基于LQI权重和改进粒子群算法的室内定位方法  

The Indoor Localization Based on LQI weight and Improved Particle Swarm Optimization Algorithm

作　　者：尚俊娜[1] 盛林[1] 程涛[1] 施浒立[2] 岳克强[3] 

机构地区：[1]杭州电子科技大学通信工程学院,杭州310018 [2]中国科学院国家天文台,北京100012 [3]杭州电子科技大学电子信息学院,杭州310018

出　　处：《传感技术学报》2017年第2期284-290,共7页Chinese Journal of Sensors and Actuators

基　　金：浙江省自然科学基金青年基金项目(LQ13F010010);浙江省重点科技创新团队"固态存储和数据安全关键技术创新团队"项目(2013TD03);浙江省"电子科学与技术"重中之重学科开放基金项目(GK13020320003/004)

摘　　要：为解决无线传感网络节点在室内定位中由非视距和多径传输等因素导致定位误差较大的问题,提出了基于三角函数的粒子群算法。针对RSSI波动性引起的测距误差,利用LQI和RSSI值之间的关系对RSSI值进行优化,提出了基于LQI权重的RSSI测距算法。改进的粒子群算法相比较于标准粒子群算法优化了权重模型和速度更新策略,避免陷入局部最优值情况。在对算法进行仿真实验后,进一步将其运用到Zigbee平台的定位实验,通过实测实验证明该算法在测试环境下平均定位误差在0.5 m以内,相比于LSE和标准PSO算法,获得较好的定位效果。In order to solve the positioning error caused by non line-of-sight and multipath transmission in indoor location of wireless sensor networks,particle swarm optimization algorithm based on trigonometric is proposed.In view of the range error caused by RSSI fluctuation,this paper uses relationship between LQI and RSSI to optimize the RSSI value,and puts forward RSSI ranging algorithm based on LQI weight.Compared to the standard particle swarm optimization algorithm,the improved algorithm improves the weight model and the velocity update strategy,which avoids the local optimal value.After the simulation experiment,it is further applied to the Zigbee platform positioning experiment.The results show that the proposed algorithm has higher accuracy compared to the traditional ranging and positioning algorithm,which average localization error is less than 0.5m.

关 键 词：室内定位 衰减模型 粒子群算法 RSSI LQI 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]