多频连续波雷达二次差频测距法的改进

在一定的信噪比下,二次差频测距法只有增加频点数才能增大测距范围或提高测距精度.本文从二次差频测距的基本原理出发详细推导了二次差频法正确测距对输出信噪比的要求,给出了测距精度与输出信噪比之间的关系,然后提出了一种新的差频测距算法——频差组合法,在相同的信噪比和频点数不增加的情况下,提高了测距性能.最后通过仿真,验证了该算法的有效性.     

多频连续波雷达隔圈解距离模糊方法研究

二次差频测距具有原理简单、结构设计相对容易等优点,文中将其应用于边搜索边跟踪模式.在介绍了二次差频测距基本原理的基础上,对该体制雷达的重难点问题隔圈解距离模糊开展研究.首先给出了隔圈解距离模糊方法,然后讨论了目标有效性判决问题,最后通过仿真验证了方法的有效性.     

噪扰下数字化多频连续波雷达的测距模糊问题

数字化多频连续波测距雷达是一种新体制雷达.它具有能同时测定多个目标、便于同时得到目标多个运动参数等突出优点.为了解决增大最大测距不模糊距离与提高测距精度之间的矛盾,我们将会在多频雷达中采用参差频差关系进行测距,这样我们可以大大提高测距最大不模糊距离.但此时噪声对测距结果有着各种有待讨论的影响,包括:能否正确解模糊;对输入信号信噪比有何要求;理论测距精度有何变化;直至对参差系数有何要求等.数字化多频雷达的这些问题,本文对这些问题进行了详细的讨论,并给出了本文结论的计算机仿真结果.从而在理论上为这种雷达的深入研究提供了一个很好的基础.     

一种提高数字化连续波雷达测距性能的新方法

通过对连续波双频测距与多频测距基本原理的分析,针对双频测距存在最大不模糊距离与测距精度相互矛盾的局限性,并考虑到多频测距对正确解模糊所需信噪比较高这一缺点,同时结合双频测距与多频测距的优势,提出了一种从多频到双频转换的连续波测距新方法。文中对这种新方法的测距步骤进行了详细论述,并进行了仿真分析,仿真结果证明了该方法的有效性。     

多频连续波测距及其解模糊

主要介绍了多频连续波雷达测距的基本原理 ,并针对其距离解模糊和相位测量两个关键部分做了详细的分析论证     

基于二次相差法的多频连续波测距雷达仿真与研究

介绍了二次相差法测距的基本原理,并对二次相差法多频连续波测距体制在分离多目标的问题上进行了研究分析。通过对二次相差法测距性能进行了仿真分析,仿真结果表明,基于双频比相测距的二次相差法多篇连续波雷达,只能求解速度不同的目标的距离值,无法求解同速度不同距离目标的距离值。     

多频连续波雷达两种测距算法研究

该文分析了连续波雷达参差多频测距算法的局限性,提出了多频连续波雷达的两种测距算法,即多频到双频测距法和二次相差法,并介绍了它们的基本原理。最后对两种方法进行了对比,并进行了仿真。仿真结果表明, 两种测距算法都达到了比较高的测距精度,在最后双频对应的最大不模糊距离相同的基础上,两种算法的测距精度相当。     

FMCW雷达差频信号的非线性分析

FMCW雷达具有大的时宽带宽积,可达到较高的测距精度,但由于压控振荡器等器件非线性的存在,使测距精度大大降低。通过理论分析这一非线性对用于测距的差频信号频谱的影响,结合直接采集和实测相应的差频信号,确定非线性误差的来源和频率范围。采用模拟窄带滤波,降低扰频信号干扰,并通过实验测量,验证了利用该方法可以达到提高测距精度的效果。     

正交频分复用和调频复合探测体制研究

针对正交频分复用探测体制存在的问题,提出一种新的正交频分复用和调频复合无线电引信探测体制与测距信号处理方案。新复合探测体制利用调频引信的特点抑制本地发射天线向接收天线的泄漏,利用OFDM信号的时频特性,在时域和频域分别通过相关检测进行距离粗估计和频域比相检测进行距离精估计。仿真结果表明,文中的测距方案能够克服多频比相法模糊距离和测距精度的矛盾,获得较高的测距精度。     

基于二次相差法的多频连续波雷达测距算法研究

本文提出一种基于双频测距的多频连续滤雷达测距新算法,即二次相差法。与传统的测距方法相比,该算法具有算法简单,实现方便,测距精度高等特点。文章介绍了二次相差法的基本原理,并给出了仿真的结果,结果表明该算法具有较高的测距精度。     

基于多小波双变换的非线性卫星信道盲均衡算法

针对非线性卫星信道Volterra盲均衡系统收敛缓慢、计算复杂高等不足,提出了基于多小波双变换的非线性卫星信道盲均衡算法.该算法用Wiener均衡器代替Volterra均衡器,减小了均衡器结构的复杂性;用平衡正交多小波对Wiener均衡器的输入信号进行变换,降低了输入信号的自相关性;在Wiener均衡器输出端增加一级判决反馈滤波器,同时对其输入信号作平衡多小波变换,又降低了判决反馈滤波器输出信号的自相关性.仿真结果验证了该算法的有效性.     

