基于RSSI高斯滤波的人工蜂群定位算法  

RSSI Gaussian Filter-Based Artificial Bee Colony Localization Algorithm

作　　者：单好民[1] 陈才学[2] SHAN Haomin;CHEN Caixue(College of artificial intelligence of Zhejiang Technical College of Posts and Telecom,Shaoxing Zhejiang 312366,China;The College of Information Engineering of Xiangtan University,Xiangtan Hu’nan 411105,China)

机构地区：[1]浙江邮电职业技术学院人工智能学院,浙江绍兴312366 [2]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《传感技术学报》2021年第7期979-983,共5页Chinese Journal of Sensors and Actuators

基　　金：浙江省教育厅科研项目(Y202044998)。

摘　　要：基于接收信号强度指示(Received signal strength Index,RSSI)定位易受到环境影响。为此,提出基于RSSI高斯滤波的人工蜂群定位(RSSI Gaussian Filter-based Artificial Bee Colony Localization,RGBL)算法。采用高斯滤波对收集的RSSI值进行处理,剔除误差较大的RSSI值,保留精度较高的RSSI值,再利用这些RSSI值测距,降低测距误差;基于测距误差建立目标函数,再利用人工蜂群算法求解目标函数,实现节点的定位。仿真结果表明,提出的RGBL算法降低了归一化平均定位误差,提升了收敛速度。When RSSI is used to locate unknown nodes of Wireless Sensor Networks, the RSSI values are easily affected by environment will cause location error. Therefore, RSSI Gaussian Filter-based Artificial Bee Colony Localization(RGBL)algorithm is proposed in this paper. In RGBL,the collected RSSI value is processed by Gaussian Filtering, and the RSSI value with large error was eliminated, and the RSSI value with high accuracy was retained. Then the RSSI value with high accuracy is used to range in order to reduce the ranging error. The objective function is established based on the ranging error, and then the artificial bee colony algorithm is used to solve the objective function to estimate position of node. The simulation results show that the proposed RGBL algorithm reduces the normalized mean positioning error and improves the convergence speed.

关 键 词：接收信号强度 人工蜂群算法 高斯滤波 定位 无线传感网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]