Multi-Information-Assisted Joint Detection and Tracking of Ground Moving Target for Airborne Radar - Ran Liu; Xiangqian Li; Jinping Sun; Tao Shan (Remote Sensing, 2025)
Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision.