BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors
Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an itera...
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Online Access: | https://www.mdpi.com/1424-8220/21/13/4457 |
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doaj-604df4e8259b4a3dba2e70a3d2e3c64e2021-07-15T15:45:34ZengMDPI AGSensors1424-82202021-06-01214457445710.3390/s21134457BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive SensorsHadar Shalev0Itzik Klein1Department of Marine Technologies, University of Haifa, Haifa 3498838, IsraelDepartment of Marine Technologies, University of Haifa, Haifa 3498838, IsraelBearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative least squares algorithm to estimate the unknown target position vector. Instead of using iterative least squares, this paper presents a deep-learning based framework for the bearing-only target tracking process, applicable for any bearings-only target tracking task. As a data-driven method, the proposed deep-learning framework offers several advantages over the traditional iterative least squares. To demonstrate the proposed approach, a scenario of tracking an autonomous underwater vehicle approaching an underwater docking station is considered. There, several passive sensors are mounted near a docking station to enable accurate localization of an approaching autonomous underwater vehicle. Simulation results show the proposed framework obtains better accuracy compared to the iterative least squares algorithm.https://www.mdpi.com/1424-8220/21/13/4457bearings-onlytarget trackingautonomous underwater vehicledeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hadar Shalev Itzik Klein |
spellingShingle |
Hadar Shalev Itzik Klein BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors Sensors bearings-only target tracking autonomous underwater vehicle deep learning |
author_facet |
Hadar Shalev Itzik Klein |
author_sort |
Hadar Shalev |
title |
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors |
title_short |
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors |
title_full |
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors |
title_fullStr |
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors |
title_full_unstemmed |
BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors |
title_sort |
botnet: deep learning-based bearings-only tracking using multiple passive sensors |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-06-01 |
description |
Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative least squares algorithm to estimate the unknown target position vector. Instead of using iterative least squares, this paper presents a deep-learning based framework for the bearing-only target tracking process, applicable for any bearings-only target tracking task. As a data-driven method, the proposed deep-learning framework offers several advantages over the traditional iterative least squares. To demonstrate the proposed approach, a scenario of tracking an autonomous underwater vehicle approaching an underwater docking station is considered. There, several passive sensors are mounted near a docking station to enable accurate localization of an approaching autonomous underwater vehicle. Simulation results show the proposed framework obtains better accuracy compared to the iterative least squares algorithm. |
topic |
bearings-only target tracking autonomous underwater vehicle deep learning |
url |
https://www.mdpi.com/1424-8220/21/13/4457 |
work_keys_str_mv |
AT hadarshalev botnetdeeplearningbasedbearingsonlytrackingusingmultiplepassivesensors AT itzikklein botnetdeeplearningbasedbearingsonlytrackingusingmultiplepassivesensors |
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1721298519864639488 |