An effective method for underwater target recognition

Abstract

To recognize the underwater target precisely is always a hard problem to all countries. In this paper, we designed a genetic-based classifier system which recognizes targets utilize sonar fingerprints. Exceptionally some improvements have been designed on it. The proposed Comparing and Matching algorithm would give the fitness value more explicitly statistical meaning, which would make user easier to explain the rules with background knowledge. The proposed hyperplasia operator can handle those instances which were not emerged before. It gives the system persistent learning abilities, so the system may be more compatible with the surroundings. The proposed refining classifier merges redundant rules and shrinks the rule set In addition, an alterable mutation probability is set in the genetic algorithm, experiment shows that this strategy increased the speed and the accuracy of the classifying operation. Sonar fingerprint technology extracts unique feature from an echo, it is similar to that one's fingerprint can identify a unique person himself, dissimilar echo leads to different fingerprint. And all these have none business with the echo's store way (analog or digital) or format (WAV, MP3, WMA, RM, and etc). The proposed underwater target classifier system is highly automatic, with quite finite hardware requirements in operating.