Positioning Algorithms for Cellular Networks Using TDOA
ABSTRACT In this paper, we investigate the performance of positioning algorithms in wireless cellular networks based on time difference of arrival (TDoA) measurements provided by the base stations. The localization process of the mobile station results in a non-linear least squares estimation problem which cannot be solved analytically. Therefore, we use iterative algorithms to determine an estimate of the mobile station position. The well-known Gauss-Newton method fails to converge for certain geometric constellations, and thus, it is not suitable for a general solution in cellular networks. Another algorithm is the steepest descent method which has a slow convergence in the final iteration steps. Hence, we apply the Levenberg-Marquardt algorithm as a new approach in the cellular network localization framework. We show that this method meets the best trade-off between accuracy and computational complexity
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ABSTRACT: In the 3 rd generation (3G) telecommunication, the subscriber radio techniques based on time difference of arrival (TDOA) became one of the key technologies. Chan-Ho is a method that is widely used in solving the TDOA hyperbolic equations, but its performance is highly dependent on the pure distance term between the home base transceiver station (BTS) and the mobile handset. The proposed procedure uses another term that will utilize the relation between the mobile handset with two base transceiver stations. The performance of the proposed algorithm and the original Chan-Ho algorithm are evaluated and the simulation results show that the proposed algorithm has the advantage over the original. time difference of arrival (TDOA).
Automation in Construction 11/2013; 35:587-594. DOI:10.1016/j.autcon.2013.01.005 · 1.82 Impact Factor
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ABSTRACT: In this paper, we address the problem of localizing sensor nodes in a static network, given that the positions of a few of them (denoted as “beacons“) are a priori known. We refer to this problem as “auto-localization.” Three localization techniques are considered: the two-stage maximum-likelihood (TSML) method; the plane intersection (PI) method; and the particle swarm optimization (PSO) algorithm. While the first two techniques come from the communication-theoretic “world,” the last one comes from the soft computing “world.” The performance of the considered localization techniques is investigated, in a comparative way, taking into account (i) the number of beacons and (ii) the distances between beacons and nodes. Since our simulation results show that a PSO-based approach allows obtaining more accurate position estimates, in the second part of the paper we focus on this technique proposing a novel hybrid version of the PSO algorithm with improved performance. In particular, we investigate, for various population sizes, the number of iterations which are needed to achieve a given error tolerance. According to our simulation results, the hybrid PSO algorithm guarantees faster convergence at a reduced computational complexity, making it attractive for dynamic localization. In more general terms, our results show that the application of soft computing techniques to communication-theoretic problems leads to interesting research perspectives.Applied Soft Computing 12/2014; 25:426-434. DOI:10.1016/j.asoc.2014.07.025 · 2.68 Impact Factor