Abstract
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.
Highlights
Estimation of the source location has been a subject of research for decades and continues to receive much interest in the signal processing research community [1,2,3], including radar [4], sonar [5], sensor network [6], wireless communication [2], etc
If there is relative motion between the source and sensors, the frequency difference of arrival (FDOA) can be incorporated with the time difference of arrival (TDOA) [8], which can significantly improve the source location accuracy and estimate the position and velocity of the source simultaneously [9]
A new bias compensation method based on MLE for distributed source localization using TDOA and FDOA with sensor location errors is presented in this paper
Summary
Estimation of the source location has been a subject of research for decades and continues to receive much interest in the signal processing research community [1,2,3], including radar [4], sonar [5], sensor network [6], wireless communication [2], etc. In order to avoid it, Hao put forward a bias reduction method an estimation result, and do not consider the sensor location uncertainties which are very sensitive for passive source localization using TDOA and gain ratios of arrival (GROA) [32]. New which bias is still high for an estimation result, andfor dodistributed not consider the sensor location uncertainties compensation method based on MLE source localization using TDOA and FDOAwhich with are verysensor sensitive to the source locationinaccuracy location errors is presented this paper.[10]. A new bias compensation method based on MLE for distributed source localization using TDOA and FDOA with sensor location errors is presented in this paper.
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