Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

被引：0
|
作者：
Feng, Xiang [1 ]
Sun, Ping [1 ]
Zhang, Lu [1 ]
Jia, Guangle [1 ]
Wang, Jun [1 ,2 ]
Zhou, Zhiquan [1 ]
机构：
[1] School of Information Science and Engineering, Harbin Institute of Technology, Weihai,264209, China
[2] Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems, Ministry of Industry and Information Technology, Weihai,264209, China
来源：
Remote Sensing | 2024年 / 16卷 / 21期
关键词：
In this paper; we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform; aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model; combining linear and nonlinear states; is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework; our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm; using the IMCS method for adaptive parameter selection; exhibits high accuracy and robustness in tracking a low-altitude maneuvering target; resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms. © 2024 by the authors;
D O I：
10.3390/rs16213951
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