Greedy Integration-Based Information Geometry Track-Before-Detect Method for Weak Radar Targets

This study addresses the difficulty of detecting weak radar targets in complex clutter backgrounds. The superior 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 radar 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 information 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 demonstrates 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 performance like that of the matrix information geometry dynamic programming TBD algorithm. It also reduces the computation 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.