Microwave Imaging of Buried Inhomogeneous Objects Using Parallel Genetic Algorithm Combined With FDTD Method

Abstract

Abstract-Microwave imaging of buried objects has been widely used in sensing and remote-sensing applications. It can be formulated and solved as inverse scattering problems.In this paper, we propose a hybrid numerical technique based on the parallel genetic algorithm (GA) and the finite-difference time-domain (FDTD) method for determining the location and dimensions of two-dimensional inhomogeneous objects buried in a lossy earth. The GA, a robust stochastic optimization procedure, is employed to recast the inverse scattering problem to a global optimization problem for its solution. To reduce its heavy computation burden, the GA-based inverse computation is parallelized and run on a multiprocessor cluster system. The FDTD method is selected for the forward calculation of the scattered field by the buried inhomogeneous object because it can effectively model an inhomogeneous object of arbitrary shape. Sample numerical results are presented and analyzed. The analysis of the numerical results shows that the proposed hybrid numerical technique is able to determine the location and dimension of a 2D buried inhomogeneous object, and the parallel computation can effectively reduce the required computation time.

A Novel Two-Layer Stacked Microstrip Antenna Array Using Cross Snowflake Fractal Patches

“…The GA is a powerful and efficient optimization technique and has been widely applied to the optimization of various applications [22][23][24][25][26]. From the Fig.…”

Time-Domain Inverse Scattering of a Two-Dimensional Metallic Cylinder in Slab Medium Using Asynchronous Particle Swarm Optimization

“…In recent year, most of the researchers have applied APSO together with the frequency domain EM solver for the inverse problems [13]. Fewer researchers had applied the genetic/evolutionary algorithms in time domain for the inverse scattering problem for target identification [14,15] and penetrable object reconstruction [16]. To the best of our knowledge, a comparative study about the performances of particle swarm optimization (PSO) and asynchronous particle swarm optimization (APSO) has not yet been reported with application to the electromagnetic inverse scattering problems.…”

“…In the past twenty years, the inversion techniques are developed intensively for the microwave imaging both in frequency domain and time domain [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Most of the inversion techniques are investigated for the inverse problem using only single frequency scattering data (monochromatic source) [3][4][5].…”

Microwave Imaging Within the Interval Analysis Framework

“…In order to avoid nonuniqueness and instability as well as to prevent the retrieval of false solutions [28], several inversion strategies have been proposed based on (a) a suitable definition of the integral equations either in exact [29,30] or approximated [31][32][33][34][35] forms to model the scattering phenomena, (b) the exploitation of the available a-priori information on some features of the scenario/scatterers under test [15,[36][37][38][39] or/and the knowledge of input-output samples of data and reference solutions [40][41][42] and/or the information acquired during the inversion process [43][44][45][46][47], and (c) the use of suitable global optimization strategies [48][49][50][51][52][53][54][55]. Whatever the approach, inversion methods generally consider an optimization step aimed at minimizing/maximizing a suitably defined data-mismatch cost function through gradient or evolutionarybased algorithms with still not fully resolved drawbacks.…”