JPBA of ARN for target tracking in clutter

Sun, Hao; Li, Ming; Zuo, Lei; Cao, Runqing

doi:10.1049/iet-rsn.2019.0038

Cited by 13 publications

(7 citation statements)

References 33 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Focusing on resource scheduling, extensive researches have been conducted in radar nodes, dwell time, illumination power and so on, for different optimisation objective [11][12][13][14][15][16][17][18][19][20][21]. For example, Yan et al propose a cooperative target assignment and dwell allocation algorithm to optimise the target-to-radar assignment and the limited time resource for improving the multiple target tracking accuracy [11].…”

Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The work of Ref. [14] researches joint power and bandwidth allocation optimisation to improve target tracking accuracy in clutter. In Ref.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Collaborative transmit resource scheduling and waveform selection for target tracking in multistatic radar system

Wang

Shi

Wang

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

This paper proposes a collaborative transmit resource scheduling and waveform selection (CTRSWS) strategy for target tracking in multistatic radar system which consists of one transmitter and an arbitrary number of receivers. The main mechanism of the proposed CTRSWS strategy is to exploit the optimisation technique to jointly optimise the illumination power, dwell time, waveform bandwidth, and pulse length under the constraints of several resource budgets and the predefined waveform library, aiming at improving the low probability of intercept (LPI) performance and target tracking accuracy of multistatic radar system simultaneously. The analytical expressions for the probability of intercept and the trace of the predicted error covariance matrix corresponding to the target state estimation are derived and adopted to evaluate the LPI performance and target tracking accuracy, respectively. Subsequently, the resulting non-convex and non-linear optimisation problem is resolved by an efficient and fast four-stage solution methodology. Several numerical results are provided to verify the effectiveness and superiority of the proposed CTRSWS scheme in terms of the achievable LPI performance and target tracking accuracy of multistatic radar system. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative transmit resource scheduling and waveform selection for target tracking in multistatic radar system

Wang

Shi

Wang

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…Numerous studies have demonstrated that there is a trade-off between transmitting resource consumption and tracking performance. [16][17][18]. For single target tracking, Ghadian et al [19] studied the waveform parameters selection and time resource management of CR.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…A two-step semi-definite programming-based solution approach is presented to solve the resulting problem. In [8], Sun et al concentrate on the problem of joint power and bandwidth allocation for target tracking in clutter with an asynchronous radar network system, and the branch-reduce-and-bound technique is adopted to deal with the non-convex optimization model. It is validated that the target state estimation error can be efficiently decreased with the optimized illumination resource allocation.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Wang

Salous

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

JPBA of ARN for target tracking in clutter

Cited by 13 publications

References 33 publications

Collaborative transmit resource scheduling and waveform selection for target tracking in multistatic radar system

Collaborative transmit resource scheduling and waveform selection for target tracking in multistatic radar system

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

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

Contact Info

Product

Resources

About