LS-CS-residual (LS-CS): Compressive Sensing on Least Squares Residual

TL;DR

This paper introduces LS-CS-residual, a recursive method for reconstructing time-varying sparse signals from limited noisy measurements, improving accuracy when sparsity patterns change slowly over time.

Contribution

The paper proposes LS-CS-residual, a novel recursive compressive sensing approach that leverages previous support estimates to improve reconstruction accuracy for slowly changing sparse signals.

Findings

LS-CS-residual reduces reconstruction error bounds compared to standard CS.

The method maintains support estimate stability over time with bounded misses and extras.

Numerical experiments confirm the theoretical advantages of LS-CS-residual.

Abstract

We consider the problem of recursively and causally reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of noisy linear measurements. The sparsity pattern is assumed to change slowly with time. The idea of our proposed solution, LS-CS-residual (LS-CS), is to replace compressed sensing (CS) on the observation by CS on the least squares (LS) residual computed using the previous estimate of the support. We bound CS-residual error and show that when the number of available measurements is small, the bound is much smaller than that on CS error if the sparsity pattern changes slowly enough. We also obtain conditions for "stability" of LS-CS over time for a signal model that allows support additions and removals, and that allows coefficients to gradually increase (decrease) until they reach a constant value (become zero). By "stability", we mean that the number of misses and extras in the support estimate remain bounded by time-invariant values (in turn implying a time-invariant bound on LS-CS error). The concept is meaningful only if the bounds are small compared to the support size. Numerical experiments backing our claims are shown.