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Applications of sparse signal processing

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016
Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and ...
Farokh Marvasti, Masoumeh Azghani
exaly   +2 more sources

Strong Impossibility Results for Sparse Signal Processing

IEEE Signal Processing Letters, 2014
This letter derives strong impossibility results for several sparse signal processing problems. It is shown that regardless of the allowed error probability in identifying the salient support set (as long as this probability is below one), the required number of measurements is almost the same as that required for the error probability to be ...
George Atia, Vincent Tan
exaly   +3 more sources

A Tutorial on Sparse Signal Reconstruction and Its Applications in Signal Processing

Circuits, Systems, and Signal Processing, 2018
Sparse signals are characterized by a few nonzero coefficients in one of their transformation domains. This was the main premise in designing signal compression algorithms. Compressive sensing as a new approach employs the sparsity property as a precondition for signal recovery.
Irena Orović, Miloš Dakovic, Srdjan Stanković   +2 more
exaly   +2 more sources

Design of sparse-signal processing in radar systems

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Sparse-signal processing (SSP) is interpreted in this paper as a sparse model-based refinement of typical steps in radar processing. Matched filtering remains vital within SSP but joined with radar detection promoting the sparsity. Realistic measurements are also supported in SSP by using Monte-Carlo (MC) methods.
Radmila Pribic, Ioannis Kyriakides
openaire   +2 more sources

Sparse Signal Processing

2014
Conventional sampling techniques are based on Shannon-Nyquist theory which states that the required sampling rate for perfect recovery of a band-limited signal is at least twice its bandwidth. The band-limitedness property of the signal plays a significant role in the design of conventional sampling and reconstruction systems.
Masoumeh Azghani, Farokh Marvasti
openaire   +1 more source

Sparse Array Signal Processing

2023
Diese Dissertation beschreibt drei Ansätze zur Richtungsschätzung (DOA) oder Beamforming in der Array-Signalverarbeitung aus der Perspektive der Sparsity. Im ersten Teil dieser Dissertation betrachten wir das Design von Sparse-Array-Beamformern basierend auf der Alternating Direction Method of Multipliers (ADMM); im zweiten Teil dieser Dissertation ...
openaire   +1 more source

Signal processing with the sparseness constraint

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired.
openaire   +1 more source

Sparse sampling of non-stationary signal for radar signal processing

2013 IEEE International Conference on Communications Workshops (ICC), 2013
Estimating the spectrogram of non-stationary signal relates to many important applications in radar signal processing. In recent years, coprime sampling and array attract attention for their potential of sparse sensing with derivative to estimate autocorrelation coefficients with all lags, which could in turn calculate the power spectrum density.
Qiong Wu 0006, Qilian Liang
openaire   +1 more source