Efficient Multitarget Tracking Approach for UAV-Borne Video SAR

Abstract

Shadow-based moving target tracking is essential for uncrewed aerial vehicle (UAV)-borne video synthetic aperture radar (ViSAR). Traditional detect-before-track (DBT) algorithms are ineffective for dim shadows, while track-before-detect (TBD) algorithms incur prohibitive computational costs. Moreover, improving tracking accuracy for dim shadows often increases false alarms in cluttered areas. This article presents an adaptive shadow-aware tracking (ASAT) algorithm and its implementation for low-power edge deployment. The algorithm adaptively extracts the moving target shadow aggregation area (MSAA) from current frames using prior tracking results. Shadows within the MSAA are classified into strong and dim categories using multivalue constant false alarm rate (CFAR) detection and morphological processing. Dim shadow traces are then processed by a TBD-based algorithm, while strong shadow traces are handled by a DBT-based algorithm. Together, adaptive MSAA extraction and subsequent classification-based processing form a joint strategy that enhances tracking accuracy and improves real-time performance on low-power edge platforms. The ASAT algorithm is deployed on a low-power edge system-on-module (SoM) featuring a multicore central processing unit (CPU) and an embedded graphics processing unit (GPU). To enable low-power edge-oriented algorithm design and lightweight implementation, an algorithm–hardware codesign methodology is introduced. Experiments on UAV-borne ViSAR data demonstrate that the proposed approach effectively outperforms traditional approaches in both tracking accuracy and real-time performance on UAV-borne low-power edge platforms.