A broadband array electronic detection anti-interference method and device and storage medium
By constructing interference signal models and target signal models, and decomposing and weighting the signals, the problem of target signal loss caused by interference suppression in broadband array reconnaissance systems is solved, and efficient target signal preservation with adaptive anti-interference is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUILIN CHANGHAI DEV
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-29
AI Technical Summary
When faced with interference in complex electromagnetic environments, existing broadband array reconnaissance systems often suffer from signal loss due to fixed suppression methods, which affects the integrity and accuracy of the detection mission.
By constructing an interference signal model, decomposing it into a two-dimensional interference data matrix, extracting interference feature vectors, constructing a target signal model, estimating the covariance matrix, and solving for the optimal weight vector based on optimization constraints, the interference is suppressed through weighted processing.
It achieves adaptive suppression of interference in complex electromagnetic environments while fully preserving target signal information, thus improving the integrity and accuracy of broadband array electronic detection.
Smart Images
Figure CN122110012A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of array signal processing technology, specifically to a broadband array electronic detection anti-interference method, device, and storage medium. Background Technology
[0002] As a core technology in the fields of electronic warfare, signals intelligence, and spectrum monitoring, radar signal electronic detection has the core task of accurately intercepting, analyzing, identifying, and locating various radiation source signals in complex electromagnetic environments. It is of irreplaceable importance in ensuring strategic situation awareness and operational decision-making.
[0003] Early electronic detection systems were mostly designed based on a single-antenna architecture and narrowband signal assumptions. Limited by hardware structure and processing logic, their angular resolution, adaptability to complex signals, and data processing efficiency all had significant shortcomings. With the rapid development of electronic countermeasures technology, radar and other electronic devices have widely adopted broadband / ultra-wideband technologies such as frequency hopping, spread spectrum, and pulse compression to improve their anti-detection capabilities and combat performance. This has greatly increased the complexity of the electromagnetic environment and the diversity of signal patterns, making traditional narrowband detection systems unable to meet the needs of modern detection missions.
[0004] To address this challenge, broadband array electronic detection technology has emerged. This technology, by deeply integrating the core advantages of broadband signal processing and array signal processing, achieves high-precision, high-sensitivity, and high-reliability sensing and analysis of complex electromagnetic signals, effectively supporting modern electronic detection missions. However, in increasingly harsh electromagnetic warfare environments, deliberate targeted electronic interference has become a key bottleneck affecting the performance of detection systems.
[0005] Existing broadband array reconnaissance and receiving systems primarily rely on fixed processing methods such as time-domain-frequency filtering to avoid interference signals in specific time periods or frequency domains, thus ensuring normal system operation. However, these fixed suppression methods have inherent drawbacks: while avoiding interference, they are highly susceptible to losing specific target signals within that time period or frequency band, leading to the synchronous loss of the core target signal and severely impacting the integrity and accuracy of the detection mission. Therefore, developing a broadband array electronic detection anti-interference technology that can adaptively suppress interference without losing target signals has become a pressing technical challenge in this field. Summary of the Invention
[0006] The technical problem to be solved by the present invention is to provide a broadband array electronic detection anti-interference method, device and storage medium to address the shortcomings of the prior art.
[0007] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A broadband array electronic detection anti-interference method, comprising the following steps: Define the core parameters of the signal receiving carrier, and construct a model of the interference signal based on the core parameters to obtain the interference signal model; Based on the interference signal model, the interference signal is arranged in two dimensions in the spatial and temporal domains to form a two-dimensional interference data matrix; The set of interference feature vectors is obtained by decomposing the two-dimensional interference data matrix. Construct a target signal model corresponding to the target signal, and construct a mixed signal matrix corresponding to the mixed signal detected on a specified channel based on the interference signal model and the target signal model; The covariance matrix is estimated based on the mixed signal matrix; A target guidance vector is constructed based on the search direction, and optimization constraints are established based on the target guidance vector, the set of interference feature vectors, and the covariance matrix. The optimization constraints are solved to obtain an intermediate weight vector, and the optimal weight vector is calculated based on the intermediate weight vector and the target guidance vector. The mixed signals in the mixed signal matrix are weighted based on the optimal weight vector to output the target signal after interference suppression.
