STAP clutter covariance matrix estimation method

Abstract

The invention belongs to the technical field of radars, and particularly relates to a STAP clutter covariance matrix estimation method based on CUT self priori knowledge. The invention discloses the clutter covariance matrix estimation method based on CUT self priori knowledge. The method comprises the following steps of firstly, reconstructing clutter of a to-be-detected frequency channel of CUT,further extracting clutter of other frequency components except the to-be-detected frequency from the CUT, and finally calculating the clutter covariance matrix of the CUT by the reconstructed clutter. By adoption of the method, the problem of estimation of the clutter covariance matrix in the STAP is solved, the estimated clutter covariance matrix is more accurate, and the rejection performanceof the clutter is improved.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to the technology of estimating the clutter covariance matrix of the unit to be detected by using its own prior knowledge. Background technique [0002] Space-time adaptive processing (STAP) is a key technology in radar and communication signal processing, and is widely used in radar and communication signal processing. The optimal weight vector designed by STAP , the output signal-to-noise ratio (SCNR) can be maximized. Among them, s is the space-time steering vector of the target signal, R CUT is the clutter covariance matrix of the range unit to be detected. However in practice R CUT is unknown and needs to be estimated through the selected training samples. [0003] In the current STAP method, the CCM (clutter covariance matrix) of CUT is completely estimated with the selected training samples. Typical is: the document "Wu Y, Wang T, Wu J, et al.Training Sample ...

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Problems solved by technology

Typical is: the document "Wu Y, Wang T, Wu J, et al.Training Sample Selection for Space-TimeAdaptive Processing in Heterogeneous Environments[J].IEEE Geoscience&RemoteSensing Letters,2014,12(4):691-695." Select samples similar to the sub-aperture covariance matrix of CUT (Cell Under Test, test unit) to estimate the CCM of CUT. However, this method only considers the local similarity, thus missing some locally dissimilar but overall similar reference units; for This problem, the literature "Wu Y, Wang T, Wu J, etal. Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments [J]. Radar Sonar & Navigation Iet, 2015, 9 (7): 778-782." "X. Zhang, Q. Yang, W. Deng, Weak target detection within the nonhomogeneous ionospheric clutter background of HFSWR based on STAP, Int. J. Antennas Propag. 2013 (2013), doi: 10.1155 / 2013 / 382516. "Choose training samples similar to the overall waveform of CUT to estimate CCM

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