Air Defense and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.
Sensors (Basel). 2022 Apr 14;22(8):3001. doi: 10.3390/s22083001.
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.
在分布式防御中，多传感器网络需要能够进行合理的规划和调度，以实现对目标的连续、准确和快速检测。本文提出了一种基于部分可观测马尔可夫决策过程的多传感器协同调度模型。通过研究部分可观测马尔可夫决策过程和后验克拉美-罗下界，建立了多传感器协同调度模型和优化目标函数。研究了通过甲壳虫群算法对粒子滤波算法的改进，以提高粒子滤波的跟踪精度。最后，采用改进的象群优化算法作为调度方案的求解算法，进一步提高了求解模型的算法性能。仿真结果表明，该模型能够很好地解决分布式多传感器协同调度问题，比其他算法具有更高的求解性能，并且满足实时性要求。
