Real-time monitoring method and device for navigation satellite signal power

Abstract

The application relates to the technical field of satellite communication, and discloses a real-time monitoring method and device for navigation satellite signal power. The real-time monitoring method for navigation satellite signal power comprises the following steps: acquiring real-time C / N0 observation of a target reference station for a target satellite and a frequency point; based on the acquired historical C / N0 observation, using SSA and LRR methods, obtaining C / N0 prediction error and C / N0 prediction at the current time; determining the C / N0 deviation of the target satellite and the frequency point at the target reference station; selecting the median of the C / N0 deviation and the median of the C / N0 prediction error of multiple target reference stations as the comprehensive C / N0 deviation and the comprehensive threshold value respectively; if the comprehensive C / N0 deviation is greater than the comprehensive threshold value, the target satellite and the frequency point are subjected to power adjustment. The SSA and LRR methods are combined innovatively, the navigation satellite signal power adjustment is monitored in real time, the monitoring cost is low, and the optimal monitoring effect can be achieved without manual parameter adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite communication, in particular to a real-time monitoring technology of navigation satellite signal power. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) is a space-based radio navigation positioning system that can provide users with three-dimensional coordinates, speed and time information at any location on the earth's surface or near space. GNSS satellites usually transmit radio signals with fixed power. However, in order to better meet the global or regional operation requirements, GNSS satellites may adjust the strength of a single signal through flexible power (Flex Power) to achieve the effect of increasing or decreasing the transmission power of a specific frequency of the satellite. The adjustment of navigation satellite signal power will cause the discontinuous change of the Differential Code Bias (DCB) of the satellite, and thus the accuracy of Single Point Positioning (SPP) will decrease. Therefore, how to monitor the impact of navigation satellite signal power adjustment on GNSS application is particularly important.

[0003] At present, there are few researches on this aspect at home and abroad. The most mature method in the existing method is to use GNSS observation stations distributed around the world combined with a semi-sphere model to monitor the navigation satellite signal power adjustment. This method is divided into two parts: modeling and monitoring.

[0004] 1. Modeling: using the carrier-to-noise ratio (C / N0) observation of the observation station, combined with a semi-sphere model to model the carrier-to-noise ratio of the target satellite and frequency point;

[0005] 2. Monitoring: using the above model as a reference, evaluating the difference between the carrier-to-noise ratio observation value and the model value and placing it in a sliding window. If the standard deviation of the sliding window is greater than the threshold, the satellite power is adjusted.

[0006] However, the inventors of the present application found that the prior art has the following disadvantages:

[0007] 1. Large memory overhead: the higher the monitoring sensitivity, the larger the resolution of the semi-sphere model, and the larger the required memory overhead. When global observation stations are used, the required memory overhead is too large, and the monitoring cost is too high;

[0008] 2. Hysteresis: due to the existence of the sliding window, the monitoring of the navigation satellite signal power adjustment often has hysteresis;

[0009] 3. Model parameters need to be optimized: due to the inherent defects of the existing method, there is no optimal parameter model to compatible all satellite systems and frequency points.

[0010] Therefore, there is an urgent need for a real-time monitoring method of navigation satellite signal power that overcomes the above-mentioned shortcomings of the prior art. SUMMARY

[0011] The purpose of the present application is to provide a real-time monitoring method of navigation satellite signal power and a device thereof, which innovatively combines SSA and LRR methods to monitor the adjustment of navigation satellite signal power in real time, has low monitoring cost, and can achieve optimal monitoring effect without manual parameter adjustment.

[0012] To solve the above technical problems, an embodiment of the present application discloses a real-time monitoring method of navigation satellite signal power, comprising the following steps:

[0013] obtaining real-time C / N0 observations of a target satellite and a frequency point at a target reference station;

[0014] based on the obtained historical C / N0 observations, using SSA and LRR methods to obtain C / N0 prediction errors and C / N0 predictions at the current time;

[0015] determining the C / N0 deviation of the target satellite and the frequency point at the target reference station, wherein the C / N0 deviation is equal to the C / N0 observation at the current time minus the C / N0 prediction at the current time;

[0016] selecting the median of the C / N0 deviations and the median of the C / N0 prediction errors of multiple target reference stations as the comprehensive C / N0 deviation and the comprehensive threshold, respectively;

[0017] comparing the size of the comprehensive C / N0 deviation and the comprehensive threshold, if the comprehensive C / N0 deviation is greater than the comprehensive threshold, the target satellite and the frequency point have power adjustment.

