Robust geolocation estimation using adaptive RANSAC algorithm

This paper deals with location estimation in the presence of unreliable measurements (outliers) in large scale geolocation systems. In order to find a way of identifying these outliers, we propose an adaptive RANSAC (Random Sample Consensus) algorithm that maintains a consensus set which iteratively includes inliers and rejects outliers. Numerical experiments are performed to apply the proposed algorithm to linearized TDOA (time difference of arrival) and AOA (angle of arrival) geolocation systems to quantify the location performance versus the least-squares estimate.