Registration for Data Fusion by Exact Maximum Likelihood Method

Abstract

The combination of data and information from multiple sources or sensors constitutes one of the most important problems in signal processing to-day. This process of data fusion has been defined as one of dealing with the association, correlation and combination of data. In this report, an exact maximum likelihood method is used to solve the problem of registration errors in data fusion. Registration is defined as the co-ordinate conversion of multiple source data. Error-free registration is a pre-requisite in the process of data fusion. In the exact maximum likelihood method (EML), a likelihood function is constructed based on the errors of co-ordinate transformation of the local sensor locations to a common system plane. Optimization is then carried out in a recursive two-step Gauss-Newton type procedure, which produces the correct system biases for proper registration. Simulation results show that EML is more efficient when compared with conventional methods of registration.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1996
Accession Number
ADA636773

Entities

People

  • Patrick Yip
  • Yifeng Zhou

Organizations

  • McMaster University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Simulations
  • Convergence
  • Covariance
  • Data Analysis
  • Data Fusion
  • Data Sets
  • Detectors
  • Equations
  • Errors
  • Information Science
  • Maximum Likelihood Estimation
  • Measurement
  • Radar
  • Signal Processing
  • Simulations

Readers

  • Image Processing and Computer Vision.
  • Operations Research
  • Systems Analysis and Design