基于Cramér-Rao下限的多传感器跟踪资源协同分配

Multi-Sensor Tracking Resource Coordinated Allocation Based on Cramér-Rao Low Bound

针对防空指挥控制系统中多传感器管理问题,提出了一种基于目标跟踪精度Cramér-Rao下限的多传感器跟踪资源协同分配方法。该方法首先利用目标战术重要性函数求解目标优先级;然后在目标优先级函数和目标—传感器配对效能函数的基础上,构造了多传感器资源协同分配一般模型,并根据目标跟踪过程特点将Cramér-Rao下限引入到协同分配的模型中,使得在进行跟踪资源协同分配时无需考虑目标跟踪滤波算法的选择。对于分配过程中出现的NP(Non-deterministic polynomial)难问题,探讨了利用匈牙利算法寻求满足条件的目标传感器最优组合,给出了模型求解的步骤。仿真结果表明,这种多传感器跟踪资源协同分配方法的可行性与模型求解的快速性。

A method for multi-sensor tracking resource coordinated allocation based on Cramer-Rao low bound is proposed for CAISR systems. First, the target tactical significance function is used to solve the target priority. Then, on the basis of target priority function and efficiency function, a multi-sensor coordinated allocation model is established. The Cramer-Rao low bound is introduced into the multi-sensor tracking resource coordinated allocation model, and it needn＇ t choose target tracking algorithm. With regard to the non-deterministic polynomial-hard question in the course of allocation, a Hungarian algorithm is researched for finding the optimization target sensor combination. The simulation results show feasibility and model solution speed of the method for multi-sensor tracking resource coordinated allocation.