Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles
In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater...
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doaj-44c78536548f43beb8006e326ccc05902020-11-25T01:15:22ZengMDPI AGSensors1424-82202019-12-0120110210.3390/s20010102s20010102Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater VehiclesTiedong Zhang0Shuwei Liu1Xiao He2Hai Huang3Kangda Hao4National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, ChinaSchool of Materials Science and Engineering, Tianjin University, Tianjin 300072, ChinaIn the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.https://www.mdpi.com/1424-8220/20/1/102auvunderwater target trackinggaussian particle filteradaptive fusion strategy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tiedong Zhang Shuwei Liu Xiao He Hai Huang Kangda Hao |
spellingShingle |
Tiedong Zhang Shuwei Liu Xiao He Hai Huang Kangda Hao Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles Sensors auv underwater target tracking gaussian particle filter adaptive fusion strategy |
author_facet |
Tiedong Zhang Shuwei Liu Xiao He Hai Huang Kangda Hao |
author_sort |
Tiedong Zhang |
title |
Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles |
title_short |
Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles |
title_full |
Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles |
title_fullStr |
Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles |
title_full_unstemmed |
Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles |
title_sort |
underwater target tracking using forward-looking sonar for autonomous underwater vehicles |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-12-01 |
description |
In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments. |
topic |
auv underwater target tracking gaussian particle filter adaptive fusion strategy |
url |
https://www.mdpi.com/1424-8220/20/1/102 |
work_keys_str_mv |
AT tiedongzhang underwatertargettrackingusingforwardlookingsonarforautonomousunderwatervehicles AT shuweiliu underwatertargettrackingusingforwardlookingsonarforautonomousunderwatervehicles AT xiaohe underwatertargettrackingusingforwardlookingsonarforautonomousunderwatervehicles AT haihuang underwatertargettrackingusingforwardlookingsonarforautonomousunderwatervehicles AT kangdahao underwatertargettrackingusingforwardlookingsonarforautonomousunderwatervehicles |
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1725153622610673664 |