An Efficient Structure-Algorithm Co-Design for Doppler Radar-Based Target Tracking With Reservoir Computing

作者：Yipeng Ding, Runjin Liu, Pung Hok, Minhao Ding, Ping Lv · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3555037 · 被引用次数：2 · 研究领域：Neural Networks and Reservoir Computing、Neural Networks and Applications、Advanced Memory and Neural Computing

Doppler radar is a cost-effective Internet of Things (IoT) device widely utilized in smart homes, urban management, and health monitoring. Conventional Doppler radars, which detect targets from a single perspective, can only extract the radial information from the radar echoes and struggle to detect stationary targets or targets moving tangentially to the radar. Furthermore, the receivers commonly encounter the issue of ambiguous frequency (AF) simultaneously, making it difficult for conventional Doppler radar to track multiple targets accurately. To address these limitations, this article enhances the target detection capabilities of Doppler radars through the design of both radar hardware structure and Doppler frequency (DF) estimation algorithms. First, a multiperspective radar system is proposed to provide richer target information and substantially minimize the AF area. Second, a novel DF estimation algorithm, based on reservoir computing (RC) theory, is proposed to estimate the DFs of targets in these reduced ambiguous intervals. Lastly, an error compensation process, adapted to the characteristics of the echoes, is designed to reduce the accumulation of estimation errors. Compared to conventional Doppler radar systems, this approach reveals more precise target information and suppresses AF interference, a critical advantage in multitarget tracking environments.