Method and device for suppressing folding clutter of agile radar based on iterative subspace projection

Abstract

The application discloses a method and device for agile radar foldover clutter suppression based on iterative subspace projection, and relates to the technical field of radar signal processing, and comprises the following steps: when the number of effective pulses is sufficient, a known baseband waveform spectrum is used as prior information to modulate a traditional PSWF base function, so that the low-rank clutter subspace of the agile radar is effectively described; meanwhile, the modulated PSWF base function provides separability for the clutters of different distance sections and different frequency points, and then the foldover clutter suppression can be realized through an iterative alternating projection algorithm. Therefore, the problem that the prior art cannot effectively suppress the foldover clutter in the scene of low-altitude detection with strong ground clutter or sea clutter is 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 suppressing clutter in agile radar based on iterative subspace projection. Background Technology

[0002] In real-world low-altitude detection scenarios, radars often face strong clutter generated by complex terrain or sea conditions. Using the maximum unambiguous range as a single range segment, the received echo inevitably contains components from multiple range segments. Therefore, clutter within multiple range segments between the target and the radar will be received simultaneously, causing strong short-range clutter to superimpose across multiple PRI (Pulse Repetition Intervals), forming a folded clutter phenomenon that severely degrades the radar's detection performance.

[0003] Existing technologies for suppressing folded clutter primarily focus on traditional Pulse Doppler (PD) radars. This suppression is mainly achieved through waveform agility-based methods. However, these PD radar methods rely on passively suppressing folded clutter in the received signal. In low-altitude detection scenarios, densely distributed clutter often appears in all pulses within the coherent processing interval, leading to an increased noise floor and limited suppression effectiveness. In contrast, agile radars, by transmitting random carrier frequency sequences and utilizing bandpass filters at the receiver, can filter out folded clutter in unwanted ranges during the down-conversion stage, providing raw echo data with less folded clutter for target detection. However, on the one hand, the available frequency resources of the system are limited; on the other hand, the coherent accumulation gain decreases with the increase in the number of frequency points. Therefore, the number of frequency points actually available for agile radars cannot be arbitrarily increased, and the suppression of strong short-range folded clutter remains a challenging technical problem.

[0004] The frequency agility and code pattern agility of agile radars present specific technical challenges to folded clutter suppression. First, because frequency agility is incompatible with the constant PRI required by Moving Target Detection (MTD) technology, existing techniques typically utilize the low-rank characteristics of clutter to achieve clutter suppression within the same frequency point. Second, considering code pattern agility, the low-rank characteristics of clutter within the same frequency point become difficult to model under the influence of Range Sidelobe Modulation (RSM) effects. Existing PSWF (Prolate Spheroidal Wave Functions) subspace models based on range-velocity steering vector calculations do not match the clutter characteristics under current multidimensional agility conditions. Third, under the combined influence of frequency agility and code pattern agility, clutter models differ across different range segments and frequencies. Therefore, sequential processing methods are insufficient to achieve folded clutter suppression in agile radars.

[0005] In summary, existing technologies are insufficient for effective folding clutter suppression in scenarios such as low-altitude detection with strong ground / sea clutter, and this problem urgently needs to be solved. Summary of the Invention

[0006] This application provides a method and apparatus for suppressing folded clutter in agile radar based on iterative subspace projection, in order to solve the problem that existing technologies are unable to effectively suppress folded clutter in scenarios such as low-altitude detection with strong ground / sea clutter.

[0007] The first aspect of this application provides a method for suppressing clutter in agile radar based on iterative subspace projection, comprising the following steps: receiving and preprocessing the echo signal of a preset agile radar to generate a frequency domain echo data matrix; acquiring the baseband waveform spectrum of the transmitted pulse train of the agile radar, and using the baseband waveform spectrum to perform spectral modulation on a preset PSWF basis function to generate a corresponding modulation PSWF basis set, and constructing a corresponding modulation clutter subspace based on a preset range-velocity steering vector; and based on the frequency domain echo data matrix and the modulation clutter subspace... In the space, a corresponding objective function is constructed, and the objective function is iteratively solved until the relative change value of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit; the reconstructed clutter is subjected to inverse Fourier transform to obtain the corresponding time domain signal, and the time domain signal is subtracted from the original time domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal, and the clutter suppression signal is subjected to preset matched filtering, coherent accumulation and constant false alarm rate detection operations to generate the target detection result corresponding to the agile radar.

[0008] Optionally, in one embodiment of this application, the step of receiving and preprocessing the echo signal of the preset agile radar to generate a frequency domain echo data matrix includes: receiving the echo signal of the target under test through the radar receiver of the target agile radar, and using the radar receiver to perform preset down-conversion and baseband sampling operations on the echo signal to obtain a corresponding time domain echo data matrix; performing a discrete Fourier transform on the time domain echo data matrix along the first target time dimension to obtain a corresponding frequency domain echo data matrix, and extracting the data vector of the second target time dimension corresponding to each frequency unit.

[0009] Optionally, in one embodiment of this application, the step of obtaining the baseband waveform spectrum of the agile radar's transmitted pulse train, and using the baseband waveform spectrum to perform spectral modulation on a preset PSWF basis function to generate a corresponding modulated PSWF basis set, and constructing a corresponding modulated clutter subspace in combination with a preset range-velocity steering vector, includes: obtaining the baseband waveform spectrum of each pulse in the radar transmitted pulse train of the agile radar, and using the baseband waveform spectrum to perform spectral modulation on the PSWF basis function, so as to perform a complex dot product operation between the baseband waveform spectrum of each pulse and the PSWF basis function in the frequency domain to generate a corresponding modulated PSWF basis set; based on the modulated PSWF basis set and the range-velocity steering vector, constructing a corresponding modulated clutter subspace in each pulse's transmitted pulse train. For each range segment, the clutter covariance matrix of each frequency unit and each transmit carrier frequency point is determined, and the matrix rank of the clutter covariance matrix is ​​determined. Multiple principal eigenvectors of the clutter covariance matrix are extracted based on the matrix rank, and the modulation clutter subspace basis matrix corresponding to each frequency unit for each range segment and each transmit carrier frequency point is constructed using the multiple principal eigenvectors. Based on the frequency agility characteristics of the target agile radar, the corresponding effective pulse selection relationship is determined, and the corresponding window function matrix is ​​determined based on the effective pulse selection relationship. Based on the window function matrix, the multiple principal eigenvectors, and the preset basis coefficient vectors to be estimated, the modulation clutter subspace corresponding to different range segments that meets the preset rank constraint characterization requirements is obtained.

