Method and device for estimating and suppressing wideband distributed radar jamming waveform

Abstract

The application relates to a broadband distributed radar jamming waveform estimation and suppression method and device, wherein the method comprises the following steps: inputting a receiving sampling signal of a distributed radar, including a jamming signal, a target echo and receiving noise; constructing an observation matrix according to jamming parameters; converting a time-domain signal model of the distributed radar against multiple jamming into a frequency-domain model through Fourier transform; initializing the jamming parameters through design of orthogonal waveforms and cross-correlation analysis, optimizing the jamming signal, a complex coefficient matrix and the jamming parameters through inner and outer nested loops, realizing jamming waveform estimation; and removing the jamming signal after waveform estimation from the receiving signal, and completing jamming suppression. Therefore, the problems of invalidation of traditional space domain jamming suppression algorithms caused by jamming decorrelation of a current broadband distributed radar system are solved.

Description

Technical Field

[0001] This application relates to the field of radar signal processing technology, and in particular to a method and apparatus for estimating and suppressing broadband distributed radar interference waveforms. Background Technology

[0002] Modern radars face a severe threat from mainlobe jamming in electronic warfare scenarios. Traditional monostatic radar anti-jamming algorithms are ill-suited to complex mainlobe jamming environments due to the limited aperture of the array. Distributed radar, by leveraging multi-station collaboration to achieve spatial diversity, has become a research hotspot in the field of anti-jamming.

[0003] However, existing research on distributed radar anti-jamming mainly targets narrowband signals or far-field scenarios. In wideband signals or long-baseline distributed radar systems, the interference decorrelation problem is significant, leading to temporal envelope shift and frequency-domain phase change of the interference signal. This renders traditional methods based on the sampled covariance matrix (such as feature projection and pre-whitening algorithms) ineffective. These methods rely on the accurate alignment of the interference signal to estimate the covariance matrix. However, in multi-interference scenarios, the interference parameters are different, and simultaneous alignment cannot be achieved by simple time shifting. This results in significant deviations in the covariance matrix estimation and a substantial decrease in interference suppression performance.

[0004] Furthermore, real-world interference types are complex and diverse, such as noise interference and Interrupted Sampling Repeater Jamming (ISRJ). ISRJ forms deceptive interference by sampling radar signals and retransmitting them. Its time-frequency characteristics are highly similar to those of the target echo, making it difficult for existing technologies to effectively distinguish, and thus urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for estimating and suppressing broadband distributed radar interference waveforms, in order to solve the problem that traditional airspace interference suppression algorithms fail due to interference decorrelation in current broadband distributed radar systems.

[0006] The first aspect of this application provides a broadband distributed radar interference waveform estimation and suppression method, comprising the following steps: acquiring a received signal corresponding to at least one target under test through a target distributed radar, constructing a corresponding time-domain signal model based on the received signal, wherein the received signal includes an interference signal, a target echo, and a noise signal; performing a discrete Fourier transform on the time-domain signal model to obtain a corresponding frequency-domain signal model, and constructing a corresponding multi-parameter optimization problem based on the frequency-domain signal model; initializing multiple parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the multiple parameters to be optimized include an interference signal, a complex coefficient matrix, and interference parameters; performing a cyclic optimization operation on the multiple parameters to be optimized based on a preset nested cyclic optimization framework to obtain an interference signal with waveform estimation, and removing the interference signal with waveform estimation from the received signal.

[0007] Optionally, in one embodiment of this application, the step of acquiring the received signal corresponding to at least one target under test through a target-distributed radar, and constructing a corresponding time-domain signal model based on the received signal, wherein the received signal includes interference signal, target echo, and noise signal, includes: transmitting radar signal to each of the at least one target under test through each radar in the target-distributed radar, and receiving the target echo, the noise signal, and interference signals emitted by a plurality of preset interference sources corresponding to each target under test through each radar; characterizing the interference parameters through preset unknown parameters, and constructing a corresponding time-domain observation matrix based on the interference parameters; and constructing the time-domain signal model based on the target echo, the noise signal, the interference signal, and the time-domain observation matrix.

[0008] Optionally, in one embodiment of this application, performing a discrete Fourier transform on the time-domain signal model to obtain a corresponding frequency-domain signal model includes: performing a discrete Fourier transform on the time-domain signal model to obtain the interference signal, the complex coefficient matrix, the frequency-domain observation matrix, and the interference parameters; and constructing the frequency-domain signal model based on the interference signal, the complex coefficient matrix, the interference parameters, and the frequency-domain observation matrix.

[0009] Optionally, in one embodiment of this application, the initialization of multiple parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the multiple parameters to be optimized include an interference signal, a complex coefficient matrix, and interference parameters, includes: determining the random phase corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and initializing the complex coefficient matrix according to the random phase; calculating the cross-correlation spectrum between each radar, and marking all peaks in the cross-correlation spectrum in chronological order to obtain corresponding marked peaks, and randomly matching the marked peaks with the multiple interference sources to generate corresponding multiple matching combinations; based on a preset initialization strategy and a random frequency coding waveform strategy, the initialization of multiple parameters to be optimized in the multi-parameter optimization problem ..., and initializing the complex coefficient matrix according to the random phase; calculating the cross-correlation spectrum between each radar, and marking all peaks in the cross-correlation spectrum according to chronological order to obtain corresponding marked peaks, and randomly matching the marked peaks with the multiple interference sources to generate corresponding multiple matching combinations; based on a preset initialization strategy and a random frequency coding waveform strategy, the initialization of multiple parameters to be optimized in the multi-parameter optimization problem includes: determining the random phase corresponding to the complex coefficient matrix, and initializing the complex coefficient matrix according to the random phase; calculating the cross-correlation spectrum between each radar, and initializing the complex coefficient matrix according to the random phase; calculating the cross-correlation spectrum between each radar, and initializing the complex coefficient matrix according to the random phase; calculating the cross-correlation spectrum between each radar, and A time difference of arrival (TDOA) algorithm is used to locate the multiple interference sources to obtain the location result for each interference source, and the corresponding location variance is calculated based on the location result. The minimum location variance among the multiple interference sources is extracted, and the target matching combination corresponding to the minimum location variance among the multiple matching combinations is determined. Based on a preset set of frequency-coded waveforms, the waveform to be transmitted for each radar is determined, and the target waveform to be transmitted with the minimum peak value in all cross-correlation spectra and a peak-to-sidelobe ratio higher than a preset sidelobe ratio threshold is selected to initialize the interference parameters through the target matching combination and the target waveform to be transmitted. Based on the initialized interference parameters and complex coefficient matrix, the interference signal is initialized.

