Compressed Sensing Multiscale Sample Entropy Feature Extraction Method for Underwater Target Radiation Noise

Abstract

Accurate underwater target detection and recognition in complex marine environments has always been a challenge. There is a lot of information in underwater target radiation noise that is important for underwater target recognition. However, the traditional underwater target radiation noise process is inefficient and inaccurate, severely limiting underwater target recognition. This paper proposed a new method for underwater target recognition based on compressed sensing multiscale entropy. For starters, compressing a signal improves its signal-to-noise ratio and broadens its linear spectrum. The multiscale sample entropy for the signal is then calculated after it has been denoised, and the most separated sample entropy is chosen by comparing the different scales of sample entropy to achieve effective underwater target radiation noise recognition. The experimental results show that the feature extraction method proposed in the paper can classify underwater target radiation noise quickly and effectively, improving recognition efficiency.