Blind single channel identification based on signal intermittency and second-order statistics

Authors:
Tiemin Mei
Affiliations:
School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

Citing 5
Cited 0

Adaptive multi-channel least mean square and Newton algorithms for blind channel identification

Signal Processing
Room impulse response shortening with infinity-norm optimization

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A class of frequency-domain adaptive approaches to blind multichannel identification

IEEE Transactions on Signal Processing
A least-squares approach to blind channel identification

IEEE Transactions on Signal Processing
Blind identification and equalization based on second-order statistics: a time domain approach

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

For intermittent channel output signal, namely, the active periods followed by nonactive periods alternatively, the blind single-input single-output (SISO) system identification problem can be transformed into a blind multichannel identification problem. It is possible and feasible to blindly identify the channel using only second-order statistics from the channel output signal. A two-stage approach is proposed in this paper. At the first stage, two or more segments of channel input signal are estimated from the single channel observation; at the second stage, the channel impulse response is identified by exploiting the estimated channel input signal segments and their corresponding channel output signal segments. Simulations show that the proposed approach works well.