Beamforming Performance Enhancement by Adaptive Hyperbola Array Shape Estimation By Michael Kaiping Liu B.S. in Chemical Engineering, University of California Los Angeles (2011) B.S. in Biochemistry, University of California Los Angeles (2011) Submitted to the Department of Mechanical Engineering and System Design and Management Program in partial fulfillment of the requirements for the degrees of Naval Engineer and Master of Science in Engineering and Management at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020 © Massachusetts Institute of Technology 2020. All rights reserved. Author ………………………………………………………………………………………………………. Department of Mechanical Engineering and System Design and Management Program May 6, 2020 Certified by …………………………………………………………………………………………………. Henrik Schmidt Professor of Mechanical and Ocean Engineering Thesis Supervisor Certified by …………………………………………………………………………………………………. Bryan R. Moser Academic Director and Sr. Lecturer, System Design and Management Thesis Supervisor Accepted by …………………………………………………………………………………………………. Joan Rubin Executive Director, System Design and Management Accepted by …………………………………………………………………………………………………. Daniel Frey Professor of Mechanical Engineering Chairman, Committee on Graduate Programs 1 THIS PAGE INTENTIONALLY LEFT BLANK 2 Beamforming Performance Enhancement by Adaptive Hyperbola Array Shape Estimation By Michael Kaiping Liu Submitted to the Department of Mechanical Engineering and System Design and Management Program on May 6, 2020, in partial fulfillment of the requirements for the degrees of Naval Engineer and Master of Science in System Design and Management ABSTRACT Analysis of U.S. Navy Ice Exercise 2016 (ICEX16) data, through a collaboration with MIT Lincoln Laboratory, demonstrated that towed array curvature commonly exhibited heading differences up to 100° and never maintained heading differences less than 30° between the forward compass and the aft compass. These deviations reflected a disparity from the underlying assumption that the towed array remained rigid with no deviations from a rigid, straight-line configuration. Using lessons learned from ICEX16, a field experiment in Massachusetts Bay 2019 (FEX19) tested whether a hexagonal search pattern would sufficiently address the curvature concern, thereby, validate the underlying rigid, straight-line beamformer assumption more commonly used. Results from the experiment showed that a hexagonal search pattern maintained a heading differences of less than 4° within 79 seconds of an initiation of a 60° maneuver. This was a marked improvement when compared to ICEX16’s vehicle maneuvers, which never maintained a heading difference of less than 30°. Even with this improvement in FEX19, 39.6% of the acoustic data was collected when the towed array did not meet the straight-line assumption. Use of the hexagonal search pattern, in two instances during U.S. Navy Ice Exercise 2020 (ICEX20), showed that 45.1% and 27.1% of the collected acoustic data did not meet the towed-array straight-line assumption. Although this realization will influence operators to minimize maneuvers that introduce significant deviations from the underlying beamforming model, field experiments often call for sharper maneuvers. This realization spurred the development of a beamformer that modeled towed array curvature using headings, effectively tangential slopes, at either end of the hydrophone portion of the towed array with a known fixed length to predict how the towed array bends. Analysis of FEX19 showed that the hyperbola-shaped beamformer output aligned to GPS heading data over 30% of the experimental window compared to less than 10% for the straight-line beamformer. This improvement held true even when the towed array had little or no curvature. Thesis Supervisor: Henrik Schmidt Title: Professor of Mechanical and Ocean Engineering Thesis Supervisor: Bryan Moser Title: Academic Director and Sr. Lecturer of System Design and Management 3 THIS PAGE INTENTIONALLY LEFT BLANK 4 ACKNOWLEDGEMENTS First, I must thank the U.S. Navy for providing me this fantastic opportunity to study at both MIT’s School of Engineering and MIT’s Sloan School of Management. I would like to express my deep and sincere gratitude to both my Mechanical Engineering Research Advisor, Professor Henrik Schmidt, for giving me the opportunity to work on underwater acoustic research and my System Design and Management Advisor, Professor Moser, for providing invaluable mentorship. Thank you Brian Lewis for facilitating research collaboration with Lincoln Laboratory Group 37. To my fellow researchers passionate in the technological advancements in the undersea domain, Jon Collis, Joseph Edwards, Benjamin Evans, Christopher Lloyd, and Nicholas Pulsone, thank you for providing me the