Satellite navigation signal abnormality cause analysis method

Abstract

The application provides a satellite navigation signal abnormal reason analysis method, establishes a satellite signal transmission channel combination distortion model, generates an ideal signal, and constitutes a combination distortion distortion model library; collects measured satellite downlink signal data to obtain a measured signal quality evaluation result, compares and analyzes the measured signal quality evaluation result with a set evaluation index, finds out a group of the most matched evaluation result data and pre-stored data in the distortion model library; and according to the best matching relationship between the measured satellite signal evaluation result and the distortion model library data, obtains the corresponding satellite signal transmission channel distortion condition. The application can efficiently and credibly give an abnormal signal specific reason analysis result, helps a navigation system to quickly lock the signal abnormal reason, and provides valuable reference and important support for channel distortion compensation and stable operation of the system.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of satellite navigation signal quality evaluation, and particularly relates to a satellite navigation signal abnormality cause analysis method. BACKGROUND

[0002] Satellite navigation signal abnormality can cause system service performance to decrease, and can even cause immeasurable loss to military and civilian applications. Navigation signal quality evaluation can reflect signal abnormality in a timely manner, and through in-depth analysis of signal quality evaluation results, satellite signal quality and service performance can be fully understood.

[0003] At present, research on satellite navigation signal quality evaluation technology mainly uses collected measured signal data to analyze signal quality, and evaluation contents mainly focus on frequency domain, modulation domain, time domain, correlation domain and measurement domain, and various evaluation methods are proposed and various indexes are established to realize evaluation and analysis of signal quality. Common indexes include ground receiving power, combined power spectrum deviation, correlation loss, S-curve zero-crossing point deviation, S-curve zero-crossing point slope, time domain waveform distortion and signal coherence, and through comparison of measured evaluation results and index requirements, whether the collected signal quality meets system service requirements can be determined. However, the traditional evaluation method can only determine the degree of signal quality, and it is not easy to determine the specific abnormality cause of the signal, and it is impossible to provide direct and effective suggestions for navigation system maintenance.

[0004] In addition, in the traditional research, the 2OS model adopted by ICAO and the corresponding extended model are mainly used to preliminarily judge the signal abnormality of PSK and simple BOC signals, and the main method is to compare the measured signal waveform with the ideal signal waveform to determine whether the measured signal is abnormal. However, the traditional method can only preliminarily determine whether the signal has digital distortion or analog distortion, and cannot accurately locate the abnormality cause; at the same time, modern navigation signals mainly use multiplexed signals to improve frequency band utilization, the number of signal channels increases, and more intermodulation terms are added to ensure the constant envelope characteristics of the combined signal, resulting in more complex signal waveform distortion, and the traditional single index analysis method is difficult to distinguish the quality of the signal, so it is of great significance to jointly analyze the signal abnormality cause by judging the data results of multiple indexes, and there is currently a lack of research in this regard. SUMMARY

[0005] In order to overcome the shortcomings of the prior art, the application provides a satellite navigation signal abnormality cause analysis method, which realizes signal abnormality cause analysis by matching the measured evaluation results with the distortion model library data, can realize more accurate judgment of the distortion type and size of the abnormal signal, is simple and easy to operate, and has high accuracy.

[0006] The technical solution adopted by the application to solve the technical problem comprises the following steps:

[0007] (1) Establish a combined distortion model of satellite signal transmission channel, let i represent the i-th distortion type, and the parameter interval is d in the possible distortion range i , then there are N i values for each distortion, and the number of all possible combined distortion types is M;

[0008] (2) Generate an ideal signal, pass the ideal signal through the combined distortion model established in step (1), and evaluate the signal quality of the output signal. Each output signal obtains a set of evaluation results, including combined power spectrum deviation, correlation loss, S-curve zero-crossing slope deviation, S-curve zero-crossing deviation, and time-domain waveform distortion. Record the above five index results to form a set of data, and keep the data corresponding relationship with the combined distortion type; all data results and their corresponding relationship with the distortion type are one-to-one corresponding to form a combined distortion distortion model library;

[0009] (3) Collect and analyze the measured satellite downlink signal data to obtain the measured signal quality evaluation results, including combined power spectrum deviation, correlation loss, S-curve zero-crossing slope deviation, S-curve zero-crossing deviation, and time-domain waveform distortion;

[0010] (4) Compare and analyze the measured signal quality evaluation results with the set evaluation index, if the evaluation result is greater than or equal to the set evaluation index, it is considered that the signal may be abnormal; find out the most matched one from the abnormal signal evaluation result data and the pre-stored data in the distortion model library;

[0011] (5) According to the best matching relationship between the measured satellite signal evaluation results and the distortion model library data, the corresponding satellite signal transmission channel distortion situation is obtained.

