Cognitive Radar Target Tracking Using Intelligent Waveforms Based on Reinforcement Learning

Zhu, Peikun; Liang, Jing; Luo, Zihan; Shen, Xiaofeng

doi:10.1109/tgrs.2023.3298355

Cited by 2 publications

(4 citation statements)

References 30 publications

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“…In this section, we consider a radar system consisting of a transmitter located at t and a receiver located at r . Differing from the discussion in [8], the receiver in our work uses a novel PDA-RBPF algorithm to obtain local tracking trajectories. Thus, the dynamic hybrid model of the maneuvering target can be defined as…”

Section: System Overview and Problem Formulationmentioning

confidence: 99%

“…Q-learning is a model-free method that updates the Q-table by calculating the reward at each step and selecting the optimal path to complete the specified task [8]. The Q-value in the Q-table represents the cumulative discounted reward, indicating the expected return when taking action a(a ∈ A) in state s(s ∈ S) at a specific moment.…”

Section: The Proposed Max-q-based Criterionmentioning

confidence: 99%

“…Therein, such criteria include the minimum mean square error criterion (Min-MSE) [4], the maximum mutual information criterion (Max-MI) [5,6] and the minimum gate criterion (Min-Gate) [7]. A series of simulations and results show that these criteria-based optimization methods have high tracking accuracy and simple calculation principles, but with relatively high time costs [8]. Reference [9] proposed a new method using the particle swarm optimization (PSO) algorithm based on adaptive Kalman filtering to optimize the radar waveform parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, their strategy is more general, and it provides better performance. Moreover, the authors in [8,12] utilized supervised learning and reinforcement learning for waveform selection, where these adaptive waveform selection methods further reduce the state estimation error and improve the tracking accuracy with much less processing time compared to the criteria-based optimization methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: System Overview and Problem Formulationmentioning

confidence: 99%

Section: The Proposed Max-q-based Criterionmentioning

confidence: 99%