Seven. Quantitative Stochastic Reconstruction Methods

  1. Matteo Pastorino

Published Online: 26 APR 2010

DOI: 10.1002/9780470602492.ch7

Microwave Imaging

Microwave Imaging

How to Cite

Pastorino, M. (2010) Quantitative Stochastic Reconstruction Methods, in Microwave Imaging, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470602492.ch7

Publication History

  1. Published Online: 26 APR 2010
  2. Published Print: 24 MAR 2010

Book Series:

  1. Wiley Series in Microwave and Optical Engineering

Book Series Editors:

  1. Kai Chang

Series Editor Information

  1. Texas A&M University, USA

ISBN Information

Print ISBN: 9780470278000

Online ISBN: 9780470602492

SEARCH

Keywords:

  • ant colony optimization method;
  • genetic algorithm;
  • particle swarm optimization method;
  • population-based algorithms;
  • simulated annealing technique;
  • stochastic minimization methods

Summary

The simulated annealing technique was one of the first stochastic minimization methods applied to electromagnetic imaging. It is an iterative method that, at each iteration, updates the current trial solution by exploiting probabilistic concepts. Population-based algorithms can be used for imaging purposes with different implementation schemes. It should also be noted that population-based algorithms seem to be particularly suitable for the inclusion of a priori information into the model. From an engineering standpoint, a priori information is very important, since it can be used for accelerating the reconstruction procedure. In this chapter, simulated annealing is discussed first. Then, some population-based methods, which have already been proposed for microwave imaging applications are considered. Specifically, there are the genetic algorithm, the differential evolution method, the particle swarm optimization method, and the ant colony optimization method.

Controlled Vocabulary Terms

ant colony optimisation; genetic algorithms; particle swarm optimisation; stochastic processes