Bayesian unified registration and tracking
Access Status
Authors
Date
2011Type
Metadata
Show full item recordCitation
Source Title
ISBN
School
Collection
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. © 2011 SPIE.
Related items
Showing items related by title, author, creator and subject.
-
Mallick, M.; Rubin, S.; Vo, Ba-Ngu (2013)Space object (satellite or space-debris) tracking (SOT) has not received much attention in the Information Fusion community, although the first Fusion conference was held in 1998. A special session on SOT was organized ...
-
Liang, M.; Kim, Du Yong; Kai, X. (2015)Multi-static Doppler-shift has re-emerged recently in the target tracking literature along with passive sensing, especially for aircraft tracking. Tracking with multi-static Doppler only measurement requires efficient ...
-
El-Mowafy, Ahmed ; Imparato, D. (2018)© 2018 Institute of Navigation. All rights reserved. Intelligent transportation systems (ITS) and autonomous vehicles need accurate localization solutions for applications such as lane identification and collision avoidance. ...