Water surface and underwater target classification method based on interference fringes and deep learning

Abstract

The invention relates to a water surface and underwater target classification method based on interference fringes and deep learning. Firstly, a Gaussian beam acoustic model is used for simulation toobtain a large number of acoustic field interference fringe images, which comprise a clear image, a blurred image and an image with random deep errors, as a training set for a deep belief network (DBN) and a CNN; the well-trained DBN is then used as a front-end processing module of the CNN, and beams are added at the same time to form a pre-processing module of the system; and finally, a pre-processed image is inputted to the DBN, and the inputted fringe image is thus autonomously classified. The method solves the problem that the training set sample is too small in deep learning by simulatinga large number of acoustic field interference fringe images. The DBN model is designed to optimize the blur and fringe shift problems in the actual input image. The CNN is designed to autonomously classify water surface and underwater targets.

Description

technical field [0001] The invention belongs to the fields of underwater acoustic engineering, marine engineering technology, etc., and relates to a method for classifying objects on the surface and underwater based on interference fringes and deep learning. A classification method based on interference fringe images and deep neural networks DBN and CNN to classify surface and underwater targets, suitable for surface targets with a target depth of more than 20m and underwater targets below 20m. Background technique [0002] In the field of image recognition, the classification of surface and underwater objects has always been an urgent problem to be solved. The traditional method is to use signal processing methods such as beamforming and DOA (beamforming and direction of arrival) estimation to estimate the source depth, and then classify the surface and underwater targets according to the estimated target depth. Although this method can describe the physical characteristic...

AI Technical Summary

Problems solved by technology

Under the condition that the sound speed profile SSP (sound speed profile) is known, a large number of sound field interference fringe images can be obtained through simulation, and there is no report on the application of sound field interference fringe images to the classification of water surface and underwater targets

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