[0010] Optionally, in one embodiment of this application, the step of constructing a corresponding objective function based on the frequency domain echo data matrix and the modulation clutter subspace, and iteratively solving the objective function until the relative change value of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit, includes: constructing a corresponding subspace basis coefficient vector set based on the basis coefficient vector to be estimated for each frequency unit and each transmit carrier frequency point in each range segment, and using the subspace basis coefficient vector set as the corresponding variable to be estimated; establishing a corresponding objective function based on the variable to be estimated to minimize the F-norm of the difference between the frequency domain echo data matrix and the modulation clutter subspace that satisfies the preset rank constraint characterization requirement, and constructing a folded clutter suppression optimization model based on the objective function; and using the folded clutter suppression optimization model... The algorithm is decomposed into multiple independent optimization sub-models, and all variables to be estimated in the multiple independent optimization sub-models are initialized. Under each frequency unit, an alternating projection operation is iteratively performed on each range segment and each transmit carrier frequency point to subtract the modulation clutter subspace basis matrix of all other range segments except the current range segment from the frequency domain echo data matrix to obtain the corresponding current residual. The residual sub-vectors corresponding to the current range segment and the current transmit carrier frequency point are extracted according to the effective pulse selection relationship, and the current modulation subspace coefficient vector is updated. The clutter components of the current range segment and the current transmit carrier frequency point are reconstructed using the updated modulation subspace coefficient vector and the modulation PSWF basis function set until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit.

[0011] Optionally, in one embodiment of this application, the mathematical expression of the objective function is:

[0012] in, Represents the set of basis coefficient vectors in the modulation subspace; Describe the objective function; Represents the window function matrix; Indicates the preceding The modulation clutter subspace basis matrix is ​​composed of principal eigenvectors; This represents the vector of basic coefficients to be estimated; This represents the frequency domain echo vector within the current frequency unit. Indicates the number of distance segments; Indicates the number of carrier frequency points; This indicates the frequency value of the current frequency unit.

[0013] A second aspect of this application provides a clutter suppression device for agile radar based on iterative subspace projection, comprising: a preprocessing module for receiving and preprocessing a preset echo signal from agile radar to generate a frequency domain echo data matrix; a spectrum modulation module for acquiring the baseband waveform spectrum of the transmitted pulse train from the agile radar, and using the baseband waveform spectrum to perform spectrum modulation on a preset PSWF basis function to generate a corresponding modulation PSWF basis set, and constructing a corresponding modulation clutter subspace by combining a preset range-velocity steering vector; and an iterative solution module for solving the frequency domain echo data matrix and... The modulated clutter subspace is used to construct a corresponding objective function, and the objective function is iteratively solved until the relative change value of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit. The clutter suppression module is used to perform inverse Fourier transform on the reconstructed clutter to obtain the corresponding time-domain signal, and to subtract the time-domain signal from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. The clutter suppression signal is then subjected to preset matched filtering, coherent accumulation and constant false alarm rate detection operations to generate the target detection result corresponding to the agile radar.

[0014] Optionally, in one embodiment of this application, the preprocessing module includes: a sampling unit, configured to receive the echo signal of the target under test through the radar receiver of the target agile radar, and perform preset down-conversion and baseband sampling operations on the echo signal using the radar receiver to obtain a corresponding time-domain echo data matrix; and an extraction unit, configured to perform discrete Fourier transform on the time-domain echo data matrix along the first target time dimension to obtain a corresponding frequency-domain echo data matrix, and extract the data vector of the second target time dimension corresponding to each frequency unit.

[0015] Optionally, in one embodiment of this application, the spectrum modulation module includes: a first acquisition unit, configured to acquire the baseband waveform spectrum of each pulse in the radar transmit pulse train of the agile radar, and use the baseband waveform spectrum to perform spectrum modulation on the PSWF basis function, so as to perform a complex dot product operation between the baseband waveform spectrum of each pulse and the PSWF basis function in the frequency domain to generate a corresponding modulation PSWF basis set; and a first construction unit, configured to construct the clutter covariance matrix for each frequency unit and each transmit carrier frequency point under each range segment based on the modulation PSWF basis set and the range-velocity steering vector, and determine the clutter covariance matrix. The matrix rank is used to extract multiple principal eigenvectors of the clutter covariance matrix based on the matrix rank, and to construct the modulation clutter subspace basis matrix corresponding to each frequency unit under each range segment and each transmit carrier frequency point through the multiple principal eigenvectors; the determination unit is used to determine the corresponding effective pulse selection relationship based on the frequency agile characteristics of the target agile radar, and to determine the corresponding window function matrix based on the effective pulse selection relationship; the second acquisition unit is used to obtain the modulation clutter subspace corresponding to different range segments that meets the preset rank constraint characterization requirements based on the window function matrix, the multiple principal eigenvectors and the preset basis coefficient vector to be estimated.

[0016] Optionally, in one embodiment of this application, the iterative solution module includes: a second construction unit, configured to construct a corresponding subspace basis coefficient vector set based on the basis coefficient vectors to be estimated for each frequency unit and each transmit carrier frequency point under each distance segment, and to use the subspace basis coefficient vector set as the corresponding variable to be estimated; a third construction unit, configured to establish a corresponding objective function based on the variable to be estimated, to minimize the F-norm of the difference between the frequency domain echo data matrix and the modulation clutter subspace that satisfies the preset rank constraint characterization requirement, and to construct a folded clutter suppression optimization model based on the objective function; and a model decomposition unit, configured to decompose the folded clutter suppression optimization model into multiple independent optimization sub-models, and initialize all variables to be estimated in the multiple independent optimization sub-models. The variables are used to iteratively perform alternating projection operations on each range segment and each transmit carrier frequency point under each frequency unit to subtract the modulation clutter subspace basis matrix of all other range segments except the current range segment from the frequency domain echo data matrix to obtain the corresponding current residual; the update unit is used to extract the residual subvector corresponding to the current range segment and the current transmit carrier frequency point according to the effective pulse selection relationship, update the current modulation subspace coefficient vector, and reconstruct the clutter components of the current range segment and the current transmit carrier frequency point using the updated modulation subspace coefficient vector and the modulation PSWF basis function set until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit.

[0017] Optionally, in one embodiment of this application, the mathematical expression of the objective function is:

[0018] in, Represents the set of basis coefficient vectors in the modulation subspace; Describe the objective function; Represents the window function matrix; Indicates the preceding The modulation clutter subspace basis matrix is ​​composed of principal eigenvectors; This represents the vector of basic coefficients to be estimated; This represents the frequency domain echo vector within the current frequency unit. Indicates the number of distance segments; Indicates the number of carrier frequency points; This indicates the frequency value of the current frequency unit.

[0019] 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 agile radar folding clutter suppression method based on iterative subspace projection as described in the above embodiments.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for suppressing agile radar folding clutter based on iterative subspace projection.

