一种改进的多元传感器协同探测粒子群算法

An Improved Particle Swarm Optimization for Multi-sensor Cooperative Detection

为了研究粒子群算法在协同探测任务分配中的适用性,将多元传感器协同探测任务分配归纳描述成一个典型的多约束整数规划问题。构建了基于探测装备跟踪探测可行域的粒子编码方案,以缩小规划问题的解空间,提高算法的求解效率。采用一种基于多样性控制策略的改进粒子群算法,以解决粒子群优化中早熟现象,摆脱局部最优解的限制。以“低慢小”无人机目标协同探测任务为例,对其探测效能与约束条件进行讨论,并对其任务分配进行仿真分析。结果表明,该方法可加快粒子搜索速度,避免了算法早熟现象,可为多元传感器跟踪探测的任务分配问题研究提供参考。

In order to study the applicability of particle swarm optimization(PSO)in cooperative detection task allocation,the multi-sensor cooperative detection task allocation is described as a typical multi-constrained integer programming problem.A particle encoding scheme based on the feasible region of tracking and detection of detection equipment is constructed to reduce the solution space of the planning problem and improve the efficiency of the algorithm.An improved PSO algorithm based on diversity control strategy is adopted to solve the premature phenomenon in PSO and get rid of the limitation of local optimal solution.Taking the cooperative target detection task of"low slow and small(LSS)"UAV as an example,its detection efficiency and constraints is discussed,and its task assignment is simulated and analyzed.The result shows that the algorithm can speed up particle search and avoid premature phenomena.It can provide reference for multisensory cooperative detection task allocation.