Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target
In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/5/550 |
| _version_ | 1797535681460830208 |
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| author | Wasiq Ali Wasim Ullah Khan Muhammad Asif Zahoor Raja Yigang He Yaan Li |
| author_facet | Wasiq Ali Wasim Ullah Khan Muhammad Asif Zahoor Raja Yigang He Yaan Li |
| author_sort | Wasiq Ali |
| collection | DOAJ |
| description | In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model. |
| first_indexed | 2024-03-10T11:48:49Z |
| format | Article |
| id | doaj.art-13453fd90eac4ac2b05b06f671735c22 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T11:48:49Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-13453fd90eac4ac2b05b06f671735c222023-11-21T17:50:15ZengMDPI AGEntropy1099-43002021-04-0123555010.3390/e23050550Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive TargetWasiq Ali0Wasim Ullah Khan1Muhammad Asif Zahoor Raja2Yigang He3Yaan Li4School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaIn this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.https://www.mdpi.com/1099-4300/23/5/550nonlinear autoregressive with exogenous input (NARX)state estimationartificial neural networkmeasurement noisenonlinear filteringintelligent computing |
| spellingShingle | Wasiq Ali Wasim Ullah Khan Muhammad Asif Zahoor Raja Yigang He Yaan Li Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target Entropy nonlinear autoregressive with exogenous input (NARX) state estimation artificial neural network measurement noise nonlinear filtering intelligent computing |
| title | Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target |
| title_full | Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target |
| title_fullStr | Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target |
| title_full_unstemmed | Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target |
| title_short | Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target |
| title_sort | design of nonlinear autoregressive exogenous model based intelligence computing for efficient state estimation of underwater passive target |
| topic | nonlinear autoregressive with exogenous input (NARX) state estimation artificial neural network measurement noise nonlinear filtering intelligent computing |
| url | https://www.mdpi.com/1099-4300/23/5/550 |
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