2015/02/17 by Thomas Markovich, Markovich, Thomas, Samuel M. Blau +5
Engineering · #Advanced Electrical Measurement Techniques #Analog and Mixed-Signal Circuit Design #Chemical Physics (physics.chem-ph) #Data Analysis #FOS: Physical sciences #Quantum Physics (quant-ph) #Sparse and Compressive Sensing Techniques #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1502.06579
openalex publication_date 2015/02/17 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
Signal processing techniques have been developed that use different\nstrategies to bypass the Nyquist sampling theorem in order to recover more\ninformation than a traditional discrete Fourier transform. Here we examine\nthree such methods: filter diagonalization, compressed sensing, and\nsuper-resolution. We apply them to a broad range of signal forms commonly found\nin science and engineering in order to discover when and how each method can be\nused most profitably. We find that filter diagonalization provides the best\nresults for Lorentzian signals, while compressed sensing and super-resolution\nperform better for arbitrary signals.\n