[0010] Optionally, in one embodiment of this application, the step of performing cyclic optimization operations on the plurality of parameters to be optimized based on a preset nested cyclic optimization framework to obtain an interference signal with waveform estimation, and removing the interference signal with waveform estimation from the received signal, includes: fixing the interference parameters during the inner loop optimization process in the nested cyclic optimization framework, and cyclically solving the closed-form solution of the interference signal and the complex coefficient matrix based on a preset minimum mean square error criterion; linearizing the interference parameters using a preset Taylor expansion strategy during the outer loop optimization process in the nested cyclic optimization framework to obtain a corresponding linearization result, and updating the interference parameters based on the linearization result and a preset Jacobian matrix; and cyclically performing the inner loop optimization process and the outer loop optimization process based on the nested cyclic optimization framework until the closed-form solution and the interference parameters meet a preset convergence requirement to generate the interference waveform estimation signal.

[0011] Optionally, in one embodiment of this application, the mathematical expression of the multi-parameter optimization problem is:

[0012]

[0013] Where A represents the complex coefficient matrix; x(f) represents the frequency domain signal model; z(f) represents the interference signal; H(f) represents the frequency domain observation matrix; Δτ represents the interference parameter; and f represents the frequency.

[0014] A second aspect of this application provides a broadband distributed radar interference waveform estimation and suppression device, comprising: a time-domain model construction module, configured to acquire a received signal corresponding to at least one target under test through a target distributed radar, and construct a corresponding time-domain signal model based on the received signal, wherein the received signal includes an interference signal, a target echo, and a noise signal; an optimization problem construction module, configured to perform a discrete Fourier transform on the time-domain signal model to obtain a corresponding frequency-domain signal model, and construct a corresponding multi-parameter optimization problem based on the frequency-domain signal model; a parameter initialization module, configured to initialize multiple parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the multiple parameters to be optimized include an interference signal, a complex coefficient matrix, and interference parameters; and an interference waveform estimation and suppression module, configured to perform cyclic optimization operations on the multiple parameters to be optimized based on a preset nested cyclic optimization framework to obtain a waveform-estimated signal corresponding to the interference signal, and remove the interference signal after interference waveform estimation from the received signal.

[0015] Optionally, in one embodiment of this application, the time-domain model construction module includes: a transmitting unit, configured to transmit radar signals to each of the at least one target under test through each radar in the target distributed radar, so as to receive the target echo, the noise signal, and the interference signal emitted by a plurality of preset interference sources corresponding to each target under test through each radar; a calculation unit, configured to characterize the interference parameters through preset unknown parameters, so as to construct a corresponding time-domain observation matrix based on the interference parameters; and a first modeling unit, configured to construct the time-domain signal model based on the target echo, the noise signal, the interference signal, and the time-domain observation matrix.

[0016] Optionally, in one embodiment of this application, the optimization problem construction module includes: a transformation unit, used to perform a discrete Fourier transform on the time-domain signal model to obtain the interference signal, the complex coefficient matrix, the frequency-domain observation matrix, and the interference parameters; and a second modeling unit, used to construct the frequency-domain signal model based on the interference signal, the complex coefficient matrix, the interference parameters, and the frequency-domain observation matrix.

[0017] Optionally, in one embodiment of this application, the parameter initialization module includes: a determination unit, configured to determine the random phase corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and initialize the complex coefficient matrix according to the random phase; a marking unit, configured to calculate the cross-correlation spectrum between each radar, and mark all peaks in the cross-correlation spectrum in chronological order to obtain corresponding marked peaks, and randomly match the marked peaks with the multiple interference sources to generate corresponding multiple matching combinations; and a positioning unit, configured to locate the multiple interference sources based on a preset time difference of arrival algorithm to obtain the positioning value corresponding to each interference source. The system generates positioning results and calculates the corresponding positioning variance based on the positioning results; an extraction unit is used to extract the minimum positioning variance among the multiple interference sources and determine the target matching combination corresponding to the minimum positioning variance among the multiple matching combinations; a matching unit is used to determine the waveform to be transmitted for each radar based on a preset set of frequency-coded waveforms, and select the target waveform to be transmitted with the minimum peak value in all cross-correlation spectra and a peak-to-sidelobe ratio higher than a preset sidelobe ratio threshold, so as to initialize the interference parameters through the target matching combination and the target waveform to be transmitted; an initialization unit is used to initialize the interference signal based on the initialized interference parameters and complex coefficient matrix.

[0018] Optionally, in one embodiment of this application, the interference waveform estimation and suppression module includes: a solution unit, configured to fix the interference parameters during the inner loop optimization process in the nested loop optimization framework, and cyclically solve the closed-form solution of the interference signal and the complex coefficient matrix based on a preset minimum mean square error criterion; a linearization unit, configured to linearize the interference parameters using a preset Taylor expansion strategy during the outer loop optimization process in the nested loop optimization framework to obtain the corresponding linearization result, and update the interference parameters based on the linearization result and a preset Jacobian matrix; and an iterative optimization unit, configured to iterate the inner loop optimization process and the outer loop optimization process based on the nested loop optimization framework until the closed-form solution and the interference parameters meet a preset convergence requirement to generate the interference waveform estimation signal.

[0019] Optionally, in one embodiment of this application, the mathematical expression of the multi-parameter optimization problem is:

[0020]

[0021] Where A represents the complex coefficient matrix; x(f) represents the frequency domain signal model; z(f) represents the interference signal; H(f) represents the frequency domain observation matrix; Δτ represents the interference parameter; and f represents the frequency.

[0022] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the broadband distributed radar interference waveform estimation and suppression method as described in the above embodiments.

[0023] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the broadband distributed radar interference waveform estimation and suppression method described above.

[0024] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described broadband distributed radar interference waveform estimation and suppression method.