[0021] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described method for suppressing agile radar folding clutter based on iterative subspace projection.

[0022] Therefore, the embodiments of this application have the following beneficial effects: The embodiments of this application can receive and preprocess the echo signal of a pre-defined agile radar to generate a frequency domain echo data matrix; acquire the baseband waveform spectrum of the transmitted pulse train of the agile radar, and use the baseband waveform spectrum to perform spectral modulation on a pre-defined PSWF basis function to generate a corresponding modulation PSWF basis set, and construct a corresponding modulation clutter subspace by combining a pre-defined range-velocity steering vector; construct a corresponding objective function based on the frequency domain echo data matrix and the modulation clutter subspace, and iteratively solve the objective function until the relative change value of the overall residual energy during the iteration process is less than a pre-defined change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit; perform an inverse Fourier transform on the reconstructed clutter to obtain the corresponding time domain signal, and subtract the time domain signal from the original time domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal, and perform pre-defined matched filtering, coherent accumulation and constant false alarm rate detection operations on the clutter suppression signal to generate the target detection result corresponding to the agile radar. This application enables the modulation of a traditional PSWF basis using known waveform spectra as prior information when the number of effective pulses is sufficient, thereby effectively describing the low-rank clutter subspace of agile radar. Simultaneously, the modulated PSWF basis provides separability for clutter at different ranges and frequencies, allowing for folded clutter suppression through an iterative alternating projection algorithm. This solves the problem of existing technologies struggling to effectively suppress folded clutter in scenarios such as low-altitude detection with strong ground / sea clutter.

[0023] 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

[0024] 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: Figure 1 This is a flowchart of a method for suppressing clutter folding in agile radar based on iterative subspace projection, according to an embodiment of this application. Figure 2 A schematic diagram of a folding clutter suppression process is provided for one embodiment of this application; Figure 3 A schematic diagram of the logical architecture of a folding clutter suppression system for agile radar based on iterative subspace projection is provided for one embodiment of this application; Figure 4 This is an example diagram of a folding clutter suppression device for agile radar based on iterative subspace projection according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0025] Among them, 10-Agile radar folding clutter suppression device based on iterative subspace projection, 100-Preprocessing module, 200-Spectrum modulation module, 300-Iterative solution module, 400-Clutter suppression module, 501-Memory, 502-Processor, 503-Communication interface. Detailed Implementation

[0026] 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.

[0027] The following describes, with reference to the accompanying drawings, a method and apparatus for suppressing clutter in agile radar based on iterative subspace projection, according to embodiments of this application. To address the problems mentioned in the background, this application provides a clutter suppression method for agile radar based on iterative subspace projection. In this method, the echo signal from a pre-defined agile radar is received and pre-processed to generate a frequency domain echo data matrix. The baseband waveform spectrum of the transmitted pulse train from the agile radar is acquired, and the baseband waveform spectrum is used to perform spectral modulation on a pre-defined PSWF basis function to generate a corresponding set of modulated PSWF basis functions. A corresponding modulated clutter subspace is constructed by combining this with a pre-defined range-velocity steering vector. Based on the frequency domain echo data matrix and the modulated clutter subspace, a corresponding objective function is constructed and iteratively solved until the relative change in the overall residual energy during the iteration process is less than a pre-defined change threshold, thereby outputting the reconstructed clutter for each range segment and frequency unit. An inverse Fourier transform is performed on the reconstructed clutter to obtain the corresponding time-domain signal. The time-domain signal is subtracted from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. Pre-defined matched filtering, coherent accumulation, and constant false alarm rate (CFAR) detection operations are performed on the clutter suppression signal to generate the target detection result corresponding to the agile radar. This application enables the modulation of a traditional PSWF basis using known waveform spectra as prior information when the number of effective pulses is sufficient, thereby effectively describing the low-rank clutter subspace of agile radar. Simultaneously, the modulated PSWF basis provides separability for clutter at different ranges and frequencies, allowing for folded clutter suppression through an iterative alternating projection algorithm. This solves the problem of existing technologies struggling to effectively suppress folded clutter in scenarios such as low-altitude detection with strong ground / sea clutter.

[0028] Specifically, Figure 1 This is a flowchart illustrating a method for suppressing clutter in agile radar based on iterative subspace projection, provided in an embodiment of this application.

[0029] like Figure 1 As shown, the agile radar clutter suppression method based on iterative subspace projection includes the following steps: In step S101, the echo signal of the preset agile radar is received and preprocessed to generate a frequency domain echo data matrix. The embodiments of this application first receive radar echo signals and perform down-conversion processing, baseband sampling, and discrete Fourier transform processing on the echo signals to output a frequency domain echo data matrix and slow-time data vectors for each frequency unit.

[0030] Optionally, in one embodiment of this application, receiving and preprocessing the echo signal of a preset agile radar to generate a frequency domain echo data matrix includes: receiving the echo signal of the target under test through the radar receiver of the target agile radar, and using the radar receiver to perform preset down-conversion and baseband sampling operations on the echo signal to obtain the corresponding time domain echo data matrix; performing a discrete Fourier transform on the time domain echo data matrix along the first target time dimension to obtain the corresponding frequency domain echo data matrix, and extracting the data vector of the second target time dimension corresponding to each frequency unit.

[0031] It should be noted that the radar system parameters in this embodiment are set as follows: the initial carrier frequency is... The frequency step size is The total number of available frequency points is Frequency point number The total number of pulses transmitted within one Coherent Processing Interval (CPI) is: Pulse Index .remember For the first The carrier frequency of each pulse can be expressed as:

[0032] in, For the first The frequency point number of each pulse. .

[0033] For agile radar, since time-domain agility can be achieved by changing the time alignment relationship of the inter-pulse baseband waveform, the embodiments of this application can... The signal model for a single transmitted pulse is represented as follows:

[0034] Wherein, the pulse width is ;No. The baseband encoded waveform of each pulse is Each pulse To achieve code agility by being different from each other; The rectangular window function has the following mathematical expression:

[0035] In addition, the pulse repetition interval is The maximum unambiguous distance is ,in It is the speed of light. The total number of distance segments is... The target is located at the first A distance segment (usually taken) ).

[0036] In actual implementation, the echo signal is down-converted and sampled in baseband to obtain a time-domain echo data matrix. ,in The number of fast-time sampling points within one CPI; secondly, embodiments of this application can process time-domain echo data. The matrix is ​​subjected to a Discrete Fourier Transform along the fast time dimension (i.e., the first target time dimension) to transform it into the frequency domain, thus obtaining the frequency domain echo data matrix. ,in The total number of frequency units is used to convert the time-domain echo to the frequency domain; then, for each frequency unit... The embodiments of this application can extract the data vector of the corresponding slow time dimension (i.e., the second target time dimension). , This represents the sampling rate, thus providing reliable data support for subsequent clutter subspace construction and iterative suppression.

