阅读说明：本技术 一种卫星导航定位接收机，及其自主正直性检测方法 (Satellite navigation positioning receiver and autonomous straightness detection method thereof ) 是由 宋挥师 赵海龙 徐雄伟 刘晓燕 孙涛 于 2020-12-23 设计创作，主要内容包括：本发明公开了一种卫星导航定位接收机及其自主正直性检测方法,在保证卫星导航接收机的定位精度和可靠性的同时降低运算量。所述方法包括：接收机获得卫星的观测量后,对同一时刻的所有观测量采用卡尔曼滤波序贯处理法,逐一对同一时刻的所有观测量进行检测,如果不满足要求,则当前观测量含有粗大误差,不适合用于定位解算,剔除所述当前观测量,如果满足要求,则当前观测量不含粗大误差,保留所述当前观测量以用于定位解算。采用本发明实施例方法和设备,避免了矩阵变换、矩阵求逆等复杂运算,大大降低了接收机自主正直性检测的复杂度,增加检测的可靠性。(The invention discloses a satellite navigation positioning receiver and an autonomous straightness detection method thereof, which can reduce the calculation amount while ensuring the positioning precision and reliability of the satellite navigation receiver. The method comprises the following steps: after the receiver obtains the observed quantities of the satellites, a Kalman filtering sequential processing method is adopted for all the observed quantities at the same moment, all the observed quantities at the same moment are detected one by one, if the observed quantities do not meet the requirement, the current observed quantities contain coarse errors and are not suitable for positioning calculation, the current observed quantities are rejected, if the observed quantities meet the requirement, the current observed quantities do not contain the coarse errors, and the current observed quantities are reserved for positioning calculation. By adopting the method and the equipment of the embodiment of the invention, the complex operations such as matrix transformation, matrix inversion and the like are avoided, the complexity of autonomous straightness detection of the receiver is greatly reduced, and the reliability of detection is increased.)

1. A method for autonomous straightness detection of a satellite navigation positioning receiver, the method comprising:

after the receiver obtains the observed quantities of the satellites, a Kalman filtering sequential processing method is adopted for all the observed quantities at the same moment, all the observed quantities at the same moment are detected one by one, if the observed quantities do not meet the requirement, the current observed quantities contain coarse errors and are not suitable for positioning calculation, the current observed quantities are rejected, if the observed quantities meet the requirement, the current observed quantities do not contain the coarse errors, and the current observed quantities are reserved for positioning calculation.

2. The method of claim 1,

the method for detecting all the observed quantities at the same moment one by adopting a Kalman filtering sequential processing method comprises the following steps:

and selecting the observed quantities one by one to correct the state quantities to obtain detection parameters, and judging whether the detection parameters meet requirements or not.

3. The method of claim 2,

the detection parameters include: a state quantity error covariance matrix corrected by the current observed quantity;

the judging whether the detection parameters meet the requirements includes: and judging whether the state quantity error covariance matrix is positive, if positive, meeting the requirement, and if not, not meeting the requirement.

4. The method of claim 3,

selecting the observed quantities one by one to correct the state quantities to obtain detection parameters, and the method comprises the following steps:

selecting an observed quantity as a current observed quantity, calculating Kalman gain of the current observed quantity, and correcting a state quantity error covariance matrix prior matrix by using the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the detection is finished, selecting the next observed quantity as the current observed quantity, taking the state quantity error covariance matrix corrected by the observed quantity meeting the requirement in the last detection process as the state quantity error covariance matrix prior matrix of the detection process, and repeating the detection process and the judgment process of whether the detection parameters meet the requirement until all the observed quantities are processed.

5. The method of claim 3,

the detecting parameters further include: (ii) an observed quantity residual of the observed quantity;

the judging whether the detection parameters meet the requirements includes: and after the state quantity error covariance matrix is determined positively, the observed quantity residual of the observed quantity is adopted to carry out observed quantity consistency judgment, if the observed quantity consistency is met, the requirement is met, and if the observed quantity consistency is not met, the requirement is not met.

6. The method of claim 5,

selecting the observed quantities one by one to correct the state quantities to obtain detection parameters, and the method comprises the following steps:

selecting an observed quantity as a current observed quantity, calculating Kalman gain of the current observed quantity, correcting a state quantity estimated value by utilizing the Kalman gain of the current observed quantity to obtain a corrected value of the state quantity and observed quantity residue of the current observed quantity, and correcting a state quantity error covariance matrix prior matrix by utilizing the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the current inspection is finished, selecting the next observed quantity as the current observed quantity, taking the latest state quantity and the state quantity error covariance matrix obtained in the last detection process as initial values of the current detection process, repeating the detection process and the judgment process for judging whether the detection parameters meet the requirements or not until all the observed quantities are processed.

7. The method of claim 5,

the method for judging the consistency of the observed quantity by adopting the observed quantity residue of the observed quantity comprises the following steps:

and calculating a first detection quantity by adopting the observed quantity residue of the observed quantity, judging whether the first detection quantity is greater than a preset first detection threshold, if so, indicating that the current observed quantity does not accord with the observed quantity consistency, and if not, indicating that the current observed quantity accords with the observed quantity consistency.

8. The method according to any one of claims 1 to 7,

the method further comprises the following steps: and performing Kalman filtering integral inspection on all the reserved observations meeting the requirements, calculating a second detection quantity by using the observation quantity residues of the observations, judging whether the second detection quantity is smaller than a preset second detection threshold, outputting all the reserved observations if the second detection quantity is smaller than the preset second detection threshold, performing local detection on all the reserved observations if the second detection quantity is larger than or equal to the preset second detection threshold, determining the problematic observations, and rejecting the problematic observations.

9. The method of claim 8,

the local detection of all the reserved observation quantities and the determination of the observation quantity with the problem comprise the following steps:

and calculating a third detection amount, and determining the observation amount corresponding to the maximum value of the third detection amount as the problematic observation amount.

10. A satellite navigation positioning receiver, characterized in that the receiver comprises an observation quantity acquisition module and a detection module, wherein:

the observation quantity acquisition module is used for acquiring the observation quantity of the satellite;

the detection module is used for detecting all the observed quantities at the same moment one by adopting a Kalman filtering sequential processing method, if the observed quantities do not meet the requirement, the current observed quantity contains a coarse error and is not suitable for positioning calculation, the current observed quantity is rejected, if the observed quantities meet the requirement, the current observed quantity does not contain the coarse error, and the current observed quantity is reserved for positioning calculation.

11. The receiver of claim 10,

the detection module adopts a Kalman filtering sequential processing method to all the observed quantities at the same moment, and detects all the observed quantities at the same moment one by one, and the method comprises the following steps:

the detection module selects the observed quantities one by one to correct the state quantities, obtains detection parameters and judges whether the detection parameters meet requirements or not.

12. The receiver of claim 11,

the detection parameters include: a state quantity error covariance matrix corrected by the current observed quantity;

the detection module judges whether the detection parameters meet the requirements or not, and comprises the following steps: the detection module judges whether the state quantity error covariance matrix is positive, if positive, the requirement is met, and if not, the requirement is not met.

13. The receiver of claim 12,

the detection module selects the observed quantity one by one to correct the state quantity to obtain detection parameters, and the detection parameters comprise:

the detection module selects an observed quantity as a current observed quantity, calculates Kalman gain of the current observed quantity, and corrects a state quantity error covariance matrix prior matrix by using the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the detection is finished, selecting the next observed quantity as the current observed quantity, taking the state quantity error covariance matrix corrected by the observed quantity meeting the requirement in the last detection process as the state quantity error covariance matrix prior matrix of the detection process, and repeating the detection process and the judgment process of whether the detection parameters meet the requirement until all the observed quantities are processed.

14. The receiver of claim 12,

the detecting parameters further include: (ii) an observed quantity residual of the observed quantity;

the detection module judges whether the detection parameters meet the requirements or not, and comprises the following steps: and after the detection module judges that the state quantity error covariance matrix is positive definite, the observed quantity residual of the observed quantity is adopted to carry out observed quantity consistency judgment, if the observed quantity consistency is met, the requirement is met, and if the observed quantity consistency is not met, the requirement is not met.

15. The receiver of claim 14,

the detection module selects the observed quantity one by one to correct the state quantity to obtain detection parameters, and the detection parameters comprise:

the detection module selects an observed quantity as a current observed quantity, calculates Kalman gain of the current observed quantity, corrects a state quantity estimated value by utilizing the Kalman gain of the current observed quantity to obtain a corrected value of the state quantity and observed quantity residue of the current observed quantity, and corrects a state quantity error covariance matrix prior matrix by utilizing the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the current inspection is finished, selecting the next observed quantity as the current observed quantity, taking the latest state quantity and the state quantity error covariance matrix obtained in the last detection process as initial values of the current detection process, repeating the detection process and the judgment process for judging whether the detection parameters meet the requirements or not until all the observed quantities are processed.

16. The receiver of claim 14,

the detection module adopts the observed quantity residue of the observed quantity to carry out the observed quantity consistency judgment, and the method comprises the following steps:

the detection module calculates a first detection quantity by adopting the observed quantity residual of the observed quantity, judges whether the first detection quantity is larger than a preset first detection threshold, if so, indicates that the current observed quantity is not consistent with the observed quantity consistency, and if not, indicates that the current observed quantity is consistent with the observed quantity consistency.

17. The receiver according to any of the claims 10-16,

the detection module is further configured to: and performing Kalman filtering integral inspection on all the reserved observations meeting the requirements, calculating a second detection quantity by using the observation quantity residues of the observations, judging whether the second detection quantity is smaller than a preset second detection threshold, outputting all the reserved observations if the second detection quantity is smaller than the preset second detection threshold, performing local detection on all the reserved observations if the second detection quantity is larger than or equal to the preset second detection threshold, determining the problematic observations, and rejecting the problematic observations.

18. The receiver of claim 17,

the detection module carries out local detection on all the reserved observed quantities, determines the observed quantities with problems, and comprises the following steps:

the detection module calculates a third detection amount, and determines the observation amount corresponding to the maximum value of the third detection amount as the observation amount with problems.

19. A satellite navigation positioning receiver comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program realizes the steps of the method according to any of claims 1-9.

Technical Field

The invention relates to the technical field of global satellite navigation, in particular to a satellite navigation positioning receiver and an autonomous straightness detection method of the satellite navigation positioning receiver.

Background

Global Navigation Satellite Systems (GNSS) have been widely used in various fields. Currently, there are mainly four global navigation positioning systems: global Positioning System (GPS) in the united states, 24 operating Medium Earth Orbiting Satellites (MEOs); in the Beidou satellite navigation system (Compass or Bei Dou, BD for short) of China, the number of MEO satellites in actual work reaches 27; the number of MEO satellites actually operated by the russian Global Navigation Satellite System (GLONASS for short) reaches 21; the civil satellite navigation and positioning system with the largest scale, namely Galileo (Galileo), launched by the european union will reach 27 MEO satellites in actual work. Among them, GPS is the most mature, BD and GLONASS have already progressed in stages, while galileo system is still in the onset stage.

With the rapid development of satellite navigation technology and applications, in order to improve the accuracy, usability, continuity and integrity of navigation positioning, a single satellite positioning system receiver cannot meet the requirements of users. Receiver manufacturers have turned gradually to multimode system receivers, i.e., receivers that support multiple navigation systems simultaneously. Currently, dual-mode and three-mode navigation receivers exist, and with the improvement of the process and the development and improvement of navigation systems, it is expected that more navigation systems of the satellite navigation receiver are integrated in the future.

