Satellite navigation jamming signal database and construction method

Abstract

The application provides a satellite navigation interference signal database and a construction method, and belongs to the field of satellite navigation interference signal identification. The construction method first judges whether an interference signal exists in a complete navigation signal; for the interference signal judged to exist, classification is performed according to membership, the classification result is taken as a first label, the optimized features of the interference signal are extracted for classification, a second label is added, the time-domain radio frequency form of the interference signal is collected, a trained convolutional neural network is used for classification, a third label is added, a label function is constructed, and the first label is corrected; an identification algorithm module index matched with the interference signal grading label is established, and accurate search and calling of the interference signal identification algorithm are realized. The application improves the fault tolerance and robustness of the database, and improves the interference signal identification efficiency and accuracy based on the database.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of satellite navigation signal interference, and particularly relates to a satellite navigation interference signal database and a construction method thereof. BACKGROUND

[0002] In modern navigation systems, satellite navigation technology plays a vital role. However, navigation signals are often affected by various interferences, resulting in decreased positioning accuracy or even complete failure. Studying and classifying interference signals is conducive to in-depth analysis of the characteristics of interference signals and targeted inhibition and elimination of certain types of interference signals. The method of constructing an interference signal database is usually used to manage, identify and inhibit interference signals.

[0003] In the prior art, a satellite navigation interference signal database is generally managed in a hierarchical manner, so as to facilitate the application of different interference inhibition algorithms and signal identification algorithms to different types of interference signals. However, the existing database with a hierarchical architecture mainly stores and records the tree structure classification results of data for searching, and the database management is greatly affected by the classification accuracy, and the database robustness is poor. SUMMARY

[0004] In view of the above defects or deficiencies in the prior art, the present application aims to provide a satellite navigation interference signal database and a construction method thereof, which classifies and manages interference signals in a multi-level and phased manner, improves the classification efficiency, fault tolerance and robustness of the database, and further improves the accuracy of interference signal identification.

[0005] In order to achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, the embodiments of the present application provide a construction method of a satellite navigation interference signal database, which comprises the following steps:

[0007] Step S1: collecting complete navigation signals from a satellite receiver, and judging whether there is an interference signal in the complete navigation signals;

[0008] Step S2: classifying the interference signal determined to exist according to the membership degree, and taking the classification result as a first label;

[0009] Step S3: extracting the optimized features of the interference signal for classification, and adding a second label;

[0010] Step S4: collecting the time domain radio frequency form signal of the interference signal, classifying by using a trained convolutional neural network, and adding a third label;

[0011] Step S5: constructing a label function, and correcting the first label;

[0012] Step S6, the index of the identification algorithm module matched with the interference signal classification label is established, so as to realize accurate search and calling of the interference signal identification algorithm.

[0013] As a preferred embodiment of the present application, the step S1 further comprises:

[0014] Step S11, using a satellite receiver to receive navigation signals from no less than 4 satellites, and demodulating the received navigation signals to convert them from radio frequency signals to baseband signals as complete navigation signals;

[0015] Step S12, decoding the complete navigation signals to extract satellite orbit parameters, signal propagation time and receiver position information, and recording signal power through the radio frequency front-end circuit of the receiver and recording noise power through the noise measurement circuit of the receiver;

[0016] Step S13, calculating the carrier-to-noise ratio using the signal power and the noise power, and the calculation formula (1) is as follows:

[0017]

[0018] In formula (1), CNR is the carrier-to-noise ratio, lg() is the logarithm operation with base 10, P1 is the signal power, and P2 is the noise power;

[0019] Step S14, multiplying the signal propagation time measured by the receiver by the speed of light to calculate the actual measured pseudo-range, using the satellite orbit parameters and the receiver position to calculate the theoretical pseudo-range by the least square method, and subtracting the theoretical pseudo-range from the actual measured pseudo-range to obtain the pseudo-range residual ρ;

[0020] Step S15, constructing a navigation interference signal preliminary identification network, calculating the probability of existing interference signals, and judging whether there is an interference signal according to the probability of existing interference signals.

