Undersea Signal Processing

The primary goal of our program is to improve the Navy’s ability to conduct its anti-submarine warfare (ASW) mission. To this end, we fund basic and applied research projects focused on developing advanced signal and information processing algorithms to detect, localize, and classify submarines in shallow and deep ocean environments. These projects are conducted by researchers at universities, university-affiliated research centers (UARCs), federal laboratories and industry. Fundamental Research is our core program, and is a portfolio of basic and early applied research projects. We organize and manage the Fundamental Research portfolio by the following technical thrusts:

• Active Sonar Fundamental Research

  Projects focus on active sonar systems for wide area surveillance, distributed search and battle-group defense.

• Passive Sonar Fundamental Research

  Projects focus on signal processing algorithms that detect, classify and locate modern diesel-electric submarines at tactically useful ranges.

• Fundamental Research Initiatives

  This program supports basic research in statistical and ocean acoustic signal processing projects conducted at universities throughout North America.

Research Concentration Areas

The Undersea Signal Processing program aims to improve the Navy’s ability to conduct anti-submarine warfare by exploiting active and passive acoustics. Broadly, research topics of interest for the Fundamental Research program are as follows:

• Active Sonar Signal Processing Fundamental Research
  • Feature development for clutter characterization and clutter versus target discrimination
  • Application of advanced machine learning techniques for improved active sonar automation
  • Artificial intelligence for next-generation active sonar automation
  • Signal and information processing technologies which exploit in situ environmental knowledge
• Passive Sonar Signal Processing Fundamental Research
  • Basic research in statistical signal processing
  • Application of advanced machine learning techniques for improved passive sonar automation
  • Optimal combination of information produced by disparate sensors, integration times, bandwidths and ranges
  • Computationally efficient implementation of signal processing algorithms
  • Long-term association, classification, tracking and localization of intermittent contacts

Research Challenges and Opportunities

• Basic research advancing the state-of-the-art of modern detection and estimation theory
• Emerging topics in signal processing theory and methods. Recent examples include co-prime sampling theory, random matrix theory, and signal processing on graphs. Projects in emerging topics are low-level efforts exploring the potential Navy value of new classes of algorithms—not large projects aimed at demonstrating proof-of-concept with operational data.
• Automatic surface ship classification at long ranges
• Advancements in multi-sensor contact association, including exploitation of external information sources, automatic detection and correction of incorrect associations, theoretical performance bounds, and methods that leverage existing classifiers, tracker and association capabilities
• Computationally efficient implementations that reduce processing hardware and memory footprints

Specific topics of interest may be found in the current call for white papers, which may be found via the link given below.

Call for White Papers

Please contact Dr. Keith Davidson by email for more information on white papers, here: keith.l.davidson4.civ@us.navy.mil