A deep learning algorithm for multiple observation data fusion in sonar system

Abstract

The data fusion of multiple sensor observations is an important topic in sonar design. For multiple array sonar signal processing systems, data fusion problem is often not only the problem of multiple observations of a single array but also the data fusion for multiple arrays. In this paper, the basic principle of data fusion is studied, that is the ensemble observation error of data fusion should be, in the statistical average, no larger error than the error of any individual observation data, which is considered as one of the entities in data fusion, regardless of any trash data addition. In other words, in the data fusion process, the increase of observation data quantities should always result in some advantages in the sense of statistical average meaning. This conclusion is the same as deep learning in artificial intelligence. A deep learning algorithm is proposed in this paper for multiple observation data fusion. The optimum linear weighted combination for the independent or dependent multiple observation data is considered as a method of carrying out decision level data fusion. The wild values are picked up before further data processing, and the input data are segmented into several blocks. Data fusion is performed in each block such that we can get minimum observation error results. The results of the system simulation conducted show that in the case where the observation data are interrupted by the interferences, i.e. the wild value, the deep learning algorithm in data fusion, derived in this paper, can considerably reduce the observation error in decision level data fusion.