Line spectrum extraction based on autoassociative neural networks

Line spectrum is an important feature for the detection and classification of underwater targets. This letter presents a method for extracting the line spectrum submerged in underwater ambient noise through autoassociative neural networks (AANN). Compared with the traditional methods, the proposed method based on AANN can directly enhance the line spectrum from the raw time-domain noise data without relying on prior information and spectral features. Moreover, the proposed method can suppress the background noise while extracting the line spectrum. Both the numerical simulation and experimental data test results demonstrate that the proposed method provides a good ability to extract the line spectrum from the strong background noise.