STAP method based on wide symmetrical characteristic of covariance matrix

Abstract

The invention discloses an STAP method based on a wide symmetrical characteristic of a covariance matrix. The method comprises the steps that (1) space-time two-dimensional echo data are received; (2) Doppler filtering processing is carried out; (3) wide symmetrical training samples are acquired; (4) a wide symmetrical covariance matrix is estimated; (5) a self-adaption weight vector is calculated; (6) self-adaption filtering processing is carried out; (7) a result is output. The method mainly solves the problem that the covariance matrix is inaccurately estimated due to the fact that the training samples meeting independent identically distributed conditions are insufficient when an airborne radar is in a heterogeneous clutter environment. The method is based on the wide symmetrical characteristic of the covariance matrix and can effectively improve the use ratio of the echo data, the clutter covariance matrix is estimated more accurately under the condition of few independent identically distributed training samples, and the better clutter retraining and target detection effect is obtained, and the method has better application prospects in the actual extremely-heterogeneous clutter environment.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to a space-time adaptive processing (STAP) method of airborne radar based on the wide symmetry characteristic of covariance matrix in the technical field of radar. The invention is mainly used to solve the problem of inaccurate estimation of the covariance matrix caused by insufficient training samples satisfying the independent and identical distribution conditions in the non-uniform clutter environment of the airborne radar, which can effectively increase the training samples and improve the performance of adaptive signal processing Clutter suppression performance, improve the detection probability of the target. Background technique [0002] The main task of airborne early warning radar is to detect targets in complex clutter background and to locate and track them. Effective suppression of clutter is the core means to improve the performance of early warning radar. ...

AI Technical Summary

Problems solved by technology

In practical applications, the STAP processor mainly has the following two problems: First, in a non-uniform clutter environment, it is necessary to obtain enough independent and identically distributed (IID) training for estimating the covariance matrix The sample is very difficult; secondly, even if the demand of the training sample is met, the problem of excessive calculation in all-time processing will make it difficult to guarantee real-time performance

However, the main disadvantage of this method is that the number of overcomplete bases for sparsely representing the clutter spectrum is undetermined, but it is much larger than the system degrees of freedom. In practice, the system degrees of freedom are usually tens of thousands, so that in each distance unit sample The amount of calculation required in the process of reconstruction of the covariance matrix is ​​very large, which is not conducive to real-time processing, thus affecting its actual engineering application effect

However, this method still has the disadvantage that the widening degree of the clutter notch is artificially set, and it cannot adaptively perceive the clutter ridge in the actual data. When the widening amount is too large, the minimum detectable speed will be caused. Larger, it is very unfavorable for the detection of low-speed small targets

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