Fast Envelope Correlation for Passive Ranging

Abstract

Application of classic triangulation methods will allow the location of a radar to be determined by passive sensors. Through the use of modern digital signal processing techniques this estimate can be made in a simpler fashion using a conventional receiver. In this thesis a technique is developed for time difference of arrival (TDOA) estimation using a frequency domain based correlation detector driven by an envelope detector. Time lag boundaries are defined on the output of the correlator. A fixed detection threshold is calculated to permit constant false alarm rate (CFAR) detection. The performance of the correlation detector is plotted as a receiver operating characteristic (ROC) curve as a function of signal to noise ratio (SNR). An interactive MATLAB software program is provided to perform either spectral domain or time domain based correlation. Spectral domain based correlation uses the Fast Fourier Transform (FFT). Implicit with the use of FFT are finite arithmetic internal processing errors which are modeled as independent uncorrelated noise sources. A method is presented to account for SNR degradation at the output of the FFT.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA246333

Entities

People

  • Frank J. Mika

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Electronic Warfare
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Angle Of Arrival
  • Detection
  • Detectors
  • Digital Signal Processing
  • Doppler Radar
  • False Alarms
  • Fast Fourier Transforms
  • Frequency
  • Frequency Domain
  • Operating Systems
  • Passive Sensors
  • Radar
  • Radar Signals
  • Signal Processing
  • Time Domain
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference