阅读说明：本技术 一种卫星接收机自适应ckf算法实现ins辅助gnss导航定位方法 (Method for realizing INS (inertial navigation system) assisted GNSS (global navigation satellite system) navigation and positioning by satellite receiver self-adaptive CKF (CKF) algorithm ) 是由 王天保 林城誉 车洪峰 王大维 于 2021-08-26 设计创作，主要内容包括：本发明公开了一种卫星接收机自适应CKF算法实现INS辅助GNSS导航定位方法,具体包括以下步骤:S1、INS辅助GNSS导航系统结构：a.状态方程,b.测量方程；S2、自适应CKF算法：a.CKF算法,b.自适应CKF算法；S3、采用假设检验的方法来判断过程噪声的不确定度,本发明涉及卫星接收机技术领域。该卫星接收机自适应CKF算法实现INS辅助GNSS导航定位方法,通过为了提升其在高动态和存在复杂干扰的环境中的导航定位精度和稳定性,本发明将使用INS/GNSS超紧组合结构,利用卫星接收机基带的I路和Q路观测数据,以及INS提供的位置、速度、姿态信息进行深度数据融合,修正基带跟踪误差,提升观测质量,使用一种非线性自适应CKF算法(ACKF),处理观测方程的非线性问题。(The invention discloses a method for realizing INS assisted GNSS navigation positioning by a satellite receiver self-adaptive CKF algorithm, which specifically comprises the following steps of S1, INS assisted GNSS navigation system structure: a. equation of state, b. measurement equation; s2, self-adaptive CKF algorithm: a, CKF algorithm, b, adaptive CKF algorithm; s3, adopting a hypothesis test method to judge the uncertainty of the process noise, and the invention relates to the technical field of satellite receivers. In order to improve the navigation positioning precision and stability of the satellite receiver in a high-dynamic environment with complex interference, the method uses an INS/GNSS ultra-tight combined structure, performs depth data fusion by using the I-path observation data and the Q-path observation data of a satellite receiver baseband and the position, speed and attitude information provided by the INS, corrects the baseband tracking error, improves the observation quality, and uses a nonlinear adaptive CKF Algorithm (ACKF) to process the nonlinear problem of an observation equation.)

1. A method for realizing INS assisted GNSS navigation positioning by a satellite receiver self-adaptive CKF algorithm is characterized by comprising the following steps: the method specifically comprises the following steps:

s1, an INS assisted GNSS navigation system structure:

a. the state equation is as follows: the state of the INS/GNSS ultra-tight combination system is The state equation of the system is x_k+1＝Φ_kx_k+w_k(formula 2);

b. the measurement equation: the satellite signal received by the satellite receiver is y (t) a · ca (t) d (t) cos (2 π ft + θ) + n₀(equation 3), the satellite signal correlation operation can be expressed as For the tracking frequency error of the carrier loop,for the phase error of the code tracking loop, T is the pre-integration time, η_I、η_QIn order to be a noise, the noise is,R_uand v_uRespectively representing the observed position and velocity of the receiver,andrespectively representing the estimated position and velocity of the receiver,andthe I and Q way observation equations can be written as

Let I (P)_e0,V_e0)＝I_INS(formula 10) and Q (P)_e0,V_e0)＝Q_INS(formula 11) then Let z ═ δ I, δ Q }_i(equation 14), the subscript 'i' indicates the number of tracked channels, and the observation equation for the system can be expressed as z_k＝h_k(x_k)+v_k(equation 15), where h (-) describes a non-linear function of the observation equation, z_kAnd v_kRespectively measuring vector and measuring noise;

s2, self-adaptive CKF algorithm: non-linear systems described by formulae (2) and (15) such as As shown in the drawings, the above-described,

