Extracting Signal Information from Contaminated Data with a Two-Dimensional Array.

Abstract

The use of sensors configured into various geometrical shapes called arrays are important in extracting signal information in many applications. This paper explores the effectiveness of a two-dimensional or planar array in extracting signal information from digitized data when additive Gaussian noise and additive impulsive interference are present. Based on the likelihood ratio approach the data output of each sensor is first passed through a zero-memory device. The zero-memory device is neither a nonlinearity or linearity depending on whether the interference is present or not present, respectively. Further processing utilizes a receiver which converts the data at the output of each zero-memory device into frequency components using a discrete-Fourier transform. The frequency components are then weighted and summed over all spatial sensors. The weights are chosen by maximizing a defined performance measure. It is shown that this optimization procedure does not take into account the debilitating effects of impulsive interference. In order to treat theoretically the inclusion of a nonlinearity, to combat impulsive interference, the concept of an ideal nonlinearity is introduced.

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Document Details

Document Type
Technical Report
Publication Date
Apr 05, 1985
Accession Number
ADA162316

Entities

People

  • Roger F. Dwyer

Organizations

  • Naval Underwater Systems Center

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Data Science
  • Detection
  • Detectors
  • Frequency
  • Gaussian Noise
  • Gaussian Processes
  • Information Science
  • Phase Shift
  • Plane Waves
  • Probability
  • Signal Detection
  • Signal Processing
  • Spectra
  • Three Dimensional
  • Two Dimensional
  • Wave Phenomena
  • Wavefronts

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Phased Array Antenna Design.
  • Statistical inference.