Hybrid Bayesian fusion of range-based and sourceless location estimates under varying observability

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Title: Hybrid Bayesian fusion of range-based and sourceless location estimates under varying observability
Authors: Yadav, Nagesh
Bleakley, Chris J.
Permanent link: http://hdl.handle.net/10197/3965
Date: 6-Sep-2012
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)
DOI: 10.1109/IS.2012.6335119
Language: en
Status of Item: Peer reviewed
Is 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
Appears in Collections:Computer Science Research Collection

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