阅读说明：本技术 一种提高多普勒精度的方法 (Method for improving Doppler accuracy ) 是由 谭敏强 关威 于 2018-07-11 设计创作，主要内容包括：本发明提供一种提高多普勒精度的方法,a、卡尔曼滤波,用载波相位作为滤波器观测量,建立动态模型,滤波得到相位率和相位二次变化率。b、超时补偿,对卡尔曼滤波得到的相位率进行补偿,即给相位率加上一个补偿值,使其适用于高动态下跟踪环路的短时间间隔特性。补偿值是利用滤波器的相位二次变化率乘以超前时间得到的。c、延时补偿,对相位率补偿,使其适用于高动态特性。以估计的相位二次变化率差分除以时间间隔作为滤波器输入,用一阶卡尔曼滤波器滤波得到相位三次变化率,再用相位率乘以时延得到补偿值。采用上述方案,能提高接收机载波相位率的精度,对噪声有很好的抑制能力,为高精度速度测量提供精确的观测信息。(The invention provides a method for improving Doppler accuracy, which comprises the steps of a, Kalman filtering, establishing a dynamic model by using a carrier phase as a filter observed quantity, and filtering to obtain a phase rate and a phase secondary change rate. b. And overtime compensation, namely compensating the phase rate obtained by the Kalman filtering, namely adding a compensation value to the phase rate to ensure that the phase rate is suitable for the short time interval characteristic of a tracking loop under high dynamic. The compensation value is obtained by multiplying the second-order rate of change of the phase of the filter by the lead time. c. Delay compensation, compensating for phase rate, makes it suitable for high dynamic characteristics. And dividing the estimated phase quadratic change rate difference by the time interval as filter input, filtering by using a first-order Kalman filter to obtain a phase cubic change rate, and multiplying the phase cubic change rate by the time delay to obtain a compensation value. By adopting the scheme, the accuracy of the carrier phase rate of the receiver can be improved, the noise suppression capability is good, and accurate observation information is provided for high-accuracy speed measurement.)

1. A method for improving doppler accuracy, comprising the steps of:

step 1: signal capture tracking, wherein a tracking loop demodulates a carrier phase and an instantaneous Doppler observed quantity;

step 2: for the carrier phase given by the tracking loop, smooth filtering is used for removing noise, and a high-precision Doppler observed value is extracted;

and step 3: and measuring and calculating the user speed by using the high-precision Doppler value.

2. The method according to claim 1, wherein the step 2 further comprises the following steps:

step 201: obtaining minimum variance estimation of the phase rate by using a third-order Kalman filter;

step 202: compensating the lead of the filter estimation value by using the acceleration estimation value;

step 203: and filtering the acceleration difference value by using a first-order Kalman filter, and compensating the delay of the phase rate estimation by using the estimation value.

3. The method of claim 2, wherein the step 201 further comprises: obtaining minimum variance estimation of the phase rate by using a third-order Kalman filter; namely Kalman filtering, namely using a phase value as an observed quantity, and using a phase, a phase rate and a phase second-order change rate as state variables to estimate values of the state variables; specifically, the formula (1) is shown as follows:

4. the method of claim 3, wherein said step 202 further comprises: compensating the lead of the filter estimation value by using the acceleration estimation value; namely, timeout compensation; multiplying the second order rate of change of phase by the estimate of the lead time compensated phase rate; specifically, the formula (2) is shown as follows:

5. the method of claim 4, wherein step 203 further comprises: filtering the acceleration difference value by using a first-order Kalman filter, and compensating the delay of the phase rate estimation by using the estimation value; namely delay compensation, the phase rate estimation has delay due to the fact that the filter estimation uses the old estimation; the specific method of the delay compensation is to divide the difference of the second-order rate of change of the phase by the time interval as the observed quantity, estimate the third-order rate of change of the phase by the first-order Kalman filtering, multiply the time delay, and compensate the delay of the phase rate, which is specifically expressed by the formula (3):

the parameters in the above equations (1), (2) and (3) are described as: k: the number of interruptions, and the number of iterations calculated; theta_k: the carrier phase is directly given by the tracking loop, and each interruption takes a value once;

Technical Field

The invention relates to the technical field of digital signal processing in a satellite receiver, in particular to a method for improving Doppler accuracy.

