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

Li, P.; Ge, H.; Yang, Jingxin

doi:10.13164/re.2017.0573

Cited by 5 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future works, we plan to apply the proposed algorithm to extended target Gaussian inverse wishart PHD (ET-GIW-PHD) filter in [13], [24]. Compared with ET-GM-PHD filter, the ET-GIW-PHD filter can estimate the target extension states.…”

Section: Discussionmentioning

confidence: 99%

“…When all the partitions are used for ET-PHD update, it will make ET-PHD filter computationally intractable. To reduce computational complexity, various measurement set partitioning algorithms have been proposed in [6][7][8][9][10][11][12][13][14]. They consider the most likely subset of all possible partitions to update the ET-PHD filter.…”

Section: The Description Of Partitioning the Measurement Setmentioning

confidence: 99%

“…Although the AP clustering reduces the computational complexity, it is influenced by the initial value of the similarity matrix. In [13], an adaptive partitioning approach is proposed. However, it needs distance partitioning to partition the measurement set first and burdens high computation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Measurement Set Partitioning Algorithm Based on CFSFDP for Multiple Extended Target Tracking in PHD Filter

Gong¹,

Cui²

2021

RADIOENGINEERING

View full text Add to dashboard Cite

The extended target probability hypothesis density (ET-PHD) filter is a promising approach for multiple extended target tracking. One crucial problem of the ET-PHD filter is partitioning the measurement set. This paper proposes a partitioning algorithm based on clustering by fast search and find density peaks (CFSFDP). Firstly, we adopt CFSFDP algorithm to partition the measurement set and the field theory is introduced to determine the cutoff distance of the CFSFDP algorithm. Then, the cluster center of the CFSFDP algorithm is determined according to solved cutoff distance and measurement rate. Finally, as the CFSFDP algorithm cannot handle the case in which targets are spatially close, an improved sub-partitioning method is implemented. Simulation results show that the proposed algorithm has less computational complexity and stronger robustness than the existing algorithm without losing tracking performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Description Of Partitioning the Measurement Setmentioning

confidence: 99%

See 1 more Smart Citation

A Measurement Set Partitioning Algorithm Based on CFSFDP for Multiple Extended Target Tracking in PHD Filter

Gong¹,

Cui²

2021

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…Based on previous work, [21] developed a statistical method to watch how close two targets were to each other, and the system still had the ability to recognize them, by checking whether the LLR peaks of the two targets coincided. Li proposed an improved partitioning algorithm for a Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter to solve the problem where the sub-partitioning algorithm failed to handle cases where targets were of different sizes, and the Mahalanobis distances was employed to distinguish among measurement cells of different sizes for extended targets [22]. However, this approach seems to not be sensitive to either differences in target size or target maneuvering.…”

Section: Related Workmentioning

confidence: 99%

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

Wang

Zhao

Wang

et al. 2019

Sensors

View full text Add to dashboard Cite

In wireless sensor networks (WSNs), the problem of measurement origin uncertainty for observed data has a significant impact on the precision of multi-target tracking. In this paper, a novel algorithm based on least squares support vector machine (LS-SVM) is proposed to classify measurement points for adjacent targets. Extended Kalman filter (EKF) algorithm is firstly adopted to compute the predicted classification line for each sampling period, which will be used to classify sampling points and calculate observed centers of closely moving targets. Then LS-SVM algorithm is utilized to train the classified points and get the best classification line, which will then be the reference classification line for the next sampling period. Finally, the locations of the targets will be precisely estimated by using observed centers based on EKF. A series of simulations validate the feasibility and accuracy of the new algorithm, while the experimental results verify the efficiency and effectiveness of the proposal.

show abstract

“…Modified Bayesian adaptive resonance theory (MB-ART) [21] can also achieve good performance. For more details about other partition algorithms, please see [22,23,24].…”

Section: Introductionmentioning

confidence: 99%

Anti-Clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

Huang

Wang

2019

Sensors

View full text Add to dashboard Cite

The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter overestimates the number of targets under high clutter density. The reason for this is that the source of measurements cannot be determined correctly if only the number of measurements is used. To address this problem, we proposed an anti-clutter filter with hypothesis testing, we take into account the number of measurements in cells, the target state and spatial distribution of clutter to decide whether the measurements in cell are clutter. Specifically, the hypothesis testing method is adopted to determine the origination of the measurements. Then, the likelihood functions of targets and clutter are deduced based on the information mentioned above, resulting in the likelihood ratio test statistic. Next, the likelihood ratio test statistic is proved to be subject to a chi-square distribution and a threshold corresponding to the confidence coefficient is introduced and the measurements below this threshold are considered as clutter. Then the correction step of ET-GIW-PHD is revised based on hypothesis testing results. Extensive experiments have demonstrated the significant performance improvement of our proposed method.

show abstract

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

Abstract: Abstract. Use of the Gaussian inverse Wishart probability hypothesis density (GIW-PHD

Cited by 5 publications

References 18 publications

A Measurement Set Partitioning Algorithm Based on CFSFDP for Multiple Extended Target Tracking in PHD Filter

A Measurement Set Partitioning Algorithm Based on CFSFDP for Multiple Extended Target Tracking in PHD Filter

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

Anti-Clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

Contact Info

Product

Resources

About