Analytic Performance Evaluation Results of the Mid-Frequency Active Classification Processor CW and LFM Normalizers

Abstract

Performance of the Mid-Frequency Active Classification Processor (MFACP) phase 1 Continuous Wave (CW) and Linear Frequency Modulated (LFM) normalizers based on an analytic evaluation methodology is presented. The performance results are quantified in the form of Receiver Operating Characteristic (ROC) curves at the normalizer output. The ROC curves determine the single bin probability of detection (P(D)) as a function of signal-to-noise ratio (SNR) for specified design probability of false alarm (P(F)) at the normalizer output. The ROC curves so generated are for normalizer performance when subjected to both stationary noise backgrounds and nonstationary noise background variations. The nonstationary background considered has a generalized sinusoidal variation of the Rayleigh parameter of the envelope detected, matched filter output where both the amplitude and period of the sinusoid are adjustable. It is shown that the worse case performance as reflected in the ROC curves occurs at the local minimum and maximum intensities of the sinusoidal nonstationary. The ROC curves at all other points of the sinusoid oscillate between the minimum and maximum point ROC curves. The Constant False Alarm Rate (CFAR) output capability of the normalizers is also evaluated for fixed detection thresholds as the normalizers operate on the stationary and nonstationary backgrounds.

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Document Details

Document Type
Technical Report
Publication Date
Sep 09, 1992
Accession Number
ADA271606

Entities

People

  • Carl J. Wenk
  • Fyzodeen Khan

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Amplitude
  • Background Noise
  • Classification
  • Continuous Waves
  • Convergence Zones (Sonar)
  • Detection
  • Detectors
  • Engineering
  • False Alarms
  • Filters
  • Frequency
  • Intensity
  • Matched Filters
  • Probability
  • Stationary
  • Test And Evaluation
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Radar Systems Engineering.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference