Multiple Target Tracking With Uncertain Sensor State Applied To Autonomous Vehicle Data
Paper in proceedings, 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.

Author

Markus Fröhle

Chalmers, Electrical Engineering, Kommunikationssystem, informationsteori och antenner, Communication Systems

Karl Granström

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Henk Wymeersch

Chalmers, Electrical Engineering, Kommunikationssystem, informationsteori och antenner, Communication Systems

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

European Commission (Horizon 2020), 2015-01-01 -- 2017-12-31.

COPPLAR CampusShuttle cooperative perception & planning platform

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

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Probability Theory and Statistics

Signal Processing

DOI

10.1109/SSP.2018.8450842

More information

Latest update

10/18/2018