A Gaussian Mixture Filter for Range-Only Tracking

Clark, John M.; Kountouriotis, P.A.; Vinter, R. B.

doi:10.1109/tac.2010.2072590

Cited by 20 publications

(16 citation statements)

References 19 publications

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“…3. This test was conducted with the simulation parameters of Table I, excepted for the target velocity, which was related to the ratios = [ 1,2,3,4,5,6,7,8,9,10 ] . The 10 overlaid results are represented in the following plots.…”

Section: B Speed Ratiomentioning

confidence: 99%

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Underwater mobile target tracking with particle filter using an autonomous vehicle

Masmitja

Bouvet

Gomáriz

et al. 2017

OCEANS 2017 - Aberdeen

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Abstract-. This paper describes an underwater mobile target localization and tracking by using an autonomous surface vehicle for which the successive ranges between the target and the reference are the only information. In a dynamic system, such as range-only single-beacon underwater target tracking, a statespace model can be characterized, where the state vector may include position, and velocity of the mobile underwater target. Moreover, the range observations can come from a mobile autonomous vehicle, which is used as a moving landmark. Then, a nonlinear Bayesian filtering algorithm can be used to make extrapolations on the state vector from the observations, in order to obtain the target position at each instant of time. In this paper we consider the use of Particle Filter (PF) to perform such localization and tracking where its performance and characterization is studied under different scenarios.

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Section: B Speed Ratiomentioning

confidence: 99%

“…On the other hand, [4] and [5] have used PF for target tracking using Direction Of Arrival (DOA) or angle-only observations. Other authors, such as [6], have derived different methods to improve the PF performances, for example by introducing Gaussian Mixture Range-Only Filter (GMROF) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Underwater mobile target tracking with particle filter using an autonomous vehicle

Masmitja

Bouvet

Gomáriz

et al. 2017

OCEANS 2017 - Aberdeen

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“…cases. Two scenarios have been considered: in the first one, the target's trajectory is observable, and in the second one, proposed in [3], the target's trajectory is unobservable. For each scenario, 500 Monte Carlo simulations have been run.…”

Section: B Efficiency Of the Linear Estimatementioning

confidence: 99%

“…In [3] the authors proposed the following scenario whose duration is 30 min to test a particle filter. The first heading and the speed of the observer are respectively -80° and 2.57 m/s.…”

Section: B Unobservable Scenario(see [3])mentioning

confidence: 99%

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Performance of range-only TMA

Pérez¹,

Jauffret²,

Pillon³

2017

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Adaptive control and signal processing literature survey (No. 24)

2011

Int. J. Adapt. Control Signal Process.

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please

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A Gaussian Mixture Filter for Range-Only Tracking

Cited by 20 publications

References 19 publications

Underwater mobile target tracking with particle filter using an autonomous vehicle

Underwater mobile target tracking with particle filter using an autonomous vehicle

Performance of range-only TMA

Adaptive control and signal processing literature survey (No. 24)

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