[0018] An embodiment of the present application also discloses a real-time monitoring device of navigation satellite signal power, comprising:

[0019] a C / N0 observation acquisition module for obtaining real-time C / N0 observations of a target satellite and a frequency point at a target reference station;

[0020] an SSA and LRR module for obtaining C / N0 prediction errors and C / N0 predictions at the current time based on the obtained historical C / N0 observations using SSA and LRR methods;

[0021] a C / N0 deviation determination module for determining the C / N0 deviation of the target satellite and the frequency point at the target reference station, wherein the C / N0 deviation is equal to the C / N0 observation at the current time minus the C / N0 prediction at the current time;

[0022] The median selection module is configured to select the median of the C / N0 bias of the plurality of target reference stations and the median of the C / N0 prediction error as the comprehensive C / N0 bias and the comprehensive threshold, respectively.

[0023] The judging module is configured to compare the comprehensive C / N0 bias and the comprehensive threshold, and if the comprehensive C / N0 bias is greater than the comprehensive threshold, the target satellite and the frequency point are subjected to power adjustment.

[0024] Compared with the prior art, the main difference and effect of the embodiments of the present application are as follows:

[0025] The SSA and LRR methods are innovatively combined to monitor the power adjustment of the navigation satellite signals in real time, and the monitoring cost is low, and the optimal monitoring effect can be achieved without manual parameter adjustment.

[0026] Further, the present application does not need to establish a semi-sphere model for modeling, and therefore, the memory overhead is very small, and the monitoring cost is low.

[0027] Further, the present application does not need to use a sliding window to store historical data, and therefore, there is no hysteresis, and the effect of real-time monitoring can be achieved.

[0028] Further, the present application uses a parameter-free method, and therefore, the optimal monitoring effect can be achieved without manual parameter adjustment.

[0029] Further, when performing singular value decomposition on the matrix, according to the data characteristics, the standard SVD and the truncated SVD are combined, and on the premise of the same accuracy, the SSA operation amount can be greatly reduced.

[0030] A large number of technical features are described in the specification of the present application, which are distributed in various technical solutions. If all possible combinations (i.e. technical solutions) of technical features of the present application are listed, the specification will be too long. In order to avoid this problem, each technical feature disclosed in the above invention content, each technical feature disclosed in the following embodiments and examples, and each technical feature disclosed in the drawings can be freely combined to form various new technical solutions (these technical solutions are considered to have been described in the specification), unless such combination of technical features is technically infeasible. For example, features A+B+C are disclosed in one example, features A+B+D+E are disclosed in another example, features C and D are equivalent technical means that play the same role, and only one of them can be used technically, and feature E can be combined with feature C technically. Therefore, the scheme of A+B+C+D should not be considered to have been described because it is technically infeasible, and the scheme of A+B+C+E should be considered to have been described. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is a flowchart of a real-time monitoring method of navigation satellite signal power in the first embodiment of the present application;

[0032] Figure 2 is a structural diagram of a real-time monitoring device of navigation satellite signal power in the second embodiment of the present application. DETAILED DESCRIPTION

[0033] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without such specific details and that numerous implementation variations and modifications can be possible from the overall description provided herein.

[0034] Explanation of some concepts:

[0035] GNSS (Global Navigation Satellite System): Global Navigation Satellite System

[0036] C / N0 (Carrier-to-Noise Density): Carrier-to-Noise Density

[0037] SSA (Singular Spectrum Analysis): Singular Spectrum Analysis

[0038] LRR (Linear Recurrence Relation): Linear Recurrence Relation

[0039] SPP (Single Point Positioning): Single Point Positioning

[0040] RMSE (Root Mean Square Error): Root Mean Square Error

[0041] SVD (Singular Value Decomposition): Singular Value Decomposition

[0042] To make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

[0043] The first embodiment of the present application relates to a real-time monitoring method of navigation satellite signal power. Figure 1 is a flowchart of the real-time monitoring method of navigation satellite signal power.

