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Owing to the atrocious oceanic operating environment of autonomous underwater vehicles(AUV), there are many uncertainties of sensors data with big noises, and high rate of wild points, especially underwater acoustic sensors. An improvement Self-adaptive Kalman filter(SAKF) is designed with forgetting factor introduced, and the optimal forgetting factor is given by the predictive residual error method. In addition, some methods are adopted to avoid probable filter divergence caused by the low estimation accuracy of noise statistic characteristics. The improved SAKF is applied to integrated navigation system of AUV based on dead-reckoning(DR). The integrated navigation system architecture of AUV is described and DR navigation algorithm and motion model are presented in detail. Experimental results show that the improved SAKF is effective, and the navigation system is reliable and feasible.

The reliability of structural systems is affected by many factors, therefore it is necessary to analyze the structural reliability. The spatial beam-plate structures were simulated by the space beam elements and stiffened plate elements. Using stochastic finite element method and advanced first-order second-moment method, the element reliability analysis was performed. The branch-and-bound algorithm was used to find the main failure modes, and the reliability index of structure system was calculated by the failure mode correlation and probabilistic network evaluation technique (PNET). The stochastic finite element program of a three-dimensional spatial thin-walled structure was developed which considers the effect of several random variables including member area, thickness, material's modulus of elasticity, loads and strength. Finally, an example was presented, explaining the application by this means.