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消除WSN目标功率影响的信号强度差LSSVR定位法

DOI: 10.3969/j.issn.1006-7043.2009.12.013

Keywords: 无线传感器网络 目标定位 信号强度 最小二乘支持向量回归机 wireless sensor networks target localization signal strength least squares support vector regression machine

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Abstract:

针对目标发射功率变化下的无线传感器网络(WSN)目标定位问题,分析了无线信道衰减特性,探讨目标功率无关的信号强度差特征提取方法,结合WSN信息交换与处理过程,提出能消除WSN目标功率变化影响的信号强度差LSSVR建模定位方法(TL-LMSD),该方法利用不同探测节点平均信号强度差构造特征向量,通过LSSVR回归建模获得表征特征向量与目标坐标映射关系的LSSVR模型,将各节点目标信号强度测量值的差值所构造特征向量输入LSSVR模型可实现目标定位.基于CC2430无线传感网络实验平台证明TL-LMSD方法目标定位均方根误差RMSE比MLE方法可减小29%~37%;TL-LMSD方法在LSSVR建模、无需重新建模2种情况下的目标定位耗时分别约为0.4s、0.04s.这表明TL-LMSD方法能显著减小信号强度值变化对目标定位结果的影响,提高目标定位准确度,并具有较好的实时性能. 号强度差构造特征向量,通过LSSVR回归建模获得表征特征向量与目标坐标映射关系的LSSVR模型,将各节点目标信号强度测量值的差值所构造特征向量输入LSSVR模型可实现目标定位.基于CC2430无线传感网络实验平台证明TL-LMSD方法目标定 均方根误差RMSE比MLE方法可减小29%~37%;TL-LMSD方法在LSSVR建模、无需重新建模2种情况下的目标定位耗时分别约为0.4s、0.04s.这表明TL-LMSD方法能显著减小信号强度值变化对目标定位结果的影响,提高目标定位准确度,并具有较好的实时性能. 号强度差构造特征向量,通过LSSVR回归建模获得

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