面向机动目标跟踪的多传感器长时调度策略

Non-myopic multi-sensor scheduling policy for maneuvering target tracking

摘要:

为解决杂波环境下机动目标跟踪以及系统辐射风险控制的问题,提出了一种面向机动目标跟踪的多传感器长时调度策略.该方法首先以交互式多模型和概率数据关联算法为基础,估计杂波环境下机动目标跟踪精度.然后以辐射度影响量化辐射代价、推导有限时域内辐射代价,以后验克拉美-罗下界衡量目标跟踪性能、预测机动目标有限时域内后验克拉美-罗下界.最后,引入传感器切换代价,考虑跟踪精度约束,建立基于代价函数和后验克拉美-罗下界的多传感器长时调度策略,并将该约束调度问题转化为决策树优化问题,采用阈值剪枝搜索技术求解最优策略.仿真结果表明:该方法验证了所提策略的有效性,与标准代价搜索相比,所提搜索算法能够以辐射风险略上升为代价,显著降低节点打开数、加快搜索空间；与随机调度、最近调度和贪婪调度相比,所提调度策略能够在满足跟踪任务需求下获得更低的辐射代价；与随机调度和贪婪调度相比,所提调度策略切换代价更低,有效克服了传感器频繁调度问题,更利于实际实现.

Abstract:

To control the system radiation risk and track the maneuvering target in clutter, a non-myopic multi-sensor scheduling policy for maneuvering target tracking is proposed. Firstly, the maneuvering target tracking accuracy in clutter was estimated by the interacting multiple model and probability data association (IMMPDA) algorithm. Secondly, the radiation cost was quantized by the emission level impact (ELI) and the posterior carmér-rao lower bound (PCRLB) was utilized to represent the target tracking performance. Then the radiation cost and the PCRLB over the future finite time horizon were predicted, respectively. Finally, considering the switching cost and the tracking accuracy constraint, a non-myopic multi-sensor scheduling policy with cost function and PCRLB was set up. The constrained scheduling problem was converted to a decision optimization problem which can be solved by a search algorithm with threshold pruning technique. Simulation results show the effectiveness of the proposed policy. Compared with the uniform cost search (UCS), the proposed search algorithm can reduce the number of nodes opened and improve the search speed with a slightly increased radiation cost. Compared with random scheduling, closest scheduling, and greedy scheduling, the proposed policy can obtain lower radiation cost while satisfying the target tracking requirement. Furthermore, the proposed policy also has the lowest switching cost, and the sensor scheduling frequency has been reduced effectively.