Satellite positioning interference identification method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of satellite positioning interference recognition, in particular to a satellite positioning interference recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: receiving a satellite signal, and carrying out the preprocessing of the satellite signal, and obtaining a preprocessed baseband signal; jointly extracting a phase feature, a carrier-to-noise ratio feature, a power intensity feature, a ranging residual feature, a Doppler change feature, a correlation peak distortion feature and a time-space consistency feature from the preprocessed baseband signal to form a multi-dimensional feature set; screening the multi-dimensional feature set to obtain key features after screening; according to the screened key features, constructing a feature combination vector in a unified format; and identifying the type of the interference signal according to the feature combination vector. Therefore, the problem of incomplete coverage of single-dimensional feature recognition is solved, the interference signal features can be captured more comprehensively and accurately, misjudgment is reduced, and the recognition rate of deception, suppression and other interference signals is improved.

Description

Technical Field

[0001] This application relates to the field of satellite positioning interference identification technology, and in particular to a satellite positioning interference identification method, device, electronic device and storage medium. Background Technology

[0002] Satellite positioning interference identification technology is used to identify and judge interference signals that may occur during satellite positioning, in order to determine whether the satellite signal is interfered with and what type of interference it is (such as spoofing interference, suppression interference, etc.). This ensures the normal operation and positioning accuracy of the satellite positioning system, avoids positioning errors or failures caused by interference, and provides reliable support for various applications that rely on satellite positioning (such as navigation, military, etc.). Existing satellite positioning interference identification technologies have many shortcomings, such as single-dimensional feature recognition, incomplete coverage, low recognition rate for single scenarios under the influence of different types of interference on satellite signals, poor adaptability, insufficient algorithm robustness, and the possibility of misjudging noise and other interference by single algorithms, resulting in low recognition rates. Summary of the Invention

[0003] This application provides a satellite positioning interference identification method, device, electronic device, and storage medium, which can solve at least one of the technical problems in the background art to a certain extent.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for identifying satellite positioning interference is provided, the method comprising: Receive satellite signals and preprocess the satellite signals to obtain preprocessed baseband signals; From the preprocessed baseband signal, phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are jointly extracted to form a multidimensional feature set; The multidimensional feature set is filtered to obtain the filtered key features; Based on the selected key features, construct a feature combination vector in a unified format; The type of interference signal is identified based on the feature combination vector.

[0005] Secondly, a satellite positioning interference identification device is provided, comprising: A receiving module is used to receive satellite signals and preprocess the satellite signals to obtain preprocessed baseband signals; The joint extraction module is used to jointly extract phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features from the preprocessed baseband signal to form a multidimensional feature set. The filtering module is used to filter the multidimensional feature set to obtain the filtered key features; The construction module is used to construct a feature combination vector in a unified format based on the filtered key features; The identification module is used to identify the type of interference signal based on the feature combination vector.

[0006] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the satellite positioning interference identification method as described in any one of the first aspects above.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the satellite positioning interference identification method as described in any one of the first aspects above.

[0008] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the satellite positioning interference identification method described in any of the first aspects above.

[0009] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0010] In this embodiment, satellite signals are first received and preprocessed to obtain preprocessed baseband signals. Then, phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are jointly extracted from the preprocessed baseband signals to form a multi-dimensional feature set. This multi-dimensional feature set is then filtered to obtain key features. Based on these key features, a unified feature combination vector is constructed. The type of interference signal is identified based on this feature combination vector. This overcomes the problem of incomplete coverage in single-dimensional feature recognition, enabling more comprehensive and accurate capture of interference signal features, reducing false positives, and improving the recognition rate of deception and suppression interference signals. By integrating algorithms with different scenarios, the system can adapt to various situations and maintain a relatively stable recognition rate, improving its adaptability to complex environments. This helps ensure the accuracy of the recognition results and allows for more timely detection of weak or progressive interference, reducing recognition delay and improving the response speed to such interference.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating the satellite positioning interference identification method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the satellite positioning interference identification device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0013] The embodiments of the technical solutions of this application will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of this application, and are therefore merely examples and should not be used to limit the scope of protection of this application. When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0014] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0015] It should be noted that satellite positioning systems have become core infrastructure in fields such as civil transportation, surveying and mapping, smart cities, and aviation and maritime navigation. Positioning accuracy, signal continuity, and data reliability directly determine the effectiveness of related applications. In complex electromagnetic environments and malicious attack scenarios, satellite positioning signals are highly susceptible to various types of interference, including suppression interference, deception interference, pulse interference, multipath interference, and gradual weak interference. This can lead to decreased positioning accuracy and timing deviations, or even serious consequences such as positioning failure, data tampering, and path misdirection, and may even threaten the operational safety of critical facilities.

