Broadband distributed radar interference waveform estimation and suppression method and device

Abstract

The invention relates to a broadband distributed radar interference waveform estimation and suppression method and device, and the method comprises the steps: inputting a receiving sampling signal of a distributed radar, including an interference signal, a target echo and receiving noise; constructing an observation matrix according to the interference parameters; through Fourier transform, converting the time domain signal model of the distributed radar for confronting multiple interferences into a frequency domain model; interference parameters are initialized through design of orthogonal waveforms and cross-correlation analysis, interference signals, a complex coefficient matrix and the interference parameters are optimized through internal and external nested loops, and interference waveform estimation is achieved. And eliminating the interference signal after waveform estimation from the received signal to complete interference suppression. Therefore, the problem that a traditional airspace interference suppression algorithm fails due to interference decorrelation of a current broadband distributed radar system is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, in particular to a wideband distributed radar jamming waveform estimation and suppression method and device. BACKGROUND

[0002] Modern radars are seriously threatened by mainlobe jamming in electronic countermeasure scenarios. Traditional monostatic radar anti-jamming algorithms are difficult to cope with complex mainlobe jamming environments due to the limited array aperture. Distributed radars obtain spatial diversity advantage through multi-station cooperation and become a research hotspot in the field of anti-jamming.

[0003] However, existing distributed radar anti-jamming research mainly targets narrowband signals or far-field scenarios. However, in wideband signal or long-baseline distributed radar systems, the jamming decorrelation problem is significant, resulting in time-domain envelope shift and frequency-domain phase change of the jamming signal, which makes the traditional method based on sample covariance matrix (such as eigenprojection, pre-whitening algorithm) invalid. Such methods rely on the accurate alignment of the jamming signal to estimate the covariance matrix, but in the multi-jamming scenario, the jamming parameters are different, and it is impossible to achieve simultaneous alignment through simple time shift, resulting in significant covariance matrix estimation bias and significant decline in jamming suppression performance.

[0004] In addition, the types of jamming in actual scenarios are complex and diverse, such as noise jamming, interrupted sampling repeater jamming (ISRJ), etc. ISRJ forms deceptive jamming by sampling radar signals and forwarding them, and its time-frequency characteristics are highly similar to those of target echoes. Existing technologies are difficult to effectively distinguish, and this problem needs to be solved urgently. SUMMARY

[0005] The present application provides a wideband distributed radar jamming waveform estimation and suppression method and device to solve the problem of invalidation of traditional spatial domain jamming suppression algorithms caused by jamming decorrelation in current wideband distributed radar systems.

[0006] The first aspect embodiment of the application provides a wideband distributed radar interference waveform estimation and suppression method, comprising the following steps: acquiring a receiving signal corresponding to at least one to-be-measured target through a target distributed radar, to construct a corresponding time-domain signal model according to the receiving signal, wherein the receiving signal comprises an interference signal, a target echo and a noise signal; performing 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 according to the frequency-domain signal model; initializing a plurality of to-be-optimized parameters in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the plurality of to-be-optimized parameters comprise an interference signal, a complex coefficient matrix and an interference parameter; performing a cyclic optimization operation on the plurality of to-be-optimized parameters based on a preset inner-outer nested loop optimization framework, to obtain a waveform-estimated interference signal, and eliminating the waveform-estimated interference signal from the receiving signal.

[0007] Optionally, in an embodiment of the application, the acquiring a receiving signal corresponding to at least one to-be-measured target through a target distributed radar, to construct a corresponding time-domain signal model according to the receiving signal, wherein the receiving signal comprises an interference signal, a target echo and a noise signal, comprises: transmitting a radar signal to each to-be-measured target in the at least one to-be-measured target through each radar in the target distributed radar, to receive the target echo corresponding to each to-be-measured target, the noise signal and an interference signal transmitted by a plurality of preset interference sources through each radar; representing the interference parameter by a preset unknown parameter, to construct a corresponding time-domain observation matrix according to the interference parameter; 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 an embodiment of the application, the performing discrete Fourier transform on the time-domain signal model to obtain a corresponding frequency-domain signal model comprises: performing discrete Fourier transform on the time-domain signal model to obtain the interference signal, a complex coefficient matrix, a frequency-domain observation matrix and an interference parameter; constructing the frequency-domain signal model based on the interference signal, the complex coefficient matrix, the interference parameter and the frequency-domain observation matrix.

