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.

Författare

Markus Fröhle

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

2018 IEEE Statistical Signal Processing Workshop (SSP)

628-632

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

High precision positioning for cooperative ITS applications

Europeiska kommissionen (Horisont 2020), 2015-01-01 -- 2017-12-31.

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA, 2016-01-01 -- 2018-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.1109/SSP.2018.8450842

Mer information

Senast uppdaterat

2018-10-18