Robust multisensor multitarget tracker with application to passive multistatic radar tracking

This paper presents a novel approach to multitarget multisensor tracking, based on the combination of a probability hypothesis density (PHD) smoother and hard multisensor multiscan data association (MMDA) used in a feedback connection. The PHD smoother allows to initiate target tracks without resort...

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Main Authors: Battistelli, G., Chisci, L., Morrocchi, S., Papi, Francesco, Farina, A., Graziano, A.
Format: Journal Article
Published: 2012
Online Access:http://hdl.handle.net/20.500.11937/37924
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author Battistelli, G.
Chisci, L.
Morrocchi, S.
Papi, Francesco
Farina, A.
Graziano, A.
author_facet Battistelli, G.
Chisci, L.
Morrocchi, S.
Papi, Francesco
Farina, A.
Graziano, A.
author_sort Battistelli, G.
building Curtin Institutional Repository
collection Online Access
description This paper presents a novel approach to multitarget multisensor tracking, based on the combination of a probability hypothesis density (PHD) smoother and hard multisensor multiscan data association (MMDA) used in a feedback connection. The PHD smoother allows to initiate target tracks without resorting to complicated measurement-to-measurement association procedures while the feedback from the hard MMDA, besides providing track labeling, makes the PHD smoother, and hence the overall tracker, more robust to missed detections and false alarms. An application of the proposed tracker to passive multistatic radar tracking is worked out in order to demonstrate its effectiveness in critical situations where the lack of single-sensor observability prevents the use of traditional track initiation methods. As a further contribution, an extension to the multisensor case of the multicommodity approach to multiscan data association, originally presented for the single-sensor case, is provided. © 2011 IEEE.
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institution Curtin University Malaysia
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spelling curtin-20.500.11937-379242017-09-13T14:11:37Z Robust multisensor multitarget tracker with application to passive multistatic radar tracking Battistelli, G. Chisci, L. Morrocchi, S. Papi, Francesco Farina, A. Graziano, A. This paper presents a novel approach to multitarget multisensor tracking, based on the combination of a probability hypothesis density (PHD) smoother and hard multisensor multiscan data association (MMDA) used in a feedback connection. The PHD smoother allows to initiate target tracks without resorting to complicated measurement-to-measurement association procedures while the feedback from the hard MMDA, besides providing track labeling, makes the PHD smoother, and hence the overall tracker, more robust to missed detections and false alarms. An application of the proposed tracker to passive multistatic radar tracking is worked out in order to demonstrate its effectiveness in critical situations where the lack of single-sensor observability prevents the use of traditional track initiation methods. As a further contribution, an extension to the multisensor case of the multicommodity approach to multiscan data association, originally presented for the single-sensor case, is provided. © 2011 IEEE. 2012 Journal Article http://hdl.handle.net/20.500.11937/37924 10.1109/TAES.2012.6324726 restricted
spellingShingle Battistelli, G.
Chisci, L.
Morrocchi, S.
Papi, Francesco
Farina, A.
Graziano, A.
Robust multisensor multitarget tracker with application to passive multistatic radar tracking
title Robust multisensor multitarget tracker with application to passive multistatic radar tracking
title_full Robust multisensor multitarget tracker with application to passive multistatic radar tracking
title_fullStr Robust multisensor multitarget tracker with application to passive multistatic radar tracking
title_full_unstemmed Robust multisensor multitarget tracker with application to passive multistatic radar tracking
title_short Robust multisensor multitarget tracker with application to passive multistatic radar tracking
title_sort robust multisensor multitarget tracker with application to passive multistatic radar tracking
url http://hdl.handle.net/20.500.11937/37924