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Nowadays, the Direct-Sequence Spread Spectrum (DSSS) technology has been widely used. It's urgent to research blind detection algorithms on DSSS signal. In this paper, a new method is proposed. It based on a combination of wavelet noise reduction and Estimation-based Time-domain Sliding Correlating Accumulation, which can be used to detect the DSSS-QPSK signal. The results show that when the length of spreading code is 15 bits and the Signal-to-Noise Ratio (SNR) is -12 dB, the method could easily detect whether the DS/SS-QPSK signal exists. Introduction Direct-Sequence Spread Spectrum is widely used in the field of spread spectrum and it shows excellent performance in covert communication. The DSSS signal has a wider bandwidth and it is difficult to detect non-cooperatively. For the DSSS Signal blind detection, we need to consider characteristic features of spread spectrum signal in the time domain, frequency domain and power spectrum to judge whether the DSSS signal is existing. There are many non-parametric approaches include common correlation methods, periodogram methods, higher-order statistics methods (HOS) , maximum likelihood (ML) methods, methods based on Yule-Walker equations, iterative filtering methods, and nonlinear least squares (NLS) methods[1]. However, all these method are not applicable to detect DSSS-QPSK signal just because the SNR required is much higher than the DSSS-QPSK signal working at. Aiming at the problem of the DSSS-QPSK signal blind detection under the low SNR, this paper proposes a new method that uses wavelet denoise in the preprocessing [2]. And then updates the estimated spreading code and estimated data to make the noise further suppressed. Finally we analyses the correlation of the signal to judge the presence of the DSSS signal. The method proposed can also detect the signal’s information including the length of spreading code and the synchronization position. Estimation model DSSS-QPSK signal ( ) S t can be expressed as ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 2 sin cos S t d t c t t d t c t t n t ω ω = + + ( ) ( ) m c i c t c p t mT ∞ =−∞ = − ∑ (1) In equation (1), ( ) 1 d t and ( ) 2 d t are two Quadrature sequences of the messege sequences to be transmitted; ( ) c t is the spread function; ( ) n t is the White Gaussian noise; [ ] 1, 1 n d ∈ + − is the messege code; [ ] 1, 1 m c ∈ + − is the spreading code; in this paper we use the m-sequence as the spreading codes; b T is the bandwidth of the information code and c T is the bandwidth of the spreading code. Firstly we use the wavelet to reduce the noise of DSSS-QPSK signal. The main purpose of this section is to minimize the noise in the DSSS-QPSK signal and filter the noise at other frequency bands. 5th International Conference on Advanced Materials and Computer Science (ICAMCS 2016) © 2016. The authors Published by Atlantis Press 856 For ( ) ( ) 2 f t L R ∈ its fourier transform is ( ) ψ ω .We called ( ) t ψ a mother wavelet when ( ) ψ ω satisfies ( ) 0 t dt ψ ∞ −∞ = ∫ . We will obtain wavelet sequence after scaling and translating the mother wavelet ( ) , 1 a t t a a t t ψ ψ −   =     (2) In equation (2), , a R t ∈ ; a is the scale factor and 0 a > ; t is the translate factor. ( ) , a t t ψ is the wavelet basis function. For the acquired DSSS-QPSK signal ( ) S t , its wavelet transform is ( ) ( ) , 1 , , f a b R t b W a b f S t dt a a ψ ψ −   = =     ∫ (3) There are four main kinds of threshold selection rules: rigrsure, heursure, sqtwolog and minimaxi[3]. In this paper, the SNR of the signal is low. The estimation of the signal threshold has a large error. In the blind detection, we do not know the bandwidth of the signal. The heursure threshold can remove the noise of the signal and retain high-frequency effectively. So we choose the heursure threshold rule. The specific selection of threshold is as following. If s is the square sum of the wavelet coefficients, when ( ) / s n n η = − and ( ) 3 2 2 log / n n μ = , then we get ( ) 1 3 1 2 min , Th Th Th Th η μ η μ ≤  =  >  (4) After wavelet denosing, the noise of signal has been suppressed to a great degree. Then we use the Estimation-based Time-domain Sliding Correlating Accumulation approach to detect the DSSS-QPSK signal[4]. First, intercept a period of the acquired signal as the estimated spreading code ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) sin cos E a b E Cm t d t c t t d t c t t n t ω ω = + + (5) In equation (5), ( ) a d t and ( ) b d t are two Quadrature sequences of acquired signal; ( ) E n t is the noise of Estimate. The estimate spreading signal is multiplied by the same length of signal ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 2 2 2 1 2 1 2 1 2 sin cos sin cos sin cos sin cos sin cos sin cos i E i i a b E

A Method for the Detection of DSSS-QPSK Signals Based on Wavelet Denoise