On the other hand, it is certainly possible that additional transitions may also become important with respect to more general measures, if the linear measurement vectors employed here fail to capture their contribution to global dynamics.
In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors.
Our results are uniform in the sense that one random choice of the measurement vectors aj guarantees recovery of all rank r-matrices simultaneously with high probability.
This contribution proposes a robust recursive algorithm for state estimation of linear multi-output systems with unknown but bounded disturbances corrupting both the state and measurement vectors.
Distributed Compressive Sensing (DCS) is an extension of compressive sensing from single measurement vector problem to Multiple Measurement Vectors (MMV) problem.
In this paper, we address the multiple measurement vectors problem, which is now a hot topic in the compressed sensing theory and its various applications.
High-pass digital filters based on inverted Blackman windowed sinc smoothing coefficients are employed to provide point estimates of noise from measurement vectors.
This contribution proposes a robust recursive algorithm for the state estimation of linear models with unknown but bounded disturbances corrupting both the state and measurement vectors.
We consider the particular scenario where the measurements are Frobenius inner products with random rank-one matrices of the form aja⁎j for some measurement vectors a1,…,am, i.e., the measurements are given by bj= tr(Xajaj⁎).
With a goal of proposing an MMV-type algorithm that is robust to outliers (absence of common sparsity pattern), we propose Greedy Pursuits Assisted Basis Pursuit for Multiple Measurement Vectors (GPABP-MMV).
In this paper, we analytically evaluate the Cramer-Rao type performance bounds for these two schemes for sparsity-aware MTT algorithms and show that the recursive learning structure outperforms the conventional approach, when the measurement vectors are corrupted by missing samples and additive noise.
