Quantized Spectral Compressed Sensing: Cramer–Rao Bounds and Recovery Algorithms
نویسندگان
چکیده
منابع مشابه
Quantized Spectral Compressed Sensing: Cramer-Rao Bounds and Recovery Algorithms
Efficient estimation of wideband spectrum is of great importance for applications such as cognitive radio. Recently, sub-Nyquist sampling schemes based on compressed sensing have been proposed to greatly reduce the sampling rate. However, the important issue of quantization has not been fully addressed, particularly for high-resolution spectrum and parameter estimation. In this paper, we aim to...
متن کاملRecovery of quantized compressed sensing measurements
The mathematical theory of Compressed Sensing has been applied to various engineering areas ranging from one-pixel cameras, to range imaging and medical ultrasound imaging, to name a few. The theory developed within CS suggests that one can achieve perfect reconstruction of a signal x P R from a small number of random measurements y P R , far below the typical Shannon-Nyquist sampling limit. Re...
متن کاملAlgorithms for Sparse Signal Recovery in Compressed Sensing
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
متن کاملCompressed Sensing Recovery: A Survey
Candes and Tao [1] introduced the following isometry condition on matrices Φ and established its important role in CS. Given a matrix Φ ∈ Rm×n and any set T of column indices, we denote by ΦT the m × #(T) (i.e., m × |T|) matrix composed of these columns. Similarly, for a vector x ∈ Rn, we denote by xT the vector obtained by retaining only the entries in x corresponding to the column indices T. ...
متن کاملConsistent Signal and Matrix Estimates in Quantized Compressed Sensing
This paper focuses on the estimation of low-complexity signals when they are observed through M uniformly quantized compressive observations. Among such signals, we consider 1-D sparse vectors, low-rank matrices, or compressible signals that are well approximated by one of these two models. In this context, we prove the estimation efficiency of a variant of Basis Pursuit Denoise, called Consist...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2018
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2018.2827326