Study On Bind Deconvolution Technique Of Underwater Acoustic Signals Processing

Multipath effect impacts passive SONAR detection performance seriously. To solve this problem, this dissertation explored multi-channel blind deconvolution (MBD) techniques for underwater acoustic signals separation. Combined with existing research work, we studied three kinds of algorithm with the linear convolutive mixture model, which are concerned with theories in time-domain, frequency-domain and the case of source numbers unknown. Several algorithms, which are applicable to underwater acoustic engineering for multi-channel phenomenon, were proposed. Simulation and measured underwater acoustic data processing results show the good performance. The main contents of this paper are as follows:(1) Based on the real data of warship radiated noise, non-gaussianity of the underwater acoustic signal was analyzed. It provided prior knowledge for the underwater acoustic source signals separation using blind signal processing (BSP). Then, aimed at the non-stationarity of warship radiated noise, two kinds of nonstationary signal processing methods, i.e. cycle frequency evaluation and empirical modal decomposition, were studied to extract low frequency line spectrum of propeller noise. It established theoretical basis for the following work of thoroughly analyzing the evaluated source signals from the real measured data via blind source separation.(2) Based on two-order moment, two kinds of acoustic multi-channel blind deconvolution algorithms were proposed in time domain. Aimed at the obscure amplitude during blind source separation (BSS), a new blind source signal restoration algorithm was proposed, which evaluated channel parameters through reference signals, and separ..ted source signals through inverse adaptive filtering. It could obtain both source signals'time structure and amplitude information, which is helpful to SONAR signal processing. Then, to improve large computation of BSS with huge delay in time domain, a fast multi-channel blind deconvolution algorithm was proposed. It updated weight vectors in frequency domain and separated the source signals in temporal domain.(3) MBD algorithm was investigated in transform domain. Owing to the actual source signals'mutual dependency, the performance of BSS algorithms descended heavily. So, a new algorithm, which used wavelet packets transformation for sub-band decomposition, was proposed to process statistically dependent sources. By minimizing the source signals'mutual information, the dependency of the source signals was reduced. The algorithm can be carried out both in temporal domain and wavelet domain.(4) After investigating the whole tenor of underdetermined sparse source separation technique when the source numbers unknown, a novel blind source separation algorithm was proposed, which utilized binary mask (BM) method, and extended the unrestricted receiving sensors both in dimension and the condition of spatial distribution, based on short-time Fourier transformation. In the end, a general framework for BSS problem was set up and applied in actual underwater acoustic signal processing for warship radiated noise seperation.