Box-Particle Labeled Multi-Bernoulli Filter for Mltiple Extended Target Tracking

Li, M.; Lin, Zaiping; An, Wei; Zhou, Yusong

doi:10.13164/re.2016.0527

Cited by 8 publications

(5 citation statements)

References 16 publications

(28 reference statements)

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“…In addition, a track maintenance approach is included in the GIW PHD filter. In the future works, we plan to apply ASP to other METT particle filters as was done in [22], [23]. ASP is a promising approach for dividing particle swarms.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targets

Li¹,

Ge²,

Yang³

2017

RADIOENGINEERING

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Section: Discussionmentioning

confidence: 99%

Adaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targets

Li¹,

Ge²,

Yang³

2017

RADIOENGINEERING

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“…Due to the unsolved set integration in Bayesian filtering based on the RFS framework, the suboptimal solution is used to approximate the standard Bayesian filtering in practice. Currently, RFS filters used in GTT mainly include probability hypothesis density (PHD) filters [16,21], Cardinality PHD (CPHD) filter [46], Multi-Bernoulli (MB) filter [47], Labeled multi-Bernoulli filter (LMB) [48,49], and Generalized LMB (GLMB) filter [47]. However, the output (including prediction) of RFS filtering is a discrete, unordered, set-based state estimate.…”

Section: Related Workmentioning

confidence: 99%

Group Target Tracking for Highly Maneuverable Unmanned Aerial Vehicles Swarms: A Perspective

Chen

Jiao

et al. 2023

Sensors

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Group target tracking (GTT) is a promising approach for countering unmanned aerial vehicles (UAVs). However, the complex distribution and high mobility of UAV swarms may limit GTTs performance. To enhance GTT performance for UAV swarms, this paper proposes potential solutions. An automatic measurement partitioning method based on ordering points to identify the clustering structure (OPTICS) is suggested to handle non-uniform measurements with arbitrary contour distribution. Maneuver modeling of UAV swarms using deep learning methods is proposed to improve centroid tracking precision. Furthermore, the group’s three-dimensional (3D) shape can be estimated more accurately by applying key point extraction and preset geometric models. Finally, optimized criteria are proposed to improve the spawning or combination of tracking groups. In the future, the proposed solutions will undergo rigorous derivations and be evaluated under harsh simulation conditions to assess their effectiveness.

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“…In recent years, multiple extended target tracking algorithms based on random finite set (RFS) have attracted the attention of many scholars [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The extended target probability hypothesis density (ET-PHD), extended target cardinalized PHD (ET-CPHD), extended target cardinality balanced multi-target multi-Bernoulli (ET-CBMeMBer), extended target generalized labeled multi-Bernoulli (ET-GLMB), extended target labeled multi-Bernoulli (ET-LMB) and extended target Poisson multi-Bernoulli mixture (ET-PMBM) filters have been proposed in [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the box particle implementation is used to reduce the amount of computation. However, the existing filters implemented by SMC or box particles, including the standard PHD and LMB filters implemented by box particles for extended target tracking [22], [23], either do not consider the extension states of targets [15,22,23], or only consider the extension states of simple shapes [16], [17]. As far as we know, there is no report on non-elliptic extension state estimation using RFS filter implemented by box particles.…”

Section: Introductionmentioning

confidence: 99%

Extended Target Fast Labeled Multi-Bernoulli Filter

Cheng¹,

Ji²,

Zhang³

2023

RADIOENGINEERING

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Focusing on the real-time tracking of the extended target labeled multi-Bernoulli (ET-LMB) filter, this paper proposes an extended target fast labeled multi-Bernoulli (ET-FLMB) filter based on beta gamma box particle (BGBP) and Gaussian process (GP), called ET-BGBP-GP-FLMB filter. First, a new ET-FLMB filter is derived to reduce the computational complexity of the ET-LMB filter. Then, by modeling the target state as an augmented state including detection probability, measurement rate, kinematic state and extension state, the BGBP-GP implementation of the ET-FLMB filter is presented. Compared with the traditional sequential Monte Carlo (SMC) implementation, the proposed implementation can not only greatly reduce the number of particles and the amount of computation, but also estimate the detection probabilities, measurement rates and extension states while estimating the number and kinematic states of extended targets. Finally, the simulation results show that the proposed filter can significantly reduce the computational burden and improve the real-time performance.

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Box-Particle Labeled Multi-Bernoulli Filter for Mltiple Extended Target Tracking

Abstract: Abstract. This paper focuses on real-time tracking of multiple extended targets in clutter based

Cited by 8 publications

References 16 publications

Adaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targets

Adaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targets

Group Target Tracking for Highly Maneuverable Unmanned Aerial Vehicles Swarms: A Perspective

Extended Target Fast Labeled Multi-Bernoulli Filter

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