MATEC Web Conf.
Volume 283, 2019The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|Number of page(s)||5|
|Published online||28 June 2019|
Assessment of multi-target distinguishing using deconvolved conventional beamforming
1 Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, 150001, China
2 Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin, 150001, China
3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, 150001, China
* Corresponding author: email@example.com
Multi-target distinguishing based on beamforming is a popular topic in array signal processing. Conventional beamforming as a frequently used method is robust but constrained by the Rayleigh limit. Deconvolved conventional beamforming is a better choice since point scattering function could be derived by deconvolution based on Lucy-Richardson, with narrower beam width and lower sidelobe levels. Besides, the robustness of the conventional beamforming is maintained. In this paper, a new method of combined deconvolved conventional beamforming with Dolph-Chebyshev weights is proposed. The proposed method could overcome the deficit of deconvolved conventional beamforming on low mainlobe of weak target caused by iteration. Firstly, principles of the method are given including conventional beamforming, deconvolved conventional beamforming and the proposed algorithm combined deconvolved conventional beamforming with Dolph-Chebyshev weights. Then, performance of the proposed method for bi-target signals with the equivalent strength, in terms of the effect of signal frequency on distinguishing performance of two closed spaced targets coexisted is analysed. For weak target detection existed strong interference, the superiority of the proposed algorithm is analysed. Finally, proposed method is validated with sea trial data of two ship target noise recorded by a 48-element array.
© The Authors, published by EDP Sciences, 2019
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