opportunity to learn from your decades of experience. I am particularly grateful to Jon Collis and Joseph Edwards for providing me a baseline acoustic analysis code to build off of for this thesis. Lincoln Laboratory Group 37 holds a special place in my memories at MIT. To my fellow members of Laboratory Autonomous Marine Sensing Systems (LAMSS), especially Eeshan Bhatt, Rui Chen, Supun Randeni, and Oscar Viquez Rojas, your help and friendship meant more to me than you know. An extra big thank you to Eeshan Bhatt for helping me debug the underlying code used to generate various hyperbola shapes to more accurately beamform received acoustic signals. Thank you to everyone in MIT’s Mechanical Engineering Department, MIT Lincoln Laboratory, and MIT’s Sloan School of Management for making my time at MIT one I will never forget. Last, but not least, I have to thank my incredible wife and children for all of your support and encouragement. 5 THIS PAGE INTENTIONALLY LEFT BLANK 6 TABLE OF CONTENTS Abstract ................................................................................................................. 3 Acknowledgements ................................................................................................ 5 List of Figures ........................................................................................................ 8 List of Tables ........................................................................................................11 Chapter 1: Problem Statement and Research Approach ........................................12 Chapter 2: U.S. Historical Context ........................................................................14 Chapter 3: Arctic Research Platforms ...................................................................24 Chapter 4: Underwater Acoustics ..........................................................................33 Chapter 5: Sonar Signal Processing ......................................................................39 Chapter 6: Sonar Signal Processing Methodology.................................................48 Chapter 7: ICEX16 Results and Analysis ..............................................................51 Chapter 8: Revised Problem Statement and Research Approach ...........................66 Chapter 9: Massachusetts Bay FEX19 Design ......................................................67 Chapter 10: Massachusetts Bay FEX19 Results and Analysis ...............................71 Chapter 11: Towed Array Curvature Analysis ......................................................85 Chapter 12: Conclusion ....................................................................................... 102 References .......................................................................................................... 107 Appendix A: Wave Equation Derivation ............................................................. 113 Appendix B: Ray Tracing ................................................................................... 114 Appendix C: Ice Events Corrected to Global Reference Coordinates .................. 118 Appendix D: Additional ICEX16 Spectrograms ................................................. 120 Appendix E: FEX19 Vehicle Data [Active 900Hz Lubell Source] ...................... 122 Appendix F: Energy Spectrum Using the Simple Hyperbola Model ................... 124 Appendix G: Hyperbola Generation Code........................................................... 126 7 LIST OF FIGURES Figure 1: Human Endurance, RMDL Peary Expedition ........................................14 Figure 2: Technological Strength, USS NAUTILUS (SSN 571) .............................14 Figure 3: SCICEX Transects from 1993 to 2005 ...................................................15 Figure 4: Arctic Transit Routes Availability .........................................................16 Figure 5: Anticipated Arctic Transit Routes ..........................................................16 Figure 6: Spatial Extent of the Beaufort Lens .......................................................22 Figure 7: RV Sikuliaq26 .........................................................................................25 Figure 8: U.S. and U.K. Submarines Broaching Sea Ice for ICEX1630 ..................26 Figure 9: ATL03 GT3R Georeferenced Photon Returns .......................................27 Figure 10: Ice Camp 201830 ..................................................................................28 Figure 11: Bluefin-2137 .........................................................................................30 Figure 12: AUV Macrura Bluefin 21 AUV and MIT DURIP Towed Array40 .......31 Figure 13: Generic Sound Speed Profile41 .............................................................35