The Method Research Of Parameter Estimation Of The Direct Sequence Spread Spectrum

Direct sequence spread spectrum communication due to its good anti-jamming,anti-reconnaissance capability, and low intercepted, in the field of modern militarycommunications and civilian communication field has been widely used, the DSSScommunication confrontation with the corresponding technology will become againstthe field of communication problems to be solved.In the case of low signal-to-noise ratio, we research the DSSS signal detectionand parameter estimation theory. First introduced the basic concepts and principles ofspread spectrum communication system, the knowledge of the classification andDSSS spread spectrum communication system; then introduced the m-sequences andGold sequences of two pseudo-random code; baseband DSSS short code signalparameters estimated power secondary spectrum method to estimate the pseudo-codeperiod, spectrum method to estimate the pseudo-code chip width and matrixfactorization method to estimate the pseudo-code sequence.This paper is a Matlab software simulation, it is mainly used by the Math Works,Inc. developed a numerical computing and visualization graphics and imageprocessing software engineering. Numerical analysis, matrix computation, graphics,image processing, signal processing, and simulation and many other powerful featuresintegrated programming environment and toolkit form easier to use interactivecomputer environment, for the purposes of scientific research and engineeringapplications provides a strong function, high efficiency, scalable programming tools.In the case of low signal-to-noise ratio, DSSS signal parameters by Monte Carlosimulation shows that is estimated to traverse characteristics in accordance with thesignal and additive white Gaussian noise, parameter estimation algorithm and setalgorithm to improve the performance of the estimated. Collections average algorithm input signal is divided into several segments, respectively, into its respective segmentsignal processing, and then summing the result of the processing of each signalsegment averaged. To this end, the first received signal Break, for each segment signalis used to estimate the processing result obtained with the estimation algorithm, andthen sets the average in order to suppress noise, the more accurate extraction of thecharacteristic parameters. In a signal-to-noise ratio,"this parameter is estimated toaverage, do many experiments to generate a set of the average number of sequences,and then calculated the mean of the sequence judgment conditions to decide whetherthe need to set standard deviation. Ultimately, set the average number of the mean andstandard deviation of the curve with the signal-to-noise ratio to measure theperformance of the algorithm.In the case of low signal-to-noise ratio, the DSSS signal pseudo-code sequencevalue estimates are estimated waveform, iteration (or adaptive) noise suppressionalgorithm to improve the signal-to-noise ratio, and then estimate the noisy DSSSsignal pseudo-code sequence value.