학술논문

Reducing the computational complexity of reconstruction in compressed sensing nonuniform sampling
Document Type
Conference
Source
21st European Signal Processing Conference (EUSIPCO 2013) Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European. :1-5 Sep, 2013
Subject
Signal Processing and Analysis
Discrete Fourier transforms
Complexity theory
Benchmark testing
Compressed sensing
Nonuniform sampling
Signal reconstruction
Matching pursuit algorithms
compressed sensing
multi-coset sampling
nonuniform sampling
reconstruction algorithm
Language
ISSN
2219-5491
2076-1465
Abstract
This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.