Comparer des méthodes

	Acquisition et reconstruction par échantillonnage parcimonieux	Estimation de la densité spectrale de puissance
Domaine	Traitement du signal	Traitement du signal
Famille	Process / pipeline	Process / pipeline
Année d'origine	2006	1967
Auteur d'origine	Emmanuel Candès, Justin Romberg, and Terence Tao	Peter Welch
Type	Sparse signal recovery	Frequency domain signal analysis
Source fondatrice	Candes, E. J., Romberg, J., & Tao, T. (2006). Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete and Inaccurate Measurements. IEEE Transactions on Information Theory, 52(2), 489–509.	Welch, P. (1967). The Use of Fast Fourier Transform for Estimation of Power Spectra: A Method Based on Time Averaging over Short, Modified Periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73.
Alias	Compressed Sensing, CS, Sparse Recovery, Sub-Nyquist Sampling	PSD Estimation, Spectral Density Analysis, Power Spectrum Estimation
Apparentées	4	4
Résumé	Compressive Sensing (CS) is a signal acquisition and reconstruction technique that exploits signal sparsity to recover high-resolution signals from far fewer samples than required by the Nyquist sampling theorem. Developed by Emmanuel Candès, Justin Romberg, and Terence Tao in 2006, compressive sensing challenges the traditional sampling paradigm by showing that signals with sparse representations can be reconstructed from sub-Nyquist random measurements using nonlinear optimization.	Power Spectral Density (PSD) estimation is a set of methods for determining how the power of a signal is distributed across different frequencies. Proposed by Peter Welch in 1967, PSD estimation techniques are fundamental to frequency domain signal analysis, providing insights into the frequency composition of signals for applications ranging from communications to biomedical monitoring.