Localization Method Of Noise Sources Based On Compressive Focused Beamforming

In recent years,because of the rapid development of the noise-reduction and vibration-isolation and the reduction of the noise level,the noise sources may be in the near field of the array,so the research on the localization of the noise sources in the near field is very significant.Because the resolution of the conventional focused beamforming is low,and traditional focused beamforming requires a mass of samples and a higher signal to noise radio,in this paper we present an underwater noise sound source near-field localization method based on a sparse representation of vector sensor array measurements.The main contents of this paper are listed as follows:Section 1 starts from the development history of the theory,and describes the related background knowledge of focused beamforming and compressived sensing in detail.Due to the advantages of compressived sensing in array singal processing and the characteristics of this theory,compressived sensing is applied to the study of noise localization.Section 2 focuses on the three core elements of the theory of compressived sensing,including the sparse representation of the signal,the construction of the measurement matrix and the algorithm of signal sparse reconstruction,and the conditions of sparse reconstruction RIP and MIP properties did a simple analysis.Section 3 sets up an underwater noise sound source near-field localization method based on a sparse representation of vector sensor array measurements that be based on the theory of compressed sensing and the sparseness of the target sound source in spatial region.And,using a large number of simulation esperiments to explain the advantages of the algorithms.Finally the relationship of dictionary refinement and the estimation accuracy is studied.Section 4 introduces two different sparse reconstruction methods to the acoustic focus localization algorithm-L1-SVD algorithm and L1-SRACV algorithm,describes their theoretical process in detail,and analyzes the advantages and disadvantages of the algorithms.through lots of simulation experiments.Section 5 studies the experiments in Songhua Lake,and proves the feasibility and effectiveness of the proposed algorithm by processing the experimental data.