[0008] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: A broadband array electronic detection anti-interference device, comprising: The interference modeling module is used to define the core parameters of the signal receiving carrier, and to build a model of the interference signal based on the core parameters to obtain the interference signal model. The two-dimensional arrangement module for interference signals is used to arrange interference signals in two dimensions, spatial and temporal domains, based on the interference signal model, to form a two-dimensional interference data matrix. An interference feature vector extraction module is used to obtain a set of interference feature vectors based on the decomposition of the interference two-dimensional data matrix. The hybrid matrix construction module is used to construct the target signal model corresponding to the target signal, and to construct the hybrid signal matrix corresponding to the hybrid signal detected on the specified channel based on the interference signal model and the target signal model. A mixed-signal covariance estimation module is used to estimate the covariance matrix based on the mixed-signal matrix; The optimal weight solution module is used to construct the target guidance vector of the target signal based on the search direction, establish optimization constraints based on the target guidance vector, the set of interference feature vectors and the covariance matrix R, solve the optimization constraints to obtain the intermediate weight vector, and calculate the optimal weight vector based on the intermediate weight vector and the target guidance vector. The target signal output module is used to perform weighted processing on the mixed signals in the mixed signal matrix based on the optimal weight vector, and output the target signal after suppressing interference.
[0009] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the broadband array electronic detection anti-interference method as described above.
[0010] The beneficial effects of this invention are as follows: Through a complete process of interference modeling, feature extraction, mixed signal processing, optimal weight solution, and weighted output, a data-adaptive broadband array anti-interference logic is constructed. The core is to achieve accurate anti-interference by separating interference features from target signal features. It does not rely on fixed filtering parameters and can adaptively adapt to various interferences in complex electromagnetic environments. While suppressing interference, it completely preserves the target signal information, solving the pain point of traditional fixed suppression methods that easily lose the target signal, and significantly improving the integrity and accuracy of broadband array electronic detection. Attached Figure Description
[0011] Figure 1 A flowchart of a broadband array electronic detection anti-interference method provided in an embodiment of the present invention; Figure 2 This is a flowchart of the signal processing of the broadband array electronic detection interference method provided in an embodiment of the present invention; Figure 3 This is a diagram showing the spatial decomposition results of interference feature values provided in an embodiment of the present invention. Figure 4 The conventional method provided in this embodiment of the invention relates to the interference signal and target signal spectrum; Figure 5 This is a diagram illustrating the interference suppression effect provided in an embodiment of the present invention; Figure 6 This is a functional block diagram of a broadband array electronic detection anti-interference device provided in an embodiment of the present invention. Detailed Implementation
[0012] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.
[0013] Example 1: As Figure 1 As shown, this embodiment of the invention provides a broadband array electronic detection anti-interference method, including the following steps: S1. Define the core parameters of the signal receiving carrier, and construct a model of the interference signal based on the core parameters to obtain the interference signal model; S2. Based on the interference signal model, the interference signal is arranged in two dimensions in the spatial and temporal domains to form a two-dimensional interference data matrix; S3. Obtain the set of interference feature vectors based on the decomposition of the interference two-dimensional data matrix; S4. Construct the target signal model corresponding to the target signal, and construct the mixed signal matrix corresponding to the mixed signal detected on the specified channel based on the interference signal model and the target signal model; S5. Estimate the covariance matrix based on the mixed signal matrix; S6. Construct a target guidance vector for the target signal based on the search direction, and establish optimization constraints based on the target guidance vector, the set of interference feature vectors and the covariance matrix, and solve the optimization constraints to obtain an intermediate weight vector. Calculate the optimal weight vector based on the intermediate weight vector and the target guidance vector. S7. Based on the optimal weight vector, perform weighted processing on the mixed signals in the mixed signal matrix, and output the target signal after suppressing interference.
