TY  - RPRT
N2  - In this work, the retrieval of the dielectric characteristics of unknown objects buried in a lossy half-space is dealt with. An innovative multi-resolution multi-frequency (MF) stochastic microwave imaging technique is proposed to solve the buried inverse scattering problem by processing wide-band ground penetrating radar (GPR) data. The proposed MF-IMSA-PSO method exploits a particle swarm optimization (PSO)-based algorithm to find the global optimum of the MF cost function measuring the mismatch between available and retrieved data at a fixed set of frequencies. Such a stochastic solver is nested within the iterative multi-scaling approach (IMSA)  in order to reduce the ratio between problem unknowns and informative data, as well as to adaptively enforce increasing resolutions only within the regions of interest in which the scatterers have been detected. A preliminary numerical validation is shown, in order to assess the robustness of the developed approach with respect to noise, as well as to compare its performance to state-of-the-art competitive approaches.
M1  - technical_report
UR  - http://www.eledia.org/students-reports/732/
Y1  - 2017///
TI  - Multi-Frequency Multi-Resolution Stochastic Optimization for GPR Microwave Imaging
PB  - University of Trento
KW  - Ground Penetrating Radar (GPR)
KW  -  Inverse Scattering (IS)
KW  -  Frequency-Hopping (FH)
KW  -  Multi-Frequency (MF)
KW  -  Particle Swarm Optimization (PSO)
KW  -  Stochastic Optimization
KW  -  Wide-band Data
KW  -  Iterative Multi-Scaling Approach (IMSA) 
AV  - public
ID  - elediasc12732
A1  - Salucci, M.
A1  - Poli, L.
A1  - Anselmi, N.
A1  - Massa, A.
ER  -