[0044] Specifically, as Figure 1As shown, the real-time monitoring method of navigation satellite signal power comprises the following steps:

[0045] In step 101, the real-time C / N0 observation of the target reference station for the target satellite and frequency point is obtained.

[0046] In this embodiment, preferably, before step 101, the following steps can also be included:

[0047] According to the predetermined standard, the target reference station is selected from the global GNSS observation station.

[0048] Further, preferably, the predetermined standard comprises:

[0049] The time of continuously observing the target satellite and frequency point is more than 1 minute, preferably more than 5 minutes;

[0050] The elevation angle of the target satellite is greater than 5 degrees, preferably greater than 15 degrees.

[0051] In addition, the predetermined standard can also include:

[0052] The observation data can output SPP positioning results, and the SPP positioning results can pass the chi-square test.

[0053] Then enter step 102, based on the obtained historical C / N0 observation, using SSA and LRR method, the C / N0 prediction error and C / N0 prediction at the current time are obtained.

[0054] In this embodiment, preferably, in step 102, the following sub-steps can be included:

[0055] Based on the obtained historical C / N0 observation, the original time sequence [x1, x2, … xN] is formed in time sequence, wherein N is the length of the original time sequence; N

[0056] According to the window length L, the Hankel matrix X of the original time sequence is obtained, wherein the Hankel matrix X is represented as follows:

[0057] Wherein, K=N-L+1, L is an integer and is 0.5N, 0.5*(N+1) or 0.5*(N-1);

[0058] The rank number corresponding to the principal component occupying a given proportion of the total components of the matrix X is calculated, denoted as r;

[0059] The singular value decomposition of the matrix X is carried out to obtain X=UΣV T , wherein U and V are standard orthogonal matrices;​

[0060] reorganize the eigenvalues of the matrix X after singular value decomposition, and obtain a reorganized time series by using diagonalization average, and obtain the C / N0 prediction error of the current moment by taking the standard deviation of the difference between the reorganized time series and the original time series;

[0061] Based on the matrix U, the C / N0 prediction of the current moment is obtained by using the LRR method.

[0062] In the embodiment, preferably, the given proportion can include, but is not limited to, 80%, 85%, 90% or 95%.

[0063] Further, preferably, the step of performing singular value decomposition on the matrix X can include the following sub-steps:

[0064] determining whether the r is greater than half of K,

[0065] If yes, performing standard singular value decomposition on the matrix X; if no, performing truncated singular value decomposition on the matrix X.

[0066] In the embodiment, according to the data characteristics, the method of combining standard SVD and truncated SVD can greatly reduce the SSA operation amount under the premise of unchanged accuracy.

[0067] In addition, preferably, the determination of the length N of the original time series needs to consider indexes including the root mean square error of the prediction result, the false alarm rate of the prediction, the observation data sampling rate and the operation efficiency, and when the indexes simultaneously reach the optimal value, the length N of the original time series is obtained.

[0068] Further, preferably, the optimal judgment standard can include the minimum mean, the minimum median or the minimum cost function.

[0069] Further, preferably, the method of determining whether the indexes simultaneously reach the optimal value can include the following steps:

[0070] For each index, a weight is determined, and when the weighted sum of all indexes is the minimum, all indexes simultaneously reach the optimal value.

[0071] In addition, in the embodiment, preferably, the length N can also be referred to as the optimal length N of the original time series.

[0072] Then, step 103 is entered to determine the C / N0 deviation of the target satellite and the frequency point at the target reference station, wherein the C / N0 deviation is equal to the C / N0 observation at the current moment minus the C / N0 prediction at the current moment.

[0073] Then, step 104 is entered, and the median of the C / N0 bias of the target reference stations and the median of the C / N0 prediction error are selected as the comprehensive C / N0 bias and the comprehensive threshold, respectively.

[0074] Then, step 105 is entered, and the comprehensive C / N0 bias and the comprehensive threshold are compared, and if the comprehensive C / N0 bias is greater than the comprehensive threshold, the target satellite and frequency point are subjected to power adjustment.

[0075] Then, the process ends.

[0076] In summary, the embodiments of the present application innovatively combine the SSA and LRR methods to monitor the power adjustment of the navigation satellite signals in real time, with low monitoring cost and optimal monitoring effect without the need for manual parameter adjustment.

