Xiaohui X Shen

20100267397, Oct 21, 2010

May 5, 2009

12/435806

Xiaohui Shen - Mundelein IL, US
Sheng-Yue Hu - Beijing, CN
Hong-Lei Liu - Beijing, CN
Yinyan Wang - Palatine IL, US

MOTOROLA, INC. - Schaumburg IL

H04W 64/00

4554561

An infrastructure-based mobililty determination module (MDM) receives multiple measurement reports from the MS and, for each received measurement report, stores values corresponding to values included in the report that are associated with measurements of parameters associated with received signals, wherein each stored value is stored in association with a wireless access node sourcing the corresponding signal. For each of one or more pairings of received measurement reports, the MDM then determines a signal parameter measurement change value corresponding to a change from the signal parameter measurement values associated with a first measurement report of the pairing to the signal parameter measurement values associated with a second measurement report of the pairing. Based on the one or more signal parameter measurement change values, the MDM determines an average signal parameter measurement change value and, based on the average signal parameter measurement change value, determines a mobility of the MS.

20120322460, Dec 20, 2012

Jan 27, 2010

13/522726

Hong-Lei Liu - Beijing, CN
Kwok Lam Cheung - Beijing, CN
Sheng-Yu Hu - Beijing, CN
XiaoHui Shen - Mundelein IL, US

MOTOROLA MOBILITY LLC - Libertyville IL

H04W 24/00

4554561

An apparatus and method is provided for real-time location estimation of a mobile station (MS) on a map of roads using non-Global Positioning Satellite location data () of the MS and includes a step of determining () a mobility of the MS. A next step () includes identifying a region that covers all location data. A next step () includes dividing the region into smaller blocks, where each block constitutes a Hidden Markov Model state. A next step () includes determining a distance between blocks, to be used in a varied continuous probability distribution to determining a state transition probability of each block to represent a likelihood of the MS moving to any one block. A next step () includes using a univariate continuous distribution as a function of a distance between each block and a raw location data. A next step () includes finding a most likely state/block sequence of the MS motion using the HMM state transition probability and the univariate continuous distribution as the location of the MS.

20160124072, May 5, 2016

Jun 24, 2013

13/925319

Hong-Lei Liu - Beijing, CN
Kwok Lam Cheung - Beijing, CN
Sheng-Yu Hu - Beijing, CN
XiaoHui Shen - Mundelein IL, US

MOTOROLA MOBILITY LLC - Libertyville IL

G01S 5/02
H04W 8/08
H04W 4/02

An apparatus and method is provided for real-time location estimation of a mobile station (MS) on a map of roads using non-Global Positioning Satellite location data of the MS and includes a step of determining that the MS is stationary. A next step includes assigning each raw location datum with an initial mass such that the data exhibit an attractive force therebetween. A next step includes calculating a net attractive force for all data using a distance between the data. A next step includes moving each datum a normalized step on the map in response to the net attractive force. A next step includes merging any data that are within a predefined distance on the map and adding their masses. A next step includes repeating the calculating, moving, and merging steps until there are no consecutive merges for a predetermined number of iterations. A next step includes removing any datum with a mass less than a threshold. If there is more than one merged datum left, and a total number of iterations is less than a predefined number, going to the calculating step. Otherwise, using a mean of the locations of any remaining data as a location of the mobile station.