Maximizing the Convergence Rate of the EM Algorithm for the Multiple Broad-Band Signal Estimation and Detection

Abstract

The following results were obtained under the contract: numerical acceleration of convergence for the EM algorithm; a characterization of the rank-n ambiguity; a characterization of the phase delay ambiguity; determination of the minimal number of known and unknown signals for sensor location observability; a large class of cyclic regression algorithms for multiple signal direction finding; array sensor localization and calibration by cyclic regression; cyclic regression for the weighted subspace fitting for direction finding: cyclic regression for harmonic retrieval from colored noise; a statistical approach to training multilayer perceptrons.

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Document Details

Document Type
Technical Report
Publication Date
Mar 26, 1992
Accession Number
ADA248230

Entities

People

  • James T. Lo

Organizations

  • University of Maryland, Baltimore County

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Ambiguity
  • Calibration
  • Computational Science
  • Computations
  • Computer Science
  • Convergence
  • Detectors
  • Direction Finding
  • Frequency
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Measurement
  • Signal Processing
  • Statistical Analysis
  • Training

Readers

  • Mechanical Engineering/Mechanics of Materials.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.