Skewness Multi-Bernoulli-TBD Filter for Tracking Multiple Maneuvering Extended Objects from ISAR Images

Barbary, M., Hamed, M. and Le Kernec, J. (2023) Skewness Multi-Bernoulli-TBD Filter for Tracking Multiple Maneuvering Extended Objects from ISAR Images. In: International Conference on Radar Systems (RADAR 2022), Edinburgh, UK, 24 - 27 October 2022, ISBN 9781839537776 (doi: 10.1049/icp.2022.2313)

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Abstract

The simultaneous imaging and tracking of manoeuvring extended objects (M-EOTs) is one of the most challenging problems in inverse synthetic aperture radar (ISAR) signal processing and has received significant attention recently. In this paper, we address multiple M-EOTs based on the track-before-detect (TBD) - Multi-Bernoulli (MB) approach, which is an efficient way to track low observable M-EOTs from ISAR images. To this end, we introduce a sub-Random Matrix Model (RMM)- MB-TBD composed of sub-ellipses; each one is represented by a RMM to denote the M-EOTs' extension. We also, propose a new ISAR observation model using a skewed (SK) normal distribution.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Elsayed, Mostafa Hamed Abdalla and Le Kernec, Dr Julien
Authors: Barbary, M., Hamed, M., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
ISBN:9781839537776
Copyright Holders:Copyright © The Institution of Engineering and Technology 2023
First Published:First published in International Conference on Radar Systems (RADAR 2022)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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