Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection
This work addresses a specific bottleneck in signal processing for applications requiring high-resolution frequency analysis, representing an incremental improvement over existing sparsity-based methods.
The paper tackles the problem of super-resolution spectrum estimation in the presence of high dictionary coherence and noise, proposing a new regularization approach that achieves parsimonious frequency selection with improved efficacy and efficiency in challenging scenarios like small sample sizes and low signal-to-noise ratios.
Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio.