Multiple Target Tracking With Uncertain Sensor State Applied To Autonomous Vehicle Data
Paper i proceeding, 2018

In a conventional multitarget tracking (MTT) scenario, the sensor position is assumed known. When the MTT sensor, e.g., an automotive radar, is mounted to a moving vehicle with uncertain state, it becomes necessary to relax this assumption and model the unknown sensor position explicitly. In this paper, we compare a recently proposed filter that models the unknown sensor state [1], to two versions of the track-oriented marginal MeMBer/Poisson (TOMB/P) filter: the first does not model the sensor state uncertainty; the second models it approximately by artificially increasing the measurement variance. The results, using real measurement data, show that in terms of tracking performance, the proposed filter can outperform TOMB/P without sensor state uncertainty, and is comparable to TOMB/P with increased variance.

Kalman filter


multi-target tracking


Markus Fröhle

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

2018 IEEE Statistical Signal Processing Workshop (SSP)

978-153861570-6 (ISBN)

2018 IEEE Statistical Signal Processing Workshop (SSP)
Freiburg, Germany,

High precision positioning for cooperative ITS applications

Europeiska kommissionen (EU) (EC/H2020/636537), 2015-01-01 -- 2017-12-31.

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.


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