基于秩滤波和裴波那契树的信号强度定位算法
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摘要
针对接收信号强度指示(RSSI)测距定位精度和鲁棒性差的问题,提出了一种基于秩滤波和裴波那契树的信号强度定位(RF-RSSI-FTO)算法.采用秩滤波方法对RSSI值进行去干扰滤波处理,可提高测距精度及鲁棒性;引入裴波那契树优化算法对定位坐标进行全局和局部搜索寻优处理,可减小定位误差.仿真结果表明,RF-RSSI-FTO算法能有效改善测距精度和鲁棒性,增强全局和局部搜索能力,提高定位精度.
In order to solve the problem of poor ranging accuracy and robustness of received signal strength indication( RSSI),a signal strength localization algorithm based on rank filter and Fibonacci tree optimization( RF-RSSI-FTO) was proposed. First,the rank filtering method was used to remove the interference of the RSSI value to improve the accuracy and robustness of the ranging. Then,the Fibonacci tree optimization algorithm was introduced to optimize the global and local search of the positioning coordinates to reduce the positioning error. Simulation results show that the RF-RSSI-FTO algorithm can effectively improve ranging accuracy and robustness,enhance the global and local search ability,and improve positioning accuracy.
In order to solve the problem of poor ranging accuracy and robustness of received signal strength indication( RSSI),a signal strength localization algorithm based on rank filter and Fibonacci tree optimization( RF-RSSI-FTO) was proposed. First,the rank filtering method was used to remove the interference of the RSSI value to improve the accuracy and robustness of the ranging. Then,the Fibonacci tree optimization algorithm was introduced to optimize the global and local search of the positioning coordinates to reduce the positioning error. Simulation results show that the RF-RSSI-FTO algorithm can effectively improve ranging accuracy and robustness,enhance the global and local search ability,and improve positioning accuracy.
引文
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[2]何杰,吴得泱,李希飞,等.利用测距值优化的室内三维定位算法[J].北京邮电大学学报,2017,40(3):37-42.He Jie,Wu Deiyang,Li Xifei,et al.A 3-D indoor localization algorithm using distance optimization[J].Journal of Beijing University of Posts and Telecommunications,2017,40(3):37-42.
[3]Mardini W,Khamayseh Y,Almodawar A A,et al.Adaptive RSSI-based localization scheme for wireless sensor networks[J].Peer-to-Peer Networking and Applications,2016,9(6):991-1004.
[4]Zhou Cheng,Yuan Jiazheng,Liu Hongzhe,et al.Bluetooth indoor positioning based on RSSI and Kalman filter[J].Wireless Personal Communications,2017,96(3):4115-4130.
[5]Guan Bo,Li Xin.An RSSI-based wireless sensor network localization algorithm with error checking and correction[J].International Journal of Online and Biomedical Engineering,2017,13(12):52-66.
[6]Tseng C H,Yen J S.Enhanced Gaussian mixture model of RSSI purification for indoor positioning[J].Journal of Systems Architecture,2017,81(11):1-6.
[7]Zou Tengyue,Lin Shouying,Li Shuyuan.Blind RSSD-based indoor localization with confidence calibration and energy control[J].Sensors,2016,16(6):788-811.
[8]Svecko J,Malajner M,Gleich D.Distance estimation using RSSI and particle filter[J].ISA Transactions,2015,55(16):275-285.
[9]冯秀芳,吕淑芳.基于RSSI和分步粒子群算法的无线传感器网络定位算法[J].控制与决策,2014,29(11):1966-1972.Feng Xiufang,LüShufang.Wireless sensor networks locating algorithm based on RSSI and split-step particle swarm optimization algorithm[J].Control and Decision,2014,29(11):1966-1972.
[10]Wu Zheng,Jedari E,Muscedere R,et al.Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization[J].Computer Communications,2016,83(3):64-71.
[11]张松海,施心陵,李鹏,等.多峰函数优化的黄金分割斐波那契树优化算法[J].电子学报,2017,45(4):791-798.Zhang Songhai,Shi Xinling,Li Peng,et al.Golden section Fibonacci tree optimization algorithm for multimodal function optimization[J].Acta Electronic Sinica,2017,45(4):791-798.
[12]傅惠民,肖强,娄泰山,等.非线性非高斯秩滤波方法[J].航空动力学报,2015,30(10):2318-2322.Fu Huimin,Xiao Qiang,Lou Taishan,et al.Nonlinear and non-Guassian rank filter method[J].Journal of Aerospace Power,2015,30(10):2318-2322.
[13]董易,吕丹桔,王霞,等.斐波那契树优化算法全局随机性概率收敛分析[J].控制与决策,2018,33(3):439-446.Dong Yi,LüDanjv,Wang Xia,et al.A global randomness-based probability convergence analysis of Fibonacci tree optimization algorithm[J].Control and Decision,2018,33(3):439-446.