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Proceedings Paper

Bayesian unified registration and tracking
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Paper Abstract

Multitarget detection and tracking algorithms typically presume that sensors are spatially registered-i.e., that all sensor states are precisely specified with respect to some common coordinate system. In actuality, sensor observations may be contaminated by unknown spatial misregistration biases. This paper demonstrates that these biases can be estimated by exploiting the data collected from a sufficiently large number of unknown targets, in a unified methodology in which sensor registration and multitarget tracking are performed jointly in a fully unified fashion. We show how to (1) model single-sensor bias, (2) integrate the biased sensors into a single probabilistic multiplatform-multisensor-multitarget system, (3) construct the optimal solution to the joint registration/tracking problem, and (4) devise a principled computational approximation of this optimal solution. The approach does not presume the availability of GPS or other inertial information.

Paper Details

Date Published: 5 May 2011
PDF: 11 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500H (5 May 2011); doi: 10.1117/12.885145
Show Author Affiliations
Ronald Mahler, Unified Data Fusion Sciences, Inc. (United States)
Adel El-Fallah, Scientific Systems Co., Inc. (United States)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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