A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F17%3APU140251" target="_blank" >RIV/00216305:26220/17:PU140251 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7889057" target="_blank" >https://ieeexplore.ieee.org/document/7889057</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2017.2688966" target="_blank" >10.1109/TSP.2017.2688966</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors

Popis výsledku v původním jazyce

We propose an algorithm for tracking an unknown number of targets based on measurements provided by multiple sensors. Our algorithm achieves lowcomputational complexity and excellent scalability by running belief propagation on a suitably devised factor graph. A redundant formulation of data association uncertainty and the use of "augmented target states" including binary target indicators make it possible to exploit statistical independencies for a drastic reduction of complexity. An increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. As a consequence, the complexity of our method scales only quadratically in the number of targets, linearly in the number of sensors, and linearly in the number of measurements per sensor. The performance of the method compares well with that of previously proposed methods, including methods with a less favorable scaling behavior. In particular, our method can outperform multisensor versions of the probability hypothesis density (PHD) filter, the cardinalized PHD filter, and the multi-Bernoulli filter.

Název v anglickém jazyce

A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors

Popis výsledku anglicky

We propose an algorithm for tracking an unknown number of targets based on measurements provided by multiple sensors. Our algorithm achieves lowcomputational complexity and excellent scalability by running belief propagation on a suitably devised factor graph. A redundant formulation of data association uncertainty and the use of "augmented target states" including binary target indicators make it possible to exploit statistical independencies for a drastic reduction of complexity. An increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. As a consequence, the complexity of our method scales only quadratically in the number of targets, linearly in the number of sensors, and linearly in the number of measurements per sensor. The performance of the method compares well with that of previously proposed methods, including methods with a less favorable scaling behavior. In particular, our method can outperform multisensor versions of the probability hypothesis density (PHD) filter, the cardinalized PHD filter, and the multi-Bernoulli filter.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE TRANSACTIONS ON SIGNAL PROCESSING

ISSN

1053-587X

e-ISSN

1941-0476

Svazek periodika

65

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

3478-3493

Kód UT WoS článku

000401090900012

EID výsledku v databázi Scopus

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