Background

In global positioning satellite systems (GNSS), there are generally two ways of velocity measurement: firstly, carrier phase rate is extracted from carrier phase to calculate, and secondly, instantaneous Doppler original observed quantity obtained by a tracking loop of a receiver is used for calculating. The carrier phase given by the tracking loop has higher precision than the Doppler value, and most receivers adopt the carrier phase to extract the carrier phase rate. The filtering technique can remove phase noise, but this results in large output delay, and under high dynamic condition, the carrier phase rate changes rapidly, and the result precision is poor due to the delay. Therefore, it is necessary to process the phase rate of the filter output to obtain a doppler value with high accuracy, and to use the doppler value for user velocity calculation.

The standard deviation of the phase noise obtained by the tracking loop processing is about 0.001rad, and the noise is larger under the high dynamic environment. It was found through experimentation that for a velocity accuracy requirement of 0.2m/s, the phase rate noise standard deviation used to calculate velocity should be less than 0.2 rad/s. The phase rate obtained by directly utilizing the carrier phase difference is used for speed calculation, and the high-precision calculation requirement cannot be met. It is therefore necessary to process the carrier phase to obtain a high accuracy phase rate.

The prior art includes Taylor series expansion, least square waveform fitting, Kalman filtering, dynamic window and nonlinear tracking differentiators. The taylor series expansion method uses the existing phase data difference to calculate each order derivative of the phase, and then uses the taylor series formula to calculate the phase rate. This method can approximate the rapid change of the phase by using the derivative of the phase, but has no filtering function, and the derivative is calculated by using more old phase values, and the calculation result has time delay. The least square waveform fitting method uses polynomial expression of sampling interval to express phase, uses multiple phase value to obtain polynomial coefficient which can make variance of phase estimation value be minimum, and uses polynomial coefficient to calculate phase rate. The method has a good denoising effect, the result precision of least square estimation is high, but an old phase value is used, and the result has time delay. The kalman filtering technique is also a minimum variance based technique, and has higher estimation accuracy due to the inclusion of data on statistical characteristics of the observed values. However, this method increases the amount of computation as the state quantity increases, and the system estimate preserves the old phase estimate, with a delay in the result. The dynamic window method uses the difference between the latest phase value and an old phase value to obtain the phase rate, the time interval between the two phase values is dynamically changed, the phase difference value is used to calculate a value to compare with the threshold value, and the time interval is adjusted. This method has an averaging effect and can adjust the time to meet the dynamic change requirements, but requires a long time interval to make the result accurate enough, but has a large time delay. The nonlinear tracking differentiator adopts a nonlinear function to approximate the quadratic change rate of the phase, then estimates the phase change rate and compensates the time delay. The method can well filter noise and contain dynamic changes of phases, but the nonlinear function has large calculation amount and is not suitable for real-time calculation.

Accordingly, the prior art is deficient and needs improvement.

Disclosure of Invention

The invention solves the technical problem of how to obtain the high-precision phase rate from the carrier phase given by the tracking loop. The invention utilizes the carrier phase given by the tracking loop to obtain the high-precision phase rate, and the prior art has the defects of incapability of meeting the dynamic environment, time delay, large calculation amount and the like. Aiming at the defects, the invention provides a method for acquiring the high-precision phase rate based on the concepts of Kalman filtering and time delay compensation. And a third-order Kalman filter is used for obtaining the minimum variance estimation of the phase rate, the advance of the filtering estimation value is compensated by the acceleration estimation value, then the acceleration difference value is filtered by the first-order Kalman filter, and the estimation value is used for compensating the delay of the phase rate estimation value. The method has the advantages of noise filtering and dynamic characteristic retention, small calculation amount, and capability of obtaining high-precision phase change rate in a high dynamic environment, wherein the phase change rate is used for estimating an average Doppler observed value in unit time and the Doppler change rate and is finally used for measuring the user speed. The method can also be used for smoothing the Doppler observed value after being simply modified, and the smoothed Doppler observed value can improve the Doppler accuracy and stability, weaken the noise thereof and further improve the speed measurement accuracy of the user receiver.