With the development of satellite navigation systems and multi-mode receiver systems, the number of satellites received by a receiver at the same time tends to increase greatly, for example, a dual-mode system receiver of GPS and BDS is at the same position, and can theoretically receive about 30 satellites at the same time. Although the positioning accuracy of the receiver is greatly improved, the calculation amount of the receiver is also greatly increased. In order to ensure the accuracy, continuity and robustness of positioning and time service of the Receiver, the straightness of the Receiver must be detected, that is, Receiver Autonomous straightness detection (RAIM for short). The RAIM algorithm has the basic idea that the observed quantity participating in the calculation is judged, and the observed quantity which is not suitable for participating in the calculation, such as large error, too small elevation angle and the like, is removed.

The conventional RAIM algorithm is performed in a least squares positioning process. The method comprises the following specific steps:

calculation of satellite information and preliminary satellite kicking

And after the receiver completes the bit synchronization and the frame synchronization of the signals, ephemeris and time information of the satellite are obtained, and the position and the speed of the satellite are calculated according to the ephemeris and the time information. And calculating the elevation angle of each satellite according to the preset value of the position of the receiver. And eliminating the observed quantity with the elevation angle lower than a preset elevation angle threshold.

Establishing an observation equation

Wherein, (X, Y, Z) is the position coordinate of the satellite, (X, Y, Z) are the preset values of the receiver position, n is the number of navigation satellite systems, and rho is the measured value of the observed quantity. Equation (1) can be transformed into an observation equation containing only one clock difference by the clock conversion factor of the system.

Third, choosing the star

Because the spatial distribution of the satellites directly influences the positioning accuracy, although the multimode receiver can receive more visible satellites at the same time, in order to reduce the calculation amount of the receiver, m satellites are selected from N visible satellites, so that the rationality of the geometric distribution of the satellites is improved. Meanwhile, the RAIM algorithm needs certain redundancy, so m is more than 5 generally. And reflecting the satellite space distribution quality by adopting a geometric precision factor (GDOP) value.

Wherein, the matrix G is an observation matrix obtained by the linearization of the formula (2), and trace () is a matrix tracing operation. The smallest combination of GDOPs is the best satellite geometry distribution.

Fourthly, observing the residual quantity

Is located according to the weighted least squares method,

b＝GΔx (4)

Δx＝(G^TCG)G^TCb (5)

where b is the observation residual before positioning, Δ x is the correction value of the state quantity, and the matrix C is W^TW, W is a weight matrix of the observed quantity.

After positioning, the residue is

Wherein I is an identity matrix.

Global detection

The detected quantity is a weighted residual sum of squares T_WSSE

From equation (6), the residual after positioning follows a normal distribution with a mean value of 0, so the weighted residual sum of squares follows X with a degree of freedom (DOF) of m-4²And (4) distribution. According to a preset false alarm rate P_faAlarm-missing rate P_mdThen the threshold value of the quantity is detected.

If T is_WSSEIf < Threshold, then the satellite participating in the solution is not problematic. The RAIM algorithm is finished, and the resolving result is reliable。

If T is_WSSE> Threshold, there are problematic observations in the satellites that participate in the solution. Subsequent local detection is required to determine which observation is problematic and kick it off.

Sixth, local detection

And step five, if the observed quantity is judged to contain the problematic satellite, the step determines which observed quantity has the problem through local detection. The detected quantity is

SLOPE(i)＝b(i)σ(i)/S_ii (9)

Wherein SLOPE (i) is a characteristic slope of the observed quantity of the ith satellite, b (i) is the observed quantity residual of the ith satellite, σ (i) is the root mean square of the observed quantity error of the ith satellite, and S_iiAs an element of the matrix S, S ═ I-G (G) can be seen from formula (6)^TCG)G^TC。

The quantity with the largest slope (i) value is the quantity which is most difficult to detect, namely the observed quantity which may have problems, and the observed quantity is removed. And then selecting a satellite combination optimal geometric combination from the N-m satellites. Judging whether the satellite number at the moment meets the resolving requirement, and returning to the third step for processing if the satellite number meets the resolving requirement; and if the satellite number at the moment does not meet the resolving requirement, giving out an insufficient satellite warning.

In the conventional RAIM algorithm, matrix operations including matrix multiplication and matrix inversion are involved, and only one problematic observation can be detected at a time. As the observed quantity increases, the amount of calculation is multiplied. Not only the complexity of detection is increased, but also the detection reliability is inevitably reduced. With the increase of various satellite navigation systems and the rapid development of multimode receivers, the search for a simple and reliable RAIM method is urgent.

Disclosure of Invention

In order to solve the technical problems, the invention provides a satellite navigation positioning receiver and an autonomous straightness detection method thereof, which can reduce the calculation amount while ensuring the positioning accuracy and reliability of the satellite navigation receiver.

In order to achieve the object of the present invention, the present invention provides an autonomous straightness detection method for a satellite navigation positioning receiver, the method comprising:

after the receiver obtains the observed quantities of the satellites, a Kalman filtering sequential processing method is adopted for all the observed quantities at the same moment, all the observed quantities at the same moment are detected one by one, if the observed quantities do not meet the requirement, the current observed quantities contain coarse errors and are not suitable for positioning calculation, the current observed quantities are rejected, if the observed quantities meet the requirement, the current observed quantities do not contain the coarse errors, and the current observed quantities are reserved for positioning calculation.

Further, the detecting all the observed quantities at the same time one by one includes: and selecting the observed quantities one by one to correct the state quantities to obtain detection parameters, and judging whether the detection parameters meet requirements or not.

