Application of acoustic image processing in underwater terrain aided navigation

This paper proposes an idea that treats multi-beam bathymetric sonar data as images for underwater terrain aided navigation. Bathymetric measurements are converted into the form of digital images by interpolation at the beginning of the process. Next, image texture features are obtained and integrated into terrain navigability parameters to define an underwater terrain feature vector. Then, by scanning the underwater terrain map using real-time data, the best match between their feature vectors are considered as the position estimation. Simulation results suggest that this method is reliable when signal to noise ratio is under 2 dB. In addition, the method is shown to be robust to image rotation as large as 20° and scale variance as small as 50×50 pixels, which means it is not sensitive to direction errors and the distance between the underwater vehicle and the bottom may vary. When image resolution is 1 m2/pixel and scanning step is 1 m/move, accuracy of the method can be as high as 1 m in suitable terrains. In comparison with terrain contour matching algorithm, performance of the proposed method is more stable when direction error is bigger than 10° and the accuracy is approximately 50% better while noise and scale vary in the real-time images.