[0021] As a preferred embodiment of the present application, the formula (2) for calculating the probability of existing interference signals is as follows:

[0022]

[0023] In formula (2), pro is the probability of existing interference signals, sigmoid() is the sigmoid function, and MLP() is the multi-layer perception operation.

[0024] According to the probability of existing interference signals, it is judged whether there is an interference signal: if pro=1, it is considered that there is a navigation interference signal, and step S2 is entered; if pro=0, it is considered that there is no navigation interference signal.

[0025] As a preferred embodiment of the present application, step S2 comprises:

[0026] Step S21, normalize the interference signal, normalize the interference signal into a normal distribution signal with mean 0 and standard deviation 1;

[0027] Step S22, based on the normalized interference signal, calculate the membership degree of the interference signal belonging to space-based dynamic interference, ground-based dynamic interference and static interference, and the calculation formula (3) is as follows:

[0028]

[0029] In formula (3), μ kj represents the membership degree of the interference signal belonging to space-based dynamic interference, μ dj represents the membership degree of the interference signal belonging to ground-based dynamic interference, μ jt represents the membership degree of the interference signal belonging to static interference, f kj represents the membership function of space-based dynamic interference, f dj represents the membership function of ground-based dynamic interference; f jt represents the membership function of static interference; feature represents the feature of the interference signal.

[0030] Select the type with the largest membership degree as the classification result of the interference signal, and add a first label first_label to the interference signal according to the classification result.

[0031] As a preferred embodiment of the present application, when normalizing in step S21, the mean mean and the standard deviation std_dev of the interference signal are calculated, and for each data point of the interference signal, the following formula is used for normalization:

[0032] normalized_data=(data-mean) / std_dev

[0033] Wherein, normalized_data represents the normalized interference signal, and data represents the unnormalized interference signal.

[0034] As a preferred embodiment of the present application, step S3 comprises:

[0035] Step S31, identify the center frequency point, bandwidth, power and repetition period of the interference signal by spectrum detection means, and construct a feature matrix:

[0036] charac=[center,band,power,cycle] (5)

[0037] In formula (5), charac represents a characteristic matrix, center represents a center frequency point of the interference signal, band represents a bandwidth of the interference signal, power represents a power of the interference signal, and cycle represents a repetition period of the interference signal;

[0038] In step S32, a target function of self-expression learning of the interference signal is established:

[0039] obj_func = min∑||charac T ·Auni-charac T ·Auni·struc||+α||charac||+β||struc|| (6)

[0040] In formula (6), obj_func represents a target function of self-expression learning, charac T represents a transpose of the matrix charac, Auni represents a matrix satisfying charac T ·Auni·Auni T ·charac = I, Auni T represents a transpose of the matrix Auni, I represents a unit matrix, struc represents a signal structure factor, || || represents a norm constraint, and α, β represent regularization parameters;

[0041] In step S33, an optimization factor is defined:

[0042] opt = (I-struc)(I-struc) T (7)

[0043] In formula (7), opt represents an optimization factor, (I-struc) T represents a transpose of the matrix (I-struc);

[0044] The target function obj_func of self-expression learning is updated as:

[0045] obj_func' = min∑||charac T ·Auni·opt·Auni T ·struc||+α||charac|| (8)

[0046] In formula (8), obj_func' represents an updated value of the target function of self-expression learning;

[0047] In step S34, a value of struc is determined, is brought into formula (7), and an optimization factor opt is calculated;

[0048] characteristic decomposition is performed on the optimization factor opt, a characteristic vector corresponding to the smallest N characteristic values is calculated, and a characteristic selection matrix eig_matrix is constructed based on the characteristic vector; the characteristic selection matrix is an optimization of the characteristic matrix charac;

[0049] In step S35, the optimization characteristic of the interference signal is extracted by using the characteristic selection matrix, and the optimization characteristic is input into the support vector machine, so that the interference signal is classified according to a wideband interference, a narrowband interference, a pulse interference or a sweep frequency interference, and a second label sec_label is added to the interference signal according to the classification result.

[0050] As a preferred embodiment of the present application, in step S4, the interference signal is classified according to a single carrier, AM, FM, BOC, BPSK, QPSK or FSK, and a third label thir_label is added to the interference signal according to the classification result.