[0012] The distortion types include nonlinear distortion and linear distortion, wherein the nonlinear distortion is the influence of high-power amplifier on the satellite, and the linear distortion includes band-limited distortion, amplitude ripple distortion, amplitude asymmetry distortion, phase ripple distortion, quadratic phase distortion, and cubic phase distortion. The values of the above seven distortions are represented by N1, N2, N3, N4, N5, N6, and N7, respectively. The number of all possible combined distortion types M=N1·N2·N3·N4·N5·N6·N7.

[0013] The step (3) receives the navigation satellite downlink signal, and after the signal passes through the low-noise amplifier and the receiving channel, the respective branch signals of the navigation signal are captured, tracked and demodulated, and the evaluation results of the measured signal are calculated.

[0014] Step (3) involves using an antenna with a gain greater than 50 dBi to receive downlink signals from navigation satellites. The signals are sampled after passing through a low-noise amplifier and a receiving channel. The sampling frequency is greater than or equal to 250 MHz and the sampling bit depth is greater than or equal to 14 bits. The collected data is read, and the signals of each branch of the navigation signal data are captured, tracked, and demodulated. The evaluation results of the measured signals are calculated.

[0015] Step (4) uses the minimum sum of squared errors as the criterion to find the set of abnormal signal evaluation results data that best matches the pre-stored data in the distortion model library.

[0016] In step (4), the abnormal signal evaluation results are assumed to be: synthetic power spectrum deviation P, correlation loss C, S-curve zero-crossing slope deviation D, S-curve zero-crossing deviation S, time-domain waveform distortion W, and sum of squared errors L. i =(PP i ) 2 +(CC i ) 2 +(DD i ) 2 +(SS i ) 2 +(WW i ) 2 , where P i C i D i ,S i W i These represent the evaluation results data of the i-th group in the distortion model library.

[0017] If multiple groups of L appear in step (4) i In cases where values ​​are the same, abnormal data are collected, evaluated, and analyzed multiple times at different time periods, and the average value is taken for further analysis. Alternatively, individual analysis of each branch signal and joint analysis of multiple branch signals can be combined to further pinpoint the cause of the anomaly.

[0018] Step (4) involves separating the individual branch signals in the multiplexed signal and then evaluating the signal quality of each individual branch signal; finding the set of abnormal signal evaluation results that best matches the pre-stored data in the distortion model library.

[0019] Step (4) involves separating the individual branch signals in the multiplexed signal, selecting the two worst-performing indicators relative to the ICD indicator requirements for each branch signal evaluation result, performing optimal matching of all indicators with the distortion model library data, and finding the set of abnormal signal evaluation result data that best matches the pre-stored data in the distortion model library.

[0020] The step (4) separates each single branch signal in the multiplexed signal, then signal quality of the single branch signal is evaluated, meanwhile, the evaluation results of each branch signal are selected, two indexes with the worst evaluation results are selected according to the relative ICD index requirements, all indexes are best matched with the distortion model library data, the evaluation results of the single branch signal are intersected with the matching results of all indexes, and a group of the best matching data of the abnormal signal evaluation results data and the pre-stored data in the distortion model library is found in the intersection.

[0021] The beneficial effects of the application are that a complete satellite navigation signal transmission channel combined distortion model and signal distortion model library including all possible linear distortion and nonlinear distortion and combinations thereof are constructed, and the mutual influence between the combined distortions is comprehensively considered, it is pointed out that different evaluation parameters have different sensitivities to different distortion types, the single branch signal analysis method and the multi-branch signal joint analysis method for judging the abnormal signal reasons are first proposed, and the best matching of the abnormal signal evaluation results and the distortion model library data is realized by combining the two methods, so that the specific reason analysis results of the abnormal signal can be efficiently and reliably given, the problems that the traditional method cannot realize the comprehensive modeling of the transmission channel combined distortion and cannot realize the rapid troubleshooting of the satellite signal abnormality according to the signal quality evaluation results are overcome, and the method can help the navigation system to quickly lock the signal abnormality reason, and provide valuable reference and important support for the channel distortion compensation and stable operation of the system. The method can be widely applied to various satellite navigation systems, including Beidou of China, GPS of the United States, GLONASS of Russia and Galileo of Europe, and can provide important guarantee for the normal operation of the navigation system. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The flow chart of the satellite navigation signal abnormality reason analysis method is shown in the figure.

