Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Systematic Characterization Framework of Row-Column Ultrasound Imaging Systems

3-D ultrasound imaging offers unique opportunities in the field of non-destructive testing that cannot be easily found in A-mode and B-mode images. To acquire a 3-D ultrasound image without a mechanically moving transducer, a 2-D array can be used. The row column technique is preferred over a fully addressed 2-D array as it requires a significantly lower number of interconnections. Recent advances in 3-D row-column ultrasound imaging systems were largely focused on sensor design. However, these imaging systems face three intrinsic challenges which cannot be addressed by improving sensor design alone: speckle noise, sparsity of data in the imaged volume, and the spatially dependant point spread function of the imaging system. There is no characterization model that describes these intrinsic challenges. In this research, we will propose a characterization framework for ultrasound imaging systems that are based on the row column method. The proposed framework will include a joint statistical image formation and noise modeling and characterization as well as a characterization of the system's beam profile using a spatially-variant point spread function. Our proposed framework has many potential applications including building a more adequate image reconstruction model, providing a better metric for comparison of different row column systems, allowing for a better optimization of a row column system's performance, and giving us a better understanding of images acquired from row column systems