CN-121978649-A - Robust space-time self-adaptive processing method for improving FRACTA structure

CN121978649ACN 121978649 ACN121978649 ACN 121978649ACN-121978649-A

Abstract

The invention discloses a robust space-time self-adaptive processing method with an improved FRACTA structure, which aims at carrying out robust clutter suppression and target detection on a heterogeneous clutter environment, adopts a sample selection method combining target-oriented constraint and energy to carry out sample selection on echo data to obtain initial sample data, carries out covariance matrix estimation and self-adaptive weight calculation on the initial sample data to obtain an initial weight vector, carries out primary clutter suppression on the echo data based on the initial weight vector to obtain a residual diagram, divides the initial sample data into an initial target sample dataset and a clutter sample dataset based on the residual diagram, carries out background covariance matrix and self-adaptive weight calculation on the clutter sample dataset to obtain a secondary weight vector, carries out clutter suppression on the initial target sample dataset based on the secondary weight vector to obtain candidate target sample data, and determines effective target sample data based on the candidate target sample data and a preset signal-to-noise ratio threshold. The method can realize stable nonuniform clutter suppression.

Inventors

XU XINGYUAN
YANG ZHIWEI
LIU JIE
ZHANG JIAN
ZHANG QINGJUN
LI XIANGHAI
LIU LEI

Assignees

西安电子科技大学

Dates

Publication Date: 20260505
Application Date: 20260203

Claims (8)

1.A robust space-time adaptive processing method for improving FRACTA structure, comprising: acquiring echo data received by a target satellite-borne radar, and finishing data preprocessing; Preliminary sample selection is carried out on the preprocessed echo data by adopting a sample selection method combining target guiding constraint and energy, so as to obtain initial sample data; Performing covariance matrix estimation and self-adaptive weight calculation on the initial sample data to obtain an initial weight vector; Performing primary clutter suppression on the preprocessed echo data based on the initial weight vector to obtain a residual diagram, and dividing the initial sample data into an initial target sample data set and a clutter sample data set based on the residual diagram; performing background covariance matrix and self-adaptive weight calculation on the clutter sample data set to obtain a secondary weight vector; Performing secondary clutter suppression on the initial target sample data set based on the secondary weight vector to obtain candidate target sample data; and determining effective target sample data based on the candidate target sample data and a preset signal-to-noise ratio threshold.
2. The method for robust space-time adaptive processing of an improved FRACTA architecture of claim 1, wherein said sample selection of said echo data using a sample selection method combining target-oriented constraints with energy results in initial sample data, comprising: determining an airspace included angle between each sample data in the echo data and the beam center; Determining sample data to be screened according to the airspace included angle between each sample data and the beam center and a preset airspace included angle threshold; And determining initial sample data according to the sample data to be screened and a preset sample energy threshold value.
3. The method for robust space-time adaptive processing of an improved FRACTA architecture of claim 1, wherein said performing covariance matrix estimation and adaptive weight calculation on said initial sample data to obtain an initial weight vector comprises: Calculating a sample covariance matrix of the initial sample data; Carrying out layered loading on the sample covariance matrix to obtain a covariance matrix of robust estimation; And carrying out self-adaptive weight calculation on the covariance matrix of the robust estimation according to the linear constraint minimum variance criterion to obtain an initial weight vector.
4. A robust space-time adaptive processing method for improving a FRACTA structure according to claim 3, wherein said performing hierarchical loading on said sample covariance matrix to obtain a robust estimated covariance matrix comprises: Performing feature decomposition on the sample covariance matrix to obtain a plurality of feature values and orthogonal feature vectors, and arranging the feature values in descending order from large to small to obtain a feature value sequence; Determining estimated characteristic values corresponding to the characteristic values based on a preset loading rule and the characteristic value sequence; and calculating to obtain a covariance matrix of robust estimation according to the estimated eigenvalue and the orthogonal eigenvector.
5. A robust space-time adaptive processing method for improving a FRACTA structure as described in claim 4, the method is characterized in that the sample covariance matrix is expressed as: Wherein, the Representing the covariance matrix of the samples, For the total number of initial sample data, Represent the first Initial sample data; the preset loading rule is expressed as: Wherein, the Represent the first The number of estimated characteristic values is chosen, Represent the first The value of the characteristic is a value of, A sequence of characteristic values is represented and, The total number of feature values is represented, The mean value is represented by the average value, The number of spatial channels is represented and, Representing a robust scaling factor; the covariance matrix of the robust estimate is expressed as: Wherein, the A covariance matrix representing the robust estimate is presented, Representing orthogonal feature vectors; The initial weight vector is expressed as: Wherein, the The initial weight vector is represented as such, Representing the target space-time steering.
6. The method for robust space-time adaptive processing of claim 1, wherein said performing primary clutter suppression on said echo data based on said initial weight vector to obtain a residual map comprises: and carrying out inner product processing on the echo data based on the initial weight vector to obtain a residual image.
7. The method of claim 6, wherein said dividing the initial sample data into an initial target sample data set and a clutter sample data set based on the residual map comprises: dividing the initial sample data into an initial target sample data set and a clutter sample data set based on a preset target energy threshold and the energy of each initial sample data in the residual diagram; The initial target sample data set comprises initial sample data with energy larger than the preset target energy threshold, and the clutter sample data set comprises initial sample data with energy smaller than or equal to the preset target energy threshold.
8. The method of claim 1, wherein determining valid target sample data based on the candidate target sample data and a predetermined signal-to-noise ratio threshold comprises: Determining background clutter plus noise energy based on the initial weight vector and the clutter sample dataset; Determining candidate target energies based on the secondary weight vector and the candidate target sample data; Determining a candidate target signal-to-noise ratio corresponding to the candidate target sample data according to the background clutter plus noise energy and the target energy; And determining candidate target sample data with the candidate target signal-to-noise ratio greater than the preset signal-to-noise ratio threshold as effective target sample data.