[0014] In the above embodiments, a data-adaptive broadband array anti-interference logic is constructed through a complete process of interference modeling, feature extraction, mixed signal processing, optimal weight solution, and weighted output. The core is to achieve accurate anti-interference by separating interference features from target signal features. It does not rely on fixed filtering parameters and can adaptively adapt to various interferences in complex electromagnetic environments. While suppressing interference, it completely preserves the target signal information, solving the pain point of traditional fixed suppression methods that are prone to losing target signals, and significantly improving the integrity and accuracy of broadband array electronic detection.
[0015] Figure 2 The signal processing flowchart of the broadband array electronic detection interference method provided in the embodiments of the present invention intuitively illustrates the full-link logic of "array reception - split sampling - adaptive processing - multi-channel suppression output" under the multi-channel architecture.
[0016] The core components and functions of this process are as follows: The initial signal reception stage: A multi-element receiving array is used as the signal input carrier through the receiving array antenna module to capture radiation source signals (including targets, interference, and noise) from different spatial directions in a complex electromagnetic environment, providing the initial signal input for subsequent channel-specific processing; AD sampling stage: For the multiple analog signals output by the receiving array, synchronous digital conversion is performed through independent AD sampling units to convert the analog signal received by each array element into a digital signal, ensuring the time-domain synchronization and sampling accuracy of each channel signal. The adaptive processing step for interference data characteristics of different channels: receiving the digital signals output by each AD sampling unit, and independently extracting the interference data features of each channel (corresponding to the interference modeling, two-dimensional matrix construction, and feature vector extraction process in S1-S3 of the method), to achieve accurate characterization of the interference features of each channel. Multi-channel adaptive interference suppression stage: It includes M independent adaptive interference suppression units from channel 1 to channel M. Each unit performs mixed signal construction, covariance estimation, optimal weight vector solution and weighting processing based on the interference characteristics of the corresponding channel (corresponding to the process in S4-S7 in the method) to achieve interference suppression in a single channel; Adaptive interference suppression output stage: Integrates the interference-suppressed signals from M channels and outputs the target signal after multi-channel collaborative processing, thus completing interference suppression and target signal enhancement in multi-channel scenarios.
[0017] Preferably, S1, defining the core parameters of the signal receiving carrier, and constructing a model of the interference signal based on the core parameters to obtain the interference signal model, including: A one-dimensional uniform linear array is used as the signal receiving carrier. The core parameters of the signal receiving carrier are defined, including the number of receiving array elements. Number of time-domain snapshots Signal wavelength Array spacing Number of interferences , No. Interference baseband and direction angle Based on the core parameters, a model of the interference signal is constructed to obtain the interference signal model, which is as follows: .
[0018] In the above embodiments, a one-dimensional uniform linear array is explicitly used as the receiving carrier. By defining core parameters and constructing a spatial-temporal mathematical model of the interference signal, the precise quantitative characterization of the interference signal is achieved, providing structured basic data for subsequent interference feature extraction. The array elements, wavelength, interference direction and other parameters included in the model ensure the accurate restoration of the propagation characteristics of the interference signal, enabling subsequent processing to specifically capture the spatial distribution pattern of the interference, and providing a reliable mathematical basis for interference suppression.
[0019] Preferably, S2, based on the interference signal model, the interference signal is arranged in two dimensions in the spatial and temporal domains to form a two-dimensional interference data matrix, including: Based on the defined number of receiver array elements And the number of time-domain snapshots Based on the interference signal model, the dispersed interference signals are arranged in two dimensions: spatial and temporal. The rows of the matrix correspond to the spatial dimension, with each row representing one receiving array element, and the columns correspond to the temporal dimension, with each column representing one temporal snapshot, forming a two-dimensional interference data matrix. , is represented as: , Among them, matrix elements Indicates the first The array element in the first The interference signal sample values received at each snapshot moment are calculated using an interference signal model. , .
