The use of predictors in seabed mapping: A simulation approach

Abstract

This paper compares two methods of prediction applied to seabed mapping; the K-Nearest-Neighbour (KNN) and the Adaptive Linear Neural Network (ADALINE). In order to study the performance of these predictors, a simulated sonar system platform was developed. The platform includes a seabed simulator based on fractal geometry, and an echo sounder whose outcome is the measured depth of the seabed. Matlab was used to build the simulator and to assess the performance of the predictors. The results show the dynamic ADALINE gives a better performance than KNN.