Pseudolite anti-multipath method based on data preprocessing and result evaluation

Abstract

The application provides a pseudolite anti-multipath method based on data preprocessing and result evaluation, and comprises the following steps: S1, performing signal evaluation preprocessing on the received intermediate frequency signal to obtain an intermediate frequency signal evaluation index; S2, performing quality evaluation on a tracking result to obtain a tracking quality evaluation index; and S3, modifying the acquisition tracking algorithm parameters through a comprehensive evaluation function according to the intermediate frequency signal evaluation index obtained in step S1 and the tracking quality evaluation index obtained in step S2; the application can intelligently adjust the algorithm parameters according to the positioning environment and the positioning effect, so that the positioning precision in the indoor complex multipath environment is improved.

Description

Technical Field

[0001] This invention relates to a pseudo-satellite anti-multipath method based on data preprocessing and result evaluation, belonging to the field of navigation and positioning technology. Background Technology

[0002] In indoor positioning, current research focuses primarily on Wi-Fi positioning, SLAM positioning, UWB positioning, and pseudosatellite positioning. Among these, pseudosatellite technology offers advantages such as high accuracy and seamless indoor / outdoor positioning, making it a relatively effective indoor positioning method. However, various walls and obstructions indoors generate a large amount of complex multipath signals, which become a major cause of interference with indoor positioning accuracy. In recent years, in addition to many outdoor GNSS anti-multipath methods, numerous new anti-multipath methods have been applied to pseudosatellite positioning systems.

[0003] Currently, technologies similar to this invention include the narrow correlation method, the strobe correlator algorithm, the MEDLL algorithm, and the vector tracking receiver algorithm. Among these, the narrow correlator method leads to decreased tracking robustness, while the strobe correlator method and the MEDLL algorithm have very high hardware computing power requirements. At the same time, a common drawback of these algorithms is that their parameters are all manually preset values ​​and cannot be intelligently adjusted according to the positioning environment and results, thus leading to poor positioning accuracy in complex multipath indoor environments. Summary of the Invention

[0004] Technical Problem: The positioning accuracy of pseudosatellite receivers is poor under complex multipath conditions indoors. This invention provides a pseudosatellite anti-multipath method based on data preprocessing and result evaluation. This method can intelligently adjust algorithm parameters according to the positioning environment and positioning effect, thereby improving positioning accuracy in complex multipath indoor environments.

[0005] Technical solution: The method of the present invention to solve the above-mentioned technical problems is achieved through the following technical solution:

[0006] The present invention provides a pseudo-satellite anti-multipath method based on data preprocessing and result evaluation, comprising the following steps:

[0007] S1. Perform signal evaluation preprocessing on the received intermediate frequency signal to obtain intermediate frequency signal evaluation indicators;

[0008] S2. Conduct a quality assessment of the tracking results to obtain tracking quality assessment indicators;

[0009] S3. Based on the intermediate frequency signal evaluation index obtained in step S1 and the tracking quality evaluation index obtained in step S2, modify the capture and tracking algorithm parameters through a comprehensive evaluation function;

[0010] Furthermore, the intermediate frequency signal received during docking in step S1 undergoes signal evaluation preprocessing, specifically as follows:

[0011] S11. Obtain the intermediate frequency signal z(t) with the carrier eliminated through the radio frequency front end and carrier loop;

[0012] S12. Let the current time be t0, set a time interval T, extract the intermediate frequency signal z(t) within a certain time interval, t∈(t0-T,t0) for signal evaluation preprocessing, and obtain the intermediate frequency signal evaluation index α(t0). The detailed steps are as follows:

[0013] S121. Correlate the locally constructed signal with the intermediate frequency signal to obtain the intermediate frequency signal correlation curve R. x By finding the maximum correlation peak (τ), the amplitude, delay, and phase estimation parameters of the direct signal can be obtained.

[0014] S122. Remove the direct signal obtained in the first step from the received signal, and correlate the locally constructed signal with the received signal again. Find the maximum correlation peak value to obtain the amplitude, delay, and phase estimation parameters of the first multipath signal. At this point, the first multipath signal is removed from the received signal.

