Estimation of
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.