[0025] Therefore, the embodiments of this application have the following beneficial effects:

[0026] The embodiments of this application can acquire the received signal corresponding to at least one target under test through a target-distributed radar, and construct a corresponding time-domain signal model based on the received signal, wherein the received signal includes interference signal, target echo, and noise signal; perform a discrete Fourier transform on the time-domain signal model to obtain the corresponding frequency-domain signal model, and construct a corresponding multi-parameter optimization problem based on the frequency-domain signal model; initialize multiple parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the multiple parameters to be optimized include interference signal, complex coefficient matrix, and interference parameters; perform cyclic optimization operations on the multiple parameters to be optimized based on a preset inner and outer nested cyclic optimization framework to obtain the waveform-estimated interference signal, and remove the waveform-estimated interference signal from the received signal. This application can achieve robust suppression of multiple types of interference in broadband or long-baseline distributed radar systems that consider interference parameters, while retaining target echo information, and significantly improve the radar anti-jamming performance in complex scenarios. Therefore, it solves the problem of the failure of traditional spatial interference suppression algorithms caused by interference decorrelation in current broadband distributed radar systems.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0029] Figure 1 This is a flowchart of a broadband distributed radar interference waveform estimation and suppression method provided according to an embodiment of this application;

[0030] Figure 2 A schematic diagram of a process for initializing interference parameters by inter-radar cross-correlation spectrum peaks is provided as an embodiment of this application;

[0031] Figure 3 A waveform optimization diagram based on frequency-coded waveforms is provided as an embodiment of this application;

[0032] Figure 4 A schematic diagram of the execution logic of a broadband distributed radar interference waveform estimation and suppression method provided in one embodiment of this application;

[0033] Figure 5 A schematic diagram of pulse compression results after interference suppression for intermittent sampling interference is provided as an embodiment of this application;

[0034] Figure 6 A schematic diagram of pulse compression results after interference suppression for noise interference is provided as an embodiment of this application;

[0035] Figure 7 This is an example diagram of a broadband distributed radar interference waveform estimation and suppression device according to an embodiment of this application;

[0036] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0037] Among them, 10-wideband distributed radar interference waveform estimation and suppression device; 100-time domain model construction module, 200-optimization problem construction module, 300-parameter initialization module, 400-interference waveform estimation and suppression module; 801-memory, 802-processor, 803-communication interface. Detailed Implementation

[0038] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0039] The following describes a broadband distributed radar interference waveform estimation and suppression method and apparatus according to embodiments of this application with reference to the accompanying drawings. Addressing the problems mentioned in the background art, this application provides a broadband distributed radar interference waveform estimation and suppression method. In this method, a received signal corresponding to at least one target is acquired through a target distributed radar. A corresponding time-domain signal model is constructed based on the received signal, wherein the received signal includes interference signal, target echo, and noise signal. A discrete Fourier transform is performed on the time-domain signal model to obtain a corresponding frequency-domain signal model, and a corresponding multi-parameter optimization problem is constructed based on the frequency-domain signal model. Based on a preset initialization strategy and a random frequency coding waveform strategy, multiple parameters to be optimized in the multi-parameter optimization problem are initialized, wherein the multiple parameters to be optimized include interference signal, complex coefficient matrix, and interference parameters. Based on a preset nested cyclic optimization framework, cyclic optimization operations are performed on the multiple parameters to be optimized to obtain the interference signal after waveform estimation, and the interference signal after waveform estimation is removed from the received signal. This application can achieve robust suppression of multiple types of interference in broadband or long-baseline distributed radar systems considering different interference parameters, while retaining target echo information, significantly improving the radar anti-interference performance in complex scenarios. This solves the problem of traditional airspace interference suppression algorithms failing due to interference decorrelation in current broadband distributed radar systems.

[0040] Specifically, Figure 1 This is a flowchart illustrating a broadband distributed radar interference waveform estimation and suppression method provided in an embodiment of this application.

[0041] like Figure 1 As shown, the broadband distributed radar interference waveform estimation and suppression method includes the following steps:

[0042] In step S101, the received signal corresponding to at least one target is acquired by the target distributed radar, so as to construct a corresponding time-domain signal model based on the received signal. The received signal includes interference signal, target echo and noise signal.

[0043] The embodiments of this application first obtain the target echo, interference signal and receiver noise (i.e. noise signal) corresponding to each target under test by different radars in the distributed radar. Therefore, the embodiments of this application integrate the independent received noise of each radar to form a time-domain received signal. Then, the embodiments of this application can linearly combine the target echo, interference signal and receiver noise to construct a time-domain signal model of the distributed radar signal under multiple interferences.

[0044] Optionally, in one embodiment of this application, a received signal corresponding to at least one target under test is acquired through a target-distributed radar to construct a corresponding time-domain signal model based on the received signal. The received signal includes interference signal, target echo, and noise signal. The process includes: transmitting radar signal to each of the at least one target under test through each radar in the target-distributed radar, and receiving target echo, noise signal, and interference signal emitted by multiple preset interference sources corresponding to each target under test through each radar; characterizing interference parameters through preset unknown parameters to construct a corresponding time-domain observation matrix based on the interference parameters; and constructing a time-domain signal model based on the target echo, noise signal, interference signal, and time-domain observation matrix.

[0045] It should be noted that, in the embodiments of this application, each radar in the distributed radar can transmit radar signals to each target under test to receive target echoes, noise signals and interference signals emitted by interference sources, and construct a time-domain observation matrix considering interference parameters; then, the embodiments of this application can construct a time-domain signal model through target echoes, noise signals, interference signals and time-domain observation matrix.

[0046] As one possible implementation, this application embodiment assumes that the distributed radar system consists of I radars, the radar transmits a signal s(t); the number of observed targets is L, and the time delay for the i-th radar to receive the l-th target is γ. il The number of interference sources is J, and the interference signal of the j-th interference source is z. j (t), whose time delay to the i-th radar is τ. ij .

[0047] Based on the above parameters, the embodiments of this application can construct a time-domain signal model for distributed radar countering multi-interference scenarios, the mathematical expression of which is:

[0048]

[0049] Where, x i (t) represents the received signal of the i-th radar, n i (t) corresponds to the received noise, α ij and β il These are the complex coefficients of the interference signal and the target echo, respectively, Δτ ij =τ ij -τ 1j It is the relative time delay of the interference signal under radar 1 as a reference radar, i.e., the interference parameter.

