The Use of Predictors in Seabed Mapping: a Simulation Approach

Ivan Jordanov, Ghedhban Swadi, Dave Holifield

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1038-1043
Number of pages6
Issue number6
Publication statusPublished - Jul 2011


  • Seabed mapping; Sonar systems; Echo sounder; Neural network; K-Nearest-Neighbour; Prediction


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