Robust space-time adaptive processing method for super-low-altitude target detection by moving platform radar

Abstract

The invention discloses a robust space-time adaptive processing method for super-low-altitude target detection by a moving platform radar, which mainly solves the problem of performance loss caused as a training sample is polluted by a mirror false target generated by the super-low-altitude target in a multipath environment. The method comprises steps: 1, optimized frequencies of four constraint points are designed; 2, a Doppler domain constraint condition is built; 3, an airspace constraint condition is built; 4, an optimal weight vector is solved; and 5, output data are obtained. Through replacing an equality constraint in the previous method by an inequality constraint in the Doppler domain, an amplitude-phase joint constraint in the airspace is used for replacing an amplitude constraint in the previous method, main lobe shape preserving is realized on the premise of not losing degree of freedom, robustness of the space-time adaptive processing method is realized, the performance of super-low-altitude target detection by the moving platform radar is improved, and the method of the invention can be used for detecting the super-low-altitude target by the moving platform radar.

Description

technical field [0001] The invention belongs to the technical field of radar, and further relates to a space-time self-adaptive signal processing technology, which can be used for monitoring and early warning of ultra-low-altitude moving targets in a multipath environment. Background technique [0002] Moving platform radar provides monitoring and tracking functions for moving targets in the air or on the ground, and has important military and civilian values. However, the moving platform radar faces the problem of ground clutter extended by Doppler, so that the weak target signal will be submerged in the clutter background. The space-time adaptive processing STAP combines space and time two-dimensional information, which can realize the detection of weak moving targets in the background of strong clutter, and provides effective detection of low-altitude penetration targets, which has attracted extensive attention from scholars at home and abroad. [0003] In the STAP metho...

AI Technical Summary

Problems solved by technology

The disadvantage of the LCMV method is that the degrees of freedom for suppressing clutter and interference are reduced due to the increase of linear constraints, resulting in higher sidelobes of the beamformer or shallower nulls in the direction of interference

In fact, the factors that cause STAP performance degradation are not only the inaccuracy of the target constraints, but also the error of the estimation of the clutter covariance matrix

For non-side-looking modes such as forward-looking arrays, the clutter also has a serious distance dependence, resulting in errors in the estimation of the clutter covariance matrix, which leads to a sharp decline in the performance of the STAP method

Images