A Knowledge-Assisted Sparse Restoration Stap Color Loading Method

Abstract

The invention discloses a knowledge-assisted sparse restoration STAP color loading method. The steps include: establishing a sample data model; calculating a priori space-time steering vector matrix; calculating a sparse restoration vector; calculating sample clutter information; and calculating a sample covariance matrix and the prior clutter covariance matrix; calculate the STAP color loading matrix and perform filtering. The scheme of the present invention combines the color loading and the sparse method, that is, the sample covariance matrix of the color loading is to obtain the covariance matrix of the sparse sample after the sample is sparsely processed; Algorithm for suppressing dense interference. In the process of solving the color loading sample covariance matrix, the sample sparse processing is carried out; the knowledge-based method for suppressing dense interference, and the minimum threshold value method for judging clutter elements solve the problem of moving target cancellation. good robustness.

Description

technical field [0001] The invention belongs to the field of main detection of airborne radar, especially a robust space-time self-adaptive method under the condition of small samples and dense interference, in particular a knowledge-assisted sparse recovery STAP color loading method. Background technique [0002] The main application of airborne radar is to detect moving targets, but in some areas, the range of clutter distribution is relatively wide and the power is relatively large, which greatly affects the accuracy of detecting moving targets. Space-time adaptive processing (STAP) technology jointly processes space-time two-dimensional echo data, which can effectively suppress clutter and improve the moving target detection performance of airborne radar. One of the difficulties faced by the application of STAP technology is that the clutter environment is uneven, complex and changeable. Traditional STAP technology requires enough independent and identically distributed...

AI Technical Summary

Problems solved by technology

However, the problem with the CPW-STAP method is that it needs a sufficient number of samples that satisfy the independent and identical distribution (IID). Compared with the sparse processing method, the IID samples required by CPW-STAP are several tens of times sparser, and the actual The radar moving target detection scene cannot provide the number of samples required by the CPW-STAP method

Therefore, the existing STAP algorithm cannot avoid the phenomenon of target and outlier cancellation, or false alarm.

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