National Digital Switching System Engineering and Technological Research Center (NDSC), Zhengzhou 450002, China.
Institute of Surveying and Mapping, Information Engineering University; Zhengzhou 450002, China.
Sensors (Basel). 2018 Nov 2;18(11):3747. doi: 10.3390/s18113747.
Current bias compensation methods for distributed localization consider the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements noise, but ignore the negative influence by the sensor location uncertainties on source localization accuracy. Therefore, a new bias compensation method for distributed localization is proposed to improve the localization accuracy in this paper. This paper derives the theoretical bias of maximum likelihood estimation when the sensor location errors and positioning measurements noise both exist. Using the rough estimate result by MLE to subtract the theoretical bias can obtain a more accurate source location estimation. Theoretical analysis and simulation results indicate that the theoretical bias derived in this paper matches well with the actual bias in moderate noise level so that it can prove the correctness of the theoretical derivation. Furthermore, after bias compensation, the estimate accuracy of the proposed method achieves a certain improvement compared with existing methods.
当前用于分布式定位的偏置补偿方法考虑了到达时间差（TDOA）和到达频率差（FDOA）测量噪声，但忽略了传感器位置不确定性对源定位精度的负面影响。因此，本文提出了一种新的分布式定位偏置补偿方法，以提高定位精度。本文推导了传感器位置误差和定位测量噪声同时存在时最大似然估计的理论偏置。使用MLE 的粗略估计结果减去理论偏置，可以得到更准确的源位置估计。理论分析和仿真结果表明，本文推导的理论偏置在中等噪声水平下与实际偏置吻合较好，从而验证了理论推导的正确性。此外，偏置补偿后，与现有方法相比，所提方法的估计精度有一定提高。
