2018/10/31 by Saeid Haghighatshoar, Giuseppe Caire, Haghighatshoar, Saeid +1
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1810.13421
openalex publication_date 2018/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study a Compressed Sensing (CS) problem known as Multiple Measurement Vectors (MMV) problem, which arises in joint estimation of multiple signal realizations when the signal samples have a common (joint) sparse support over a fixed known dictionary. Although there is a vast literature on the analysis of MMV, it is not yet fully known how the number of signal samples and their statistical correlations affects the performance of the joint estimation in MMV. Moreover, in many instances of MMV the underlying sparsifying dictionary may not be precisely known, and it is still an open problem to quantify how the dictionary mismatch may affect the estimation performance. In this paper, we focus on ℓ2,1-norm regularized least squares (ℓ2,1-LS) as a well-known and widely-used MMV algorithm in the literature. We prove an interesting decoupling property for ℓ2,1-LS, where we show that it can be decomposed into two phases: i) use all the signal samples to estimate the signal covariance matrix (coupled phase), ii) plug in the resulting covariance estimate as the true covariance matrix into the Minimum Mean Squared Error (MMSE) estimator to reconstruct each signal sample individually (decoupled phase). As a consequence of this decomposition, we are able to provide further insights on the performance of ℓ2,1-LS for MMV. In particular, we address how the signal correlations and dictionary mismatch affects its performance. Moreover, we show that by using the decoupling property one can obtain a variety of MMV algorithms with performances even better than that of ℓ2,1-LS. We also provide numerical simulations to validate our theoretical results.