[0051] As a preferred embodiment of the present application, in step S5, the first label is corrected by using the following formula:

[0052]

[0053] In formula (9), label_func represents a label function of the interference signal, label_func=1 represents that the label of the interference signal is reasonable, that is, the interference signal exists with the first label first_label, the second label sec_label and the third label thir_label; and label_func=0 represents that the label of the interference signal is unreasonable, that is, the interference signal does not exist with the first label first_label, the second label sec_label and the third label thir_label.

[0054] If label_func=0, the first label is corrected, the type with the second largest membership degree is selected as the first classification result of the interference signal, and the first label first_label of the interference signal is corrected according to the classification result.

[0055] As a preferred embodiment of the present application, in step S6, the index of the recognition algorithm module is constructed as follows:

[0056] Index:(cate,layer)=alori (10)

[0057] In formula (10), layer represents an index level, cate represents the type of each layer, and alori represents an algorithm that can be called; the algorithm that can be called includes a method based on machine learning and a traditional signal processing technology.

[0058] When layer = 1, the set of type cate is:

[0059] {empty base dynamic interference, ground base dynamic interference, static interference}

[0060] When layer = 2, the set of type cate is:

[0061] {wideband interference, narrowband interference, impulse interference, sweep interference}

[0062] When layer = 3, the set of type cate is:

[0063] {single carrier, AM, FM, BOC, BPSK, QPSK, FSK}.

[0064] In a second aspect, the embodiment of the present application further provides a database constructed by the method for constructing a satellite navigation interference signal database, the database comprising a recognition algorithm module and a storage module; wherein,

[0065] The storage module stores interference signals collected based on complete navigation signals carrying interference signals, the interference signals being assigned a first label, a second label and a third label according to classification results and saved in corresponding storage modules according to the labels;

[0066] The recognition algorithm module is used for inputting interference signals to be recognized, calling recognition algorithms in the corresponding recognition algorithm module according to the interference signals, and recognizing the interference signals to be recognized based on the interference signal types stored in the storage.

[0067] The technical solution provided by the embodiment of the present application has the following beneficial effects:

[0068] The satellite navigation interference signal database and the method for constructing the same provided by the embodiment of the present application classify and manage interference signals in a multi-level and phased manner, and improve the accuracy of interference signal recognition through the judgment of classification combination; in the hierarchical classification stage, the fuzzy classification method based on membership and the feature selection method are used to improve the classification efficiency; on the basis of classification, further refinement is performed, and the fault tolerance and robustness of the database are improved through feedback modification of the first label. For different levels and types of interference signals, the recognition algorithm is selected to ensure that the source and type of the interference signal can be quickly and accurately located.

[0069] Of course, implementing any product or method of the present application does not necessarily require all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only show some of the embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0071] Figure 1 is a flow chart of the method for constructing the satellite navigation interference signal database provided by the embodiments of the present application. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict, if possible.

[0073] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present application, the terms "first", "second", "third", "fourth" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0074] In view of the problems existing in the satellite navigation database in the prior art, the embodiments of the present application provide a satellite navigation interference signal database and a construction method. Referring to Figure 1 , the construction method of the satellite navigation interference signal database comprises the following steps:

[0075] Step S1, collecting complete navigation signals from a satellite receiver, and judging whether there is an interference signal in the complete navigation signals.

[0076] In this step, the judgment of whether there is an interference signal comprises the following steps:

[0077] Step S11, using a satellite receiver to receive navigation signals from no less than 4 satellites, and demodulating the received navigation signals to convert them from radio frequency signals to baseband signals as complete navigation signals;

[0078] Step S12, decoding the complete navigation signals to extract satellite orbit parameters, signal propagation time and receiver position information, and recording signal power through the radio frequency front-end circuit of the receiver and recording noise power through the noise measurement circuit of the receiver;

[0079] Step S13, the carrier-to-noise ratio is calculated by using the signal power and the noise power, and the calculation formula (1) is as follows:

[0080]

[0081] In formula (1), CNR is the carrier-to-noise ratio, lg() is the logarithm operation with base 10, P1 is the signal power, and P2 is the noise power.

