Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

Shi, Chenguang; Wang, Yijie; Salous, Sana; Zhou, Jianjaing; Yan, Junkun

doi:10.1109/taes.2021.3138869

Cited by 90 publications

(16 citation statements)

References 39 publications

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“…where I 3 is the identity matrix of order 3, ⊗ is the Kronecker product, and τ q denotes the process noise intensity [33].…”

Section: A Dynamic Model For Target Qmentioning

confidence: 99%

“…The PC-FIM (33) shows that the positions of multi-UAV have an impact on the tracking performance, which motivates us to optimize the trajectories of multiple UAVs to achieve the optimal MTT performance. Since the elements of the PC-CRLB matrix have different units, it cannot be adopted as an objective function directly, we perform a trace operation on the normalized PC-CRLB as follows:…”

Section: B Cto Modelmentioning

confidence: 99%

See 1 more Smart Citation

Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

Dai

Yan

et al. 2023

IEEE Trans. Geosci. Remote Sensing

Self Cite

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“…where I 3 is the identity matrix of order 3, ⊗ is the Kronecker product, and τ q denotes the process noise intensity [33].…”

Section: A Dynamic Model For Target Qmentioning

confidence: 99%

Section: B Cto Modelmentioning

confidence: 99%

Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

Dai

Yan

et al. 2023

IEEE Trans. Geosci. Remote Sensing

Self Cite

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“…They proposed a cost function that could adapt to several radar measurement errors to improve tracking performance and could save the time resources of CR. For target tracking in radar networks, Shi et al [20] investigated the joint transmit resource management and waveform selection to coordinate the time resource and waveform resource of each radar node to improve tracking performance as well as the low probability of intercept (LPI) performance. A particle swarm optimisation-based three-stage solution was developed to solve the RRM problem.…”

Section: Introductionmentioning

confidence: 99%

Memory‐based deep reinforcement learning for cognitive radar target tracking waveform resource management

Qin,

Zhu,

Pan

et al. 2023

IET Radar Sonar & Navi

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A cognitive radar (CR) system can offer enhanced target tracking performance due to its intelligence on the perception‐action cycle, wherein a CR adaptively allocates the limited transmitting resources based on its perception of surrounding environments. To effectively manage the transmit waveform resource for the target tracking task, CR resource management problem is formulated under the partially observable Markov decision process framework. The sequential decision‐making and the inherent partial observability for target tracking problem are considered. In the proposed method, a long short‐term memory (LSTM)‐based twin delayed deep deterministic policy gradient (TD3) algorithm is developed to effectively solve the problem. A reward function is designed considering Haykin's cognitive executive attention mechanism for radar systems such that the CR resource management policy has stability in the decision of transmit waveform, which follows the principle of minimum disturbance. Simulation results demonstrate the superiority of the proposed LSTM memory‐based TD3 with improved target tracking performance and increased mean rewards for CR.

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“…This is the question which we pursue here. We acknowledge that a practical characterization of online learning performance for all waveform selection tasks is beyond the scope of a single investigation, but present insight towards the general question by examining compelling performance measures for two broad applications of practical interest, namely radar dynamic spectrum access [8], [35], [36] and multiple target tracking [2], [37], [38]. We apply tools from decision theory and information theory to compare the performance of an online learning-based waveform selection algorithm with fixed rule-based strategies.…”

Section: Introductionmentioning

confidence: 99%

Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking

Thornton

Buehrer

Martone

2022

2022 IEEE Radar Conference (RadarConf22)

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This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse. We seek general insights through an examination of two decision-making scenarios, namely dynamic spectrum access and multiple-target tracking. The radar scene is characterized by inducing a statespace model and examining the structure of its underlying Markov state transition matrix, in terms of entropy rate and diagonality. It is found that entropy rate is a strong predictor of online learning-based waveform selection, while diagonality is a better predictor of fixed rule-based waveform selection. We show that these measures can be used to predict first and second-order stochastic dominance relationships, which can allow system designers to make use of simple decision rules instead of more cumbersome learning approaches under certain conditions. We validate our findings through numerical results for each application and provide guidelines for future implementations.

show abstract

Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

Cited by 90 publications

References 39 publications

Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

Memory‐based deep reinforcement learning for cognitive radar target tracking waveform resource management

Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking

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