Inversion of Lossy Dielectric Profiles Using Particle Swarm Optimization

Shaltout, Amr M.; Hashish, Essam A.; Hassan, and Mohamed I.

doi:10.2528/pierm09072604

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…The estimation of wall parameters associated with a multilayered building wall has been extensively studied in the past decade within the framework of one‐dimensional inverse scattering problem. We would refer the authors to Thajudeen and Hoorfar [2010], Thajudeen et al [2011], Zhang et al [2001], Nakhkash et al [1999], and Emad Eldin et al [2009] for the estimation of wall parameters associated with multilayered building walls. The radar operating condition and electrical parameters (permittivity and conductivity) of the wall are the same as in previous example.…”

Section: Numerical and Experimental Resultsmentioning

confidence: 99%

Full polarimetric beam‐forming algorithm for through‐the‐wall radar imaging

et al. 2011

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[1] Development of an imaging algorithm that accurately models the wave propagation physical process has become an important topic in through-the-wall radar imaging (TWRI). In this paper, a full polarimetric beam-forming algorithm for through-the-wall radar imaging for a general multilayer wall case is presented and applied to various 2D and 3D simulated and measured scenarios. Polarimetric TWRI not only enhances the target characterization but also mitigates the wall ringing effect in cross polarizations. The far field layered medium Green's function is incorporated in the proposed TWRI algorithm for the quad-polarization of the target returns, namely, VV, HV, VH and HH. Due to the incorporation of the layered medium Green's function, the imaging algorithm not only takes into account the wall reflection, bending, and delay effects but also accounts for the complex scattering mechanism due to the presence of the wall. Numerical and experimental results show that the proposed full polarimetric beam former can provide high quality focused images in various wall-target scenarios, in particular, when the technique is combined with a wall parameter estimation technique.

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Section: Numerical and Experimental Resultsmentioning

confidence: 99%

Full polarimetric beam‐forming algorithm for through‐the‐wall radar imaging

et al. 2011

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“…It has received a huge attention and popularity due to its algorithmic simplicity and effectiveness for solving design problems such as antenna design [19,20]. Recently, the application of PSO has been extended to the reconstruction of microwave images and excellent results have been reported in the literature [21][22][23]. One of the main advantages of PSO over other stochastic optimization methods such as GA or SA, lies in the ease with which it can be tuned and implemented, using only a velocity operator to drive the search through out the hyperspace [24].…”

Section: Introductionmentioning

confidence: 97%

Hybrid of Particle Swarm Optimization, Simulated Annealing and Tabu Search for the Reconstruction of Two-Dimensional Targets From Laboratory-Controlled Data

Mhamdi¹,

Grayaa²,

Aguili³

2011

PIER B

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Abstract-Recently, the use of the particle swarm optimization (PSO) technique for the reconstruction of microwave images has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, the basic PSO algorithm is easily trapping into local minimum and may lead to the premature convergence. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To overcome the premature convergence of PSO, we propose a new hybrid algorithm of particle swarm optimization (PSO), simulated annealing (SA) and tabu search algorithm (TS) for solving the scattering inverse problem. The incorporation of tabu search (TS) and simulated annealing (SA) as local improvement approaches enable the hybrid algorithm to overleap local optima and intensify its search ability in local regions. Reconstructions of dielectric scatterers from experimental inversescattering data are finally presented to demonstrate the accuracy and efficiency of the hybrid technique.

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“…In the reported numerical results, the PSO shows slightly better convergence rate, but the DE allows obtaining a more precise reconstruction. A similar approach is proposed in [52], too. In [53], the authors propose the use of the PSO for the localization of dielectric circular cylinders under a TM timedomain formulation.…”

Section: Psomentioning

confidence: 99%

Swarm Optimization Methods in Microwave Imaging

Randazzo

2012

International Journal of Microwave Science and Technology

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Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.). Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed.

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Inversion of Lossy Dielectric Profiles Using Particle Swarm Optimization

Cited by 10 publications

References 24 publications

Full polarimetric beam‐forming algorithm for through‐the‐wall radar imaging

Full polarimetric beam‐forming algorithm for through‐the‐wall radar imaging

Hybrid of Particle Swarm Optimization, Simulated Annealing and Tabu Search for the Reconstruction of Two-Dimensional Targets From Laboratory-Controlled Data

Swarm Optimization Methods in Microwave Imaging

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