[0082] Step S14, the actual measured pseudo-range is calculated by multiplying the signal propagation time measured by the receiver by the speed of light; the theoretical pseudo-range is calculated by using the satellite orbit parameters and the receiver position by using the least square method; and the pseudo-range residual error p is obtained by subtracting the theoretical pseudo-range from the actual measured pseudo-range.

[0083] Step S15, a navigation interference signal preliminary identification network is constructed, and step S15, the probability of the existence of an interference signal is calculated, and the calculation formula (2) of the existence of the interference signal is as follows:

[0084]

[0085] In formula (2), pro is the probability of the existence of the interference signal, sigmoid() is the sigmoid function, and MLP() is the multi-layer perception operation.

[0086] According to the probability of the existence of the interference signal, it is judged whether the interference signal exists: if pro=1, it is considered that the navigation interference signal exists, and step S2 is entered; if pro=0, it is considered that the navigation interference signal does not exist.

[0087] Step S2, for the existing interference signal, classification is performed according to the membership degree, and the classification result is taken as the first label.

[0088] This step further includes:

[0089] Step S21, the interference signal is normalized, specifically including: the interference signal is normalized to a normal distribution signal with a mean of 0 and a standard deviation of 1, and the specific steps are to calculate the mean mean and the standard deviation std_dev of the interference signal, and for each data point of the interference signal, the following formula is used for normalization:

[0090] normalized_data=(data-mean) / std_dev

[0091] Wherein, normalized_data represents the normalized interference signal, and data represents the unnormalized interference signal.

[0092] Step S22, based on the normalized interference signal, the membership degree of the interference signal belonging to the space-based dynamic interference, the ground-based dynamic interference and the static interference is calculated, and the calculation formula (3) is as follows:

[0093]

[0094] In formula (3), μ kj represents the membership degree of the interference signal belonging to the space-based dynamic interference, μ dj represents the membership degree of the interference signal belonging to the ground-based dynamic interference, μ jt represents the membership degree of the interference signal belonging to the static interference, f kj represents the membership degree function of the space-based dynamic interference, f dj represents the membership degree function of the ground-based dynamic interference; f jt represents the membership degree function of the static interference; feature represents the feature of the interference signal. The type with the maximum membership degree is selected as the classification result of the interference signal, and a first label first_label is added to the interference signal according to the classification result.

[0095] Step S3, the optimized feature of the interference signal is extracted for classification, and a second label is added.

[0096] This step further comprises:

[0097] Step S31, the center frequency point, the bandwidth, the power and the repetition period of the interference signal are identified by a spectrum detection means, and a feature matrix is constructed:

[0098] charac=[center,band,power,cycle](5)

[0099] In formula (5), charac represents the feature matrix, center represents the center frequency point of the interference signal, band represents the bandwidth of the interference signal, power represents the power of the interference signal, and cycle represents the repetition period of the interference signal.

[0100] In the embodiment of the application, a spectrum analyzer can be used to perform frequency domain analysis on the interference signal to find the peak frequency in the spectrum of the interference signal, which is the center frequency point. The autocorrelation function of the interference signal can also be calculated to determine the periodicity of the interference signal, and the peak value of the signal energy in a period is found, which is the center frequency point of the interference signal.

[0101] A spectrum analyzer can be used to find the peak frequency and the valley frequency in the spectrum of the interference signal, and the interpolation of the peak frequency and the valley frequency is the bandwidth of the interference signal. The autocorrelation function of the interference signal can also be calculated to determine the periodicity of the signal, and the peak value and the valley of the signal energy in a period are found, and the interpolation of the peak frequency and the valley frequency is the bandwidth of the interference signal.

[0102] Step S32, a target function of the interference signal self-expression learning is established:

[0103] obj_func = min∑||charac T ·Auni-charac T ·Auni·struc||+α||charac||+β||struc|| (6)

[0104] In formula (6), obj_func represents the target function of the self-expression learning, charac T represents the transpose of the matrix charac, Auni represents a matrix satisfying charac T ·Auni·Auni T ·charac = I, Auni T represents the transpose of the matrix Auni, I represents the unit matrix, struc represents the signal structure factor, || || represents the norm constraint, and α, β represent the regularization parameters.

