Results 1 to 10 of about 359 (123)
A Novel Sparsity Adaptive Algorithm for Underwater Acoustic Signal Reconstruction [PDF]
Sensors, 2022 In view of the fact that most of the traditional algorithms for reconstructing underwater acoustic signals from low-dimensional compressed data are based on known sparsity, a sparsity adaptive and variable step-size matching pursuit (SAVSMP) algorithm is
Na Li, Xinghui Yin, Haitao Li
doaj +2 more sources
An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing [PDF]
Sensors, 2022 Interrupted sampling repeater jamming (ISRJ) is an attracted coherent jamming method to inverse synthetic aperture radar (ISAR) in the past decades. By means of different jamming parameters settings, realistic dense false targets can be formed around the
Yingxi Liu +5 more
doaj +2 more sources
An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery
IEEE Access, 2021 Joint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix.
Lili Pan, Xunzhi Zhu
doaj +1 more source
Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery
, 2021 Restricted isometry property (RIP), essentially stating that the linear measurements are approximately norm-preserving, plays a crucial role in studying low-rank matrix recovery problem. However, RIP fails in the robust setting, when a subset of the measurements are grossly corrupted with noise.
Ma, Jianhao, Fattahi, Salar
openaire +2 more sources
A Method of Reweighting the Sensing Matrix for Compressed Sensing
IEEE Access, 2021 In compressed sensing, a small enough restricted isometry constant (RIC) of the sensing matrix satisfying the restricted isometry property (RIP) is the powerful guarantee on the precise reconstruction of a sparse discrete signal.
Lei Shi, Gangrong Qu, Qian Wang
doaj +1 more source
EURASIP Journal on Advances in Signal Processing, 2021 The interrupted transmitting and receiving (ITR) can be used in anechoic chamber to solve the coupling between the transmitted and reflected signals. When the ITR periods are random, the fake peaks in high-resolution range profile (HRRP) of ITR echo can ...
Xiaobin Liu +3 more
doaj +1 more source
On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit
IEEE Access, 2019 Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been ...
Xiaobo Zhang +4 more
doaj +1 more source
Improved RIP Conditions for Compressed Sensing with Coherent Tight Frames
Discrete Dynamics in Nature and Society, 2017 This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compressed sensing with coherent tight frames.
Yao Wang, Jianjun Wang
doaj +1 more source
New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted
IEEE Access, 2019 In this paper, we consider using the weighted ℓ2,1 minimization to reconstruct X from Y = AX + Z. This method has been applied to recover multichannel signal in resent years since it exploits both the interchannel correlation and multisource prior.
Huanmin Ge, Run Cao
doaj +1 more source
Deterministic Construction of Compressed Sensing Matrices via Vector Spaces Over Finite Fields
IEEE Access, 2020 Compressed Sensing (CS) is a new signal processing theory under the condition that the signal is sparse or compressible. One of the central problems in compressed sensing is the construction of sensing matrices.
Xuemei Liu, Lihua Jia
doaj +1 more source