[0037] In step S102, the baseband waveform spectrum of the agile radar transmit pulse train is obtained, and the preset PSWF basis function is spectrally modulated using the baseband waveform spectrum to generate the corresponding modulated PSWF basis set, and the corresponding modulated clutter subspace is constructed by combining the preset range-velocity steering vector. Furthermore, embodiments of this application can obtain the baseband waveform spectrum of the radar transmitted pulse train, and use the baseband waveform spectrum to perform spectrum modulation on the PSWF basis functions to generate a modulated PSWF basis function set; secondly, embodiments of this application can use the modulated PSWF basis function set, combined with the range-velocity steering vector, to calculate the clutter covariance matrix of each frequency unit and each transmitted carrier frequency point, and based on PSWF theory, perform eigenvalue decomposition on the clutter covariance matrix to extract the principal eigenvectors to form the modulated clutter subspace basis matrix, and generate the corresponding subspace basis matrix and window function matrix according to the effective pulse selection relationship of different range segments.

[0038] Optionally, in one embodiment of this application, the baseband waveform spectrum of the transmitted pulse train of the agile radar is obtained, and the baseband waveform spectrum is used to perform spectrum modulation on a preset PSWF basis function to generate a corresponding modulated PSWF basis set. Furthermore, a corresponding modulated clutter subspace is constructed by combining the baseband waveform spectrum with a preset range-velocity steering vector. This includes: obtaining the baseband waveform spectrum of each pulse in the radar transmitted pulse train of the agile radar, and performing spectrum modulation on the PSWF basis function using the baseband waveform spectrum to perform a complex dot product operation between the baseband waveform spectrum of each pulse and the PSWF basis function in the frequency domain to generate a corresponding modulated PSWF basis set; and constructing a corresponding modulated clutter subspace based on the modulated PSWF basis set and the range-velocity steering vector. Clutter covariance matrices are constructed for each frequency cell and each transmit carrier frequency point within each range segment. The rank of the clutter covariance matrix is ​​determined, and multiple principal eigenvectors of the clutter covariance matrix are extracted based on the rank. The modulation clutter subspace basis matrix corresponding to each frequency cell within each range segment and each transmit carrier frequency point is constructed using these principal eigenvectors. Based on the frequency agility characteristics of the target agile radar, the corresponding effective pulse selection relationship is determined, and the corresponding window function matrix is ​​determined based on the effective pulse selection relationship. Based on the window function matrix, multiple principal eigenvectors, and the preset basis coefficient vectors to be estimated, the modulation clutter subspaces corresponding to different range segments that meet the preset rank constraint characterization requirements are obtained.

[0039] Specifically, the embodiments of this application can first obtain the baseband waveform spectrum of each pulse in the radar transmitted pulse train. (No. The spectrum of each pulse is used as prior information, and the baseband waveform spectrum is used as a priori information. Secondly, the embodiments of this application can use the baseband waveform spectrum to perform spectral modulation on the PSWF basis function. The waveform spectrum of each pulse is multiplied with the PSWF basis function in the frequency domain to generate a new set of basis functions, namely the modulated PSWF basis function. The modulated PSWF basis function inherits the time-limited and band-limited characteristics and high energy concentration of the PSWF basis function, while incorporating the prior information of the waveform spectrum.

[0040] For each frequency and each carrier frequency point In this application embodiment, a corresponding modulation steering vector is defined. For distance... and speed Distance-velocity steering vector The The elements are:

[0041] Secondly, the embodiments of this application can construct waveform spectrum vectors. ,in, Indicates transpose. The modulation steering vector is , It represents the Hadamardi (or Hadama) stack.

[0042] Assuming the clutter scattering point is in the range segment and speed range If the distribution is continuous, then the frequency clutter covariance matrix for:

[0043] in, This indicates the conjugate transpose.

[0044] matrix The Middle Line 1 List The elements have the following parsing expressions:

[0045] in,

[0046] For the Kronecker function, i.e. when It is 1 if it is true, otherwise it is 0.

[0047] Therefore, the clutter covariance matrix can be determined. Each frequency unit can be determined independently, thus allowing clutter itself to be represented independently by frequency unit. Furthermore, , and Indicates the first and the pulse index, Represents the matrix of the first Line 1 The values ​​that the elements of a column can take.

[0048] Due to frequency agility Regarding frequency point segmentation diagonally:

[0049] in, Corresponding frequency submatrix, For frequency point The number of pulses.

[0050] The embodiments of this application can be used for... The eigenvalues ​​are decomposed as shown in the following equation:

[0051] This application embodiment uses the method of taking the previous step. The principal eigenvectors constitute the modulation clutter subspace basis matrix. ,rank Approximated by PSWF theory:

[0052] For folded clutter (the first A distance segment, Due to frequency agility, only a portion of the pulses are effective. For the first... The distance segment and the first The set of effective pulse indices for each frequency point is: , The number of elements in the middle is The embodiments of this application can define a window function matrix. As shown in the following formula:

[0053] make Represents frequency Upper The distance segment, the first The clutter covariance matrix at each frequency point can be obtained by eigendecomposition as follows:

[0054] in, Includes tensor frequency The front of the upper clutter subspace One principal feature vector.

[0055] Therefore, in frequency From the above The clutter in each range segment can be represented as:

[0056] in, Let be the vector of basic coefficients to be estimated.

[0057] Therefore, the embodiments of this application utilize the modulation PSWF basis function set and combine it with the range-velocity steering vector to construct the clutter covariance matrix for each frequency unit and each transmit carrier frequency point. Based on PSWF theory, the rank of the clutter covariance matrix is ​​determined, and the first few principal eigenvectors are extracted to form the modulation clutter subspace basis matrix. In addition, for different range segments, the embodiments of this application can extract the corresponding subspace basis matrix using a window function matrix based on the effective pulse selection relationship introduced by the frequency agility characteristics of the radar. This provides a low-rank clutter subspace description for different frequency units, different range segments, and different carrier frequency points, effectively ensuring the subsequent realization of folded clutter suppression.

