Time Invariant Error Bounds for Modified-CS based Sparse Signal Sequence Recovery

TL;DR

This paper establishes time-invariant error bounds for modified-CS algorithms in recursive sparse signal recovery, demonstrating robustness under mild conditions and slow support change assumptions, with theoretical guarantees supported by simulations.

Contribution

It provides novel, time-invariant error bounds for modified-CS and its variant, under weaker measurement conditions and specific support change models.

Findings

Support recovery error remains small and bounded over time.

Results hold under weaker measurement assumptions than traditional $\,l_1$ minimization.

Simulation results confirm theoretical guarantees.

Abstract

In this work, we obtain performance guarantees for modified-CS and for its improved version, modified-CS-Add-LS-Del, for recursive reconstruction of a time sequence of sparse signals from a reduced set of noisy measurements available at each time. Under mild assumptions, we show that the support recovery error of both algorithms is bounded by a time-invariant and small value at all times. The same is also true for the reconstruction error. Under a slow support change assumption, (i) the support recovery error bound is small compared to the support size; and (ii) our results hold under weaker assumptions on the number of measurements than what $\ell_1$ minimization for noisy data needs. We first give a general result that only assumes a bound on support size, number of support changes and number of small magnitude nonzero entries at each time. Later, we specialize the main idea of these results for two sets of signal change assumptions that model the class of problems in which a new element that is added to the support either gets added at a large initial magnitude or its magnitude slowly increases to a large enough value within a finite delay. Simulation experiments are shown to back up our claims.