[0020] In the above embodiments, the scattered interference signals are reorganized into a two-dimensional data matrix according to the spatial domain (array elements) and temporal domain (snapshot) dimensions, thus completing the structured regularization of the interference signals. The originally scattered signal sample values are transformed into a matrix form that meets the requirements of covariance estimation and eigenvalue decomposition, eliminating the impact of signal dimension disorder on subsequent processing, greatly improving the efficiency and accuracy of interference feature extraction, and laying a regular data foundation for the separation of interference and noise.
[0021] Preferably, S3, obtaining a set of interference feature vectors based on the decomposition of the interference two-dimensional data matrix includes: Interference two-dimensional data matrix Statistical covariance matrix estimation is performed to obtain the mixed statistical covariance matrix of interference and noise. , , in, For the number of time-domain snapshots, This is a conjugate operation. For the number of receiving array elements, To interfere with the two-dimensional data matrix; The interference-noise mixture statistical covariance matrix It can be decomposed into a superposition of interference energy characteristic terms and noise energy characteristic terms, expressed as: , in, The interference energy characteristic term represents the energy and characteristic distribution of the interference signal in the covariance matrix. The noise energy feature term represents the energy and characteristic distribution of the background noise in the covariance matrix. For the number of large eigenvalues, For the first Large eigenvalues, For the first The eigenvectors corresponding to the large eigenvalues represent the feature space formed by the interference. These are the eigenvalues corresponding to the noise; Extracting large eigenvalues The corresponding set of interference feature vectors The set of interference feature vectors consists of the feature vectors corresponding to the interference.
[0022] In the above embodiments, by estimating the covariance matrix and decomposing the eigenvalues, the set of interference feature vectors is separated from the interference-noise mixed signal, thus achieving accurate extraction of interference features. The feature space of interference and noise is clearly distinguished by the difference in eigenvalue energy. The extracted set of interference feature vectors becomes the core template for subsequent interference suppression, enabling the system to accurately identify the spatial features of interference. This provides a key basis for constructing targeted interference suppression constraints and ensures that the subsequent optimization weight vectors can accurately target the interference suppression objective.
[0023] Preferably, S4, constructing a target signal model corresponding to the target signal, and constructing a mixed signal matrix corresponding to the mixed signal detected on the specified channel based on the interference signal model and the target signal model, including: Based on the propagation characteristics of array signals, the target signal model formed at each array element after propagation in the spatial domain is as follows: , in, The array steering vector for the t-th target is expressed as: , in, This is the conjugate transpose operation. The phase term in the steering vector, which is an imaginary unit, reflects the difference in propagation delay of the target signal between different array elements. A hybrid signal matrix corresponding to the hybrid signal detected on a specified channel is constructed based on the interference signal model and the target signal model. The expression is: , in, for A background noise matrix, where each element represents the noise sample value of each array element at the corresponding snapshot time, and a mixed signal matrix. The corresponding row Each receiving array element (spatial dimension), corresponding to the column. Each time-domain snapshot (time-domain dimension), matrix elements Indicates the first The array element in the first The mixed signal sample values captured in a single snapshot. , ; Arrange the mixed signals into a structured matrix form according to the spatial and temporal dimensions to obtain the mixed signal matrix: .
[0024] In the above embodiments, a target signal model is constructed and an interference signal model is fused to form a hybrid signal matrix containing the target, interference, and noise, which fully represents the signal composition of the actual detection scenario. It comprehensively covers all kinds of signal components in the detection scenario, avoids the deviation of a single signal model from the actual environment, and provides input data that is close to the real scenario for subsequent covariance matrix estimation. This ensures that the subsequent weight vector solution can be based on the complete signal distribution law and achieve a balance between interference suppression and target preservation.
[0025] Preferably, S5, estimating the covariance matrix based on the mixed signal matrix, includes: After performing a conjugate transpose operation on the mixed signal matrix, multiplying it with the mixed signal matrix and then dividing by the number of time-domain snapshots. To reduce estimation errors, the mixed signal covariance matrix is obtained, expressed as: , in, For the number of receiving array elements, For the number of time-domain snapshots, This is the conjugate transpose operation of a matrix. For mixed signal matrix The conjugate transpose of .
