基于粒子滤波的RSSI测距优化的牛顿定位算法
|
摘要
针对以基于RSSI测距为基础的无线传感网络定位算法存在定位精度低的问题,提出了基于粒子滤波的RSSI测距优化的牛顿定位算法PF-RSSI-NL。在RSSI测距方面,采用粒子滤波对RSSI值进行预处理,降低测距误差;在定位计算方面,运用牛顿法估计未知节点的位置。先用最小二乘法估计牛顿迭代算法的初始值,再用牛顿法对未知节点估计值进行迭代修正。仿真结果表明,与传统的基于统计均值RSSI测距相比,基于粒子滤波的RSSI优化的测距误差降低0.6 m。与同类的定位算法相比,归一化平均定位误差下降36%。
Newton Localization algorithm based on Particle filter RSSI( PF-RSSI-NL) was proposed to solve the common problems in RSSI-ranging localization algorithm in wireless sensor network,namely low localization accuracy. In terms of RSSI ranging,RSSI( Received Signal Strength Index) value was preprocessed by particle filter,and thus reducing ranging error. In terms of localization,the position of unknown node was estimated by Newton algorithm. The initial value of Newton algorithm was computed by least-squares,and iterative refinement of unknown node coordinate was done by Newton algorithm. Simulation results show that the ranging error based on RSSI particle filter reduces 0.6 m compared with ranging error based on RSSI statistical mean. Compared with similar localization algorithm,the normalization localization error of PF-RSSI-NL reduce 36%.
Newton Localization algorithm based on Particle filter RSSI( PF-RSSI-NL) was proposed to solve the common problems in RSSI-ranging localization algorithm in wireless sensor network,namely low localization accuracy. In terms of RSSI ranging,RSSI( Received Signal Strength Index) value was preprocessed by particle filter,and thus reducing ranging error. In terms of localization,the position of unknown node was estimated by Newton algorithm. The initial value of Newton algorithm was computed by least-squares,and iterative refinement of unknown node coordinate was done by Newton algorithm. Simulation results show that the ranging error based on RSSI particle filter reduces 0.6 m compared with ranging error based on RSSI statistical mean. Compared with similar localization algorithm,the normalization localization error of PF-RSSI-NL reduce 36%.
引文
[1]任春华,潘英俊,赵雪江,等.小口径光纤陀螺快速精密定向测斜研究[J].仪器仪表学报,2010,31(5):1126-1131.
[2]张延顺,王月.一种新型井眼轨迹连续测量方法究[J].北京航空航天大学学报,2010,36(2):210-213.
[3]陶为戈,朱昳华,贾子彦.基于RSSI混合滤波和最小二乘参数估计的测距算法[J].传感技术学报,2012,25(12):1748-1753.
[4]王新芳,张冰,冯友兵.基于粒子群优化的改进加权质心定位算法[J].计算机工程,2012(1):90-92.
[5]程秀芝,朱达荣,张申,朱广.基于RSSI差分校正的最小二乘法-拟牛顿定位算法[J].传感技术学报,2014,27(1):123-128.
[6]罗炬锋,付耀先,王营冠.基于RSSI测距的WLS定位算法[J].华中科技大学学报(自然科学版).2011,39(11):34-38.
[7]SO H C,LIN L.Linear least squares approach for accurate received signal strength based source localization[J].IEEE Transactions on Signal Processing,2011,59(8):4035-4040.
[8]陈昌祥,达维,周洁.基于RSSI的无线传感器网络距离修正定位算法[J].通信技术,2011,44(2):65-68.
[9]李时辉.基于测距的蒙特卡罗盒传感网定位算法研究[J].计算工程与应用,2016,52(7):110-117.
[10]乔欣,常飞,丁恩杰,等.基于跳距修正的WSN拟牛顿迭代定位算法[J].传感技术学报,2014,27(6):797-02.
[11]赵珊,付敬奇.基于粒子滤波模型的RSSI测距优化研究[J].电子测量技术,2016,39(3):122-127.
[12]史洪宇,燕莎.WSN中一种改进的DV-Hop节点定位算法[J].电光与控制,2011(4):93-96.
[2]张延顺,王月.一种新型井眼轨迹连续测量方法究[J].北京航空航天大学学报,2010,36(2):210-213.
[3]陶为戈,朱昳华,贾子彦.基于RSSI混合滤波和最小二乘参数估计的测距算法[J].传感技术学报,2012,25(12):1748-1753.
[4]王新芳,张冰,冯友兵.基于粒子群优化的改进加权质心定位算法[J].计算机工程,2012(1):90-92.
[5]程秀芝,朱达荣,张申,朱广.基于RSSI差分校正的最小二乘法-拟牛顿定位算法[J].传感技术学报,2014,27(1):123-128.
[6]罗炬锋,付耀先,王营冠.基于RSSI测距的WLS定位算法[J].华中科技大学学报(自然科学版).2011,39(11):34-38.
[7]SO H C,LIN L.Linear least squares approach for accurate received signal strength based source localization[J].IEEE Transactions on Signal Processing,2011,59(8):4035-4040.
[8]陈昌祥,达维,周洁.基于RSSI的无线传感器网络距离修正定位算法[J].通信技术,2011,44(2):65-68.
[9]李时辉.基于测距的蒙特卡罗盒传感网定位算法研究[J].计算工程与应用,2016,52(7):110-117.
[10]乔欣,常飞,丁恩杰,等.基于跳距修正的WSN拟牛顿迭代定位算法[J].传感技术学报,2014,27(6):797-02.
[11]赵珊,付敬奇.基于粒子滤波模型的RSSI测距优化研究[J].电子测量技术,2016,39(3):122-127.
[12]史洪宇,燕莎.WSN中一种改进的DV-Hop节点定位算法[J].电光与控制,2011(4):93-96.