The technical scheme of the invention is as follows:

a method of improving doppler accuracy comprising the steps of:

step 1: signal capture tracking, wherein a tracking loop demodulates a carrier phase and an instantaneous Doppler observed quantity;

step 2: for the carrier phase given by the tracking loop, smooth filtering is used for removing noise, and a high-precision Doppler observed value is extracted;

and step 3: and measuring and calculating the user speed by using the high-precision Doppler value.

Further, the step 2 specifically includes the following steps:

step 201: obtaining minimum variance estimation of the phase rate by using a third-order Kalman filter;

step 202: compensating the lead of the filter estimation value by using the acceleration estimation value;

step 203: and filtering the acceleration difference value by using a first-order Kalman filter, and compensating the delay of the phase rate estimation by using the estimation value.

Further, the step 201 further includes: obtaining minimum variance estimation of the phase rate by using a third-order Kalman filter; namely Kalman filtering, namely using a phase value as an observed quantity, and using a phase, a phase rate and a phase second-order change rate as state variables to estimate values of the state variables; specifically, the formula (1) is shown as follows:

further, the step 202 further includes: compensating the lead of the filter estimation value by using the acceleration estimation value; namely, timeout compensation; multiplying the second order rate of change of phase by the estimate of the lead time compensated phase rate; specifically, the formula (2) is shown as follows:

further, the step 203 further includes: filtering the acceleration difference value by using a first-order Kalman filter, and compensating the delay of the phase rate estimation by using the estimation value; namely delay compensation, the phase rate estimation has delay due to the fact that the filter estimation uses the old estimation; the specific method of the delay compensation is to divide the difference of the second-order rate of change of the phase by the time interval as the observed quantity, estimate the third-order rate of change of the phase by the first-order Kalman filtering, multiply the time delay, and compensate the delay of the phase rate, which is specifically expressed by the formula (3):

the parameters in the above equations (1), (2) and (3) are described as: k: the number of interruptions, and the number of iterations calculated; theta_k: the carrier phase is directly given by the tracking loop, and each interruption takes a value once;

a carrier phase predicted by a kalman filter;

the state quantity predicted by the Kalman filter comprises a phase, a phase rate and a phase quadratic change rate;

a state quantity estimated by a kalman filter;a predicted value of error covariance of the state quantity; p_k: an estimate of the error covariance of the state quantities; phi_k: a transition matrix of state quantities; q_k: a system noise covariance matrix; r_k: observing a noise covariance matrix; h_k: an observation matrix of a Kalman filter; k_k: a gain matrix of a Kalman filter; i: an identity matrix;

carrier phase rate estimated by a kalman filter;

a carrier phase quadratic rate of change estimated by a kalman filter; t is t_h: system lead time;

the carrier phase rate after the primary compensation; t: an interrupt time interval; z is a radical of_k: an observed value of the carrier phase cubic change rate; p'_k: an estimate of the error covariance of the second Kalman filter state quantity; q'_k: a second Kalman filter system noise covariance matrix; r'_k: a second Kalman filter observes a noise covariance matrix; k'_k: a gain matrix of a second Kalman filter;an estimate of the third rate of change of the carrier phase.

By adopting the scheme, the method of Kalman filtering, overtime compensation, delay compensation and the like is utilized, the noise in the carrier phase is effectively inhibited, the dynamic characteristic of the phase rate can be well kept, the calculated amount is small, and the real-time property is met. Through analysis and simulation experiments, the following results are obtained: the algorithm can improve the precision of the phase rate of carrier waves of the receiver, has good inhibition capacity on noise, and provides accurate observation information for high-precision speed measurement. The method can also be used to smooth doppler observations, with only minor changes to the filter, and can also obtain accurate velocity estimates under high dynamic conditions.

Drawings

FIG. 1 is a schematic diagram of a phase processing method according to the present invention.

FIG. 2 is a schematic diagram of a simulation system according to the present invention.

FIG. 3 is a schematic diagram of the third order rate of change of the phase according to the present invention.

FIG. 4 is a schematic diagram of phase error of the present invention.

FIG. 5 is a diagram of differential noise according to the present invention.

FIG. 6 is a noise diagram of a primary filtering result according to the present invention.

FIG. 7 is a diagram illustrating the residual noise after correction according to the present invention.

FIG. 8 is a schematic diagram of the third order rate of change of the phase according to the present invention.

