Stability of Modified-CS and LS-CS for Recursive Reconstruction of Sparse Signal Sequences

TL;DR

This paper establishes conditions under which the LS-CS and modified-CS algorithms reliably reconstruct sparse signal sequences over time, maintaining bounded errors despite noise and support changes, with practical implications demonstrated via simulations.

Contribution

It provides new stability conditions for LS-CS and modified-CS algorithms, ensuring bounded support errors and reconstruction accuracy in dynamic sparse signal scenarios.

Findings

Support errors remain bounded over time.

Reconstruction error is bounded by a time-invariant value.

Stability holds under mild assumptions with slow support changes.

Abstract

In this work, we obtain sufficient conditions for the "stability" of our recently proposed algorithms, Least Squares Compressive Sensing residual (LS-CS) and modified-CS, for recursively reconstructing sparse signal sequences from noisy measurements. By "stability" we mean that the number of misses from the current support estimate and the number of extras in it remain bounded by a time-invariant value at all times. We show that, for a signal model with fixed signal power and support set size; support set changes allowed at every time; and gradual coefficient magnitude increase/decrease, "stability" holds under mild assumptions -- bounded noise, high enough minimum nonzero coefficient magnitude increase rate, and large enough number of measurements at every time. A direct corollary is that the reconstruction error is also bounded by a time-invariant value at all times. If the support set of the sparse signal sequence changes slowly over time, our results hold under weaker assumptions than what simple compressive sensing (CS) needs for the same error bound. Also, our support error bounds are small compared to the support size. Our discussion is backed up by Monte Carlo simulation based comparisons.