[0050] Therefore, the embodiments of this application can construct a time-domain signal model for distributed radar to counter multiple interferences, thereby providing reliable data guidance and basis for the subsequent construction of the corresponding frequency-domain signal model.

[0051] In step S102, the time-domain signal model is subjected to a discrete Fourier transform to obtain the corresponding frequency-domain signal model, and a corresponding multi-parameter optimization problem is constructed based on the frequency-domain signal model.

[0052] Furthermore, embodiments of this application can transform the time-domain signal model to the frequency domain through Fourier transform to form a set of frequency-domain observation equations (i.e., a frequency-domain signal model) to characterize the phase changes caused by the interference parameters. Subsequently, embodiments of this application can establish a multi-parameter optimization problem concerning the interference parameters, the complex coefficients of the observation matrix (i.e., the complex coefficient matrix), and the interference signal based on the principle of minimum mean square interference signal waveform estimation error.

[0053] Optionally, in one embodiment of this application, performing a Discrete Fourier Transform on the time-domain signal model to obtain the corresponding frequency-domain signal model includes: performing a Discrete Fourier Transform on the time-domain signal model to obtain an interference signal, a complex coefficient matrix, a frequency-domain observation matrix, and interference parameters; and constructing a frequency-domain signal model based on the interference signal, the complex coefficient matrix, the interference parameters, and the frequency-domain observation matrix.

[0054] In the specific implementation process, the embodiments of this application can first use Discrete Fourier Transform (DFT) to convert the time-domain received signal to the frequency domain, as shown in the following equation:

[0055]

[0056] Subsequently, embodiments of this application stack all received signals into a matrix representation to obtain the corresponding frequency domain signal model, as shown in the following equation:

[0057]

[0058] Where H(f) represents the frequency domain observation matrix, and A represents the complex coefficient matrix. The observation matrix can vary with frequency.

[0059] Therefore, the embodiments of this application effectively ensure the construction of subsequent multi-parameter optimization problems by constructing a time-frequency domain signal model containing interference parameters.

[0060] Optionally, in one embodiment of this application, the mathematical expression for the multi-parameter optimization problem is:

[0061]

[0062] Where A represents the complex coefficient matrix; x(f) represents the frequency domain signal model; z(f) represents the interference signal; H(f) represents the frequency domain observation matrix; Δτ represents the interference parameter; and f represents the frequency.

[0063] In actual implementation, embodiments of this application can construct a multivariate optimization problem (i.e., a multi-parameter optimization problem) for interference waveform estimation based on the above-mentioned frequency domain signal model, as shown in the following equation:

[0064]

[0065] Where A represents the complex coefficient matrix; x(f) represents the frequency domain signal model; z(f) represents the interference signal; H(f) represents the frequency domain observation matrix; Δτ represents the interference parameter; and f represents the frequency.

[0066] It should be noted that the multi-parameter optimization problem in this application embodiment includes three parts of parameters: the interference signal z(f), the interference parameter Δτ, and the complex coefficients (i.e., the complex coefficient matrix) A of the interference signal. This optimization problem is a non-convex optimization problem. The optimization process depends on the initial values ​​of the parameters and the corresponding optimization method. Its purpose is to search for the optimal A, Δτ, z(f) to minimize the loss function, thereby realizing the waveform estimation of the interference signal.

[0067] In step S103, based on the preset initialization strategy and random frequency coding waveform strategy, multiple parameters to be optimized in the multi-parameter optimization problem are initialized, including the interference signal, complex coefficient matrix and interference parameters.

[0068] Subsequently, the embodiments of this application also need to combine the cross-correlation initialization strategy and the random frequency encoded waveform design to initialize the interference signal, complex coefficient matrix and interference parameters waiting to be optimized in the multi-parameter optimization problem, thereby facilitating the implementation of the subsequent inner and outer nested loop optimization process.

[0069] Optionally, in one embodiment of this application, based on a preset initialization strategy and a random frequency coding waveform strategy, multiple parameters to be optimized in a multi-parameter optimization problem are initialized, wherein the multiple parameters to be optimized include interference signals, complex coefficient matrices, and interference parameters. This includes: determining the random phase corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and initializing the complex coefficient matrix according to the random phase; calculating the cross-correlation spectrum between each radar, and marking all peaks in the cross-correlation spectrum in chronological order to obtain corresponding marked peaks, and randomly matching the marked peaks with multiple interference sources to generate corresponding multiple matching combinations; based on a preset initialization strategy and a random frequency coding waveform strategy, multiple parameters to be optimized in a multi-parameter optimization problem are initialized, wherein multiple parameters to be optimized include interference signals, complex coefficient matrices, and interference parameters. The proposed time difference of arrival algorithm is used to locate multiple interference sources to obtain the location result for each interference source, and to calculate the corresponding location variance based on the location result. The minimum location variance among multiple interference sources is extracted, and the target matching combination corresponding to the minimum location variance among multiple matching combinations is determined. Based on multiple preset frequency-coded waveforms, the waveform to be transmitted for each radar is determined, and the target waveform to be transmitted with the minimum peak value in all cross-correlation spectra and a peak-to-sidelobe ratio higher than a preset sidelobe ratio threshold is selected to initialize the interference parameters through target matching combination and target waveform to be transmitted. Based on the initialized interference parameters and complex coefficient matrix, the interference signal is initialized.

[0070] Those skilled in the art will understand that interference signals can take many forms, including noise interference and intermittent sampling-forwarding interference. Therefore, frequency domain signal models treat the interference signal z(f) as an unknown parameter to be estimated. In this case, embodiments of this application may not initialize z(f), but instead directly estimate z(f) using the initial values ​​of the other parameters to be optimized.

[0071] For a complex coefficient matrix A, each element α ij It is a complex value. Since the inherent phases of different radars may differ, and the phases of different interference signals will change after down-conversion, therefore α... ij The phase is usually unknown, and its amplitude |α ij | Corresponding energy of the received interference signal;|α ij |Depth of different paths and receiver gain η i Decision, that is

[0072] Δτ is the most critical parameter to be optimized, as it characterizes the spatial distribution of the interference source and directly determines the observation matrix as a function of frequency. Choosing a suitable initial value for Δτ helps to confine the problem to the correct local region. For a specific interference signal, the signal envelopes observed by different radars have similarities. Based on this, embodiments of this application can identify the peak value corresponding to the interference parameter in the cross-correlation spectrum by calculating the cross-correlation between the signals received by different radars.

