非视距环境下基于粒子群的超宽带定位算法

将智能算法应用到无线传感器网络定位技术中是一种全新的尝试,粒子群算法是其中的一种典型算法.根据超宽带(UWB)定位原理,建立基于粒子群算法的定位模型,在非视距(NLOS)环境下,利用NLOS误差导致的附加时延和由信道决定的均方根时延扩展的联合统计特性,进行NLOS误差补偿,在迭代过程中采用线性递减的惯性权重,粒了群通过不断追踪个体极值和局部极值,更新自身的位置与速度,从而找到全局最优解,仿真结果表明正确率达90％以上.

UWB localization algorithm based on particle swarm optimization in NLOS environment

Appling intelligent algorithm to wireless sensor networks (WSNs)positioning technology is a new attempt,particle swarm optimization(PSO) algorithm is one of the typical algorithms.According to the principle of ultra wideband(UWB) localization,positioning model based on PSO algorithm is established,in non-line-of-sight (NLOS)environment,use joint statistical properties of additional time delay caused by NLOS error and the root mean square delay spread decided by channel for NLOS error compensation,in the process of iterative,linear decreasing inertia weight is used,particle swarm update their own position and velocity by continuously tracking individual extremum and local extremum,so as to find the global optimal solution and the simnulation results show that the accuracy is above 90 ％.