FIG. 9 is a schematic diagram of phase rate noise of the Kalman filter of the present invention.

Detailed Description

In order to facilitate an understanding of the invention, the invention is described in more detail below with reference to the accompanying drawings and specific examples. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention provides a method for improving doppler accuracy,

step 1: signal capture tracking, wherein a tracking loop demodulates a carrier phase and an instantaneous Doppler observed quantity;

step 2: for the carrier phase given by the tracking loop, smooth filtering is used for removing noise, and a high-precision Doppler observed value is extracted;

and step 3: and measuring and calculating the user speed by using the high-precision Doppler value.

As shown in fig. 1, in step 2, the extracting the high-precision doppler observation specifically includes the following steps:

step 201: obtaining minimum variance estimation of the phase rate by using a third-order Kalman filter;

step 202: compensating the lead of the filter estimation value by using the acceleration estimation value;

step 203: and filtering the acceleration difference value by using a first-order Kalman filter, and compensating the delay of the phase rate estimation by using the estimation value.

Step 201: obtaining minimum variance estimation of the phase rate by using a third-order Kalman filter; namely Kalman filtering, namely using a phase value as an observed quantity, and using a phase, a phase rate and a phase second-order change rate as state variables to estimate values of the state variables; specifically, the formula (1) is shown as follows:

step 202: compensating the lead of the filter estimation value by using the acceleration estimation value; i.e., time-out compensation, the second order rate of change estimate of the phase is advanced since the third order rate of change of the phase under high dynamic conditions is not contained in the state quantity, so that the filter can be kept stable. Thus, multiplying the second order rate of change of phase by the estimate of the lead time compensated phase rate; specifically, the formula (2) is shown as follows:

step 203: filtering the acceleration difference value by using a first-order Kalman filter, and compensating the delay of the phase rate estimation by using the estimation value; i.e., delay compensation, the phase rate estimate has a delay due to the filter estimate using the old estimate. The specific method of the delay compensation is to divide the difference of the second-order rate of change of the phase by the time interval as the observed quantity, estimate the third-order rate of change of the phase by the first-order Kalman filtering, multiply the time delay, and compensate the delay of the phase rate, which is specifically expressed by the formula (3):

the parameters in the above equations (1), (2) and (3) are described as:

1) k: the number of interruptions, and the number of iterations calculated;

2)θ_k: the carrier phase is directly given by the tracking loop, and each interruption takes a value once;

3)

a carrier phase predicted by a kalman filter;

4)

the state quantity predicted by the Kalman filter comprises a phase, a phase rate and a phase quadratic change rate;

5)

states estimated by Kalman filterAn amount;

6)

a predicted value of error covariance of the state quantity;

7)P_k: an estimate of the error covariance of the state quantities;

8)Φ_k: a transition matrix of state quantities;

9)Q_k: a system noise covariance matrix;

10)R_k: observing a noise covariance matrix;

11)H_k: an observation matrix of a Kalman filter;

12)K_k: a gain matrix of a Kalman filter;

13) i: an identity matrix;

14)

carrier phase rate estimated by a kalman filter;

15)

a carrier phase quadratic rate of change estimated by a kalman filter;

16)t_h: system lead time;

17)the carrier phase rate after the primary compensation;

18) t: an interrupt time interval;

19)z_k: an observed value of the carrier phase cubic change rate;

20)P′_k: an estimate of the error covariance of the second Kalman filter state quantity;

21)Q′_k: a second Kalman filter system noise covariance matrix;

22)R′_k: a second Kalman filter observes a noise covariance matrix;

23)K′_k: a gain matrix of a second Kalman filter;

24)

an estimated value of the carrier phase cubic change rate;

as shown in fig. 2, fig. 2 is a structure of the simulation system of the present invention, and the algorithm effect of the present invention is evaluated, and the basic processes of steps 1 and 2 are as follows: the digital source of the receiver generates carrier phase and noiseless accurate Doppler, then adds noise to the carrier phase, then carries out filtering processing on the carrier phase added with the noise by the filtering algorithm (shown in figure 1) of the invention, extracts high-precision phase rate, compares the high-precision phase rate with the accurate Doppler parallel analysis, and analyzes the precision of the filtering result of the invention.