[0026] In the above embodiments, the covariance matrix is estimated based on the mixed signal matrix, which condenses the spatial-temporal statistical characteristics of the mixed signal. The estimation error is reduced by the time averaging method. The obtained covariance matrix fully reflects the energy distribution and correlation of the target, interference and noise, providing core statistical data support for the establishment of subsequent optimization constraints. This enables the solution of the optimal weight vector to conform to the actual statistical law of the mixed signal, and improves the accuracy of interference suppression and target gain.
[0027] Preferably, S6 involves constructing a target guidance vector for the target signal based on the search direction, establishing optimization constraints based on the target guidance vector, the interference features, and the covariance matrix R, solving the optimization constraints to obtain an intermediate weight vector, and calculating the optimal weight vector based on the intermediate weight vector and the target guidance vector, including: When searching for a signal in a certain direction, the search direction angle is set to... Based on search direction angle Number of receiving array elements Signal wavelength and array spacing By combining the spatial propagation characteristics of array signals, the target steering vector of the target signal is constructed. , is represented as: , in, This is a matrix transpose operation. The imaginary unit; Based on the target guidance vector And the set of interference feature vectors, construct a constraint matrix, the dimension of which is The expression is: Construct the target vector The target vector The dimension is ( The expression is: , in, This represents the conjugate transpose operation; exist When performing target detection in the search direction, the mixed signal covariance matrix is used. Based on this, establish a weighted vector To optimize the constrained optimization problem of the variables, specifically: , The objective function is to minimize the gain of the weight vector on the mixed signal, and the constraints limit the response characteristics of the weight vector to the target steering vector and the interference feature vector. Solve the constrained optimization problem to obtain the intermediate weight vector. The expression is: , in, for The target signal vector in the search direction For matrix The inverse matrix; Based on the target guidance vector With intermediate weight vector The optimal weight vector is calculated. The expression is: .
[0028] In the above embodiments, optimization constraints are constructed based on the target steering vector, interference features, and covariance matrix, and the optimal weight vector is solved to achieve a dual-objective balance of "maximizing target gain and maximizing interference suppression". By limiting the weight vector's gain to the target and its zero response to interference through constraint conditions, the optimal weight vector obtained has the characteristics of "main lobe aligned with target and null align with interference", which fundamentally solves the contradiction between interference suppression and target preservation in traditional methods and greatly improves the signal-to-interference-plus-noise ratio of the target signal.
[0029] Preferably, S7, weighting the mixed signals in the mixed signal matrix based on the optimal weight vector and outputting the target signal after interference suppression, includes: Using the optimal weight vector as the weighting coefficient and the mixed signal matrix as the processing object, and based on the conjugate transpose weighting method, the optimal weight vector and the mixed signal matrix are multiplied to obtain the weighted output signal in the time domain. , , in, The optimal weight vector The conjugate transpose of the matrix is used to perform an inner product operation between the weight vector and each column of the mixed signal matrix, and the output is... for 3D row vector, elements Indicates the first The signal value after weighted processing of each snapshot moment ; Extract weighted output signal The effective time-domain data is calibrated for signal amplitude and then output as the target signal after interference suppression.
[0030] In the above embodiments, the optimal weight vector is used to perform conjugate transpose weighting on the mixed signal to achieve interference suppression and target signal output. During the weighting operation, the target signal is coherently superimposed, the interference signal is inversely canceled, and the noise is attenuated. While effectively suppressing interference energy, the key information such as the amplitude, frequency, and phase of the target signal is fully preserved. The output target signal can be directly used for subsequent demodulation and identification, significantly improving the practical performance of the broadband array electronic detection system.
[0031] This invention conducts systematic verification by building a simulation environment that closely resembles actual application scenarios, fully demonstrating the effectiveness and superiority of the proposed data-adaptive broadband array electronic detection anti-interference method.