Description

Robust space-time self-adaptive processing method for improving FRACTA structure Technical Field The invention belongs to the technical field of clutter suppression of spaceborne radars, and particularly relates to a robust space-time self-adaptive processing method with an improved FRACTA structure. Background The spaceborne radar has important application value in the fields of ground detection, military reconnaissance and the like because of the wide observation range. However, the special working environment of the radar under the satellite-borne platform brings serious challenges to clutter suppression and moving target detection. The space-borne radar has wide irradiation range and various earth surface coverage types, so that ground clutter received by the radar is unevenly and non-stably distributed on a space-time two-dimensional spectrum. Meanwhile, due to the fact that the satellite platform is high in height and long in acting distance, clutter from different distance units is folded in the time domain and the space domain, the clutter freedom degree is obviously increased, and the clutter spectrum is seriously widened. Space-time adaptive processing (STAP) is a common clutter suppression technique, which generally uses the sample data of a reference unit to calculate a clutter covariance matrix by assuming that the clutter statistics of the unit to be detected are consistent, and the optimal filter is formed by self-adaptive algorithms such as sample covariance matrix inversion, however, in the practical application of the satellite-borne radar system, clutter has the non-uniformity, so that the performance of the traditional STAP algorithm based on the sample covariance matrix inversion is drastically reduced. The existing robust adaptive algorithm improves the matrix inversion condition through methods such as diagonal loading, but the selection of the loading amount of the robust adaptive algorithm often depends on experience, lacks the targeted processing capability of non-uniform scattering points, and is difficult to keep the detection performance of a moving target while inhibiting strong non-uniform clutter. Therefore, how to realize effective clutter suppression in a non-uniform clutter environment and improve the reliability of moving target detection is a technical problem to be solved at present. At present, clutter suppression performance is deteriorated due to the influence of clutter non-uniformity in the processing of the prior art, doppler center and radial velocity estimation accuracy is reduced, and the problem that target energy is seriously lost due to incomplete sample selection exists, so that the accuracy of target detection and target parameter estimation is influenced. Disclosure of Invention In order to solve the above problems in the prior art, the present invention provides a robust space-time adaptive processing method with an improved FRACTA structure. The technical problems to be solved by the invention are realized by the following technical scheme: The invention provides a robust space-time self-adaptive processing method for improving FRACTA structures, which comprises the following steps: acquiring echo data received by a target satellite-borne radar, and finishing data preprocessing; Preliminary sample selection is carried out on the preprocessed echo data by adopting a sample selection method combining target guiding constraint and energy, so as to obtain initial sample data; performing covariance matrix estimation and self-adaptive weight calculation on initial sample data to obtain an initial weight vector; performing primary clutter suppression on the preprocessed echo data based on the initial weight vector to obtain a residual diagram, and dividing the initial sample data into an initial target sample data set and a clutter sample data set based on the residual diagram; performing background covariance matrix and self-adaptive weight calculation on the clutter sample data set to obtain a secondary weight vector; performing clutter suppression on the initial target sample data set based on the secondary weight vector to obtain candidate target sample data; and determining effective target sample data based on the candidate target sample data and a preset signal-to-noise ratio threshold. The robust space-time self-adaptive processing method with the improved FRACTA structure provided by the invention aims at non-uniform clutter, and combines the dimension-reducing space-time self-adaptive processing (STAP) technology of robust loading on the basis of FRACTA processing framework and combined target guiding constraint and energy sample selection, so as to effectively inhibit the non-uniform clutter. The method can effectively solve the problem of performance deterioration of the traditional STAP technology in a non-uniform clutter scene of the spaceborne radar, and improves the detection probability of a moving target. The present invention will be describ