Passive radar seeker anti-interference identification method based on dual-polarization array

Abstract

The invention provides a passive radar seeker anti-interference identification method based on a dual-polarization array, and the method comprises the steps: obtaining a first signal and a second signal, and carrying out the time-space alignment calibration of the first signal and the second signal, and obtaining a first polarization signal and a second polarization signal; performing polarization-Doppler-frequency band three-dimensional tensor construction on the first polarization signal and the second polarization signal; performing dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain a target polarization characteristic parameter and an interference polarization characteristic parameter; performing interference suppression processing on the first polarization signal and the second polarization signal through polarization matched filtering to generate an anti-interference enhanced signal; performing polarization scattering mechanism decomposition processing on the target polarization characteristic parameter to generate a target surface scattering component and a dihedral angle scattering component; and outputting an anti-interference identification result based on the target surface scattering component, the dihedral angle scattering component and the polarization phase consistency parameter of the anti-interference enhancement signal. The method can improve the anti-interference recognition precision of the radar.

Description

Technical Field

[0001] The invention belongs to the field of radar anti-interference, and in particular relates to an anti-interference identification method for a passive radar seeker based on a dual-polarization array. Background Art

[0002] In recent years, passive radar seeker technology has gradually become an important development direction in the field of radar detection due to its strong concealment and great anti-interference potential. As the core component of this technology, the dual-polarization array can simultaneously obtain the horizontal and vertical polarization signals of the target, and enhance the target feature extraction capability through polarization difference analysis. Traditional methods mainly achieve interference suppression through signal processing of a single polarization channel or static polarization parameter matching (such as polarization ratio screening), and have achieved certain results in target scenarios with uniform materials and gentle movement. For example, polarization entropy analysis or spatial domain beamforming technology based on a fixed frequency band can effectively distinguish between conventional targets and typical interference signals in a steady-state environment.

[0003] However, with the development of new weaponry, target characteristics are becoming increasingly complex. For targets such as stealth aircraft and hypersonic weapons, which utilize hybrid metal and composite structures, their polarization scattering characteristics can exhibit significant frequency-dependent jumps and dynamic coupling effects due to material differences and changes in motion. Traditional technologies, when processing such scenarios, struggle to distinguish between polarization fluctuations caused by material heterogeneity and Doppler-polarization coupling interference caused by high-speed motion, resulting in signal aliasing and a significant decrease in recognition accuracy.

[0004] Current technologies are unable to effectively solve the joint ambiguity and feature confusion problems caused by the coupling of polarization dispersion frequency jumps and Doppler effect in high-speed moving targets of heterogeneous materials in wide frequency bands, which restricts the anti-interference recognition accuracy in complex confrontation scenarios. Summary of the Invention

[0005] Based on this, it is necessary to provide an anti-interference identification method for a passive radar seeker based on a dual-polarization array to address the above technical problems. It can achieve high-precision target recognition in wide frequency bands and strong interference scenarios, and improve the anti-interference robustness against hidden targets in complex confrontation environments.

[0006] In a first aspect, the present application provides an anti-interference identification method for a passive radar seeker based on a dual-polarization array, comprising:

[0007] Acquire a first signal and a second signal received by a dual-polarization array, perform spatiotemporal alignment calibration on the first signal and the second signal, and obtain a first polarization signal and a second polarization signal;

[0008] Performing polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor;

[0009] Perform dynamic core tensor decomposition on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters;

[0010] Based on the target polarization characteristic parameters and the interference polarization characteristic parameters, interference suppression processing is performed on the first polarization signal and the second polarization signal through polarization matched filtering to generate an anti-interference enhanced signal;

[0011] Perform polarization scattering mechanism decomposition on the target polarization characteristic parameters to generate target surface scattering component and dihedral angle scattering component;

[0012] Based on the target surface scattering component, dihedral angle scattering component and polarization phase consistency parameter of the anti-interference enhancement signal, target classification and recognition processing are performed, and the anti-interference recognition result is output.

[0013] In a possible embodiment, performing polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor includes:

[0014] Performing short-time Fourier transform on the first polarization signal and the second polarization signal to generate a first polarization time-frequency distribution and a second polarization time-frequency distribution;

[0015] Based on the amplitude ratio of the first polarization time-frequency distribution and the second polarization time-frequency distribution, dynamic noise floor adjustment processing is performed to generate an anti-noise polarization ratio parameter;

[0016] Performing Doppler-polarization coupling analysis based on the phase gradients of the first polarization time-frequency distribution and the second polarization time-frequency distribution to generate polarization phase difference and Doppler frequency shift parameters;

[0017] According to the frequency band, the anti-noise polarization ratio parameter, polarization phase difference and Doppler frequency shift parameters are combined into a polarization-Doppler-frequency band three-dimensional tensor.

[0018] In a possible embodiment, dynamic core tensor decomposition processing is performed on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters, including:

[0019] Perform high-order singular value decomposition on the polarization-Doppler-frequency three-dimensional tensor to obtain the core tensor and multidimensional factor matrix;

[0020] Calculate the real-time interference energy ratio threshold based on the anti-noise polarization ratio parameter and polarization phase difference;

[0021] Based on the real-time interference energy ratio threshold, the core tensor is dynamically truncated to retain the target principal component tensor;

[0022] Reconstructing a target polarization scattering matrix according to the target principal component tensor and the multidimensional factor matrix to obtain target polarization characteristic parameters, wherein the target polarization characteristic parameters include a target polarization ratio and a target polarization phase difference;

[0023] An interference polarization scattering matrix is ​​extracted from the truncated interference component tensor to obtain interference polarization characteristic parameters, wherein the interference polarization characteristic parameters include an interference polarization ratio and an interference polarization phase difference.

[0024] In a possible embodiment, based on target polarization characteristic parameters and interference polarization characteristic parameters, interference suppression processing is performed on the first polarization signal and the second polarization signal through polarization matched filtering to generate an anti-interference enhanced signal, including:

[0025] The polarization ratio difference is calculated based on the target polarization ratio and the interference polarization ratio using the following formula:

[0026]

[0027] Among them, Δρ(t,f) is the polarization ratio difference, (t,f) is the time-frequency point, ρ target is the target polarization ratio, ρ inter is the interference polarization ratio;

[0028] Calculate the difference between the target polarization phase difference and the interference polarization phase difference to obtain the polarization phase difference difference;

[0029] Generate a space-polarization joint filtering weight matrix according to the polarization ratio difference and the polarization phase difference;

[0030] Based on the space-polarization joint filtering weight matrix, weighted synthesis processing is performed on the first polarization signal and the second polarization signal to generate an anti-interference enhanced signal.

[0031] In a possible embodiment, target classification and recognition processing is performed based on the target surface scattering component, the dihedral angle scattering component, and the polarization phase consistency parameter of the anti-interference enhancement signal, and an anti-interference recognition result is output, including:

[0032] Calculate the scattering entropy of the target surface scattering component and the anisotropy of the dihedral angle scattering component, and generate the target scattering feature vector based on the scattering entropy and anisotropy;

[0033] Extract polarization phase consistency parameters from the anti-interference enhancement signal, perform feature fusion processing on the target scattering feature vector and the polarization phase consistency parameters to generate a classification feature vector;

[0034] The classification feature vector is classified by a pre-trained classifier and the anti-interference recognition result is output.

[0035] In a second aspect, the present application also provides an anti-interference identification device for a passive radar seeker based on a dual-polarization array, comprising:

[0036] a signal acquisition module, configured to acquire a first signal and a second signal received by the dual-polarization array, perform spatiotemporal alignment calibration on the first signal and the second signal, and obtain a first polarization signal and a second polarization signal;

[0037] A time-frequency analysis module, configured to perform polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor;

[0038] The feature decomposition module is used to perform dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain the target polarization characteristic parameters and the interference polarization characteristic parameters;

[0039] a polarization matching module, configured to perform interference suppression processing on the first polarization signal and the second polarization signal through polarization matching filtering based on the target polarization characteristic parameters and the interference polarization characteristic parameters, thereby generating an anti-interference enhanced signal;

[0040] The scattering characteristic decomposition module is used to perform polarization scattering mechanism decomposition processing on the target polarization characteristic parameters to generate the target surface scattering component and dihedral angle scattering component;

[0041] The interference identification module is used to perform target classification and identification processing based on the target surface scattering component, dihedral angle scattering component and polarization phase consistency parameters of the anti-interference enhancement signal, and output the anti-interference identification result.

[0042] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned passive radar seeker anti-interference identification method based on a dual-polarization array.

[0043] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned anti-interference identification method for a passive radar seeker based on a dual-polarization array.

[0044] The above-mentioned passive radar seeker anti-interference identification method based on dual-polarization array obtains the first signal and the second signal received by the dual-polarization array and performs time-space alignment calibration on them to generate the calibrated first polarization signal and the second polarization signal, thereby solving the synchronization deviation problem of the dual-polarization channel signal and providing high-precision input for subsequent processing; by performing polarization-Doppler joint time-frequency analysis on the calibrated polarization signal, a polarization-Doppler-frequency band three-dimensional tensor containing polarization, Doppler and frequency band multi-dimensional features is constructed to realize the extraction of the time-frequency coupling characteristics of the target and the interference in the wide-band signal; by performing dynamic core tensor decomposition processing on the three-dimensional tensor, the target polarization characteristic parameters and Interference polarization characteristic parameters are used to decouple anti-interference features by utilizing the differences between the target and interference in the polarization-Doppler domain. Based on the differences in polarization characteristics between the target and interference, the original polarization signal is subjected to interference suppression processing through polarization matched filtering to generate an anti-interference enhancement signal, effectively suppressing clutter and deception signals in strong interference scenarios. The target polarization characteristic parameters are further subjected to polarization scattering mechanism decomposition processing to extract the target surface scattering component and dihedral scattering component. The polarization phase consistency parameters of the anti-interference enhancement signal are combined for target classification and identification, thereby improving the feature resolution capability and anti-interference robustness of complex concealed targets in wide-band, strong interference scenarios, and achieving high-precision identification. The above method can accurately separate target and interference features, effectively solve the problem of feature confusion, and improve the anti-interference identification accuracy in complex adversarial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flowchart of an anti-interference identification method for a passive radar seeker based on a dual-polarization array provided by an embodiment of the present invention;

[0047] Figure 2 A flowchart of a method for constructing a polarization-Doppler-frequency band three-dimensional tensor provided in an embodiment of the present invention;

[0048] Figure 3 A schematic structural diagram of a passive radar seeker anti-interference identification device based on a dual-polarization array provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0050] First, a brief introduction is given to the terms involved in the embodiments of this application.

[0051] A dual-polarization array is an antenna array structure that simultaneously supports two orthogonal polarization modes (such as horizontal and vertical polarization). It synchronously receives the target's scattered signals through dual-polarization channels, acquiring multi-dimensional characteristics such as the target's amplitude, phase, and time-frequency response under different polarization states. Compared to single-polarization radar, a dual-polarization array can enhance the decoupling capability of target scattering mechanisms through polarization domain information fusion. It is particularly suitable for capturing the polarization-scattering frequency-variable characteristics of high-speed moving targets made of heterogeneous materials. It provides the hardware foundation for separating polarization-Doppler coupling interference and improving anti-ambiguity recognition capabilities. It is a key technical carrier for solving the problem of dynamic target feature confusion in wide frequency bands.

[0052] Passive radar seekers are an advanced radar technology that detects and tracks targets by receiving electromagnetic signals radiated or reflected by them, rather than actively transmitting signals. This technology offers exceptional stealth and anti-interference capabilities. They are widely used in the military, particularly in complex adversarial environments, effectively detecting and tracking high-speed moving targets, providing critical targeting information for precision-guided weapons. However, conventional passive radar seekers are often limited by the combined ambiguity and feature confusion caused by the coupling of polarization scattering, radiofrequency hopping, and the Doppler effect when encountering high-speed moving targets made of heterogeneous materials. This makes it difficult to achieve high-precision anti-interference recognition across a wide frequency band.

