Satellite navigation interference signal database and construction method

Abstract

The invention 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 comprises the following steps: firstly, judging whether interference signals exist in complete navigation signals or not; carrying out classification on interference signals judged to exist according to membership degrees, taking a classification result as a first label, extracting optimization features of the interference signals for classification, adding a second label, collecting signals in a time domain radio frequency form of the interference signals, carrying out classification by adopting a trained convolutional neural network, adding a third label, and constructing a label function; the first label is corrected; and establishing an identification algorithm module index matched with the interference signal grading tag to realize accurate searching and calling of the interference signal identification algorithm. According to the method, the fault tolerance and robustness of the database are improved, and meanwhile, the interference signal identification efficiency and accuracy based on the database are improved.

Description

Technical Field

[0001] The present invention 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 Art

[0002] In modern navigation systems, satellite navigation technology plays a crucial role. However, navigation signals are often affected by various interferences, resulting in a decrease in positioning accuracy or even complete failure. Studying and classifying interference signals is beneficial for in-depth analysis of the characteristics of interference signals and for targeted suppression and elimination of certain types of interference signals. Generally, a method of constructing an interference signal database is used to manage, identify, and suppress interference signals.

[0003] In the prior art, satellite navigation interference signal databases generally adopt hierarchical management, which is beneficial for applying different interference suppression algorithms and signal recognition algorithms for different types of interference signals. However, currently existing databases with a hierarchical architecture mainly store and record the classification results of the tree-like structure of data for searching, and database management is greatly affected by the classification accuracy, and the database has poor robustness. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present invention aims to provide a satellite navigation interference signal database and a construction method thereof, which classify and manage interference signals in a multi-level and phased manner, improve the classification efficiency, fault tolerance, and robustness of the database, and thus further improve the accuracy of interference signal recognition.

[0005] To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

[0006] In a first aspect, the embodiments of the present invention provide a construction method of a satellite navigation interference signal database, and the construction method includes the following steps:

[0007] Step S1, collect complete navigation signals from a satellite receiver, and determine whether there are interference signals in the complete navigation signals;

[0008] Step S2, classify the determined interference signals according to the membership degree, and use the classification result as the first label;

[0009] Step S3, extract optimized features of the interference signals for classification, and add a second label;

[0010] Step S4, collect signals in the time-domain radio frequency form of the interference signals, and use a trained convolutional neural network for classification, and add a third label;

[0011] Step S5, construct a label function, and correct the first label;

[0012] Step S6, establish an index of the recognition algorithm module that matches the interference signal classification label to achieve an accurate search and call of the interference signal recognition algorithm.

[0013] As a preferred embodiment of the present invention, the step S1 further includes:

[0014] 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.

[0015] Step S12, decode the complete navigation signals, extract information such as satellite orbit parameters, signal propagation time, and receiver position, and at the same time record the signal power through the radio frequency front-end circuit of the receiver and record the noise power through the noise measurement circuit of the receiver.

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

[0017]

[0018] In formula (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.

[0019] 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 the 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.

[0020] Step S15, construct a preliminary recognition network for navigation interference signals. Calculate the probability of the existence of interference signals, and judge whether there are interference signals according to the probability of the existence of interference signals.

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

[0022]

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

[0024] Judge whether there are interference signals according to the probability of the existence of interference signals: if pro = 1, it is considered that there are navigation interference signals and enter step S2; if pro = 0, it is considered that there are no navigation interference signals.

[0025] As a preferred embodiment of the present invention, step S2 includes:

[0026] Step S21, perform normalization processing on the interference signal to normalize the interference signal into a normal distribution signal with a mean of 0 and a standard deviation of 1;

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

[0028]

[0029] In formula (3), μ kj represents the membership degree of the interference signal belonging to airborne 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 airborne 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 characteristics of the interference signal;

[0030] Select the type with the largest 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.

