Research On The Blind Detection Of DSSS Signals In Lower SNR

With the coming of the information age,human daily life and military action depend on communication more and more.The Direct Sequence Spread Spectrum(DSSS)system has good anti-interference and information is not easily intercepted,so DSSS has been widely used today.In non-cooperative communication,the blind detection of DSSS signals is the premise of parameter estimation and blind estimation of DSSS signals.Therefore,blind detection of DSSS signals had attracted much attention.However,in the past many detection algorithms,some of them are semi blind detection,and some of the detection algorithms require much higher signal-to-noise ratio(SNR)than the DSSS signal working at,so they cannot meet the requirements of practical applications.In this paper,we propose two blind detection algorithms for DSSS signals at low SNR on the basis of deep research and analysis of DSSS signals.Firstly,this paper modifies Estimation-based Time-domain Sliding Correlating Accumulation(ETSCA)algorithm based on the correlation detection algorithm in the time domain.The algorithm greatly suppresses the noise by updating the estimated spreading code and the estimated data.The algorithm is used to detect the DSSS signal produced by the vector signal generator,and the result shows that the algorithm has good detection performance for the actual signal data.Secondly,the Autocorrelation-based Matrix Analysis(ACMA)algorithm is proposed on the basis of eigenvalue decomposition algorithm.In the algorithm,the DSSS signal is detected by analyzing the eigenvalue of DSSS Signals' autocorrelation matrix.Simulation result shows that under the same conditions,the detection performance of this algorithm is about 2d B higher than that of ETSCA algorithm.It is found that the synchronization offset has an influence on the performance of the algorithm.Through theoretical deduction,it is found that the performance of the algorithm is the best under synchronous condition,and the performance is worst when the normalized offset is 1/2.Finally,the Estimation-based Autocorrelation Matrix Analysis(EACMA)algorithm is proposed on the basis of the previous two algorithms.The algorithm solves the problem that the detection performance of ACMA algorithm fluctuates with the variation of synchronization offset,and improves the detection performance of the algorithm.The good performance of this algorithm is achieved in improving the algorithm complexity.In order to reduce the complexity of the algorithm,a simplified version of the algorithm is proposed.The fast search scheme reduces the detection time to a fraction of the original detection time at the expense of little detection performance,and the length of the detection time is related to the step length.