Low SNR BPSK signal chip rate estimation using a wavelet based spectral correlation algorithm

Author(s):  
Ying-Xiang Li ◽  
Min Yi ◽  
Qin Yang ◽  
Xian-Ci Xiao ◽  
Heng-Ming Tai
2019 ◽  
Vol 26 (7) ◽  
pp. 991-995 ◽  
Author(s):  
Chaochao Sun ◽  
Peizhong Lu ◽  
Kai Cao

2014 ◽  
Vol 945-949 ◽  
pp. 2297-2300 ◽  
Author(s):  
Xing Hua Xia ◽  
Fang Jun Luan ◽  
Meng Xin Li

Spectrum sensing performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a primary users sensing approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, primary user signal has been detected. Simulations indicate that the overall success rate is above 90.2% when SNR is equal to-5dB and 80.1% in-15dB. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm in building indoor environment.


2011 ◽  
Vol 88-89 ◽  
pp. 615-620
Author(s):  
Cheng Zhi Li ◽  
Fu Qun Shao ◽  
Zhe Kan ◽  
Hai Xiang Fan

The acoustic pyrometer system uses physical properties of the gas and registers the perceivable temperature without the effects of radiation, and the measuring accuracy of acoustic wave flight time is the major factor in its application. The traditional correlation algorithm could not overcome convolution interference and calculate the time delay of acoustic signals accurately, under the low SNR (Signal Noise Ratio) and complex reverberation noise conditions. This paper presents a generalized cross power spectrum algorithm to filter the product and convolution interferences, and improves the performances of anti-noise and anti-convolution by whitening the sample signals and adjusting the weighted value of cross power spectrum algorithm with the variation of SNR. The experimental result and theoretical analysis showed that the new generalized cross power spectrum algorithm compared to the traditional correlation function analytical algorithm, can overcome the convolution interferences from reverberation noise, and sharpen peak value, thereby estimate the time delays of signals accurately.


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