Blind Extraction of Nonstationary Signal with Four Order Correlation Kurtosis Deconvolution Name: Chong Shan Affiliation: School of Electrical and Information Engineering, Yunnan Minzu University, Kunming, China

The reasons of absence conference Sorry, I have no passport and can not handled in a short time. My professor has agreed. Professor name:Guangyong Yang Email:guangyong_yang@126.com

1. The development status of the subject In the past 20 years, the main research focus of blind extraction is: Independent component analysis (ICA); Maximum entropy spectrum estimation; High order statistic adaptive filter; Two order correlation kurtosis deconvolution inverse filter (it is mainly used for earthquake, forecast, intelligent fault diagnosis, medical imaging analysis etc); The three order correlation kurtosis deconvolution inverse filter (laser displacement measurement, subpixel peak position extraction);

The characteristics of correlated kurtosis deconvolution inverse filter 1. Don't need mean processing ; 2. Don't need whitening process; 3. Don't need the reference model or when the system is difficult to established an accurate reference model; The research objectives of four order correlation kurtosis deconvolution inverse filter 1. Improving signal to noise ratio; 2. Accelerating the convergence speed

2.Research contents We established mathematical model of four order correlation kurtosis deconvolution inverse filter and obtained analytical solutions; four order correlation kurtosis: Where is sampling period. The necessary condition of optimal iterative solution for inverse filter is the four order correlation kurtosis get maximum. Assuming Where, is the convolution output vector of the observation sequence, is the delay time, is observation measurement of noise

The simulation signal is constructed by using the window function, the gauss white noise (stationary process), the rayleigh noise (nonstationary process) and the exponential distribution noise (nonstationary process). S(k) M4CKD g(H,G,R,E)

3.Simulstion results and analysis Fig1.Mixed signal and blind signal extraction of M4CKD algorithm Fig2. Results of signal extraction based on FastICA algorithm

Fig3. Unit impulse response of inverse filter Fig4 Fig3. Unit impulse response of inverse filter Fig4. Iterative results of M4CKD algorithm

Signal noise component separation Performance simulation results of three algorithms are shown in table 1. table 1. Performance simulation results of three algorithms Comparison items M4CKD FastICAV2.5 M3CKD Iteration times 3 5 SNR(dB) 3.3735 1.1060 0.9210 Reference model nonessential essential Signal pretreatment Signal noise component separation nonexistence existence

4.Conlusion This paper is proposed the four order correlation deconvolution algorithm, which is a kind of adaptive filter. According to the statistical characteristics of correlated kurtosis, mixed signal of nonstationary that is subpixel peak detection model is blind extracted. The algorithm can more effectively suppress the Super- Gaussian and sub-Gaussian noise and improve the signal to noise ratio and get faster convergence rate. It can extract multiple signals, especially for Single-Input Multi-Output (MISO system).The feasibility and operability of M4CKD algorithm is better than FastICA algorithm and M3CKD algorithm.

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