摘要
在利用多传感器观测值进行空间配准时,系统误差模型难以构建、目标运动模型确定困难。针对此问题,从误差配准的基本原则出发,构造了目标函数,进而将配准问题转化为与系统误差模型无关的非线性优化问题,提出了基于异步学习因子的粒子群算法,对该优化问题进行求解。该粒子群算法简单,易于实现,收敛速度较快。最后通过仿真验证了该算法的有效性和可行性。
During the space registration of multi-sensor observation values,system error model and target motion model are hard to be constructed.Aiming at solving this problem,the objective function based on the principles of error registration is constructed,and further,transform the registration into the non-linear optimization problem,and propose an improved particle swarm optimization（PSO） based on asynchronous learning factors to solve this non-linear optimization problem.This algorithm is easy to be implemented,and fast to be converged.The simulation results show the effectiveness and feasibility of the proposed algorithm.
出处
《电力电子技术》
CSCD
北大核心
2010年第9期44-46,共3页
Power Electronics
关键词
传感器
空间配准
粒子群算法
sensor
space registration
particle swarm optimization