[0016] Therefore, there is an urgent need for a set of efficient, accurate, and adaptable satellite positioning interference identification technology to achieve rapid perception, type determination, and accurate identification of interference signals, providing support for subsequent interference suppression, signal repair, and positioning error correction, and ensuring the stable operation of the satellite positioning system in all scenarios.

[0017] The existing technology has the following defects and shortcomings: Single-dimensional feature recognition has incomplete coverage and poor adaptability: relying solely on single features such as carrier-to-noise ratio and power to judge interference cannot fully characterize the signal distortion patterns of different types of interference such as deception, suppression, and weak interference. The recognition rate is extremely low for complex mixed interference and interference in edge scenarios, and it is prone to missed judgments and false judgments.

[0018] The algorithm lacks robustness and has weak noise resistance: It uses a single fixed algorithm for recognition without dynamic optimization based on the scene. In environments with strong noise and multipath coupling, it is easy to judge normal signal fluctuations as interference or ignore weak interference, resulting in large fluctuations in recognition accuracy.

[0019] Limited scene adaptability and poor versatility: Most technologies are designed for ideal scenarios such as open and unobstructed areas and static reception. In scenarios such as urban areas with obstructions, high-speed movement, and complex electromagnetic fields, the recognition rate drops significantly and cannot be reused across scenarios.

[0020] Lagging perception and untimely response to weak interference: Existing technologies are not sensitive enough to gradual weak interference with varying power and concealed features, as well as minor deception and tampering. Often, the interference causes a positioning deviation before identification is triggered, resulting in a significant identification delay and making it difficult to achieve proactive protection.

[0021] Weak signal preprocessing and poor source data quality: No fine preprocessing was performed on satellite signals to address multipath distortion, phase jumps, and amplitude-phase distortion. The raw signals had high noise and many errors, further increasing the difficulty of subsequent identification.

[0022] This application addresses the vulnerability of satellite and navigation signals to interference by proposing a solution that integrates multi-frequency observation data with independent, interference-free navigation testing technology. Through data preprocessing, feature extraction, multi-source information fusion, and algorithm-based identification system construction, it enables the identification of interference signals such as deception and suppression.

[0023] See Figure 1 This is a flowchart illustrating the satellite positioning interference identification method provided in this application embodiment. The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] like Figure 1 As shown, the satellite positioning interference identification method provided in this embodiment includes the following steps: Step 101: Receive satellite signals and preprocess the satellite signals to obtain preprocessed baseband signals.

[0025] As one possible implementation, step 101 may include the following sub-steps: (1) Multi-channel parallel acquisition technology is adopted to receive satellite signals from multiple frequency points simultaneously; Among them, multi-channel parallel acquisition technology refers to the simultaneous activation of multiple independent signal acquisition channels in the signal receiving module to acquire, track and demodulate multiple satellite signals in parallel, synchronously and in real time.

[0026] As one implementation method, multi-constellation and multi-frequency parallel acquisition technology can be adopted to support the reception and adaptive selection of multi-frequency signals from multiple satellite navigation systems such as BeiDou, GPS, GLONASS, and GALILEO. This avoids signal loss due to interference from a single system or frequency. In electromagnetic interference environments, it can automatically switch to available satellite systems and frequencies to ensure continuous and reliable positioning and timing.

[0027] (2) The satellite signal is downconverted to baseband, and then amplified and filtered in sequence to obtain the filtered signal; Specifically, the received satellite radio frequency signal is down-converted to the baseband frequency band, and signal amplification, in-band and out-of-band filtering are performed in sequence to filter out out-of-band spurious noise and irrelevant interference components, while retaining the complete characteristics of the effective signal.