[0073] Specifically, such as Figure 2 As shown, the embodiments of this application can calculate the cross-correlation spectrum between radar 1 and other radars; when all interference signals are independent of each other, J peaks will appear in the cross-correlation spectrum. However, due to the lack of prior knowledge about the interference signals, the correspondence between these peaks and the interference signals cannot be directly determined.

[0074] As one possible approach, this embodiment of the application can first randomly match peak values ​​with interference sources. Specifically, this embodiment of the application can label the peak values ​​of the cross-correlation spectrum between radar 2 and radar 1 as 1,...,J in chronological order to obtain labeled peak values; for the i-th receiving station, its correlation spectrum peak values ​​have J! possible matching combinations; taking radar 1 as a reference, I-1 cross-correlation results can be obtained. Since R 12 The peak value in (t) has been marked, so the total number of possible matching schemes is (J!). I-2 For each matching scheme, this embodiment of the application can use the time difference of arrival algorithm to locate J interference sources; finally, this embodiment of the application can select the scheme that minimizes the localization variance of all interference sources as the optimal match (i.e., the target matching combination), and based on the matching scheme (i.e., the target matching combination), obtain a rough estimate of the interference parameter Δτ.

[0075] To enhance the distinguishability of peaks in the cross-correlation spectrum, the waveform design should ensure that the cross-correlation between different interference signals approaches zero, meaning the interference signals should be as orthogonal as possible. Specifically, for example... Figure 3 As shown, the frequency-coded waveform has good waveform uncorrelation. In this embodiment, several sets of frequency-coded waveforms can be pre-designed as the waveform to be transmitted by the radar (i.e. the waveform to be transmitted). The target waveform to be transmitted with the smallest peak value in all cross-correlation spectra is selected, while ensuring that the peak sidelobe ratio of the transmitted waveform is higher than the preset sidelobe ratio threshold.

[0076] Therefore, embodiments of this application can use target matching combination and target to be transmitted waveform (i.e. orthogonal waveform) to initialize interference parameters, thereby realizing the initialization of interference parameters by designing orthogonal waveforms and cross-correlation analysis, and using the initialized interference parameters and complex coefficient matrix to initialize the interference signal.

[0077] It is understood that the embodiments of this application are based on a cross-correlation initialization strategy. The peak position is calculated by cross-correlation of signals between radars. The interference parameters of multiple interference sources are matched by combining the time difference of arrival localization algorithm to reduce the complexity of combination matching. By using random frequency coding waveform design, the orthogonality of different interference signals is improved, the cross-correlation peak of intermittent sampling and forwarding interference is suppressed, and the effective distinction between spectral peaks on the cross-correlation spectrum of the received signal is ensured, thereby initializing the interference signal, complex coefficient matrix and interference parameters.

[0078] In step S104, based on a preset nested loop optimization framework, multiple parameters to be optimized are cyclically optimized to obtain the interference signal after waveform estimation, and the interference signal after waveform estimation is removed from the received signal.

[0079] Therefore, the embodiments of this application can be based on a nested loop optimization algorithm. The inner loop fixes the disturbance parameters and combines them with the minimum mean square error criterion to solve the disturbance and complex coefficient matrix in a loop. The outer loop of the nested loop optimization algorithm fixes the disturbance and complex coefficient matrix and linearizes the disturbance parameters through Taylor expansion. The disturbance parameters are updated one step at a time, thereby iterating and optimizing until all parameters to be optimized converge.

[0080] Therefore, the embodiments of this application utilize nested loops to optimize the interference signal, complex coefficient matrix, and interference parameters, thereby achieving interference waveform estimation and removing the interference signal from the received signal, thus completing interference suppression.

[0081] It is understood that the embodiments of this application solve the problem of interference alignment caused by interference parameters by time-frequency domain signal modeling and inner and outer nested loop optimization, thereby improving the adaptability to complex interference types and providing a new approach for reliable target detection of distributed radar in strong interference environments.

[0082] Optionally, in one embodiment of this application, based on a preset nested loop optimization framework, multiple parameters to be optimized are iteratively optimized to obtain an interference signal with waveform estimation, and the interference signal with waveform estimation is removed from the received signal. This includes: fixing the interference parameters in the inner loop optimization process of the nested loop optimization framework, and iteratively solving the closed-form solution of the interference signal and the complex coefficient matrix based on a preset minimum mean square error criterion; linearizing the interference parameters using a preset Taylor expansion strategy in the outer loop optimization process of the nested loop optimization framework to obtain the corresponding linearization result, and updating the interference parameters based on the linearization result and a preset Jacobian matrix; and iteratively performing the inner loop optimization process and the outer loop optimization process based on the nested loop optimization framework until the closed-form solution and the interference parameters meet the preset convergence requirements to generate an interference waveform estimation signal.

[0083] Specifically, embodiments of this application can construct a nested loop optimization framework based on the established multi-parameter optimization problem and parameter initialization method. The inner and outer loop processes of this nested loop optimization framework are described below:

[0084] 1. Internal circulation:

[0085] With the interference parameter Δτ fixed, the interference signal z(f) and the complex coefficient matrix A are iteratively optimized; the closed-form solution is obtained using the Moore-Penrose pseudo-inverse through the minimum mean square error criterion.

[0086]

[0087] Subsequently, in this embodiment of the application, the estimated z(f) can be fixed, and A can be updated; based on the minimum mean square error criterion, the closed-form update expression of A can be obtained as follows:

[0088]

[0089] Where H = blkdiag(Z1,...,Z I ), z j A column vector is constructed for all frequency values ​​of the j-th interference signal.

[0090] Repeat the above operation until convergence, thus completing one inner loop.

[0091] 2. External circulation:

[0092] The embodiments of this application can linearize the interference parameters based on Taylor expansion and update the interference parameters Δτ through the Jacobian matrix to improve alignment accuracy.

[0093] In actual implementation, the embodiments of this application can make δ ij Indicates the initial Δτ ij The error between the true value and the actual value, due to δ ij The value is relatively small and can be obtained using a first-order Taylor expansion:

[0094]

[0095] Secondly, in the embodiments of this application, A and z(f) can be fixed, and the optimization problem can be further written as:

[0096]

[0097] in, And x has the same definition, Jacobian matrix Therefore, the update expression for the interference parameters can be obtained as follows:

[0098]

[0099] In the embodiments of this application, the outer loop is a one-step iteration, and the inner and outer loops are iterated repeatedly until all parameters converge to complete the interference waveform estimation.

