DETECTION OF SONAR SINUSOIDS OF UNKNOWN FREQUENCY AND KNOWN OR UNKNOWN PHASE

Abstract

The problem of detecting a constant sine wave of unknown frequency and amplitude in gaussian noise is discussed. It is assumed that the sinusoid may appear at any one of a finite number of known frequencies, and the probability of its occurrence at each of these frequencies is assumed to be equal. Two cases are treated. The first assumes that the frequency is not known and the phase of the signal is known, allowing coherent detection. The second acknowledges that the initial phase indeed could not be known, and an analysis of the incoherent detector is made. For large output signal-to-noise ratios the problem in both cases becomes that of detecting one of m approximately orthogonal signals in a noise background. The magnitude of the error in the orthogonality approximation is considered. A physical realization of an approximately optimum detector structure is studied in some detail, and the effect of finite observation time is considered. The results indicate that the difference between the two cases is small and quite predictable. It can be assumed that the initial phase is known, the gaussian character of the quantities that arise may be retained, and finally, the answers can be adjusted to account for the actual lack of knowledge concerning the phase.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1965
Accession Number
AD0627645

Entities

People

  • F. S. Hill Jr.

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Amplitude
  • Detection
  • Detectors
  • False Alarms
  • Frequency
  • Frequency Bands
  • Gaussian Noise
  • Observation
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Sine Waves
  • Statistics
  • Warning Systems
  • Waveforms
  • Waves

Readers

  • Control Systems Engineering.
  • Radar Systems Engineering.
  • Statistical inference.