DETECTION OF RANDOM ACOUSTIC SIGNALS BY RECEIVERS WITH DISTRIBUTED ELEMENTS, OPTIMUM RECEIVER STRUCTURES FOR NORMAL SIGNAL AND NOISE FIELDS,

Abstract

This paper deals with the passive detection of noiselike signals in the presence of both external (environmental) noise and self generated (receiver) noise, using an array of transducers. Starting with the Bayesian formulation of the general detection problem, a set of matrix integral equations is derived whose solution yields the optimum detector function. By regarding the resultant time-varying filters as operators, and the defining matrix integral equations as a set of operational equations, it is possible to examine the underlying structure of the optimum detector most easily. It is shown thereby that factorization of the space-time operations (i.e. separation of the required filter into two successive operations, the first depending only on the geometry of the array, the second depending only on the statistics of the noise processes) is not, in general, possible in optimum systems. Only in the strong signal case has it been possible to show that factorization analogous to conventional beam forming can be utilized in optimum array detection. Another interesting conclusion is that an optimum detector is not superdirective in the limiting case of strong external directive noise. This paper also summarizes the important general properties of the matrix integral operators needed in the analysis. (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1963
Accession Number
AD0420829

Entities

People

  • David Middleton
  • H. L. Groginsky

Tags

DTIC Thesaurus Topics

  • Acoustic Signals
  • Beam Forming
  • Detection
  • Detectors
  • Directives
  • Equations
  • Geometry
  • Integral Equations
  • Integrals
  • Mathematics
  • Statistics
  • Transducers

Fields of Study

  • Engineering

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Phased Array Antenna Design.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space