[0058] In step S103, based on the frequency domain echo data matrix and the modulation clutter subspace, a corresponding objective function is constructed, and the objective function is iteratively solved until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit. The system receives frequency domain echo data, as well as the modulation clutter subspace basis matrix and window function matrix. It establishes an objective function that minimizes the F-norm of the difference between the echo data and the reconstructed clutter, and solves this function using an iterative alternating projection algorithm. The subspace coefficient vector is initialized, and residual calculation, least-squares coefficient update, and clutter reconstruction are performed sequentially for each frequency cell, each range segment, and each frequency point. This process iterates until convergence, and the reconstructed clutter for each range segment and frequency cell is output. Optionally, in one embodiment of this application, based on the frequency domain echo data matrix and the modulation clutter subspace, a corresponding objective function is constructed, and the objective function is iteratively solved until the relative change value of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit. This includes: constructing a corresponding subspace basis coefficient vector set based on the basis coefficient vector to be estimated for each frequency unit and each transmit carrier frequency point in each range segment, and using the subspace basis coefficient vector set as the corresponding variable to be estimated; establishing a corresponding objective function based on the variable to be estimated to minimize the F-norm of the difference between the frequency domain echo data matrix and the modulation clutter subspace that meets the preset rank constraint characterization requirements, and constructing a folded clutter suppression optimization model based on the objective function; dividing the folded clutter suppression optimization model into... The solution is divided into multiple independent optimization sub-models, and all variables to be estimated in the multiple independent optimization sub-models are initialized. Under each frequency unit, alternating projection operation is performed iteratively for each range segment and each transmit carrier frequency point to subtract the modulation clutter subspace basis matrix of all other range segments except the current range segment from the frequency domain echo data matrix to obtain the corresponding current residual. The residual sub-vectors corresponding to the current range segment and the current transmit carrier frequency point are extracted according to the effective pulse selection relationship, and the current modulation subspace coefficient vector is updated. The clutter components of the current range segment and the current transmit carrier frequency point are reconstructed using the updated modulation subspace coefficient vector and the modulation PSWF basis function set until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit.

[0059] It should be noted that, in the embodiments of this application, the subspace basis coefficient vector set of each range segment and each transmit carrier frequency point can be used as the variable to be estimated, and the clutter can be reconstructed using the modulation PSWF basis function matrix.

[0060] In embodiments of this application, a frequency can be defined. The set of basis coefficient vectors of the subspace on is:

[0061] The received frequency domain echo is:

[0062] in, and These are the target and the noise, respectively.

[0063] For each frequency The embodiments of this application can establish an objective function that minimizes the F-norm of the difference between the echo data and the reconstructed clutter, forming an optimization problem for folded clutter suppression, i.e., a least squares optimization problem:

[0064] in, Represents the set of basis coefficient vectors in the modulation subspace; Represent the objective function; Represents the window function matrix; Indicates the preceding The modulation clutter subspace basis matrix composed of principal eigenvectors; This represents the vector of basic coefficients to be estimated; This represents the frequency domain echo vector within the current frequency unit. Indicates the number of distance segments; Indicates the number of carrier frequency points; This indicates the frequency value of the current frequency unit.

[0065] In frequency The total amount needed to be estimated A vector of basis coefficients in each subspace. Because the objective function is independent of each other across different frequency units (different... Corresponding variables (Independent), the embodiments of this application can decompose the global optimization problem into There are several independent subproblems, as shown in the following equation:

[0066] The above optimization problem is based on minimizing the received data. The coefficients are constructed from the squared residuals between the reconstructed clutter and the reconstructed clutter. By solving this problem, the optimal coefficients can be obtained. .

[0067] As one possible approach, embodiments of this application can initialize all subspace coefficient vectors to zero or small random values, and for each frequency unit, sequentially perform the following iterative alternating projection solution operation for each range segment and each transmit carrier frequency point: 1. After all frequency units have converged iteratively, the reconstructed clutter for each range segment is then processed. (by all) (Assembled) and transformed back to the time domain via inverse DFT ; 2. Calculate the current residual to obtain the suppressed time-domain echo by subtracting the reconstruction clutter of all range segments from the original time-domain echo (i.e., subtracting the reconstruction clutter of all other range segments except the current range segment from the echo data). ; 3. Based on the effective pulse selection of frequency agility, extract the residual sub-vectors corresponding to the current range segment and the current transmission carrier frequency point; 4. Update the current subspace coefficient vector using the least squares method; 5. Reconstruct the clutter components of the current range segment using the updated coefficient vector and the modulation PSWF basis function set.

[0068] Therefore, the embodiments of this application repeat the above-described alternating update process until the relative change in the overall residual energy is less than a preset threshold, so as to output the reconstructed clutter of each distance segment and each frequency unit.

[0069] In step S104, the reconstructed clutter is subjected to inverse Fourier transform to obtain the corresponding time-domain signal. The time-domain signal is then subtracted from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. The clutter suppression signal is subjected to preset matched filtering, coherent accumulation and constant false alarm rate detection operations to generate the target detection result corresponding to the agile radar. Furthermore, in the embodiments of this application, the frequency domain clutter (i.e. reconstructed clutter) reconstructed in each range segment can be converted back to the time domain by inverse Fourier transform to obtain the corresponding time domain signal. The time domain signal is then subtracted from the original time domain echo data to obtain the clutter-suppressed echo data (i.e., the clutter-suppressed signal).

[0070] Subsequently, in this embodiment, the echo data after clutter suppression can be processed by matched filtering and coherent accumulation to output a range-velocity spectrum. The range-velocity spectrum is then subjected to constant false alarm rate detection to output the range and velocity information of the target under test, i.e., the target detection result, thereby completing the target detection and parameter estimation after clutter suppression.

[0071] The following description of the folding clutter suppression process, in conjunction with the accompanying drawings, illustrates this application.

[0072] Figure 2 This is a schematic diagram of the folding clutter suppression process in this application. Figure 2 As shown, the process of folded clutter suppression in this application is as follows: 1. Initialize all coefficient vectors Reconstructing clutter Let the convergence threshold be... .

[0073] 2. For each frequency unit Repeat the following steps until convergence: (1) For each distance segment : 1) Calculate the residuals: .

[0074] 2) For each carrier frequency point : Extracting effective pulse residuals: ; Update coefficients (least square solution): ; Update noise: ; (2) Calculate the current global residual If the relative change is less than Then stop.

[0075] 3. After all frequency units have been processed, the reconstructed clutter for each range segment will be... Returning to the time domain via inverse DFT transformation:

[0076] 4. Subtract the reconstructed clutter for all range segments from the original time-domain echo: .

[0077] Furthermore, this application can also construct a corresponding agile radar folding clutter suppression system based on iterative subspace projection according to the execution logic of the agile radar folding clutter suppression method based on iterative subspace projection.

[0078] Figure 3 This is a schematic diagram of the logical architecture of the agile radar clutter suppression system based on iterative subspace projection according to this application. Figure 3 As shown, the agile radar folding clutter suppression system based on iterative subspace projection of this application includes an echo reception and preprocessing module, a clutter subspace generation module, a folding clutter suppression module, a target detection module, and a control and parameter configuration module.