[0105] Step S33, an optimization factor is defined:

[0106] opt = (I-struc)(I-struc) T (7)

[0107] In formula (7), opt represents the optimization factor, (I-struc) T represents the transpose of the matrix (I-struc).

[0108] The target function obj_func of the self-expression learning is updated as:

[0109] obj_func' = min∑||charac T ·Auni·opt·Auni T ·struc||+α||charac|| (8)

[0110] In formula (8), obj_func' represents the updated value of the target function of the self-expression learning.

[0111] Step S34, the value of struc is determined, which is brought into formula (7) to calculate the optimization factor opt.

[0112] The optimization factor opt is subjected to eigenvalue decomposition, the eigenvectors corresponding to the smallest N eigenvalues are calculated, and a feature selection matrix eig_matrix is constructed based on the eigenvectors. The feature selection matrix here is the optimization of the feature matrix charac.

[0113] Step S35, the optimized features of the interference signal are extracted by using the feature selection matrix, and the optimized features are input into the support vector machine, so as to classify the interference signal according to a wideband interference, a narrowband interference, a pulse interference or a sweep frequency interference, and add a second label sec_label to the interference signal according to the classification result.

[0114] Step S4, the signal in the form of a time domain radio frequency of the interference signal is collected, a trained convolutional neural network is used for classification, and a third label is added.

[0115] In this step, the interference signal is classified according to a single carrier, AM, FM, BOC, BPSK, QPSK or FSK, and a third label thir_label is added to the interference signal according to the classification result.

[0116] Step S5, a label function is constructed, and the first label is corrected.

[0117] Specifically, the correction of the first label in this step uses the following formula:

[0118]

[0119] In formula (9), label_func represents a label function of the interference signal, label_func=1 represents that the label of the interference signal is reasonable, that is, the interference signal exists with the first label first_label, the second label sec_label and the third label thir_label; and label_func=0 represents that the label of the interference signal is unreasonable, that is, the interference signal does not exist with the first label first_label, the second label sec_label and the third label thir_label.

[0120] If label_func=0, the first label is corrected, the type with the second largest membership degree is selected as the first classification result of the interference signal, and the first label first_label of the interference signal is corrected according to the classification result.

[0121] Step S6, an index of a recognition algorithm module matched with the classification label of the interference signal is established, so as to realize accurate search and calling of the interference signal recognition algorithm.

[0122] In this step, the index of the recognition algorithm module is constructed as follows:

[0123] Index:(cate,layer)=alori (10)

[0124] In formula (10), layer represents an index level, cate represents the type of each layer, and alori represents an algorithm that can be invoked. The algorithm that can be invoked includes, but is not limited to, a machine learning-based method, a traditional signal processing technique, and the like.

[0125] When layer = 1, the set of types cate is:

[0126] {empty base dynamic interference, ground base dynamic interference, static interference}

[0127] When layer = 2, the set of types cate is:

[0128] {wideband interference, narrowband interference, impulse interference, sweep interference}

[0129] When layer = 3, the set of types cate is:

[0130] {single carrier, AM, FM, BOC, BPSK, QPSK, FSK}.

[0131] Based on the same idea, the embodiment of the application further provides a database constructed by using the construction method of the satellite navigation interference signal database, the database comprising an identification algorithm module and a storage module; wherein,

[0132] The storage module stores interference signals collected based on complete navigation signals carrying interference signals, the interference signals being assigned a first label, a second label and a third label according to classification results and being saved in corresponding storage modules according to the labels;

[0133] The identification algorithm module is used for inputting an interference signal to be identified, calling an identification algorithm in the corresponding identification algorithm module according to the interference signal, and identifying the interference signal to be identified based on the interference signal types stored in the storage.

[0134] The modules in the embodiments are implemented by a processor, and a memory is appropriately increased when storage is needed. The processor can be, but is not limited to, a microprocessor (MPU), a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The memory can include a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0135] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode.

[0136] In addition, it should be noted that the satellite navigation interference signal database described in the embodiments is constructed by using the construction method of the satellite navigation interference signal database, and the description and limitation of the construction method are also applicable to the database, which will not be described here.

