Abstract
Seabed terrain modelling is one of the key technologies in the Subsea Environmental Information System, and this system is critical for underwater vehicle path planning. A composite fractal interpolation algorithm based on improved fractional Brownian motion (FBM) and an improved iterative function system (IFS) is proposed in this paper to increase the precision of the seabed terrain model for submarine topography and to account for the complexity and irregularity of fractal properties in each region. The MATLAB simulation experiment showed that fractal properties of the model built by the complex composite fractal interpolation algorithm were closer to real surface features. After calculation analysis, the model built by the complex composite fractal interpolation algorithm, when compared with the model built by the traditional interpolation algorithm or by the single fractal interpolation algorithm, had higher precision and was more suitable for path planning for underwater vehicles.
Highlights
Three-dimensional seabed terrain modelling is helpful to simulate the real seabed surface and to verify the static path planning technique for underwater vehicles under the sea [1]
It is very difficult to obtain the altitude data of the seabed, which requires substantial financial and material resources; we use an interpolation method to establish a seabed terrain model based on the limited scattered data points [1]
This paper presents a composite fractal interpolation algorithm based on the improved fractional Brownian motion (FBM) and iterative function system (IFS) to establish a seabed terrain model that can reflect the real surface characteristics
Summary
Three-dimensional seabed terrain modelling is helpful to simulate the real seabed surface and to verify the static path planning technique for underwater vehicles under the sea [1]. It is very difficult to obtain the altitude data of the seabed, which requires substantial financial and material resources; we use an interpolation method to establish a seabed terrain model based on the limited scattered data points [1]. The Kriging interpolation algorithm has higher precision when compared with the traditional linear interpolation algorithm. This algorithm is still a smooth interpolation method with high geometric accuracy [3]. A surface modelled by the Kriging interpolation algorithm is smooth, whereas the real surface is disorganized and rugged. The model constructed with the Kriging interpolation algorithm hides the most important features of the real surface [4, 5]. It is necessary to find an interpolation algorithm that can show real characteristics of the surface
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