Clutter covariance matrix estimation method based on two-order statistics similarity

Abstract

The invention relates to the technical field of signal processing, and particularly provides a clutter covariance matrix estimation method based on a two-order statistics similarity, so as to estimation of a clutter covariance matrix in the case of space-time adaptive processing. Original samples are taken near a to-be-processed unit in a sliding window mode firstly; then, the two-order statistics of each sample is estimated; then, according to the two-order statistics of the to-be-processed unit and the original sample, the similarity between the to-be-processed unit and the original sample is estimated; when the covariance matrix of the to-be-processed unit is estimated, the weight of the similarity is added before each training sample, if a certain sample is comparatively similar to the to-be-processed unit, the training sample occupies a large proportion when the covariance matrix of the to-be-processed unit is estimated, and thus, the clutter covariance matrix estimation precision of the to-be-processed unit is thus improved. The method can accurately estimate the covariance matrix of the to-be-processed unit, and the detection performance of an airborne radar is improved.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a clutter covariance matrix estimation method based on second-order statistical similarity. Background technique [0002] When the airborne radar works in the down-looking state to detect moving targets, it will receive the clutter reflected from the ground. Strong ground clutter will drown the target signal and reduce the radar detection performance. The space-time adaptive processing technology can suppress clutter well and improve the performance of airborne radar in ground detection through the joint processing of airspace and time domain. Space-time adaptive processing needs to estimate the covariance matrix of the clutter when calculating the adaptive weight, which is usually estimated by the nearby distance units of the processing unit. When the radar works in a non-uniform clutter environment, the clutter statistical characteristics of different range gates may...

AI Technical Summary

Problems solved by technology

[0004] 1) Divide the radar echo data evenly into several segments, and directly use the samples of the segment to estimate the clutter covariance matrix of all range units in the segment, but when the radar echo data is non-uniform, the estimated clutter covariance matrix is inaccurate;

[0005] 2) Divide the radar echo data evenly into several segments, and use the non-uniform detector to eliminate non-uniform samples for each segment, and then use the remaining "uniform" samples to estimate the clutter covariance matrix of the unit to be processed. Although this method eliminates However, sometimes the elimination effect is not good, and when the unit to be processed itself is a non-uniform sample, it is obviously impossible to estimate the clutter covariance matrix of the unit to be processed with a uniform sample, which leads to a space-time Performance degradation with adaptive processing

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