Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the RFS-based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state-of-the-art MTT algorithms.
%0 Book Section
%1 doi:https://doi.org/10.1002/047134608X.W8275
%A Vo, Ba-ngu
%A Mallick, Mahendra
%A Bar shalom, Yaakov
%A Coraluppi, Stefano
%A Osborne III, Richard
%A Mahler, Ronald
%A Vo, Ba-tuong
%B Wiley Encyclopedia of Electrical and Electronics Engineering
%D 2015
%I John Wiley & Sons, Ltd
%K multitarget_tracking
%P 1-15
%R https://doi.org/10.1002/047134608X.W8275
%T Multitarget Tracking
%U https://onlinelibrary.wiley.com/doi/abs/10.1002/047134608X.W8275
%X Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the RFS-based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state-of-the-art MTT algorithms.
%@ 9780471346081
@inbook{doi:https://doi.org/10.1002/047134608X.W8275,
abstract = {Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the RFS-based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state-of-the-art MTT algorithms.},
added-at = {2023-12-01T17:02:16.000+0100},
author = {Vo, Ba-ngu and Mallick, Mahendra and Bar shalom, Yaakov and Coraluppi, Stefano and Osborne III, Richard and Mahler, Ronald and Vo, Ba-tuong},
biburl = {https://www.bibsonomy.org/bibtex/241e790d1432589bee1d95ec0bef03d7f/ng9q1nfk},
booktitle = {Wiley Encyclopedia of Electrical and Electronics Engineering},
doi = {https://doi.org/10.1002/047134608X.W8275},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/047134608X.W8275},
interhash = {8a7ea73ac6ec7ed4a63633bd94c9048c},
intrahash = {41e790d1432589bee1d95ec0bef03d7f},
isbn = {9780471346081},
keywords = {multitarget_tracking},
pages = {1-15},
publisher = {John Wiley & Sons, Ltd},
timestamp = {2023-12-01T17:02:16.000+0100},
title = {Multitarget Tracking},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/047134608X.W8275},
year = 2015
}