[0077] In order to better understand the technical solutions of the present application, a preferred embodiment is described below, and the details listed in the preferred embodiment are mainly for the purpose of understanding and do not limit the protection scope of the present application.

[0078] The technical solutions of the preferred embodiment include the following steps:

[0079] 1. The global GNSS observation stations that meet the requirements are determined as target reference stations. The requirements for the observation stations include:

[0080] 1.1 The target satellite and frequency point can be observed continuously for more than 5 minutes;

[0081] 1.2 The elevation angle of the target satellite is greater than 15 degrees;

[0082] 1.3 The observation data quality is good. One method to judge the data quality is that the SPP positioning result can be output and pass the chi-square test.

[0083] Preferably, the criteria for judging the quality of the reference data include: the pseudorange multipath is less than 2.5 meters or the spatial geometric intensity factor of the satellite distribution is less than 6 when the cutoff elevation angle is 5

[0084] degrees.

[0085] 2. The real-time C / N0 observation of the target reference station for the target satellite and frequency point is obtained.

[0086] 3. The C / N0 prediction and its prediction error (denoted as threshold) at the current time are obtained by combining the SSA and LRR methods. The specific process includes:

[0087] 3.1 The optimal length of the original C / N0 time series is selected as the input to the SSA method. Specifically, based on the acquired historical C / N0 observations, the original time series [x1, x2, ... x] is formed in chronological order. N Determining the length of the original time series requires considering factors such as the root mean square error (RMSE) of the prediction results, the false alarm rate, the sampling rate of the observed data, and the computational efficiency. The optimal length is obtained when all these indicators reach their optimal values ​​simultaneously. One method to determine whether all indicators have reached their optimal values ​​is to assign weights to each indicator. The optimal length is obtained when the weighted sum of all indicators is minimized; this is denoted as N.

[0088] The optimal criteria include: minimum mean, minimum median, or minimum cost function.

[0089] 3.2 Based on the optimal length N of the original time series, the window length is L (generally half of N), denoted as...

[0090] K = N - L + 1;

[0091] 3.3 Calculate the Hankel matrix X based on the original time series, where X is defined as follows:

[0092]

[0093] 3.4 Calculate the rank of the principal component that occupies 90% of the total components of X, and denote it as k;

[0094] 3.5 Perform Singular Value Decomposition (SVD) on X. If k is greater than half of K, perform Standard Singular Value Decomposition (SSVD) on X; otherwise, perform Truncation SSVD. Based on the data characteristics, a combination of Standard SVD and Truncation SVD is used to significantly reduce the computational complexity of SSA without maintaining accuracy. The final decomposition form is X = UΣV T , where U and V are orthogonal matrices;

[0095] 3.6 Reconstruct X and use diagonalized averaging to obtain the reconstructed time series. Subtract the reconstructed time series from the original time series to obtain the prediction error.

[0096] 3.7 Based on matrix U and the number of predicted epochs, the C / N0 prediction at the current time is obtained using the LRR method. Since this method is common, it will not be elaborated upon here.

[0097] 4. Determine the C / N0 deviation of the target satellite and frequency point at the target reference station, where the C / N0 deviation is equal to the current observation minus the prediction.

[0098] 5. Select the median of the C / N0 deviation and the median of the threshold for all target reference stations as the final comprehensive C / N0 deviation and threshold.

[0099] 6. When the integrated C / N0 bias is greater than a threshold value, power adjustment of the target satellite and frequency point occurs.

[0100] In summary, the present application innovatively combines singular spectrum analysis (SSA) and linear recurrence relation (LRR) to monitor the power adjustment of navigation satellite signals in real time, and has the following advantages compared with the prior art:

[0101] 1. No semi-sphere model needs to be established for modeling, so the memory overhead is very small, and the monitoring cost is low;

[0102] 2. No sliding window is needed to store historical data, so there is no hysteresis, and real-time monitoring can be achieved;

[0103] 3. The non-parametric method combining SSA and LRR is used, so the optimal monitoring effect can be achieved without manual parameter adjustment.

[0104] Each method embodiment of the present application can be implemented in software, hardware, firmware, etc. Regardless of whether the present application is implemented in software, hardware, or firmware, the instruction code can be stored in any type of computer-accessible memory (for example, permanent or modifiable, volatile or non-volatile, solid-state or non-solid-state, fixed or replaceable medium, etc.). Similarly, the memory can be, for example, a programmable array logic (PAL), a random access memory (RAM), a programmable read-only memory (PROM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic disk, an optical disk, a digital versatile disc (DVD), etc.

