Tracking multiple extended targets with multi‐Bernoulli filter

2021

IET Radar Sonar & Navi

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi-Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MBfilter-based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM-MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track-before-detect (TBD) approach, which is an efficient way to track low-observable ESTs, is proposed. In EST-RMM-TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST-sub-RMM-TBD composed of sub-ellipses is introduced, each representing an RMM. Based on such models, a sub-RMM-MB-TBD filter is used to estimate the kinematic states and extensions of sub-objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non-linear kinematic EST state is applied. The results indicate that the proposed SMC-sub-RMM-MB-TBD filter has more accurate cardinality estimation and smaller optimal sub-pattern assignment errors than the recent extended tracking filters. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Problem Formulationmentioning

confidence: 99%

Multiple extended stealth target tracking from image observation based on non‐linear sub‐random matrices approach

2021

IET Radar Sonar & Navi

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“…In the most recent research for ET tracking [1–9], the shape model of an ET is usually assumed by the RMM because it is an optimum combination of an informative model for ET and low computational complexity. In the framework of RMM, the extended state is modelled by a symmetric positive definite (SPD) random matrix to simplify the extension of ET as an ellipsoid.…”

Section: Problem Formulationmentioning

confidence: 99%

“…During the last decades, joint detection and tracking for multiple extended targets (ETs) is always a challenging problem in modern radar systems [1–12]; especially for extended stealth targets (ESTs) [1]. Modern sensors (e.g.…”

Section: Introductionmentioning

confidence: 99%

Joint detection and tracking of non‐ellipsoidal extended targets based on cubature Kalman‐CBMeMBer sub‐random matrices filter

2020

IET image process

Joint detection and tracking of multiple extended targets (ETs) from image observations is a challenging radar technology; especially for 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 cardinality‐balanced multi‐target multi‐Bernoulli (SMC‐CBMeMBer) filter provides a good framework to cope with TBD approach. However, this filter suffers from the particles’ degradation problem seriously; especially for ETs tracking. Recently, the cubature Kalman (CK)‐CBMeMBer filter which employs a third‐degree spherical‐radical cubature rule has been proposed to handle the non‐linear models, the CK‐CBMeMBer filter is more accurate and more principled in mathematical terms compared to SMC‐CBMeMBer filter. To this point, the authors address a TBD of ESTs with extended CK‐CBMeMBer filter based on random matrix 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, they introduce a filter composed of sub‐ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed filter.

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

2022

Multimed Tools Appl