Spatial coding in sonar imaging

• Published in: Electronics letters Vol. 53; no. 17; pp. 1222 - 1224
• Main Authors: Wang, Jingxi, Guo, Xin, Ge, Feng-Xiang, Arce, Gonzalo R
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.08.2017
• Subjects: array signal processing; beamforming; compressive sensing method; image coding; image reconstruction; inverse problem; inverse problems; iterative methods; iterative optimisation algorithm; numerical simulation; object detection; optimisation; probability; Radar, sonar and navigation; receiver; receivers; restricted isometry property; sonar detection; sonar imaging; sonar imaging reconstruction; sparse signal reconstruction framework; spatial coding; target detection; target localisation; transmitter; transmitters; iterative methods; target localisation; object detection; compressive sensing method; optimisation; restricted isometry property; receivers; inverse problems; transmitter; numerical simulation; array signal processing; inverse problem; transmitters; receiver; probability; sonar imaging; image reconstruction; sonar detection; iterative optimisation algorithm; sonar imaging reconstruction; target detection; spatial coding; sparse signal reconstruction framework; beamforming; image coding
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2017.2095

A practical deployment is presented for sonar imaging with fewer transmitters and receivers. Spatial coding via transmit beamforming is used for target detection and localisation. Transmit beamforming reduces the ambiguity in a model and improves imaging performance. To reconstruct the resultant underdetermined system of equations, a sparse signal reconstruction framework is used, and the inverse problem is solved by using an iterative optimisation algorithm. Deployment is optimised for better target detection and localisation performance. Successful sonar imaging reconstruction is guaranteed by the restricted isometry property with high probability. A numerical simulation shows that the proposed method can detect and localise targets efficiently, with better imaging results than those attained by conventional methods or the compressive sensing method without spatial coding.