Parametric and Model Based Adaptive Detection Algorithms for Non-Gaussian Interference Backgrounds

Abstract

This report presents model based adaptive signal processing methods for target detection in a background of non-Gaussian interference. Several candidate algorithms are derived and important insights pertaining to their structure are documented in this report. Performance analysis of these algorithms is discussed in some detail. It is seen that the parametric adaptive matched filter (PAMF) offers the potential for significantly improved performance in non-Gaussian interference scenarios, while leading to considerably lower secondary data support requirements compared to classical adaptive processing methods. This is due to the use of a parametric method that employs a low model order to approximate the interference spectral characteristics. Another reduced rank adaptive algorithm considered in this study is the principal component inverse (PCI) method.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1999
Accession Number
ADA369457

Entities

People

  • Muralidhar Rangaswamy

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computational Complexity
  • Data Science
  • Detection
  • Detectors
  • Electrical Engineering
  • Estimators
  • Information Science
  • Matched Filters
  • Monte Carlo Method
  • Radar
  • Radar Clutter
  • Random Variables
  • Signal Detection
  • Signal Processing

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radio communications and signal processing.
  • Sensor Fusion and Tracking Systems.