[0009] Optionally, in an embodiment of the present application, the initializing the plurality of to-be-optimized parameters in the multi-parameter optimization problem based on the preset initialization strategy and the random frequency coded waveform strategy, wherein the plurality of to-be-optimized parameters include an interference signal, a complex coefficient matrix and an interference parameter, comprises: determining a 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 cross-correlation spectrums between each radar, and marking all peak values in the cross-correlation spectrums in time sequence to obtain corresponding marked peak values, and randomly matching the marked peak values and the plurality of interference sources to generate a plurality of matching combinations; positioning the plurality of interference sources based on a preset time difference of arrival algorithm to obtain a positioning result corresponding to each interference source, and calculating a positioning variance corresponding to the positioning result; extracting a minimum positioning variance in the plurality of interference sources, and determining a target matching combination corresponding to the minimum positioning variance in the plurality of matching combinations; determining a to-be-transmitted waveform of each radar based on a plurality of preset frequency coded waveforms, selecting a target to-be-transmitted waveform having a minimum peak value in all cross-correlation spectrums and a peak side lobe ratio higher than a preset side lobe ratio threshold, and initializing the interference parameter by using the target matching combination and the target to-be-transmitted waveform; and initializing the interference signal based on the initialized interference parameter and the complex coefficient matrix.

[0010] Optionally, in an embodiment of the present application, the cyclic optimization operation of the plurality of to-be-optimized parameters based on the preset inner-outer nested loop optimization framework to obtain a waveform-estimated interference signal, and the waveform-estimated interference signal is removed from the received signal, comprises: fixing the interference parameter in an inner loop optimization process in the inner-outer nested loop optimization framework, and cyclically solving a closed-form solution of the interference signal and the complex coefficient matrix based on a preset minimum mean square error criterion; linearizing the interference parameter by using a preset Taylor expansion strategy in an outer loop optimization process in the inner-outer nested loop optimization framework to obtain a corresponding linearization result, and updating the interference parameter based on the linearization result and a preset Jacobian matrix; cyclically operating the inner loop optimization process and the outer loop optimization process based on the inner-outer nested loop optimization framework until the closed-form solution and the interference parameter meet a preset convergence requirement, so as to generate the interference waveform-estimated signal.

[0011] Optionally, in an embodiment of the present application, a mathematical expression of the multi-parameter optimization problem is:

[0012]

[0013] wherein, 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 frequency.

[0014] The second aspect embodiment of the present application provides a wideband distributed radar interference waveform estimation and suppression device, comprising: a time domain model construction module, configured to acquire a receiving signal corresponding to at least one to-be-detected target through a target distributed radar, and to construct a corresponding time domain signal model according to the receiving signal, wherein the receiving signal comprises 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 to construct a corresponding multi-parameter optimization problem according to the frequency domain signal model; a parameter initialization module, configured to initialize a plurality of to-be-optimized parameters in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency encoding waveform strategy, wherein the plurality of to-be-optimized parameters comprise the interference signal, a complex coefficient matrix and an interference parameter; and an interference waveform estimation and suppression module, configured to perform a loop optimization operation on the plurality of to-be-optimized parameters based on a preset inner-outer nested loop optimization framework, to obtain a waveform-estimated signal corresponding to the interference signal, and to eliminate the interference waveform-estimated interference signal from the receiving signal.

[0015] Optionally, in an embodiment of the present application, the time domain model construction module comprises: a transmitting unit, configured to transmit a radar signal to each to-be-detected target in the at least one to-be-detected target through each radar in the target distributed radar, to receive the target echo, the noise signal and an interference signal transmitted by a plurality of preset interference sources corresponding to each to-be-detected target through each radar; a calculation unit, configured to represent the interference parameter by a preset unknown parameter, to construct a corresponding time domain observation matrix according to the interference parameter; 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 an embodiment of the present application, the optimization problem construction module comprises: a transformation unit, configured to perform a discrete Fourier transform on the time domain signal model to obtain the interference signal, a complex coefficient matrix, a frequency domain observation matrix and an interference parameter; and a second modeling unit, configured to construct the frequency domain signal model based on the interference signal, the complex coefficient matrix, the interference parameter and the frequency domain observation matrix.