Further, the detecting parameters include: a state quantity error covariance matrix corrected by the current observed quantity; the judging whether the detection parameters meet the requirements includes: and judging whether the state quantity error covariance matrix is positive, if positive, meeting the requirement, and if not, not meeting the requirement.

Further, the selecting the observed quantities one by one to correct the state quantity to obtain the detection parameters includes: selecting an observed quantity as a current observed quantity, calculating Kalman gain of the current observed quantity, and correcting a state quantity error covariance matrix prior matrix by using the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the detection is finished, selecting the next observed quantity as the current observed quantity, taking the state quantity error covariance matrix corrected by the observed quantity meeting the requirement in the last detection process as the state quantity error covariance matrix prior matrix of the detection process, and repeating the detection process and the judgment process of whether the detection parameters meet the requirement until all the observed quantities are processed.

Further, the detecting parameters further include: (ii) an observed quantity residual of the observed quantity; the judging whether the detection parameters meet the requirements includes: and after the state quantity error covariance matrix is determined positively, the observed quantity residual of the observed quantity is adopted to carry out observed quantity consistency judgment, if the observed quantity consistency is met, the requirement is met, and if the observed quantity consistency is not met, the requirement is not met.

Further, the selecting the observed quantities one by one to correct the state quantity to obtain the detection parameters includes: selecting an observed quantity as a current observed quantity, calculating Kalman gain of the current observed quantity, correcting a state quantity estimated value by utilizing the Kalman gain of the current observed quantity to obtain a corrected value of the state quantity and observed quantity residue of the current observed quantity, and correcting a state quantity error covariance matrix prior matrix by utilizing the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the current inspection is finished, selecting the next observed quantity as the current observed quantity, taking the latest state quantity and the state quantity error covariance matrix obtained in the last detection process as initial values of the current detection process, repeating the detection process and the judgment process for judging whether the detection parameters meet the requirements or not until all the observed quantities are processed.

Further, the method for judging the consistency of the observed quantity by using the observed quantity residual of the observed quantity comprises the following steps: and calculating a first detection quantity by adopting the observed quantity residue of the observed quantity, judging whether the first detection quantity is greater than a preset first detection threshold, if so, indicating that the current observed quantity does not accord with the observed quantity consistency, and if not, indicating that the current observed quantity accords with the observed quantity consistency.

Further, the method further comprises: and performing Kalman filtering integral inspection on all the reserved observations meeting the requirements, calculating a second detection quantity by using the observation quantity residues of the observations, judging whether the second detection quantity is smaller than a preset second detection threshold, outputting all the reserved observations if the second detection quantity is smaller than the preset second detection threshold, performing local detection on all the reserved observations if the second detection quantity is larger than or equal to the preset second detection threshold, determining the problematic observations, and rejecting the problematic observations.

Further, the locally detecting all the remaining observations to determine the problematic observations includes: and calculating a third detection amount, and determining the observation amount corresponding to the maximum value of the third detection amount as the problematic observation amount.

In order to achieve the object of the present invention, the present invention further provides a satellite navigation positioning receiver, which includes an observation quantity obtaining module and a detection module, wherein:

the observation quantity acquisition module is used for acquiring the observation quantity of the satellite;

the detection module is used for detecting all the observed quantities at the same moment one by adopting a Kalman filtering sequential processing method, if the observed quantities do not meet the requirement, the current observed quantity contains a coarse error and is not suitable for positioning calculation, the current observed quantity is rejected, if the observed quantities meet the requirement, the current observed quantity does not contain the coarse error, and the current observed quantity is reserved for positioning calculation.

Further, the detecting module adopts a kalman filtering sequential processing method for all the observed quantities at the same time, and detects all the observed quantities at the same time one by one, including: the detection module selects the observed quantities one by one to correct the state quantities, obtains detection parameters and judges whether the detection parameters meet requirements or not.

Further, the detecting parameters include: a state quantity error covariance matrix corrected by the current observed quantity;

the detection module judges whether the detection parameters meet the requirements or not, and comprises the following steps: the detection module judges whether the state quantity error covariance matrix is positive, if positive, the requirement is met, and if not, the requirement is not met.

Further, the detecting module selects the observed quantities one by one to correct the state quantity, and obtains detecting parameters, including:

the detection module selects an observed quantity as a current observed quantity, calculates Kalman gain of the current observed quantity, and corrects a state quantity error covariance matrix prior matrix by using the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the detection is finished, selecting the next observed quantity as the current observed quantity, taking the state quantity error covariance matrix corrected by the observed quantity meeting the requirement in the last detection process as the state quantity error covariance matrix prior matrix of the detection process, and repeating the detection process and the judgment process of whether the detection parameters meet the requirement until all the observed quantities are processed.

Further, the detecting parameters further include: (ii) an observed quantity residual of the observed quantity;

the detection module judges whether the detection parameters meet the requirements or not, and comprises the following steps: and after the detection module judges that the state quantity error covariance matrix is positive definite, the observed quantity residual of the observed quantity is adopted to carry out observed quantity consistency judgment, if the observed quantity consistency is met, the requirement is met, and if the observed quantity consistency is not met, the requirement is not met.

Further, the detecting module selects the observed quantities one by one to correct the state quantity, and obtains detecting parameters, including:

the detection module selects an observed quantity as a current observed quantity, calculates Kalman gain of the current observed quantity, corrects a state quantity estimated value by utilizing the Kalman gain of the current observed quantity to obtain a corrected value of the state quantity and observed quantity residue of the current observed quantity, and corrects a state quantity error covariance matrix prior matrix by utilizing the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix;

and after the current inspection is finished, selecting the next observed quantity as the current observed quantity, taking the latest state quantity and the state quantity error covariance matrix obtained in the last detection process as initial values of the current detection process, repeating the detection process and the judgment process for judging whether the detection parameters meet the requirements or not until all the observed quantities are processed.

