Sensor fusion for AUV localization

Rigaud, V.; Marcé, Lionel; Michel, Jean-Louis; Borot, P.

doi:10.1109/auv.1990.110452

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…DOA estimation of an acoustic source in the underwater environment is an important topic and lies at the heart of many underwater signal processing applications such as sonar detection and localization [1]- [4], geoacoustic inversion [5], [6], underwater vehicle navigation [7], and ocean biological signal analysis [8]. It is a challenging problem since various uncertainties can arise from following aspects: 1) Source dynamics.…”

Section: Introductionmentioning

confidence: 99%

Direction of arrival tracking of an underwater acoustic source using particle filtering: Real data experiments

Zhong

Premkumar

Wang

2013

IEEE 2013 Tencon - Spring

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Abstract-In the past, both theoretical work and practical implementation of particle filtering (PF) method have been extensively studied. However, its application in underwater signal processing has received much less attention. This paper intends to introduce PF approach for underwater acoustic signal processing. Particularly, we are interested in direction of arrival (DOA) estimation using PF. A detailed introduction along with this perspective is presented in this paper. Since the noise usually spreads the mainlobe of likelihood function and causes problem in subsequent particle resampling step, an exponential weighted likelihood model is developed to emphasize particles at more relevant area. Hence, the the effect due to background noise can be reduced. Real underwater acoustic data collected in SWELLEx-96 experiment are employed to demonstrate the performance of the proposed PF approaches for underwater DOA tracking.Index Terms-Direction of arrival, localization and tracking, underwater acoustics, particle filtering, SWELLEx-96 experiment.

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Section: Introductionmentioning

confidence: 99%

Direction of arrival tracking of an underwater acoustic source using particle filtering: Real data experiments

Zhong

Premkumar

Wang

2013

IEEE 2013 Tencon - Spring

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“…A different approach on acoustic sensor fusion for AUV navigation was performed by Rigaut et al, showing the advantages of acoustic data fusion, versus a dead reckoning strategy Rigaud et al (1990). The matching of the absolute and relative reckoning modules permits to compute a new hypothesis, and to reset the relative estimation in an asynchronous way with the help of the operator.…”

Section: Seabed Transpondersmentioning

confidence: 99%

AUV localisation: a review of passive and active techniques

Maurelli

Krupínski

Xia

et al. 2021

Int J Intell Robot Appl

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Localisation, i.e. estimation of one’s position in a given environment is a crucial element of many mobile systems, manned and unmanned. Due to the high demand for autonomous exploration, patrolling and inspection services and a rapid improvement of batteries, sensors and machine learning algorithms, the quality of localisation becomes even more important for smart robotic systems. The underwater domain is a very challenging environment due to the water blocking most of the signals over short distances. Recent results in localisation techniques for underwater vehicles are summarised in two principal categories: passive techniques, which strive to provide the best estimation of the vehicle’s position (global or local) given the past and current information from sensors, and active techniques, which additionally produce guidance output that is expected to minimise the uncertainty of estimated position.

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“…RELATED WORK AUV localisation is a problem which has been studied from the very early stages of marine autonomy. Already in 1990, Rigaud et al proposed a system based on sensor fusion to correct dead reckoning [1], using a discrete stochasticaccumulation method. The results are shown postprocessing sonar data from a pool.…”

Section: Introductionmentioning

confidence: 99%

A Semantic-Aided Particle Filter Approach for AUV Localization

Maurelli

Krupínski

2018

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)

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This paper presents a novel approach to AUV localization, based on a semantic-aided particle filter. Particle filters have been used successfully for robotics localization since many years. Most of the approaches are however based on geometric measurements and geometric information and simulations. In the past years more and more efforts from research goes towards cognitive robotics and the marine domain is not exception. Moving from signal to symbol becomes therefore paramount for more complex applications. This paper presents a contribution in the well-known area of underwater localization, incorporating semantic information. An extension to the standard particle filter approach is presented, based on semantic information of the environment. A comparison with the geometric approach shows the advantages of a semantic layer to successfully perform selflocalization.

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Sensor fusion for AUV localization

Cited by 7 publications

References 7 publications

Direction of arrival tracking of an underwater acoustic source using particle filtering: Real data experiments

Direction of arrival tracking of an underwater acoustic source using particle filtering: Real data experiments

AUV localisation: a review of passive and active techniques

A Semantic-Aided Particle Filter Approach for AUV Localization

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