
Accession Number : ADA189050
Title : Statistical Signal Processing Using a Class of Iterative Estimation Algorithms.
Descriptive Note : Technical rept.,
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE RESEARCH LAB OF ELECTRONICS
Personal Author(s) : Feder, Meir
PDF Url : ADA189050
Report Date : Sep 1987
Pagination or Media Count : 208
Abstract : Many Signal Processing problems may be posed as statistical parameter estimation problems. A desired solution for the statistical problem is obtained by maximizing the Likelihood(ML), the APosteriori probability (MAP) or by optimizing other criterion, depending on the apriori knowledge. However, in many practical situations, the original signal processing problem may generate a complicated optimization problem e.g. when the observed signals are noisy and 'incomplete'. A framework of iterative procedures for maximizing the likelihood, the EM algorithm, is widely used in statistics. In the EM algorithm, the observations are considered 'incomplete' and the algorithm iterates between estimating the sufficient statistics of the 'complete data' given the observations and a current estimate of the parameters (the E step) and maximizing the likelihood of the complete data, using the estimated sufficient statistics (the M step). When this algorithm is applied to signal processing problems, it yields, in many cases, an intuitively appealing processing scheme.
Descriptors : *ALGORITHMS, *ESTIMATES, *ITERATIONS, *SIGNAL PROCESSING, *STATISTICAL PROCESSES, PARAMETERS, PROCESSING, STATISTICS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE