Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking

Gongguo XU; Ganlin SHAN; Xiusheng DUAN; Chenglin QIAO; Haotian WANG

doi:10.11999/JEIT181129

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2201-2208

Gongguo XU, Ganlin SHAN, Xiusheng DUAN, Chenglin QIAO, Haotian WANG. Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2201-2208. doi: 10.11999/JEIT181129

Citation:

Gongguo XU, Ganlin SHAN, Xiusheng DUAN, Chenglin QIAO, Haotian WANG. Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2201-2208. doi: 10.11999/JEIT181129

Citation:

Gongguo XU, Ganlin SHAN, Xiusheng DUAN, Chenglin QIAO, Haotian WANG. Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2201-2208. doi: 10.11999/JEIT181129

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Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking

doi: 10.11999/JEIT181129

1.
Department of Electronic and Optical Engineering, Army Engineering University Shijiazhuang Campus, Shijiazhuang 050003, China
2.
Department of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050003, China

Received Date: 2018-12-06
Rev Recd Date: 2019-05-26

Available Online: 2019-06-03

Publish Date: 2019-09-10

Abstract

Abstract

In order to solve the problem of sensor scheduling in the multi-task scenario, a multi-sensor scheduling method for target cooperative detection and tracking is proposed. Firstly, the sensor scheduling model is built based on the Partially Observable Markov Decision Process (POMDP) and an objective function is designed based on Posterior Carmér-Rao Lower Bound (PCRLB). Then, considering sensor switching time and the change of target number, the randomly distributed particles are used to calculate the detection probability of new target, and the sensor scheduling methods are given for the situations with fixed target number and time-varying target number. At last, to meet the real-time requirement of online scheduling, an Adaptive Multi-swarm Cooperative Differential Evolution (AMCDE) algorithm is used to solve the sensor scheduling scheme. Simulation results show that the method can effectively deal with multi-task scenarios and realize reasonable scheduling of multi-sensor resources.
- Detection and tracking,
- Sensor scheduling,
- Markov decision process,
- Differential evolution

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References(15)

References

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