基于改进型粒子群优化的节点自定位算法

为了提高测距误差影响下无线传感器网络节点自定位精度,提出一种基于距离的节点自定位新算法.对混沌搜索与粒子群优化进行算法融合,给出一种改进型粒子群优化算法,将其应用于节点自定位.新算法利用未知节点与信标节点之间的距离信息,通过改进型粒子群优化算法获取未知节点的位置.仿真结果表明,改进型粒子群优化算法对两种标准测试函数的搜索结果优于一般的粒子群优化算法.在测距误差和信标节点数量相同的条件下,相对于最小二乘估计法,新算法在各个测距误差级上的定位精度更高,其定位误差随测距误差增大而上升的趋势更缓慢.新算法具有更好的鲁棒性,适用于测距误差较大、信标节点数量较少的情况.

Node self-localization algorithm based on modified particle swarm optimization

In order to improve the node self-localization precision in wireless sensor networks despite ranging error,a new range-based node self-localization algorithm was proposed.A modified particle swarm optimization that combines chaotic and particle swarm optimization was studied and used in the field of node self-localization.The new algorithm uses range information between unknown node and anchor node and gets the node location through the modified particle swarm optimization.The simulation results show that the modified particle swarm optimization algorithm is better than the original in terms of searching results of two standard test functions.When ranging error and anchor node number are the same,the new algorithm,compared with the least square method,has higher localization precision at each ranging error level.As the ranging error increases,the localization error grows more slowly.It enjoys better robustness and is suitable for the situation of big ranging error and few anchor nodes.