一种粒子群优化的RSSI室内定位算法  

A RSSI indoor positioning algorithm based on particle swarm optimization

作　　者：张毅[1] 孙伟英 ZHANG Yi;SUN Weiying(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《通信技术》2024年第12期1267-1274,共8页Communications Technology

摘　　要：针对基于接收信号强度指示(Received Signal Strength Indicator,RSSI)测距模型的室内定位算法在复杂多变的室内环境下难以根据当前室内环境实时更新模型参数而导致定位精度下降的问题,提出了一种粒子群优化的RSSI室内定位算法。该算法利用蓝牙低功耗网状(Bluetooth Low Energy Mesh,BLE Mesh)网络中各节点可以相互通信的特点及对数路径损耗模型参数对距离转换的直接影响,将各锚节点间预测距离和真实距离的均方误差作为约束条件,采用粒子群优化对对数路径损耗模型的参数进行迭代优化,最终获得符合当前室内环境的模型参数,以进行室内定位。实验结果表明,提出的算法具有较好的收敛性能,且定位误差在1 m以内,能够有效满足室内定位的实际需求。To address the issue of decreased positioning accuracy in indoor positioning algorithm based on RSSI(Received Signal Strength Indicator)ranging model,which arises due to the difficulty in real-time updating of model parameters in complex and dynamically changing indoor environment,a RSSI indoor positioning algorithm based on particle swarm optimization is proposed.Based on the characteristics that each node in BLE(Bluetooth Low Energy)Mesh network can communicate with each other and the direct impact of the parameters of the logarithmic path loss model on the distance conversion,the mean square error of the predicted distance and the real distance between each anchor node is taken as a constraint condition,and the parameters of the logarithmic path loss model are iteratively optimized by particle swarm optimization to finally obtain model parameters that match the current indoor environment for indoor positioning.The experimental results demonstrate that the proposed algorithm exhibits superior convergence performance and the positioning error is within 1 m,which can effectively meet the actual needs of indoor positioning.

关 键 词：室内定位 低功耗蓝牙 接收信号强度指示 对数路径损耗模型 粒子群优化 

分 类 号：TN98[电子电信—信息与通信工程]