一种基于卡尔曼和线性插值滤波的改进三角质心定位算法
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摘要
基于RSSI的目标定位技术,由于复杂度低、定位精度较高,被广泛用于无线传感网络节点定位中。为进一步提高无线移动定位精度,首先利用CC2530构成的参考节点的RSSI实测数值得到无线信道模型中优化射频参数A和传播因子n,通过卡尔曼滤波和线性插值法对随机误差进行补偿以获得较精确的定位节点与参考节点间的估计距离;然后采用一种改进的三角质心定位算法进行测试与仿真。通过仿真证明,采用的改进算法较传统的加权质心算法在定位精度上有较明显的提高,在所测试环境下平均定位精度为0.7米左右,获得了较好的效果。
The target location technology based on RSSI with the features of low complexity and high locating accu-racy has been widely applied to nodes location in WSN. For further improving moving target locating accuracy theoptimized RF parameter A and signal transmission constant n in wireless channel model are calculated firstlythrough RSSI real values received from reference nodes made up of CC2530. Then a more precise estimated dis-tance value between reference and location nodes can be achieved via Kalman filtering and linear interpolation forcompensating random errors. Finally an improved triangle centroid location algorithm was tested and simulated onbasis of above-mentioned distance value. The simulation results showed that the improved algorithm used in the pa-per achieved an average about 0.7 m location precision in the testing environment,which is markedly better thanthat of the existing location algorithms.
The target location technology based on RSSI with the features of low complexity and high locating accu-racy has been widely applied to nodes location in WSN. For further improving moving target locating accuracy theoptimized RF parameter A and signal transmission constant n in wireless channel model are calculated firstlythrough RSSI real values received from reference nodes made up of CC2530. Then a more precise estimated dis-tance value between reference and location nodes can be achieved via Kalman filtering and linear interpolation forcompensating random errors. Finally an improved triangle centroid location algorithm was tested and simulated onbasis of above-mentioned distance value. The simulation results showed that the improved algorithm used in the pa-per achieved an average about 0.7 m location precision in the testing environment,which is markedly better thanthat of the existing location algorithms.
引文
[1]周雅琴,谭定忠.无线传感器网络应用及研究现状[J].传感器世界,2009(5):35-40.
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[6]Yang Zheng,Liu Yunhao.Understanding Node Localizability ofWireless Ad Hoc and Sensor Networks[J].IEEE Transactions onParallel and Distributed Systems,2012,11:1249-1260.
[7]刘志华,马征征,陈嘉兴.无线传感器网络中移动节点的后验信号滤波定位法[J].传感技术学报,2013,26(5):739-744.
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[12]杨博雄,倪玉华,刘琨,等.基于加权三角质心RSSI算法的Zig-Bee室内无线定位技术研究[J].传感器世界,2012,11:31-35.
[13]Wang Gang,Yang Kehu.A New Approach to Sensor Node Local-ization Using RSS Measurements in Wireless Sensor Networks[J].IEEE Transactions on Wireless Communications,2011,10(5):1389-1395.
[14]Bulusu N,Heidemann J,Estrin D.GPS-Less Low Cost OutdoorLocalization for Very Small Devices[J].IEEE Personal Communi-cations Magazine,2000,7(5):28-34.
[15]袁鑫,吴晓平,王国英.线性最小二乘法的RSSI定位精确计算方法[J].传感技术学报,2014,27(10):1412-1417.
[16]刘志华,李改燕,刘晓爽.基于最小二乘法的蒙特卡洛移动节点定位算法[J].传感技术学报,2012,25(4):541-544.
[17]吕振,赵鹏飞.一种改进的无线传感器网络加权质心定位算法[J].计算机测量与控制,2013,21(4):1102-1104.
[18]刘运杰,金明录,崔承毅.传感器网络修正加权质心定位算法[J].传感技术学报,2010,23(5):717-721.
[19]张橙,宋学瑞.基于Zig Bee的RSSI测距方精度研究[J].湖南工业大学学报,2011,25(5):37-41.
[2]Liu Yunhao,Yang Zheng,Wang Xiaoping.Location,Localizationand Localizability[J].Journal of Computer Science and Technolo-gy,2010,25:274-297.
[3]吴晓平,陆炳斌,沈浩.基于RSSI定位模型的非视距关系识别方法[J].传感技术学报,2013,26(11):1584-1589.
[4]Zhang Shigeng,Cao Jiannong,Chen Lijun,et al.Accurate and En-ergy-Efficient Range-Free Localization for Mobile Sensor Net-works[J].IEEE Transactions on Mobile Computing,2010,9(6):897-910.
[5]Junyang Shen,Andreas F Molisch,Jussi Salmi.Accurate PassiveLocation Estimation Using TOA Measurements[J].IEEE Transac-tions on Wireless Communications,2012,11(6):2182-2192.
[6]Yang Zheng,Liu Yunhao.Understanding Node Localizability ofWireless Ad Hoc and Sensor Networks[J].IEEE Transactions onParallel and Distributed Systems,2012,11:1249-1260.
[7]刘志华,马征征,陈嘉兴.无线传感器网络中移动节点的后验信号滤波定位法[J].传感技术学报,2013,26(5):739-744.
[8]朱明强,侯建军,刘颖,等.一种基于卡尔曼数据平滑的分段曲线拟合室内定位算法[J].北京交通大学学报,2012,36(5):95-99.
[9]邓胡滨,张磊,吴颖,等.基于卡尔曼滤波算法的轨迹估计研究[J].传感器与微系统,2012,31(5):4-7.
[10]Paul A S,Wan E A.RSSI-Based Indoor Localization and TrackingUsing Sigma-Point Kalman Smoothers[J].IEEE J of Selected Top-ics in Signal Processing,2009,3(5):860-873.
[11]曹春萍,罗玲莉.基于卡尔曼滤波算法的室内无线定位系统[J].计算机系统应用,2011,20(11):76-79.
[12]杨博雄,倪玉华,刘琨,等.基于加权三角质心RSSI算法的Zig-Bee室内无线定位技术研究[J].传感器世界,2012,11:31-35.
[13]Wang Gang,Yang Kehu.A New Approach to Sensor Node Local-ization Using RSS Measurements in Wireless Sensor Networks[J].IEEE Transactions on Wireless Communications,2011,10(5):1389-1395.
[14]Bulusu N,Heidemann J,Estrin D.GPS-Less Low Cost OutdoorLocalization for Very Small Devices[J].IEEE Personal Communi-cations Magazine,2000,7(5):28-34.
[15]袁鑫,吴晓平,王国英.线性最小二乘法的RSSI定位精确计算方法[J].传感技术学报,2014,27(10):1412-1417.
[16]刘志华,李改燕,刘晓爽.基于最小二乘法的蒙特卡洛移动节点定位算法[J].传感技术学报,2012,25(4):541-544.
[17]吕振,赵鹏飞.一种改进的无线传感器网络加权质心定位算法[J].计算机测量与控制,2013,21(4):1102-1104.
[18]刘运杰,金明录,崔承毅.传感器网络修正加权质心定位算法[J].传感技术学报,2010,23(5):717-721.
[19]张橙,宋学瑞.基于Zig Bee的RSSI测距方精度研究[J].湖南工业大学学报,2011,25(5):37-41.