Robust STAP method based on array manifold priori knowledge having measuring error

Abstract

The invention discloses a robust STAP method based on array manifold priori knowledge having measuring error, and the method comprises the steps: S1, a clutter space-time steering vector set is acquired according to a given error range; S2, an important space-time steering vector is selected in the clutter space-time steering vector set, and an eigenvalue and an eigenvector of the important space-time steering vector is calculated; and S3, a clutter covariance matrix is obtained according to the eigenvalue and the eigenvector of the important space-time steering vector, and a filter weight vector is obtained according to the clutter covariance matrix. In the method, errors are unavoidably existed in the acquisition of array manifold knowledge and directly causes processing performance limitation of STAP based on the array manifold knowledge. Compared with the STAP method based on the array manifold knowledge in the prior art, the method provided by the invention reduces the requirement for the accuracy of priori knowledge, has robust characteristic for the priori knowledge with certain errors, and can avoid a process of clutter covariance matrix inversion in a process of designing a filter, so that a purpose of reducing system computation complexity can be achieved.

Description

technical field [0001] The invention belongs to the field of radar signal processing, and more specifically relates to a robust STAP method based on prior knowledge of array manifolds with measurement errors. Background technique [0002] In traditional space-time adaptive processing (Space-Time Adaptive Processing, STAP), such as principal component method (Principle Components, PC), local joint processing (joint domain localized, JDL) algorithm, method of selecting feature subspace according to cross-spectrum scale (Cross-Spectral Metric, CSM), multistage Wiener filter (Multistage Winer Filter, MWF) algorithm, joint iterative optimal rank reduction adaptive filter (JIOAF) method and a series of dimensionality reduction or rank reduction algorithms, will The required number of training samples is reduced to 2x dimensionality reduction or 2x rank reduction clutter rank. Parametric Adaptive Matched Filter (PAMF) based on multi-channel vector autoregressive (Auto-Regressive, ...

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Problems solved by technology

[0004] Aiming at the defects of the prior art, the purpose of the present invention is to provide a robust STAP method based on the prior knowledge of array manifolds with measuremen

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