Constrained multiple model probability hypothesis density filter for maneuvering ground target tracking

Yang, Feng; Shi, Xi; Liang, Yan; Wang, Yongqi

doi:10.1109/cac.2013.6775836

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…The GM-PHD-based filters extract the targets based on their weights [4], [11], [10], [13]. Therefore, when the measurement of a target is missed, the target weight is decreased and therefore its estimation is lost.…”

Section: Proposed Methodsmentioning

confidence: 99%

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Yazdian-Dehkordi

Azimifar

2015

2015 23rd Iranian Conference on Electrical Engineering

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Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed as a closed form solution of PHD filter to estimate the first-order moment of the multi-target posterior density. Recently, different methods such as Competitive GM-PHD (CGM-PHD), Penalized GM-PHD (PGM-PHD) and Collaborative PGM-PHD (CPGM-PHD) are proposed to enhance the performance of GM-PHD filter for tracking closely spaced targets. These methods have no assumption about possible subsequent missed detections which occur in some practical applications. For this reason, the performance of these filters degrades in this condition. In this paper, we propose a novel improvement on GM-PHD filter to track targets in possible subsequent missed detections. In addition to targets weight, we define a probability of confirm (PC) for each target which is adaptively calculated in time. We also propose a new state refinement and state extraction methods based on the defined PC. The experimental results provided for different uncertainties show the effectiveness of the proposed method.

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Section: Proposed Methodsmentioning

confidence: 99%

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Yazdian-Dehkordi

Azimifar

2015

2015 23rd Iranian Conference on Electrical Engineering

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“…Referring to the method proposed in Refs. [ 48 , 49 , 50 ], the target state estimation of the filter can be modified by the above constraints.…”

Section: Gmphdf Incorporating Map Informationmentioning

confidence: 99%

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Zheng

Gao

2018

Sensors

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The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

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“…In recent years, the GM-PHD filter has been widely applied to various fields such as tracking visual targets [13][14][15], Doppler-only targets [16,17], radar targets [18,19] and extended targets [20,21]. Tracking of multi-sensor multi-target based on the GM-PHD filter is also studied in [22] and tracking of manoeuvring targets using this filter is considered in [23,24]. Recently, Yazdian-Dehkordi et al [25][26][27] proposed the competitive GM-PHD (CGM-PHD) filter and the penalised GM-PHD (PGM-PHD) filter to improve the performance of the GM-PHD filter for tracking closely spaced and occluded targets.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the GM‐PHD filter has been widely applied to various fields such as tracking visual targets [13–15], Doppler‐only targets [16, 17], radar targets [18, 19] and extended targets [20, 21]. Tracking of multi‐sensor multi‐target based on the GM‐PHD filter is also studied in [22] and tracking of manoeuvring targets using this filter is considered in [23, 24]. Recently, Yazdian‐Dehkordi et al .…”

Section: Introductionmentioning

confidence: 99%

Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

Yazdian-Dehkordi

Azimifar

2016

IET signal process.

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The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.

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Constrained multiple model probability hypothesis density filter for maneuvering ground target tracking

Cited by 4 publications

References 18 publications

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

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