Συντάχθηκε 04-07-2011 10:47
από Aggelos Bletsas
Email συντάκτη: abletsas<στο>tuc.gr
Ενημερώθηκε:
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Ιδιότητα: ΔΕΠ ΗΜΜΥ.
...στην αίθουσα 137.Π39
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Speaker:
Dr. Costas Pelekanakis, Acoustic Research Lab, National University of Singapore.
Title:
Two sparse adaptive algorithms for underwater acoustic channel identification.
Abstract:
The first algorithm is derived for underwater acoustic channel estimation in the presence of impulsive SaS noise. The algorithm is based on the minimization of a new cost function, which is the sum of two terms. The first term is the distance between the previous and the current channel estimate. The distance metric is chosen as Riemannian, as in the improved-proportionate normalized least-mean-square (IPNLMS) algorithm, such that the sparse (non-isotropic) nature of the filter taps is taken into account. The second term depends
on an appropriately defined 1-norm of the a posteriori estimation error and ensures robustness under impulsive SaS noise. The resulting algorithm shows a linear computational complexity with respect to its filter coefficients. Algorithm performance is investigated by identification of two real, sparse, time-varying, underwater acoustic channels under the presence of snapping shrimp ambient noise. The proposed algorithm demonstrates 2-4 dB better performance than IPNLMS when loud snaps occur. Moreover, the clear superiority of the new algorithm over the ubiquitous NLMS and RLS algorithms is also demonstrated.
The second algorithm is proposed for sparse underwater acoustic (UWA) channel estimation by incorporating the Riemannian metric of the IPNLMS and an appropriately defined L0 norm of the channel taps into the same cost function. To cope with the high Doppler frequencies often encountered in UWA channels, carrier-phase estimation is incorporated into the algorithm. Based on data recorded over a sparse acoustic link, the superior performance of the proposed algorithm to existing natural gradient (NG) and stochastic gradient (SG) algorithms is validated.
Short Bio:
Costas received his Diploma from the Department of Electronic and Computer Engineering, Technical University of Crete, Greece, in 2001 and his M.Sc. and Ph.D. degrees in Mechanical and Ocean Engineering from the Massachusetts Institute of Technology (MIT), Cambridge, in 2004 and 2009, respectively.
Currently, he is a Research Fellow with the Acoustic Research Laboratory (ARL) at the National University of Singapore (NUS). His current research interests lie in the areas of MIMO underwater acoustic communication systems, coded OFDM, statistical signal processing in non-Gaussian noise, and adaptive algorithms for sparse system identification.
