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In this paper, we propose an improved scattering-matrix method (ISMM) to deal with the propagation of electromagnetic (EM) wave in plasma. As a stratification-based method, the ISMM is based on the scattering-matrix method (SMM) and thus inherits the advantages of the SMM in accuracy and high efficiency. But different from the SMM, the ISMM improves the scattering matrices by adjusting the reference point for each layer interface, making the matrices more robust in calculation. This improvement enhances the robustness of the method in dealing with various media situations, especially for the case of a metal as the floor layer and that of extreme plasmas, which is a weakness for the SMM. After presenting the ISMM, we use it to analyze the EM wave reflected by a reentry plasma sheath, which is an important field but with few new results. With the study of various plasma parameters of the reentry plasma sheath, the ISMM is applied to reveal the characteristics of the sheath. In simulations, the validity of the ISMM is first demonstrated, and then the reflection, transmission, and absorption properties of the EM wave propagating in the plasma sheath are studied by the ISMM. The phenomena of “sunken regions” and “phase jumps” of the plasma sheath are revealed in simulations, which may cause energy loss and distortion of the EM wave. [ABSTRACT FROM AUTHOR]

Conventional sparsity-based direction of arrival (DOA) estimation algorithms suffer from performance degradation due to the off-grid problem. To address it, several research studies focused on 1-D off-grid problem have been conducted. However, due to the bias coupling between the azimuth and elevation directions, those works cannot be directly extended to the uniform rectangular array. In this paper, we propose an effective sparsity-based 2-D DOA estimation algorithm. The proposed algorithm provides the following benefits: 1) we introduce a more accurate observation model where the bias coupling problem is eliminated and each grid bias can be compensated individually and 2) besides the sparsity constraint, we engage additional energy constraint over the biases to improve the estimation performance. Due to the presence of multiple unknown parameters, an alternating descent method is adopted to solve the associated joint optimization problem. Finally, simulational results demonstrate that the proposed algorithm, compared with competitors, exhibits superior performance including high precision DOA estimation and low computational complexity. [ABSTRACT FROM PUBLISHER]