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The sea has a very wide, irregular, and continuously changing surface and is usually a mixed sea composed of several wave systems. Each wave system is generated from different locations and conditions and has its own characteristics. The Fourier domain approach using sea wave spectra is an effective technique for the realistic simulation of sea surfaces in real time, but the conventional Fourier domain approach cannot independently simulate the characteristics of each wave system. In this paper, we propose a realistic and real-time simulation method of the mixed sea using multiple spectrum-based wave systems for maritime simulators. We recognize the importance of the visual and physical contributions of each wave system and faithfully reproduce all wave systems in the mixed sea. In order to simulate the mixed sea, our method generates and combines multiple spectrum-based wave systems using adaptive spectral sampling of the separated spectrum of the multi-peaked spectrum. The unique characteristics of each wave system can be set independently through spectral parameters, sampling number and range, wave direction and spread, and the shape factor of waves. The proposed method also supports the smooth transition between sea states, such as wind sea, swell, and mixed sea. Through the experiments, we verify that the proposed method effectively reflects sea wave spectra and the reproduced sea has very similar statistical characteristics to the actual sea. Experimental results also show that our approach can simulate the mixed sea, which has high-frequency wind sea and low-frequency swell.

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Abstract:In this paper, an enhanced method for attitude determination is proposed for systems using an IMU (Inertial Measurement Unit). In attitude determination with IMU, it is generally assumed that the IMU can be located in the center of gravity on the vehicle. If the IMU is not located in the center of gravity, the accelerometers of the IMU are disturbed from additive accelerations such as centripetal acceleration and tangential acceleration. Additive accelerations are derived from the lever arm which is the distance between the center of gravity and the position of the IMU. The performance of estimation errors can be maintained in system with a non-zero lever arm, if the lever arm is estimated to remove the additive accelerations from the accelerometer's measurements. In this paper, an estimation using Kalman filter is proposed to include the lever arm in the state variables of the state space equation. For the Kalman filter, the process model and the measurement model for attitude determination are made up by using quaternion. In order to evaluate the proposed algorithm, both of the simulations and the experiments are performed for the simplified scenario of motion.

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