基于贪婪积累的信息几何雷达弱小目标检测前跟踪方法OA

This study addresses the difficulty of detecting weak radar targets in complex clutter backgrounds.The supe-rior capability of information geometry detection methods in distinguishing targets from clutter on manifolds and the multi-frame information integration ability of track-before-detect(TBD)algorithms are leveraged.A greedy integration-based information geometry TBD method is proposed.By establishing a high-dimensional manifold representation of ra-dar multi-frame echo signals,the distance between the target and clutter on the manifold is used as a feature difference.Through the integration of multi-frame feature differences,the target and clutter feature trajectories on the multi-frame manifold gradually separate,significantly increasing their dissimilarity.This method uses feature differences on the manifold as multi-frame integration quantities and uses a greedy integration algorithm to achieve multi-frame informa-tion integration.This distinguishes targets from complex clutter backgrounds and outputs target trajectories.Through a series of locally optimal forward decisions made by the greedy integration algorithm,multi-frame joint processing is achieved while outputting the current best estimate frame-by-frame.Experimental validation using measured data demon-strates that the greedy integration-based information geometry TBD algorithm achieves superior detection performance compared with traditional energy-feature-based TBD algorithms.The proposed method can achieve a detection perfor-mance like that of the matrix information geometry dynamic programming TBD algorithm.It also reduces the computa-tion time by 80%.Compared to traditional energy-based TBD algorithms,the proposed method improves the detection signal-to-clutter ratio by approximately 2 dB,increases the detection probability by more than 10%,and achieves a higher trajectory estimation accuracy.