CKF algorithm is mainlyThe method comprises the following steps: step1 initialization: initializationAnd P₀(ii) a Step2 time update: since the equation of state is linear,and P_k/k-1Can be used for Step3 cumquat points calculation: by Cholesky decomposition, P_k/k-1Can be expressed asComputing Then Z_i,k/k-1＝h(X_i,k/k-1)(i＝1,2,...,2n_x) (equation 22), Step4: and

b. the adaptive CKF algorithm: assuming that there is uncertain process statistical noise in the system described by (equation 16), a time-varying adaptive factor can be used to reduce the effect of a priori information on the current state estimation in the prediction covariance matrix, so thatWherein λ is_kFor the adaptive attenuation factor to be calculated from the residual error,is composed ofWherein H (k) is an observation matrix(equation 30) covariance matrix of residuals may be expressed asChanging the Kalman gain matrix when the following conditions are satisfiedWherein, tr [ ·]Is a trace of the correlation matrix, if Q_kIn which there is a large deviation, estimated observation vectorAnd the observed quantity z_kCompared with the abrupt change, the real filtering estimation error exceeds the theoretical error, so an adaptive factor lambda must be used_kTo match the process noise lambda_kMemory for recordingUse of (formula 28) in place of P_k，Bringing (equation 29) into (equation 35) yields:due to the fact thatThe adaptive factor can be calculated as

S3, adopting a hypothesis test method to judge the uncertainty of the process noise: two assumptions are as follows: gamma ray₀The process noise statistics are accurate; gamma ray₁The process noise statistic has an uncertainty, which can be verified by the following function:due to the fact thatTherefore, when γ is assumed₁Correct, beta_kThe value will be much larger than the threshold.

2. The method of claim 1, wherein the method for realizing INS assisted GNSS navigation positioning by using the satellite receiver adaptive CKF algorithm comprises: in the step S1, in the step S,where dx, dy and dz are position errors,andfor velocity error, φ x, φ y and φ z for attitude angle error, a_x,a_yAnd a_zIs the accelerometer bias, g_x,g_yAnd g_zIs the gyro deviation, c_bAnd c_dClock bias and clock skew of the satellite receiver.

3. The method of claim 1, wherein the method for realizing INS assisted GNSS navigation positioning by using the satellite receiver adaptive CKF algorithm comprises: in the S1, x_k+1＝Φ_kx_k+w_kMiddle phi_kState transition matrix, x_kAnd w_kState vectors and process noise.

4. The method of claim 1, wherein the method for realizing INS assisted GNSS navigation positioning by using the satellite receiver adaptive CKF algorithm comprises: in the step S2, in the step S,in (1),is w_kAnd v_kThe covariance matrix of (2).

5. The method of claim 1, wherein the method for realizing INS assisted GNSS navigation positioning by using the satellite receiver adaptive CKF algorithm comprises: in the step S2, in the step S,in (1),n_xis the dimension of the state, [1 ]]Is a set of points, [1]_iIs a set of points [1]Point i.

6. The method of claim 1, wherein the method for realizing INS assisted GNSS navigation positioning by using the satellite receiver adaptive CKF algorithm comprises: in the step S3, in the step S,in, the statistical function obeys x of m dimension²Distribution, where m is the residual vectorDimension (d) of (a).

Technical Field

The invention relates to the technical field of satellite receivers, in particular to a method for realizing INS (inertial navigation system) assisted GNSS (global navigation satellite system) navigation and positioning by a satellite receiver self-adaptive CKF (CKF) algorithm.

Background

The satellite television receiver is equipment for receiving satellite television signals, generally refers to an indoor unit of a satellite television receiving station, and comprises a 2 nd down converter, a channel selection circuit, an intermediate frequency band-pass filter, a main intermediate frequency amplifier, an intermediate frequency demodulation circuit, a baseband processing circuit and the like, and the working process is as follows: sending the 1 st intermediate frequency signal with 500 MHz bandwidth and 24 channels into the 2 nd down converter to select channels, sending the output 2 nd intermediate frequency signal to an intermediate frequency band-pass filter, an intermediate frequency amplifier and a demodulator to demodulate a baseband signal, respectively outputting a video signal and an accompanying sound signal through image processing and accompanying sound demodulation, sending the video signal and the accompanying sound signal to a video and sound interface of a monitor, and reproducing images and sound; or the RF end output of a VHF or UHF television modulator arranged in the satellite television receiver is directly sent to an antenna socket of a household color television receiver for people to watch satellite television programs, the INS inertial navigation system is a navigation positioning system for positioning and resolving by utilizing three-dimensional angular velocity and three-dimensional acceleration information provided by an inertial sensor, the inertial device is a passive device, the work of the inertial navigation system is not interfered by the outside, the angular velocity and acceleration information of the inertial navigation system can be output in real time, the INS and the satellite navigation system are combined, the defects of respective systems can be overcome, and more accurate and stable navigation positioning results can be obtained.