[0105] The second embodiment of the present application relates to a real-time monitoring device for navigation satellite signal power. Figure 2 FIG. 1 is a structural schematic diagram of the real-time monitoring device for navigation satellite signal power.

[0106] Specifically, as shown in the figure, the real-time monitoring device for navigation satellite signal power comprises: Figure 2

[0107] a C / N0 observation quantity acquisition module configured to acquire real-time C / N0 observation quantities of a target reference station for a target satellite and a frequency point;​

[0108] an SSA and LRR module, configured to obtain a C / N0 prediction error and a C / N0 prediction at a current time based on the obtained historical C / N0 observations by using SSA and LRR methods;

[0109] a C / N0 bias determination module, configured to determine a C / N0 bias of the target satellite and frequency at the target reference station, wherein the C / N0 bias is equal to the C / N0 observation at the current time minus the C / N0 prediction at the current time;

[0110] a median selection module, configured to select a median of the C / N0 biases of the target reference stations and a median of the C / N0 prediction errors as a comprehensive C / N0 bias and a comprehensive threshold, respectively;

[0111] a judgment module, configured to compare the comprehensive C / N0 bias and the comprehensive threshold, and if the comprehensive C / N0 bias is greater than the comprehensive threshold, the target satellite and frequency are subjected to power adjustment.

[0112] In the embodiment, preferably, the real-time monitoring device for the navigation satellite signal power further comprises:

[0113] a selection module, configured to select the target reference station from global GNSS observation stations according to a predetermined standard.

[0114] Further, preferably, the predetermined standard comprises:

[0115] the target satellite and frequency can be continuously observed for more than 1 minute, preferably more than 5 minutes;

[0116] an elevation angle of the target satellite is greater than 5 degrees, preferably greater than 15 degrees.

[0117] In addition, the predetermined standard can further comprise:

[0118] the observation data can output an SPP positioning result, and the SPP positioning result can pass a chi-square test.

[0119] In the embodiment, preferably, the SSA and LRR module performs the following steps:

[0120] based on the obtained historical C / N0 observations, an original time sequence [x1, x2, … xN] is formed in chronological order, wherein N is a length of the original time sequence; N

[0121] a Hankel matrix X of the original time sequence is obtained according to a window length L, wherein the Hankel matrix X is represented as follows: ​

[0122] wherein K=N-L+1, L is an integer and is 0.5N, 0.5*(N+1) or 0.5*(N-1);

[0123] calculating the rank number corresponding to the principal components occupying a given proportion of the total components of the matrix X, denoted as r;

[0124] performing singular value decomposition on the matrix X to obtain X=UΣV T wherein U and V are standard orthogonal matrices;

[0125] reorganizing the eigenvalues of the matrix X after singular value decomposition, and obtaining the reorganized time series by using diagonalization average, and obtaining the C / N0 prediction error of the current moment by taking the standard deviation of the difference between the reorganized time series and the original time series;

[0126] obtaining the C / N0 prediction of the current moment by using the LRR method based on the matrix U.

[0127] In the embodiment, preferably, the given proportion can include but is not limited to 80%, 85%, 90% or 95%.

[0128] Further, preferably, the step of performing singular value decomposition on the matrix X can include the following sub-steps:

[0129] judging whether the r is greater than half of K,

[0130] if yes, performing standard singular value decomposition on the matrix X; if no, performing truncated singular value decomposition on the matrix X.

[0131] In the embodiment, according to the data characteristics, the method of combining standard SVD and truncated SVD can greatly reduce the SSA operation amount under the premise of unchanged accuracy.

[0132] In addition, preferably, the determination of the length N of the original time series needs to consider the indicators including the root mean square error of the prediction result, the false alarm rate of the prediction, the observation data sampling rate and the operation efficiency, and when the indicators simultaneously reach the optimal value, the length N of the original time series can be obtained.

[0133] Further, preferably, the optimal judgment standard can include the minimum mean value, the minimum median value or the minimum cost function.

