A Novel Method for Improving Quality of Oblique Incidence Sounding Ionograms Based on Eigenspace-Based Beamforming Technology and Capon High-Resolution Range Profile

Abstract

Ground-based oblique incidence sounding (OIS) is an important means to investigate the ionosphere. As the OIS ionogram is a visual representation of the OIS parameters, such as group distance and maximum usable frequency (MUF), it is of great significance for improving the quality and the range resolution. This will facilitate the automatic interpretation and inversion of OIS ionograms to obtain the fine structure and spatial–temporal evolutions of the ionosphere. In this paper, a novel OIS signal processing scheme is proposed based on the Eigenspace-based (ESB) beamforming technology and Capon high-resolution range profile (HRRP). First, by applying the ESB beamformer to a compact L-shaped antenna array, the energy of the OIS signals received by multiple antennas can be added, while the interference and noise will be suppressed. Subsequently, the Capon HRRP algorithm is used to improve the range resolution. This is achieved due to the slow variation in the characteristics of the ionosphere resulting in good short-term coherence of the narrowband signals. The experimental results show that the two-stage signal-processing method significantly improves the imaging quality of OIS ionograms. In particular, the structure inside the ionosphere and its temporal and spatial evolution can be observed more precisely after the range resolution of the OIS ionogram is improved; therefore, it has great application potential.

ADMM-Net for Beamforming Based on Linear Rectification with the Atomic Norm Minimization

“…where η denotes the real number scalar, f (η) denotes the real-valued function of η. By applying the chain rule shown in (27) to the ADMM-Net, the loss function can calculate the gradient of any parameter in the parameter set. After obtaining the gradient, the training can be updated by using the gradient descent.…”

“…Nevertheless, these methods necessitate prior knowledge of the target number, a requirement that poses challenges under conditions characterized by a low signal-to-noise ratio and a high-dimensional signal-interference subspace. In addition to the aforementioned traditional methods, recent research has started to use subspace reconstruction methods for adaptive beamforming [27][28][29][30]. Sparse recovery methods are employed to recover the interference subspace matrix and calculate adaptive weights based on the reconstructed interference subspace [27], which utilizes the low-rank characteristics of the interference or clutter subspace in the entire sample matrix.…”

“…In addition to the aforementioned traditional methods, recent research has started to use subspace reconstruction methods for adaptive beamforming [27][28][29][30]. Sparse recovery methods are employed to recover the interference subspace matrix and calculate adaptive weights based on the reconstructed interference subspace [27], which utilizes the low-rank characteristics of the interference or clutter subspace in the entire sample matrix. However, the above-mentioned methods require the guiding vectors of interference and clutter to fall onto the dictionary matrix of sparse recovery, which cannot handle off-grid situations.…”

Robust Space-Time Adaptive Processing Method for GNSS Receivers in Coherent Signal Environments

“…In practice, there may be direction errors, local scattering, wavefront distortion and limited number of sampling snapshots, which lead to the covariance matrix mismatches and steering vector errors. In order to improve the beamforming performance, a large number of robust adaptive beamforming methods have been proposed to eliminate these errors, such as diagonal loading [10], eigenspace-based [11], uncertainty set-based [12], interference-plus-noise covariance matrix (INCM) reconstruction [13][14][15][16] methods. Although these SOP algorithms can effectively suppress multiple interferences, the degrees-of-freedom of interference suppression is limited by the number of antenna array elements.…”