Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo Methods

Vercauteren, Tom; Wang, Xiao Dong

doi:10.1002/9780470487068.ch14

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…[22] allow the time‐invariant class of any particles to change with a small probability which results in model mismatch. In this paper, an ad‐hoc approach inspired by Vercauteren and Wang [23] is used to maintaining robustness, in which a resample procedure is recommended to be separately performed on each class‐conditioned particle set with normalised weights

ω_{k}^{c, j}

after a recursion. Any conventional resampling technique can be applied (see [24] for further details).…”

Section: Jdtc Algorithm Of a Manoeuvring Target In The Fisst Frameworkmentioning

confidence: 99%

Joint detection, tracking and classification of a manoeuvring target in the finite set statistics framework

Yang

Wang²,

et al. 2015

IET signal process.

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Target detection, tracking and classification are three essential and closely coupled subjects for most surveillance systems. In the finite set statistics (FISST) framework, this paper presents a Bayesian and recursive solution to joint detection, tracking and classification (JDTC) of a manoeuvring target in a cluttered environment, which is inspired by previous work on joint target tracking and classification in the classical Bayesian filter framework. The derived JDTC algorithm exploits the dependence of target state on target class by using class-dependent dynamical model sets. The relative merits of this JDTC algorithm are demonstrated via a two-dimensional example using a sequential Monte Carlo implementation. It is shown that handling those three closely coupled subjects jointly can achieve comparable detection and tracking performance to that of the exact filter in the FISST framework with a prior known class. The classification results are consistent with the previous work.

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ω_{k}^{c, j}

after a recursion. Any conventional resampling technique can be applied (see [24] for further details).…”

Section: Jdtc Algorithm Of a Manoeuvring Target In The Fisst Frameworkmentioning

confidence: 99%

Joint detection, tracking and classification of a manoeuvring target in the finite set statistics framework

Yang

Wang²,

et al. 2015

IET signal process.

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“…We employ a JMTD-like particle filter, like e.g. in [3] or [4]. We jointly estimate the number of targets and the target states.…”

Section: A Simulation Examplementioning

confidence: 99%

“…Some works appeared even later, see e.g. [3], [4], and [6]. These approaches all fall in the joint multistate class, where a particle represents a multitarget state.…”

Section: Introductionmentioning

confidence: 98%

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Mixed Labelling in Multitarget Particle Filtering

Boers

Sviestins

Driessen

2010

IEEE Trans. Aerosp. Electron. Syst.

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The so-called mixed labelling problem inherent to a joint state multitarget particle filter implementation is treated. The mixed labelling problem would be prohibitive for track extraction from a joint state multitarget particle filter. It is shown, using the theory of Markov chains, that the mixed labelling problem in a particle filter is inherently self-resolving. It is also shown that the factors influencing this capability are the number of particles and the number of resampling steps. Extensive quantitative analyses of these influencing factors are provided.

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