[0053] Polarization-Doppler joint time-frequency analysis is a multidimensional signal processing method that integrates the polarization, Doppler, and time-frequency domains. Its core is to perform a joint time-frequency transform (such as a short-time Fourier transform or wavelet transform) on the time-varying signals received by a dual-polarization array, synchronously extracting the time-frequency distribution characteristics of the signal under different polarization states, and combining the Doppler frequency shift information caused by target motion to analyze the coupling relationship between polarization scattering characteristics and the Doppler effect in the time-frequency domain. By jointly modeling polarization parameters (such as dynamic polarization ratio and polarization phase difference) and Doppler frequency shift, this method tracks the polarization scattering frequency-varying jump characteristics of high-speed moving targets across a wide frequency band, effectively separating the time-frequency coupling characteristics of the target and interference, and resolving the signal ambiguity caused by polarization-Doppler cross-modulation in traditional single-domain analysis. This provides multidimensional feature support for anti-interference identification in complex adversarial scenarios.

[0054] Based on the above explanation of terms, the implementation environment of the dual-polarization array-based passive radar seeker anti-interference identification method provided in the embodiment of the present application is described. Schematically, the implementation environment includes: a dual-polarization array antenna, a signal processing unit, a terminal, and a storage device. The terminal is connected to the dual-polarization array antenna, the signal processing unit, and the storage device via a network or a high-speed signal transmission link; the processor includes but is not limited to a central processing unit, a multi-core processor, or an artificial intelligence chip; and the storage device can be a distributed storage system or a centralized storage system, which is not limited here.

[0055] In combination with the above explanations of terms and implementation environments, the application scenarios of the embodiments of this application are described. The passive radar seeker anti-interference identification method based on a dual-polarization array provided in the embodiments of this application can be applied to, but not limited to, the following scenarios:

[0056] In the field of military defense, this technical solution can be applied to anti-interference identification of high-speed stealth aircraft: for stealth targets using heterogeneous composite materials and dynamic trajectory change strategies, the dual-polarization array captures the polarization scattering frequency-variable jump characteristics under its wide frequency band, and combines Doppler time-frequency analysis to decouple the coupling effect of motion trajectory and scattering characteristics, effectively overcoming the false alarm and missed detection problems caused by the polarization-Doppler joint ambiguity of traditional radar, and significantly improving the continuous tracking capability of highly maneuverable stealth targets.

[0057] In the civilian field, this technology can be expanded to low-altitude drone monitoring and countermeasure scenarios: in response to the lightweight composite materials and random track avoidance strategies commonly used in consumer-grade drones, the dual-polarization array effectively distinguishes the micro-Doppler characteristic differences between drones and natural targets such as birds through polarization phase consistency parameter analysis. Combined with the target classification algorithm of the anti-interference enhanced signal, it improves the drone recognition accuracy and anti-environmental clutter interference capability in complex low-altitude urban environments.

[0058] In emergency rescue scenarios, such as after a natural disaster, this method can be applied to the radar systems of rescue helicopters or drones. Disaster sites are often filled with complex and changing electromagnetic environments, where reflections from debris and interference from smoke and dust can affect radar recognition of rescue targets. A dual-polarization array-based passive radar seeker anti-interference recognition method can help the radar systems of rescue helicopters or drones accurately identify weak signals reflected by life signs in such complex environments, distinguish interference between the target and the surrounding environment, improve the efficiency and accuracy of rescue operations, and buy precious time to save lives.

[0059] Illustratively, the anti-interference identification method of a passive radar seeker based on a dual-polarization array and based on sound attenuation simulation provided in the embodiment of the present application can also be applied to other application scenarios. It is only used as an example here and is not limited to the specific application scenario.

[0060] In an exemplary embodiment, Figure 1 As shown, a method for anti-interference identification of a passive radar seeker based on a dual-polarization array is provided. This embodiment uses the method as an example of a terminal in the aforementioned implementation environment. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps 101 to 106:

[0061] Step 101: Acquire a first signal and a second signal received by a dual-polarization array, perform spatiotemporal alignment calibration on the first signal and the second signal, and obtain a first polarization signal and a second polarization signal.

[0062] Specifically, a first signal and a second signal are received by a dual-polarization array antenna. Exemplarily, the first signal and the second signal correspond to horizontal polarization and vertical polarization channels, respectively. A cross-correlation peak is calculated for the first signal and the second signal to obtain a delay difference estimate, and the second signal is dynamically delayed compensated based on the delay difference estimate to generate a delay alignment signal. Further, based on the phase consistency between the delay alignment signal and the first signal, a closed-loop phase calibration method is used to eliminate the phase deviation between the polarization channels, and then the compensated signal is dynamically gain-adjusted to generate a calibrated first polarization signal and a second polarization signal. The spatiotemporal alignment calibration process solves the signal synchronization deviation problem of the dual-polarization channel and provides high-precision input for subsequent analysis. For example, calibration can be achieved through a cross-correlation peak detection algorithm or phase closed-loop feedback control.

[0063] Step 102: Perform polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor.

[0064] Specifically, the calibrated first and second polarized signals undergo joint polarization-Doppler time-frequency analysis. This analysis process aims to simultaneously capture the signal's polarization and Doppler shift characteristics, and combines frequency band information to construct a three-dimensional polarization-Doppler-frequency band tensor. Polarization-Doppler joint time-frequency analysis employs methods such as the short-time Fourier transform (SFT) to calculate the signal's polarization and Doppler shift characteristics in different time periods and frequency bands, generating a three-dimensional tensor rich in signal information. The construction of this three-dimensional tensor integrates multiple signal features, providing a more comprehensive data foundation for subsequent target and interference separation.

