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Content Provider | IEEE Xplore Digital Library |
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Author | Williams, J.L. |
Copyright Year | 2012 |
Description | Author affiliation: Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Australia (Williams, J.L.) |
Abstract | The probability hypothesis density (PHD) and multitarget multi-Bernoulli (MeMBer) filters are two leading algorithms that have emerged from random finite sets (RFS). In this paper we study a method which combines these two approaches. Our work is motivated by a recent paper, which proves that the full Bayes RFS filter naturally incorporates a Poisson component representing targets that have never been detected, and a linear combination of multi-Bernoulli components representing targets under track. Here we demonstrate the benefit (in speed of track initiation) that maintenance of a Poisson component of never detected targets provides. Subsequently, we propose a method of recycling, which projects Bernoulli components with a low probability of existence onto the Poisson component (as opposed to deleting them). We show that this allows us to achieve similar tracking performance using a fraction of the number of Bernoulli components (i.e., tracks). |
Starting Page | 1103 |
Ending Page | 1110 |
File Size | 1364793 |
Page Count | 8 |
File Format | |
ISBN | 9781467304177 |
e-ISBN | 9780982443859 |
e-ISBN | 9780982443842 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-07-09 |
Publisher Place | Singapore |
Access Restriction | Subscribed |
Rights Holder | ISIF ( Intl Society of Information Fusi |
Subject Keyword | Target tracking Sensors Time measurement Steady-state Density measurement Position measurement |
Content Type | Text |
Resource Type | Article |
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