[0100] Therefore, the embodiments of this application use a nested loop optimization algorithm to fix the interference parameters in the inner loop and solve the closed-form solution of the interference spectrum and observation matrix based on the minimum mean square error criterion to achieve waveform estimation of the interference signal. In the outer loop, Taylor expansion is used to linearize the interference parameters and the interference parameters are updated through the Jacobian matrix to improve the time-domain alignment accuracy of the interference signal, thereby effectively improving the accuracy of the frequency domain observation matrix.

[0101] Furthermore, embodiments of this application can effectively subtract interference signals from the received signal, that is:

[0102]

[0103] For the i-th receiver, the output after pulse compression can be expressed as the inverse discrete Fourier transform of the dot product of the spectrum of the received signal after interference suppression and the transmitted waveform, as shown in the following equation:

[0104]

[0105] In summary, the embodiments of this application take the received sampling signal input of the distributed radar, including the interference signal, target echo and received noise; construct the observation matrix according to the interference parameters; convert the time domain signal model of the distributed radar against multiple interferences into the frequency domain signal model through Fourier transform; optimize the interference spectrum, complex coefficients of the observation matrix and interference parameters by using nested loops; initialize the interference parameters by combining cross-correlation analysis and design orthogonal waveforms to improve the discrimination of interference on the cross-correlation spectrum.

[0106] Therefore, the embodiments of this application can address the problem of non-negligible differences in the envelope delay of interference signals in broadband or long-baseline distributed radar by using joint time-frequency modeling and cyclic algorithms to estimate the waveform of the interference signal, thereby eliminating interference from the original echo signal and achieving multi-interference suppression.

[0107] The execution logic and performance of the broadband distributed radar interference waveform estimation and suppression method of this application are explained below with reference to the accompanying drawings.

[0108] 1. Execution logic:

[0109] Figure 4 This diagram illustrates the execution effect of the broadband distributed radar interference waveform estimation and suppression method of this application. Figure 4 As shown, the execution process of the broadband distributed radar interference waveform estimation and suppression method of this application is as follows:

[0110] S401: Distributed radar receives jamming signals and target echoes, and constructs a time-domain signal model containing jamming decorrelation.

[0111] S402: Using the Discrete Fourier Transform, the time-domain signal model is converted into a frequency-domain signal model, and a multi-parameter optimization problem is constructed.

[0112] S403: Construct orthogonal waveforms based on frequency-coded waveforms, and preliminarily estimate interference parameters based on the spectral peaks of cross-correlation of different radar received signals;

[0113] S404: Based on the existing initialization parameters, nested loops are used to optimize the interference spectrum, complex coefficient matrix, and interference parameters to complete the interference waveform estimation.

[0114] S405: Eliminates multiple interference signals from the distributed radar received signals, thus completing interference suppression.

[0115] 2. Execution results:

[0116] For intermittent sampling interference, such as Figure 5 As shown, due to the existence of two deceptive interference sources, it is possible to... Figure 5 Multiple peaks were observed; the interference waveform estimation method based on this application can effectively suppress interference signals, improving the signal-to-interference-plus-noise ratio by nearly 30dB; compared with the feature projection algorithm, since it is difficult to align two interference signals simultaneously in the time domain, the simulation uses real interference parameters to align one of the interference signals (interference 1 or interference 2); Figure 5 As shown in the yellow and purple curves, the feature projection algorithm can only suppress aligned interference signals, but it is difficult to effectively suppress the remaining interference signals; for noise interference situations, such as... Figure 6 As shown, the feature projection algorithm cannot suppress two interference signals at the same time. The interference waveform estimation algorithm works effectively and achieves a signal-to-interference-plus-noise ratio improvement of 30dB.

[0117] The broadband distributed radar interference waveform estimation and suppression method proposed in this application involves acquiring the received signal corresponding to at least one target using a target-distributed radar system. A corresponding time-domain signal model is constructed based on the received signal, which includes interference signals, target echoes, and noise signals. A discrete Fourier transform is performed on the time-domain signal model to obtain the corresponding frequency-domain signal model. A multi-parameter optimization problem is then constructed based on the frequency-domain signal model. Multiple parameters to be optimized in the multi-parameter optimization problem are initialized based on a preset initialization strategy and a random frequency coding waveform strategy. These multiple parameters include interference signals, complex coefficient matrices, and interference parameters. Based on a preset nested cyclic optimization framework, cyclic optimization operations are performed on the multiple parameters to be optimized to obtain the waveform-estimated interference signal. The waveform-estimated interference signal is then removed from the received signal. This application enables robust suppression of multiple types of interference in broadband or long-baseline distributed radar systems considering varying interference parameters, while preserving target echo information, significantly improving radar anti-interference performance in complex scenarios.

[0118] Secondly, the broadband distributed radar interference waveform estimation and suppression device according to the embodiments of this application is described with reference to the accompanying drawings.

[0119] Figure 7 This is a block diagram of a broadband distributed radar interference waveform estimation and suppression device according to an embodiment of this application.

[0120] like Figure 7 As shown, the broadband distributed radar interference waveform estimation and suppression device 10 includes: a time-domain model construction module 100, an optimization problem construction module 200, a parameter initialization module 300, and an interference waveform estimation and suppression module 400.

[0121] The time-domain model construction module 100 is used to acquire the received signal corresponding to at least one target under test through the target distributed radar, so as to construct the corresponding time-domain signal model based on the received signal. The received signal includes interference signal, target echo and noise signal.

[0122] The optimization problem construction module 200 is used to perform discrete Fourier transform on the time-domain signal model to obtain the corresponding frequency-domain signal model, and to construct the corresponding multi-parameter optimization problem based on the frequency-domain signal model.

[0123] The parameter initialization module 300 is used to initialize multiple parameters to be optimized in a multi-parameter optimization problem based on a preset initialization strategy and a random frequency encoded waveform strategy. The multiple parameters to be optimized include interference signals, complex coefficient matrices, and interference parameters.

