Compressive Sampling Strategies for Multichannel Signals
Related publications (94)
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
This paper addresses the problem of correct recovery of multiple sparse correlated signals using distributed thresholding. We consider the scenario where multiple sensors capture the same event, but observe different signals that are correlated by local tr ...
We consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. A general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requir ...
Numerous applications demand that we manipulate large sets of very high-dimensional signals. A simple yet common example is the problem of finding those signals in a database that are closest to a query. In this paper, we tackle this problem by restricting ...
We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K < N. In contrast to the existing work in the sparse approximation and compress ...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M < N inner products with random vectors and then recover the signal via a sparsity-seeki ...
This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal ...
This paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabilistic signal model which assumes randomness for both the amplitude and also ...
With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...
It is well known that the support of a sparse signal can be recovered from a small number of random projections. However, in the presence of noise all known sufficient conditions require that the per-sample signal-to-noise ratio (SNR) grows without bound w ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008
In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...