Showing papers by "Silviu Ciochina published in 2023"
TL;DR: In this paper , a block symmetric matrix form is introduced to adopt a more comprehensive framework (non-restricted by reciprocity assumptions) in mapping the scattering matrix by the consimilarity equivalence relation.
Abstract: Synthetic aperture radar with polarimetric diversity is a powerful tool in remote sensing. Each pixel is described by the scattering matrix corresponding to the emission/reception polarization states (usually horizontal and vertical). The algebraic real representation, a block symmetric matrix form, is introduced to adopt a more comprehensive framework (non-restricted by reciprocity assumptions) in mapping the scattering matrix by the consimilarity equivalence relation. The proposed representation can reveal potentially new information. For example, its eigenvalue decomposition, which is itself a necessary step in obtaining the consimilarity transformation products, may be useful in characterizing the degree of reciprocity/nonreciprocity. As a consequence, it can be employed in testing the reciprocity compliance assumed with monostatic PolSAR data. Full-wave simulated polarimetric data confirm that oriented scatterers can present complex eigenvalues, even with the monostatic geometry.
02 Mar 2023
TL;DR: In this paper , the authors extended this particular symmetric filter in the context of linear system identification, aiming to estimate more general types of impulse responses, especially in more challenging scenarios (e.g., limited amount of data and/or noisy conditions).
Abstract: Recent works have focused on the identification of a type of linearly separable systems owning particular intrinsic symmetric/antisymmetric properties. This problem was formulated based on bilinear forms and Kronecker product decomposition. In this paper, we extend this particular symmetric filter in the context of linear system identification, aiming to estimate more general types of impulse responses. The developed solution is formulated as a Wiener filter, by deriving an iterative version exhibiting better performance features, especially in more challenging scenarios (e.g., limited amount of data and/or noisy conditions). Simulation results obtained in the context of echo cancellation indicate the appealing features of the proposed solution.
TL;DR: In this paper , a solution based on a third-order tensor decomposition was developed to solve the original system identification problem using a combination of two shorter filters, and the problem of approximating the rank of a tensor is avoided thanks to the control of a matrix rank.
Abstract: A wide variety of system identification problems can be efficiently addressed based on the Kronecker product decomposition of the impulse response, together with low-rank approximations. Such an approach solves the original system identification problem using a combination of two shorter filters. In this letter, targeting a higher dimensionality reduction, we develop a solution based on a third-order tensor decomposition. In addition, the problem of approximating the rank of a tensor is avoided thanks to the control of a matrix rank. Then, an iterative Wiener filter is developed, which outperforms both the conventional benchmark and the previously developed counterpart that exploits the second-order decomposition.