[0028] Down-conversion refers to the signal processing operation that converts high-frequency radio frequency signals into low-frequency baseband signals, facilitating subsequent digital analysis.

[0029] Among them, radio frequency signals refer to high-frequency radio signals emitted by satellites, which are the original transmission signals for satellite navigation.

[0030] The baseband band refers to the original signal band that has not undergone carrier modulation, and it is the core processing band for signal demodulation and feature extraction.

[0031] Signal amplification refers to the operation of increasing the amplitude of a weak signal through a gain adjustment circuit to compensate for signal transmission loss. In-band and out-of-band filtering refers to signal conditioning operations that use filtering circuits to retain the effective signal within the target frequency band and filter out noise and clutter outside the band.

[0032] (3) Perform at least one of the following on the filtered signal: multipath delay symmetric compensation, carrier phase micro-jump tracking calibration, nonlinear coupling distortion closed-loop correction, pulse interference sparse elimination, amplitude and phase coupling pre-distortion compensation and noise entropy optimization whitening to obtain the preprocessed baseband signal. Specifically, a high-precision compensation and correction operation is performed on the filtered signal. At least one of the following methods is selected: multipath delay symmetric compensation, carrier phase micro-jump tracking calibration, nonlinear coupling distortion closed-loop correction, pulse interference sparsity removal, amplitude and phase coupling pre-distortion compensation, and noise entropy optimization whitening processing to obtain the preprocessed baseband signal.

[0033] Among them, multipath delay symmetric compensation refers to a processing technique that performs symmetrical correction and compensation for the multipath propagation delay deviation caused by signal reflection and refraction, thereby eliminating the superposition distortion of multipath signals.

[0034] Among them, carrier phase micro-jump tracking calibration refers to a tracking technology that monitors minute changes in carrier phase in real time and dynamically calibrates them to ensure phase continuity.

[0035] Among them, nonlinear coupling distortion closed-loop correction refers to a processing method that corrects signal coupling distortion caused by nonlinear devices in the signal transmission link in real time through a closed-loop feedback mechanism.

[0036] Among them, sparse removal of pulse interference refers to the processing method based on sparse decomposition algorithm to identify and remove instantaneous pulse interference signals.

[0037] Among them, amplitude-phase coupling pre-distortion compensation refers to the technique of performing pre-distortion correction in advance for the coupling distortion of signal amplitude and phase, so as to restore the true amplitude and phase characteristics of the signal.

[0038] Among them, noise entropy optimization whitening processing refers to whitening and normalizing signal noise through entropy optimization algorithm, balancing noise distribution characteristics, weakening the masking effect of noise on effective signals, and improving signal recognition in weak signal environments.

[0039] Among them, noise entropy optimization whitening processing refers to whitening and normalizing signal noise through entropy optimization algorithm, balancing noise distribution characteristics, weakening the masking effect of noise on effective signals, and improving signal recognition in weak signal environments.

[0040] Step 102: From the preprocessed baseband signal, jointly extract phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features to form a multidimensional feature set.

[0041] Specifically, phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features can be extracted synchronously from the preprocessed baseband signal and integrated to form a multi-dimensional feature set, comprehensively capturing the differentiated characterization of interference signals.

[0042] Among them, phase characteristics refer to the phase change parameters of the satellite signal carrier, reflecting the phase shift and jump characteristics of the signal waveform. Power intensity characteristics refer to the instantaneous and average power values ​​of the satellite signal, characterizing the magnitude and fluctuation of signal energy. Ranging residual characteristics refer to the difference between the observed and theoretically predicted values ​​of satellite pseudorange, reflecting the degree of deviation in positioning and ranging. Correlation peak distortion characteristics refer to the morphological parameters such as the peak value, peak width, and symmetry of the autocorrelation peak of the satellite signal; normal signals have regular correlation peaks, while interference signals show significant distortion. Spatiotemporal consistency characteristics refer to the continuity and rationality of the receiver's positioning results in the time and space dimensions; normal positioning results conform to spatiotemporal logic, while interference signals show spatiotemporal jumps. Multidimensional feature set refers to a feature dataset formed by integrating multiple single-dimensional features, possessing a more comprehensive interference characterization capability.

