TY - GEN
T1 - TLS linear prediction with optimum root selection for resolving closely space sinusoids
AU - So, C. F.
AU - Ng, S. C.
AU - Leung, S. H.
PY - 2004
Y1 - 2004
N2 - Total least square linear prediction has been successfully applied to frequency estimation for closely spaced sinusoids. In low signal to noise ratio, the resolving ability of TLS is degraded and extraneous roots of the predictor are close to unit circle. Hence the performance of total least square is severely degraded in low SNR. In this paper, a generalized total least squares method with a new root selection criterion, which is based on the envelope of the signal spectrum, is presented. An optimum procedure is introduced to provide a TLS solution that can perform closer to Cramer-Rao Bound, particularly in low SNR.
AB - Total least square linear prediction has been successfully applied to frequency estimation for closely spaced sinusoids. In low signal to noise ratio, the resolving ability of TLS is degraded and extraneous roots of the predictor are close to unit circle. Hence the performance of total least square is severely degraded in low SNR. In this paper, a generalized total least squares method with a new root selection criterion, which is based on the envelope of the signal spectrum, is presented. An optimum procedure is introduced to provide a TLS solution that can perform closer to Cramer-Rao Bound, particularly in low SNR.
UR - http://www.scopus.com/inward/record.url?scp=10944252001&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2004.1381077
DO - 10.1109/IJCNN.2004.1381077
M3 - Conference contribution
AN - SCOPUS:10944252001
SN - 0780383591
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 2699
EP - 2703
BT - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
T2 - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
Y2 - 25 July 2004 through 29 July 2004
ER -