Shuffled Multi-Channel Sparse Signal Recovery

TL;DR

This paper addresses the problem of reconstructing multi-channel signals with unknown sample-channel correspondences, proposing new theoretical conditions and a robust method, demonstrated in calcium imaging applications.

Contribution

It introduces the first sampling results for shuffled multi-channel signals and extends them to sparse signals with unknown sensing matrices, offering a novel reconstruction approach.

Findings

Established sufficient conditions for unique recovery of shuffled signals.

Developed a robust reconstruction method combining sparse recovery and linear regression.

Validated the approach in whole-brain calcium imaging applications.

Abstract

Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple-target tracking or multi-person contactless vital sign monitoring may be severely affected by mismatched sample-channel assignments. To systematically address this fundamental problem, we pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. Assuming that we have a sensing matrix for the underlying signals, we show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery. To the best of our knowledge, a sampling result for the reconstruction of shuffled multi-channel signals has not been considered in the literature and existing methods for unlabeled sensing cannot be directly applied. We extend our results to the case where the signals admit a sparse representation in an overcomplete dictionary (i.e., the sensing matrix is not precisely known), and derive sufficient conditions for the reconstruction of shuffled sparse signals. We propose a robust reconstruction method that combines sparse signal recovery with robust linear regression for the two-channel case. The performance and robustness of the proposed approach is illustrated in an application related to whole-brain calcium imaging. The proposed methodology can be generalized to sparse signal representations other than the ones considered in this work to be applied in a variety of real-world problems with imprecise measurement or channel assignment.