Further, the detecting module performs observation consistency determination by using the observation residuals of the observations, and includes: the detection module calculates a first detection quantity by adopting the observed quantity residual of the observed quantity, judges whether the first detection quantity is larger than a preset first detection threshold, if so, indicates that the current observed quantity is not consistent with the observed quantity consistency, and if not, indicates that the current observed quantity is consistent with the observed quantity consistency.

Further, the detection module is further configured to: and performing Kalman filtering integral inspection on all the reserved observations meeting the requirements, calculating a second detection quantity by using the observation quantity residues of the observations, judging whether the second detection quantity is smaller than a preset second detection threshold, outputting all the reserved observations if the second detection quantity is smaller than the preset second detection threshold, performing local detection on all the reserved observations if the second detection quantity is larger than or equal to the preset second detection threshold, determining the problematic observations, and rejecting the problematic observations.

Further, the detecting module performs local detection on all the reserved observation quantities, and determines the observation quantity with the problem, including: the detection module calculates a third detection amount, and determines the observation amount corresponding to the maximum value of the third detection amount as the observation amount with problems.

In order to achieve the object of the present invention, the present invention further provides a satellite navigation positioning receiver, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method when executing the program.

By adopting the method and the equipment of the embodiment of the invention, the least square method in the prior art is replaced by the Kalman filtering sequential processing method to preprocess the observed quantities, the observed quantities are processed one by one, and the processed parameters are scalar quantities, so that complex operations such as matrix transformation, matrix inversion and the like are avoided, the complexity of autonomous orthogonality detection of a receiver is greatly reduced, and the reliability of detection is increased. The method and the device of the embodiment of the invention are particularly suitable for multimode receivers, but can also be suitable for single-mode receivers.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.

FIG. 1 is a flowchart of a method of example 1 of the present invention;

FIG. 2 is a flow chart of the receiver RAIM algorithm based on Kalman filtering in embodiment 2 of the present invention;

FIG. 3 is a flow chart of Kalman filtering sequential satellite kicking processing in embodiment 2 of the present invention;

FIG. 4 is a schematic structural diagram of a satellite navigation positioning receiver according to embodiment 3 of the present invention;

fig. 5 is another schematic structural diagram of a satellite navigation positioning receiver according to embodiment 3 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.

The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.

The Kalman filtering positioning algorithm links the state quantities at different moments through a state equation, so that the positioning track is smoother. The state quantity is connected with the observed quantity through an observation equation, and the observed quantity can be processed one by one according to the characteristic when being processed, namely the sequential processing method of Kalman filtering. Because the observed quantities are processed one by one, the matrix inversion process in the calculation process is avoided, and meanwhile, the positioning quality is not reduced. According to the characteristic, the RAIM algorithm based on the Kalman filtering is realized, the operation amount is reduced, and the reliability is improved.

Example one

As shown in fig. 1, the method comprises the following steps:

step 10, a receiver obtains the observed quantity of a satellite;

step 11, adopting a Kalman filtering sequential processing method for all the observed quantities at the same moment, detecting all the observed quantities at the same moment one by one, judging whether the observed quantities meet requirements, if so, executing step 12, and if not, executing step 13;

step 12, the current observed quantity does not contain a gross error, and the current observed quantity is reserved for positioning calculation;

and step 13, the current observed quantity contains a large error, is not suitable for positioning calculation, and is rejected.

By adopting the method of the embodiment, the observed quantity is detected one by adopting the Kalman filtering sequential processing method, and compared with the least square method observed quantity preprocessing in the prior art, the method reduces the operand and the processing complexity.

In the step 11, the step of detecting all the observed quantities at the same time one by using a kalman filtering sequential processing method includes: and selecting the observed quantities one by one to correct the state quantities to obtain detection parameters, and judging whether the detection parameters meet requirements or not.

In an alternative embodiment, the detecting parameters include: and judging whether the detection parameters meet the requirements or not by using the state quantity error covariance matrix corrected by the current observed quantity, wherein the judgment on the state quantity error covariance matrix is positive, if positive, the requirements are considered to be met, and if not, the requirements are considered not to be met. In the embodiment, the observed quantity can be eliminated only by the positive qualitative judgment of the state quantity error covariance matrix corrected by the current observed quantity, a large number of matrix operations are not needed, and the operation quantity is reduced while the positioning accuracy and the reliability of the satellite navigation receiver are ensured.

In this embodiment, the step of selecting the observed quantities one by one to correct the state quantities and obtain the detection parameters includes: selecting an observed quantity as a current observed quantity, calculating Kalman gain of the current observed quantity, and correcting a state quantity error covariance matrix prior matrix by using the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix; and after the detection is finished, selecting the next observed quantity as the current observed quantity, taking the state quantity error covariance matrix corrected by the observed quantity meeting the requirement in the last detection process as the state quantity error covariance matrix prior matrix of the detection process, and repeating the detection process and the judgment process of whether the detection parameters meet the requirement until all the observed quantities are processed.

In another alternative embodiment, the detection parameters may further include, in addition to the state quantity error covariance matrix: (ii) an observed quantity residual of the observed quantity; judging whether the detection parameters meet the requirements or not, comprising the following steps: and after the state quantity error covariance matrix is determined positively, the observed quantity residual of the observed quantity is utilized to judge the consistency of the observed quantity, if the observed quantity consistency is met, the requirement is considered to be met, and if the observed quantity consistency is not met, the requirement is considered not to be met. In the embodiment, in addition to the positive qualitative judgment of the state quantity error covariance matrix, the consistency judgment of the observed quantity is added, and the reliability of detection is further improved.

Specifically, the method for judging the consistency of the observed quantity by adopting the observed quantity residual of the observed quantity comprises the following steps: and calculating a first detection quantity by adopting the observed quantity residue of the observed quantity, judging whether the first detection quantity is greater than a preset first detection threshold, if so, indicating that the current observed quantity does not accord with the observed quantity consistency, and if not, indicating that the current observed quantity accords with the observed quantity consistency.

