Robust Tracking in Cellular Networks Using HMM Filters and Cell-ID Measurements

Abstract

A localization algorithm based on cell identification (Cell-ID) information is proposed. Instead of building the localization decisions only on the serving base station, all the detected Cell-IDs (serving or nonserving) by the mobile station are utilized. The statistical modeling of user motion and the measurements are done via a hidden Markov model (HMM), and the localization decisions are made with maximum a posteriori estimation criterion using the posterior probabilities from an HMM filter. The results are observed and compared with standard alternatives on an example whose data were collected from a worldwide interoperability for microwave access network in a challenging urban area in the Brussels capitol city.<p>©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p

A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

“…MSR-based location methods can be classified into three categories: angle of arrival (AoA) [13][14][15], time of arrival (ToA) [13,14,16], and time difference of arrival (TDoA) [9,10,13]. This approach requires higher computation power than other methods [12,[17][18][19].…”

“…These methods can be classified into three categories: a cell-ID-based method [19], a handover-based method [20][21][22], and a fingerprint positioning method [23][24][25][26][27][28]. However, the lengths of cells and handoff zones are approximately 2 km and 200 m, respectively, and the location error depends on the cell size [12,19,29]. Although precise location estimation can be obtained by the fingerprint positioning method, higher computation power and an establishment fee are required [30].…”

The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation

“…As shown in Figure 1, if more than two handover processes are performed, then the first handover sequence tags (CGI 1 , CGI 2 ) at 1 and the second handover sequence tags (CGI 2 , CGI 3 ) at 2 can be retrieved. In addition, if the handover sequence tags at 1 (CGI 1 , CGI 2 ) and 2 (CGI 2 , CGI 3 ) are known, then the distance between the two positions ( 1 , 2 ) can be determined, and the vehicle speed can be estimated according to (1). However, the location errors of some handover pairs are substantial, especially in rural areas.…”

“…Speed estimates are then computed and reported [1]. In this study, an FPA was used to increase the location and speed estimation accuracy, and a model for analyzing speedreporting rates is proposed based on the communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations for the same call to determine the optimal sampling period and estimate vehicle speed.…”

The analysis of speed-reporting rates from a cellular network based on a fingerprint-positioning algorithm

“…If this MS moves from one BS to another during a call, then a handover occurs. Therefore, cell ID, handover signals, and received signal strengths (RSS) from measure reports, which are usually used to determine the location of the MS [3][4][5][6][7][8][9][10][11][12][13], are described in the following subsections.…”