论文部分内容阅读
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。
The authors propose a new method of secondary location, which is the first time to systematically utilize a large amount of sensor information to reduce the impact of sensor error and noise on location. It is through the cross-test between multiple sets of data to improve positioning Accuracy. The process of the method is as follows: Firstly, the reliability of the data monitored by each sensor is given according to the position of the sensor and the initial location of the source is calculated by using the data with higher reliability. Then, based on the relative relationship between the initial positioning result and the position of the sensor A set of equations with the best noise tolerance are chosen and the final hypocenter location is determined by the k-mean voting method. The traditional localization method and the proposed method are compared to verify the reliability of the method. The simulation results and the field test data are respectively used for the location estimation. In the field test, when the TDOA adds the normal distribution error of N (2,2), compared with the traditional method, the positioning error of this method is reduced by 41.8%. Experimental results show that the quadratic positioning method proposed in this paper can significantly improve the fault-tolerant performance and get more accurate positioning results.