The algorithm provided by the invention mainly realizes phase rate extraction and inhibits the noise of the carrier phase provided by the tracking loop. The phase used for simulation includes first, second and third order rates of change. The noise is white gaussian noise with a mean value of 0 and a standard deviation of 0.001. The initial values of the filter state quantities are all 0. The moving speed of the receiver is 300m/s to 2700m/s, the relative motion of the receiver and the satellite generates Doppler frequency shift, and the third-order change rate of the phase is shown in figure 3.

Fig. 4 shows the noise error of the phase provided by the tracking loop. The error depends on the performance of the tracking loop and is related to receiver thermal noise, dynamic stress error, etc. For the high-precision requirement of the speed under high dynamic state, the original phase observed quantity cannot be directly used for speed measurement, and the high-precision extraction processing must be carried out on the phase to suppress the noise thereof, so that the speed precision requirement can be met. Therefore, the invention can just inhibit the phase noise in a filtering smoothing mode and improve the speed measurement precision in a high dynamic environment.

Fig. 5 shows the phase ratio obtained directly from the phase difference. In the stationary part the noise amplitude is between-0.2 and 0.1, in the widely varying part the noise amplitude exceeds 10. Such a phase rate value does not suppress noise, does not retain dynamic characteristics, and cannot satisfy the requirement for high-precision speed calculation in a high-dynamic environment.

Fig. 6 shows the noise of the primary filtering output result of the present invention, corresponding to the algorithm step 201. The first 3000 results are not shown because the filter requires time to converge. The filter has good denoising effect, the phase rate (shown in figure 5) obtained by comparing direct phase difference is obviously improved, the noise amplitude of a speed stable part is between-0.05 and-0.12, but the noise amplitude of a part with large motion state change is between-0.8 and 0.6, and the dynamic characteristic of a user is not reserved.

Fig. 7 shows the noise after the delay compensation result of the present invention, corresponding to the algorithm step 203. Here, the time interval for the source to generate data is 20ms, and the system timeout is negligible, so that the filtering result and the delay compensation result are only drawn once. After compensation, the steady partial noise is slightly increased to be between 0 and-0.2 compared with the primary filtering result, but the partial noise with large dynamic change is reduced to be between 0 and 0.1, and the dynamic characteristics of the user are well kept.

The parameter settings in the simulation are as in table 1:

table 1 simulation parameter settings

P₀And P'₀Is the initial value of the error covariance matrix. .

In order to check the robustness of the filter, different high dynamic scenes are simulated, and the simulation parameters are shown in the table 2. The speed, acceleration and jerk values in the table are large and change rapidly, and are similar to the actual high-speed motion tracks, such as missiles and fighters. Simulation results show that the filter can obtain accurate speed estimation under different high dynamic environments.

Table 2 simulation results:

since the data interval of the source is 20ms and the time interval is long, the lead time is 0. To illustrate the effect of lead time compensation, another simulation is presented here.

The simulation time interval is 0.5 ms. The simulated phase is obtained by integrating the phase three times of change rate. The noise is consistent with the first simulation. The three-fold rate of change of phase is shown in fig. 8. The initial value of the secondary phase change rate is 1.3rad/s, and the initial value of the phase rate is-2896 rad. The simulation parameters are different from those of the first simulation, and the noise matrixes R of the two filters are both 1 and t_hIs 0.0005, t_dAnd was 0.012. The simulation results are shown in FIG. 9, verifying the role and necessity of the algorithm step 202.

In fig. 9, the error of the first 3000 points is large because the filter is in the convergence process, and the initial value of the phase rate is 0, and the deviation from the actual value is large. Having converged, the filter output error is large and the error curve is skewed. After time-out compensation, the error is still large, but the curve flattens. After the delay compensation, the error decreases, but the error jitter increases, because the rate of change of the phase by three times contains a lot of noise. In a word, after two times of compensation, the result precision of the filtering is improved.

The technical features mentioned above are combined with each other to form various embodiments which are not listed above, and all of them are regarded as the scope of the present invention described in the specification; also, modifications and variations may be suggested to those skilled in the art in light of the above teachings, and it is intended to cover all such modifications and variations as fall within the true spirit and scope of the invention as defined by the appended claims.