[0079] The echo reception and preprocessing module is used to receive radar echo signals, perform down-conversion processing, baseband sampling, and discrete Fourier transform on the echo signals, and output frequency domain echo data matrix and slow-time data vectors of each frequency unit.

[0080] The clutter subspace generation module, connected to the echo reception and preprocessing module, is used to store or calculate the PSWF basis function set, obtain the baseband waveform spectrum of the radar transmitted pulse train, and use the baseband waveform spectrum to perform spectral modulation on the PSWF basis functions to generate a modulated PSWF basis function set. Then, using the modulated PSWF basis function set, combined with the range-velocity steering vector, the clutter covariance matrix of each frequency cell and each transmitted carrier frequency point is calculated. Based on PSWF theory, eigenvalue decomposition is performed to extract the principal eigenvectors to form the modulated clutter subspace basis matrix, and the corresponding subspace basis matrix and window function matrix are generated according to the effective pulse selection relationship of different range segments.

[0081] The folded clutter suppression module, connected to the echo reception and preprocessing module and the clutter subspace generation module, is used to receive frequency domain echo data as well as the modulation clutter subspace basis matrix and window function matrix. It establishes an objective function that minimizes the F-norm of the difference between the echo data and the reconstructed clutter, and solves it using an iterative alternating projection algorithm. It initializes the subspace coefficient vector, and sequentially performs residual calculation, least squares coefficient update, and clutter reconstruction for each frequency unit, each range segment, and each frequency point. It iterates until convergence and outputs the reconstructed clutter for each range segment and each frequency unit.

[0082] The target detection module, connected to the folded clutter suppression module, is used to convert the reconstructed frequency domain clutter back to the time domain via inverse Fourier transform, subtract the reconstructed clutter for each range segment from the original time domain echo, perform matched filtering, coherent accumulation, and constant false alarm rate detection on the clutter-suppressed signal, and output the target detection result.

[0083] The control and parameter configuration module is connected to each of the above modules and is used to configure radar operating parameters, set iteration termination thresholds, and control the coordinated operation of each module.

[0084] The module includes an echo reception and preprocessing module, a clutter subspace generation module, a folded clutter suppression module, a target detection module, and a control and parameter configuration module. In summary, such as Figure 3 As shown, this application can input radar echoes to an echo receiving and preprocessing module to output corresponding frequency domain echoes, and input the frequency domain echoes to a folded clutter suppression module; simultaneously, this application can input baseband waveform spectrum and transmitted carrier frequency sequence to a clutter subspace generation module to output a clutter subspace described by a modulated PSMF basis function, and input the clutter subspace to a folded clutter suppression module; then, the folded clutter suppression module can output suppressed echo data based on the frequency domain echoes and clutter subspace, and input the suppressed echo data to a target detection module to output the range and velocity information of the target under test.

[0085] According to the embodiment of this application, the method for suppressing clutter in agile radar based on iterative subspace projection involves receiving and preprocessing the echo signal of a pre-defined agile radar to generate a frequency domain echo data matrix; acquiring the baseband waveform spectrum of the transmitted pulse train of the agile radar and using the baseband waveform spectrum to perform spectral modulation on a pre-defined PSWF basis function to generate a corresponding set of modulated PSWF basis functions, and constructing a corresponding modulated clutter subspace by combining a pre-defined range-velocity steering vector; constructing a corresponding objective function based on the frequency domain echo data matrix and the modulated clutter subspace, and iteratively solving the objective function until the relative change value of the overall residual energy during the iteration process is less than a pre-defined change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit; performing an inverse Fourier transform on the reconstructed clutter to obtain the corresponding time domain signal, and subtracting the time domain signal from the original time domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal; and performing pre-defined matched filtering, coherent accumulation, and constant false alarm rate detection operations on the clutter suppression signal to generate the target detection result corresponding to the agile radar. This application can effectively describe the low-rank clutter subspace of agile radar by modulating a traditional PSWF basis using known waveform spectrum as prior information when there is a sufficient number of effective pulses. At the same time, the modulated PSWF basis provides separability for clutter at different ranges and frequencies, thereby enabling folded clutter suppression through an iterative alternating projection algorithm.

[0086] Secondly, with reference to the accompanying drawings, a clutter suppression device for agile radar based on iterative subspace projection, according to an embodiment of this application, is described.

[0087] Figure 4 This is a block diagram of a folding clutter suppression device for agile radar based on iterative subspace projection, according to an embodiment of this application.

[0088] like Figure 4 As shown, the agile radar folding clutter suppression device 10 based on iterative subspace projection includes: a preprocessing module 100, a spectrum modulation module 200, an iterative solution module 300, and a clutter suppression module 400.

[0089] The preprocessing module 100 is used to receive and preprocess the echo signal of the preset agile radar to generate a frequency domain echo data matrix.

[0090] The spectrum modulation module 200 is used to acquire the baseband waveform spectrum of the transmitted pulse train of the agile radar, and to use the baseband waveform spectrum to perform spectrum modulation on the preset PSWF basis function to generate the corresponding modulation PSWF basis set, and to construct the corresponding modulation clutter subspace by combining the preset range-velocity steering vector.

[0091] The iterative solution module 300 is used to construct the corresponding objective function based on the frequency domain echo data matrix and the modulation clutter subspace, and iteratively solve the objective function until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit.

[0092] The clutter suppression module 400 is used to perform inverse Fourier transform on the reconstructed clutter to obtain the corresponding time-domain signal, and to subtract the time-domain signal from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. Furthermore, it performs preset matched filtering, coherent accumulation, and constant false alarm rate detection operations on the clutter suppression signal to generate the target detection result corresponding to the agile radar.

[0093] Optionally, in one embodiment of this application, the preprocessing module 100 includes a sampling unit and an extraction unit.

[0094] The sampling unit is used to receive the echo signal of the target under test through the radar receiver of the target agile radar, and to perform preset down-conversion and baseband sampling operations on the echo signal using the radar receiver to obtain the corresponding time-domain echo data matrix.

[0095] The extraction unit is used to perform a discrete Fourier transform on the time-domain echo data matrix along the first target time dimension to obtain the corresponding frequency-domain echo data matrix, and extract the data vector of the second target time dimension corresponding to each frequency unit.

[0096] Optionally, in one embodiment of this application, the spectrum modulation module 200 includes: a first acquisition unit, a first construction unit, a determination unit, and a second acquisition unit.

[0097] The first acquisition unit is used to acquire the baseband waveform spectrum of each pulse in the radar transmit pulse train of the agile radar, and to use the baseband waveform spectrum to perform spectrum modulation on the PSWF basis function, so as to perform complex point multiplication operation between the baseband waveform spectrum of each pulse and the PSWF basis function in the frequency domain to generate the corresponding modulated PSWF basis set.