[0043] Among them, the carrier-to-noise ratio (CNR) refers to the ratio of signal carrier power to noise power, and is used to quantify signal transmission quality and channel status. Doppler variation characteristics refer to the amount of signal frequency shift caused by the relative motion between the satellite and the receiver, and can reflect abnormal fluctuations in the signal transmission path.

[0044] It should be noted that phase features and Doppler variation features can effectively identify signal distortion and spoofing interference; carrier-to-noise ratio features and power intensity features can complement each other to characterize signal quality and channel state; ranging residual features and spatiotemporal consistency features are used to correct positioning errors and verify timing rationality; and correlation peak distortion features are used to identify multipath and maliciously tampered signals. These multi-dimensional features mutually verify each other and complement each other's advantages, overcoming the shortcomings of insufficient coverage and poor robustness of single-dimensional features. An adaptive matching algorithm is used to achieve accurate extraction for different feature signals.

[0045] It is understood that this application embodiment takes into account that spoofing interference can forge satellite signal phase, leading to abnormal receiver phase tracking. By detecting the phase transition frequency and amplitude, spoofing attacks can be identified. This application embodiment extracts carrier-to-noise ratio features and calculates the ratio of signal carrier power to noise power to reflect signal quality.

[0046] This application's embodiments take into account that suppressing interference reduces the carrier-to-noise ratio. Suppression interference can be identified by setting a dynamic threshold. Power intensity features are extracted, and instantaneous changes in signal power are monitored to identify sudden interference. Ranging residual features are extracted, and the residual between BeiDou pseudorange observations and predicted values ​​is used to detect abnormal ranging data. This is because deceptive interference can forge pseudorange observations, causing the residual to deviate significantly from the normal range.

[0047] Feature weight refers to the proportion of each feature dimension in the interference identification process. A higher weight indicates a stronger ability to identify the corresponding interference type. Deceptive interference refers to malicious interference that misleads the receiver into producing incorrect positioning and timing results by forging false satellite signals. Multipath interference refers to signal distortion interference caused by the superposition of satellite signals with direct signals after reflection from buildings and the ground, forming multipath signals.

[0048] In the process of jointly extracting multidimensional feature sets, differentiated feature weights are used for different types of interference: To counter deception interference, the extraction weights of phase features, Doppler variation features, and ranging residual features are increased. To address interference suppression, the extraction weights of carrier-to-noise ratio and power intensity features are increased. To address multipath interference, the extraction weights of relevant peak distortion features and spatiotemporal consistency features are increased. Understandably, to combat deception interference, the extraction weights of phase features, Doppler variation features, and ranging residual features are increased to accurately capture the phase, frequency shift, and ranging deviation of spoofed signals; to combat suppression interference, the extraction weights of carrier-to-noise ratio features and power intensity features are increased to quickly identify abnormal states such as sudden changes in signal power and a sharp drop in quality; and to combat multipath interference, the extraction weights of correlation peak distortion features and spatiotemporal consistency features are increased to effectively distinguish multipath reflection signals from maliciously tampered signals.

[0049] Step 103: Filter the multidimensional feature set to obtain the filtered key features.

[0050] Specifically, the Pearson correlation coefficient between each feature in the multidimensional feature set can be calculated, redundant features with Pearson correlation coefficients greater than a first preset threshold can be removed to obtain each candidate feature, the interference sensitivity of each candidate feature can be determined, and then candidate features with interference sensitivity higher than a second preset threshold can be screened to obtain the screened key features.

[0051] Redundant features refer to features that are highly linearly correlated with other features and carry repetitive information.

[0052] Interference sensitivity refers to the degree to which the feature value changes significantly with the appearance of interference signals; the higher the sensitivity, the greater the interference identification value.

[0053] The first preset threshold refers to a pre-set feature correlation threshold, which is used to determine whether a feature is redundant.

[0054] The second preset threshold refers to a pre-set threshold for feature interference sensitivity, which is used to screen effective key features.

[0055] Among them, candidate features refer to the feature data initially retained after removing redundant features.

