Multitarget Tracking Using Multiple Hypothesis Tracking

Mallick, Mahendra; Coraluppi, Stefano; Carthel, Craig

doi:10.1002/9781118450550.ch05

Cited by 16 publications

(25 citation statements)

References 125 publications

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“…A number of terms such as target, track, track hypothesis, hypothesis, global hypothesis, and association hypothesis, etc. (1,2,25,95,96) are commonly used in the MTT literature, which are not clearly explained. Often a target and a track are used interchangeably.…”

Section: Multiple Hypothesis Trackingmentioning

confidence: 98%

“…The dynamic and measurement models in many real-world problems such as the bearing-only tracking, angle-only tracking, radar tracking, and video tracking (1,2,22,23,25) are nonlinear. The process noise and measurement noise can also be non-additive and nonGaussian.…”

Section: The Kalman Filtermentioning

confidence: 99%

“…Two types of tracking architectures, centralized and distributed, are used in multisensor multitarget tracking (1,2,25). This article addresses only centralized tracking.…”

Section: The Mtt Problemmentioning

confidence: 99%

“…There are two types of MHT, the hypothesis-oriented MHT (HOMHT) (1,2,94) and TOMHT (1,2,25,95,110,124,125). Reid first proposed the HOMHT (94).…”

Section: Types Of Mht Algorithmsmentioning

confidence: 99%

“…MHT (1,2,25,(94)(95)(96)) is a deferred decision approach to data association-based MTT. At each observation time, the MHT algorithm attempts to propagate and maintain a set of association hypotheses with high posterior probability or track score.…”

Section: The Mtt Problemmentioning

confidence: 99%

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Multitarget Tracking

Mallick²,

Bar‐Shalom

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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195

150

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Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the RFS‐based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state‐of‐the‐art MTT algorithms.

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Section: Multiple Hypothesis Trackingmentioning

confidence: 98%

Section: The Kalman Filtermentioning

confidence: 99%

“…Two types of tracking architectures, centralized and distributed, are used in multisensor multitarget tracking (1,2,25). This article addresses only centralized tracking.…”

Section: The Mtt Problemmentioning

confidence: 99%

“…There are two types of MHT, the hypothesis-oriented MHT (HOMHT) (1,2,94) and TOMHT (1,2,25,95,110,124,125). Reid first proposed the HOMHT (94).…”

Section: Types Of Mht Algorithmsmentioning

confidence: 99%

Section: The Mtt Problemmentioning

confidence: 99%

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Multitarget Tracking

Mallick²,

Bar‐Shalom

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

Self Cite

195

150

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Survey: mobile sensor networks for target searching and tracking

Albert

Imsland

2018

Cyber-Physical Systems

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Mobile sensor networks can be employed in multiple applications, such as search and rescue, border patrol, battle scenarios, and environmental monitoring. In this survey, we review the literature utilizing mobile sensor networks in applications classified as target searching and/or tracking. Our contribution is threefold. First, we focus on the diverse types of filters applied to estimating the state of the targets. Second, we present the most common approaches to high-level trajectory planning for the sensors in the network to do target searching and/or tracking. Finally, we classify the literature based on the problem formulation used and solution characteristics. At the end of the survey, we discuss the current state of the literature and possible directions for future research efforts.

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Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking

Liu

Jiang²,

Tian

et al. 2020

IEEE Access

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We propose a novel algorithm, state propagation based dynamic compressed sensing (SP-DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a fixed number of targets. To track a time-varying number of targets using raw measurements from a Doppler radar, we also propose a novel hybrid particle filter based dynamic compressed sensing (HPF-DCS) algorithm. We calculate the support set in a Bayesian framework and a particle filter approximates the posterior probability mass function (pmf) of the support set. HPF-DCS is a combination of random and deterministic sampling. In random sampling, a number of predicted existing sub-particles are sampled from the prior pmf of the existing support set to handle the scenario when targets disappear randomly at a scan time. In deterministic sampling, the new support set corresponding to newly appearing targets is calculated by solving a sparsity promoting optimization problem. Our simulation results show that the proposed algorithm can track a time-varying number of targets successfully. It also outperforms the sequential Monte Carlo based probability hypothesis density (SMC-PHD) filter, as well as the multi-mode, multi-target track before detect (MM-MT-TBD) filter. INDEX TERMS Multitarget tracking, dynamic compressed sensing, state propagation based dynamic compressed sensing (SP-DCS), hybrid particle filter based dynamic compressed sensing (HPF-DCS), Doppler radar raw measurement based tracking.

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Multitarget Tracking Using Multiple Hypothesis Tracking

Cited by 16 publications

References 125 publications

Multitarget Tracking

Multitarget Tracking

Survey: mobile sensor networks for target searching and tracking

Hybrid Particle Filter Based Dynamic Compressed Sensing for Signal-Level Multitarget Tracking

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