Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices

Liang, Zhibing; Fu-xian, Liu; Gao, Jiale

doi:10.1371/journal.pone.0192473

Cited by 18 publications

(13 citation statements)

References 24 publications

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“…1, more detailed information about target can be easily obtained by multiple ellipses. In this case, the set of non-ellipsoidal extended targets can be described as [20] …”

Section: B．non-ellipsoid Extended Target Modelmentioning

confidence: 99%

“…But only a single extended target is considered without clutter in [19]. The multiple sub-objects approach has been further applied into PHD (called NET-GGIW-PHD) filter [20], CBMeMBer filter [21], GLMB filter [22] and realizes the multiple non-ellipsoidal extended targets tracking. However, it is assumed that sub-objects number for each NET remains constant during the whole process.…”

Section: Introductionmentioning

confidence: 99%

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A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

Gong

Wu²

2021

IEEE Access

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When the extension state of the non-ellipsoidal extended target (NET) changes, the performance of traditional multiple target tracking algorithms based on the constant number of sub-objects will decrease. To solve this problem, this paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for non-ellipsoidal extended targets with varying number of sub-objects, called VN-NET-GGIW-PHD filter. In the proposed filter, each NET is considered as a combination of multiple spatially close sub-objects, and the label management is introduced to realize the association between the NET and corresponding sub-objects. Then, by target spawning and combination, the number of sub-objects for approximating the extension state of each NET can be adjusted automatically. Furthermore, to obtain the partition of the measurement set, an approach based on the clustering by fast search and find of density peaks (CFSFDP) algorithm and expectation maximization (EM) algorithm is proposed. Simulation results show that the proposed filter can adaptively adjust the number of sub-objects and has better performance when the extension state of the NET changes.

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“…1, more detailed information about target can be easily obtained by multiple ellipses. In this case, the set of non-ellipsoidal extended targets can be described as [20] …”

Section: B．non-ellipsoid Extended Target Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

Gong

Wu²

2021

IEEE Access

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“…Due to the non-linearity of the resulting estimation problem, tracking a single EST is a highly complex problem for which elaborate non-linear estimation techniques are required. The conventional EST tracking approaches depend on measurements (position and range rate), which are extracted by thresholding the received signal in a surveillance radar [2,4,5]. The primary role of thresholding is to reduce the data flow.…”

Section: Introductionmentioning

confidence: 99%

“…For the high-resolution radar, or the distance between the ST and radar is small, the radar may be able to resolve individual features on the ST. Each ST may generate more than one measurement per scan, and the assumption of point STs is not appropriate. Hence, extended ST (EST) tracking arises [4,5]. Modern sensors (e.g., phased-array radar) with increased resolution can generate strong fluctuating number of measurements from EST, which provides not only target kinematic information but also more information about the target extension including size, shape, and orientation.…”

Section: Introductionmentioning

confidence: 99%

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

Barbary

ElAzeem

2021

IET Radar Sonar & Navi

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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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“…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

Barbary

ElAzeem

2020

IET image process

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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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Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices

Cited by 18 publications

References 24 publications

A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

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

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

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