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Probabilistic data association for tracking extended targets under clutter using random matrices

  • The use of random matrices for tracking extended objects has received high attention in recent years. It is an efficient approach for tracking objects that give rise to more than one measurement per time step. In this paper, the concept of random matrices is used to track surface vessels using highresolution automotive radar sensors. Since the radar also receives a large number of clutter measurements from the water, for the data association problem, a generalized probabilistic data association filter is applied. Additionally, a modification of the filter update step is proposed to incorporate the Doppler velocity measurements. The presented tracking algorithm is validated using Monte Carlo Simulation, and some performance results with real radar data are shown as well.

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Metadaten
Author:Michael SchusterGND, Johannes ReuterORCiD, Gerd WanielikGND
URL:http://ieeexplore.ieee.org/document/7266663/
ISBN:978-0-9824-4386-6
Parent Title (English):18th International Conference on Information Fusion (Fusion), 6-9 July 2015, Washington, DC, USA
Document Type:Conference Proceeding
Language:English
Year of Publication:2015
Release Date:2018/03/06
Tag:Doppler radar; filtering theory; marine engineering; marine navigation; target tracking
First Page:961
Last Page:968
Note:
Volltextzugriff f├╝r Hochschulangeh├Ârige via Datenbank IEEE Xplore
Open Access?:Nein