[0065] Step 103 : Perform dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters.

[0066] Specifically, dynamic core tensor decomposition is performed on the three-dimensional polarization-Doppler-frequency tensor. For example, through alternating least squares iterative optimization, target polarization characteristic parameters that characterize target scattering characteristics and interference polarization characteristic parameters that reflect interference characteristics can be separated. For example, a tensor decomposition method based on the Tucker model or a dynamic kernel update strategy combined with gradient descent can be used. This processing can effectively distinguish the polarization modulation differences between the target and the interference, significantly improving anti-interference recognition capabilities.

[0067] Step 104 : Based on the target polarization characteristic parameters and the interference polarization characteristic parameters, interference suppression processing is performed on the first polarization signal and the second polarization signal through polarization matched filtering to generate an anti-interference enhanced signal.

[0068] Specifically, polarization-matched filtering is performed on dual-polarization channel signals based on the polarization characteristics of the target and interference. By constructing filter weights that match the target polarization characteristics, the energy in the interference polarization direction is suppressed, generating an enhanced anti-interference signal. For example, polarization whitening filtering can be used to eliminate the interference polarization component, or a polarization filter can be designed based on the maximum signal-to-interference-noise ratio criterion. This method effectively suppresses sidelobe interference and cross-polarization interference while preserving the target signal.

[0069] Step 105 : performing polarization scattering mechanism decomposition processing on the target polarization characteristic parameters to generate target surface scattering components and dihedral angle scattering components.

[0070] Specifically, the entropy value and scattering angle parameters of surface scattering can be extracted through H / A / α decomposition, or the even-order reflection components of dihedral scattering can be separated through Freeman decomposition. For example, Cloude decomposition is used to obtain the target polarization entropy parameters, and combined with the target polarization azimuth information, the surface scattering characteristics of ship-type targets and the dihedral scattering characteristics of building-type targets can be distinguished. Polarization scattering mechanism decomposition analyzes the intrinsic polarization response of the target and converts abstract polarization parameters into scattering components with clear physical meaning, thereby enhancing feature interpretability. The separation of surface scattering and dihedral scattering components can effectively distinguish different structural targets, such as flat surface scattering of ships and dihedral scattering of aircraft wings, significantly improving the identification accuracy of complex target types. The combination of Doppler domain characteristics further suppresses distributed interference such as meteorological clutter and enhances feature stability in anti-interference scenarios.

[0071] Step 106 : performing target classification and recognition processing based on the target surface scattering component, the dihedral angle scattering component and the polarization phase consistency parameter of the anti-interference enhancement signal, and outputting an anti-interference recognition result.

[0072] For example, a multi-dimensional classification feature set can be constructed based on the surface scattering component, dihedral scattering component and polarization phase consistency parameters of the anti-interference enhancement signal. Engineers use machine learning classifiers to train and identify the feature set, such as dividing ships, aircraft and other categories through support vector machines, or using random forest classifiers to fuse scattering components and phase consistency parameters. In specific implementations, the polarization phase consistency parameters can be combined to verify the target scattering stability, such as using the polarization interference phase coherence coefficient to exclude false targets. This step outputs high-confidence anti-interference recognition results in a strong interference environment by combining time-frequency-polarization-phase multi-dimensional features.

[0073] The above-mentioned passive radar seeker anti-interference identification method based on dual-polarization array obtains the first signal and the second signal received by the dual-polarization array and performs time-space alignment calibration on them to generate the calibrated first polarization signal and the second polarization signal, thereby solving the synchronization deviation problem of the dual-polarization channel signal and providing high-precision input for subsequent processing; by performing polarization-Doppler joint time-frequency analysis on the calibrated polarization signal, a polarization-Doppler-frequency band three-dimensional tensor containing polarization, Doppler and frequency band multi-dimensional features is constructed to realize the extraction of the time-frequency coupling characteristics of the target and the interference in the wide-band signal; by performing dynamic core tensor decomposition processing on the three-dimensional tensor, the target polarization characteristic parameters and Interference polarization characteristic parameters are used to decouple anti-interference features by utilizing the differences between the target and interference in the polarization-Doppler domain. Based on the differences in polarization characteristics between the target and interference, the original polarization signal is subjected to interference suppression processing through polarization matched filtering to generate an anti-interference enhancement signal, effectively suppressing clutter and deception signals in strong interference scenarios. The target polarization characteristic parameters are further subjected to polarization scattering mechanism decomposition processing to extract the target surface scattering component and dihedral scattering component. The polarization phase consistency parameters of the anti-interference enhancement signal are combined for target classification and identification, thereby improving the feature resolution capability and anti-interference robustness of complex concealed targets in wide-band, strong interference scenarios, and achieving high-precision identification. The above method can accurately separate target and interference features, effectively solve the problem of feature confusion, and improve the anti-interference identification accuracy in complex adversarial scenarios.

[0074] like Figure 2 As shown, in a possible embodiment, performing polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor includes:

[0075] Step 201: Perform short-time Fourier transform on the first polarization signal and the second polarization signal to generate a first polarization time-frequency distribution and a second polarization time-frequency distribution.

[0076] Exemplarily, a short-time Fourier transform can be performed on the first polarization signal and the second polarization signal respectively, and a sliding time window can be used to intercept the signal segments and calculate the spectrum to generate the first polarization time-frequency distribution and the second polarization time-frequency distribution. Specifically, the spectrum leakage can be reduced by Hamming window weighting, or the time-frequency resolution can be optimized using an adaptive window length. For example, in a ship target detection scenario, the time-frequency distribution can highlight the target micro-Doppler modulation characteristics while suppressing the broadband interference of sea clutter. This processing maps the polarization signal into a time-frequency domain energy distribution, providing basic data for subsequent noise suppression and feature extraction.

[0077] Step 202 : Perform dynamic noise floor adjustment processing based on the amplitude ratio of the first polarization time-frequency distribution and the second polarization time-frequency distribution to generate an anti-noise polarization ratio parameter.

