Probabilistic Cooperative Positioning in Wireless Networks
Date Issued
2011
Date
2011
Author(s)
Lan, Pang-Chang
Abstract
Indoor user positioning has drawn lots of interests in recent years. To find the positions of the concerned users, researchers have developed numerous algorithms mostly based on Kalman filters, particle filters, or hidden Markov
model (HMM). Despite the distinctions, existing works generally conduct the position estimation individually for each user. The inter-user information has been seldom considered. In this thesis, on the other hand, we integrate cooperative schemes into our proposed three HMM-based algorithms. Designed for different requirements for complexity, these algorithms generally utilize the estimation results of the encountered users to help improving accuracy. We further introduce an iterative method that can greatly enhance the positioning performance. For verification, simulation results show that our cooperative algorithms provide significant gains over the conventional non-cooperative works. As expected, the contribution of the cooperative scheme to positioning accuracy rises considerably with the increase of the number
of collaborative users. By conducting the iterative positioning algorithm, an even more gain in accuracy can be achieved. In all, the proposed algorithms set new paradigms for the concept of cooperative positioning.
Subjects
Collaborative Positioning
User Cooperation
Location Estimation, Hidden Markov Model (HMM)
Encountering Information
Type
thesis
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