In this embodiment, the step of selecting the observed quantities one by one to correct the state quantities and obtain the detection parameters includes: selecting an observed quantity as a current observed quantity, calculating Kalman gain of the current observed quantity, correcting a state quantity estimated value by utilizing the Kalman gain of the current observed quantity to obtain a corrected value of the state quantity and observed quantity residue of the current observed quantity, and correcting a state quantity error covariance matrix prior matrix by utilizing the Kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix; and after the current inspection is finished, selecting the next observed quantity as the current observed quantity, taking the latest state quantity and the state quantity error covariance matrix obtained in the last detection process as initial values of the current detection process, repeating the detection process and the judgment process for judging whether the detection parameters meet the requirements or not until all the observed quantities are processed.

The state quantity detection method comprises the steps of obtaining a state quantity error covariance matrix, obtaining a state quantity correction value. It should be noted that, only if the detection passes and the last detection result is that the observed quantity meets the requirement, the parameter in the detection process can be used as the initial value of the next detection process.

In another optional embodiment, in order to further improve reliability, after rejecting the observation quantities that do not meet the requirement, a kalman filter overall test may be performed on all the remaining observation quantities that meet the requirement, a second detection quantity is calculated by using the observation quantity residuals of the observation quantities, whether the second detection quantity is smaller than a preset second detection threshold is determined, if the second detection quantity is smaller than the preset second detection threshold, all the remaining observation quantities are output, and if the second detection quantity is larger than or equal to the preset second detection threshold, all the remaining observation quantities are locally detected, and the observation quantities that have the problem are determined and rejected.

In this embodiment, the local detection process includes: and calculating a third detection amount, and determining the observation amount corresponding to the maximum value of the third detection amount as the problematic observation amount.

By adopting the method of the embodiment of the invention, the least square method in the prior art is replaced by the Kalman filtering sequential processing method to preprocess the observed quantities, the observed quantities are processed one by one, and the processed parameters are scalar quantities, so that complex operations such as matrix transformation, matrix inversion and the like are avoided, the complexity of autonomous orthogonality detection of the receiver is greatly reduced, and the reliability of detection is increased. The above embodiment methods may be applied to multimode receivers, but may also be applied to single mode receivers.

Example two

The embodiment of the method described above is specifically described in this embodiment, and as shown in fig. 2, the method includes the following steps:

step 20, calculating the satellite elevation angle according to the satellite observation quantity, and kicking the satellite by utilizing an elevation angle (EL) threshold value;

the state equation and the observation equation of the Kalman filtering algorithm are respectively as follows:

x_k＝Ax_k-1+w_k-1 (10)

y_k＝Hx_k+v_k (11)

wherein, x is a state vector, k is a positioning epoch, A is a state transition matrix, w is a process noise vector, a covariance matrix thereof is Q, y is an observed quantity vector, H is a transition matrix between the state quantity and the observed quantity, v is a measurement noise vector, and a covariance matrix thereof is R. Let P be the covariance matrix of the state quantity error.

The Kalman filtering positioning algorithm comprises two steps of prediction and updating, wherein a covariance matrix prior matrix of state quantity and state quantity errors is obtained through the prediction step, and the updating step comprises the following steps: calculating Kalman gain, updating the state quantity and the covariance matrix of the state quantity error by using the Kalman gain, and specifically obtaining the following parameters:

prediction of state quantity:

prior matrix of covariance matrix of state quantity error:

kalman gain:

updating the state quantity:

updating of covariance matrix of state quantities:and I is an identity matrix.

The position and velocity of the satellite are calculated from ephemeris provided by the satellite. And calculating the elevation angle of each satellite according to the preset value of the receiver, and setting an elevation angle threshold to eliminate the satellites with low elevation angles by considering that the satellites with low elevation angles have large influence on multipath and other factors. Let the number of observations at this time be N.

Step 21, preprocessing the obtained observed quantity by adopting a Kalman filtering sequential processing method, namely performing Kalman filtering sequential processing on a satellite kicking process;

the kalman filtering sequential processing method is applied to all the observations, as shown in fig. 3, and includes the following steps:

step 30, predicting the state quantity, namely determining an initial value of the state quantity;

in this embodiment, the least square method positioning result is used as the initial quantity of the kalman filter, and in other embodiments, other values may be used as the initial quantity of the kalman filter, and the higher the accuracy of the initial quantity, the better the satellite kicking effect. And judging the motion state of the receiver, determining elements of a state vector according to the motion state, and listing the state equation. Will observe the quantity y_kAnd corresponding observation matrix H with each row number, y_k，j,H_jJ is 1,2 … … N, there is no precedence between observations themselves.

Step 31, detecting the jth observed quantity and calculating K_k,j，x_k,j，P_k,j：

Obtaining a state estimation value of k epoch according to an initial value of the state quantitySum-state error covariance matrix prior matrixUsing the observed quantity y_k,jCorrecting the state quantities of the k epochs one by one;

kalman gain of observed quantity:wherein r is_jIs the variance of the observation error of the observed quantity. Due to the fact thatIs scalar, so no matrix inversion operation is required.

Correction value of state quantity: x is the number of_k,j＝x_k.j-1+K_k,j(y_k,j-H_jx_k,j-1)

Covariance matrix of corrected state quantity error: p_k,j＝(I-K_k,jH_j)P_k,j-1

And after the observation quantity detection is successful, the corrected state quantity error covariance matrix is used as a prior matrix of the next state quantity error covariance matrix, and the state quantity correction value is used as an initial value of the next state quantity.

