Optimal feature extraction techniques to improve classification performance, with application to sonar signals

Feature extraction is an important preliminary step to classification of complex signals. By reducing a high-dimensional signal to a lower-dimensional feature set which preserves the relevant structure of the signal, classification performance is enhanced. A classification system was developed to classify sonar signals as to whether the object detected is minelike or nonminelike. Results are presented comparing classification performance when various feature extraction methods are implemented