The satellite receiver receives satellite signals, independent navigation and positioning are carried out without depending on other external sensor information, however, in a dynamic situation or an environment with interference around, due to Doppler deviation or large environmental interference, the receiver is likely to lose lock, the INS/GNSS combination mode can be divided into three modes of loose combination, tight combination and ultra-tight combination, the traditional loose combination and tight combination method carries out data fusion by using positioning information of a GNSS system or pseudo range and Doppler observed quantity information, navigation and positioning accuracy and stability of the whole system can be improved, but because the satellite receiver baseband is not used for tracking original observed quantity information, the INS cannot assist the satellite receiver baseband to carry out stable tracking under the condition of dynamic or complex interference, the traditional KF algorithm is often used in sensor data fusion as a linear estimation method, in the INS/GNSS ultra-tight combination system, the observation equation is nonlinear, and a linear method generates larger estimation deviation in a high dynamic and complex electromagnetic interference environment.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a method for realizing INS assisted GNSS navigation positioning by a satellite receiver self-adaptive CKF algorithm, which solves the problems that the receiver is likely to lose lock due to Doppler deviation or large environmental interference, the INS cannot assist a satellite receiver baseband to stably track, and a linear method generates large estimation deviation.

In order to achieve the purpose, the invention is realized by the following technical scheme: a method for realizing INS assisted GNSS navigation positioning by a satellite receiver self-adaptive CKF algorithm specifically comprises the following steps:

s1, an INS assisted GNSS navigation system structure:

a. the state equation is as follows: the state of the INS/GNSS ultra-tight combination system is The state equation of the system is x_k+1＝Φ_kx_k+w_k(formula 2);

b. the measurement equation: the satellite signal received by the satellite receiver is y (t) a · ca (t) d (t) cos (2 π ft + θ) + n₀(equation 3), the satellite signal correlation operation can be expressed as For the tracking frequency error of the carrier loop,for the phase error of the code tracking loop, T is the pre-integration time, η_I、η_QIn order to be a noise, the noise is,R_uand v_uRespectively representing the observed position and velocity of the receiver,andrespectively representing the estimated position and velocity of the receiver,andthe I and Q way observation equations can be written as

Let I (P)_e0,V_e0)＝I_INS(formula 10) and Q (P)_e0,V_e0)＝Q_INS(formula 11) then Let z be { δI,δQ}_i(equation 14), the subscript 'i' indicates the number of tracked channels, and the observation equation for the system can be expressed as z_k＝h_k(x_k)+v_k(equation 15), where h (-) describes a non-linear function of the observation equation, z_kAnd v_kRespectively measuring vector and measuring noise;

s2, self-adaptive CKF algorithm: non-linear systems described by formulae (2) and (15) such as As shown in the drawings, the above-described,

CKF algorithm the CKF algorithm comprises the following main steps: step1 initialization: initializationAnd P₀(ii) a Step2 time update: since the equation of state is linear,and P_k/k-1Can be used for Step3 cumquat points calculation: by Cholesky decomposition, P_k/k-1Can be expressed asCalculating cubiture pointsThen Z_i,k/k-1＝h(X_i,k/k-1) (i ═ 1, 2., 2nx) (equation 22), Step4: measurement update: and

b. the adaptive CKF algorithm: assuming that there is uncertain process statistical noise in the system described by (equation 16), a time-varying adaptive factor can be used to reduce the effect of a priori information on the current state estimation in the prediction covariance matrix, so thatWherein λ is_kFor the adaptive attenuation factor to be calculated from the residual error,is composed ofWherein H (k) is an observation matrix(equation 30) covariance matrix of residuals may be expressed asChanging the Kalman gain matrix when the following conditions are satisfiedWherein, tr [ ·]Is a trace of the correlation matrix, if Q_kIn which there is a large deviation, estimated observation vectorAnd the observed quantity z_kCompared with the method, the method generates mutation and real filtrationThe wave estimation error will exceed the theoretical error, and therefore an adaptive factor λ must be used_kTo match the process noise lambda_kMemory for recordingUse of (formula 28) in place of P_k，Bringing (equation 29) into (equation 35) yields:due to the fact thatThe adaptive factor can be calculated as