Wherein, y_k,jIs j observed quantity of k epoch (j observed quantity, hereinafter referred to as j observed quantity), x_k,j-1State quantities, H, obtained after correction with the j-1 st observation for the k epoch_jAn observation matrix for the j observations,is a state error covariance matrix prior matrix;

step 32, positive qualitative determination of the covariance matrix of the state quantities, if the covariance matrix P of the error of the state quantities is obtained from the j observed quantities_k,jIf not, go to step 34; covariance matrix P of state quantity error if obtained from j observation_k,jIf yes, the next step of processing is carried out, namely step 33 is executed;

step 33, set b_j＝y_k,j-H_jx_k,j-1Residual of observed quantity of j observed quantity, as b_jJudging the consistency of the observed quantities, if the observed quantities are consistent, selecting the next observed quantity, returning to the step 31 to continue sequential processing, and if the observed quantities are not consistent, executing the step 34;

the process of sequential processing is equivalent to the correction of state quantities of different observed quantities at the same moment. And b_jObedience mean is zero and variance isIs normally distributed. Let the first detection amount beSetting a first detection threshold as B according to the deviation range of the state quantity at the same moment_thresholdThe first detection threshold may be determined based on positioning accuracy requirements.

If B > B_thresholdIf the observed quantity is inconsistent with the consistency, the observed quantity is rejected.

If B < B_thresholdThen the observation is reliable. At this time, the state quantity and the covariance matrix of the state quantity obtained this time are set as initial values for the next calculation in step 31.

Step 34, eliminating problems existing in the current observed quantity, returning to step 31, and processing the next observed quantity;

the Kalman filtering sequential processing star kicking algorithm adopts a single one-by-one processing mode for observed quantities, so that the processing idea is simple, matrix inversion equal matrix operation is avoided, and the operation quantity is greatly reduced. And after all the observed quantity detection is finished, if the observed quantity has a certain redundancy, the finally output observed quantity can be used for positioning calculation.

Step 22, in order to ensure the reliability of the autonomous detection of the receiver, next, performing Kalman filtering integral detection on the observed quantity after the satellite kicking is sequentially processed to verify whether the satellite kicking is complete, if the result of the detected quantity indicates that a problem quantity exists, executing step 23, and if no problem exists, using the residual observed quantity as positioning calculation;

first, a second detection amount is determined. Let the residual vector of the observed quantity be

b_k＝y_k-Hx_k (12)

Order toThe second detection quantity is the residual weighted sum of squares T:

according to a preset false alarm rate P_faAnd rate of missed alarm P_mdCalculating a second detection threshold T_thresholdAnd if the second detection quantity is smaller than the second detection threshold value, the residual observation quantity is reliable and does not need to be processed next. If the second detection quantity is larger than or equal to the second detection threshold value, the remaining observed quantity has problematic observed quantity, and the next local detection processing is required to remove the poor observed quantity.

Step 23, if the problem observed quantity exists in the overall detection, performing local detection, determining which observed quantity has a problem, and rejecting the observed quantity;

calculating a third detection quantity:

where h is the count of remaining satellites and k ranges from [1, k]。I_hIs the h-th column of the identity matrix. Detected quantity theta_hAnd eliminating the observed quantity corresponding to the maximum value, namely the problem observed quantity, and reforming the observation matrix.

Step 24, judging whether the remaining satellites are enough (for example, if the minimum satellite number is set, namely the minimum observed quantity number is 6, the satellite number is not enough if N is less than 6, and if N is more than or equal to 6, the remaining satellites are enough), if so, returning to the overall detection to see whether the problem observed quantity still exists; if the remaining satellites are not enough, an insufficient satellite warning is issued.

Because the preliminary star kicking is completed by the sequential processing method, the probability of problems in the residual observed quantity is greatly reduced. And the later-stage overall verification and local elimination are mainly used for ensuring the reliability of the star kicking. Therefore, from the perspective of the whole satellite kicking strategy, the RAIM algorithm idea of the navigation satellite receiver based on Kalman filtering is simpler, the calculation amount is less, and the robustness is stronger. The method has more prominent application effect in the multimode navigation satellite receiver.

EXAMPLE III

The present embodiment provides a satellite navigation positioning receiver device, configured to implement the method of the first embodiment or the second embodiment, where the description in the foregoing method embodiment is also applicable to this embodiment, and as shown in fig. 4, the receiver includes an observation quantity obtaining module 41 and a detection module 42, where:

the observation quantity obtaining module 41 is configured to obtain an observation quantity of a satellite;

the detection module 42 is configured to detect all the observed quantities at the same time one by using a kalman filtering sequential processing method, if the observed quantities do not meet the requirement, the current observed quantity contains a coarse error and is not suitable for positioning calculation, reject the current observed quantity, and if the observed quantities meet the requirement, the current observed quantity does not contain a coarse error and is reserved for positioning calculation.

In an alternative embodiment, the detecting module 42 uses a kalman filtering sequential processing method to detect all the observations at the same time, and includes: the detection module 42 selects the observed quantities one by one to correct the state quantities, obtains detection parameters, and determines whether the detection parameters meet requirements.

In an optional embodiment, the detection parameters include: a state quantity error covariance matrix corrected by the current observed quantity; the detecting module 42 determines whether the detection parameters satisfy the requirements, including: the detection module 42 determines whether the state quantity error covariance matrix is positive, and if the state quantity error covariance matrix is positive, the requirement is met, and if the state quantity error covariance matrix is not positive, the requirement is not met.

The detecting module 42 selects the observed quantities one by one to correct the state quantities, and obtains detecting parameters, including: the detection module 42 selects an observed quantity as a current observed quantity, calculates kalman gain of the current observed quantity, and corrects the state quantity error covariance matrix prior matrix by using the kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix; and after the detection is finished, selecting the next observed quantity as the current observed quantity, taking the state quantity error covariance matrix corrected by the observed quantity meeting the requirement in the last detection process as the state quantity error covariance matrix prior matrix of the detection process, and repeating the detection process and the judgment process of whether the detection parameters meet the requirement until all the observed quantities are processed.

