Least-squares based adaptive source localization by mobile agents

This paper focuses on the problem of localizing a signal source by a mobile sensory agent using distance measurements. This problem was tackled in a recent paper using a gradient based adaptive algorithm. In this paper, we design a least-squares based adaptive algorithm with forgetting factor for the same task. We establish that the least-squares based algorithm we propose bears the same asymptotic stability and convergence properties as the gradient algorithm previously studied. It is further demonstrated via simulation studies that the proposed least-squares algorithm converges significantly faster to the resultant location estimates than the gradient algorithm for high values of the forgetting factor, and significantly reduces the noise effects for small values of the forgetting factor.