[0032] The simulation uses a ground-based uniform linear array as the broadband receiving array for the electronic reconnaissance system, with 32 array elements and an element spacing of λ / 2 (λ being the signal wavelength; this spacing effectively avoids grating lobes and ensures spatial resolution). It specifically simulates the anti-jamming reconnaissance effect of a single-frequency target signal under three targeted jamming signals. The target signal frequency is 450MHz, with an azimuth angle of 60 degrees relative to the receiving array. The parameters of the three jamming signals are: jamming 1 frequency 430MHz, azimuth angle 20 degrees; jamming 2 frequency 410MHz, azimuth angle 30 degrees; and jamming 3 frequency 390MHz, azimuth angle 45 degrees. All three jamming signals have a bandwidth of 20MHz, and the simulation sampling frequency is set to 2GHz to meet the requirements of broadband signal processing.
[0033] Figure 3The result of the interference eigenvalue space decomposition shows that there are three significant large eigenvalues and corresponding eigenvectors in the interference region, which clearly reflects the independent and identically distributed characteristics among the interference sources. This verifies the rationality of constructing the interference signal subspace through the eigenvectors corresponding to the large eigenvalues in this invention, and that accurate suppression can be achieved without prior direction finding of the interference. Figure 4 The signal spectrum calculated using traditional methods shows strong interference signals in the 20°, 30°, and 45° directions. These interferences severely affect the identification of target signals in the 60° direction, highlighting the limitations of traditional methods. Figure 5 The target signal spectrum calculated by the scheme of this embodiment shows that all three interference signals are effectively suppressed, and the target signal in the 60-degree direction is clearly distinguishable. The entire calculation process does not require prior direction finding of the interference signals, which is fundamentally different from traditional interference suppression methods. It is based entirely on the received data and completes adaptive processing autonomously. From the quantitative results, the interference suppression depth of the method proposed in this invention can reach more than 10dB, which significantly improves the stability and reliability of electronic detection systems in complex interference environments while ensuring the integrity of the target signal.
[0034] Example 2: As Figure 6 As shown, this embodiment of the invention also provides a broadband array electronic detection anti-interference device, comprising: The interference modeling module is used to define the core parameters of the signal receiving carrier, and to build a model of the interference signal based on the core parameters to obtain the interference signal model. The two-dimensional arrangement module for interference signals is used to arrange interference signals in two dimensions, spatial and temporal domains, based on the interference signal model, to form a two-dimensional interference data matrix. An interference feature vector extraction module is used to obtain a set of interference feature vectors based on the decomposition of the interference two-dimensional data matrix. The hybrid matrix construction module is used to construct the target signal model corresponding to the target signal, and to construct the hybrid signal matrix corresponding to the hybrid signal detected on the specified channel based on the interference signal model and the target signal model. A mixed-signal covariance estimation module is used to estimate the covariance matrix based on the mixed-signal matrix; The optimal weight solution module is used to construct a target guidance vector of the target signal based on the search direction, establish optimization constraints based on the target guidance vector, the set of interference feature vectors and the covariance matrix, solve the optimization constraints to obtain an intermediate weight vector, and calculate the optimal weight vector based on the intermediate weight vector and the target guidance vector. The target signal output module is used to perform weighted processing on the mixed signals in the mixed signal matrix based on the optimal weight vector, and output the target signal after suppressing interference.
[0035] Example 3: This embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the broadband array electronic detection anti-interference method as described above.
[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0037] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0038] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
[0039] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.
[0040] Furthermore, the functional units in the various embodiments of the present invention 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.
[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A broadband array electronic detection anti-interference method, characterized in that, Includes the following steps: S1. Define the core parameters of the signal receiving carrier, and construct a model of the interference signal based on the core parameters to obtain the interference signal model; S2. Based on the interference signal model, the interference signal is arranged in two dimensions in the spatial and temporal domains to form a two-dimensional interference data matrix; S3. Obtain the set of interference feature vectors based on the decomposition of the interference two-dimensional data matrix; S4. Construct the target signal model corresponding to the target signal, and construct the mixed signal matrix corresponding to the mixed signal detected on the specified channel based on the interference signal model and the target signal model; S5. Estimate the covariance matrix based on the mixed signal matrix; S6. Construct a target guidance vector for the target signal based on the search direction, and establish optimization constraints based on the target guidance vector, the set of interference feature vectors and the covariance matrix, and solve the optimization constraints to obtain an intermediate weight vector. Calculate the optimal weight vector based on the intermediate weight vector and the target guidance vector. S7. Based on the optimal weight vector, perform weighted processing on the mixed signals in the mixed signal matrix, and output the target signal after suppressing interference.
