Abstract
The interpolation-reconstruction of local underwater terrain using the underwater digital terrain map (UDTM) is an important step for building an underwater terrain matching unit and directly affects the accuracy of underwater terrain matching navigation. The Kriging method is often used in terrain interpolation, but, with this method, the local terrain features are often lost. Therefore, the accuracy cannot meet the requirements of practical application. Analysis of the geographical features is performed on the basis of the randomness and self-similarity of underwater terrain. We extract the fractal features of local underwater terrain with the fractal Brownian motion model, compensating for the possible errors of the Kriging method with fractal theory. We then put forward an improved Kriging interpolation method based on this fractal compensation. Interpolation-reconstruction tests show that the method can simulate the real underwater terrain features well and that it has good usability.
Highlights
The ever increasing capabilities of the autonomous underwater vehicle (AUV) allow for extended period long-range precision autonomous underwater navigation [1,2,3,4]
The terrain obtained by GMA based method and inverse distance weighting (IDW) based method is relatively smooth
There are large differences in terrain local details. This is because GMA based method and IDW based method are approaching-point distance weighting
Summary
The ever increasing capabilities of the autonomous underwater vehicle (AUV) allow for extended period long-range precision autonomous underwater navigation [1,2,3,4]. The underwater terrain matching navigation (UTMN) has been studied in depth. The traditional terrain interpolation methods include Kriging [7], Gaussian-weighted average (GWA) [8], inverse distance weighting (IDW) [9], and bilinear interpolation [10]. These methods do too much smoothing of the original data. The overall underwater terrain trends can be expressed in general, but too many details are lost and the interpolation accuracy is low These methods are difficult to apply in practice. The existing studies are limited to the application of Kriging in related fields, and the defects of Kriging are not corrected
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