[0023] Figure 2 The multiplexing method block diagram of BDS-3B1 signal is shown in the figure.

[0024] Figure 3 The flow chart of the satellite signal transmission channel combined distortion model and the combined distortion model library is shown in the figure.

[0025] Figure 4 The schematic diagram of the satellite signal combined distortion distortion model library is shown in the figure.

[0026] Figure 5 The matching method schematic diagram of the satellite navigation signal evaluation results and the distortion model library data is shown in the figure.

[0027] Figure 6 The single branch signal analysis method schematic diagram is shown in the figure.

[0028] Figure 7 The three branch signal joint analysis method schematic diagram is shown in the figure. DETAILED DESCRIPTION

[0029] The application will be further described below in connection with the accompanying drawings and examples, which include but are not limited to the following examples.

[0030] The technical solution adopted by the application includes the following steps:

[0031] (1) Establish a satellite signal transmission channel combined distortion model.

[0032] The various types of distortion that may exist in the satellite navigation signal full-link transmission channel mainly include nonlinear distortion and linear distortion, where the nonlinear distortion is mainly the influence of the high-power amplifier on the satellite, and the linear distortion includes band-limited distortion, amplitude ripple distortion, amplitude asymmetry distortion, phase ripple distortion, quadratic phase distortion, and cubic phase distortion. By setting different parameters to control the size of each type of distortion, the traversal method is adopted within the range of each parameter to represent each type of distortion. Let i represent the i-th type of distortion, and the parameter interval within the possible distortion range is d i , then each type of distortion can have N i values. The values of the above seven types of distortion are represented by N1, N2, N3, N4, N5, N6, and N7, respectively, and the number of all possible combined distortion types can be represented as:

[0033] M = N1·N2·N3·N4·N5·N6·N7

[0034] Where M represents the number of combined distortion types, and the smaller the interval d i , the more comprehensive the distortion degree traversal, and the more accurate the distortion cause analysis.

[0035] (2) Establish a satellite signal combined distortion distortion model library.

[0036] Firstly, the ideal signal is generated, and the ideal signal is subjected to various combined distortion models established in step (1), and then the output signal is subjected to signal quality evaluation. Five typical signal evaluation parameters are selected as evaluation indexes, including signal power spectrum deviation, correlation loss, S-curve zero-crossing point slope deviation, S-curve zero-crossing point deviation, and time-domain waveform distortion (length difference between positive and negative chips and ideal chips). The above evaluation index results can be expressed in a quantitative form. Since there are M combined distortion types in total, each output signal corresponds to one combined distortion type, and therefore the ideal signal subjected to all possible combined distortions has M corresponding output signals. Then, the output signals subjected to each combined distortion are subjected to signal quality evaluation, and a set of evaluation results can be obtained, including combined power spectrum deviation, correlation loss, S-curve zero-crossing point slope deviation, S-curve zero-crossing point deviation, and time-domain waveform distortion. The above five index results are recorded to form a set of data, and the data and the corresponding relationship with the combined distortion type are maintained. M output signals can obtain M sets of data and M corresponding relationships. The entire data results and the corresponding relationships with the distortion types are one-to-one corresponding to form a combined distortion distortion model library.

[0037] (3) Collecting measured satellite downlink signal data by using a high-gain large-aperture antenna, and analyzing and evaluating by using a signal quality evaluation software to obtain measured signal quality evaluation results.

[0038] The navigation satellite downlink signal is received by using a high-gain antenna (gain greater than 50 dBi). After the signal passes through a low-noise amplifier and a receiving channel, it reaches a high-speed synchronous data collection device (for higher precision offline analysis, the sampling frequency needs to be greater than or equal to 250 MHz, and the sampling bit number needs to be greater than or equal to 14 bits). The offline collected data is read by using a software receiver, and each branch signal of the navigation signal data is subjected to capture, tracking and demodulation processing, and then the combined power spectrum deviation, correlation loss, S-curve zero-crossing point slope deviation, S-curve zero-crossing point deviation, and time-domain waveform distortion evaluation results of the measured signal are calculated.

