TY - JOUR
T1 - Superfast Line Spectral Estimation
AU - Hansen, Thomas Lundgaard
AU - Fleury, Bernard Henri
AU - Rao, Bhaskar D.
PY - 2018/2/19
Y1 - 2018/2/19
N2 - A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques. These methods are preferable over classical line spectral estimation algorithms because they inherently estimate the model order. However, they all have computation times that grow at least cubically in the problem size, thus limiting their practical applicability in cases with large dimensions. To alleviate this issue, we propose a low-complexity method for line spectral estimation, which also draws on ideas from sparse estimation. Our method is based on a Bayesian view of the problem. The signal covariance matrix is shown to have Toeplitz structure, allowing superfast Toeplitz inversion to be used. We demonstrate that our method achieves estimation accuracy at least as good as current methods and that it does so while being orders of magnitudes faster
AB - A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques. These methods are preferable over classical line spectral estimation algorithms because they inherently estimate the model order. However, they all have computation times that grow at least cubically in the problem size, thus limiting their practical applicability in cases with large dimensions. To alleviate this issue, we propose a low-complexity method for line spectral estimation, which also draws on ideas from sparse estimation. Our method is based on a Bayesian view of the problem. The signal covariance matrix is shown to have Toeplitz structure, allowing superfast Toeplitz inversion to be used. We demonstrate that our method achieves estimation accuracy at least as good as current methods and that it does so while being orders of magnitudes faster
KW - Bernoulli-Gaussian model
KW - Parameter estimation
KW - Toeplitz matrices
KW - computational efficiency
KW - line spectral estimation
KW - sparse estimation
KW - super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85042178656&partnerID=8YFLogxK
U2 - 10.1109/TSP.2018.2807417
DO - 10.1109/TSP.2018.2807417
M3 - Journal article
SN - 1053-587X
VL - 66
SP - 2511
EP - 2526
JO - I E E E Transactions on Signal Processing
JF - I E E E Transactions on Signal Processing
IS - 10
ER -