Title :
Maximum Likelihood trajectory estimation of a mobile node from RSS measurements
Author :
Coluccia, Angelo ; Ricciato, Fabio
Author_Institution :
Univ. of Salento, Lecce, Italy
Abstract :
In this paper we present a Maximum Likelihood (ML) trajectory estimation of a mobile node from Received Signal Strength (RSS) measurements. The reference scenario includes a number of nodes in fixed and known positions (anchors) and a target node (blind) in motion whose instantaneous position is unknown. We first consider the dynamic estimation of the channel parameters from anchor-to-anchor measurements, statistically modeled according to the well-known Path-Loss propagation model. Then, we address the ML estimation problem for the position and velocity of the blind node based on a set of blind-to-anchor RSS measurements. We compare also the algorithm with a ML-based single-point localization algorithm, and discuss the applicability of both methods for slowly moving nodes. We present simulation results to assess the accuracy of the proposed solution in terms of localization error and velocity estimation (modulus and angle). The distribution of the localization error on the initial and final point is analyzed and closed-form expressions are derived.
Keywords :
blind source separation; maximum likelihood estimation; mobile radio; ML estimation problem; RSS measurements; blind node; localization error; maximum likelihood trajectory estimation; mobile node; received signal strength measurements; reference scenario; single point localization algorithm; target node; velocity estimation; Channel estimation; Fading; Maximum likelihood estimation; Position measurement; Trajectory; Wireless sensor networks;
Conference_Titel :
Wireless On-demand Network Systems and Services (WONS), 2012 9th Annual Conference on
Conference_Location :
Courmayeur
Print_ISBN :
978-1-4577-1721-5
Electronic_ISBN :
978-1-4577-1720-8
DOI :
10.1109/WONS.2012.6152222