[0124] The interference waveform estimation and suppression module 400 is used to perform cyclic optimization operations on multiple parameters to be optimized based on a preset nested cyclic optimization framework to obtain the interference signal after waveform estimation, and to remove the interference signal after waveform estimation from the received signal.

[0125] Optionally, in one embodiment of this application, the time-domain model construction module 100 includes: a transmission unit, a calculation unit, and a first modeling unit.

[0126] The transmitting unit is used to transmit radar signals to each of at least one target in the target distributed radar through each radar in the target distributed radar, so as to receive the target echo, noise signal and interference signals transmitted by multiple preset interference sources corresponding to each target through each radar.

[0127] The computational unit is used to characterize the interference parameters using preset unknown parameters, so as to construct the corresponding time-domain observation matrix based on the interference parameters.

[0128] The first modeling unit is used to construct a time-domain signal model based on the target echo, noise signal, interference signal, and time-domain observation matrix.

[0129] Optionally, in one embodiment of this application, the optimization problem construction module 200 includes: a transformation unit and a second modeling unit.

[0130] The transformation unit is used to perform discrete Fourier transform on the time-domain signal model to obtain the interference signal, complex coefficient matrix, frequency domain observation matrix, and interference parameters.

[0131] The second modeling unit is used to construct a frequency domain signal model based on the interference signal, complex coefficient matrix, interference parameters, and frequency domain observation matrix.

[0132] Optionally, in one embodiment of this application, the parameter initialization module 300 includes: a determination unit, a marking unit, a positioning unit, an extraction unit, a matching unit, and an initialization unit.

[0133] The determining unit is used to determine the random phase corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and to initialize the complex coefficient matrix according to the random phase.

[0134] The marking unit is used to calculate the cross-correlation spectrum between each radar, and to mark all the peaks in the cross-correlation spectrum in chronological order to obtain the corresponding marked peaks. The marked peaks are then randomly matched with multiple interference sources to generate various matching combinations.

[0135] The positioning unit is used to locate multiple interference sources based on a preset time difference of arrival algorithm, so as to obtain the positioning result corresponding to each interference source, and calculate the corresponding positioning variance based on the positioning result.

[0136] The extraction unit is used to extract the minimum localization variance among multiple interference sources and determine the target matching combination corresponding to the minimum localization variance among multiple matching combinations.

[0137] The matching unit is used to determine the waveform to be transmitted for each radar based on multiple preset frequency-coded waveforms, and select the target waveform to be transmitted that has the smallest peak value in all cross-correlation spectra and whose peak sidelobe ratio is higher than the preset sidelobe ratio threshold, so as to initialize the interference parameters through target matching combination and target waveform to be transmitted.

[0138] The initialization unit is used to initialize the interference signal based on the initialized interference parameters and complex coefficient matrix.

[0139] Optionally, in one embodiment of this application, the interference waveform estimation and suppression module 400 includes: a solution unit, a linearization unit, and an iterative optimization unit.

[0140] The solution unit is used to fix the interference parameters in the inner loop optimization process of the nested loop optimization framework, and to solve the closed-form solution of the interference signal and complex coefficient matrix in a loop based on the preset minimum mean square error criterion.

[0141] The linearization unit is used to linearize the interference parameters during the outer loop optimization process in the nested loop optimization framework using a preset Taylor expansion strategy to obtain the corresponding linearization result, and update the interference parameters based on the linearization result and the preset Jacobian matrix.

[0142] The iterative optimization unit is used to generate an estimated interference waveform signal based on a nested loop optimization framework, performing both inner and outer loop optimization processes until the closed-form solution and interference parameters meet the preset convergence requirements.

[0143] Optionally, in one embodiment of this application, the mathematical expression for the multi-parameter optimization problem is:

[0144]

[0145] Where A represents the complex coefficient matrix; x(f) represents the frequency domain signal model; z(f) represents the interference signal; H(f) represents the frequency domain observation matrix; Δτ represents the interference parameter; and f represents the frequency.

[0146] It should be noted that the foregoing explanation of the broadband distributed radar interference waveform estimation and suppression method embodiment also applies to the broadband distributed radar interference waveform estimation and suppression device of this embodiment, and will not be repeated here.

[0147] The broadband distributed radar interference waveform estimation and suppression device proposed in this application includes a time-domain model construction module 100, used to acquire the received signal corresponding to at least one target under test through a target distributed radar, and to construct a corresponding time-domain signal model based on the received signal, wherein the received signal includes interference signal, target echo and noise signal; an optimization problem construction module 200, used to perform discrete Fourier transform on the time-domain signal model to obtain the corresponding frequency-domain signal model, and to construct a corresponding multi-parameter optimization problem based on the frequency-domain signal model; a parameter initialization module 300, used to initialize multiple parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the multiple parameters to be optimized include interference signal, complex coefficient matrix and interference parameters; and an interference waveform estimation and suppression module 400, used to perform cyclic optimization operation on multiple parameters to be optimized based on a preset inner and outer nested cyclic optimization framework to obtain the interference signal after waveform estimation, and to remove the interference signal after waveform estimation from the received signal. This application achieves effective suppression of various types of interference, such as noise interference and slice forwarding interference, through signal acquisition, waveform design, initialization, and interference waveform estimation and suppression operations, thereby improving the radar anti-interference performance in complex scenarios.

[0148] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0149] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0150] When the processor 802 executes the program, it implements the broadband distributed radar interference waveform estimation and suppression method provided in the above embodiments.

[0151] Furthermore, electronic devices also include:

[0152] Communication interface 803 is used for communication between memory 801 and processor 802.

[0153] The memory 801 is used to store computer programs that can run on the processor 802.

[0154] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0155] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0156] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0157] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0158] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described broadband distributed radar interference waveform estimation and suppression method.

[0159] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described broadband distributed radar interference waveform estimation and suppression method.