S3, adopting a hypothesis test method to judge the uncertainty of the process noise: two assumptions are as follows: gamma ray₀The process noise statistics are accurate; gamma ray₁The process noise statistic has an uncertainty, which can be verified by the following function:due to the fact thatTherefore, when γ is assumed₁Correct, beta_kThe value will be much larger than the threshold.

Preferably, in said S1,where dx, dy and dz are position errors,andthe velocity error, phi x,phi y and phi z are attitude angle errors, a_x,a_yAnd a_zIs the accelerometer bias, g_x,g_yAnd g_zIs the gyro deviation, c_bAnd c_dClock bias and clock skew of the satellite receiver.

Preferably, in S1, x_k+1＝Φ_kx_k+w_kMiddle phi_kState transition matrix, x_kAnd w_kState vectors and process noise.

Preferably, in said S2,in (1),is w_kAnd v_kThe covariance matrix of (2).

Preferably, in said S2,in (1),n_xis the dimension of the state, [1 ]]Is a set of points, [1]_iIs a set of points [1]Point i.

Preferably, in said S3,in, the statistical function obeys x of m dimension²Distribution, where m is the residual vectorDimension (d) of (a).

Advantageous effects

The invention provides a method for realizing INS (inertial navigation system) assisted GNSS (global navigation satellite system) navigation and positioning by a satellite receiver self-adaptive CKF (CKF) algorithm. Compared with the prior art, the method has the following beneficial effects: the method for realizing INS assisted GNSS navigation positioning by the satellite receiver self-adaptive CKF algorithm comprises the following steps of S1, INS assisted GNSS navigation system structure: a. equation of state, b. measurement equation; s2, self-adaptive CKF algorithm: a, CKF algorithm, b, adaptive CKF algorithm; s3, adopting a hypothesis test method to judge the uncertainty of the process noise, and in order to improve the navigation positioning precision and stability of the process noise in the environment with high dynamic and complex interference, the invention uses an INS/GNSS super-tight combined structure, uses the I-path and Q-path observation data of a satellite receiver baseband and the position, speed and attitude information provided by the INS to carry out depth data fusion, corrects the baseband tracking error and improves the observation quality, and simultaneously uses a nonlinear adaptive CKF Algorithm (ACKF) to process the nonlinear problem of an observation equation, and uses the hypothesis test method to detect the uncertainty of the process noise, uses an adaptive factor to adjust the prior weight, processes the uncertainty disturbance in the filtering process, and improves the tolerance and robustness of the model, thereby improving the stability of the system from the algorithm level.

Drawings

FIG. 1 is a block diagram of an INS assisted GNSS navigation positioning system according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, the present invention provides a technical solution: a method for realizing INS assisted GNSS navigation positioning by a satellite receiver self-adaptive CKF algorithm specifically comprises the following steps:

s1, an INS assisted GNSS navigation system structure:

a. the state equation is as follows: the state of the INS/GNSS ultra-tight combination system is The state equation of the system is x_k+1＝Φ_kx_k+w_k(formula 2), an INS auxiliary mode in the satellite receiver adopts an INS/GNSS ultra-tight combined structure, and depth data fusion is carried out by utilizing the I-path observation data and the Q-path observation data of a satellite receiver baseband and the position, speed and attitude information provided by the INS, so that the baseband tracking error is corrected, and the observation quality is improved;

b. the measurement equation: the satellite signal received by the satellite receiver is y (t) a · ca (t) d (t) cos (2 π ft + θ) + n₀(equation 3), the satellite signal correlation operation can be expressed as For the tracking frequency error of the carrier loop,for the phase error of the code tracking loop, T is the pre-integration time, η_I、η_QIn order to be a noise, the noise is,R_uand v_uRespectively representing the observed position and velocity of the receiver,andrespectively representing the estimated position and velocity of the receiver,andthe I and Q way observation equations can be written as

Let I (P)_e0,V_e0)＝I_INS(formula 10) and Q (P)_e0,V_e0)＝Q_INS(formula 11) then Let z ═ δ I, δ Q }_i(equation 14), the subscript 'i' indicates the number of tracked channels, and the observation equation for the system can be expressed as z_k＝h_k(x_k)+v_k(equation 15), where h (-) describes a non-linear function of the observation equation, z_kAnd v_kRespectively measuring vector and measuring noise;

the satellite receiver uses the INS to assist the GNSS to perform positioning solution, improves the positioning performance of the satellite receiver in high dynamic and complex interference environment,

s2, self-adaptive CKF algorithm: non-linear systems described by formulae (2) and (15) such as As shown, the INS auxiliary mode in the satellite receiver adopts an INS/GNSS ultra-tight combined structure, the fusion filtering method uses an adaptive CKF algorithm, the method can not only process the nonlinear problem of an observation equation, but also process the uncertainty disturbance existing in the filtering process, and improve the tolerance and the robustness of a model, thereby improving the stability of the system from the aspect of the algorithm,

CKF algorithm the CKF algorithm comprises the following main steps: step1 initialization: initializationAnd P₀(ii) a Step2 time update: since the equation of state is linear,and P_k/k-1Can be used for Step3 cumquat points calculation: by Cholesky decomposition, P_k/k-1Can be expressed asCalculating cubiture pointsThen Z_i,k/k-1＝h(X_i,k/k-1)(i＝1,2,...,2n_x) (equation 22), Step4: and

b. the adaptive CKF algorithm: assuming that there is uncertain process statistical noise in the system described by (equation 16), a time-varying adaptive factor can be used to reduce the effect of a priori information on the current state estimation in the prediction covariance matrix, so thatWherein λ is_kFor the adaptive attenuation factor to be calculated from the residual error,is composed ofWherein H (k) is an observation matrix(equation 30) covariance matrix of residuals may be expressed asChanging the Kalman gain matrix when the following conditions are satisfiedWherein, tr [ ·]Is a trace of the correlation matrix, if Q_kIn which there is a large deviation, estimated observation vectorAnd the observed quantity z_kCompared with the abrupt change, the real filtering estimation error exceeds the theoretical error, so an adaptive factor lambda must be used_kTo match the process noise lambda_kMemory for recordingUse of (formula 28) in place of P_k，Bringing (equation 29) into (equation 35) yields:due to the fact thatThe adaptive factor can be calculated as

S3, adopting a hypothesis test method to judge the uncertainty of the process noise: two assumptions are as follows: gamma ray₀The process noise statistics are accurate; gamma ray₁The process noise statistic has an uncertainty, which can be verified by the following function:due to the fact thatTherefore, when γ is assumed₁Correct, beta_kThe value will be much larger than the threshold.

In the present invention, in S1,where dx, dy and dz are position errors,andfor velocity error, φ x, φ y and φ z for attitude angle error, a_x,a_yAnd a_zIs the accelerometer bias, g_x,g_yAnd g_zIs the gyro deviation, c_bAnd c_dClock bias and clock skew of the satellite receiver.

In the present invention, in S1, x_k+1＝Φ_kx_k+w_kMiddle phi_kState transition matrix, x_kAnd w_kState vectors and process noise.

In the present invention, in S2,in (1),is w_kAnd v_kThe covariance matrix of (2).

In the present invention, in S2,in (1),n_xis the dimension of the state, [1 ]]Is a set of points, [1]_iIs a set of points [1]Point i.

In the present invention, in S3,in, the statistical function obeys x of m dimension²Distribution, where m is the residual vectorDimension (d) of (a).

And those not described in detail in this specification are well within the skill of those in the art.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.