Optimal Statistical Techniques for Combining Pieces of Information Applied to 3-D Complex Object Position Estimation

This paper uses techniques appropriate to the analysis of probability density functions for the large sample case. These techniques permit controlled decomposition of a large problem into small problems where maximum likelihood estimation or Bayesian estimation or recognition can be realized locally, and the results can be combined to arrive at globally optimum estimation or recognition or both.