[0056] Among them, key features refer to core features that are highly sensitive to interference and have strong characterization capabilities.

[0057] Specifically, firstly, the Pearson correlation coefficients between features in the multidimensional feature set are calculated, and redundant features with Pearson correlation coefficients greater than a first preset threshold are removed to obtain a candidate feature set. Secondly, the interference sensitivity of each candidate feature is calculated based on historical interference data, and candidate features with interference sensitivity higher than a second preset threshold are selected to finally obtain the selected key features.

[0058] The Pearson correlation coefficient measures the degree of linear correlation between two feature variables; a coefficient closer to 1 indicates a higher correlation. This coefficient can be used to quickly locate redundant features. Interference sensitivity refers to the degree to which a feature value deviates from its normal range, quantifying the strength of a feature's response to interference signals.

[0059] First, the Pearson correlation coefficients among the features in the multidimensional feature set are calculated. Redundant features with Pearson correlation coefficients greater than a first preset threshold are removed to obtain a candidate feature set. Then, the interference sensitivity of each candidate feature is calculated based on historical interference data. Candidate features with interference sensitivity higher than a second preset threshold are selected to obtain the filtered key features.

[0060] Step 104: Construct a feature combination vector in a unified format based on the selected key features.

[0061] Optionally, the key features after screening can be normalized, and the phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features can be concatenated into a feature combination vector in a preset order.

[0062] For example, the filtered feature values ​​are mapped to the [0,1] interval to eliminate dimensional differences. For instance, the phase feature range is [-π,π], which is normalized to [0,1]; the carrier-to-noise ratio range is [20dB-Hz, 50dB-Hz], which is normalized to [0,1].

[0063] The normalized key features are combined into a combined vector, such as [phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features], which is then used as input for subsequent algorithms.

[0064] Step 105: Identify the type of interference signal based on the feature combination vector.

[0065] Specifically, a preset interference identification algorithm library can be called to classify and identify the feature combination vectors to obtain the type of interference signal; the preset interference identification algorithm library includes at least one of support vector machine, random forest, and long short-term memory network.

[0066] Optionally, a dynamic weighted fusion strategy can be adopted to perform weighted calculations on the output results of at least two interference identification algorithms to obtain a fused identification result. Based on the fused identification result and a preset interference judgment threshold, the type of interference signal can be determined.

[0067] The dynamic weighted fusion strategy includes adjusting the weight coefficients of each interference identification algorithm according to the current application scenario; specifically, increasing the weight coefficients of the Long Short-Term Memory Network algorithm in open scenarios and increasing the weight coefficients of the Support Vector Machine and Random Forest algorithms in complex occlusion scenarios.

[0068] For example, in open scenes, the weights of the LSTM recognition results can be increased. By weighted fusion of the outputs of various algorithms, such as SVM outputting a spoofing / interference probability of 0.8, RF outputting 0.7, and LSTM outputting 0.6, the result is determined to be spoofing / interference after fusion based on the weight coefficients. The scene can be dynamic (determined through environmental perception data, user behavior data, etc., or based on machine learning combined with multi-dimensional features of the input) or static (such as a fixed base station).

[0069] The types of interference signals include at least one of deception interference, suppression interference, pulse interference, and multipath interference.

[0070] In this embodiment, satellite signals are first received and preprocessed to obtain preprocessed baseband signals. Then, phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are jointly extracted from the preprocessed baseband signals to form a multi-dimensional feature set. This multi-dimensional feature set is then filtered to obtain key features. Based on these key features, a unified feature combination vector is constructed. The type of interference signal is identified based on this feature combination vector. This overcomes the problem of incomplete coverage in single-dimensional feature recognition, enabling more comprehensive and accurate capture of interference signal features, reducing false positives, and improving the recognition rate of deception and suppression interference signals. By integrating algorithms with different scenarios, the system can adapt to various situations and maintain a relatively stable recognition rate, improving its adaptability to complex environments. This helps ensure the accuracy of the recognition results and allows for more timely detection of weak or progressive interference, reducing recognition delay and improving the response speed to such interference.

