An Improved Algorithm of Underwater Target Feature Abstracting Based on Target Azimuth Tendency

ing underwater target azimuth tendency feature is a common method to identify underwater target dimension. The cross-spectrum method of splitting beam is often used to abstracting underwater target azimuth tendency feature. But when the SNR is very low, this method is inefficient. In this thesis, an improved algorithm is presented to abstract underwater target azimuth tendency feature which can be used in the low SNR. In the algorithm, the underwater target highlight structure feature is used, and by least square fitting twice the underwater target azimuth tendency feature will be finally obtained. The simulation experiment results show that the proposed algorithm can be used to abstract underwater target azimuth tendency feature even when the SNR is very low. The improved algorithm has made an important foundation for further studying the feature detection and recognition of an underwater target dimension.