[0017] Optionally, in an embodiment of the present application, the parameter initialization module comprises: a determination unit configured to determine random phases corresponding to the complex coefficient matrix in the multi-parameter optimization problem, and initialize the complex coefficient matrix according to the random phases; a marking unit configured to calculate cross-correlation spectrums between each radar, and mark all peaks in the cross-correlation spectrums in time sequence to obtain corresponding marked peaks, and randomly match the marked peaks and the plurality of interference sources to generate corresponding multiple matching combinations; a positioning unit configured to position the plurality of interference sources based on a preset time difference of arrival algorithm to obtain a positioning result corresponding to each interference source, and calculate a positioning variance corresponding to the positioning result; an extraction unit configured to extract a minimum positioning variance in the plurality of interference sources, and determine a target matching combination corresponding to the minimum positioning variance in the multiple matching combinations; a matching unit configured to determine a to-be-transmitted waveform of the each radar based on a preset plurality of frequency encoding waveforms, select a target to-be-transmitted waveform having a minimum peak value in all cross-correlation spectrums and a peak side lobe ratio higher than a preset side lobe ratio threshold, and initialize the interference parameters by using the target matching combination and the target to-be-transmitted waveform; and an initialization unit configured to initialize the interference signal based on the initialized interference parameters and the complex coefficient matrix.

[0018] Optionally, in an embodiment of the present application, the interference waveform estimation and suppression module comprises: a solving unit configured to fix the interference parameters in an inner loop optimization process in the inner-outer nested loop optimization framework, and cyclically solve a 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 by using a preset Taylor expansion strategy in an outer loop optimization process in the inner-outer nested loop optimization framework to obtain a corresponding linearization processing result, and update the interference parameters based on the linearization processing result and a preset Jacobian matrix; and an iterative optimization unit configured to cyclically perform the inner loop optimization process and the outer loop optimization process based on the inner-outer nested loop optimization framework until the closed-form solution and the interference parameters satisfy a preset convergence requirement, so as to generate the interference waveform estimation signal.

[0019] Optionally, in an embodiment of the present application, a mathematical expression of the multi-parameter optimization problem is as follows:

[0020]

[0021] wherein 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 parameters; and f represents frequency.

[0022] The third aspect of the embodiments of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the wideband distributed radar interference waveform estimation and suppression method as described in the above embodiments.

[0023] The fourth aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the wideband distributed radar interference waveform estimation and suppression method as described above.

[0024] The fifth aspect of the embodiments of the present application provides a computer program product comprising a computer program, which is executed to implement the wideband distributed radar interference waveform estimation and suppression method as described above.

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

[0026] The embodiments of the present application can obtain a receiving signal corresponding to at least one to-be-measured target through a target distributed radar, construct a corresponding time-domain signal model according to the receiving signal, wherein the receiving signal comprises an interference signal, a target echo, and a noise signal; 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 according to the frequency-domain signal model; initialize a plurality of to-be-optimized parameters in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coding waveform strategy, wherein the plurality of to-be-optimized parameters comprise the interference signal, a complex coefficient matrix, and an interference parameter; perform a loop optimization operation on the plurality of to-be-optimized parameters based on a preset inner-outer loop optimization framework to obtain a waveform-estimated interference signal, and eliminate the waveform-estimated interference signal from the receiving signal. The present application can realize robust suppression of multiple types of interference in a wideband or long-baseline distributed radar system considering the interference parameter, while retaining the target echo information, thereby effectively improving the radar anti-interference performance in a complex scene. Thus, the problems such as invalidation of traditional spatial domain interference suppression algorithms caused by interference decorrelation of the current wideband distributed radar system are solved.

[0027] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0028] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein:

[0029] Figure 1 A flowchart of a wideband distributed radar interference waveform estimation and suppression method according to an embodiment of the present application is provided.

[0030] Figure 2 A flowchart of initializing interference interference parameters by radar inter-correlation spectrum peak provided for an embodiment of the present application;

[0031] Figure 3 A waveform optimization schematic diagram based on frequency encoding waveform provided for an embodiment of the present application;

[0032] Figure 4 An execution logic schematic diagram of a wideband distributed radar interference waveform estimation and suppression method provided for an embodiment of the present application;

[0033] Figure 5 A pulse compression result schematic diagram after interference suppression for intermittent sampling interference provided for an embodiment of the present application;

[0034] Figure 6 A pulse compression result schematic diagram after interference suppression for noise interference provided for an embodiment of the present application;

[0035] Figure 7 An example diagram of a wideband distributed radar interference waveform estimation and suppression device according to an embodiment of the present application;

[0036] Figure 8 A structural schematic diagram of an electronic device provided for an embodiment of the present 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 DESCRIPTION

[0038] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present 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 an embodiment of the present application, the receiving signal corresponding to at least one target to be detected is acquired by the target distributed radar to construct a corresponding time domain signal model according to the receiving signal, wherein the receiving signal includes an interference signal, a target echo and a noise signal, and the method comprises: transmitting a radar signal to each target to be detected by each radar in the target distributed radar to receive a target echo, a noise signal and an interference signal emitted by a preset plurality of interference sources corresponding to each target to be detected by each radar; characterizing an interference parameter by a preset unknown parameter to construct a corresponding time domain observation matrix according to the interference parameter; and constructing a time domain signal model based on the target echo, the noise signal, the interference signal and the time domain observation matrix.