[0071] Corresponding to the satellite positioning interference identification method described in the above embodiments, Figure 2 This is a structural block diagram of the satellite positioning interference identification device provided in the embodiments of this application. It should be noted that the satellite positioning interference identification device can be deployed on the terminal side receiving satellite signals (such as vehicles, handheld devices, timing protection devices), or on the network side (such as ground monitoring stations, cloud platforms), or it can be deployed to form a collaborative interference identification and protection system of "terminal + network".

[0072] Reference Figure 2 The satellite positioning interference identification device 200 includes: The receiving module 210 is used to receive satellite signals and preprocess the satellite signals to obtain preprocessed baseband signals; The joint extraction module 220 is used to jointly extract phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features from the preprocessed baseband signal to form a multidimensional feature set. The filtering module 230 is used to filter the multidimensional feature set to obtain the filtered key features; The construction module 240 is used to construct a feature combination vector in a unified format based on the filtered key features; The identification module 250 is used to identify the type of interference signal based on the feature combination vector.

[0073] Optional, receiving module, specifically used for: It employs multi-channel parallel acquisition technology to simultaneously receive satellite signals from multiple frequency points; The satellite signal is down-converted to baseband, then amplified and filtered sequentially to obtain the filtered signal. The filtered signal is subjected to at least one of the following processes: multipath delay symmetry compensation, carrier phase micro-jump tracking calibration, nonlinear coupling distortion closed-loop correction, pulse interference sparsity removal, amplitude and phase coupling pre-distortion compensation, and noise entropy optimization whitening processing, to obtain the preprocessed baseband signal. Optionally, during the joint extraction of multidimensional feature sets, differentiated feature weights can be used for different types of interference: To counter deception interference, the extraction weights of the phase features, Doppler change features, and ranging residual features are increased. To suppress interference, the extraction weights of the carrier-to-noise ratio feature and the power intensity feature are increased; To address multipath interference, the extraction weights of the relevant peak distortion features and the spatiotemporal consistency features are increased. Optional, filtering module, specifically used for: Calculate the Pearson correlation coefficient between each feature in the multidimensional feature set, and remove redundant features whose Pearson correlation coefficient is greater than a first preset threshold to obtain each candidate feature; Determine the interference sensitivity of each candidate feature; Candidate features with interference sensitivity higher than a second preset threshold are selected to obtain the selected key features. Optionally, the construction module is specifically used for: The key features selected are then normalized. The phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are concatenated into the feature combination vector in a preset order.

[0074] Optional, the recognition module is specifically used for: A preset interference identification algorithm library is invoked to classify and identify the feature combination vector to obtain the interference signal type; wherein, the preset interference identification algorithm library includes at least one of support vector machine, random forest, and long short-term memory network.

[0075] Optional, the recognition module is specifically used for: A dynamic weighted fusion strategy is adopted to calculate the weighted results of the outputs of at least two interference identification algorithms to obtain the fused identification result. Based on the fusion recognition result and the preset interference judgment threshold, the type of interference signal is determined.

[0076] In this embodiment, satellite signals are first received and preprocessed to obtain preprocessed baseband signals. Then, phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are jointly extracted from the preprocessed baseband signals to form a multi-dimensional feature set. This multi-dimensional feature set is then filtered to obtain key features. Based on these key features, a unified feature combination vector is constructed. The type of interference signal is identified based on this feature combination vector. This overcomes the problem of incomplete coverage in single-dimensional feature recognition, enabling more comprehensive and accurate capture of interference signal features, reducing false positives, and improving the recognition rate of deception and suppression interference signals. By integrating algorithms with different scenarios, the system can adapt to various situations and maintain a relatively stable recognition rate, improving its adaptability to complex environments. This helps ensure the accuracy of the recognition results and allows for more timely detection of weak or progressive interference, reducing recognition delay and improving the response speed to such interference.

[0077] in addition, Figure 2 The satellite positioning interference identification device shown can be a software unit, hardware unit, or a combination of software and hardware built into existing electronic devices. It can also be integrated into electronic devices as an independent accessory or exist as an independent electronic device.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0079] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 5 of this embodiment includes: at least one processor 50 ( Figure 3(Only one is shown in the diagram) a processor, a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, wherein the processor 50 executes the computer program 52 to implement the steps in any of the above-described embodiments of the satellite positioning interference identification method.

