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Multi-sensor fusion using an adaptive multi-hypothesis tracking algorithm

Author: Kester, L.J.H.M.
Type:article
Date:2003
Place: Bellingham,WA
Institution: TNO Fysisch en Elektronisch Laboratorium
Source:Dasarathy B.V., Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, 23-25 April 2003, Orlando, FL, USA, 164-172
series:
Proceedings of SPIE
Identifier: 237285
doi: doi:10.1117/12.487365
Keywords: Data association · MHT · Sensor fusion · Tracking · Acoustic devices · Adaptive algorithms · Cameras · Electrooptical devices · Kalman filtering · Optical sensors · Radar tracking · Tracking radar · Acoustic sensors · Data association · Electrooptic sensors · Extended Kalman filtering · Multihypothesis tracking · Sensor data fusion

Abstract

The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depends on the type of data that is provided by these sensors. It is discussed how the tracking algorithm can adapt itself, depending on the provided data, to improve data association. The core of the tracking algorithm is an extended Kalman filter using multiple hypotheses for contact to track association. Examples of various sensor suites of radars, electro-optic sensors and acoustic sensors are presented.