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State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing
In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) networ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471294/ https://www.ncbi.nlm.nih.gov/pubmed/34573749 http://dx.doi.org/10.3390/e23091124 |
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author | Ali, Wasiq Li, Yaan Raja, Muhammad Asif Zahoor Khan, Wasim Ullah He, Yigang |
author_facet | Ali, Wasiq Li, Yaan Raja, Muhammad Asif Zahoor Khan, Wasim Ullah He, Yigang |
author_sort | Ali, Wasiq |
collection | PubMed |
description | In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter. |
format | Online Article Text |
id | pubmed-8471294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84712942021-09-27 State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing Ali, Wasiq Li, Yaan Raja, Muhammad Asif Zahoor Khan, Wasim Ullah He, Yigang Entropy (Basel) Article In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter. MDPI 2021-08-29 /pmc/articles/PMC8471294/ /pubmed/34573749 http://dx.doi.org/10.3390/e23091124 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ali, Wasiq Li, Yaan Raja, Muhammad Asif Zahoor Khan, Wasim Ullah He, Yigang State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing |
title | State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing |
title_full | State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing |
title_fullStr | State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing |
title_full_unstemmed | State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing |
title_short | State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing |
title_sort | state estimation of an underwater markov chain maneuvering target using intelligent computing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471294/ https://www.ncbi.nlm.nih.gov/pubmed/34573749 http://dx.doi.org/10.3390/e23091124 |
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