面向协同检测与跟踪的多传感器长时调度方法
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(1. 陆军工程大学石家庄校区电子与光学工程系,石家庄050003;2. 国防大学联合勤务学院, 北京100858;3. 中国人民解放军63870部队,陕西华阴714200)

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E-mail: qiaochenglin@126.com.

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TP212

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武器装备预研基金项目(012015012600A2203).


Non-myopic scheduling algorithm for multi-sensor collaborative detection and tracking
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(1.Department of Electronic and Optical Engineering,Shijiazhuang Campus of Army Engineering University,Shijiazhuang050003,China;2. College of Joint Service,National Defense University,Beijing100858,China;3. Unit 63870 of PLA,Huayin714200,China)

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    摘要:

    针对目标检测与跟踪时辐射控制问题,提出一种面向协同检测与跟踪的多传感器长时调度方法.首先建立基于部分马尔可夫决策过程(POMDP)的目标跟踪与辐射控制模型;然后以随机分布粒子计算新生目标检测概率,以后验克拉美-罗下界(PCRLB)预测长时跟踪精度,以隐马尔可夫模型(HMM)滤波器推导长时辐射代价;最后构建新生目标检测概率和已有目标跟踪精度约束下辐射控制的长时优化函数,给出基于贪婪搜索的分支定界算法求解最优调度序列.仿真结果验证了所提出方法的有效性.

    Abstract:

    In consideration of the radiation control for target detection and tracking, a non-myopic scheduling algorithm for multi-sensor collaborative dectection and tracking is proposed. Firstly, the model of target tracking and radiation control is formulated as a partially observable Markov decision process(POMDP). Then, the probability of detecting new targets is calculated by the randomly distributed particles, the non-myopic tracking accuracy is predicted by the posterior carmér-rao lower bound (PCRLB), and the non-myopic radiation cost is derived by the hidden Markov model (HMM) filter. Finally, the non-myopic optimization function of radiation control is set up with the constraints of the new target detection probability and the existing target tracking accuracy. And the optimal scheduling sequence is obtained by the branch and bound algorithm based on greedy search. Simulation results show the effectiveness of the proposed algorithm.

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乔成林,单甘霖,王一川,等.面向协同检测与跟踪的多传感器长时调度方法[J].控制与决策,2020,35(4):799-806

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  • 在线发布日期: 2020-03-03
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