TY - GEN
T1 - Prior knowledge aided super-resolution line spectral estimation
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
AU - Wang, Feiyu
AU - Fang, Jun
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - This paper concerns detecting the frequency components from a spectral sparse, undersampled signal. This problem is also called super-resolution line spectral estimation because the frequencies can take arbitrary continuous values. The prior knowledge of the frequency distribution is often available in many applications. To exploit the prior knowledge, a weighting function w designed according to the frequency distribution p is introduced. The prior information can be harnessed through minimizing the corresponding weighted log-sum penalty function. We solve the optimization problem through iteratively decreasing a surrogate function majorizing the original penalty function. Simulation results show that the proposed algorithm outperforms other methods both in noiseless and noisy case, and it also presents superior performance in resolving closely-spaced frequency components.
AB - This paper concerns detecting the frequency components from a spectral sparse, undersampled signal. This problem is also called super-resolution line spectral estimation because the frequencies can take arbitrary continuous values. The prior knowledge of the frequency distribution is often available in many applications. To exploit the prior knowledge, a weighting function w designed according to the frequency distribution p is introduced. The prior information can be harnessed through minimizing the corresponding weighted log-sum penalty function. We solve the optimization problem through iteratively decreasing a surrogate function majorizing the original penalty function. Simulation results show that the proposed algorithm outperforms other methods both in noiseless and noisy case, and it also presents superior performance in resolving closely-spaced frequency components.
KW - Compressed sensing
KW - iterative reweighted method
KW - probabilistic prior
KW - super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85023776097&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023776097&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952766
DO - 10.1109/ICASSP.2017.7952766
M3 - Conference contribution
AN - SCOPUS:85023776097
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3296
EP - 3300
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Y2 - 5 March 2017 through 9 March 2017
ER -