Multiple Resolvable Group Estimation Based on the GLMB Filter with Graph Structure

Liu, Weifeng; Chi, Yudong; Zhang, Guilin

doi:10.1109/cyber.2018.8688329

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Serial refers to tracking n subgroups successively at a step, which needs to be tracked n times. (26…”

Section: Algorithm For Large-batch and Multi-structure Group Targets ...mentioning

confidence: 99%

“…In 2015, Beard et al proposed the gamma Gaussian inverse Wishart-LMB (GGIW-LMB) algorithm [24], which can estimate the number, states, and trajectories of the group targets. In the literature [25][26][27], graph theory is introduced to describe the relationship between group targets, and collaborative noise is proposed. Reference [28] proposed a resolvable group targets tracking algorithm based on graph theory and GLMB filter.…”

mentioning

confidence: 99%

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Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Liu

Zhao

Liu

et al. 2022

EURASIP J. Adv. Signal Process.

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Current resolvable group targets tracking algorithms can only track a small number of group targets assuming the standard deviation vectors (SDVs) are known. Based on generalized labeled multi-Bernoulli filter, this paper first proposes a serial tracking algorithm, in order to save the computational cost of filter tracking large-batch and multi-structure group targets. Then, the least squares and the k-means algorithms are used to jointly estimate the SDVs and the group target states. The experimental simulation results verify the effectiveness of the proposed methods.

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“…Serial refers to tracking n subgroups successively at a step, which needs to be tracked n times. (26…”

Section: Algorithm For Large-batch and Multi-structure Group Targets ...mentioning

confidence: 99%

mentioning

confidence: 99%

Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Liu

Zhao

Liu

et al. 2022

EURASIP J. Adv. Signal Process.

Self Cite

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show abstract

“…System status

estimates

and estimated error covariance

are obtained based on

[ 19 ]. The filtering equation predicts a step prediction estimation value

and corresponding step prediction error covariance matrix

.…”

Section: Higher Order Extended Kalman Filter Design Based On Maximum Correlation Entropymentioning

confidence: 99%

Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System

Wang

Sun

Wen

2021

Sensors

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This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical properties. Firstly, the transfer function and measurement function are transformed into a nonlinear random dynamic model with a polynomial form via system identification through the multidimensional Taylor network. Secondly, the higher order polynomials in the transformed state model and measurement model are defined as implicit variables of the system. At the same time, the state model and the measurement model are equivalent to the pseudolinear model based on the combination of the original variable and the hidden variable. Thirdly, higher order hidden variables are treated as additive parameters of the system; then, we establish an extended dimensional linear state model and a measurement model combining state and parameters via the previously used random dynamic model. Finally, as we only know the results of the limited sampling of the random modeling error, we use the combination of the maximum correlation estimator and the Kalman filter to establish a new higher order extended Kalman filter. The effectiveness of the new filter is verified by digital simulation.

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Labeled Multi-Bernoulli Filter Based Multiple Resolvable Group Targets Tracking With Leader-Follower Model

2023

IEEE Trans. Aerosp. Electron. Syst.

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Multiple Resolvable Group Estimation Based on the GLMB Filter with Graph Structure

Cited by 3 publications

References 12 publications

Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System

Labeled Multi-Bernoulli Filter Based Multiple Resolvable Group Targets Tracking With Leader-Follower Model

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