[0080] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0081] The processor 50 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0082] In some embodiments, the memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. In other embodiments, the memory 51 may be an external storage device of the electronic device 5, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the electronic device 5. Further, the memory 51 may include both internal storage units and external storage devices of the electronic device 5. The memory 51 is used to store operating systems, applications, boot loaders, data, and other programs, such as the program code of the computer program. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0083] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0084] This application provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0085] If the integrated unit is implemented as a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / electronic device, a recording medium, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0086] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0087] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0088] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0089] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0090] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying satellite positioning interference, characterized in that, include: Receive satellite signals and preprocess the satellite signals to obtain preprocessed baseband signals; From the preprocessed baseband signal, phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are jointly extracted to form a multidimensional feature set; The multidimensional feature set is filtered to obtain the filtered key features; Based on the selected key features, construct a feature combination vector in a unified format; The type of interference signal is identified based on the feature combination vector.

2. The method according to claim 1, characterized in that, The process of receiving satellite signals and preprocessing the satellite signals to obtain preprocessed baseband signals includes: It employs multi-channel parallel acquisition technology to simultaneously receive satellite signals from multiple frequency points; The satellite signal is down-converted to baseband, then amplified and filtered sequentially to obtain the filtered signal. The filtered signal is subjected to at least one of the following processes: multipath delay symmetry compensation, carrier phase micro-jump tracking calibration, nonlinear coupling distortion closed-loop correction, pulse interference sparsity removal, amplitude and phase coupling pre-distortion compensation, and noise entropy optimization whitening processing, to obtain the preprocessed baseband signal.

3. The method according to claim 2, characterized in that, in, In the process of jointly extracting multidimensional feature sets, differentiated feature weights are used for different types of interference: To counter deception interference, the extraction weights of the phase features, Doppler change features, and ranging residual features are increased. To suppress interference, the extraction weights of the carrier-to-noise ratio feature and the power intensity feature are increased; To address multipath interference, the extraction weights of the relevant peak distortion features and the spatiotemporal consistency features are increased.

4. The method according to claim 3, characterized in that, The process of filtering the multidimensional feature set to obtain the filtered key features includes: Calculate the Pearson correlation coefficient between each feature in the multidimensional feature set, and remove redundant features whose Pearson correlation coefficient is greater than a first preset threshold to obtain each candidate feature; Determine the interference sensitivity of each candidate feature; Candidate features with interference sensitivity higher than a second preset threshold are selected to obtain the selected key features.

5. The method according to claim 1, characterized in that, The step of constructing a feature combination vector in a unified format based on the filtered key features includes: The key features selected are then normalized. The phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features are concatenated into the feature combination vector in a preset order.

6. The method according to claim 1, characterized in that, The step of identifying the type of interference signal based on the feature combination vector includes: A preset interference identification algorithm library is invoked to classify and identify the feature combination vector to obtain the interference signal type; wherein, the preset interference identification algorithm library includes at least one of support vector machine, random forest, and long short-term memory network.

7. The method according to claim 6, characterized in that, The step of identifying the type of interference signal based on the feature combination vector includes: A dynamic weighted fusion strategy is adopted to calculate the weighted results of the outputs of at least two interference identification algorithms to obtain the fused identification result. Based on the fusion recognition result and the preset interference judgment threshold, the type of interference signal is determined.

8. A satellite positioning interference identification device, characterized in that, include: A receiving module is used to receive satellite signals and preprocess the satellite signals to obtain preprocessed baseband signals; The joint extraction module is used to jointly extract phase features, carrier-to-noise ratio features, power intensity features, ranging residual features, Doppler variation features, correlation peak distortion features, and spatiotemporal consistency features from the preprocessed baseband signal to form a multidimensional feature set; The filtering module is used to filter the multidimensional feature set to obtain the filtered key features; The construction module is used to construct a feature combination vector in a unified format based on the filtered key features; The identification module is used to identify the type of interference signal based on the feature combination vector.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which is loaded by a processor to perform the steps of the method according to any one of claims 1 to 7.

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