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 challenging and varying operating conditions) are often subject to various types of noise other external factors in the data collection transmission processes. Therefore, filtering algorithms required reduce level measured signals, thus enabling more efficient extraction useful information. This paper proposes a locally-adaptive algorithm based on radial basis function (RBF) kernel smoother with variable width. width is calculated using asymmetrical combined-window relative intersection confidence intervals (RICI) algorithm, whose parameters adjusted applying particle swarm optimization (PSO) procedure. proposed RBF-RICI algorithm’s performances analyzed several simulated, synthetic noisy showing its efficiency suppression error reduction. Moreover, compared competing algorithms, provides better or competitive performance most considered test cases. Finally, applied data, proving be possible solution for successful practical application sectors.