[0098] The first building unit is used to construct the clutter covariance matrix for each frequency cell and each transmit carrier frequency point under each range segment based on the modulation PSWF basis set and range-velocity steering vector, and to determine the matrix rank of the clutter covariance matrix so as to extract multiple principal eigenvectors of the clutter covariance matrix according to the matrix rank, and to construct the modulation clutter subspace basis matrix corresponding to each frequency cell under each range segment and each transmit carrier frequency point through multiple principal eigenvectors.

[0099] The determination unit is used to determine the corresponding effective pulse selection relationship based on the frequency agility characteristics of the target agile radar, and to determine the corresponding window function matrix based on the effective pulse selection relationship.

[0100] The second acquisition unit is used to obtain the modulation clutter subspace that meets the preset rank constraint characterization requirements for different distance segments based on the window function matrix, multiple principal eigenvectors and preset basic coefficient vectors to be estimated.

[0101] Optionally, in one embodiment of this application, the iterative solution module 300 includes: a second building unit, a third building unit, a model decomposition unit, and an update unit.

[0102] The second construction unit is used to construct a corresponding subspace basis coefficient vector set based on the basis coefficient vector to be estimated for each frequency unit and each transmit carrier frequency point in each range segment, and to use the subspace basis coefficient vector set as the corresponding variable to be estimated.

[0103] The third building unit is used to establish a corresponding objective function based on the variables to be estimated, so as to minimize the F-norm of the difference between the frequency domain echo data matrix and the modulation clutter subspace that meets the preset rank constraint characterization requirements, and to build a folded clutter suppression optimization model according to the objective function.

[0104] The model decomposition unit is used to decompose the folded clutter suppression optimization model into multiple independent optimization sub-models, initialize all the variables to be estimated in the multiple independent optimization sub-models, and iteratively perform alternating projection operations for each range segment and each transmit carrier frequency point under each frequency unit to subtract the modulation clutter subspace basis matrix of all other range segments except the current range segment from the frequency domain echo data matrix to obtain the corresponding current residual.

[0105] The update unit is used to extract the residual sub-vectors corresponding to the current range segment and the current transmit carrier frequency point according to the effective pulse selection relationship, update the current modulation subspace coefficient vector, and reconstruct the clutter components of the current range segment and the current transmit carrier frequency point using the updated modulation subspace coefficient vector and the modulation PSWF basis function set until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit.

[0106] Optionally, in one embodiment of this application, the mathematical expression of the objective function is:

[0107] in, Represents the set of basis coefficient vectors in the modulation subspace; Represent the objective function; Represents the window function matrix; Indicates the preceding The modulation clutter subspace basis matrix composed of principal eigenvectors; This represents the vector of basic coefficients to be estimated; This represents the frequency domain echo vector within the current frequency unit. Indicates the number of distance segments; Indicates the number of carrier frequency points; This indicates the frequency value of the current frequency unit.

[0108] It should be noted that the foregoing explanation of the embodiment of the agile radar folding clutter suppression method based on iterative subspace projection also applies to the agile radar folding clutter suppression device based on iterative subspace projection in this embodiment, and will not be repeated here.

[0109] The agile radar clutter suppression device based on iterative subspace projection proposed in this application includes a preprocessing module 100 for receiving and preprocessing the echo signal of a preset agile radar to generate a frequency domain echo data matrix; a spectrum modulation module 200 for acquiring the baseband waveform spectrum of the transmitted pulse train of the agile radar, and using the baseband waveform spectrum to perform spectrum modulation on a preset PSWF basis function to generate a corresponding modulation PSWF basis set, and combining it with a preset range-velocity steering vector to construct a corresponding modulation clutter subspace; and an iterative solution module 300 for solving the frequency domain echo signal. The data matrix and modulation clutter subspace are used to construct the corresponding objective function, and the objective function is iteratively solved until the relative change of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit. The clutter suppression module 400 is used to perform inverse Fourier transform on the reconstructed clutter to obtain the corresponding time-domain signal, and subtract the time-domain signal from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. The clutter suppression signal is then subjected to preset matched filtering, coherent accumulation and constant false alarm rate detection operations to generate the target detection result corresponding to the agile radar. This application can effectively describe the low-rank clutter subspace of the agile radar by modulating the traditional PSWF basis with known waveform spectrum as a priori information when the number of effective pulses is sufficient. At the same time, the modulated PSWF basis provides separability for clutter at different range segments and different frequency points, so that folded clutter suppression can be achieved through iterative alternating projection algorithm.

[0110] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0111] When the processor 502 executes the program, it implements the agile radar folding clutter suppression method based on iterative subspace projection provided in the above embodiments.

[0112] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0113] The memory 501 is used to store computer programs that can run on the processor 502.

[0114] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0115] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 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 categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 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.

[0116] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0117] Processor 502 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.

[0118] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for suppressing agile radar folding clutter based on iterative subspace projection.

[0119] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for suppressing agile radar folding clutter based on iterative subspace projection.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 method for suppressing clutter folding in agile radar based on iterative subspace projection, characterized in that, Includes the following steps: Receive and preprocess the echo signal from the preset agile radar to generate a frequency domain echo data matrix; The baseband waveform spectrum of the transmitted pulse train of the agile radar is obtained, and the baseband waveform spectrum is used to perform spectrum modulation on the preset PSWF basis function to generate the corresponding modulation PSWF basis set, and the corresponding modulation clutter subspace is constructed by combining the preset range-velocity steering vector. Based on the frequency domain echo data matrix and the modulation clutter subspace, a corresponding objective function is constructed, and the objective function is iteratively solved until the relative change value of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit. The reconstructed clutter is subjected to inverse Fourier transform to obtain the corresponding time-domain signal. The time-domain signal is then subtracted from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. The clutter suppression signal is subjected to preset matched filtering, coherent accumulation and constant false alarm rate detection operations to generate the target detection result corresponding to the agile radar. The step of acquiring the baseband waveform spectrum of the agile radar transmitted pulse train, and using the baseband waveform spectrum to perform spectral modulation on a preset PSWF basis function to generate a corresponding set of modulated PSWF basis functions, and constructing a corresponding modulated clutter subspace by combining it with a preset range-velocity steering vector, includes: The baseband waveform spectrum of each pulse in the radar transmit pulse train of the agile radar is obtained, and the PSWF basis function is spectrally modulated using the baseband waveform spectrum, so as to perform a complex dot product operation between the baseband waveform spectrum of each pulse and the PSWF basis function in the frequency domain to generate the corresponding modulated PSWF basis set. Based on the modulation PSWF basis set and the range-velocity steering vector, a clutter covariance matrix is ​​constructed for each frequency cell and each transmit carrier frequency point under each range segment, and the matrix rank of the clutter covariance matrix is ​​determined. Multiple principal eigenvectors of the clutter covariance matrix are extracted according to the matrix rank, and the modulation clutter subspace basis matrix corresponding to each frequency cell under each range segment and each transmit carrier frequency point is constructed through the multiple principal eigenvectors. Based on the frequency agility characteristics of the agile radar, the corresponding effective pulse selection relationship is determined, and the corresponding window function matrix is ​​determined according to the effective pulse selection relationship; Based on the window function matrix, the multiple principal eigenvectors, and the preset basis coefficient vectors to be estimated, the modulation clutter subspaces corresponding to different distance segments that meet the preset rank constraint characterization requirements are obtained.

