改进粒子群优化算法在TDOA定位中的应用  

Application of improved particle swarm optimization algorithm in TDOA positioning

作　　者：翟彦蓉[1] 黄欢[1] 张申[1] 马秀萍[1] 刘伟[1] 游春霞[1] 

机构地区：[1]中国矿业大学物联网(感知矿山)研究中心,江苏徐州221008

出　　处：《传感器与微系统》2013年第4期145-148,152,共5页Transducer and Microsystem Technologies

摘　　要：针对TDOA定位估计中的非线性最优化问题,提出了一种基于改进粒子群优化的TDOA定位算法。该算法在自适应粒子群优化算法的基础上,引入禁忌搜索策略,有效地解决了粒子群优化算法容易陷入局部最优的问题,使算法快速收敛到全局最优解。仿真结果表明:该算法性能稳定,定位精度高。A new time-difference of arrival（TDOA） positioning algorithm based on improved particle swarm optimization（PSO） is proposed for nonlinear optimization of TDOA positioning estimation.The algorithm uses tabu search strategy based on adaptive PSO algorithm,effectively resolves the problem that PSO algorithm easy to fall into local optimum and the proposed algorithm converges to global optimum fastly.Simulation results verify that the algorithm performs stably with high positioning precision.

关 键 词：到达时间差 非线性优化 禁忌搜索 粒子群优化算法 自适应 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]