Title :
Uncensored Indoor Positioning
Author :
Mansour, Mohamed F. ; Waters, Deric W.
Author_Institution :
Embedded Syst. Lab., Texas Instrum. Inc., Dallas, TX, USA
Abstract :
We present a new method to accommodate the unobserved access points in indoor positioning systems that use the Received Signal Strength (RSS). We formulate a probabilistic framework, and derive the estimation bounds that quantify the advantage of incorporating unobserved data. Further, we describe a realization for the maximum-likelihood estimator and establish its effectiveness in a typical indoor positioning scenario.
Keywords :
indoor radio; maximum likelihood estimation; probability; radionavigation; RSS; estimation bounds; maximum-likelihood estimator; probabilistic framework; received signal strength; uncensored indoor positioning system; unobserved access points; Channel models; Cramer-Rao bounds; Databases; Maximum likelihood estimation; Sensitivity; Vectors; Cramer-Rao bound; estimation; localization; maximum-likelihood; sensitivity; unobserved data;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2317787
