Move-stop-move Target Tracking with Low-altitude Surveillance Radars

Abstract

Low-altitude small targets, represented by rotor unmanned aerial vehicles, always adopt slow move-and-stop strategy or employ an obstacle blocking strategy to avoid radar detection and conduct point-and-point strikes or interference on important information equipment and strategic bases. This type of target can appear and disappear from the radar Field of View (FoV) multiple times, thus, it is referred to as move-stop-move targets. Dealing with this type of target using traditional tracking models and algorithms can lead to discontinuities in target identity and track fragmentation. To this end, this study investigates the tracking problem of move-stop-move targets with the Labeled Multi-Bernoulli (LMB) filter based on random finite set statistics. To describe the evolution characteristics of multiple entries to the radar FoV, first, we introduce the third type of birth procedure, that is, the Re-Birth (RB) procedure. Specifically, based on the spatial and kinematic relationships between target states before and after returning to the radar FoV, a Spatial Correlation-based RB (SC-RB) procedure is proposed. Then, in the framework of Bayesian filtering, we derive the SC-RB-LMB filter with the proposed SC-RB model, which is capable of tracking move-stop-move targets continuously with its identity unchanged. In typical low-altitude surveillance scenarios, the effectiveness of the proposed model and algorithm is highlighted.

Keywords

• low-altitude surveillance radar
• move-stop-move target tracking
• random finite set statistics
• rebirth procedure model
• lmb filter