[0039] (4) Realizing the best matching of the measured satellite navigation signal evaluation results and the distortion model library data.

[0040] The measured evaluation results are compared and analyzed with their evaluation indexes. If the evaluation results are greater than or equal to the index requirements, it is considered that the signal may be abnormal. Then, the abnormal signal evaluation result data and the pre-stored data in the distortion model library are found out by using the minimum error sum as the criterion, so as to inversely deduce the distortion type and size suffered by the signal, and realize the cause analysis of the abnormal signal. Assuming that the abnormal signal evaluation results are combined power spectrum deviation P, correlation loss C, S-curve zero-crossing point slope deviation D, S-curve zero-crossing point deviation S, and time-domain waveform distortion W, the matching process can be expressed by the following formula:

[0041] L i = (P-Pi ) 2 +(C-C i ) 2 +(D-D i ) 2 +(S-S i ) 2 +(W-W i ) 2

[0042] Wherein, L i represents error sum of squares, P i , C i , D i , S i , W i represents the i-th group of evaluation result data in the distortion model library, i takes the value range of 1~M. The k-th group of data corresponding to the minimum error sum of squares is found out, and the k-th combined distortion is obtained by backstepping, so that the distortion type and size suffered by the abnormal signal can be judged. It should be noted that when matching, if the number of M is large enough, there may be multiple groups of Li values that are the same, but because the evaluation results are all taken to 3 decimal places, the probability of this situation occurring is small. When this situation occurs, the abnormal data can be collected and evaluated multiple times in different periods, and the average value is taken to judge and analyze again, or the method of combining single analysis of each branch signal and joint analysis of multi-branch signal is used to further lock the abnormal reason.

[0043] (5) judging the distortion reason of the abnormal signal.

[0044] According to the best matching relationship between the measured satellite signal evaluation result and the distortion model library data, the corresponding satellite signal transmission channel distortion condition can be obtained.

[0045] The application proposes two methods for analyzing and judging the signal abnormal reason: single analysis method of each branch signal in multiplexing signal and joint analysis method of multi-branch signal.

[0046] ① Each branch signal separate analysis method: the satellite navigation signal will be affected by the channel in the actual transmission of a variety of distortion, the interaction between the distortion will produce, this interaction is manifested as superposition or offset effect, the influence of the evaluation index results also presents different rules. Multiplexing each single signal experienced the same space transmission channel, so the signal quality comprehensive analysis for single branch to determine the signal abnormal reason has representativeness and universality. In the signal import software receiver processing, complete capture and stable tracking, then stripping carrier wave, and then using the period accumulation processing algorithm to realize the separation of each single branch signal in multiplexing signal, and then the signal quality evaluation of single branch signal. Single branch signal evaluation parameter selection above 5 kinds of quantifiable index: synthesis power spectrum deviation, correlation loss, S curve zero crossing point slope deviation, S curve zero crossing deviation, time domain waveform distortion, the best matching of the 5 parameters and distortion model library data, so as to obtain the corresponding signal abnormal reason. The method has the advantages of only needing to evaluate and analyze the signal quality of single branch signal, reducing the processing time of receiver, and effectively improving the efficiency. The invention uses period accumulation method to separate and process each branch signal independently, and analyzes the quality of each single branch signal during evaluation and analysis. Since the code correlation is very small, other branch signals can be considered as noise after a certain period of accumulation.

[0047] ② Multi-branch signal joint analysis method: modern navigation signal is usually multiplexed, and the multiplexed signal is transmitted. The distortion types and sizes of the multiple branch signals are the same, but the effects of the combined distortion on each branch signal are different due to the different codes and modulation methods of each branch signal, and each branch signal shows different distortion phenomena. Therefore, the multi-branch joint analysis method can be used to analyze the signal abnormal reason. Two most important and representative parameters are selected for each branch signal evaluation result. Each parameter has different sensitivity to different channel distortion types, so two parameters with high sensitivity are selected in practice, that is, the relative ICD index requirement, and the two indicators with poor evaluation results. There are 2N parameters for N branch signals. The 2N parameters are best matched with the distortion model library data, so as to obtain the corresponding signal abnormal reason. The main advantage of this method is that the multi-branch signal joint analysis further improves the reliability of the analysis, and only a few main evaluation parameters are used to realize the signal abnormal reason analysis, without the need for one by one analysis and calculation of other signal quality evaluation parameters.

