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PH4454 Sonar Transducer Theory and Design

A treatment of the fundamental phenomena basic to the design of sonar transducers, specific examples of their application and design exercises. Topics include piezoelectric, magnetostrictive and hydromechanical effects. Laboratory includes experiments on measurement techniques, properties of transducer materials, characteristics of typical navy transducers, and a design project. A field trip to visit one or more transducer manufacturers is normally scheduled during the course. 

Prerequisite

PH3452 (may be taken concurrently)

Lecture Hours

4

Lab Hours

2

Course Learning Outcomes

Upon successful completion of this course, a student will:

  • Understand how to apply the SONAR equation for single and multiple sensors in realistic environments using the decibel scale
  • Understand, calculate, and convert levels between Power and Power Spectral Density with temporal and spatial windows (including Equivalent Noise Bandwidth ENBW) for a sinusoid received at an array of sensors
  • Understand how to convert from microPascal to A/D counts for sinusoidal power given a typical set of transfer functions
  • Understand the difference between auto- and cross-correlation and convolution for matched-filtering approaches with single sensors
  • Understand beamforming principles and implement phase delays in the time- and frequency-domain for a linearly spaced array of N sensors for a single source
  • Calculate conventional weights and implement a conventional beamforming (CBF) algorithm in the frequency domain for source parameter estimation (location, source level)
  • Calculate adaptive weights and implement an adaptive beamforming (ABF) algorithm (MVDR) in the frequency domain to localize multiple sources
  • Understand the principles and requirements for ABF (uncorrelated sources, full rank and invertible sample covariance matrix)
  • Implement and evaluate additive white gaussian noise defined for single-sensor or array SNR in the frequency domain
  • Execute and demonstrate learning objectives via a programming language (e.g., MATLAB) in simulations and with data