[0160] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0161] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0162] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0163] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0164] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0165] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0167] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A broadband distributed radar interference waveform estimation and suppression method, characterized in that, Includes the following steps: The received signal corresponding to at least one target is acquired by a target-distributed radar, and a corresponding time-domain signal model is constructed based on the received signal. The received signal includes interference signal, target echo and noise signal. The time-domain signal model is subjected to a discrete Fourier transform to obtain the corresponding frequency-domain signal model, and a corresponding multi-parameter optimization problem is constructed based on the frequency-domain signal model. Based on a preset initialization strategy and a random frequency encoded waveform strategy, multiple parameters to be optimized in the multi-parameter optimization problem are initialized, wherein the multiple parameters to be optimized include an interference signal, a complex coefficient matrix, and interference parameters. Based on a preset nested loop optimization framework, loop optimization operations are performed on the multiple parameters to be optimized to obtain the interference signal after waveform estimation, and the interference signal after waveform estimation is removed from the received signal. Specifically, based on a preset initialization strategy and a random frequency encoded waveform strategy, multiple parameters to be optimized in the multi-parameter optimization problem are initialized. These multiple parameters to be optimized include an interference signal, a complex coefficient matrix, and interference parameters, including: Determine the random phase corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and initialize the complex coefficient matrix according to the random phase; Calculate the cross-correlation spectrum between each radar, and mark all peaks in the cross-correlation spectrum in chronological order to obtain the corresponding marked peaks. Then, randomly match the marked peaks with multiple interference sources to generate various matching combinations. Based on a preset time difference of arrival algorithm, the multiple interference sources are located to obtain the location result corresponding to each interference source, and the corresponding location variance is calculated based on the location result. Extract the minimum localization variance among the multiple interference sources, and determine the target matching combination corresponding to the minimum localization variance among the multiple matching combinations; Based on multiple preset frequency-coded waveforms, the waveform to be transmitted for each radar is determined, and the target waveform to be transmitted with the smallest peak value in all cross-correlation spectra and a peak-to-sidelobe ratio higher than a preset sidelobe ratio threshold is selected, so as to initialize the interference parameters by the target matching combination and the target waveform to be transmitted. The interference signal is initialized based on the initialized interference parameters and complex coefficient matrix.

2. The method according to claim 1, characterized in that, The method involves acquiring received signals corresponding to at least one target using a distributed radar system, and constructing a corresponding time-domain signal model based on the received signals. The received signals include interference signals, target echoes, and noise signals, including: Each of the target distributed radars transmits radar signals to each of the at least one target to be tested, so as to receive the target echo, the noise signal and the interference signals emitted by a plurality of preset interference sources corresponding to each target to be tested through each radar. The interference parameters are characterized by preset unknown parameters, and a corresponding time-domain observation matrix is ​​constructed based on the interference parameters. The time-domain signal model is constructed based on the target echo, the noise signal, the interference signal, and the time-domain observation matrix.

3. The method according to claim 2, characterized in that, The step of performing a discrete Fourier transform on the time-domain signal model to obtain the corresponding frequency-domain signal model includes: Perform a Discrete Fourier Transform on the time-domain signal model to obtain the interference signal, complex coefficient matrix, frequency domain observation matrix, and interference parameters corresponding to the interference signal; The frequency domain signal model is constructed based on the interference signal, the complex coefficient matrix, the interference parameters, and the frequency domain observation matrix.

4. The method according to claim 1, characterized in that, The method, based on a preset nested loop optimization framework, performs loop optimization operations on the multiple parameters to be optimized to obtain the waveform-estimated interference signal, and removes the waveform-estimated interference signal from the received signal, including: In the inner loop optimization process of the nested loop optimization framework, the interference parameters are fixed, and the closed-form solution of the interference signal and the complex coefficient matrix is ​​solved cyclically based on the preset minimum mean square error criterion. In the outer loop optimization process of the nested loop optimization framework, a preset Taylor expansion strategy is used to linearize the interference parameters to obtain the corresponding linearization result, and the interference parameters are updated based on the linearization result and the preset Jacobian matrix. Based on the aforementioned nested loop optimization framework, the inner loop optimization process and the outer loop optimization process are iterated until the closed-form solution and the interference parameters meet the preset convergence requirements, so as to generate the interference waveform estimation signal.

5. The method according to claim 3, characterized in that, The mathematical expression for the multi-parameter optimization problem is: in, Represents the complex coefficient matrix; This represents the frequency domain signal model; This refers to the interference signal; Represents the frequency domain observation matrix; Indicates the interference parameter; Indicates frequency.

6. A broadband distributed radar interference waveform estimation and suppression device, characterized in that, include: The time-domain model construction module is used to acquire the received signal corresponding to at least one target under test through the target distributed radar, so as to construct a corresponding time-domain signal model based on the received signal, wherein the received signal includes interference signal, target echo and noise signal; The optimization problem construction module is used to perform discrete Fourier transform on the time-domain signal model to obtain the corresponding frequency-domain signal model, and to construct the corresponding multi-parameter optimization problem based on the frequency-domain signal model. The parameter initialization module is used to initialize multiple parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency encoded waveform strategy. The multiple parameters to be optimized include an interference signal, a complex coefficient matrix, and interference parameters. The interference waveform estimation and suppression module is used to perform cyclic optimization operations on the multiple parameters to be optimized based on a preset nested cyclic optimization framework to obtain the interference signal after waveform estimation, and to remove the interference signal after waveform estimation from the received signal. The parameter initialization module includes: A determining unit is used to determine the random phase corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and initialize the complex coefficient matrix according to the random phase; The marking unit is used to calculate the cross-correlation spectrum between each radar, and to mark all the peaks in the cross-correlation spectrum in chronological order to obtain the corresponding marked peaks. The marked peaks are then randomly matched with multiple interference sources to generate various matching combinations. The positioning unit is used to locate the multiple interference sources based on a preset time difference of arrival algorithm, so as to obtain the positioning result corresponding to each interference source, and calculate the corresponding positioning variance based on the positioning result. An extraction unit is used to extract the minimum localization variance among the multiple interference sources and determine the target matching combination corresponding to the minimum localization variance among the multiple matching combinations; The matching unit is used to determine the waveform to be transmitted for each radar based on a preset set of frequency-coded waveforms, and select the target waveform to be transmitted that has the smallest peak value in all cross-correlation spectra and whose peak sidelobe ratio is higher than a preset sidelobe ratio threshold, so as to initialize the interference parameters through the target matching combination and the target waveform to be transmitted. An initialization unit is used to initialize the interference signal based on the initialized interference parameters and complex coefficient matrix.

7. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the broadband distributed radar jamming waveform estimation and suppression method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the broadband distributed radar jamming waveform estimation and suppression method as described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the broadband distributed radar jamming waveform estimation and suppression method as described in any one of claims 1-5.

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