Microwave Imaging Using Optimization With Variable Number of Dimensions

The solution to the microwave imaging problem is often provided by systems that employ global optimization methods that search for material properties of the selected investigation domain. A novel method for the solution of the microwave imaging problem based on the optimization with a variable number of dimensions is introduced in this article. Shapes of scatterers with an arbitrary complexity can be coded in the form of decision space vectors with varying sizes. The variable number of dimensions formulation of the problem is compared to a conventional approach that uses a regular grid of sub-domains and the optimization algorithm then searches for material properties of individual sub-regions. The influence of the investigation domain parameters like the signal to noise ratio of the measured field, or scatterer's material properties on the stability of the method is discussed in the paper.