Nonparametric Message Passing Methods for Cooperative Localization and Tracking

Savic, Vladimir (2012). Nonparametric Message Passing Methods for Cooperative Localization and Tracking. Thesis (Doctoral), E.T.S.I. Telecomunicación (UPM).

Description

Title: Nonparametric Message Passing Methods for Cooperative Localization and Tracking
Author/s:
  • Savic, Vladimir
Contributor/s:
  • Zazo Bello, Santiago
Item Type: Thesis (Doctoral)
Date: 2012
Subjects:
Freetext Keywords: cooperative localization, tracking, wireless sensor networks, message passing, particle filtering, belief propagation, RFID
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The objective of this thesis is the development of cooperative localization and tracking algorithms using nonparametric message passing techniques. In contrast to the most well-known techniques, the goal is to estimate the posterior probability density function (PDF) of the position of each sensor. This problem can be solved using Bayesian approach, but it is intractable in general case. Nevertheless, the particle-based approximation (via nonparametric representation), and an appropriate factorization of the joint PDFs (using message passing methods), make Bayesian approach acceptable for inference in sensor networks. The well-known method for this problem, nonparametric belief propagation (NBP), can lead to inaccurate beliefs and possible non-convergence in loopy networks. Therefore, we propose four novel algorithms which alleviate these problems: nonparametric generalized belief propagation (NGBP) based on junction tree (NGBP-JT), NGBP based on pseudo-junction tree (NGBP-PJT), NBP based on spanning trees (NBP-ST), and uniformly-reweighted NBP (URW-NBP). We also extend NBP for cooperative localization in mobile networks. In contrast to the previous methods, we use an optional smoothing, provide a novel communication protocol, and increase the efficiency of the sampling techniques. Moreover, we propose novel algorithms for distributed tracking, in which the goal is to track the passive object which cannot locate itself. In particular, we develop distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Finally, the last part of this thesis includes the experimental analysis of some of the proposed algorithms, in which we found that the results based on real measurements are very similar with the results based on theoretical models.

More information

Item ID: 10798
DC Identifier: http://oa.upm.es/10798/
OAI Identifier: oai:oa.upm.es:10798
Deposited by: Mr Vladimir Savic
Deposited on: 05 May 2012 06:41
Last Modified: 20 Apr 2016 19:00
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