A statistical modeling approach to location estimation

Abstract

AbstractÐSome location estimation methods, such as the GPS satellite navigation system, require nonstandard features either in the mobile terminal or the network. Solutions based on generic technologies not intended for location estimation purposes, such as the cell-ID method in GSM/GPRS cellular networks, are usually problematic due to their inadequate location estimation accuracy. In order to enable accurate location estimation when only inaccurate measurements are available, we present an approach to location estimation that is different from the prevailing geometric one. We call our approach the statistical modeling approach. As an example application of the proposed statistical modeling framework, we present a location estimation method based on a statistical signal power model. We also present encouraging empirical results from simulated experiments supported by real-world field tests.

Probability kernel regression for WiFi localisation

“…We conclude this methodology section by proving the relationship between Kullback-Leibler divergence kernel regression for WiFi localisation and previous Bayesian probabilistic approaches to that problem such as (Castro et al, 2001;Roos et al, 2002). Assuming that we know the true fingerprint distributions q at every fingerprint location {x , y }, we can express the probability of observing a sequence of discrete, integer RSSI measurements S (expressed as a histogram {h 1 , h 2 , .…”

“…We can normalise the right-hand side of (Eq. 6) using a partition function Z = p (S|{x , y }), and then compute the expected value E[{x, y}] of the location having measured the signal, as in (Roos et al, 2002). Using (Eq.…”

“…The first usage of a probabilistic approach to RSSI in indoor localisation was explained in (Castro et al, 2001;Roos et al, 2002;Youssef et al, 2003). They proposed to model the distribution of RSSI at each fingerprint location as a histogram, and to use it as a prior in a Bayesian framework, to compute the probability of having a specific histogram of RSSI at a new location using Bayesian Networks (Castro et al, 2001;Roos et al, 2002) or the Naive Bayes algorithm (Youssef et al, 2003).…”

“…They proposed to model the distribution of RSSI at each fingerprint location as a histogram, and to use it as a prior in a Bayesian framework, to compute the probability of having a specific histogram of RSSI at a new location using Bayesian Networks (Castro et al, 2001;Roos et al, 2002) or the Naive Bayes algorithm (Youssef et al, 2003). Paschalidis et al (2009) use a Kullback-Leibler-based statistical framework for Wireless Sensor Networks localisation (consisting in null hypothesis testing for each fingerprint).…”

The Application of Ray-Tracing to Mobile Localization Using the Direction of Arrival and Received Signal Strength in Multipath Indoor Environments

“…Many proposed localization methods and algorithms were based on the computation of the time of arrival (TOA) [5][6][7], time differences of arrival (TDOA) [8], direction of arrival (DOA) [9][10][11] and the received signal strength (RSS) [12,13]. Conventional methods based on these four measurements increase in error with multipath propagation because they require LoS conditions between the access points and the mobile stations.…”