Multi-target Track Before Detect with Labeled Random Finite Set and Adaptive Correlation Filtering

Kim, D 2017, 'Multi-target Track Before Detect with Labeled Random Finite Set and Adaptive Correlation Filtering', in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2017), Chiang Mai, Thailand, 31 October - 3 November 2017, pp. 44-49.


Document type: Conference Paper
Collection: Conference Papers

Title Multi-target Track Before Detect with Labeled Random Finite Set and Adaptive Correlation Filtering
Author(s) Kim, D
Year 2017
Conference name ICCAIS 2017
Conference location Chiang Mai, Thailand
Conference dates 31 October - 3 November 2017
Proceedings title Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2017)
Publisher IEEE
Place of publication United States
Start page 44
End page 49
Total pages 6
Abstract In Track-Before-Detect (TBD), the aim is to jointly estimate the number of tracks and their states from low signal-to-noise ratio (SNR) images. This is a challenging problem due to the unknown and time varying number of targets as well as the nonlinearity and size of the image data. A good balance between tractability and fidelity is important in the design of the measurement model for such trackers. In this paper, we transform the raw images into predetection images via adaptive correlation filtering, then apply an efficient labeled random finite set tracking filter for image data. Moreover, instead of using a particle implementation, we use an unscented transformation implementation which is computationally efficient and does not suffer from particle depletion. Numerical studies using realistic radar-based TBD scenarios are presented to verify the efficiency of the proposed solution.
Subjects Signal Processing
DOI - identifier 10.1109/ICCAIS.2017.8217591
Copyright notice © 2017 Crown
ISBN 9781538631140
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 4 Abstract Views  -  Detailed Statistics
Created: Thu, 31 Jan 2019, 11:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us