[0045] It should be noted that in the embodiment of the present application, each radar in the distributed radar transmits a radar signal to each target to be detected to receive a target echo, a noise signal and an interference signal emitted by an interference source, and a time domain observation matrix is constructed considering the interference parameter; then, the embodiment of the present application constructs a time domain signal model based on the target echo, the noise signal, the interference signal and the time domain observation matrix.

[0046] As an implementable way, the embodiment of the present application assumes that the distributed radar system is composed of I radars, the radar transmitted signal is s(t); the number of observed targets is L, the time delay of the ith radar receiving the lth target to be detected is γ il ; the number of interference sources is J, the interference signal of the jth interference source is z j (t), and the time delay of the interference signal to the ith radar is τ ij .

[0047] Based on the above parameters, the embodiment of the present application can construct a time domain signal model of the distributed radar against a multi-interference scene, and the mathematical expression is:

[0048]

[0049] Wherein, x i (t) represents the receiving signal of the ith radar, n i (t) corresponds to the corresponding receiving noise, α ij and β il are the complex coefficients of the interference signal and the target echo respectively, Δτ ij = τ ij - τ 1j is the relative time delay of the interference signal under the reference radar 1, that is, the interference parameter.

[0050] Therefore, the embodiment of the present application can construct a time domain signal model of the distributed radar against multi-interference, thereby providing reliable data guidance and basis for the subsequent construction of a corresponding frequency domain signal model.

[0051] In step S102, 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 according to the frequency-domain signal model.

[0052] Further, the embodiment of the present application can convert the time-domain signal model to the frequency domain through Fourier transform to form a frequency-domain observation equation set (i.e., the frequency-domain signal model) to characterize the phase change caused by the interference parameter; then, the embodiment of the present application can establish a multi-parameter optimization problem about the interference parameter, the complex coefficient of the observation matrix (i.e., the complex coefficient matrix), and the interference signal according to the principle of minimizing the estimation error of the interference signal waveform.

[0053] Optionally, in an embodiment of the present application, the discrete Fourier transform is performed on the time-domain signal model to obtain the corresponding frequency-domain signal model, including: performing the 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 parameter; and constructing the frequency-domain signal model based on the interference signal, the complex coefficient matrix, the interference parameter, and the frequency-domain observation matrix.

[0054] In the specific implementation process, the embodiment of the present application can first convert the time-domain received signal to the frequency domain by using the discrete Fourier transform (DFT), as shown in the following formula:

[0055]

[0056] Then, the embodiment of the present application stacks all the received signals into a matrix to obtain the corresponding frequency-domain signal model, as shown in the following formula:

[0057]

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

[0059] Therefore, the embodiment of the present application effectively guarantees the construction of the subsequent multi-parameter optimization problem by constructing the time-frequency-domain signal model containing the interference parameter.

[0060] Optionally, in an embodiment of the present application, the mathematical expression of the multi-parameter optimization problem is as follows:

[0061]

[0062] wherein 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, the embodiment of the present application can construct a multi-variable optimization problem (i.e., a multi-parameter optimization problem) for interference waveform estimation according to the above frequency domain signal model, as shown in the following formula:

[0064]

[0065] wherein A represents a 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 the embodiment of the present application contains three parameters of the interference signal z(f), the interference parameter Δτ, and the complex coefficient of the interference signal (i.e., the complex coefficient matrix) A; the optimization problem is a non-convex optimization problem, and the optimization process depends on the initial value of the parameter and the corresponding optimization method, and the 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, the multiple to-be-optimized parameters in the multi-parameter optimization problem are initialized based on the preset initialization strategy and the random frequency coded waveform strategy, wherein the multiple to-be-optimized parameters include the interference signal, the complex coefficient matrix, and the interference parameter.

[0068] After that, the embodiment of the present application also needs to combine the initialization strategy of cross-correlation and the random frequency coded waveform design to initialize the to-be-optimized parameters of the interference signal, the complex coefficient matrix, and the interference parameter in the multi-parameter optimization problem, thereby facilitating the implementation of the subsequent inner-outer nested loop optimization process.