In an optional embodiment, the detecting the parameter further comprises: (ii) an observed quantity residual of the observed quantity;

the detecting module 42 determines whether the detected parameters satisfy the requirements, including: after the detection module 42 judges that the state quantity error covariance matrix is positive, the observed quantity residual of the observed quantity is used for judging the consistency of the observed quantity, if the observed quantity consistency is met, the requirement is met, and if the observed quantity consistency is not met, the requirement is not met.

The detecting module 42 selects the observed quantities one by one to correct the state quantities, and obtains detecting parameters, including: the detection module 42 selects an observed quantity as a current observed quantity, calculates a kalman gain of the current observed quantity, corrects the state quantity estimated value by using the kalman gain of the current observed quantity to obtain a corrected value of the state quantity and an observed quantity residual of the current observed quantity, and corrects the state quantity error covariance matrix prior matrix by using the kalman gain of the current observed quantity to obtain a corrected state quantity error covariance matrix; and after the current inspection is finished, selecting the next observed quantity as the current observed quantity, taking the latest state quantity and the state quantity error covariance matrix obtained in the last detection process as initial values of the current detection process, repeating the detection process and the judgment process for judging whether the detection parameters meet the requirements or not until all the observed quantities are processed.

In an alternative embodiment, the detection module 42 uses the observation residuals of the observations to make an observation consistency determination, including: the detection module 42 calculates a first detection quantity by using the observed quantity residue of the observed quantity, and determines whether the first detection quantity is greater than a preset first detection threshold, if so, it indicates that the current observed quantity does not accord with the observed quantity consistency, and if not, it indicates that the current observed quantity accords with the observed quantity consistency.

In an optional embodiment, the detection module 42 is further configured to: and performing Kalman filtering integral inspection on all the reserved observations meeting the requirements, calculating a second detection quantity by using the observation quantity residues of the observations, judging whether the second detection quantity is smaller than a preset second detection threshold, outputting all the reserved observations if the second detection quantity is smaller than the preset second detection threshold, performing local detection on all the reserved observations if the second detection quantity is larger than or equal to the preset second detection threshold, determining the problematic observations, and rejecting the problematic observations.

The detecting module 42 performs local detection on all the remaining observations to determine the problematic observations, including: the detection module 42 calculates a third detection amount, and determines the observation amount corresponding to the maximum value of the third detection amount as the problematic observation amount.

The satellite navigation positioning receiver may be a computer device, which may be configured as shown in fig. 5, and includes a processor 51, a memory 52, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, some or all of the steps in one or the second embodiment may be implemented.

Application example

A dual-mode system receiver composed of a GPS and a BDS is taken as an example to realize the RAIM algorithm of the Kalman filtering based multimode navigation satellite receiver. Observation site: wuhan; the observation time was 2015, 12, 20, 11, 22 min, 42 sec. And obtaining the observed satellite information position and speed according to the satellite telegraph text. The preset value for the initial position of the receiver is (-2271601.738,5009144.904,3218832.710). The visible satellites (svid is the satellite number) and the elevation angle (unit: degree) thereof under different systems are as follows:

GPS：svid＝1,el＝-0.268；svid＝3,el＝12.0；svid＝10,el＝29.7；svid＝14,el＝62.1；svid＝16,el＝13.0；svid＝22,el＝40.3；svid＝25,el＝37.7；svid＝26,el＝42.8；svid＝29,el＝19.5；svid＝31,el＝59.1；svid＝32,el＝41.3。

BDS:svid＝1,el＝45.7；svid＝3,el＝53.9；svid＝4,el＝29.8；svid＝5,el＝20.1；svid＝6,el＝54.9；svid＝7,el＝70.9；svid＝8,el＝2.049；svid＝9,el＝75.8；svid＝10,el＝55.2svid＝11,el＝13.7。

and if the elevation threshold is EL which is 10 degrees, removing satellites with svid which is 1 in the GPS and svid which is 8 in the BDS, and remaining 19 satellites.

The state equation and the observation equation of the kalman filter are respectively expressed by equation (15) and equation (16):

x_k＝Ax_k-1+w_k-1 (15)

wherein x is_kConverting the clock difference of two systems into one clock difference through a clock difference factor in order to reduce the dimensionality; state transition matrixt is a time interval; the covariance matrix Q of the process noise is a symmetric matrix.

Now separately writing the observation matrix H as H in order to distinguish the two systems_GPSAnd H_BDSAnd in practice, the two can be mixed together, and then the observation matrix is:

and taking the observed quantity corresponding to the row of the observation matrix as a sign change, wherein the corresponding measured errors of the observed quantity are respectively as follows: r is₁＝37.883；

r₂＝70.271；r₃＝160.840；r₄＝174.034；r₅＝186.759；r₆＝231.946；r₇＝299.665；

r₈＝341.870；r₉＝492.548；r₁₀＝5978.997；r₁₁＝21.071；r₁₂＝46.343；r₁₃＝47.866；

r₁₄＝54.536；r₁₅＝64.383；r₁₆＝78.399；r₁₇＝135.398；r₁₈＝370.259；r₁₉＝1271.278

According to the process of the sequential star kicking method, the satellite GPS which does not meet the conditions can be obtained: svid 16, svid 3; BDS: svid 11 and svid 5.

According to false alarm rate P_fa＝1×10^-5Alarm-missing rate P_mdAnd (4) judging whether the rest observed quantities contain the problem extracting satellite or not by the number of the observed quantities obtained by residual solution and a chi-square distribution function, wherein the number of the observed quantities is 0.2. And judging that no problem satellite exists in the residual observed quantity. Local detection is not required.

It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical units; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.