Knowledge assisted-based sparsity recovery STAP color loading method

Abstract

The invention discloses a knowledge assisted-based sparsity recovery STAP color loading method. The steps include: establishing a sample data model; calculating a priori space-time steering vector matrix; calculating a sparsity recovery vector; calculating sample clutter information; calculating a sample covariance matrix and a prior clutter covariance matrix; and calculating a STAP color loadingmatrix and performing filtering. The solution of the invention combines color loading and a sparsity method, that is, the sample covariance matrix of the color loading is a covariance matrix obtainedfrom a sparsity sample after sparsity processing is performed on a sample. In a process of sparsity recovery processing of the sample, an algorithm to suppress dense interference is performed. The sample sparsity processing is performed in a solving process of the sample covariance matrix of the color loading. A knowledge assisted-based method used to suppress the dense interference, and a minimumthreshold method for determining a clutter element solve a problem of moving target cancellation, and the algorithm has better 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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