[0069] Optionally, in an embodiment of the present application, based on a preset initialization strategy and a random frequency coding waveform strategy, a plurality of to-be-optimized parameters in a multi-parameter optimization problem are initialized, wherein the plurality of to-be-optimized parameters include an interference signal, a complex coefficient matrix and an interference parameter, and the initialization includes: determining a 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 cross-correlation spectrums between each radar, and marking all peak values in the cross-correlation spectrums in time sequence to obtain corresponding marked peak values, and randomly matching the marked peak values and a plurality of interference sources to generate corresponding multiple matching combinations; based on a preset time difference of arrival algorithm, the plurality of interference sources are located to obtain a positioning result corresponding to each interference source, and the positioning result is used to calculate a corresponding positioning variance; the minimum positioning variance is extracted from the plurality of interference sources, and a target matching combination corresponding to the minimum positioning variance is determined from the multiple matching combinations; based on a preset multiple sets of frequency coding waveforms, a to-be-transmitted waveform of each radar is determined, a target to-be-transmitted waveform having a minimum peak value in all cross-correlation spectrums and a peak side lobe ratio higher than a preset side lobe ratio threshold is selected, and the target matching combination and the target to-be-transmitted waveform are used to initialize the interference parameter; and based on the initialized interference parameter and the complex coefficient matrix, the interference signal is initialized.

[0070] As understood by those skilled in the art, the interference signal has multiple types, including noise interference, intermittent sampling and forwarding interference, etc. Therefore, the frequency domain signal model regards the interference signal z(f) as an unknown parameter to be estimated. In this case, the embodiments of the present application can not initialize z(f), but directly estimate z(f) using the initial values of the remaining to-be-optimized parameters.

[0071] For the complex coefficient matrix A, each element α ij is a complex value. Since the inherent phases of the radars can be different, and the phases of different interference signals change after frequency conversion, the phase of α ij is usually unknown, and the amplitude |α ij | corresponds to the energy of the received interference signal; |α ij | is determined by the distance of different paths and the reception gain η i , that is,

[0072] Δτ is the most critical to-be-optimized parameter, which represents the spatial distribution of the interference source and directly determines the observation matrix varying with frequency. Selecting a suitable initial value for Δτ helps to limit the problem in the correct local area. For a specific interference signal, the signal envelopes observed by different radars have similarities. Based on this, the embodiments of the present application can identify the peak values corresponding to the interference parameters in the cross-correlation spectrums by calculating the cross-correlation between the signals received by different radars.

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

[0074] As an implementable manner, the embodiments of the present application can first randomly match the peaks and the interference sources. Specifically, the embodiments of the present application can mark the peaks of the cross-correlation spectrum between the radar 2 and the radar 1 in time sequence as 1, …, J, to obtain the marked peaks; for the i-th receiving station, there are J! possible matching combinations of the peaks of the correlation spectrum; taking the radar 1 as a reference, I-1 cross-correlation results can be obtained. Since R 12 The total number of possible matching schemes is (J!) I-2 For each matching scheme, the embodiments of the present application can use the time difference of arrival algorithm to locate the J interference sources; finally, the embodiments of the present application can select the scheme that minimizes the variance of the location of all interference sources as the optimal matching (i.e., the target matching combination), and obtain a rough estimate of the interference parameter Δτ based on the matching scheme (i.e., the target matching combination).

[0075] In order to enhance the distinguishability of the peaks in the cross-correlation spectrum, the waveform design should satisfy that the cross-correlation between different interference signals approaches zero, that is, the interference signals should be as orthogonal as possible. Specifically, as shown in Figure 3 The frequency random coding waveform has good waveform irrelevance, the embodiments of the present application can pre-design several groups of frequency coding waveforms as the waveforms to be transmitted by the radar (i.e., the to-be-transmitted waveforms), select the target to-be-transmitted waveform with the smallest peak value in all cross-correlation spectra, and ensure that the peak-to-sidelobe ratio of the transmitted waveform is higher than a preset sidelobe ratio threshold.

[0076] Therefore, the embodiments of the present application can initialize the interference parameters by using the target matching combination and the target to-be-transmitted waveform (i.e., the orthogonal waveform), so as to realize the initialization of the interference parameters and the interference signals by designing the orthogonal waveform and the cross-correlation analysis, and initializing the interference parameters and the complex coefficient matrix after the initialization.

