Compressive Periodogram Reconstruction Using Uniform Binning
Dyonisius Dony Ariananda, Daniel Romero, and Geert Leus

TL;DR
This paper introduces a method for reconstructing periodograms in angular and frequency domains using uniform binning and circulant matrix structures, enabling efficient compression and accurate spectral estimation.
Contribution
It proposes a novel compressive reconstruction technique leveraging circulant structures and sparse ruler sampling patterns for improved spectral analysis.
Findings
Effective periodogram reconstruction with strong compression.
Analysis of bias and variance in the statistical performance.
Extension to correlated bins beyond the uncorrelated assumption.
Abstract
In this paper, two problems that show great similarities are examined. The first problem is the reconstruction of the angular-domain periodogram from spatial-domain signals received at different time indices. The second one is the reconstruction of the frequency-domain periodogram from time-domain signals received at different wireless sensors. We split the entire angular or frequency band into uniform bins. The bin size is set such that the received spectra at two frequencies or angles, whose distance is equal to or larger than the size of a bin, are uncorrelated. These problems in the two different domains lead to a similar circulant structure in the so-called coset correlation matrix. This circulant structure allows for a strong compression and a simple least-squares reconstruction method. The latter is possible under the full column rank condition of the system matrix, which can be…
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