Evolution of Sonar & Sonar Signal Processing Fundamentals

About This Tutorial

In sonar education, it is often useful to review the process of sonar design: historic, current, and prospects for the future. Sound navigation and ranging equipment for subsea navigation, target detection, and imaging has undergone many iterations, with improved developments in the technology since World War II. Today, we may take these developments for granted, and it is easy to forget the underlying fundamentals for processing such data. This may lead to an improper handling of sonar data and a misunderstanding of what a specific transducer is capable of doing and what its output is telling the operator. This tutorial is designed to facilitate development of next generation engineering professionals, sensors, and automation techniques for maritime surveillance, reconnaissance, navigation and communication. The material will cover the history of sonar systems, review important sonar signal processing techniques, and practice converting raw sonar signals into meaningful output.

Who Should Attend

Practicing engineers, researchers, and graduate students working in sonar sensor design or sonar signal processing. Any attendees interested in learning more about the history of sonar system development are also encouraged to attend. This tutorial is also approachable for any students or researchers with a basic digital signal processing background but little exposure to sonar signals and data.

Recommended Prerequisites

Although we plan to make all material approachable for newcomers (programming solutions, slides/notes provided), we highly recommend that attendees (at minimum) have the following skills:

Linear Algebra: Basic understanding of what eigenvalues and eigenvectors are and what they represent for a multidimensional dataset.
Digital Signal Processing: Basic knowledge of periodicity, frequency, Fourier Transforms.
Water Channels: Basic knowledge of how ocean signals propagate underwater.
Programming: Proficiency in reading, writing, and debugging Python code.

Learning Objectives

By the end of this tutorial, attendees will be able to...

Recognize the historical trend of sonar signal processing since the early 1900s.
Gain intuition about digital signal processing (DSP) techniques used to process sonar data.
Program DSP algorithms for the purpose of beamforming multi-element sonar data.
Apply matched filtering to coherently produce synthetic aperture sonar (SAS) data.

Planned Schedule

This tutorial is scheduled as a half-day, single-track event (4 hours) and currently has 3.5 hours total of planned topics/breaks. The first major topic of the tutorial will focus on lecture, but "Basics of DSP" and "Basics of Sonar DSP" will shift to multiple hands-on exercises that attendees are encouraged to use while following along. Thus, please bring your own laptop with appropriate libraries pre-installed (see Pre-Tutorial Software Setup below).

Time Length	Topic
[60 min]	The Evolution and Future of Sonar
45 min	1900s to Present Overview from the underwater signaling bell to present day sidescan and multibeam sonars. Timeline of sonar generations in the Navy and industry/academia.
15 min	Future of Sonar Covering the future of sonar sensor development and application.
[35 min]	Basics of DSP
10 min	Signal Characteristics Importance of sampling rate and signal phase. Connections between time delay and phase of returned echoes. Brief discussion on Euler's formula. Introduction of passband and baseband concept and the significance of both.
10 min	Intuition on Fourier Series and Transforms Viewing Fourier transforms as projections. Review on the usefulness of the Fourier domain when processing/filtering and shifting signal frequency.
15 min	Matched Filtering Overview of its place in the sonar processing pipeline. Derivation of the optimal filter for received signals given knowledge of projected radar or sonar pings.
[10 min]	Coffee Break
[90 min]	Basics of Sonar DSP
25 min	Single-element, Multipath, and Noise Overview of the sonar equation. Flowchart of operations when finding probability of object detection. Discussion on how sonar signals propagate through water channels and noise sources to consider. Estimating transmission loss in a water channel using simulated data from BELLHOP (Acoustics Toolbox).
30 min	Beam patterns and Beamforming Motivation for beamforming in reference to sonar equation. Derive basic delay-and-sum beamforming equations. Apply beam pattern windowing functions to suppress sidelobes and discuss tradeoffs. Compare different beamforming methods, including Minimum Variance Distortionless Response and Multiple Signal Classification.
35 min	Sidescan Sonar and SAS Image Formation Demonstrate tradeoffs of sidescan and SAS image formation algorithms. Create a SAS image with time-delay beamforming. Cover intuition on the Omega-K beamforming algorithm.
[10 min]	Review and Q & A

Tutorial Materials & Downloads

Sample files for the hands-on portions of this tutorial will be provided here prior to the event. Complete versions of the files will be provided to attendees closer to the OCEANS26. Any Tutorial Data released below are provided through publicly available data or through an acoustic simulator (Sonar-Sim/MASTODON).

Presentation Slides

Slide deck used during the tutorial to cover all topics, from the evolution of sonar to backprojection for SAS imagery.

SampleSlides.pdf

Jupyter Notebooks

Interactive Jupyter Notebook file containing hands-on coding exercises for DSP and sonar signal processing.

sonar_tutorial_sampler.ipynb

Notebook Solutions

Completed versions of the Jupyter Notebook exercises for reference and self-checking.

Download link to be posted

Tutorial Data

Simulated sonar datasets and signals used in the hands-on beamforming and SAS sessions.

Download link to be posted

Pre-Tutorial Software Setup

Attendees are expected to bring their own laptops with Python installed. To ensure compatibility and ease of setup, we strongly recommend using Miniforge3 (a community-driven, minimal installer for conda specific to conda-forge) to manage your Python environment and dependencies.

Please download and install Miniforge3 for your operating system from the official Miniforge GitHub repository.

Once Python and your package manager are installed, please open your terminal or Anaconda prompt and ensure the following packages are installed in your environment prior to arriving:

conda install joblib numpy jupyter ipykernel matplotlib scipy h5py

Note: If you prefer using standard Python and pip, you can alternatively install them via:

pip install joblib numpy jupyter ipykernel matplotlib scipy h5py

About the Authors / Instructors

Dr. Spencer J. Chang is an Electronics Engineer at the U.S. Naval Surface Warfare Center Panama City Division and works in the areas of digital signal processing, computer vision, and machine learning. In May of 2025, he received his PhD from the Department of Electrical and Computer Engineering (ECE) at the University of Florida while working with Prof. Alina Zare. While there, he was recognized with the ECE Graduate Student Service Excellence Award and ECE Graduate Student Teaching Excellence Award. His interests are in researching the development of novel deep learning architectures and teaching others in digital signal processing and machine learning.

Dr. Daniel D. Sternlicht is the Distinguished Scientist for Littoral Sensing Technologies at the U.S. Naval Surface Warfare Center Panama City Division, where he serves as technical expert in sensing technologies relevant to the full spectrum of littoral warfare systems; provides subject matter expertise across the U.S. Naval Research and Development Establishment; and leads new developments in maritime reconnaissance and surveillance for Navy and Marine Corps missions. During a career that includes scientific research and program and line management, Dr. Sternlicht’s technical work has included SAS development; through-the-sensor environmental characterization; multi-sensor fusion; buried mine detection and classification; munitions remediation; automated seabed change detection; unmanned systems autonomy, situational awareness, navigation and communications; and anti-submarine warfare and tracking.

Acknowledgements

The instructors would like to acknowledge the following for their help in developing the material in this tutorial: Dr. Adriana L. McKinney, Samuel R. Shaffer, Evan Marcinkevage, John C. Moore, Jemma A. Smith, Jacqueline A. Sternlicht, Angelica M. Velazquez-Lassalle, and Kagan Zimmerman.