[0078] Specifically, based on the amplitude ratio of the first polarization time-frequency distribution and the second polarization time-frequency distribution, a sliding window is used to calculate the local noise floor, and the signal-to-noise ratio threshold of the polarization ratio parameter is dynamically adjusted to generate a noise-resistant polarization ratio parameter. For example, quantile estimation is used to calculate the background noise level, or an adaptive threshold is used to suppress pulse interference. This processing can eliminate polarization ratio fluctuations caused by ambient noise and interference, for example, stably extracting target polarization characteristics under low signal-to-noise ratio conditions, avoiding feature misjudgment caused by noise lift.

[0079] Step 203 : Perform Doppler-polarization coupling analysis based on the phase gradients of the first polarization time-frequency distribution and the second polarization time-frequency distribution to generate polarization phase difference and Doppler frequency shift parameters.

[0080] Specifically, the instantaneous phase difference between the polarization channels is calculated based on the phase gradients of the first and second polarization time-frequency distributions, and the polarization-Doppler coupling parameters are constructed in combination with the Doppler frequency shift. For example, phase information can be extracted through a Hilbert transform, or the time-varying Doppler frequency can be estimated using a phase difference method. For example, in aircraft target recognition, the phase gradient of a uniformly moving target exhibits linear variation, while the random motion of an interference signal produces an incoherent phase difference. This analysis can accurately correlate the target's polarization response with its motion characteristics, enhancing feature separability.

[0081] Step 204 : Combining the anti-noise polarization ratio parameter, the polarization phase difference, and the Doppler frequency shift parameter into a polarization-Doppler-frequency band three-dimensional tensor according to the frequency band.

[0082] Specifically, according to preset frequency band division rules, the anti-noise polarization ratio parameters, polarization phase difference, and Doppler shift parameters are integrated along the frequency band dimension to construct a three-dimensional polarization-Doppler-frequency band tensor. For example, uniform subband division can be used to cover the entire frequency band, or wavelet packet decomposition can be used to achieve non-uniform frequency band optimization. This three-dimensional tensor preserves the differentiated distribution characteristics of targets and interference in the frequency band dimension, such as the concentrated low-frequency scattered energy of ship targets and the diffuse high-frequency characteristics of interference, providing structured input for subsequent tensor decomposition.

[0083] In a possible embodiment, dynamic core tensor decomposition processing is performed on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters, including:

[0084] The polarization-Doppler-frequency band three-dimensional tensor is subjected to high-order singular value decomposition to obtain a core tensor and a multidimensional factor matrix.

[0085] Specifically, a high-order singular value decomposition (HSVD) is performed on the three-dimensional polarization-Doppler-frequency tensor. By decomposing the tensor's factor matrices and core tensor into each dimension, the inherent correlations of the multidimensional features are extracted. Furthermore, the Tucker decomposition model can be used to decompose the original tensor into the product of the core tensor and the factor matrices of each dimension. For example, in a ship target recognition scenario, the decomposed core tensor can characterize the target's dominant modes in the polarization, Doppler, and frequency dimensions, while the factor matrix reflects the energy distribution characteristics of each dimension, providing a basis for subsequent dynamic truncation.

[0086] Calculate the real-time interference energy ratio threshold based on the anti-noise polarization ratio parameter and polarization phase difference.

[0087] Specifically, the local variance of the polarization ratio parameter can be used to estimate interference fluctuation intensity, or the incoherence of the polarization phase difference can be combined to quantify the interference energy ratio. For example, median filtering of the polarization ratio within a sliding window can be used to suppress sudden interference, or the entropy of the phase difference histogram can be used to determine interference activity. This threshold dynamically adapts to changes in interference intensity, ensuring real-time and robust truncation processing.

[0088] Based on the real-time interference energy ratio threshold, the core tensor is dynamically truncated to retain the target principal component tensor.

[0089] Specifically, the core tensor is dynamically truncated based on a real-time interference energy threshold, preserving the target principal component tensor. The goal of this truncation is to remove interference-related information and enhance target-related features. This truncated target principal component tensor more accurately describes the target's polarization characteristics, providing high-quality data support for subsequent target identification.

[0090] A target polarization scattering matrix is ​​reconstructed according to the target principal component tensor and the multidimensional factor matrix to obtain target polarization characteristic parameters, wherein the target polarization characteristic parameters include a target polarization ratio and a target polarization phase difference.

[0091] Specifically, the target's polarization scattering matrix is ​​reconstructed using the target's principal component tensor and a multidimensional factor matrix. The target's intrinsic response in the polarization domain is recovered through tensor multiplication, and the target polarization ratio and target polarization phase difference parameters are extracted. For example, the polarization ratio is obtained by eigenvalue decomposition of the polarization scattering matrix, or the target's polarization characteristics are characterized by polarization azimuth and ellipticity parameters. This reconstruction process preserves the target's true scattering characteristics, such as the horizontal polarization dominance of ship targets or the cross-polarization symmetry of aircraft targets.

[0092] An interference polarization scattering matrix is ​​extracted from the truncated interference component tensor to obtain interference polarization characteristic parameters, wherein the interference polarization characteristic parameters include an interference polarization ratio and an interference polarization phase difference.

[0093] Specifically, the interference polarization scattering matrix is ​​extracted from the truncated interference component tensor, and the interference polarization modulation characteristics are described using the interference polarization ratio and the interference polarization phase difference. For example, the randomness of the interference polarization can be quantified based on the singular value decomposition of the interference scattering matrix, or the polarization ratio standard deviation can be used to characterize the intensity of the interference polarization fluctuation. This parameter extraction can identify the rapid polarization jitter characteristics of chaff interference or the depolarization characteristics of meteorological clutter, providing prior knowledge for subsequent anti-interference filtering.

