Estimation of a Filtered Marked Poisson Process from Noisy Observations with Applications to Signal Processing

Abstract

The problem of estimating the arrival times, amplitudes, and phases of an unknown number of signals in noise is treated. The signals are assumed to have a common, known waveform. A Bayesian model using a Poisson prior for the arrival times is specified. and a real time algorithm for computing the posterior mode is developed. Alternatively, the procedure may be looked upon as a penalized likelihood estimator with a penalty team which is a generalized form of Akaike's Information Critierion. Simulation results are presented which show that this approach can improve over classical, linear methods.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1983
Accession Number
ADA127692

Entities

People

  • Dennis D. Cox
  • John E. Ehrenberg

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Amplitude
  • Bayesian Networks
  • Computational Science
  • Computations
  • Data Science
  • Electrical Engineering
  • Engineering
  • Maximum Likelihood Estimation
  • Military Research
  • Models
  • Probability
  • Random Variables
  • Signal Processing
  • Simulations
  • Statistics
  • Waveforms

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms