本文作者提出了一种新的二次定位的方法，这是首次系统地利用大量的传感器信息来降低传感器误差和噪声对定位造成的影响，它是通过多组数据之间交叉检验来提高定位的精度。方法的过程是：首先根据传感器位置等因素给出每个传感器监测到的数据的可靠度，使用可靠度较高的数据进行震源位置的初步测算，然后根据初始定位的结果和传感器位置的相对关系选择具有最优噪音容忍度的一组方程，并通过 k-mean 投票法确定最终的震源位置。对传统定位方法和本文提出方法进行了比较以验证方法的可靠性，并分别使用模拟和现场试验数据进行了定位测算。在现场试验中，当 TDOA 加入了 N(2,2)的正态分布误差，与传统方法相比，本文方法的定位误差降低了41.8%。实验结果表明本文提出的二次定位法能够显著提高容错性能，得到更为精确的定位结果。
A new method, named relocation, was proposed to reduce the impact of sensor errors systematically, especially when available data of sensors are abundant. The procedure includes evaluating the reliability of every sensors datum, processing the initial location by the credible data, and selecting a set of equations with optimal noise tolerance according to the relative relationship between the initial location and sensors location, then calculating the final location by k-mean voting. The results obtained in this research include comparing traditional location method with the presented method in both simulation and field experiment. In the field experiment, the location error of relocation method reduced 41.8% compared with traditional location method. The results suggested that relocation method can improve the fault-tolerant performance significantly.
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