[0134] Further, preferably, the method of judging whether the indicators simultaneously reach the optimal value can include the following steps:

[0135] Each index is weighted, and when the weighted sum of all indexes is the smallest, all indexes reach the optimum at the same time.

[0136] In addition, in the embodiment, the length N, which can also be referred to as the optimal length N of the original time sequence, is preferably determined.

[0137] The embodiment of the application innovatively combines the SSA and LRR methods to monitor the navigation satellite signal power adjustment in real time, has low monitoring cost, and can achieve optimal monitoring effect without manual parameter adjustment.

[0138] Compared with the prior art, the following advantages are achieved:

[0139] 1. No semi-sphere model needs to be established for modeling, so that the memory cost is very small, and the monitoring cost is low.

[0140] 2. No sliding window is needed to store historical data, so that there is no hysteresis, and real-time monitoring effect can be achieved.

[0141] 3. The parameter-free method combining the SSA and LRR is used, so that optimal monitoring effect can be achieved without manual parameter adjustment.

[0142] The embodiment is a device embodiment corresponding to the first embodiment, and the embodiment can be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in the embodiment, and to reduce repetition, they will not be described here. Correspondingly, the related technical details mentioned in the embodiment can also be applied in the first embodiment.

[0143] It should be noted that each module mentioned in each device embodiment of the application is a logical module. In the physical aspect, one logical module can be one physical module, or a part of one physical module, or a combination of multiple physical modules. The physical implementation of the logical module itself is not the most important, and the combination of the functions of the logical module is the key to solving the technical problems proposed in the application. In addition, in order to highlight the innovative part of the application, the above-mentioned device embodiments of the application do not introduce modules that are not closely related to solving the technical problems proposed in the application, which does not mean that the above-mentioned device embodiments do not have other modules.

[0144] It should be noted that the implementation functions of the modules shown in the embodiments of the above devices can be understood with reference to the related descriptions of the corresponding methods. The functions of the modules shown in the embodiments of the above devices can be realized by programs (executable instructions) running on the processor, or by specific logic circuits. The above devices of the embodiments of the present specification, if realized in the form of software function modules and sold or used as independent products, can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present specification can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present specification. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various media that can store program codes. Thus, the embodiments of the present specification are not limited to any specific hardware and software combination.

[0145] It should be noted that in the patent application file, the relationship terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including one" does not exclude the presence of another identical element in the process, method, article or device including the element. In the patent application file, if it is mentioned that a certain action is performed according to a certain element, it means that the action is performed at least according to the element, which includes two cases: the action is performed only according to the element, and the action is performed according to the element and other elements. The expressions of multiple, multiple times, multiple varieties, etc. include 2, 2 times, 2 varieties and more than 2, more than 2 times, more than 2 varieties.

[0146] All the documents mentioned in the present application are considered to be included in the disclosure of the present application as a whole, so as to be used as a modification if necessary. In addition, it should be understood that those skilled in the art can make various modifications or changes to the present application after reading the above disclosure of the present application, and these equivalent forms also fall within the scope of the present application.

Claims

1. A method for real-time monitoring of navigation satellite signal power, characterized in that, Includes the following steps: Acquire real-time C / N0 observations from the target reference station for the target satellite and frequency points; Based on the historical C / N0 observations already acquired, the C / N0 prediction error and C / N0 prediction value at the current moment are obtained using the SSA and LRR methods. Determine the C / N0 deviation of the target satellite and frequency point at the target reference station, wherein the C / N0 deviation is equal to the C / N0 observation at the current time minus the C / N0 prediction at the current time; The median of the C / N0 deviation and the median of the C / N0 prediction error of multiple target reference stations are selected as the comprehensive C / N0 deviation and comprehensive threshold, respectively. The magnitudes of the integrated C / N0 deviation and the integrated threshold are compared. If the integrated C / N0 deviation is greater than the integrated threshold, the power of the target satellite and frequency point is adjusted. The step of obtaining the C / N0 prediction error and C / N0 prediction value at the current time based on the acquired historical C / N0 observations using the SSA and LRR methods includes the following sub-steps: Based on the acquired historical C / N0 observations, an original time series [x1, x2, ... x] is formed in chronological order. N ], where N is the length of the original time series; Based on the window length L, the Hankel matrix X of the original time series is obtained, wherein the Hankel matrix X is represented as follows: Where K = N - L + 1, L is an integer and is 0.5N, 0.5*(N+1) or 0.5*(N-1); Calculate the rank of the principal component that occupies a given proportion of the total components of the matrix X, and denote it as r; Performing singular value decomposition on the matrix X yields X = UΣV T Where U and V are orthogonal matrices; The eigenvalues ​​of the singular value decomposition matrix X are recombined, and the diagonalized average is used to obtain the recombined time series. The standard deviation of the difference between the recombined time series and the original time series is taken to obtain the C / N0 prediction error at the current time. Based on the matrix U, the C / N0 prediction at the current time is obtained using the LRR method.

