Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

• Published in: Journal of Marine Science and Engineering Vol. 9; no. 4; p. 439
• Main Authors: Lopac, Nikola, Jurdana, Irena, Lerga, Jonatan, Wakabayashi, Nobukazu
• Format: Journal Article
• Language: English; Japanese
• Published: Basel MDPI AG 18.04.2021
• Subjects: 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
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse9040439

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.