短时突发信号的盲处理技术研究

【作者】 许华；

【导师】 ??；

【作者基本信息】 解放军信息工程大学 ， 军事通信学， 2005， 博士

【摘要】 对短时突发信号的盲处理是非合作通信接收中遇到的一类重要技术难题，为了对其进行系统的理论分析并解决实际遇到的各种典型问题，作者深入分析了短时突发信号盲解调处理的几个重要环节并提出了解决有关问题的多种处理算法，其中的大多数研究内容都在实际应用中得到了验证。 本文第一章首先对短时突发信号和盲处理的有关概念进行了解释和说明，然后对本文的研究内容做了简要的介绍。 本文第二章首先研究了载波同步的问题，相对于传统的载波恢复环路，前向估计的方式更适合于短时突发信号的处理，因此本章主要研究了载波频率和相位的前向估计技术。在对载波频率估计的研究中，作者通过对不同信道条件下算法的分析，提出实用的衰落信道频率估计方法应该采用“与载波偏差独立的盲均衡+非衰落信道的频率估计算法”的观点；总结了一类“粗估计—>分叉迭代精搜索”的迭代逼近参数估计方法，并进行了性能特点分析；分析了影响频率估计范围的因素，提出了利用“多采样”的方法提高算法的频率估计范围的方法。对载波相位估计，现有的捕获算法都存在估计精度较低的问题，这样会造成解调初期性能不理想，因此作者给出了两种方法来改进载波相位捕获性能：一是利用最大似然迭代逼近算法；二是结合跟踪算法(DD算法)的相位捕获方法。这两种方法都能使捕获算法的性能得到了较大的提升，从而提高解调初期的性能。 其次，第二章研究了短时突发信号的定时同步。由于分数倍均衡器具有对定时相位不敏感的特性，所以作者主要研究了利用分数倍盲均衡器进行定时同步的问题(也同时实现了信道均衡)。但是由于定时频率偏差的影响，必须采取补偿时钟频率偏差的措施，因此本章给出利用均衡器系数平移的方法来克服时钟频率偏差的影响，并提出了两种确定平移时刻的方法：a、与前向定时误差估计结合的方法；b、相关处理的方法。 本文第三章对各种盲均衡算法进行分析之后指出，基于“数据重用”的Bussgang类盲均衡算法是现阶段解决短时突发信号盲均衡问题的一种实用算法。因此，本章首先对“数据重用”的处理方法进行了深入的总结和分析：通过前人的研究成果和该章的仿真曲线可以确定，“数据重用”明显加快均衡器算法的收敛速度，但同时也增加了稳态误差，其中最有代表性的就是BNDR-LMS算法和仿射投影算法；为了提高常模盲均衡算法的收敛性能，作者将数据重用引入常模算法来提高收敛速度，并利用集员滤波器来降低稳态误差，得到了一种基于数据重用的新的常模算法(集员双归一化数据重用常模算法)。 和BNDR-LMS算法和仿射投影算法等不同，作者提出的基于数据矢量块循环重用的数据重用方式，可以挖掘了自适应算法的潜力，使一些在短输入数据下不能收敛的自适应算法收敛(如LMS算法)。将这种数据重用方法应用到常模算法中得到基于数据矢量块更多还原

【Abstract】 In uncooperative communication, the blind processing of short burst signals is an important technology problem. In order to do systematic theory analysis for the technology of short burst signals’ blind procession and solve the problems encountered in practice, the writer analyzes for several important parts of short burst signals’ blind demodulation detailedly and provides some processing algorithms to solve the corresponding problems. Most content of this dissertation is proved by practical application. In the first chapter, the conceptions of short burst signals and blind procession are explained firstly, and then main contents of this dissertation are introduced in brief.The second chapter studies the carrier synchronization for short burst signals firstly. The writer considers that comparing to the loop way, the way of forward estimation is more suited to the blind processing of short burst signals. So, the forward estimation technology of carrier frequency and phase is investigated in this chapter. In the investigation of carrier frequency, the writer presents that the method of frequency estimation for fading channel should be "the blind equalization that independent of carrier error + the frequency estimation algorithm of unfading channel" by analyzing of various algorithms for various channel. An iterative approach method for parameters estimation base on "rough estimation + iterative dichotomous fine search" is summarized. The factors that impact the range of frequency estimation are analyzed and a way to extend the range based on multi-sampling is provided. Almost all presented carrier phase acquisition algorithms have a comparative low performance, which will lead to degraded performance of demodulation. Therefore, the writer provides two methods to improve the performance of carrier phase acquisition: (a) an algorithm of ML iterative approach; (b) the acquisition algorithms combining tracking algorithm (DD algorithm). The two methods both can enhance the acquisition performance; accordingly the demodulation performance in the beginning is enhanced. Secondly, the timing synchronization is studied. According to the speciality of insensitive to timing error for fractionally spaced equalization, the writer makes a study that timing synchronization and channel equalization are both achieved by using fractionally spaced equalization. But for the impaction of timing frequency error, some means must be adopted to compensate the timing frequency error. So, the way of shifting equalizer taps’ coefficients to compensate the timing frequency error is given and two methods to decide the time of shifting are provided: (a) the way of combining forward timing error estimation; (b) the way of correlation.The third chapter indicates that the "data reuse" Bussgang class blind equalization algorithm is an applied method to solve the problem of short burst signals’ equalization by analyzing all kinds of equalization algorithms. So, the way of data reuse processing is summarized and analyzed. It can be confirmed by others’ presented results and our simulation that the "data reuse" can accelerate convergence rate and yet raise the steady error. The representative algorithms are BNDR-LMS and affine projection algorithm. In order to improve the convergence performance of constant modulus algorithm (CMA), the writer introduces the data reuse to CMA to accelerate the convergence rate and use set-member filter to reduce the steady error. Then, a new constant modulus algorithm named set-member bi-normalized data reuse constant modulus algorithm is presented.更多还原