Results 51 to 60 of about 70,734 (340)
Robust Bayesian Compressed sensing
, 2016 We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the outliers from sparse signal recovery.
Wan, Qian +3 more
openaire +2 more sources
Bayesian compressed sensing in ultrasound imaging [PDF]
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 Following our previous study on compressed sensing for ultrasound imaging, this paper proposes to exploit the image sparsity in the frequency domain within a Bayesian approach. A Bernoulli-Gaussian prior is assigned to the Fourier transform of the ultrasound image in order to enforce sparsity and to reconstruct the image via Bayesian compressed sensing.
Celine Quinsac +4 more
openaire +1 more source
, 2013 This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise,
Haupt, Jarvis +2 more
core +1 more source
Energy constraint Bayesian compressive sensing detection algorithm
Tongxin xuebao, 2012 To solve the shortage of nodes handling ability and limited energy in wireless sensor network,an energy constraint Bayesian compressive sensing detection algorithm was proposed.To balance the energy of the whole network and prevent network paralyzed due ...
Chun-hui ZHAO, Yun-long XU
doaj +2 more sources
On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
Applied Sciences, 2021 The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components.
Yosra Marnissi +4 more
doaj +1 more source
Structure-Based Bayesian Sparse Reconstruction
, 2012 Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information ...
Al-Naffouri, Tareq Y., Quadeer, Ahmed A.
core +2 more sources
Bi-fidelity adaptive sparse reconstruction of polynomial chaos using Bayesian compressive sensing
Engineering computationsIn recent years, non-intrusive polynomial chaos expansion (NIPCE) has been recognized as a practical method for uncertainty quantification (UQ) of stochastic problems. However, this method suffers from the curse of dimensionality, where the computational
M. Karimi, Ramin Mohammadi, M. Raisee
semanticscholar +1 more source
IEEE Communications Letters, 2021 In the unmanned aerial vehicle-based wireless sensor network, the compressive sensing approach can simultaneously locate multiple ground radio frequency sources, while the existing algorithms’ localization accuracies would deteriorate confronting the ...
Xinhua Jiang +5 more
semanticscholar +1 more source
Bayesian compressive sensing for primary user detection
IET Signal Processing, 2016 In compressive sensing (CS)‐based spectrum sensing literature, most studies consider accurate reconstruction of the primary user signal rather than detection of the signal. Furthermore, possible absence of the signal is not taken into account while evaluating the spectrum sensing performance.
Basaran, Mehmet +2 more
openaire +3 more sources
Full polarisation ISAR imaging based on joint sparse Bayesian compressive sensing
The Journal of Engineering, 2019 This study proposes a joint sparse algorithm based on Bayesian compressive sensing to improve full polarisation inverse synthetic aperture radar (ISAR) imaging performance.
Yalong Gu +4 more
doaj +1 more source