TY - GEN
T1 - Dynamic filtering of sparse signals using reweighted ℓ1
AU - Charles, Adam S.
AU - Rozell, Christopher J.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - Accurate estimation of undersampled time-varying signals improves as stronger signal models provide more information to aid the estimator. In class Kalman filter-type algorithms, dynamic models of signal evolution are highly leveraged but there is little exploitation of structure within a signal at a given time. In contrast, standard sparse approximation schemes (e.g., L1 minimization) utilize strong structural models for a single signal, but do not admit obvious ways to incorporate dynamic models for data streams. In this work we introduce a causal estimation algorithm to estimate time-varying sparse signals. This algorithm is based on a hierarchical probabilistic model that uses re-weighted L1 minimization as its core computation, and propagates second order statistics through time similar to classic Kalman filtering. The resulting algorithm achieves very good performance, and appears to be particularly robust to errors in the dynamic signal model.
AB - Accurate estimation of undersampled time-varying signals improves as stronger signal models provide more information to aid the estimator. In class Kalman filter-type algorithms, dynamic models of signal evolution are highly leveraged but there is little exploitation of structure within a signal at a given time. In contrast, standard sparse approximation schemes (e.g., L1 minimization) utilize strong structural models for a single signal, but do not admit obvious ways to incorporate dynamic models for data streams. In this work we introduce a causal estimation algorithm to estimate time-varying sparse signals. This algorithm is based on a hierarchical probabilistic model that uses re-weighted L1 minimization as its core computation, and propagates second order statistics through time similar to classic Kalman filtering. The resulting algorithm achieves very good performance, and appears to be particularly robust to errors in the dynamic signal model.
UR - http://www.scopus.com/inward/record.url?scp=84890506712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890506712&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638908
DO - 10.1109/ICASSP.2013.6638908
M3 - Conference contribution
AN - SCOPUS:84890506712
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6451
EP - 6455
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -