Adaptive identification of lowpass filter cutoff frequency for online vessel model tuning

Abstract

Tuning of vessel models in real-time based on vessel measurements and weather information is of great interest in order to increase the safety and efficiency of marine operations. Vessel motion signals usually contain high-frequency noise. For an unbiased model tuning algorithm, it is essential to filter the noisy signals in order to identify the power of the wave-induced first-order vessel response. Lowpass filters with high accuracy should therefore be applied. However, it is a challenge to design such a filter since the optimal cutoff frequency can vary with sea states, vessel dimensions, vessel conditions, etc. This paper proposes a novel algorithm to adaptively search for the optimal cutoff frequency for a lowpass filter with high accuracy. The algorithm is fundamentally based on the facts that the vessel naturally acts as a lowpass filter and the energy from the high-frequency components, e.g., signal noise, is significantly smaller than that from the wave-induced vessel response. The algorithm is validated by 500 numerically simulated vessel motion signals with quite high noise levels and also by analysis of several on-site full-scale vessel motion signals. The improvements to the tuning results for the vessel parameters are demonstrated.