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摘要:
为了降低有源传感器在获得目标持续量测时被敌方截获的风险，提出一种多传感器协同跟踪与辐射控制的调度算法。该算法首先采用辐射度影响（ELI）衡量传感器辐射，将目标跟踪与辐射控制过程建立为部分可观马尔可夫决策（POMDP）过程。然后以隐马尔可夫模型（HMM）滤波器更新传感器辐射状态、推导长时辐射风险，以无迹卡尔曼滤波（UKF）更新目标状态、估计跟踪精度。最后考虑跟踪任务需求，构建精度约束下辐射控制的长时调度模型，并将该长时调度问题转化为决策树寻优问题，给出决策树节点次优下界值，采用改进分支定界技术（IB & B）快速求解最优调度序列。仿真结果验证了本文算法的有效性。
Abstract:Active sensors obtain the target continuous measurements that can be intercepted by enemy system. To reduce the interception risk, a scheduling algorithm for multi-sensor collaboration tracking and radiation control is proposed. Firstly, the sensor radiation is represented by the emission level impact (ELI) and the processes of target tracking and radiation control are formulated as a partially observable Markov decision process (POMDP). Secondly, the hidden Markov model (HMM) filter is utilized to update the sensor radiation state and derive the non-myopic radiation risk. Meanwhile, the target state is updated by the unscented Kalman filter (UKF) which is also used to evaluate the target tracking accuracy. Finally, considering the tracking task requirement, the non-myopic scheduling model of radiation control is set up with tracking accuracy constraint and the scheduling problem is translated to a decision tree optimization problem. Then, the suboptimal lower bound of each decision tree node is given and the optimal scheduling sequence is obtained by improved branch and bound (IB & B) technique. Simulation results prove the validity of the proposed algorithm.
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图 6 不同策略下的累积辐射风险和累积ELI值
Figure 6. Cumulative radiation risk and cumulative ELI value of different policies
表 1 算法搜索性能对比
Table 1. Comparison of search performance among algorithms
算法 H=2 H=3 H=4 H=5 节点打开数(占比) 最大存储节点 节点打开数(占比) 最大存储节点 节点打开数(占比) 最大存储节点 节点打开数(占比) 最大存储节点 ES 20 16 84 64 340 256 1 364 1 024 UCS 17(85%) 4 69(82%) 16 240(70%) 64 856(63%) 251 ε-UCS 17(85%) 4 62(74%) 16 177(52%) 64 533(39%) 251 IB & B 9(45%) 4 19(23%) 12 36(11%) 28 73(5%) 66 
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