An Auxiliary Clutter Sample Screening Method Based on Subaperture Smoothing

Abstract

The invention discloses an auxiliary clutter sample screening method based on sub-aperture smoothing, which adopts the ultra-low side lobe sub-aperture sliding window method to greatly improve the signal-to-noise ratio of the side lobe area, and combines the method of fitting variance deviation to extract the target large signal Energy, and be eliminated, thereby improving the purity of the initial clutter covariance matrix; at the same time, combined with the sub-aperture smoothing method can significantly improve the output signal-to-noise ratio of small targets, combined with GIP and other algorithms to eliminate clutter samples containing small targets, thereby Improving the output signal-to-noise ratio of weakly discrete detection points containing such targets as micro-motion devices provides the possibility for subsequent target recognition based on detection output. It overcomes the problem that the existing non-uniform clutter sample screening method faces the problem that the initial covariance matrix contains the target signal and cannot eliminate the target information, which can greatly reduce the system operation complexity and improve the system application efficiency.

Description

technical field [0001] The invention relates to an auxiliary clutter sample screening method based on sub-aperture smoothing, which belongs to the field of radar technology, in particular to an adaptive non-uniform / target-contaminated clutter sample removal technology, which is suitable for signal noise above 30dB in actual engineering Adaptive non-uniform / target-contaminated clutter sample culling in a strong clutter background. Background technique [0002] The basic idea of ​​training sample selection is to select training samples with similar clutter statistical properties in a given set of training samples to estimate the clutter covariance matrix and use it to calculate the STAP filter weight vector. Traditional methods mainly include: Sample Matrix Inversion (SMI) method, which directly performs STAP on the original sample set, and then eliminates samples with larger residual power. The inner product rule mainly groups samples according to the power of clutter sample...

AI Technical Summary

Problems solved by technology

[0004] The technical problem of the present invention is: to overcome the deficiencies of the prior art, a kind of auxiliary clutter sample screening method based on sub-aperture smoothing is proposed, which solves the problem of strong target pollution and partial inclusion with a signal-to-noise ratio higher than 20dB in actual engineering The problem of output signal-to-noise ratio decrease caused by the pollution of micro-movement targets such as spin, precession and nutation

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