High-efficiency method for estimating covariance matrix structures

Abstract

The invention discloses a high-efficiency method for estimating covariance matrix structures, which belongs to the field of radar signal processing and mainly realizes estimating covariance matrix structures in adaptive detection of radar targets on the background with non-Gaussian clusters. The high-efficiency method aims to solve the problem that a traditional sampling covariance matrix and a traditional normalized sampling covariance matrix cannot lead an adaptive detector to a complete CFAR (constant false alarm rate) characteristic. A sampling covariance matrix is solved after pretreatment by means of dividing real-part data from virtual-part data, an obtained initiated matrix realizes the complete CFAR characteristic for clusters, a real part and a virtual part of auxiliary data are sufficiently utilized for iteration, accordingly, computation complexity in an iteration process is reduced, estimation precision is improved beneficially, and the high-efficiency method for estimating covariance matrix structures has a popularization and application value in adaptive CFAR detectors for radar targets.

Description

1. Technical field [0001] The invention belongs to the field of radar target self-adaptive detection, and in particular relates to an efficient covariance matrix structure estimation method. 2. Background technology [0002] Adaptive constant false alarm ratio (CFAR) detection of radar targets is an important content of radar target detection. For single-array radar target detection, it is only necessary to estimate the clutter power level of the detected unit to achieve adaptive CFAR detection of the target. However, when the radar is a multi-element radar, the target detection probability can be effectively improved by coherent accumulation of the echo signals of each array element, but at the same time, more clutter parameters need to be estimated. [0003] In the Gaussian clutter background, the sample covariance matrix SCM (sample covariance matrix) is the maximum likelihood estimate (MLE) of the covariance matrix, and the radar target can be obtained by directly using...

AI Technical Summary

Problems solved by technology

However, NSCM-RE needs multiple iterations to make the corresponding adaptive detector have approximate CFAR characteristics, which significantly increases the computational complexity

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