2. The method for suppressing clutter in agile radar based on iterative subspace projection according to claim 1, characterized in that, The process of receiving and preprocessing the echo signal from the preset agile radar to generate a frequency domain echo data matrix includes: The radar receiver of the agile radar receives the echo signal of the target under test, and uses the radar receiver to perform preset down-conversion and baseband sampling operations on the echo signal to obtain the corresponding time-domain echo data matrix. The time-domain echo data matrix is ​​subjected to a discrete Fourier transform along the first target time dimension to obtain the corresponding frequency-domain echo data matrix, and the data vector corresponding to the second target time dimension for each frequency unit is extracted.

3. The method for suppressing clutter in agile radar based on iterative subspace projection according to claim 1, characterized in that, Based on the frequency domain echo data matrix and the modulation clutter subspace, a corresponding objective function is constructed, and the objective function is iteratively solved until the relative change in the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter for each range segment and each frequency unit, including: Based on the estimated basis coefficient vectors of each frequency unit and each transmit carrier frequency point under each distance segment, a corresponding subspace basis coefficient vector set is constructed, and the subspace basis coefficient vector set is used as the corresponding estimated variable; Based on the variables to be estimated, a corresponding objective function is established to minimize the F-norm of the difference between the frequency domain echo data matrix and the modulation clutter subspace that satisfies the preset rank constraint characterization requirement, and a folded clutter suppression optimization model is constructed according to the objective function. The folded clutter suppression optimization model is decomposed into multiple independent optimization sub-models, and all variables to be estimated in the multiple independent optimization sub-models are initialized. Under each frequency unit, an alternating projection operation is iteratively performed on each range segment and each transmit carrier frequency point to subtract the modulation clutter subspace basis matrix of all other range segments outside the current range segment from the frequency domain echo data matrix to obtain the corresponding current residual. The residual subvectors corresponding to the current range segment and the current transmit carrier frequency point are extracted according to the effective pulse selection relationship, and the current modulation subspace coefficient vector is updated. The clutter components of the current range segment and the current transmit carrier frequency point are reconstructed using the updated modulation subspace coefficient vector and the modulation PSWF basis function set until the relative change value of the overall residual energy during the iteration process is less than the preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit.

4. The method for suppressing clutter in agile radar based on iterative subspace projection according to claim 3, characterized in that, The mathematical expression for the objective function is: in, Represents the set of basis coefficient vectors in the modulation subspace; Describe the objective function; Represents the window function matrix; Indicates the preceding The modulation clutter subspace basis matrix is ​​composed of principal eigenvectors; This represents the vector of basic coefficients to be estimated; This represents the frequency domain echo vector within the current frequency unit. Indicates the number of distance segments; Indicates the number of carrier frequency points; This indicates the frequency value of the current frequency unit.

5. A clutter suppression device for agile radar based on iterative subspace projection, characterized in that, include: The preprocessing module is used to receive and preprocess the echo signal of the preset agile radar to generate a frequency domain echo data matrix. The spectrum modulation module is used to acquire the baseband waveform spectrum of the transmitted pulse train of the agile radar, and to use the baseband waveform spectrum to perform spectrum modulation on the preset PSWF basis function to generate the corresponding modulation PSWF basis set, and to construct the corresponding modulation clutter subspace by combining the preset range-velocity steering vector. The iterative solution module is used to construct a corresponding objective function based on the frequency domain echo data matrix and the modulation clutter subspace, and iteratively solve the objective function until the relative change value of the overall residual energy during the iteration process is less than a preset change threshold, so as to output the reconstructed clutter of each range segment and each frequency unit. The clutter suppression module is used to perform inverse Fourier transform on the reconstructed clutter to obtain the corresponding time-domain signal, and to subtract the time-domain signal from the original time-domain echo corresponding to the echo signal to obtain the corresponding clutter suppression signal. Furthermore, the clutter suppression signal is subjected to preset matched filtering, coherent accumulation, and constant false alarm rate detection operations to generate the target detection result corresponding to the agile radar. The spectrum modulation module includes: The first acquisition unit is used to acquire the baseband waveform spectrum of each pulse in the radar transmit pulse train of the agile radar, and to use the baseband waveform spectrum to perform spectrum modulation on the PSWF basis function, so as to perform a complex dot product operation between the baseband waveform spectrum of each pulse and the PSWF basis function in the frequency domain to generate the corresponding modulated PSWF basis set. The first construction unit is used to construct clutter covariance matrices for each frequency unit and each transmit carrier frequency point under each range segment based on the modulation PSWF basis set and the range-velocity steering vector, and to determine the matrix rank of the clutter covariance matrix, so as to extract multiple principal eigenvectors of the clutter covariance matrix according to the matrix rank, and to construct the modulation clutter subspace basis matrix corresponding to each frequency unit under each range segment and each transmit carrier frequency point through the multiple principal eigenvectors; The determining unit is used to determine the corresponding effective pulse selection relationship based on the frequency agility characteristics of the agile radar, and to determine the corresponding window function matrix according to the effective pulse selection relationship; The second acquisition unit is used to obtain the modulation clutter subspace that meets the preset rank constraint characterization requirements for different distance segments based on the window function matrix, the multiple principal eigenvectors and the preset basis coefficient vector to be estimated.

6. The agile radar clutter suppression device based on iterative subspace projection according to claim 5, characterized in that, The preprocessing module includes: The sampling unit is used to receive the echo signal of the target under test through the radar receiver of the agile radar, and to use the radar receiver to perform preset down-conversion and baseband sampling operations on the echo signal to obtain the corresponding time-domain echo data matrix. The extraction unit is used to perform a discrete Fourier transform on the time-domain echo data matrix along the first target time dimension to obtain the corresponding frequency-domain echo data matrix, and extract the data vector of the second target time dimension corresponding to each frequency unit.

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 agile radar folding clutter suppression method based on iterative subspace projection as described in any one of claims 1-4.

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 agile radar folding clutter suppression method based on iterative subspace projection as described in any one of claims 1-4.

9. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the agile radar folding clutter suppression method based on iterative subspace projection as described in any one of claims 1-4.

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