Balanced Data Focusing: Direction of Arrival Estimation with Maximum Likelihood Performance,

Abstract

The performance of a direction of arrival estimation procedure at low signal- to-noise ratios and limited data samples is an important characteristic. The approach based on maximum likelihood (ML) estimation is considered to be among the best for this problem as long as the underlying signal model is properly chosen. Unfortunately, in most cases, there is no closed-form solution so fast search procedures are employed. Given to a priori knowledge, selecting the initial parameter values for these search procedures can be a difficult problem, especially under low signal-to-noise conditions. In this paper, a new method for uniform linear sensor arrays which overcomes the initial value problem is introduced. This method is called Balanced Data Focusing (BDF). Simulation results are included comparing the performance of this new method to that of the ML approach using the Alternating Projection Maximization search procedure and another popular estimation approach, the root-MUSiC method.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1994
Accession Number
ADA288252

Entities

People

  • William J. Read

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Angle Of Arrival
  • Computational Science
  • Computations
  • Computer Simulations
  • Eigenvalues
  • Eigenvectors
  • Equations
  • Estimators
  • Interpolation
  • Linear Arrays
  • National Security
  • Security
  • Simulations
  • Three Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.