[0048] In practical applications, the two methods can complement each other, and in an ideal case, the results obtained by the two methods should be consistent. Since M cannot be infinite in practice, the matching results will not be completely consistent. However, due to the incomplete consistency of the matching, the judgment of the distortion size is actually a range. The above two methods can be combined for further judgment, and the intersection of the two methods can reduce the judgment range as much as possible. In addition, the combination of the two analysis methods can also help to judge the case where multiple Li values are the same, making the analysis result more accurate.

[0049] Referring to Figure 1 The overall idea of the satellite navigation signal abnormality reason analysis method provided by the embodiment of the application is as follows: first, ideal signals corresponding to different navigation systems and different frequency points are simulated, a satellite signal transmission channel combination distortion model is established based on a satellite navigation signal full-link transmission channel, and then signal quality evaluation is performed on the output signals of the ideal signals after the combination distortion model to obtain corresponding evaluation results. The M distortion types of the channel distortion model and the evaluation results of the M output signals are one-to-one corresponding and a distortion model library is established. The measured signals are collected and evaluated, and the abnormal signal evaluation results greater than or equal to the index are best matched with the distortion model library according to the minimum error sum criterion, so that the corresponding transmission channel distortion can be reversely deduced. Finally, the distortion type and size of the abnormal signal are judged by the single branch signal analysis method and the multi-branch signal joint analysis method. The overall process can be divided into five steps, which will be described in detail in combination with the embodiment.

[0050] (1) According to different navigation systems and different frequency point signals, the corresponding ideal signals are simulated, and in this example, BDS-3B1 signals are used. Based on the BDS-3B1 signal structure and characteristics, each component is simulated, and POCET technology is used for multiplexing and synthesis to obtain B1 ideal signals.

[0051] At present, the BDS B1 signal is transmitted in a constant envelope manner: B1I, B1C and B1A, wherein B1C includes data component B1Cd and pilot component B1Cp, B1I and B1C provide public services, and B1A provides authorized services. The modulation mode, code length and other information are shown in Table 1.

[0052] Table 1 BDS-3 B1 signal characteristics

[0053]

[0054] In this example, the B1 signal generation adopts the same method as the actual on-board B1 signal generation. The POCET constant envelope multiplexing method is used to modulate each component signal according to the given phase lookup table. Referring to Figure 2The BDS-3B1 signal multiplexing method, the specific process is as follows:

[0055] First, based on the composition structure of BDS B1 signal, each component signal is generated: B1I, B1Q, B1Cd, B1Cpa, B1Cpb, B1Ad, B1Ap, wherein the ranging code of B1Ad and B1Ap uses a random symbol function to generate. Use S1(t), S2(t),..., S7(t) to represent the above 7 component signals in turn:

[0056] S 1，2 (t)=D(t)·C(t)

[0057] S3(t)=D(t)·C(t)·sc BOC(m,n)

[0058] S 4,5..7 (t)=C(t)·sc BOC(m,n)

[0059] In the formula, D(t) is a random text, C(t) is a ranging code, sc BOC(m,n) is the subcarrier of BOC(m, n).

[0060] The 7 signal components S1(t), S2(t),..., S7(t) to be involved in multiplexing are sent into the multiplex signal generator to complete the mapping relationship between signal value combination and complex signal phase angle. For 7 bipolar baseband signals, there are at most 2 7 different value combinations. A lookup table is stored in the multiplex signal generator, which specifies a phase angle θ k for each value combination. According to the value of the 7 signals at the current time, the corresponding phase angle is output. The synthesized complex baseband signal generator generates the complex envelope complex baseband signal after compounding according to the phase angle, which can be represented as:

[0061]

[0062] In the formula, A is the envelope amplitude, and θ k is the phase angle.

[0063] (2) Establish a satellite navigation signal transmission channel combination distortion model, and set various distortion parameters to control the distortion size. Referring to Figure 3From the perspective of linear distortion and nonlinear distortion, the distortion types existing in the transmission channel are considered, the nonlinear distortion is determined by the high-power amplifier model type and parameter setting, the linear distortion mainly includes band-limit distortion, amplitude distortion and phase distortion, the band-limit distortion size is determined by the filter bandwidth setting, the amplitude distortion type considers amplitude ripple distortion and amplitude asymmetric distortion, the phase distortion type considers the influence of phase ripple distortion, quadratic phase distortion and cubic phase distortion, the amplitude distortion and phase distortion size can be controlled by parameter setting, each type of distortion can be represented by taking traversal in each parameter range, let i represent the i-th distortion type, the parameter interval is d i , then each distortion can have N i values. In this example, the combined distortion model adopts the combination of high-power amplifier, band-limit distortion, amplitude distortion and phase distortion, the above four distortions are represented by N1, N2, N3 and N4 respectively, M represents all distortion types, then M = N1·N2·N3·N4. The high-power amplifier adopts Saleh model, the parameter setting is two groups, the distortion type corresponds to N1 = 2; the band-limit distortion filter bandwidth setting is 0-70MHz, the step is 5MHz, the distortion type corresponds to N2 = 14; the amplitude distortion model is represented by parameter A to represent the distortion degree of the channel, the value is traversed from 0 to 6dB, the value interval d is 0.1, the distortion type corresponds to N3 = 60; the phase distortion model is represented by parameter b to represent the distortion degree, the value is traversed from 0 to 1°, the value interval d is 0.02, the distortion type corresponds to N5 = 50; then all distortion types M = 84000.

[0064] (3) The B1 ideal signal passes through the combined distortion model established in step (2), and the output result is evaluated for signal quality. The distorted data is read by the software receiver, and each branch signal of the navigation signal data is captured, tracked and demodulated, and then the measured signal is calculated. The evaluation results of the combined power spectrum deviation, correlation loss, S curve zero crossing slope deviation, S curve zero crossing deviation, time domain waveform distortion (positive and negative chip length difference with ideal chip) are calculated, and the quality evaluation results of B1I, B1Cd and B1Cpa signals are obtained. The signal after combined distortion processing has M types, which can be obtained from the combined distortion parameter setting in (2). M = 84000, the software receiver needs to process M signals, and the evaluation results of the three branch signals are recorded. Referring to Figure 4 , the M-th distortion can be represented by band-limit distortion B M , amplitude ripple distortion A M , amplitude asymmetric distortion A' M , phase ripple distortion b M , quadratic phase distortion τ M and cubic phase distortion τ' MThe corresponding evaluation result of the Mth group of data can be expressed as a combined power spectrum deviation P M , a correlation loss C M , an S-curve zero-crossing slope deviation D M , an S-curve zero-crossing deviation S M , and a time-domain waveform distortion W M . The evaluation result is matched with the combined distortion type and size, and the data result is matched with the corresponding relationship to form a distortion model library. For the B1 signal in this example, each branch can form a distortion model library, forming B1I, B1Cd, and B1Cpa signal distortion model libraries.

[0065] (4) The measured satellite downlink signal data is collected by using a high-gain large-aperture antenna, and is analyzed and evaluated by using a signal quality evaluation software to obtain the measured signal quality evaluation result. The navigation satellite downlink signal is received by using a higher-gain antenna (the gain is greater than 50 dBi). After the signal passes through a low-noise amplifier and a receiving channel, it reaches a high-speed synchronous data collection device (for higher-precision offline analysis, the sampling frequency needs to be greater than or equal to 250 MHz, and the sampling bit number needs to be greater than or equal to 14). The offline collection data is read by using a software receiver, and each branch signal of the navigation signal data is captured, tracked, and demodulated, and then the combined power spectrum deviation, the correlation loss, the S-curve zero-crossing slope deviation, the S-curve zero-crossing deviation, and the time-domain waveform distortion evaluation result of the measured signal are calculated.

[0066] (5) Referring to Figure 5 , the best matching between the abnormal satellite navigation signal evaluation result and the distortion model library data is realized.

[0067] In this embodiment, the data whose evaluation result is greater than or equal to the index requirement is found out as the best matching between the abnormal signal evaluation result data and the pre-stored data in the distortion model library according to the criterion of minimum error sum, so that the channel distortion condition is obtained. In this example, the B1 abnormal signal evaluation result is represented as: a combined power spectrum deviation P, a correlation loss C, an S-curve zero-crossing slope deviation D, an S-curve zero-crossing deviation S, and a time-domain waveform distortion W. The B1 measured abnormal signal evaluation result in this example is brought in, and the matching is realized by the following method:

[0068] L i =(P-P i ) 2 +(C-C i ) 2 +(D-D i ) 2 +(S-S i ) 2 +(W-W i ) 2

[0069] The kth group of data corresponding to the minimum error sum of squares is found, and the kth combined distortion is inversely deduced, so that the type and size of the distortion suffered by the abnormal signal can be judged. It should be noted that when matching, if the number of M is large enough, multiple groups of Li values may be the same, but since the evaluation results are all taken to three decimal places, the probability of this situation is small. When this situation occurs, the abnormal data can be collected and evaluated multiple times in different periods, and the average value is taken to judge and analyze again, or the method of combining single analysis of each branch signal and joint analysis of multiple branch signals is used to further lock the abnormal reason.

[0070] After the channel distortion condition is obtained, two matching analysis methods with the distortion model library are used, as follows:

[0071] ① Single analysis method of each branch signal:

[0072] Referring to Figure 6 , the B1 signal quality evaluation results are given in the form of single branch signal, which are B1I, B1Cd and B1Cpa signals. Taking B1I signal as an example, the evaluation results of B1I signal are obtained: the combined spectrum deviation P B1I , the correlation loss C B1I , the S curve zero crossing slope deviation D B1I , the S curve zero crossing deviation S B1I , and the time domain waveform distortion W B1I . The evaluation results are best matched with the B1I signal distortion model library by the least square method, so that the type and size of the distortion suffered by the signal can be judged. The advantage of this method is that only single branch signal needs to be evaluated.

[0073] ② Joint analysis method of three branch signals:

[0074] Referring to Figure 7 , when transmitting the B1 signal, three branch signals are multiplexed into one signal for transmission, and it can be considered that the three branch signals suffer the same type and size of channel distortion, but the combined distortion affects the three branch signals differently, and each branch signal shows different evaluation results, so the joint analysis method of three branch signals can be used. In this example, the S curve zero crossing slope deviation and the S curve zero crossing deviation are taken as the main evaluation parameters, and the evaluation results of B1I signal are D B1I and S B1I . The evaluation results are best matched with the B1I signal distortion model library by the least square method, so that the type and size of the distortion suffered by the signal can be judged. The evaluation results of B1Cd signal are D B1Cd and S B1Cd . The evaluation results are best matched with the B1Cd signal distortion model library by the least square method, so that the size range of the distortion can be obtained. The evaluation results of B1Cpa signal are DB1Cpa and S B1Cpa The distortion range can be obtained by least square optimization matching with the B1Cpa signal distortion model library, and the intersection of three distortion ranges can determine the distortion type and size of the signal. The advantage of this method is that only a few main evaluation parameters are used to analyze the signal abnormality, and other parameters do not need to be analyzed and calculated one by one.

[0075] When the distortion range determined by one method is larger, the two methods are used to complement each other. Ideally, the results of the two methods should be consistent. Since M cannot be infinite in practice, the matching results will not be completely consistent. However, due to the incomplete consistency of matching, the distortion size is actually a range, which can be further judged by combining the above two methods. The intersection of the two methods can minimize the judgment range. In addition, the combination of the two analysis methods can also help to judge the case where multiple Li values are the same. The more comprehensive the combined distortion type is, the more signal quality evaluation indicators are, and the more accurate the signal abnormality analysis is.

[0076] (6) From June to November 2021, the quality of 24 MEO satellite B1 signals was evaluated. The high-gain large-aperture antenna was used to collect the measured 24 MEO satellite B1 downlink signal data, the high-speed synchronous data acquisition equipment was used for data acquisition, and the signal quality evaluation software was used for analysis and evaluation. The measured signal quality evaluation results were obtained. The slightly poor evaluation results were found, and the abnormality analysis method proposed in the application was used for analysis. Table 2 shows the analysis process and results of two abnormal cases.

[0077] Table 2 Analysis process and results of two abnormal cases

[0078]

[0079] The above two abnormal signal reason analysis results were verified, and the influence of the combined distortion in the specific value range on the signal quality was re-evaluated and analyzed. According to the minimum and maximum values of the combined distortion verification simulation data, the judgment interval was given, and the measured abnormal data was compared. The measured data was within the judgment interval of the simulation result data, which verified the effectiveness and feasibility of the application. Table 3 shows the simulation verification analysis results.

[0080] Table 3 Simulation verification of abnormal signal reason analysis results

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Claims

1. A method for analyzing the causes of satellite navigation signal anomalies, characterized in that, Includes the following steps: (1) Establish a satellite signal transmission channel combined distortion model. Let i represent the i-th distortion type. The parameter interval within the possible distortion range is di. Then each distortion has Ni values, and the number of all possible combined distortion types is M. The distortion types include nonlinear distortion and linear distortion. Nonlinear distortion is the effect of the on-board high-power amplifier. Linear distortion includes band-limited distortion, amplitude ripple distortion, amplitude asymmetry distortion, phase ripple distortion, quadratic phase distortion, and cubic phase distortion. The values ​​of the above seven distortions are represented by N1, N2, N3, N4, N5, N6, and N7, respectively. The number of all possible combined distortion types is M = N1·N2·N3·N4·N5·N6·N7. (2) Generate an ideal signal. The ideal signal is processed through various combined distortion models established in step (1) to evaluate the signal quality of the output signal. Each output signal obtains a set of evaluation results, including the composite power spectrum deviation, correlation loss, S-curve zero-crossing slope deviation, S-curve zero-crossing deviation, and time-domain waveform distortion. The results of the above five indicators are recorded to form a set of data, and the correspondence between the data and the combined distortion type is maintained. All data results and their correspondence with the distortion type are matched one by one to form a combined distortion model library. (3) Collect measured satellite downlink signal data and analyze and evaluate it to obtain the measured signal quality evaluation results, including the composite power spectrum deviation, correlation loss, S-curve zero-crossing slope deviation, S-curve zero-crossing deviation and time-domain waveform distortion; (4) Compare and analyze the measured signal quality assessment results with the set assessment indicators. If the assessment results are greater than or equal to the set assessment indicators, it is considered that the signal may be abnormal. Find the set of abnormal signal assessment results data that best matches the pre-stored data in the distortion model library. (5) Based on the best matching relationship between the measured satellite signal evaluation results and the distortion model library data, the corresponding satellite signal transmission channel distortion is obtained.

2. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, In step (3), the downlink signal of the navigation satellite is received. After the signal passes through the low noise amplifier and the receiving channel, the signals of each branch of the navigation signal are captured, tracked and demodulated respectively, and the evaluation result of the measured signal is calculated.

3. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, In step (3), the downlink signal of the navigation satellite is received using an antenna with a gain greater than 50dBi. The signal is sampled after passing through a low-noise amplifier and a receiving channel. The sampling frequency is greater than or equal to 250MHz and the sampling bit is greater than or equal to 14 bits. The system reads the collected data, captures, tracks, and demodulates the signals of each branch of the navigation signal data, and calculates the evaluation results of the measured signals.

4. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, Step (4) uses the minimum sum of squared errors as the criterion to find the set of abnormal signal evaluation results data that best matches the pre-stored data in the distortion model library.

5. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, In step (4), the abnormal signal evaluation results are assumed to be: synthetic power spectrum deviation P, correlation loss C, S-curve zero-crossing slope deviation D, S-curve zero-crossing deviation S, time-domain waveform distortion W, and sum of squared errors. ,in, These represent the evaluation results data of the i-th group in the distortion model library.

6. The method for analyzing the causes of satellite navigation signal anomalies according to claim 5, characterized in that, If multiple sets of Li values ​​are the same in step (4), the abnormal data are collected, evaluated, and analyzed multiple times at different time periods, and the average value is taken for further judgment and analysis. Alternatively, the analysis of each branch signal individually and the joint analysis of multiple branch signals are combined to further pinpoint the cause of the abnormality.

7. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, Step (4) involves separating the individual branch signals in the multiplexed signal and then evaluating the signal quality of each individual branch signal; finding the set of abnormal signal evaluation results that best matches the pre-stored data in the distortion model library.

8. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, Step (4) involves separating the individual branch signals in the multiplexed signal, selecting the two worst-performing indicators relative to the ICD indicator requirements for each branch signal evaluation result, performing optimal matching of all indicators with the distortion model library data, and finding the set of abnormal signal evaluation result data that best matches the pre-stored data in the distortion model library.

9. The method for analyzing the causes of satellite navigation signal anomalies according to claim 1, characterized in that, Step (4) involves separating the individual branch signals in the multiplexed signal and then evaluating the signal quality of each individual branch signal. Simultaneously, the two indicators with the worst evaluation results relative to the ICD indicator requirements are selected from the evaluation results of each branch signal, and all indicators are matched best with the distortion model library data. The intersection of the evaluation results of the individual branch signal and the results of matching all indicators is taken, and the set of abnormal signal evaluation results data that best matches the pre-stored data in the distortion model library is found in the intersection.

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