Abstract
The external environmental conditions around a vessel are essential for efficient and safe ship operation, among which the sea state is of key importance. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This is a challenging task since the relationships between the waves and the ship motions are hard to describe accurately. Machine learning approaches can learn these mapping without an explicit model, which is promising for sea state estimation. Current machine learning approaches represent the sea state as a set of categories or a number of wave parameters while neglecting the 2D wave spectrum. This paper proposes a sea state estimation network that estimates the 2D wave spectrum along with a discrimination network. The discrimination network can detect and correct high-order inconsistencies of the spectrum. Simulation studies are performed to show that the proposed method can provide wave spectrum estimation with high accuracy.