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ICASSP
2008
IEEE
15 years 4 months ago
Distributed compressed sensing: Sparsity models and reconstruction algorithms using annihilating filter
Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some commo...
Ali Hormati, Martin Vetterli
CORR
2010
Springer
97views Education» more  CORR 2010»
14 years 7 months ago
On the Scaling Law for Compressive Sensing and its Applications
1 minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsity, as a function of the ratio...
Weiyu Xu, Ao Tang
TIT
2010
174views Education» more  TIT 2010»
14 years 4 months ago
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation
Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few ...
Jarvis Haupt, Waheed Uz Zaman Bajwa, Gil M. Raz, R...
CISS
2011
IEEE
14 years 1 months ago
Noncoherent compressive sensing with application to distributed radar
—We consider a multi-static radar scenario with spatially dislocated receivers that can individually extract delay information only. Furthermore, we assume that the receivers are...
Christian R. Berger, Josee M. F. Moura
ICASSP
2011
IEEE
14 years 1 months ago
A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems
Due to multipath delay spread and relatively high sampling rate in OFDM systems, the channel estimation is formulated as a sparse recovery problem, where a hybrid compressed sensi...
Chenhao Qi, Lenan Wu