[0077] It can be understood that the embodiments of the present application use the initialization strategy based on the cross-correlation, calculate the peak position by the cross-correlation between the signals of the radars, match the interference parameters of the multiple interference sources by the time difference of arrival positioning algorithm, reduce the complexity of the combination matching, and use the random frequency coding waveform design to improve the orthogonality of different interference signals, suppress the cross-correlation peaks of the intermittent sampling and forwarding interference, and ensure the effective distinction between the spectrum peaks on the cross-correlation spectrum of the received signals, so as to initialize the interference signals, the complex coefficient matrix and the interference parameters.

[0078] In step S104, based on the preset inner-outer nested loop optimization framework, the multiple to-be-optimized parameters are subjected to loop optimization operation to obtain the waveform-estimated interference signal, and the waveform-estimated interference signal is removed from the received signal.

[0079] Further, the embodiment of the present application can obtain the interference and complex coefficient matrix by fixing the interference parameter in the inner loop of the inner-outer nested loop optimization algorithm and combining the minimum mean square error criterion, fix the interference and complex coefficient matrix in the outer loop of the inner-outer nested loop optimization algorithm, linearize the interference parameter by Taylor expansion, and update the interference parameter in one step, so as to repeatedly optimize until all the to-be-optimized parameters converge.

[0080] Thus, the embodiment of the present application uses the inner-outer nested loop optimization of the interference signal, the complex coefficient matrix and the interference parameter, realizes the interference waveform estimation, removes the interference signal from the received signal, and thus completes the interference suppression.

[0081] It can be understood that the embodiment of the present application solves the interference alignment problem caused by the interference parameter by time-frequency domain signal modeling and inner-outer nested loop optimization, improves the adaptability to complex interference types, and provides a new way for reliable target detection of distributed radar in a strong interference environment.

[0082] Optionally, in an embodiment of the present application, based on the preset inner-outer nested loop optimization framework, the multiple to-be-optimized parameters are subjected to loop optimization operation to obtain the waveform-estimated interference signal, and the waveform-estimated interference signal is removed from the received signal, including: fixing the interference parameter in the inner loop optimization process in the inner-outer nested loop optimization framework, and based on the preset minimum mean square error criterion, cyclically solving the closed-form solution of the interference signal and the complex coefficient matrix; in the outer loop optimization process in the inner-outer nested loop optimization framework, using the preset Taylor expansion strategy to linearize the interference parameter to obtain the corresponding linearization result, and updating the interference parameter based on the linearization result and the preset Jacobian matrix; based on the inner-outer nested loop optimization framework, cyclically performing the inner loop optimization process and the outer loop optimization process until the closed-form solution and the interference parameter meet the preset convergence requirement, to generate the interference waveform estimation signal.

[0083] Specifically, the embodiment of the present application can construct an inner-outer nested loop optimization framework according to the established multi-parameter optimization problem and the parameter initialization method, and the inner-outer loop process of the inner-outer nested loop optimization framework is as follows:

[0084] 1. Inner loop:

[0085] Fixing the interference parameter Δτ, the interference signal z(f) and the complex coefficient matrix A are subjected to loop optimization; by the minimum mean square error criterion, the closed-form solution is solved by using Moore-Penrose pseudo-inverse:

[0086]

[0087] After that, the embodiment of the present application can fix the estimated z(f), update A; based on the minimum mean square error criterion, the closed-form update expression of A can be obtained as:

[0088]

[0089] Wherein, H = blkdiag(Z1,..., Z I ), z j is a column vector constructed by all frequency values of the jth interference signal.

[0090] Repeat the above operation until convergence, thereby completing an inner loop.

[0091] 2, outer loop:

[0092] The embodiment of the present application can linearize the interference parameter based on Taylor expansion, update the interference parameter Δτ through the Jacobian matrix, and improve the alignment accuracy.

[0093] In actual execution process, the embodiment of the present application can make δ ij represent the error between the initialized Δτ ij and the true value, since δ ij is relatively small, the first-order Taylor expansion can be used to obtain:

[0094]

[0095] Secondly, the embodiment of the present application can fix A and z(f), and the optimization problem can be further written as:

[0096]

[0097] Wherein, and x have the same definition, the Jacobian matrix Therefore, the update expression of the interference parameter can be obtained as:

[0098]

[0099] In the embodiment of the present application, the outer loop is one-step iteration, and the loop iterates the inner loop and the outer loop until all parameters converge, to complete the interference waveform estimation.

[0100] Therefore, the embodiment of the present application optimizes the algorithm of the inner and outer nested loops, fixes the interference parameters in the inner loop, solves the closed-form solution of the interference spectrum and the observation matrix based on the minimum mean square error criterion, realizes the waveform estimation of the interference signal, linearizes the interference parameters by Taylor expansion in the outer loop, updates the interference parameters through the Jacobian matrix, and improves the time domain alignment accuracy of the interference signal, thereby effectively improving the accuracy of the frequency domain observation matrix.

