Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data
The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the f...
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| Published in | Journal of Marine Science and Engineering Vol. 9; no. 4; p. 439 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
Basel
MDPI AG
18.04.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2077-1312 2077-1312 |
| DOI | 10.3390/jmse9040439 |
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| Abstract | The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors. |
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| AbstractList | The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors. |
| Author | Irena Jurdana Nobukazu Wakabayashi Nikola Lopac Jonatan Lerga |
| Author_xml | – sequence: 1 givenname: Nikola orcidid: 0000-0002-0616-1265 surname: Lopac fullname: Lopac, Nikola – sequence: 2 givenname: Irena orcidid: 0000-0002-3287-1383 surname: Jurdana fullname: Jurdana, Irena – sequence: 3 givenname: Jonatan orcidid: 0000-0002-4058-8449 surname: Lerga fullname: Lerga, Jonatan – sequence: 4 givenname: Nobukazu surname: Wakabayashi fullname: Wakabayashi, Nobukazu |
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| SubjectTerms | Adaptive algorithms adaptive filtering adaptive filtering ; radial basis function ; variable-width kernel smoother ; particle swarm optimization ; maritime transport ; signal processing Adaptive filters Algorithms Bias Confidence intervals Data collection Error reduction GC1-1581 Kernels Marine transportation maritime transport Mathematical analysis Methods Naval architecture. Shipbuilding. Marine engineering Noise Noise levels Noise reduction Oceanography Optimization Particle swarm optimization Radial basis function Signal processing Transport variable-width kernel smoother VM1-989 Width |
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| Title | Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data |
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