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Hybrid Bayesian fusion of range-based and sourceless location estimates under varying observability
Alternative Title
Hybrid Bayesian Approach for Fusing Range-based and Sourceless Localization Estimates Under Non-Stationary Observability
Author(s)
Date Issued
2012-09-06
Date Available
2012-12-18T16:48:47Z
Abstract
The paper proposes a hybrid Bayesian approach
for multi-sensor data fusion for 3D localization. The approach
addresses the problem of fusing range-based and sourceless
localization estimates under conditions of varying observability in
the range-based sub-system. The proposed localization approach
uses a mixture of Single-Hypothesis-Tracking (e.g. Kalman filter)
and Multi-Hypothesis-Tracking (MHT) (e.g. Particle Filters)
Bayesian filtering to improve tracking accuracy under conditions
of varying observability. Under conditions of sufficient (or no)
range measurements a single hypothesis approach is used. Under
the condition of insufficient range measurements (i.e, 1 or 2
ranges), MHT is used, since it more accurately models the
distribution of real error in the estimated positions by means of
Gaussian mixtures rather that a single Gaussian. The results show
up to 10% improvement in 3D position estimation as compared
to Single-Constraint-at-a-Time (SCAAT) approach and upto 24%
improvement compared to an Extended Kalman Filter approach
for intermittent 3 second partial range occlusions when tracking
human arm movements.
Type of Material
Conference Publication
Publisher
IEEE
Subject – LCSH
Bayesian statistical decision theory
Three-dimensional imaging
Observers (Control theory)
Language
English
Status of Item
Peer reviewed
Part of
Intelligent Systems (IS), 2012 6th IEEE International Conference [proceedings]
Conference Details
6th IEEE International Conference on Intelligent Systems IS’12, Sofia, Bulgaria, September 6-8, 2012
ISBN
978-1-4673-2276-8
This item is made available under a Creative Commons License
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