scholarly journals BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4457
Author(s):  
Hadar Shalev ◽  
Itzik Klein
Keyword(s):  
Deep Learning ◽  
Least Squares ◽  
Target Tracking ◽  
Autonomous Underwater Vehicle ◽  
Target Position ◽  
Underwater Vehicle ◽  
Traffic Monitoring ◽  
Vehicle Simulation ◽  
Passive Sensors ◽  
Least Squares Algorithm

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.

scholarly journals The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092101
Author(s):  
Zhang Huajun ◽  
Tong Xinchi ◽  
Guo Hang ◽  
Xia Shou
Keyword(s):  
Least Squares ◽  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Six Degrees Of Freedom ◽  
Recursive Least Squares Algorithm ◽  
Least Squares Algorithm

An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV model in practice. Firstly, the autonomous underwater vehicle model is a coupled six-degrees-of-freedom model, and each state of the kinetic model influences the other five states. Secondly, there are more than 100 hydrodynamic coefficients which have different effects, and some parameters are too small to be identified. This article proposes a simplified six-degrees-of-freedom model that contains the essential parameters and employs the multi-innovation least squares algorithm based on the recursive least squares algorithm to obtain the parameters. The multi-innovation least squares algorithm leverages several past errors to identify the parameters, and the identification results are more accurate than those of the recursive least squares algorithm. It collects the practical data through an experiment and designs a numerical program to identify the model parameters. Meanwhile, it compares the performances of the multi-innovation least squares algorithm with those of the recursive least squares algorithm and the least square method, the results show that the multi-innovation least squares algorithm is the most effective way to identify parameters for the simplified six-degrees-of-freedom model.

scholarly journals An Autonomous Underwater Vehicle Simulation Using Linear Quadratic Servo Based on Open Control Platform

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Nanang Syahroni ◽  
Jae Weon Choi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Linear Quadratic ◽  
New Approach ◽  
Decentralized System ◽  
Vehicle Simulation ◽  
Depth Control ◽  
Open Control ◽  
Simulation Results ◽  
Optimal Regulator

This paper presents an optimal regulator for depth control simulation of an autonomous underwater vehicle (AUV) using a new approach of decentralized system environment called open control platform (OCP). Simulation results are presented to demonstrate performance of the proposed method.

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