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Non-ellipsoidal extended targets tracking from image observations based on ECK-MB-sub-random matrices filter

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Abstract

Joint detection and tracking of multiple extended objects from image observations is a challenging radar technology; especially for small back-scattering objects such as extended stealth targets (ESTs). This work provides a new approach for the ESTs tracking under the non-linear Gaussian system based on track-before-detect (TBD) approach. The sequential Monte Carlo multi-Bernoulli (SMC-MB) filter provides a good framework to cope with TBD approach. However, the SMC-MB filter suffers from the particles’ degradation problem seriously; especially for ETs tracking. Recently, the Cubature Kalman MB (CK-MB) filter which employs a third-degree spherical-radical cubature rule has been proposed to handle the nonlinear models, the CK-MB filter is more accurate and more principled in mathematical terms compared to SMC-MB filter. To this point, we address a TBD of ESTs with extended CK (ECK)-MB filter based on random matrices model (RMM), which is an efficient way to track ellipsoidal ESTs. In RMM-ESTs scenarios, although the extension ellipsoid is efficient, it may not be accurate enough because of lacking useful information, such as size, shape and orientation. Therefore, we introduce a filter composed of sub-ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed ECK-Sub-RMM-MB-TBD filter.

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Authors and Affiliations

  1. Department of Electrical Engineering, Alexandria University, Alexandria, Egypt

    Mohamed Barbary

  2. Research and Development Department of Radar system, EADF, Cairo, Egypt

    Mohamed Barbary

  3. Department of electronics and communication, Arab Academy for Science Technology and Maritime Transport, Cairo, Egypt

    Mohamed H. Abd El-azeem

Authors
  1. Mohamed Barbary
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  2. Mohamed H. Abd El-azeem
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Correspondence to Mohamed Barbary.

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Barbary, M., Abd El-azeem, M.H. Non-ellipsoidal extended targets tracking from image observations based on ECK-MB-sub-random matrices filter. Multimed Tools Appl 82, 3269–3302 (2023). https://doi.org/10.1007/s11042-022-13324-x

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  • DOI: https://doi.org/10.1007/s11042-022-13324-x

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