Abstract
The problem of locating mobile stations (MS) in cellular systems based on uplink time difference of arrival (UTDOA) of MS signal at spatially separated base stations with known locations is addressed in this paper. Multipath fading and channel noise are the main factors resulting in inaccurate mobile station position estimation. Therefore, use of the normalized least mean square (NLMS) algorithm based adaptive line enhancer (ALE) followed by a correlator is proposed to obtain more precise time difference of arrival (TDOA) estimation. The proposed technique applies the ALE as a pre-filter to signal cross correlation, leading to improved accuracy in TDOA estimation and consequently more precise positioning of MS. The robustness of the proposed technique is examined and analyzed through computer simulations. Simulation results indicate superior performance of the proposed ALE-UTDOA estimator over the conventional cross correlation method.
Highlights
Mobile station (MS) positioning has become a significant part of mobile communication technologies
The proposed adaptive line enhancer (ALE)-uplink time difference of arrival (UTDOA) method as well as the conventional UTDOA method have been implemented in MATLAB and comprehensively compared
A major challenge encountering UTDOA positioning method in mobile wireless systems is the noise and multipath effect due to non-line-of-sight (NLOS) channels and techniques to mitigate such channel effects are crucial to the overall performance of localization
Summary
Mobile station (MS) positioning has become a significant part of mobile communication technologies. The RSS-based positioning method is the simplest but the most inaccurate technique while the AOA, TOA and TDOA methods obtain more accurate estimation. Conventional UTDOA positioning is composed of two steps (Rappaport et al, 1996): first, obtaining TDOA parameters of a signal from the MS between pairs of BSs by a time delay estimation algorithm and forming a set of nonlinear hyperbolic equations, second, using an appropriate algorithm to solve the hyperbolic equations obtained from the first step to uniquely localize the MS. The cross correlation performance and the TDOA estimation accuracy deteriorate in low signal-to-noise ratio (SNR) circumstances. Utilization of the ALE as a pre-filter for cross correlation is proposed and examined in UTDOA based localization to improve the accuracy of TDOA estimation, and subsequently reducing the uncertainty in MS positioning. The robustness of the proposed technique is assessed through computer simulation results
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