[0015] S123. Repeat S122 to gradually eliminate each multipath signal in the received signal until the ratio of the power of the direct signal estimated in step 1 to the power of the remaining signal after multipath signal elimination reaches a threshold value. This indicates that multipath signal elimination is complete. This threshold value should be the signal-to-noise ratio value at which the maximum permissible pseudorange error is generated.

[0016] Given a pre-defined set number of M multipath signals, the calculation of the amplitude, delay, and phase parameters of the nth multipath signal is performed. When the estimated parameters are obtained, the formulas are as follows:

[0017]

[0018]

[0019]

[0020] S12.4) corresponding intermediate frequency signal evaluation index

[0021] S13. The intermediate frequency signal preprocessing calculation is performed only once every T time interval. The intermediate frequency signal intercepted in the (t0-T, t0) time interval is subjected to signal evaluation preprocessing in the (t0, t0+T) time interval. The obtained intermediate frequency signal evaluation index α(t0) is used to guide the calculation of tracking algorithm parameters in the (t0+T, t0+2T) time interval.

[0022] Furthermore, step 2 involves analyzing the tracking results to obtain tracking quality evaluation indicators, as detailed below:

[0023] S21. Record the input and output values ​​x(t) and y(t) of the phase detector;

[0024] S22. Let the current time be t0. Using the same time interval T as in step S12, extract the phase detector input and output values ​​x(t), t∈(t0-T,t0) and y(t), t∈(t0-T,t0) over a certain period for analysis to obtain the tracking quality evaluation index β(t0). The specific method is as follows:

[0025] S22 1. Plot the graph of the parametric function y(x);

[0026] S222. Find the zero-crossing point of y(x), and let its coordinates be (δ,0). Then the tracking quality assessment index β(t0) = |δ|.

[0027] S23. The calculation of the tracking quality assessment index should be performed at the same time as the calculation of the intermediate frequency signal preprocessing, and the time interval T used should also be exactly the same.

[0028] Furthermore, step 3 involves modifying the capture and tracking algorithm parameters using a comprehensive evaluation function, as detailed below:

[0029] S31. Obtain the intermediate frequency signal evaluation index α(t0) and the tracking quality evaluation index β(t0) through steps 1 and 2;

[0030] S32. Adjust the parameters that need to be adjusted using a comprehensive evaluation function. Let the parameter before adjustment be γ(t0-T), then the parameter after adjustment is... Where k, k1, and k2 are the set reliability parameters of the indicators;

[0031] The parameter that needs to be adjusted is the correlator interval d. Let k = 2, k1 = k2, then...

[0032]

[0033] The parameter to be adjusted is the phase detector coefficient k(t0). Let k = 2, k1 = k2, then...

[0034]

[0035] S33. The obtained parameter γ(t0) should be used for capture and tracking in the time period (t0+T, t0+2T).

[0036] Furthermore, the time interval is T = 0.1s.

[0037] Beneficial effects:

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] This invention employs a multipath mitigation scheme combining intermediate frequency (IF) signal preprocessing and result evaluation. IF signal preprocessing provides a general understanding of the current positioning environment, allowing for intelligent parameter adjustment to achieve a balance between robustness and accuracy. Result evaluation assesses the current positioning performance and feeds this feedback back into parameter adjustment, optimizing parameters and improving both robustness and accuracy. In summary, this invention enables pseudosatellite receivers to intelligently adjust parameters based on IF signals and positioning results under complex indoor multipath conditions, thereby enhancing positioning robustness and accuracy. Attached Figure Description

[0040] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0041] The implementation process of the present invention will be described in detail below with reference to the embodiments and the accompanying drawings.