[0101] Further, the embodiment of the present application can effectively subtract the interference signal from the received signal, that is:

[0102]

[0103] For the i-th receiver, the output after pulse compression can be represented as the inverse discrete Fourier transform of the point product of the interference-suppressed received signal spectrum and the transmit waveform, as shown in the following formula:

[0104]

[0105] In summary, the embodiment of the present application inputs the received sampling signal of the distributed radar, including the interference signal, the target echo and the received noise; constructs the observation matrix according to the interference parameters; converts the time domain signal model of the distributed radar against multiple interference into a frequency domain signal model through Fourier transform; optimizes the interference spectrum, the observation matrix complex coefficient and the interference parameters by using the inner and outer nested loops, initializes the interference parameters by using cross-correlation analysis, and designs orthogonal waveforms to improve the discrimination of the interference in the cross-correlation spectrum.

[0106] Therefore, the embodiment of the present application can solve the problem that the envelope delay difference of the interference signal in the wideband or long baseline distributed radar cannot be ignored, realize waveform estimation of the interference signal through time-frequency domain joint modeling and cyclic algorithm, subtract the interference from the original echo signal, and realize multiple interference suppression.

[0107] The execution logic and execution effect of the wideband distributed radar interference waveform estimation and suppression method of the present application are described below by combining the accompanying drawings.

[0108] 1. Execution logic:

[0109] Figure 4 The execution effect diagram of the wideband distributed radar interference waveform estimation and suppression method of the present application is shown in FIG. 1. Figure 4 As shown in the figure, the execution process of the wideband distributed radar interference waveform estimation and suppression method of the present application is described as follows.

[0110] S401: The distributed radar receives the interference signal and the target echo, and constructs a time domain signal model containing interference 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 wideband distributed radar jamming waveform estimation and suppression device according to the embodiment of the application is described with reference to the drawings.

[0119] Figure 7 FIG. 1 is a block schematic diagram of the wideband distributed radar jamming waveform estimation and suppression device according to the embodiment of the application.

[0120] As shown in FIG. 1, the wideband distributed radar jamming waveform estimation and suppression device 10 comprises a time-domain model construction module 100, an optimization problem construction module 200, a parameter initialization module 300 and a jamming waveform estimation and suppression module 400. Figure 7

[0121] The time-domain model construction module 100 is configured to acquire a receiving signal corresponding to each of the at least one target to be measured by each radar of the target distributed radar, and construct a corresponding time-domain signal model according to the receiving signal, wherein the receiving signal comprises a jamming signal, a target echo and a noise signal.

[0122] The optimization problem construction module 200 is 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 according to the frequency-domain signal model.

[0123] The parameter initialization module 300 is configured to initialize a plurality of parameters to be optimized in the multi-parameter optimization problem based on a preset initialization strategy and a random frequency coded waveform strategy, wherein the plurality of parameters to be optimized comprise a jamming signal, a complex coefficient matrix and a jamming parameter.

[0124] The jamming waveform estimation and suppression module 400 is configured to perform a loop optimization operation on the plurality of parameters to be optimized based on a preset inner-outer nested loop optimization framework, to obtain a jamming signal after waveform estimation, and eliminate the jamming signal after waveform estimation from the receiving signal.

[0125] Optionally, in an embodiment of the application, the time-domain model construction module 100 comprises a transmitting unit, a calculating unit and a first modeling unit.

[0126] The transmitting unit is configured to transmit a radar signal to each of the at least one target to be measured by each radar of the target distributed radar, to receive a target echo corresponding to each of the at least one target to be measured, a noise signal and a jamming signal transmitted by a plurality of preset jamming sources through each radar.

[0127] The calculating unit is configured to represent the jamming parameter by a preset unknown parameter, and construct a corresponding time-domain observation matrix according to the jamming parameter.

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

[0129] Optionally, in an embodiment of the present application, the optimization problem construction module 200 comprises a transformation unit and a second modeling unit.

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

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

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

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

[0134] The marking unit is configured to calculate cross-correlation spectrums between each radar, and mark all peaks in the cross-correlation spectrums in time sequence to obtain corresponding marked peaks, and randomly match the marked peaks and the multiple interference sources to generate corresponding multiple matching combinations.

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

[0136] The extraction unit is configured to extract a minimum positioning variance in the multiple interference sources, and determine a target matching combination corresponding to the minimum positioning variance in the multiple matching combinations.

