Abstract
Abstract Side scan sonar measurement platform, affected by underwater environment and its own motion precision, inevitably has posture and motion disturbance, which greatly affects accuracy of geomorphic image formation. It is difficult to sensitively and accurately capture these underwater disturbances by relying on auxiliary navigation devices. In this paper, we propose a method to invert motion and posture information of the measurement platform by using the matching relation between the strip images. The inversion algorithm is the key link in the image mosaic frame of side scan sonar, and the acquired motion posture information can effectively improve seabed topography and plotting accuracy and stability. In this paper, we first analyze influence of platform motion and posture on side scan sonar mapping, and establish the correlation model between motion, posture information and strip image matching information. Then, based on the model, a reverse neural network is established. Based on input, output of neural network, design of and test data set, a motion posture inversion mechanism based on strip image matching information is established. Accuracy and validity of the algorithm are verified by the experimental results.
Highlights
Side scan sonar describes submarine topography in the form of grids and detects seabed morphology by recording and showing submarine backscatter echo of incident sound wave
A matching pair is randomly selected from the overlapping area, to replace 88 matching pairs of two strip images obtained by side scan sonar image matching algorithm based on SURF algorithm and typical
The motion trajectory inversion algorithm of side scan sonar platform based on artificial neural network is proposed
Summary
Side scan sonar describes submarine topography in the form of grids and detects seabed morphology by recording and showing submarine backscatter echo of incident sound wave. Existing processing means have difficulty to effectively eliminate image spots, stripe noise caused by noise, marine environment changes, insufficient navigation information accuracy, track bending, speed nonuniformity, unstable towfish posture as well as cracks and local distortion etc., caused by inaccurate image mosaic. These interference and distortion problems greatly hinder applications of automatic target detection, identification, seabed material classification, navigation. Based on the theory of artificial intelligence and pattern recognition, motion state information of the platform can be extracted by self-learning and generalization process from parameters such as translation, rotation and scaling obtained by multiple strip image matching analysis, and motion trajectory of the platform can be calculated
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