Improving TOA Localization Through Outlier Detection Using Intersection of Lines of Position

Abstract

Outlier measurements often presence when locating an object from a number of sensors, which could decrease the positioning performance considerably. This paper addresses the problem of outlier detection in locating an object using TOA measurements. The detection is based on the construction of a spectral graph through pairwise intersection between the lines of position from a measurement pair. Crucial to this technique is the determination for intersection, and we have derived such conditions for 2-D and 3-D positionings. The detected outliers are removed and the remaining measurements are used for the Maximum Likelihood estimator to obtain the object position. Simulation shows that the proposed outlier detection method is very effective with the probability of detection and the probability of false alarms examined. The positioning accuracy is able to reach the CRLB performance after removing the detected outliers.