【作者】 张荣龙；

【导师】 郭亚莎；

【作者基本信息】 成都理工大学 ， 信号与信息处理， 2013， 硕士

【摘要】 直接序列扩频（Direct Sequence Spread Spectrum，简称DSSS）通信由于其抗干扰性好、反侦察能力强、截获率低等优点，已经在现代军事通信领域和民用通信领域得到了广泛的应用，与之对应的DSSS通信对抗技术也就成了通信对抗领域亟待解决的问题。本文对低信噪比情况下的DSSS信号的检测及参数的估计进行了理论研究。首先介绍了扩频通信系统的基本概念及原理，扩频的分类和DSSS通信系统的相关知识；然后介绍了m序列和Gold序列两种伪随机码；最后对基带DSSS的短码信号进行了参数估计，主要有功率二次谱法估计伪码周期、谱相关法估计伪码的码片宽度和矩阵分解法估计伪码序列。本论文是采用Matlab软件来进行仿真的，它是由Math Works公司开发的一种主要用于数值计算及可视化图形图像处理的工程软件。以编程环境和工具箱的形式将数值分析、矩阵运算、图形图像处理、信号处理和仿真等诸多强大的功能集成在较易使用的交互式计算机环境中，为科学研究、工程应用提供了一种功能强、效率高、可扩展的编程工具。通过Monte Carlo仿真可知，在低信噪比情况下对DSSS信号参数进行估计，根据信号和加性高斯白噪声具有遍历性的特点，可以将参数估计算法与集平均算法相结合以改善估计的性能。集平均算法就是把输入信号分成若干段，分别对其各段信号进处理，然后再将各个信号段的处理结果求和取平均。为此，可以先将接收到的信号分段，用估计算法求出各段信号的用于估计的处理结果，然后集平均以抑制噪声，较为准确的提取特征参数。我们将在某个信噪比下，以“这个参数被估计到”为判决条件，决定是否还需集平均，做多次实验以生成一个集平均次数的序列，再求出该序列的均值、标准差等。最终，得到集平均次数的均值和标准差随信号信噪比变化的曲线，用以衡量算法的性能。在低信噪比情况下，对DSSS信号伪码序列值的估计属于波形估计，会采用迭代（或自适应）算法抑制噪声以提高信噪比，进而估计出含噪的DSSS信号中的伪码序列值。更多还原

【Abstract】 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.更多还原