Recursive estimate-maximize (EM) algorithms for time varying parameters with applications to multiple target tracking

L. Frenkel; M. Feder

Recursive estimate-maximize (EM) algorithms for time varying parameters with applications to multiple target tracking

L. Frenkel^*, M. Feder

^*Corresponding author for this work

School of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

We investigate the application of EM algorithm to the classical problem of multiple target tracking (MTT) for a known number of targets. Conventional algorithms, have a computational complexity that depends exponentially on the targets' number, and usually divide the problem into a localization stage and a tracking stage. The new algorithms achieve a linear dependency, and integrate those two stages. Three major optimization criteria are proposed, using deterministic and stochastic dynamic models for the targets.

Original language	English
Pages (from-to)	2068-2071
Number of pages	4
Journal	Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume	3
State	Published - 1995
Event	Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA Duration: 9 May 1995 → 12 May 1995

Cite this

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title = "Recursive estimate-maximize (EM) algorithms for time varying parameters with applications to multiple target tracking",

abstract = "We investigate the application of EM algorithm to the classical problem of multiple target tracking (MTT) for a known number of targets. Conventional algorithms, have a computational complexity that depends exponentially on the targets' number, and usually divide the problem into a localization stage and a tracking stage. The new algorithms achieve a linear dependency, and integrate those two stages. Three major optimization criteria are proposed, using deterministic and stochastic dynamic models for the targets.",

author = "L. Frenkel and M. Feder",

year = "1995",

language = "אנגלית",

volume = "3",

pages = "2068--2071",

journal = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",

issn = "0736-7791",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "null ; Conference date: 09-05-1995 Through 12-05-1995",

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T1 - Recursive estimate-maximize (EM) algorithms for time varying parameters with applications to multiple target tracking

AU - Frenkel, L.

AU - Feder, M.

PY - 1995

Y1 - 1995

N2 - We investigate the application of EM algorithm to the classical problem of multiple target tracking (MTT) for a known number of targets. Conventional algorithms, have a computational complexity that depends exponentially on the targets' number, and usually divide the problem into a localization stage and a tracking stage. The new algorithms achieve a linear dependency, and integrate those two stages. Three major optimization criteria are proposed, using deterministic and stochastic dynamic models for the targets.

AB - We investigate the application of EM algorithm to the classical problem of multiple target tracking (MTT) for a known number of targets. Conventional algorithms, have a computational complexity that depends exponentially on the targets' number, and usually divide the problem into a localization stage and a tracking stage. The new algorithms achieve a linear dependency, and integrate those two stages. Three major optimization criteria are proposed, using deterministic and stochastic dynamic models for the targets.

UR - http://www.scopus.com/inward/record.url?scp=0028996440&partnerID=8YFLogxK

M3 - מאמר מכנס

AN - SCOPUS:0028996440

VL - 3

SP - 2068

EP - 2071

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

Y2 - 9 May 1995 through 12 May 1995

ER -

Recursive estimate-maximize (EM) algorithms for time varying parameters with applications to multiple target tracking

Abstract

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