A Novel Algorithm for Adaptive Detection and Tracking of Extended Targets Using Millimeter-Wave Imaging Radar

A high-resolution imaging radar is exceptionally well-suited for the detection and perception of extended targets (ETs), as it provides a comprehensive representation of the spatial distribution of target scattering characteristics. In this work, we propose an adaptive detection and tracking framework for non-cooperative ETs based on radar imaging. The framework leverages the statistical properties of ETs in radar imaging to construct a target distribution model and introduces an adaptive ET detection and tracking algorithm based on Scattering Point Shift (SPS). This algorithm is designed to track ETs with internal motion characterized by multiple scattering points. The initial target distribution is estimated using two-dimensional kernel density estimation (2D-KDE). Compared to existing ET tracking algorithms, the proposed SPS method demonstrates superior universality in accommodating diverse scattering point distributions and integrates detection and tracking into a unified process, thereby significantly improving information utilization efficiency. The effectiveness of the algorithm is validated through extensive simulations and real-world data collected using a millimeter-wave (mmWave) imaging radar operating in the Linear Frequency Modulated Continuous Wave (LFMCW) mode.