Adaptive Detection in Stationary and Nonstationary Noise Environments

Abstract

This report describes the statistical performance of several radar- based adaptive detection schemes in both stationary and nonstationary noise and interference environments. The detectors under study must be able to correctly determine the presence of a target in a range gate with a high degree of probability given that the probability of misclassification is a fixed small value. The hostile noise environment is assumed to consist of possibly time- varying, spatially correlated interference along with Gaussian background noise. In a typical radar environment, the mean value of the returned radar signal and the noise covariance matrix are unknown parameters; therefore, generalized likelihood ratio test procedures were used to develop decision rules that meet the Neyman-Pearson criterion. Three major cases of interest were examined. First, the single-pulse test developed by Kelly is reviewed. The multiple-pulse return test case is extremely complicated and was divided into distinct detector forms: noncoherent and coherent. The performance of each detector is a function of the signal-to-noise ratio, the number of radar pulse returns used in the decision rule, and the quality of the covariance estimate.

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Document Details

Document Type
Technical Report
Publication Date
Feb 24, 1994
Accession Number
ADA279581

Entities

People

  • Paul Monticciolo

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Covariance
  • Data Science
  • Detection
  • Detectors
  • Distribution Functions
  • Estimators
  • Gaussian Distributions
  • Information Science
  • Monte Carlo Method
  • Noise
  • Probability
  • Radar
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Readers

  • Radar Systems Engineering.
  • Statistical inference.
  • Systems Analysis and Design