The plasma sheath will be generated on the surface of the reentry object due to the effect of aerodynamic thermoionization, causing multicomponent signals with Doppler frequency components in the radar echo. This situation will further result in the appearance of false targets that seriously affect the detection of reentry targets by radar, causing positioning errors and even tracking losses. In this article, the echo signal model of the plasma sheath covered reentry target is established, and the multidomain characteristics of the echo signal in terms of time, time-frequency, and range are analyzed, revealing the influence mechanism of plasma sheath on radar detection of reentry target. Through range-period analysis, the influence of intrapulse Doppler frequency caused by plasma sheath is determined as well as the interpulse Doppler frequency on target range measurement. Theoretical analyses conducted in this study are verified by simulation results, laying theoretical foundation for effective detection and reliable tracking of plasma sheath covered reentry targets.

Conventional sparsity-based direction of arrival (DOA) estimation algorithms suffer from performance degradation due to the offgrid problem. To address it, several research studies focused on onedimensional (1-D) off-grid problem have been conducted. However, due to the bias coupling between the azimuth and elevation directions, those works can’t be directly extended to the uniform rectangular array (URA). In this paper, we propose an effective sparsity-based two-dimensional (2-D) DOA estimation algorithm. The proposed algorithm provides the following benefits: a) we introduce a more accurate observation model where the bias coupling problem is eliminated and each grid bias can be compensated individually. b) 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.

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.

A reentry object covered by a plasma sheath will produce two reflective signals in a radar echo, which seriously affects the detection of the reentry object by radar and results in positioning error and even tracking loss. On the basis of the scattering center model and the non-uniform plasma stratification model, the present paper establishes a radar scattering model of a reentry object covered by a plasma sheath. The model effectively retains the plasma distribution and electromagnetic wave propagation characteristics of the plasma sheath. Analysis of the signal components of radar echoes in an example shows to some extent that the non-mirror reflection signal is not generated by the plasma wake.

Compressive sensing-based synthetic aperture radar (SAR) imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach.

Traditional sparse representation-(SR) based inverse synthetic aperture radar (ISAR) imaging schemes can achieve significant performance, but they suffer from high costs of memory and computational complexity, because the SR of a 2-D image is converted into that of a 1-D vector. Instead of memory consuming vector operations, we propose a fast ISAR imaging algorithm, where the decomposition and reconstruction of a 2-D scene is implemented using matrix operations directly. Besides the spatial sparsity of a scene, its structural sparsity is presented using the range profile of the scene, where both can be used to enhance sparsity exploitation during image reconstruction. Also, benefitting from the structural sparsity of the range profile, the target energy can be accumulated during the process, which further improves performance. Compared to available SR-based ISAR imaging algorithms, the proposed algorithm reduces both memory costs and computational complexity significantly, which is proven using simulated and real data.