A track-oriented multiple hypothesis multitarget tracking algorithm

Kosuge, Yoshio; Tachibana, Yasuo; Tsujimichi, Shingo

doi:10.1002/(sici)1520-6424(199912)82:12<84::aid-ecja10>3.0.co;2-b

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…To account for this data association problem, two different approaches are proposed. The first one is based on a Track-Oriented Multi-Hypothesis Tracking (TOMHT) method [20], [21] combined with an Extended Kalman Filter (EKF), which relies on the assumption that the motion model of the target is known. In the second approach instead, an agnostic approach for robust tracking based on Expectation Maximisation (EM) is adopted both to estimate the model parameters and to track the target [22], [23].…”

Section: Motion Tracking Solutionsmentioning

confidence: 99%

Algorithms for Enhanced Indoor Positioning and Tracking based on a 60-GHz Radar Platform

Shamsfakhr

Corrà²,

Ferrari³

et al. 2022

2022 IEEE International Workshop on Metrology for Industry 4.0 &Amp; IoT (MetroInd4.0&IoT)

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Wireless technologies are increasingly adopted for indoor localisation and tracking. Among them, radars based on 60 GHz phased-array transceivers are becoming a viable and low-cost solution, which delivers high accuracy with a low computational cost while preserving people privacy. In this paper, we propose two techniques for radar-based people tracking and localisation that tackle important effects such as delayed and spurious detection. We also offer a thorough and scientifically sound experimental analysis of the performance of the proposed techniques using an off-the-shelf phased-array device (i.e., the System-on-Chip (SOC) TI IWR6843).

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Section: Motion Tracking Solutionsmentioning

confidence: 99%

Algorithms for Enhanced Indoor Positioning and Tracking based on a 60-GHz Radar Platform

Shamsfakhr

Corrà²,

Ferrari³

et al. 2022

2022 IEEE International Workshop on Metrology for Industry 4.0 &Amp; IoT (MetroInd4.0&IoT)

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“…The MHT algorithm assumes that each new measurement may originate from an existing target, a new target or a clutter, then establish multiple hypotheses in one time, and finally achieve multi-target tracking by hypothesis evaluation, deletion and merging. However, the calculation amount of the algorithm increases exponentially with the increase of the number of targets and clutters [8]. In order to reduce the computation of JPDA, some improved JPDA algorithms have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A multi-target tracking algorithm based on Gaussian mixture model

Sun

Cao

et al. 2020

J. of Syst. Eng. Electron.

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Since the joint probabilistic data association (JPDA) algorithm results in calculation explosion with the increasing number of targets, a multi-target tracking algorithm based on Gaussian mixture model (GMM) clustering is proposed. The algorithm is used to cluster the measurements, and the association matrix between measurements and tracks is constructed by the posterior probability. Compared with the traditional data association algorithm, this algorithm has better tracking performance and less computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

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“…However, the HOMHT proposed by Reid [8] forms and expands a large number of hypotheses from scan to scan for data association. In contrary to the HOMHT, the TOMHT maintains track trees for incompatible tracks on last scans and prunes all the unreliable tree branches (track hypotheses) to obtain the best global hypothesis [9]. Recent research on MHT algorithms has mainly focused on improving the association algorithm in MHT [10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion

Zhang

Sun

et al. 2019

Sensors

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In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.

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A track-oriented multiple hypothesis multitarget tracking algorithm

Cited by 10 publications

References 4 publications

Algorithms for Enhanced Indoor Positioning and Tracking based on a 60-GHz Radar Platform

Algorithms for Enhanced Indoor Positioning and Tracking based on a 60-GHz Radar Platform

A multi-target tracking algorithm based on Gaussian mixture model

Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion

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