2. The method according to claim 1, characterized in that, The determination of the length N of the original time series needs to consider the following indicators: root mean square error of the prediction result, false alarm rate of prediction, sampling rate of observation data and computational efficiency. When the indicators to be considered reach their optimal values ​​at the same time, the length N of the original time series can be obtained.

3. The method according to claim 2, characterized in that, The method for determining that the aforementioned indicators to be considered simultaneously reach their optimal state includes the following steps: Each indicator is assigned a weight, and all indicators reach their optimal state simultaneously when the weighted sum of all indicators is minimized.

4. The method according to claim 1, characterized in that, The step of performing singular value decomposition on the matrix X includes the following sub-steps: Determine whether r is greater than half of K. If yes, then perform standard singular value decomposition on the matrix X; otherwise, perform truncated singular value decomposition on the matrix X.

5. The method according to claim 1, characterized in that, Before the step of acquiring the real-time C / N0 observations of the target reference station for the target satellite and frequency points, the following steps are also included: The target reference station is selected from global GNSS observation stations according to predetermined criteria.

6. The method according to claim 5, characterized in that, The predetermined standards include: The target satellite and frequency point can be continuously observed for more than 5 minutes; The target satellite's elevation angle is greater than 15 degrees.

7. The method according to claim 5, characterized in that, The predetermined standards also include: The observation data can output SPP positioning results, and the SPP positioning results can pass the chi-square test.

8. A real-time monitoring device for navigation satellite signal power, characterized in that, include: The C / N0 observation acquisition module is used to acquire real-time C / N0 observations of the target reference station for the target satellite and frequency points; The SSA and LRR modules are used to obtain the C / N0 prediction error and C / N0 prediction value at the current moment based on the acquired historical C / N0 observations using the SSA and LRR methods. The C / N0 deviation determination module is used to determine the C / N0 deviation of the target satellite and frequency point at the target reference station, wherein the C / N0 deviation is equal to the C / N0 observation at the current time minus the C / N0 prediction at the current time; The median selection module is used to select the median of the C / N0 deviation and the median of the C / N0 prediction error of multiple target reference stations, which are used as the comprehensive C / N0 deviation and comprehensive threshold, respectively. The judgment module is used to compare the magnitude of the integrated C / N0 deviation and the integrated threshold. If the integrated C / N0 deviation is greater than the integrated threshold, the target satellite and frequency point will undergo power adjustment. The SSA and LRR modules, in obtaining the current C / N0 prediction error and C / N0 prediction value based on the acquired historical C / N0 observations using the SSA and LRR methods, include the following sub-steps: Based on the acquired historical C / N0 observations, an original time series [x1, x2, ... x] is formed in chronological order. N ], where N is the length of the original time series; Based on the window length L, the Hankel matrix X of the original time series is obtained, wherein the Hankel matrix X is represented as follows: Where K = N - L + 1, L is an integer and is 0.5N, 0.5*(N+1) or 0.5*(N-1); Calculate the rank of the principal component that occupies a given proportion of the total components of the matrix X, and denote it as r; Performing singular value decomposition on the matrix X yields X = UΣV T Where U and V are orthogonal matrices; The eigenvalues ​​of the singular value decomposition matrix X are recombined, and the diagonalized average is used to obtain the recombined time series. The standard deviation of the difference between the recombined time series and the original time series is taken to obtain the C / N0 prediction error at the current time. Based on the matrix U, the C / N0 prediction at the current time is obtained using the LRR method.

9. The apparatus according to claim 8, characterized in that, Also includes: The selection module is used to select the target reference station from global GNSS observation stations according to predetermined criteria.

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