Mobile terminal location has attracted much interest for emergency communications, location sensitive browsing, and resource allocation. The topic of this seminar is location estimation based on propagation distance measurements from fixed location base stations. The relationship between the measurements and terminal location is complicated by Non Line of Sight (NLOS) propagation when the shortest distance straight line path from receiver to transmitter is obstructed, multipath propagation, receiver noise, and interference noise. This presentation introduces non-parametric estimation and dynamic filtering for accurate location estimation.
Particular emphasis is placed on a dynamic state space model describing the physical rules governing motion and a a dynamic filter using the dynamic model that combines information from measurements made at different times to create improved location estimates. A novel generalized multiple model filter is created incorporating the
dependency of the switching probabilities of the control input on the location state into the filtering algorithm.
The location methods presented reduce the root mean square location error from 100 meters, for the previous methods, to 10 meters for a range error standard deviation of 15 meters. They allow for location prediction in resource allocation algorithms to facilitate efficient cellular networks to carry more data using less bandwidth.