2. The broadband array electronic detection anti-interference method according to claim 1, characterized in that, S1. Define the core parameters of the signal receiving carrier, and construct a model of the interference signal based on the core parameters to obtain the interference signal model, including: A one-dimensional uniform linear array is used as the signal receiving carrier. The core parameters of the signal receiving carrier are defined, including the number of receiving array elements. Number of time-domain snapshots Signal wavelength Array spacing Number of interferences , No. The baseband of the interference and direction angle Based on the core parameters, a model of the interference signal is constructed to obtain the interference signal model, which is as follows: 。 3. The broadband array electronic detection anti-interference method according to claim 2, characterized in that, S2. Based on the interference signal model, the interference signal is arranged in two dimensions in the spatial and temporal domains to form a two-dimensional interference data matrix, including: Based on the defined number of receiver array elements And the number of time-domain snapshots Based on the interference signal model, the dispersed interference signals are arranged in two dimensions: spatial and temporal. The rows of the matrix correspond to the spatial dimension, with each row representing one receiving array element, and the columns correspond to the temporal dimension, with each column representing one temporal snapshot, forming a two-dimensional interference data matrix. , represented as: ， Among them, matrix elements Indicates the first The array element in the first The interference signal sample values received at each snapshot moment are calculated using an interference signal model. , .
4. The broadband array electronic detection anti-interference method according to claim 3, characterized in that, S3. Based on the decomposition of the interference two-dimensional data matrix, a set of interference feature vectors is obtained, including: Interference two-dimensional data matrix Statistical covariance matrix estimation is performed to obtain the mixed statistical covariance matrix of interference and noise. , ， in, For the number of time-domain snapshots, This is a conjugate operation. For the number of receiving array elements, To interfere with the two-dimensional data matrix; The interference-noise mixture statistical covariance matrix It can be decomposed into a superposition of interference energy characteristic terms and noise energy characteristic terms, expressed as: ， in, For the interference energy characteristic term, For noise energy characteristic terms, For the number of large eigenvalues, For the first Large eigenvalues, For the first The eigenvectors corresponding to the large eigenvalues represent the feature space formed by the interference. These are the eigenvalues corresponding to the noise; Extracting large eigenvalues The corresponding set of interference feature vectors The set of interference feature vectors consists of the feature vectors corresponding to the interference.
5. The broadband array electronic detection anti-interference method according to claim 4, characterized in that, S4. Construct the target signal model corresponding to the target signal, and based on the interference signal model and the target signal model, construct the mixed signal matrix corresponding to the mixed signal detected on the specified channel, including: Based on the propagation characteristics of array signals, the target signal model formed at each array element after propagation in the spatial domain is as follows: ， in, The array steering vector for the t-th target is expressed as: ， in, This is the conjugate transpose operation. The phase term in the steering vector, which is an imaginary unit, reflects the difference in propagation delay of the target signal between different array elements. A hybrid signal matrix corresponding to the hybrid signal detected on a specified channel is constructed based on the interference signal model and the target signal model. The expression is: ， in, for A background noise matrix, where each element represents the noise sample value of each array element at the corresponding snapshot time, and a mixed signal matrix. The corresponding row Each receiving array element corresponds to a column. A time-domain snapshot, matrix elements Indicates the first The array element in the first The mixed signal sample values captured in a single snapshot. , ; Arrange the mixed signals into a structured matrix form according to the spatial and temporal dimensions to obtain the mixed signal matrix: 。 6. The broadband array electronic detection anti-interference method according to claim 5, S5, estimating the covariance matrix based on the mixed signal matrix, includes: After performing a conjugate transpose operation on the mixed signal matrix, the product operation is performed with the mixed signal matrix and then divided by the number of time-domain snapshots. To reduce estimation error, the covariance matrix is obtained, expressed as: ， in, For the number of receiving array elements, For the number of time-domain snapshots, This is the conjugate transpose operation of a matrix. For mixed signal matrix The conjugate transpose of .
