利用最小二乘支持向量机实现无线传感器网络的目标定位

针对接收信号强度值(RSSI)的波动直接影响无线传感器网络(WSN)目标定位准确度的问题,研究了利用最小二乘支持向量回归机(LSSVR)实现WSN的目标定位的基本原理,分析了固定探测节点和探测节点变化时的LSSVR建模定位特性,提出了基于自适应LSSVR回归建模实现WSN目标定位的方法(TL-AML)。该方法综合考虑目标定位准确度和实时性,初始时刻首先建立LSSVR回归模型来定位目标,根据后面任一时刻探测节点与前一时刻回归模型建模节点的包含关系决定是否重新建模,实现自适应建模定位过程。基于CC2430无线传感网络实验平台,进行了相关TL-AML方法性能实验,通过合理选取建模参数,TL-AML方法的目标定位均方根误差(RMSE)比MLE方法减小34%～37%,比LSE方法减小60%～65%。建模参数在较大范围内取值时,TL-AML方法目标定位准确度比MLE和LSE方法有明显提高。在LSSVR建模情况下,TL-AML方法目标定位耗时0.2～0.4s,无需重复建模时,目标定位耗时减少到0.04s。实验结果表明,TL-AML方法能够显著减小RSSI波动对目标定位结果的影响,提高目标定位准确度,减少目标定位时间,且具有较好的目标定位实时性。

Target localization based on LSSVR in wireless sensor networks

In consideration of the direct influence of Received Signal Strength Indicator(RSSI) fluctuation on the target localization accuracy in wireless sensor networks (WSN), the basic principle of target localization using Least Square Support Vector Regression(LSSVR) is discussed. Then, the characteristics of LSSVR modeling are analyzed for given and variable detection sensors, respectively. Furthermore, a method for Target Localization based on Adaptive LSSVR Modeling (TL-AML) in WSN is proposed. By considering localization accuracy and real-time performance comprehensively, LSSVR models are built for locating target at initial time, and at follow-up time it is used to decide whether new models need to be built or not according to the inclusion relation between detection nodes and sensor nodes. The performance of TL-AML is verified based on CC2430 WSN experiment platform. Results show that the Root Mean Square Error (RMSE) of target localization based on TL-AML has reduced by 34%~37% and 60%~65% as compared with those of MLE and LSE,respectively. When modeling parameters are taken in reasonable value ranges, the localization accuracy of TL-AML is improved evidently compared with MLE and LSE. Moreover,the consuming time of TL-AML is 0.2~0.4 s,If LSSVR modeling is needed. Otherwise, the consuming time is only about 0.04 s. The results indicate that TL-AML method can weaken the influence of RSSI fluctuation on the accuracy of target localization and has good real-time target localization accuracy.