Multi-sensor fusion using an adaptive multi-hypothesis tracking algorithm
conference paper
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.
Topics
TNO Identifier
237285
Source title
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, 23-25 April 2003, Orlando, FL, USA
Editor(s)
Dasarathy B.V.
Place of publication
Bellingham,WA
Pages
164-172