Research on Distributed Multi-Sensor Cooperative Scheduling Model Based on Partially Observable Markov Decision Process

• Main Authors: Luo, R. (Author), Wu, J. (Author), Zhang, Z. (Author), Zhao, Y. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Cooperative scheduling; distributed defense; Distributed defense; intelligent optimization algorithm; Intelligent optimization algorithm; Markov processes; Model-based OPC; Monte Carlo methods; Multi sensor; Multi-sensor networks; multi-sensor scheduling; Multi-sensor scheduling; Network security; Optimization; partially observable Markov decision process; Partially observable Markov decision process; Scheduling; Scheduling algorithms; Scheduling models; Sensor networks; Sensor scheduling
• Online Access: View Fulltext in Publisher

 In the context of distributed defense, multi-sensor networks are required to be able to carry out reasonable planning and scheduling to achieve the purpose of continuous, accurate and rapid target detection. In this paper, a multi-sensor cooperative scheduling model based on the partially observable Markov decision process is proposed. By studying the partially observable Markov decision process and the posterior Cramer–Rao lower bound, a multi-sensor cooperative scheduling model and optimization objective function were established. The improvement of the particle filter algorithm by the beetle swarm optimization algorithm was studied to improve the tracking accuracy of the particle filter. Finally, the improved elephant herding optimization algorithm was used as the solution algorithm of the scheduling scheme, which further improved the algorithm performance of the solution model. The simulation results showed that the model could solve the distributed multi-sensor cooperative scheduling problem well, had higher solution performance than other algorithms, and met the real-time requirements. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.