[0042] Example 1:

[0043] The present invention provides a pseudo-satellite anti-multipath method based on data preprocessing and result evaluation, comprising the following steps:

[0044] S1. Perform signal evaluation preprocessing on the received intermediate frequency signal to obtain intermediate frequency signal evaluation indicators;

[0045] S11. Obtain the intermediate frequency signal z(t) with the carrier eliminated through the radio frequency front end and carrier loop;

[0046] S12. Let the current time be t0, set a time interval T = 0.1s, extract the intermediate frequency signal z(t) within a certain time interval, t∈(t0-T,t0) for signal evaluation preprocessing, and obtain the intermediate frequency signal evaluation index α(t0). The detailed steps are as follows:

[0047] S121. Correlate the locally constructed signal with the intermediate frequency signal to obtain the intermediate frequency signal correlation curve R. x By finding the maximum correlation peak (τ), the amplitude, delay, and phase estimation parameters of the direct signal can be obtained.

[0048] S122. Remove the direct signal obtained in the first step from the received signal, and correlate the locally constructed signal with the received signal again. Find the maximum correlation peak value to obtain the amplitude, delay, and phase estimation parameters of the first multipath signal. At this point, the first multipath signal is removed from the received signal.

[0049] S123. Repeat S122 to gradually eliminate each multipath signal in the received signal until the ratio of the power of the direct signal estimated in step 1 to the power of the remaining signal after multipath signal elimination reaches a threshold value. This indicates that multipath signal elimination is complete. This threshold value should be the signal-to-noise ratio value at which the maximum permissible pseudorange error is generated.

[0050] Given a pre-defined set number of M multipath signals, the calculation of the amplitude, delay, and phase parameters of the nth multipath signal is performed. When the estimated parameters are obtained, the formulas are as follows:

[0051]

[0052]

[0053]

[0054] S12.4) corresponding intermediate frequency signal evaluation index

[0055] S13. The intermediate frequency signal preprocessing calculation is performed only once every T time interval. The intermediate frequency signal intercepted in the (t0-T, t0) time interval is subjected to signal evaluation preprocessing in the (t0, t0+T) time interval. The obtained intermediate frequency signal evaluation index α(t0) is used to guide the calculation of tracking algorithm parameters in the (t0+T, t0+2T) time interval.

[0056] S2. Conduct a quality assessment of the tracking results to obtain tracking quality assessment indicators;

[0057] S21. Record the input and output values ​​x(t) and y(t) of the phase detector;

[0058] S22. Let the current time be t0. Using the same time interval T as in step S12, extract the phase detector input and output values ​​x(t), t∈(t0-T,t0) and y(t), t∈(t0-T,t0) over a certain period for analysis to obtain the tracking quality evaluation index β(t0). The specific method is as follows:

[0059] S22 1. Plot the graph of the parametric function y(x);

[0060] S222. Find the zero-crossing point of y(x), and let its coordinates be (δ,0). Then the tracking quality assessment index β(t0) = |δ|.

[0061] S23. The calculation of the tracking quality assessment index should be performed at the same time as the calculation of the intermediate frequency signal preprocessing, and the time interval T used should also be exactly the same.

[0062] S3. Based on the intermediate frequency signal evaluation index obtained in step S1 and the tracking quality evaluation index obtained in step S2, modify the capture and tracking algorithm parameters through a comprehensive evaluation function;

[0063] S31. Obtain the intermediate frequency signal evaluation index α(t0) and the tracking quality evaluation index β(t0) through steps 1 and 2;

[0064] S32. Adjust the parameters that need to be adjusted using a comprehensive evaluation function. Let the parameter before adjustment be γ(t0-T), then the parameter after adjustment is... Where k, k1, and k2 are the set reliability parameters of the indicators;

[0065] The parameter that needs to be adjusted is the correlator interval d. Let k = 2, k1 = k2, then...

[0066]

[0067] The parameter to be adjusted is the phase detector coefficient k(t0). Let k = 2, k1 = k2, then...

[0068]

[0069] S33. The obtained parameter γ(t0) should be used for capture and tracking in the time period (t0+T, t0+2T).

