Transfer Alignment and Leveling Method Based on Characteristics of MINS Parameters

The initial alignment time directly relates to the fast prefiring performance of a torpedo. The internal parameters of traditional alignment algorithm and leveling criterion are fixed, which results in longer alignment time and poor adaptation to different alignment conditions. To solve the problem, this study proposed an alignment and leveling method based on the characteristics of main inertial navigation system(MINS) speed injection parameters and horizontal attitude angle sloshing parameter. The back propagation(BP) neural network was used to identify these characteristics, and the internal specific parameters were online adaptively updated in real time. Hence, on the premise of guaranteeing the alignment accuracy, fast convergence rate of the filter and timely leveling judgment in different working conditions could be ensured. Comparative test shows that the proposed method can effectively accelerate the convergence rate of the detuning angle, adaptively design the leveling criterion according to different working conditions, and shorten the initial alignment time.