[0031] As a preferred embodiment of the present invention, when performing normalization in step S21, calculate the mean mean and standard deviation std_dev of the interference signal, and use the following formula to normalize each data point of the interference signal:

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

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

[0034] As a preferred embodiment of the present invention, step S3 includes:

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

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

[0037] In Equation (5), charac represents the feature 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;

[0038] Step S32, establish the objective function for self-expression learning of the interference signal:

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

[0040] In Equation (6), obj_func represents the objective function of self-expression learning, charac T represents the transpose of matrix charac, Auni represents the matrix that satisfies charac T ·Auni·Auni T ·charac = I, Auni T represents the transpose of matrix Auni, I represents the identity matrix, struc represents the signal structure factor, || || represents the norm constraint, and α, β represent the regularization parameters;

[0041] Step S33, define the optimization factor:

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

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

[0044] The objective function obj_func of self-expression learning is updated to:

[0045] obj_func′ = min Σ||charac T ·Auni·opt·Auni T ·struc|| + α||charac|| (8)

[0046] In Equation (8), obj_func′ represents the updated value of the objective function of self-expression learning;

[0047] Step S34, determine the value of struc, substitute it into Equation (7), and calculate the optimization factor opt;

[0048] Perform eigen - decomposition on the optimization factor opt, calculate the eigen - vectors corresponding to the smallest N eigenvalues, and construct an eigen - selection matrix eig_matrix based on the eigen - vectors; the eigen - selection matrix is the optimization of the feature matrix charac.

[0049] Step S35: Use the eigen - selection matrix to extract the optimized features of the interference signal, input the optimized features into the support vector machine, classify the interference signal according to wide - band interference, narrow - band interference, pulse interference or frequency - swept interference, and add a second label sec_label to the interference signal according to the classification result.

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

[0051] As a preferred embodiment of the present invention, in step S5, the following formula is used to correct the first label:

[0052]

[0053] In formula (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, there exists an interference signal with the first label first_label, the second label sec_label, and the third label thir_label; label_func = 0 indicates that the label of the interference signal is unreasonable, that is, there is no interference signal with the first label first_label, the second label sec_label, and the third label thir_label.

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

[0055] As a preferred embodiment of the present invention, 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 the index level, cate represents the type of each layer, and alori represents the callable algorithm; the callable algorithms include: methods based on machine learning, traditional signal - processing techniques.

[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] {Broadband interference, narrowband interference, pulse interference, frequency 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, an embodiment of the present invention further provides a database constructed by using the method for constructing the satellite navigation interference signal database, where the database includes an identification 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 are assigned a first label, a second label, and a third label according to the classification result, and are stored in the corresponding storage module according to the labels;

[0066] The identification algorithm module is used to input the interference signal to be identified, call the identification algorithm in the corresponding identification algorithm module according to the interference signal, and then identify the interference signal to be identified based on the interference signal type stored in the memory.

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

[0068] The satellite navigation interference signal database and the construction method provided by the embodiment of the present invention classify and manage interference signals in a multi-level and phased manner, and improve the accuracy of interference signal identification through the judgment of classification combination; in the hierarchical classification stage, a fuzzy classification method and a feature selection method based on membership degree are adopted to improve the classification efficiency; on the basis of classification, it is further refined, 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, an identification algorithm is selected to ensure that the source and type of the interference signal can be quickly and accurately located.

[0069] Of course, it is not necessary for any product or method implementing the present invention to achieve all the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0071] Figure 1 It is a flowchart of a method for constructing a satellite navigation interference signal database provided by an embodiment of the present invention. Specific embodiments

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations. It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can also be combined with each other.

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

[0074] Aiming at the problems existing in the satellite navigation database in the prior art, an embodiment of the present invention provides a satellite navigation interference signal database and a construction method. See Figure 1 The construction method of the satellite navigation interference signal database includes the following steps:

[0075] Step S1: Collect complete navigation signals from a satellite receiver and determine whether there are interference signals in the complete navigation signals.

[0076] In this step, the determination of whether there are interference signals includes the following steps:

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

[0078] Step S12: Decode the complete navigation signals, extract information such as satellite orbit parameters, signal propagation time, and receiver position, and at the same time record the signal power through the radio frequency front-end circuit of the receiver and record the noise power through the noise measurement circuit of the receiver;

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

[0080]

[0081] In formula (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.