[0094] In a possible embodiment, based on target polarization characteristic parameters and interference polarization characteristic parameters, interference suppression processing is performed on the first polarization signal and the second polarization signal through polarization matched filtering to generate an anti-interference enhanced signal, including:

[0095] The polarization ratio difference is calculated based on the target polarization ratio and the interference polarization ratio using the following formula:

[0096]

[0097] Among them, Δρ(t,f) is the polarization ratio difference, (t,f) is the time-frequency point, ρ target is the target polarization ratio, ρ inter is the interference polarization ratio.

[0098] Specifically, the polarization ratio difference is calculated based on the target polarization ratio and the interference polarization ratio. The absolute difference between the target and interference polarization ratios is divided by their maximum value using a normalization method to obtain the polarization ratio difference parameter in the time-frequency dimension. For example, the stability of the target polarization ratio can be statistically analyzed using a sliding window, or the fluctuation range of the interference polarization ratio can be adjusted using an adaptive threshold. For example, in ship target recognition, the significant difference between the target horizontal polarization ratio and the interference vertical polarization ratio is used to suppress sea surface reflection interference. This difference parameter can effectively characterize the polarization separability of the target and the interference.

[0099] The difference between the target polarization phase difference and the interference polarization phase difference is calculated to obtain the polarization phase difference difference.

[0100] Specifically, the absolute difference between the target polarization phase difference and the interference polarization phase difference is calculated to obtain a polarization phase difference difference parameter. For example, the phase gradient analysis method can be used to extract the continuity characteristics of the target phase difference, or the randomness of the interference phase difference can be statistically analyzed using a phase difference histogram. For example, the polarization phase difference of an aircraft target exhibits regular Doppler modulation, while the phase difference of chaff interference has a random jump characteristic. This difference parameter can enhance the distinction between moving targets and passive interference.

[0101] According to the polarization ratio difference and the polarization phase difference, a space-polarization joint filtering weight matrix is ​​generated.

[0102] Specifically, based on the polarization ratio difference and the polarization phase difference, a spatial-polarization joint filtering weight matrix is ​​constructed. For example, there are multiple options for generating the spatial-polarization joint filtering weight matrix. For example, it can be constructed according to a certain weight distribution rule based on the size of the difference, or a mapping model can be obtained through training of a machine learning algorithm to map the difference value to the filtering weight matrix. For example, the polarization difference weight is increased in the strong interference area to suppress the interference, and the spatial domain beamforming gain is retained in the weak interference area. The matrix achieves a dynamic balance between interference suppression and target signal retention.

[0103] Based on the space-polarization joint filtering weight matrix, weighted synthesis processing is performed on the first polarization signal and the second polarization signal to generate an anti-interference enhanced signal.

[0104] Specifically, based on the space-polarization joint filtering weight matrix, the dual-polarization channel signals are weighted and synthesized, and the first polarization signal and the second polarization signal are energy-adjusted at the time-frequency point level according to the weight matrix to generate an anti-interference enhanced signal. For example, the overlapping retention method can be used to avoid the edge effect of time-frequency block, or combined with phase compensation to maintain signal coherence. For example, when suppressing foil interference, by reducing the signal weight in the interference polarization ratio difference area, the energy in the target polarization matching direction is retained, and the signal-to-interference-noise ratio of the output signal is improved. The above method can effectively suppress the influence of interference signals, enhance the strength of target signals, and improve the detection and recognition capabilities of target signals in complex electromagnetic environments.

[0105] In a possible embodiment, target classification and recognition processing is performed based on the target surface scattering component, the dihedral angle scattering component, and the polarization phase consistency parameter of the anti-interference enhancement signal, and an anti-interference recognition result is output, including:

[0106] The scattering entropy of the target surface scattering component and the anisotropy of the dihedral angle scattering component are calculated, and the target scattering feature vector is generated according to the scattering entropy and anisotropy.

[0107] Specifically, the scattering entropy of the target's surface scattering component and the anisotropy of the dihedral scattering component are calculated. Based on these scattering entropy and anisotropy, a target scattering feature vector is generated. The scattering entropy reflects the complexity of the target's surface scattering and can be calculated using various methods, such as the Shannon entropy based on the statistical distribution of the scattering amplitude. The anisotropy reflects the directional differences in dihedral scattering and can be calculated by analyzing the elements of the polarization scattering matrix. These two parameters can be used to construct the target scattering feature vector, providing information on the target's scattering characteristics for subsequent classification.

[0108] Polarization phase consistency parameters are extracted from the anti-interference enhancement signal, and the target scattering feature vector is fused with the polarization phase consistency parameters to generate a classification feature vector.

[0109] Specifically, the polarization phase consistency parameter is extracted from the anti-interference enhancement signal. The target scattering feature vector is then fused with the polarization phase consistency parameter to generate a classification feature vector. The polarization phase consistency parameter reflects the stability of the signal in terms of polarization phase and can be obtained by analyzing the phase characteristics of the anti-interference enhancement signal. Various feature fusion methods exist, such as simple concatenation, weighted summation, or feature fusion using deep learning models. The resulting classification feature vector combines the target scattering characteristics and the signal's polarization phase characteristics.

[0110] The classification feature vector is classified by a pre-trained classifier and the anti-interference recognition result is output.

[0111] Specifically, a pre-trained classifier classifies the classification feature vectors and outputs the anti-interference recognition results. Pre-trained classifiers can utilize a variety of machine learning algorithms, such as support vector machines, decision trees, and neural networks. These classifiers have already learned the characteristic patterns of different target categories during the training phase. During the classification process, the classifier matches the classification feature vectors to the corresponding target category based on the characteristic patterns, thereby achieving target classification and recognition. This approach effectively utilizes the target's scattering and polarization phase characteristics, improving target classification and recognition accuracy and anti-interference capabilities.

