SNR estimation algorithm of LFM signal based on FRFT for long range and shallow underwater acoustic communication systems

It is very important to estimate the signal to noise ratio (SNR) of receive signal accurately because this parameter will play an important role in the equalization and detection modules. LFM signal is often used as a synchronization signal due to its good autocorrelation property and Doppler tolerance in underwater acoustic (UWA) channels. A new SNR estimation algorithm making use of the LFM signal is proposed in this paper which works in the fractional Fourier transformation (FRFT) domain. Since the characteristics of the LFM signal and the Gaussian white noise are different in the FRFT domain, it is much easier to separate the LFM signal and the Gaussian noise and estimate the power of them, respectively. Computer simulation results show that the proposed algorithm is more accurate than the traditional spectrum-based algorithm in AWGN channel though the estimation accuracy reducing slightly when employed in multipath fading channels. The results of pool experiments and outfield experiments show that this algorithm is more accurate and more robust than the spectrum-base algorithm even in the UWA channels.

[1]  Luís B. Almeida,et al.  The fractional Fourier transform and time-frequency representations , 1994, IEEE Trans. Signal Process..

[2]  Yun Q. Shi,et al.  An investigation of non-data-aided SNR estimation techniques for analog modulation signals , 2010, 2010 IEEE Sarnoff Symposium.

[3]  You Xiaohu,et al.  A scheme for the SNR estimation and its application in Doppler shift estimation of mobile communication systems , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[4]  P. Welch The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[5]  A. G. Kebkal,et al.  A frequency-modulated-carrier digital communication technique for multipath underwater acoustic channels , 2004 .

[6]  Norman C. Beaulieu,et al.  Comparison of four SNR estimators for QPSK modulations , 2000, IEEE Communications Letters.

[7]  Sofiène Affes,et al.  SNR Estimation Over SIMO Channels From Linearly Modulated Signals , 2010, IEEE Transactions on Signal Processing.

[8]  Ephraim Speech enhancement using a minimum mean square error short-time spectral amplitude estimator , 1984 .

[9]  R. Matzner,et al.  An SNR estimation algorithm using fourth-order moments , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[10]  Stefano Cioni,et al.  On the adaptive DVB-S2 physical layer: design and performance , 2005, IEEE Wireless Communications.

[11]  Norman C. Beaulieu,et al.  A comparison of SNR estimation techniques for the AWGN channel , 2000, IEEE Trans. Commun..