[0137] The matching unit is configured to determine a to-be-transmitted waveform of each radar based on a preset multiple sets of frequency encoding waveforms, select a target to-be-transmitted waveform having a minimum peak value in all cross-correlation spectrums and a peak sidelobe ratio higher than a preset sidelobe ratio threshold, and initialize the interference parameters by using the target matching combination and the target to-be-transmitted waveform.

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

[0139] Optionally, in an embodiment of the present application, the interference waveform estimation and suppression module 400 comprises a solving unit, a linearization unit and an iterative optimization unit.

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

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

[0142] The iterative optimization unit is configured to perform the inner loop optimization process and the outer loop optimization process based on the inner-outer nested loop optimization framework until the closed-form solution and the interference parameter meet a preset convergence requirement, to generate the interference waveform estimation signal.

[0143] Optionally, in an embodiment of the present application, the mathematical expression of the multi-parameter optimization problem is as follows:

[0144]

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

[0146] It should be noted that the aforementioned explanation and description of the embodiment of the wideband distributed radar interference waveform estimation and suppression method also applies to the wideband distributed radar interference waveform estimation and suppression device of the embodiment, which will not be described herein again.

[0147] The wideband distributed radar jamming waveform estimation and suppression device provided by the embodiment of the application comprises a time domain model construction module 100, which is configured to acquire a receiving signal corresponding to at least one target to be detected through a target distributed radar, and to construct a corresponding time domain signal model according to the receiving signal, wherein the receiving signal comprises a jamming signal, a target echo and a noise signal; an optimization problem construction module 200, which is configured to perform a discrete Fourier transform on the time domain signal model to obtain a corresponding frequency domain signal model, and to construct a corresponding multi-parameter optimization problem according to the frequency domain signal model; a parameter initialization module 300, which is configured to initialize a plurality of 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 plurality of parameters to be optimized comprise a jamming signal, a complex coefficient matrix and a jamming parameter; and a jamming waveform estimation and suppression module 400, which is configured to perform a cyclic optimization operation on the plurality of parameters to be optimized based on a preset inner-outer nested loop optimization framework, to obtain a jamming signal after waveform estimation, and to eliminate the jamming signal after waveform estimation from the receiving signal. Through signal acquisition, waveform design, initialization and jamming waveform estimation and suppression operation, the application realizes effective suppression of multiple types of interference such as noise interference and slice forwarding interference, and improves the radar anti-interference performance in a complex scene.

[0148] Figure 8 The structure schematic diagram of the electronic device provided by the embodiment of the application is provided. The electronic device can comprise:

[0149] The memory 801, the processor 802 and the computer program stored in the memory 801 and executable on the processor 802.

[0150] The processor 802 implements the wideband distributed radar jamming waveform estimation and suppression method provided in the above embodiment when executing the program.

[0151] Further, the electronic device further comprises:

[0152] The communication interface 803 is configured to communicate between the memory 801 and the processor 802.

[0153] The memory 801 is configured to store the computer program executable on the processor 802.

[0154] The memory 801 can comprise a high-speed RAM memory, and can also comprise a non-volatile memory, for example, at least one disk memory.

[0155] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 8 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

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

[0157] The processor 802 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0158] The embodiments of the present application also provide a computer readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement the above method for estimating and suppressing a wideband distributed radar jamming waveform.

[0159] The embodiments of the present application also provide a computer program product, which includes a computer program, and the computer program is executed to implement the above method for estimating and suppressing a wideband distributed radar jamming waveform.

[0160] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0161] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features of the application, and do not imply or connote relative importance or a specific order of precedence. Thus, features defined with "first", "second", etc. can include at least one of the features, either explicitly or implicitly.

[0162] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts or otherwise described herein are not necessarily performed in the order shown or discussed, including, for example, performing or depending from other operations or stages, in parallel, in reverse order, or in some other suitable manner. Blocks can also be omitted.

[0163] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for example, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0164] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0165] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiments is included.

[0166] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of 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 magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present 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, the 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.

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 3, characterized in that, The method, based on a preset initialization strategy and a random frequency encoded waveform strategy, initializes multiple parameters to be optimized in the multi-parameter optimization problem. These multiple parameters 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 the 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.

5. The method according to claim 4, 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.

6. The method according to claim 4, characterized in that, The mathematical expression for the multi-parameter optimization problem is: 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.

7. 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 a 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.

8. 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 1-6.

9. 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 1-6.

10. 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 1-6.

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