[0070] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the specific embodiments described above. The specific embodiments and descriptions in the specification are merely for further illustrating the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A method for pseudolite anti-multipath based on data pre-processing and result evaluation, characterized in that, The method comprises the following steps: S1. Signal evaluation preprocessing is performed on the received intermediate frequency signal to obtain an intermediate frequency signal evaluation index; S2. Quality evaluation is performed on the tracking result to obtain a tracking quality evaluation index; S3. According to the intermediate frequency signal evaluation index obtained in step S1 and the tracking quality evaluation index obtained in step S2, the acquisition and tracking algorithm parameters are modified through a comprehensive evaluation function; The signal evaluation preprocessing performed on the received intermediate frequency signal in step S1 is specifically as follows: S11. The intermediate frequency signal z(t) from which the carrier is eliminated is obtained through a radio frequency front end and a carrier loop; S12. Assuming that the current time is t0, a time interval T is set, and the intermediate frequency signal z(t) in a time period t∈(t0-T, t0) is intercepted for signal evaluation preprocessing to obtain an intermediate frequency signal evaluation index α(t0), and the detailed steps are as follows: S121. Correlate the locally constructed signal with the intermediate frequency signal through a correlator to obtain an intermediate frequency signal correlation curve R x (τ), find the maximum correlation peak value, and obtain the amplitude, delay, and phase estimation parameters of the direct signal S122. The direct signal obtained in step S121 is removed from the received intermediate frequency signal, and the locally constructed signal is again correlated with the received signal to find the maximum correlation peak and obtain the amplitude, delay, and phase estimation parameters of the first multipath signal At this time, the first multipath signal is eliminated from the received intermediate frequency signal; S123. S122 is repeated, and each multipath signal in the received intermediate frequency signal is gradually eliminated until the ratio of the power of the direct signal estimated in step S121 to the power of the residual signal after the multipath signal is eliminated reaches a threshold value, which indicates that the multipath signal elimination is completed, and the threshold value is the signal-to-noise ratio value when the maximum allowable pseudorange error is generated, The number of multipath signals is preset as M, and when the amplitude, delay and phase parameters of the nth multipath signal are calculated The estimation parameter acquisition formula is: Where j is an imaginary unit, and the subscript m represents other multipath signals except the n th multipath signal; S124. Corresponding intermediate frequency signal evaluation index S13. The intermediate frequency signal preprocessing calculation is performed once every T, the intermediate frequency signal intercepted in the time period (t0-T, t0) is subjected to signal evaluation preprocessing in the time period (t0, t0+T), and the intermediate frequency signal evaluation index α(t0) obtained is used to guide the calculation of the tracking algorithm parameters in the time period (t0+T, t0+2T); The tracking result is analyzed in step 2 to obtain a tracking quality evaluation index, and the specific method is as follows: S21. The input and output values x(t) and y(t) of the phase detector are recorded; S22. Assuming that the current time is t0, the same time interval T as in step S12 is adopted, and the phase detector input and output values x(t), t∈(t0-T, t0) and y(t), t∈(t0-T, t0) in a time period are intercepted for result analysis to obtain a tracking quality evaluation index β(t0), and the specific method is as follows: S221. A parameter function image y(x) is drawn; S222. The zero-crossing point of y(x) is found, and the coordinates are set as (δ, 0), so the tracking quality evaluation index β(t0) = |δ|; S23. The calculation of the tracking quality evaluation index is performed at the same time as the intermediate frequency signal preprocessing calculation, and the same time interval T is adopted; The acquisition and tracking algorithm parameters are modified through a comprehensive evaluation function in step 3, and the specific method is as follows: S31. The intermediate frequency signal evaluation index α(t0) and the tracking quality evaluation index β(t0) are obtained through steps 1 and 2; S32. Adjust the parameters that need to be adjusted through the comprehensive evaluation function, and set the parameters before adjustment as γ(t0-T), and the parameters after adjustment as wherein k, k1, k2 are set index credibility parameters; The parameter to be adjusted is the correlator interval d, and assuming that k=2 and k1=k2, then The parameter to be adjusted is the phase detector coefficient k(t0), and assuming that k=2 and k1=k2, then S33. The obtained parameter γ(t0) is used for acquisition and tracking in the time period (t0+T, t0+2T).

2. The method of claim 1, wherein the method is based on data pre-processing and result evaluation for pseudolite anti-multipath. The time interval is T=0.1 s.

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