[0137] From the above technical solutions, the satellite navigation interference signal database and the construction method provided by the embodiment of the present application classifies and manages the interference signals in a multi-level and phased manner, and improves the accuracy of interference signal identification through the judgment of classification combination; in the hierarchical classification stage, the fuzzy classification method and the feature selection method based on membership are used to improve the classification efficiency; on the basis of classification, further refinement is made, and the fault tolerance and robustness of the database are improved through the feedback modification of the first label. For different levels and types of interference signals, the identification algorithm is selected to ensure that the source and type of the interference signal can be quickly and accurately located.

[0138] The above description is merely the preferred embodiments of the present application and the explanation of the applied technical principles, and is not intended to limit the scope of the claimed present application, but merely represents the preferred embodiments of the present application. Those skilled in the art should understand that the scope of the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

Claims

1. A method for constructing a satellite navigation interference signal database, characterized in that, The construction method includes the following steps: Step S1: Collect the complete navigation signal from the satellite receiver and determine whether there is interference signal in the complete navigation signal; Step S2: Classify the interference signals that are identified as existing based on their membership degree, and use the classification result as the first label; Step S3: Extract optimized features of the interference signal, classify them, and add a second label; Step S4: Collect the interference signal in the time domain radio frequency form, classify it using a trained convolutional neural network, and add a third label; Step S5: Construct the tag function and correct the first tag; Step S6: Establish an index of identification algorithm modules that match the graded labels of interference signals to achieve accurate search and invocation of interference signal identification algorithms; in, Step S2 includes: Step S21: Normalize the interference signal to a normal distribution signal with a mean of 0 and a standard deviation of 1. Step S22: Based on the normalized interference signal, calculate the membership degree of the interference signal to airborne dynamic interference, ground-based dynamic interference, and static interference. The calculation formula (3) is as follows: In equation (3), μ kj μ represents the membership degree of the interference signal to airborne dynamic interference. dj μ represents the membership degree of the interference signal to ground-based dynamic interference. jt f represents the membership degree of the interference signal as static interference. kj The membership function f represents the dynamic disturbance of the airborne system. dj The membership function representing the dynamic disturbance of the foundation; f jt The membership function represents static interference; feature represents the characteristics of the interference signal. Select the type with the highest membership degree as the classification result of the interference signal, and add the first label first_label to the interference signal according to the classification result; Step S3 includes: Step S31: Identify the center frequency, bandwidth, power, and repetition period of the interference signal using spectrum detection methods, and construct a feature matrix. charac=[center,band,power,cycle] (5) In equation (5), charac represents the characteristic matrix, center represents the center frequency of the interference signal, band represents the bandwidth of the interference signal, power represents the power of the interference signal, and cycle represents the repetition period of the interference signal. Step S32, establish the objective function for learning the self-expression of the interference signal: obj_func=min∑||character T ·Uni-character T ·Auni·struc||+α||charac||+β||struc|| (6) In equation (6), obj_func represents the objective function of self-expressive learning, and charac T Auni represents the transpose of matrix charac, and Auni represents the matrix satisfying charac. T ·Auni·Auni T A matrix charac = I, Auni T Let Auni denote the transpose of matrix Auni, I denote the identity matrix, struc denote the signal structure factor, || denotes the norm constraint, and α and β denote the regularization parameters. Step S33, define the optimization factor: opt=(I-struc)(I-struc) T (7) In equation (7), opt represents the optimization factor, (I-struc) T Represents the transpose of matrix (I-struc); The objective function obj_func for self-expressive learning is updated to: obj_func′=minΣ||character T ·Auni·opt·Auni T ·struct||+α||character|| (8) In equation (8), obj_func′ represents the update value of the objective function of self-expressive learning; Step S34: Determine the value of struc, substitute it into equation (7), and calculate the optimization factor opt; perform eigenvalue decomposition on the optimization factor opt, calculate the eigenvectors corresponding to the N smallest eigenvalues, and construct the feature selection matrix eig_matrix based on the eigenvectors; the feature selection matrix is ​​an optimization of the feature matrix charac; Step S35: Extract optimized features of the interference signal using the feature selection matrix, input the optimized features into the support vector machine, classify the interference signal into broadband interference, narrowband interference, pulse interference or frequency sweep interference, and add a second label sec_label to the interference signal according to the classification result. In step S4, the interference signals are classified according to single carrier, AM, FM, BOC, BPSK, QPSK or FSK, and a third label thir_label is added to the interference signals according to the classification results.

2. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that, Step S1 further includes: Step S11: Use a satellite receiver to receive navigation signals from no less than 4 satellites, demodulate the received navigation signals, and convert them from radio frequency signals to baseband signals as complete navigation signals; Step S12: Decode the complete navigation signal to extract satellite orbit parameters, signal propagation time and receiver position information. At the same time, record the signal power through the receiver's radio frequency front-end circuit and the noise power through the receiver's noise measurement circuit. Step S13: Calculate the carrier-to-noise ratio using the signal power and noise power. The calculation formula (1) is as follows: In equation (1), CNR is the carrier-to-noise ratio, lg() is the logarithmic operation with base 10, P1 is the signal power, and P2 is the noise power. Step S14: Multiply the signal propagation time measured by the receiver by the speed of light to calculate the actual measured pseudorange; use the satellite orbit parameters and receiver position to calculate the theoretical pseudorange using the least squares method; subtract the theoretical pseudorange from the actual measured pseudorange to obtain the pseudorange residual ρ. Step S15: Construct a preliminary identification network for navigation interference signals, calculate the probability of the existence of interference signals, and determine whether interference signals exist based on the probability of the existence of interference signals.

3. The method for constructing a satellite navigation interference signal database according to claim 2, characterized in that, The probability formula (2) for calculating the presence of interference signals is as follows: In equation (2), pro is the probability of the presence of interference signal, sigmoid() is the sigmoid function, and MLP() is the multilayer perceptron operation; Based on the probability of the presence of an interference signal, determine whether an interference signal exists: if pro = 1, then it is considered that a navigation interference signal exists, and proceed to step S2; if pro = 0, then it is considered that no navigation interference signal exists.

4. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that, In step S21, during normalization, the mean and standard deviation (std_dev) of the interference signal are calculated. For each data point of the interference signal, normalization is performed using the following formula: normalized_data=(data-mean) / std_dev Where normalized_data represents the normalized interference signal, and data represents the unnormalized interference signal.

5. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that, In step S5, the first label is corrected using the following formula: In equation (9), label_func represents the label function of the interference signal. label_func = 1 indicates that the label of the interference signal is reasonable, that is, the interference signal with the first label is first_label, the second label is sec_label, and the third label is thir_label exists; label_func = 0 indicates that the label of the interference signal is unreasonable, that is, the interference signal with the first label is first_label, the second label is sec_label, and the third label is thir_label does not exist. If label_func = 0, the first label is corrected, and the type with the second largest membership degree is selected as the first-level classification result of the interference signal. The first label first_label of the interference signal will be corrected according to the classification result.

6. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that, In step S6, the index for constructing the recognition algorithm module is as follows: Index:(cate,layer)=alori (10) In equation (10), layer represents the index level, cate represents the type of each level, and alori represents the callable algorithm; When layer = 1, the set of type cate is: {Airborne dynamic interference, ground-based dynamic interference, static interference} When layer = 2, the set of type cate is: Broadband interference, narrowband interference, impulse interference, frequency sweep interference. When layer = 3, the set of type cate is: {Single carrier, AM, FM, BOC, BPSK, QPSK, FSK}.

7. A database constructed using the method for constructing a satellite navigation interference signal database as described in any one of claims 1-6, characterized in that, The database includes a recognition algorithm module and a storage module; wherein... The storage module stores the interference signals collected based on the complete navigation signal carrying the interference signal. The interference signals are assigned a first tag, a second tag, and a third tag according to the classification results, and are stored in the corresponding storage module according to the tags. The identification algorithm module is used to input the interference signal to be identified, and to call the identification algorithm in the corresponding identification algorithm module according to the interference signal, and then to identify the interference signal to be identified based on the interference signal type stored in the memory.

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