7. The broadband array electronic detection anti-interference method according to claim 6, S6, constructing a target steering vector of the target signal based on the search direction, establishing optimization constraints based on the target steering vector, the interference characteristics, and the covariance matrix R, solving the optimization constraints to obtain an intermediate weight vector, and calculating the optimal weight vector based on the intermediate weight vector and the target steering vector, including: When searching for a signal in a certain direction, the search direction angle is set to... Based on search direction angle Number of receiving array elements Signal wavelength and array spacing By combining the spatial propagation characteristics of array signals, the target steering vector of the target signal is constructed. , represented as: ， in, This is a matrix transpose operation. The imaginary unit; Based on the target guidance vector And the set of interference feature vectors, construct a constraint matrix, the dimension of which is The expression is: Construct the target vector The target vector The dimension is ( The expression is: , in, This represents the conjugate transpose operation; exist When performing target detection in the search direction, the mixed signal covariance matrix is used. Based on this, establish a weighted vector To optimize the constrained optimization problem of the variables, specifically: ， The objective function is to minimize the gain of the weight vector on the mixed signal, and the constraints limit the response characteristics of the weight vector to the target steering vector and the interference feature vector. Solve the constrained optimization problem to obtain the intermediate weight vector. The expression is: ， in, for The target signal vector in the search direction For matrix The inverse matrix; Based on the target guidance vector With intermediate weight vector The optimal weight vector is calculated. The expression is: 。 8. The broadband array electronic detection anti-interference method according to claim 7, S7, weighting the mixed signals in the mixed signal matrix based on the optimal weight vector, and outputting the target signal after suppressing interference, includes: Using the optimal weight vector as the weighting coefficient and the mixed signal matrix as the processing object, and based on the conjugate transpose weighting method, the optimal weight vector and the mixed signal matrix are multiplied to obtain the weighted output signal in the time domain. , ， in, The optimal weight vector The conjugate transpose of the matrix is used to perform an inner product operation between the weight vector and each column of the mixed signal matrix, and the output is... for 3D row vector, elements Indicates the first The signal value after weighted processing of each snapshot moment ; Extract weighted output signal The effective time-domain data in the signal is calibrated for signal amplitude, and the target signal after interference suppression is output.
9. A broadband array electronic detection anti-interference device, characterized in that, include: The interference modeling module is used to define the core parameters of the signal receiving carrier, and to build a model of the interference signal based on the core parameters to obtain the interference signal model. The two-dimensional arrangement module for interference signals is used to arrange interference signals in two dimensions, spatial and temporal domains, based on the interference signal model, to form a two-dimensional interference data matrix. An interference feature vector extraction module is used to obtain a set of interference feature vectors based on the decomposition of the interference two-dimensional data matrix. The hybrid matrix construction module is used to construct the target signal model corresponding to the target signal, and to construct the hybrid signal matrix corresponding to the hybrid signal detected on the specified channel based on the interference signal model and the target signal model. A mixed-signal covariance estimation module is used to estimate the covariance matrix based on the mixed-signal matrix; The optimal weight solution module is used to construct a target guidance vector of the target signal based on the search direction, establish optimization constraints based on the target guidance vector, the set of interference feature vectors and the covariance matrix, solve the optimization constraints to obtain an intermediate weight vector, and calculate the optimal weight vector based on the intermediate weight vector and the target guidance vector. The target signal output module is used to perform weighted processing on the mixed signals in the mixed signal matrix based on the optimal weight vector, and output the target signal after suppressing interference.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the broadband array electronic detection anti-interference method as described in any one of claims 1 to 6.