Adaptive Waveform Selection Algorithm based on Reinforcement Learning for Cognitive Radar

Cao, Xin; Zheng, Zhe; An, Di

doi:10.1109/auteee48671.2019.9033413

Cited by 3 publications

(1 citation statement)

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“…The proposed method can reduce the velocity error and range error by 50% and 60%, respectively. The authors in [10] proposed an adaptive waveform selection algorithm based on indirect reinforcement learning, addressing uncertainties in the target-state space. Simulation results show that the algorithm has better computational efficiency and fewer state estimation errors, which improve the tracking accuracy.…”

Section: Introductionmentioning

confidence: 99%

Radar Waveform Selection for Maneuvering Target Tracking in Clutter with PDA-RBPF and Max-Q-Based Criterion

Feng,

Sun,

Liang

et al. 2024

Remote Sensing

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In this paper, to track maneuvering unmanned surface vehicles (USVs) in scenarios with clutter, we propose a novel method based on the probabilistic data association (PDA) algorithm and Rao-Blackwellized particle filter (RBPF) algorithm, and we further improve the tracking performance by Max-Q criterion-based waveform selection. This work develops a maneuvering target model in the context of clutter, integrating linear and nonlinear states as well as observations with false alarms. In order to jointly tackle the mixed-state tracking problem, the PDA algorithm is integrated into the RBPF framework. This allows it to be used with the complex nonlinear and linear hybrid system and helps to minimize the state dimensions of conventional particle filtering (PF). Additionally, by utilizing Q-learning principles, we provide a Max-Q-based criterion to select the waveform parameters, which guarantees low measurement errors and efficiently handles measurement uncertainties. Our simulation results show that the PDA-RBPF algorithm, which has a more appropriate tracking mechanism, produces results that are more accurate than those of the EKF or PF algorithms alone. Furthermore, the RMSE derived by the Max-Q-based criterion is smaller and more robust than that of other selection methods, as well as yielding a fixed waveform. Our proposed mechanism, which combines the concepts of PDA-RBPF and Max-Q waveform selection, performs well in target tracking tasks and exhibits relatively good performance over some existing ones.

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Section: Introductionmentioning

confidence: 99%