Future ESM Systems and the Potential for Neural Processing,

Abstract

The projected radar electromagnetic environments (eme) for the 1990's and beyond include: higher pulse densities (several million pps); frequencies to 40 GHz and higher; stable, jittered, staggered and pseudo-random pulse repetition intervals (PRIs) with multiple frequencies; spread spectrum techniques; multiple agile radar beams an multi-mode missile seekers. Electronic Support Measures (ESM) concerns the passive detection and identification of radar signals. Thus, an ESM system which can measure such signal characteristics will most likely flood its main processor with information to such an extent that it may not be able to cope. In addition, missing pulses and receiver/processor shadowing times may lead to degraded input data to the processor. A number of likely solutions exist ranging from special purpose hardware to new processing techniques. In the past few years, neural networks have shifted from being primarily a research technology to active use in wide-ranging defence applications. This paper will indicate the likely applicability of a neural processing approach to a range of ESM functions together with results from some preliminary proof-of-concept investigations.

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADP006334

Entities

People

  • Arthur G. Self
  • Gregory Bourassa

Organizations

  • AGARD

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Detection
  • Electromagnetic Environments
  • Electronic Support Measures
  • Environment
  • Frequency
  • Identification
  • Intervals
  • Neural Networks
  • Portugal
  • Radar Beams
  • Radar Signals
  • Signal Processing
  • Spread Spectrum

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Microelectronics