Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar

Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward lookin...

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Bibliographic Details
Main Authors: Witold Kazimierski, Grzegorz Zaniewicz
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Remote Sensing
Subjects:
AUV
Online Access:https://www.mdpi.com/2072-4292/13/5/1014
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spelling doaj-315e4553bda542e9aac138e8a19ee07f2021-03-09T00:00:59ZengMDPI AGRemote Sensing2072-42922021-03-01131014101410.3390/rs13051014Determination of Process Noise for Underwater Target Tracking with Forward Looking SonarWitold Kazimierski0Grzegorz Zaniewicz1Faculty of Navigation, Chair of Geoinformatics, Maritime University of Szczecin, 70-500 Szczecin, PolandFaculty of Navigation, Chair of Geoinformatics, Maritime University of Szczecin, 70-500 Szczecin, PolandTarget tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.https://www.mdpi.com/2072-4292/13/5/1014sonar target trackingAUVanti-collisionKalman filterunderwater surveillance
collection DOAJ
language English
format Article
sources DOAJ
author Witold Kazimierski
Grzegorz Zaniewicz
spellingShingle Witold Kazimierski
Grzegorz Zaniewicz
Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
Remote Sensing
sonar target tracking
AUV
anti-collision
Kalman filter
underwater surveillance
author_facet Witold Kazimierski
Grzegorz Zaniewicz
author_sort Witold Kazimierski
title Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
title_short Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
title_full Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
title_fullStr Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
title_full_unstemmed Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar
title_sort determination of process noise for underwater target tracking with forward looking sonar
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-03-01
description Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.
topic sonar target tracking
AUV
anti-collision
Kalman filter
underwater surveillance
url https://www.mdpi.com/2072-4292/13/5/1014
work_keys_str_mv AT witoldkazimierski determinationofprocessnoiseforunderwatertargettrackingwithforwardlookingsonar
AT grzegorzzaniewicz determinationofprocessnoiseforunderwatertargettrackingwithforwardlookingsonar
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