Convex Estimation of Sparse-Smooth Power Spectral Densities from Mixtures of Realizations with Application to Weather Radar

TL;DR

This paper introduces a convex optimization method for accurately estimating sparse and smooth power spectral densities from mixtures of realizations, with applications to weather radar signal analysis.

Contribution

It proposes a novel convex model that jointly estimates frequency components and PSDs, effectively enforcing smoothness without nonconvex penalties.

Findings

Achieves superior estimation accuracy over existing methods

Effectively enforces smoothness via convex optimization

Demonstrates robustness on weather radar data

Abstract

In this paper, we propose a convex optimization-based estimation of sparse and smooth power spectral densities (PSDs) of complex-valued random processes from mixtures of realizations. While the PSDs are related to the magnitude of the frequency components of the realizations, it has been a major challenge to exploit the smoothness of the PSDs, because penalizing the difference of the magnitude of the frequency components results in a nonconvex optimization problem that is difficult to solve. To address this challenge, we design the proposed model that jointly estimates the complex-valued frequency components and the nonnegative PSDs, which are respectively regularized to be sparse and sparse-smooth. By penalizing the difference of the nonnegative variable that estimates the PSDs, the proposed model can enhance the smoothness of the PSDs via convex optimization. Numerical experiments on the phased array weather radar, an advanced weather radar system, demonstrate that the proposed model achieves superior estimation accuracy compared to existing sparse estimation models, regardless of whether they are combined with a smoothing technique as a post-processing step or not.