[0082] 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 the receiver position, and adopt the least squares method to calculate the theoretical pseudorange; subtract the theoretical pseudorange from the actual measured pseudorange to obtain the pseudorange residual ρ.

[0083] Step S15: Construct a preliminary identification network for navigation interference signals. Calculate the probability of the existence of interference signals. The calculation formula (2) is as follows:

[0084]

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

[0086] Based on the probability of the existence of interference signals, determine whether there are interference signals: If pro = 1, it is considered that there are navigation interference signals, and go to step S2; if pro = 0, it is considered that there are no navigation interference signals.

[0087] Step S2: Classify the determined interference signals according to the membership degree, and use the classification result as the first label.

[0088] This step further includes:

[0089] Step S21: Perform normalization processing on the interference signals. Specifically, it includes: Normalize the interference signals into a normal distribution signal with a mean of 0 and a standard deviation of 1. The specific steps are to calculate the mean mean and the standard deviation std_dev of the interference signals, and for each data point of the interference signals, use the following formula for normalization:

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

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

[0092] Step S22: Based on the normalized interference signal, calculate the membership degrees of the interference signal belonging to airborne dynamic interference, ground-based dynamic interference, and static interference. The calculation formula (3) is as follows:

[0093]

[0094] In formula (3), μ kj represents the membership degree of the interference signal belonging to airborne 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 degree function of airborne dynamic interference, f dj represents the membership degree function of ground-based dynamic interference; f jt represents the membership degree function of static interference; feature represents the characteristics of the interference signal. Select the type with the largest 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.

[0095] Step S3: Extract the optimized features of the interference signal for classification and add the second label.

[0096] This step further includes:

[0097] Step S31: Through spectrum detection means, identify the center frequency, bandwidth, power, and repetition period of the interference signal, and construct a feature matrix:

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

[0099] In formula (5), charac represents the feature 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.

[0100] In the embodiments of the present invention, a spectrum analyzer can be used to perform frequency-domain analysis on the interference signal to find the peak frequency in the interference signal spectrum, which is the center frequency; alternatively, the autocorrelation function of the interference signal can be calculated to determine the periodicity of the interference signal, and the peak of the signal energy is found within one period, which is the center frequency of the interference signal;

[0101] A spectrum analyzer can be used to find the peak frequency and valley frequency in the interference signal spectrum, and the interpolation between the peak frequency and valley frequency is the bandwidth of the interference signal; alternatively, the autocorrelation function of the interference signal can be calculated to determine the signal periodicity, and the peak and valley of the signal energy are found within one period, and the interpolation between the peak frequency and valley frequency is the bandwidth of the interference signal.

[0102] Step S32, establish the objective function for self-expression learning of interference signals:

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

[0104] In formula (6), obj_func represents the objective function of self-expression learning, charac T represents the transpose of matrix charac, Auni represents the matrix that satisfies charac T ·Auni·Auni T ·charac = I, Auni T represents the transpose of matrix Auni, I represents the identity matrix, struc represents the signal structure factor, || || represents the norm constraint, and α, β represent the regularization parameters;

[0105] Step S33, define the optimization factor:

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

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

[0108] The objective function obj_func of self-expression learning is updated to:

[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 objective function of self-expression learning.

[0111] Step S34, determine the value of struc, substitute it into formula (7), and calculate the optimization factor opt;

[0112] Perform eigenvalue decomposition on the optimization factor opt, calculate the eigenvectors corresponding to the smallest N eigenvalues, and construct the feature selection matrix eig_matrix based on the eigenvectors. Here, the feature selection matrix is the optimization of the feature matrix charac.

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

[0114] Step S4: Collect the signal in the time-domain RF form of the interference signal, classify it using the trained convolutional neural network, and add a third label.

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

[0116] Step S5: Construct a label function and correct the first label.

[0117] Specifically, the formula for correcting the first label in this step is as follows:

[0118]

[0119] 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, there exists an interference signal with the first label as first_label, the second label as sec_label, and the third label as thir_label; label_func = 0 indicates that the label of the interference signal is unreasonable, that is, there does not exist an interference signal with the first label as first_label, the second label as sec_label, and the third label as thir_label.

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

[0121] Step S6: Establish an index of the recognition algorithm module that matches the hierarchical label of the interference signal to achieve accurate search and call of the interference signal recognition algorithm.

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

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

[0124] In Equation (10), "layer" represents the index level, "cate" represents the type of each layer, and "alori" represents the callable algorithm. The callable algorithm includes, but is not limited to, machine learning-based methods, traditional signal processing techniques, etc.

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

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

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

[0128] {Broadband interference, narrowband interference, pulse interference, frequency sweep interference}

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

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

[0131] Based on the same idea, the embodiment of the present invention also provides a database constructed by using the construction method of the satellite navigation interference signal database. The database includes an identification algorithm module and a storage module; wherein,

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

[0133] The identification algorithm module is used to input the interference signal to be identified, call the identification algorithm in the corresponding identification algorithm module according to the interference signal, and then identify the interference signal to be identified based on the type of interference signal stored in the memory.

[0134] In this embodiment, each module is implemented by a processor, and a memory is appropriately added when storage is required. Among them, the processor may 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 gate, transistor logic devices, discrete hardware components, etc. The memory may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0135] In the above embodiment, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part 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, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may 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 may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.).

[0136] In addition, it should be noted that the satellite navigation interference signal database described in this embodiment is constructed by using the construction method of the satellite navigation interference signal database. The description and definition of the construction method also apply to the database, and will not be repeated here.

[0137] As can be seen from the above technical solutions, the satellite navigation interference signal database and the construction method provided by the embodiments of the present invention 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, a fuzzy classification method based on membership degree and a feature selection method are adopted to improve the classification efficiency; on the basis of classification, it is further refined, 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, recognition algorithms are selected to ensure that the source and type of interference signals can be quickly and accurately located.

[0138] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles applied, and is not intended to limit the scope of the present invention claimed, but only represents the preferred embodiments of the present invention. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but 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 invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

Claims

1. A method for constructing a satellite navigation interference signal database, characterized in that: The construction method comprises the following steps: Step S1, collecting a complete navigation signal from a satellite receiver, and determining whether there is an interference signal in the complete navigation signal; Step S2, classifying the interference signal that is determined to exist according to the degree of membership, and using the classification result as the first label; Step S3, extracting the optimized features of the interference signal for classification and adding a second label; Step S4, collecting the interference signal in the time domain radio frequency form, classifying it using the trained convolutional neural network, and adding a third label; Step S5, constructing a label function and correcting the first label; Step S6, establishing an identification algorithm module index that matches the interference signal classification label to achieve accurate search and call of the interference signal identification algorithm.

2. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that: The step S1 further comprises: Step S11, using a satellite receiver to receive navigation signals from no less than four satellites, and demodulating the received navigation signals, converting them from radio frequency signals to baseband signals as complete navigation signals; Step S12, decoding the complete navigation signal, extracting satellite orbit parameters, signal propagation time and receiver position information, and recording the signal power through the RF front-end circuit of the receiver and recording the noise power through the noise measurement circuit of the receiver; Step S13, using the signal power and the noise power to calculate the carrier-to-noise ratio, the calculation formula (1) is as follows: 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; Step S14, multiplying the signal propagation time measured by the receiver by the speed of light to calculate the actual measured pseudorange; using the satellite orbit parameters and the receiver position, the least square method is used to calculate the theoretical pseudorange; subtracting the theoretical pseudorange from the actual measured pseudorange to obtain the pseudorange residual ρ; Step S15, constructing a preliminary identification network for navigation interference signals, step S15, constructing a preliminary identification network for navigation interference signals, calculating the probability of the existence of interference signals, and judging whether there are interference signals according to 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 an interference signal is as follows: In formula (2), pro is the probability of the existence of interference signal, sigmoid ( ) is the sigmoid function, and MLP ( ) is the multi-layer perceptron operation; According to the probability of the existence of the interference signal, determine whether there is an interference signal: if pro=1, it is considered that there is a navigation interference signal, and enter step S2; if pro=0, it is considered that there is no navigation interference signal.

4. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that: Step S2 includes: Step S21, normalizing the interference signal to a normally distributed signal with a mean of 0 and a standard deviation of 1; Step S22, based on the normalized interference signal, calculate the membership of the interference signal to air-based dynamic interference, ground-based dynamic interference and static interference, and the calculation formula (3) is as follows: In formula (3), μ kj Indicates the membership degree of the interference signal belonging to air-based dynamic interference, μ dj Indicates the membership degree of the interference signal belonging to the ground-based dynamic interference, μ jt Indicates the membership degree of the interference signal belonging to static interference, f kj represents the membership function of airborne dynamic interference, f dj represents the membership function of the foundation dynamic disturbance; f jt represents the membership function of static interference; feature represents the characteristics of the interference signal; The type with the largest membership degree is selected as the classification result of the interference signal, and the first label first_label is added to the interference signal according to the classification result.

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

6. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that: Step S3 includes: Step S31, by means of spectrum detection, the center frequency, bandwidth, power, and repetition period of the interference signal are identified, and a feature matrix is ​​constructed: charac=[center,band,power,cycle] (5) In formula (5), charac represents the characteristic matrix, center represents the central 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, establishing the objective function of interference signal self-expression learning: obj_func=min∑||character T ·Uni-character T ·Auni·struc||+α||charac||+β||struc|| (6) In formula (6), obj_func represents the objective function of self-expression learning, character T Represents the transpose of the matrix charac, and Auni represents the matrix that satisfies charac T ·Auni·Auni T · charac=I matrix, Auni T represents the transpose of the matrix Auni, I represents the identity matrix, struc represents the signal structure factor, || || represents the norm constraint, and α, β represent the regularization parameters; Step S33, defining optimization factors: opt=(I-struc)(I-struc) T (7) In formula (7), opt represents the optimization factor, (I-struc) T Represents the transpose of the matrix (I-struc); The objective function obj_func of self-expression learning is updated as: obj_func′=min∑||character T ·Auni·opt·Auni T ·struct||+α||character|| (8) In formula (8), obj_func′ represents the updated value of the objective function of self-expression learning; Step S34, determine the value of struc, substitute it into formula (7), calculate the optimization factor opt; perform eigendecomposition on the optimization factor opt, calculate the eigenvectors corresponding to the smallest N eigenvalues, and construct a feature selection matrix eig_matrix based on the eigenvectors; the feature selection matrix is ​​the optimization of the feature matrix charac; Step S35, using the feature selection matrix to extract the optimized features of the interference signal, and inputting the optimized features into the support vector machine, classifying the interference signal according to broadband interference, narrowband interference, pulse interference or swept frequency interference, and adding a second label sec_label to the interference signal according to the classification result.

7. The method for constructing a satellite navigation interference signal database according to claim 1, characterized in that: In step S4, the interference signal is classified according to 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.

8. 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 formula (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 first_label, the second label sec_label, and the third label 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 first_label, the second label sec_label, and the third label thir_label does not exist; If label_func=0, the first label is modified, and the type with the second largest membership is selected as the first-level classification result of the interference signal. The first label first_label of the interference signal is modified according to the classification result.

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

10. A database constructed by using the method for constructing a satellite navigation interference signal database according to any one of claims 1 to 9, characterized in that: The database includes a recognition algorithm module and a storage module; wherein, The storage module stores the interference signal collected based on the complete navigation signal carrying the interference signal, and the interference signal is assigned a first label, a second label and a third label according to the classification result, and is stored in the corresponding storage module according to the label; The identification algorithm module is used to input the interference signal to be identified, call the identification algorithm in the corresponding identification algorithm module according to the interference signal, and then identify the interference signal to be identified based on the interference signal type stored in the memory.

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