This paper describes the approach developed to model the current return networks installed aboard aircrafts having parts made in composite materials. The surface partial element equivalent circuit (PEEC) method is adopted for its high-fidelity modeling capabilities, and its accuracy in the low-frequency region, which is of interest for the characterization of the return networks. State of the art of PEEC modeling is implemented in order to allow real-life aircrafts to be modeled. A special complex mock-up has been realized and measured. The numerical results are compared with measurements to assess their adequacy.Index Terms-Almost equipotential electrical network, composite aircrafts, current return network, partial element equivalent circuit (PEEC) method.
In this letter, we present a Green's function approximation valid in the weak-interaction region that can be used with the sparse-matrix canonical grid (SMCG) method. It can be easily introduced into existing SMCG codes, allowing a reduction in size of the neighborhood region and, consequently, of the dynamic memory and the computation time requirements.Index Terms-Electromagnetic scattering by rough surfaces, Green function, iterative methods, least mean square methods, moment methods. I NTEREST has grown in the Monte Carlo simulations of random rough surface scattering as a result of the increasing computational power of computers. Several techniques have recently been developed for a fast computation of electromagnetic scattering from a deterministic terrain profile. In particular, the banded matrix Iterative approach/canonical grid (BMIA/CAG) method [1]-[4] and the sparse-matrix canonical grid (SMCG) [5]- [7], which is its extension to the analysis of the scattering from two-dimensional rough surfaces, seem to be two of the most efficient methods, since they provide low dynamic memory and computation time.These techniques, based on a modification of the classical method of moments (MoM), allow a fast evaluation of the reaction integral and, when an iterative solver is used, a fast matrix-vector multiplication. In both methods, the MoM solution technique is applied to the surface integral equation, and the matrix equation is . The key feature is to introduce a neighborhood distance separating two regions: a near-interaction region and a weak-interaction region. The impedance matrix is then divided into the sum of a strong-and a weak-matrix, i.e., , where represents the strong near-field interaction and the weak nonnear-field interaction. The near-interaction region requires that several reaction integrals be evaluated and stored in the strong-matrix , which is a sparse matrix. Concerning the weak-matrix, for each planar distance between the basis and weighting functions greater than , a Taylor series expansion of the free space Green's function is introduced. This allows us to operate a canonical grid expansion of the exact weak-matrix elements into a series of translationally invariant terms times powers of the height differences between the basis and weighting functions. This rewritten matrix is then applied in a conjugate gradient solution of the matrix equation with Manuscript Fig. 1. Accuracy of the Taylor series approximation (TSA) and of the least square minimization (LSM) technique when only three terms are considered.Range of minimization: 0:3 < d < 1 ; N = 50, N = 25. Fig. 2. Accuracy of the Taylor series approximation (TSA) and of the least square minimization (LSM) technique when a different number of series terms are considered. Range of minimization: 0:3 < d < 1; N = 50, N = 25.required matrix-vector multiplies performed rapidly due to the sparse nature of the strong-matrix and the use of the fast Fourier transform (FFT) and its inverse for the weak-matrix multiplies. Storage limitations for larg...
We present an original application of fuzzy logic to restoration of phase images from interferometric synthetic aperture radar (InSAR), which are affected by zero-mean uncorrelated noise, whose variance depends on the underlying coherence, thereby yielding a nonstationary random noise process. Spatial filtering of the phase noise is recommended, either before phase unwrapping is accomplished, or simultaneously with it. In fact, phase unwrapping basically relies on a smoothness constraint of the phase field, which is severely hampered by the noise. Space-varying linear MMSE estimation is stated as a problem of matching pursuit, in which the estimator is obtained as an expansion in series of a finite number of prototype estimators, fitting the spatial features of the different statistical classes encountered, for example, fringes and steep slope areas. Such estimators are calculated in a fuzzy fashion through an automatic training procedure. The space-varying coefficients of the expansion are stated as degrees of fuzzy membership of a pixel to each of the estimators. Neither a priori knowledge on the noise variance is required nor particular signal and noise models are assumed. Filtering performances on simulated phase images show a steady SNR improvement over conventional box filtering. Applications of the proposed filter to interferometric phase images demonstrate a superior ability of restoring fringes yet preserving their discontinuities, together with an effective noise smoothing performance, irrespective of locally varying coherence characteristics
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