Sentence examples for measurement vector problem from inspiring English sources

The joint sparse recovery problem is a generalization of the single measurement vector problem widely studied in compressed sensing.

In this paper, we study a sparse multiple measurement vector problem in which we need to recover a set of jointly sparse vectors from incomplete measurements.

Distributed Compressive Sensing (DCS) is an extension of compressive sensing from single measurement vector problem to Multiple Measurement Vectors (MMV) problem.

This problem is well known in sparse approximation and has been termed the multiple measurement vector problem [2, 3] or simultaneous sparse approximation (SSA) problem [4].

We formulate the multiple measurement vector problem in CS-based RFID and demonstrate how a joint recovery of the signal vectors strongly improves the identification speed and noise robustness.

Furthermore, it needs to be pointed out that the conclusion in Theorems 2 and 3 is valid in a single measurement vector problem, i.e., (l_{p} -minimization al_{p} -minimization originalsonique solution to (l_{0})-minimization when (0< p< p^{ast}).

In fact, a lot of sparse signals may satisfy the form of block-sparse signal in practice, such as multi-band signal, DNA array (DNA microarray), radar pulse signal, and multiple measurement vector problems [16].

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.

To solve this problem, we can reconstruct the original signal by turning IMV problem into multiple measurement vector (MMV) problem using continuous to finite (CTF) modular [7] under the premise of joint sparse [8].

By doing this, we reformulate the MD/MM problem as a single measurement vector recovery problem and therefore reduce the unknown variables greatly.

The recovery of a single vector is often called single measurement vector (SMV) problem.