[0112] In summary, the anti-interference identification method for a passive radar seeker based on a dual-polarization array provided in the embodiment of the present application performs spatiotemporal alignment calibration on the dual-channel signals received by the dual-polarization array, eliminates the time delay and phase deviation between polarization channels, and ensures the accuracy of subsequent polarization feature extraction; further, a polarization-Doppler joint time-frequency analysis is used to construct a three-dimensional tensor including polarization, Doppler frequency shift and frequency band division, and the dynamic core tensor decomposition technology is used to separate the polarization feature parameters of the target and the interference, thereby solving the problem of target and interference feature aliasing in the traditional method in a wide frequency band scenario; further, based on the polarization ratio difference and phase difference between the target and the interference, the target and interference are separated. The spatial-polarization joint filtering weights are designed based on the difference difference. The interference signal energy is suppressed through time-frequency point-level weighted synthesis, while retaining the effective information of the target polarization matching direction, thereby improving the signal quality in the strong interference background. By decomposing the target polarization characteristics through the scattering mechanism, the scattering entropy of the surface scattering component and the anisotropy of the dihedral scattering component are extracted. Combined with the polarization phase consistency parameter of the anti-interference enhancement signal, a multi-dimensional classification feature is constructed that integrates the target physical characteristics and signal stability, effectively overcoming the feature distortion problem caused by multipath scattering and camouflage interference. The pre-trained classifier is used to make a joint decision on the multi-dimensional features and output the anti-interference recognition result. The above method can achieve high-precision target recognition in wide frequency band and strong interference scenarios, and improve the anti-interference robustness against concealed targets in complex confrontation environments.

[0113] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0114] Based on the same inventive concept, the embodiments of the present application further provide a device 10 for anti-interference identification of a passive radar seeker based on a dual-polarization array, for implementing the aforementioned method for anti-interference identification of a passive radar seeker based on a dual-polarization array. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device 10 for anti-interference identification of a passive radar seeker based on a dual-polarization array provided below can be found in the above-mentioned limitations of the method for anti-interference identification of a passive radar seeker based on a dual-polarization array, and will not be repeated here.

[0115] In an exemplary embodiment, Figure 3 As shown, a passive radar seeker anti-interference identification device 10 based on a dual-polarization array is provided, comprising:

[0116] The signal acquisition module 11 is configured to acquire a first signal and a second signal received by a dual-polarization array, perform time-space alignment calibration on the first signal and the second signal, and obtain a first polarization signal and a second polarization signal.

[0117] The time-frequency analysis module 12 is configured to perform polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor.

[0118] The characteristic decomposition module 13 is used to perform dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters.

[0119] The polarization matching module 14 is configured to perform interference suppression processing on the first polarization signal and the second polarization signal through polarization matching filtering based on the target polarization characteristic parameters and the interference polarization characteristic parameters to generate an anti-interference enhanced signal.

[0120] The scattering characteristic decomposition module 15 is used to perform polarization scattering mechanism decomposition processing on the target polarization characteristic parameters to generate target surface scattering components and dihedral angle scattering components.

[0121] The interference identification module 16 is used to perform target classification and identification processing based on the target surface scattering component, the dihedral angle scattering component and the polarization phase consistency parameter of the anti-interference enhancement signal, and output the anti-interference identification result.

[0122] In a possible embodiment, the time-frequency analysis module 12 includes:

[0123] The time-frequency transform unit is configured to perform short-time Fourier transform on the first polarization signal and the second polarization signal to generate a first polarization time-frequency distribution and a second polarization time-frequency distribution.

[0124] The anti-noise processing unit is configured to perform dynamic noise floor adjustment processing based on the amplitude ratio of the first polarization time-frequency distribution and the second polarization time-frequency distribution to generate an anti-noise polarization ratio parameter.

[0125] The coupling analysis unit is used to perform Doppler-polarization coupling analysis according to the phase gradients of the first polarization time-frequency distribution and the second polarization time-frequency distribution, and generate polarization phase difference and Doppler frequency shift parameters.

[0126] The tensor construction unit is used to combine the anti-noise polarization ratio parameter, polarization phase difference and Doppler frequency shift parameter into a polarization-Doppler-frequency band three-dimensional tensor according to frequency band division.

[0127] In a possible embodiment, the feature decomposition module 13 includes:

[0128] The tensor decomposition unit is used to perform high-order singular value decomposition on the polarization-Doppler-frequency band three-dimensional tensor to obtain a core tensor and a multidimensional factor matrix.

[0129] The threshold calculation unit is used to calculate the real-time interference energy ratio threshold based on the anti-noise polarization ratio parameter and the polarization phase difference.

[0130] The truncation processing unit is used to dynamically truncate the core tensor based on the real-time interference energy ratio threshold to retain the target principal component tensor.

[0131] The target reconstruction unit is used to reconstruct the target polarization scattering matrix according to the target principal component tensor and the multidimensional factor matrix to obtain target polarization characteristic parameters, wherein the target polarization characteristic parameters include a target polarization ratio and a target polarization phase difference.

[0132] The interference extraction unit is used to extract the interference polarization scattering matrix from the truncated interference component tensor to obtain interference polarization characteristic parameters, wherein the interference polarization characteristic parameters include an interference polarization ratio and an interference polarization phase difference.

[0133] In a possible embodiment, the polarization matching module 14 includes:

[0134] The difference calculation unit is configured to calculate the polarization ratio difference based on the target polarization ratio and the interference polarization ratio using the following formula:

[0135]

[0136] Among them, Δρ(t,f) is the polarization ratio difference, (t,f) is the time-frequency point, ρ target is the target polarization ratio, ρ inter is the interference polarization ratio.

[0137] The phase difference processing unit is used to calculate the difference between the target polarization phase difference and the interference polarization phase difference to obtain the polarization phase difference difference.

[0138] The weight generating unit is used to generate a space-polarization joint filtering weight matrix according to the polarization ratio difference and the polarization phase difference difference.

[0139] The signal synthesis unit is configured to perform weighted synthesis processing on the first polarization signal and the second polarization signal based on a space-polarization joint filtering weight matrix to generate an anti-interference enhanced signal.

[0140] In a possible embodiment, the interference identification module 16 includes:

[0141] The scattering feature unit is used to calculate the scattering entropy of the target surface scattering component and the anisotropy of the dihedral angle scattering component, and generate the target scattering feature vector according to the scattering entropy and anisotropy.

