Third-Order Statistics Reconstruction from Compressive Measurements

TL;DR

This paper introduces a compressive sensing framework for reconstructing third-order statistics from fewer measurements, reducing hardware costs and computational load while maintaining estimation accuracy for high-dimensional signals.

Contribution

It derives a linear system linking compressive measurements to third-order cumulants and proposes efficient reconstruction methods with conditions for lossless recovery.

Findings

Achieves significant reduction in sampling rates for third-order statistics

Provides conditions for lossless reconstruction via least-squares

Validates methods through extensive simulations

Abstract

Estimation of third-order statistics relies on the availability of a huge amount of data records, which can pose severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption, and unaffordably high sampling rate especially when dealing with high-dimensional data such as wideband signals. To overcome these challenges, this paper focuses on the reconstruction of the third-order cumulants under the compressive sensing framework. Specifically, this paper derives a transformed linear system that directly connects the cross-cumulants of compressive measurements to the desired third-order statistics. We provide sufficient conditions for lossless third-order statistics reconstruction via solving simple least-squares, along with the strongest achievable compression ratio. To reduce the computational burden, we also propose an approach to recover diagonal cumulant slices directly from compressive measurements, which is useful when the cumulant slices are sufficient for the inference task at hand. All the proposed techniques are tested via extensive simulations. The developed joint sampling and reconstruction approach to third-order statistics estimation is able to reduce the required sampling rates significantly by exploiting the cumulant structure resulting from signal stationarity, even in the absence of any sparsity constraints on the signal or cumulants.