ISAR Imaging by Two-Dimensional Convex Optimization-Based Compressive Sensing

Hyperparameter-Free Receiver for Grant-Free NOMA Systems With MIMO-OFDM

“…This scheme does not require prior impractical knowledge at the BS, unlike the approach in [12]. Moreover, we show the performance of the scheme of [18], . ., 2D-FISTA, with 100 iterations to evaluate the case that the sparsity in the angle domain is exploited in the estimation 3 .…”

“…These results show that the proposed scheme can outperform NNLS significantly and is comparable to ML. Note that our objective is to achieve superior performance without any hyperparameters that need to be designed via an 3 Although the Lipschitz constant is calculated by max X =1 A H AX in [18], it is computed via A H A based on the relation A H AX ≤ A H A X since X = 1 is not usually satisfied in this study. Fig.…”

Bayesian 3‐D interferometric ISAR imaging for the targets with limited pulses

“…Since the azimuth echoes of the non‐cooperative target is always sparse in Doppler domain, it is possible to yield high‐resolution ISAR images of the target with limited pluses by using the CS algorithms. [7–10] proposed a series of CS algorithms (such as OMP, SL0, Bayesian etc.) to yield high‐quality 2‐D images of a target with limited or missing echoes.…”

Echo Preprocessing to Enhance SNR for 2D CS-Based ISAR Imaging Method

“…However, that increases the computational cost and memory consumption enormously. With the aim to reduce the computational cost of the conventional 1D CS-based algorithms, a few 2D sparse recovery algorithms based on the 2D CS, such as the two-dimensional iterative adaptive algorithm (2D IAA) [7], the two-dimensional sparse learning via iterative minimization (2D SLIM) [8], the two-dimensional fast iterative shrinkage-thresholding algorithm (2D-FISTA) [9] and the two-dimensional smoothed l 0 norm algorithm (2D SL0) [10], were proposed. The smoothed l 0 norm (SL0) approach was expanded into two dimensions, making the 2D smoothed l 0 norm algorithm able to deal with the sparse reconstruction of 2D signals on dictionaries with separable atoms [10].…”