[0142] The feature fusion unit is used to extract the polarization phase consistency parameter from the anti-interference enhancement signal, perform feature fusion processing on the target scattering feature vector and the polarization phase consistency parameter, and generate a classification feature vector.

[0143] The classification processing unit is used to classify the classification feature vector through a pre-trained classifier and output an anti-interference recognition result.

[0144] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the aforementioned method for anti-interference identification of a passive radar seeker based on a dual-polarization array are implemented.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0146] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0147] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A passive radar seeker anti-interference identification method based on a dual-polarization array, characterized in that: The method comprises: Acquire a first signal and a second signal received by a dual-polarization array, perform spatiotemporal alignment calibration on the first signal and the second signal, and obtain a first polarization signal and a second polarization signal; Performing polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor; Performing dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters; performing interference suppression processing on the first polarized signal and the second polarized signal through polarization matched filtering based on the target polarization characteristic parameter and the interference polarization characteristic parameter to generate an anti-interference enhanced signal; performing polarization scattering mechanism decomposition processing on the target polarization characteristic parameters to generate a target surface scattering component and a dihedral angle scattering component; Based on the target surface scattering component, the dihedral angle scattering component and the polarization phase consistency parameter of the anti-interference enhancement signal, target classification and recognition processing is performed, and an anti-interference recognition result is output.

2. The method according to claim 1, characterized in that The performing polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor includes: Performing short-time Fourier transform on the first polarized signal and the second polarized signal to generate a first polarization time-frequency distribution and a second polarization time-frequency distribution; performing dynamic noise floor adjustment processing based on an amplitude ratio of the first polarization time-frequency distribution to the second polarization time-frequency distribution to generate an anti-noise polarization ratio parameter; Performing Doppler-polarization coupling analysis based on the phase gradients of the first polarization time-frequency distribution and the second polarization time-frequency distribution to generate polarization phase difference and Doppler frequency shift parameters; The anti-noise polarization ratio parameter, the polarization phase difference, and the Doppler frequency shift parameter are combined into the polarization-Doppler-frequency band three-dimensional tensor according to frequency band division.

3. The method according to claim 2, characterized in that The performing dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization characteristic parameters and interference polarization characteristic parameters includes: Performing high-order singular value decomposition on the polarization-Doppler-frequency band three-dimensional tensor to obtain a core tensor and a multidimensional factor matrix; Calculating a real-time interference energy proportion threshold based on the anti-noise polarization ratio parameter and the polarization phase difference; Based on the real-time interference energy ratio threshold, dynamically truncate the core tensor to retain the target principal component tensor; reconstructing a target polarization scattering matrix according to the target principal component tensor and the multidimensional factor matrix to obtain the target polarization characteristic parameters, wherein the target polarization characteristic parameters include a target polarization ratio and a target polarization phase difference; An interference polarization scattering matrix is ​​extracted from the truncated interference component tensor to obtain the interference polarization characteristic parameters, wherein the interference polarization characteristic parameters include an interference polarization ratio and an interference polarization phase difference.

4. The method according to claim 3, characterized in that The performing interference suppression processing on the first polarization signal and the second polarization signal by polarization matched filtering based on the target polarization characteristic parameter and the interference polarization characteristic parameter to generate an anti-interference enhanced signal includes: The polarization ratio difference is calculated based on the target polarization ratio and the interference polarization ratio using the following formula: Wherein, Δρ(t,f) is the polarization ratio difference, (t,f) is the time-frequency point, ρ target is the target polarization ratio, ρ inter is the interference polarization ratio; Calculating a difference between the target polarization phase difference and the interference polarization phase difference to obtain a polarization phase difference difference; generating a space-polarization joint filtering weight matrix according to the polarization ratio difference and the polarization phase difference difference; Based on the space-polarization joint filtering weight matrix, weighted synthesis processing is performed on the first polarization signal and the second polarization signal to generate an anti-interference enhanced signal.

5. The method according to claim 1, wherein The target classification and recognition processing is performed based on the target surface scattering component, the dihedral angle scattering component, and the polarization phase consistency parameter of the anti-interference enhancement signal, and the anti-interference recognition result is output, including: Calculating the scattering entropy of the target surface scattering component and the anisotropy of the dihedral angle scattering component, and generating a target scattering feature vector according to the scattering entropy and the anisotropy; Extracting a polarization phase consistency parameter from the anti-interference enhancement signal, performing feature fusion processing on the target scattering feature vector and the polarization phase consistency parameter to generate a classification feature vector; The classification feature vector is classified by a pre-trained classifier, and the anti-interference recognition result is output.

6. A passive radar seeker anti-interference identification device based on a dual-polarization array, characterized in that: The device comprises: a signal acquisition module, configured to acquire a first signal and a second signal received by a dual-polarization array, perform spatiotemporal alignment calibration on the first signal and the second signal, and obtain a first polarization signal and a second polarization signal; a time-frequency analysis module, configured to perform polarization-Doppler joint time-frequency analysis on the first polarization signal and the second polarization signal to construct a polarization-Doppler-frequency band three-dimensional tensor; a feature decomposition module, configured to perform dynamic core tensor decomposition processing on the polarization-Doppler-frequency band three-dimensional tensor to obtain target polarization feature parameters and interference polarization feature parameters; a polarization matching module, configured to perform interference suppression processing on the first polarization signal and the second polarization signal through polarization matching filtering based on the target polarization characteristic parameter and the interference polarization characteristic parameter, to generate an anti-interference enhanced signal; a scattering characteristic decomposition module, configured to perform polarization scattering mechanism decomposition processing on the target polarization characteristic parameters to generate target surface scattering components and dihedral angle scattering components; The interference identification module is used to perform target classification and identification processing based on the target surface scattering component, the dihedral angle scattering component and the polarization phase consistency parameter of the anti-interference enhancement signal, and output an anti-interference identification result.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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