Conventional gyrocompass alignment methods are based on relatively small azimuth misalignment angles. However, a marine strapdown inertial navigation system may face large azimuth misalignment angle caused by a failed coarse alignment algorithm. This paper provides a novel gyrocompass alignment method to solve the problem. Effects of system parameters are analyzed and the proper scenario of parameter switch based on the classic control theories is derived. Test results show that compared with the conventional methods, our method can accomplish the initial alignment quickly and accurately under large azimuth misalignment angle.

No abstract

Abstract:Transfer learning is a method that studies how to identify the useful knowledge and skills in the previous tasks, and uses them to the new tasks or domains. At present, the research on transfer learning mostly focuses on the field of long texts. However, the source data should be given for the transportation from long texts to the short ones, and the priori probability distribution of the data should be given at the same time. In order to solve the problems, the algorithm which is called FSFP (Free Source selection Free Priori probability distribution) is proposed. It can transfer knowledge from the long texts to the short ones. Latent semantic analysis is used to extract the key words as seed characteristic sets, which are semantically related to the long texts from the target domain. And then the graph structure of online information is built. With the help of the improved Laplacian Eigenmaps, the feature representations of highdimensional data are mapped to a low-dimensional space. Lastly, the target data are classified in the constraint of minimizing the mutual information between the instance and the feature representation. The experimental results on large data sets show the effectiveness of the new algorithm.