Location estimation using RSS measurements with unknown path loss exponents

Abstract

The location of a mobile station (MS) in a cellular network can be estimated using received signal strength (RSS) measurements that are available from control channels of nearby base stations. Most of the recent RSS-based location estimation methods that are available in the literature rely on the rather unrealistic assumption that signal propagation characteristics are known and independent of time variations and the environment. In this paper, we propose an RSS-based location estimation technique, so-called multiple path loss exponent algorithm (RSS-MPLE), which jointly estimates the propagation parameters and the MS position. The RSS-MPLE method incorporates antenna radiation pattern information into the signal model and determines the maximum likelihood estimate of unknown parameters by employing the Levenberg-Marquardt method. The accuracy of the proposed method is further examined by deriving the Cramer-Rao bound. The performance of the RSS-MPLE algorithm is evaluated for various scenarios via simulation results which confirm that the proposed scheme provides a practical position estimator that is not only accurate but also robust against the variations in the signal propagation characteristics.

SVM-based Models for Mobile Users' Initial Position Determination

“…The use of the Okumura propagation model for modelling probability distribution functions of RSS values (Yamamoto et al, 2001) achieved accuracy of 53 m|67% when tested on field data in an urban environment. RSS Multiple Path-loss Exponent Algorithm (RSS-MPLE) positioning algorithm, which estimates unknown propagation parameters of the path-loss model and determines MS position based on the RSS measurements and known BSs positions, achieved accuracy of 160 m|67% in a simulated environment (Zeytinci et al, 2013). However, the implementations of previously mentioned positioning models require knowledge of BSs positions, transmitted powers, antenna directions, heights, gains and radiation patterns, etc.…”

Unified Fingerprinting/Ranging Localization in Harsh Environments

“…In order to generalize the localization framework, we here consider the most general case where path-loss exponents are dynamically estimated, for which a variety of algorithms can be found in the literature [34,[52][53][54][55][56]. Specifically, we adhere to the technique proposed in [34] based on maximizing the compatibility of the distances between the target and the anchors given a set of received RSS values and subject to a set of feasible solutions, Λ.…”

Performance limit of AOA-based localization using MIMO-OFDM channel state information

“…For wireless localization, a method requiring hardware of just an existing wireless network is of particular interest, because no extra hardware is required for both the infrastructure and the mobile devices (MDs). Unfortunately, although many studies have investigated wireless localization [1][2][3][4][5], it is not straightforward to apply their results without requiring dedicated hardware or affecting the network protocol structure.…”