Electromagnetic Imaging of Dielectric Objects Using a Multidirectional-Search-Based Simulated Annealing

Abstract

In this paper, we introduce a global optimization method that is a novel combination of the simulated annealing method and the multi-directional search algorithm. We demonstrate the use of the algorithm for a microwave-imaging system to obtain the electrical properties of objects. The proposed global optimizer significantly improves the performance and speed of the simulated annealing method by utilizing a nonlinear simplex search, starting from an initial guess, and taking effective steps in obtaining the global solution of the minimization problem. Due to the efficient performance of the proposed global optimization method, we are able to obtain the shape, location, and material properties of the target without considering any a priori information about them. The accuracy and applicability of the proposed imaging method is demonstrated with some numerical results in which two-dimensional images of multiple objects are successfully reconstructed. Based on the optimization techniques utilized in the inverse-scattering problems, microwave imaging methods could be classified as two major groups, namely, local and global optimization methods. Due to the substantially higher speed of convergence in local optimization algorithms [1], they have been more popular in solving the inverse problsms. Born iterative method, for example, is widely used as an effective imaging method to obtain

GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

“…CYGNSS L1B science data version 3.1, along with ancillary data, were used to retrieve soil moisture based on a local/global hybrid method that uses multi-directional search and simulated annealing discussed in detail in [28]. In the inverse-scattering problem, this method proved to be substantially faster than the standard simulated annealing method in converging to the global minimum [28]. The physics-based forward model, SSBM DDM in Sections 2 and 3, was used with parameters from CYGNSS data and ancillary data as inputs to the forward model.…”

“…The level of sensitivity to soil moisture depends on the other parameters of the forward model. The tuning parameters of the multidirectional-search-based simulated annealing algorithm [28] are N md , N s , N t , N, l step , and f stop . The parameters N md , N s , and N t are the maximum number of iterations of the algorithm inner searches.…”

“…The circularly polarized BRCS values computed by the forward model were then used to construct the desired DDM. The inversion technique proposed in this paper is based on the hybrid global and local optimization method of [28]. The hybrid global and local optimization scheme used in the inversion algorithm takes advantage of the speed of a local optimization method while ensuring convergence to the global minimum of the inverse problem [28].…”

“…The inversion technique proposed in this paper is based on the hybrid global and local optimization method of [28]. The hybrid global and local optimization scheme used in the inversion algorithm takes advantage of the speed of a local optimization method while ensuring convergence to the global minimum of the inverse problem [28]. Compared to the conventional simulated annealing methods, this optimization technique was shown to be faster and more accurate [28].…”

“…The hybrid global and local optimization scheme used in the inversion algorithm takes advantage of the speed of a local optimization method while ensuring convergence to the global minimum of the inverse problem [28]. Compared to the conventional simulated annealing methods, this optimization technique was shown to be faster and more accurate [28]. The proposed inversion method was developed and validated in simulation, as well as with data over the SMAP Yanco site located in Australia.…”