Microwave Imaging in Frequency Domain for Through-Wall Multiple Conductors

Abstract

This paper presents an inverse scattering problem for through-wall imaging. Two separate perfect-conducting cylinders of unknown shapes are behind a homogeneous building wall and illuminated by the transverse magnetic (TM) plane wave. After an integral formulation, a discretization using the method of moment (MoM) is applied. The through-wall imaging (TWI) problem is recast as a nonlinear optimization problem with an objective function defined by the norm of a difference between the measured and calculated scattered electric field. Thus, the shape of the metallic cylinder can be obtained by minimizing the objective function. The asynchronous particle swarm optimization (APSO) is employed to find out the global extreme solution of the object function. Numerical results demonstrate that even when the initial guesses are far away from the exact shapes, and the multiple scattered fields between two conductors are serious, good reconstruction still can be obtained. In addition, the effect of Gaussian noise on the reconstruction result is investigated and the numerical simulation shows that even though the signal-noise ratio (SNR) is 20 dB, we can still get good results of reconstructions.

Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments

“…The evolution method of the self-adaptive dynamic differential evolution (SADDE) algorithm is shown in the literature [21][22][23]. The dynamic difference evolution method treats each solution as a parameter vector and selects the difference in the vector of the parameters of each group.…”

“…If the new parametrical vector performs better than the optimal vector, then the new vector becomes the optimal vector, so the population in the dynamic differential algorithm is updated in a dynamic form. Please refer to the literature [22][23][24] for details.…”

Self-Adaptive Dynamic Differential Evolution Applied to Ber Reduction With Beamforming Techniques for Ultra Wideband Mu-Mimo Systems

“…The SADDE is based on the DDE scheme with the ability to automatically adjust the scaling factors without increasing the time complexity [23][24][25]. The SADDE algorithm starting from the initial population consists of a randomly generated set of individual coordinates that represent each location of the transmitter antenna.…”

“…where α l and β l are the lower limits of α and β, and their values are set to 0.1. α u and β u are the upper limits of α and β, and their values are set to 0.9 [24,32]. rand1, rand2, rand3, and rand4 are random numbers with values uniformly distributed between 0 and 1.…”