无线电工程2024，Vol.54Issue(6)：1489-1496,8.DOI:10.3969/j.issn.1003-3106.2024.06.018
扩展卡尔曼滤波的改进蛇定位算法在WSN中的应用
Application of Improved Snake Localization Algorithm with Extended Kalman Filter in WSN
摘要
Abstract
In order to address the impact of environmental factors on the Received Signal Strength Index(RSSI)for localization,a based RSSI Extended Kalman Filter Improved Snake Optimization Localization Algorithm(RSSI-EISL)is proposed.This algorithm utilizes the Extended Kalman Filter(EKF)model to smooth the RSSI signal values and suppress the influence of noise and outliers on the estimation results,thereby improving the accuracy and robustness of distance measurement.By introducing the Improved Snake Optimization Algorithm(ISO)with Levy flight and nonlinear convergence factor,the ability of the Snake Optimization Algorithm(SO)is enhanced,enabling more accurate calculation of the coordinates of the nodes to be measured.According to simulation results,the proposed RSSI-EISL improves the localization accuracy by about 26.4％,8.75％,and 5.6％compared to RSSI Ordinary Least Squares Localization Algorithm(ROL),RSSI Extended Kalman Filter-based Grey Wolf Optimization Algorithm(REGL),and RSSI EKF-based Snake Optimization Localization Algorithm(RESL)algorithms,respectively,while the convergence speed and global search capability of the algorithm are also improved.关键词
无线传感器网络/接收信号强度/蛇优化算法/扩展卡尔曼滤波/Levy飞行/非线性收敛因子Key words
wireless sensor network/received signal strength/SO/EKF/Levy flight/nonlinear convergence factor分类
信息技术与安全科学引用本文复制引用
彭铎,刘明硕,谢堃..扩展卡尔曼滤波的改进蛇定位算法在WSN中的应用[J].无线电工程,2024,54(6):1489-1496,8.基金项目
国家自然科学基金(62265010,62061024) （62265010,62061024）
甘肃省科技计划(23YFGA0062) （23YFGA0062）
甘肃省创新基金(2022A-215)National Natural Science Foundation of China(62265010,62061024) （2022A-215）
Gansu Provincial Science and Technology Plan(23YFGA0062) （23YFGA0062）
Gansu Provincial Innovation Fund(2022A-215) （2022A-215）
