非定常船体動揺データの時系列解析について [in Japanese] Time Series Analysis of Non-stationary Ship Motion Data [in Japanese]
Access this Article
Search this Article
Time-varying coefficient autoregressive (TVAR) modeling is applied to the spectral analysis of non-stationary ship motion data. Based on the ship's maneuvers, such as course and speed changes, the ship motions in waves are regarded as non-stationary random processes, although the seaway can be considered as a stationary stochastic process. Generally, TVAR models are transformed into state space models, and the time-varying coefficients can be evaluated by using the Kalman filter algorithm. Using the estimated time-varying coefficients, the instantaneous power spectra of ship motions can be calculated at every moment. On the assumption that variance of observation noise to be a constant, total amount of numerical calculation can be effectively reduced. For this, the trend and time-varying variance models are introduced to normalize the non-stationary time series. Reliability of the TVAR modeling was examined using the data of on-board tests. Optimum order of the model and Akaike's information criterion were also examined for several changes of parameters. Under stationary conditions, at a constant speed and course, the TVAR modeling shows good agreement with Stationary autoregressive (SAR) modeling analysis. Moreover, it is confirmed that the TVAR modeling can estimate the instantaneous power spectra of ship motions even under non-stationary conditions, showing that TVAR modeling is a powerful tool for real-time analysis of non-stationary ship motion data.
- The Journal of Japan Institute of Navigation
The Journal of Japan Institute of Navigation 112(0), 301-306, 2005
Japan Institute of Navigation