阵列天线系统的最小互信息盲接收器

提出了平衰落信道中阵列天线多输入多输出(MIMO)系统的基于广义高斯分布近似的最小互信息盲接收器(GGMIR)。该接收器采用输出信号的广义高斯分布近似，基于互信息目标函数最小化的方法自适应调整接收器的系数。比较了基于广义高斯分布近似和非线性变换(NLMIR)的两种最小互信息盲接收算法。实验表明基于广义高斯分布近似自适应盲接收算法GGMIR比NLMIR算法有更快的收敛速率，得到的信号星座图有更大的距离和更好的误码性能。     

Least squares approach to blind channel equalization

We present a least squares (LS) algorithm for blind channel equalization based on a reformulation of the Godard algorithm. A transformation for the equalizer parameters is considered to convert the nonlinear LS problem inherent in the Godard algorithm to a linear LS problem. Unlike the Godard (1980) algorithm, the proposed LS approach does not suffer from ill-convergence to closed-eye local minima. Methods for extracting the equalizer parameters from their transformed version are developed. Offline and recursive implementations of the LS algorithm are presented. The algorithm requires only a small number of channel output observations to estimate the equalizer parameters and is therefore fast vis-a-vis the Godard algorithm. The channel input correlation does not impose any restriction on the application of the algorithm, as long as a weak sufficient-excitation condition is satisfied. Simulation examples are presented to demonstrate the LS approach and to compare it with the Godard algorithm     

Application of Recurrent Wavelet Neural Networks to the Digital Communications Channel Blind Equalization

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Combined multiuser signal classification and blind equalization

A multiuser automatic modulation classifier (MAMC) is an important signal processing component of a multiantenna cognitive radio (CR) receiver that helps the radio in identifying modulation format employed by multiple users in a frequency band simultaneously. In a typical wireless communication, transmitted signals are subjected to multipath fading and interference from other users. Multipath fading not only affects symbol detection performance but also affects the performance of the automatic modulation classifier. A multi input multi output (MIMO) blind equalizer is another important component of a multiantenna CR receiver that improves symbol detection performance by reducing inter symbol interference and inter user interference. In a CR scenario it is preferable to consider the performance of the AMC also while adapting the parameters of the blind equalizer. In this paper we propose MIMO blind equalizers that improves the performance of both multiuser symbol detection and cumulants based MAMC. Computer simulations are provided to illustrate this concept and the proposed algorithm.     

Timing phase offset recovery based on dispersion minimization

This paper presents a blind timing phase offset recovery scheme that attempts to optimize the baud spaced equalizer output mean square error (MSE) for a realistic equalizer length that is usually shorter than the ideal length. Among the existing blind timing recovery schemes, few are designed for equalizer output MSE optimization, and none are designed for the realistic case when the equalizer is short. The proposed algorithm (that is based on a cost function that minimizes the dispersion of the received signal) attempts to minimize the MSE of a one-tap equalizer output. It also exhibits good performance for relatively short equalizers. Conditions for the unimodality of the dispersion minimization cost are investigated, and a geometric relationship to the minimum MSE (MMSE) timing offset is shown qualitatively. The detailed MSE performance of the algorithm is investigated for the representing classes of channels by comparing existing blind timing offset estimation schemes.     

多径衰落信道MPSK信号超指数迭代盲解调算法

为了提高多径衰落信道下的盲解调性能,提出了一种结构简单的MPSK信号盲解调算法。首先利用超指数迭代分数间隔盲均衡器实现联合定时同步与均衡,然后对均衡器输出信号进行非线性变换实现载波频偏的估计,最后利用二阶数字判决锁相环跟踪相位变化纠正剩余频偏和相偏。仿真结果表明,在多径衰落信道条件下,与现有算法相比,基于超指数迭代分数间隔盲均衡器的盲解调算法实现简单,误码率低,而且具有收敛速度快、性能稳定等优点。     

基于频域波形合成的盲空间分集均衡器设计

针对频率选择性衰落信道下的多天线盲接收问题,本文提出了一种新的"合成器-均衡器"盲空间分集均衡器结构.基于频域波形合成、均衡器与软符号信息的联合处理框架,首先设计了频域BLOCK-SUMPLE迭代算法,对多路信号频域合成权值进行联合估计,实现频率选择性衰落信道下的波形合成.在此基础上,利用均衡器输出软符号信息重构合成参考,对权值相位予以修正,实现对等效合成信道的优化补偿.与传统盲空间分集均衡算法相比,上述算法实现无需训练序列辅助,并且优化了信号分集合并性能,降低了均衡器的设计难度.仿真结果表明,采用新型结构可有效改善衰落信道下的盲接收效果,逼近最佳空间分集均衡器性能.     

基于最小二乘支持向量机的自适应盲均衡器

提出了一种普适性较强的基于最小二乘支持向量机(LSSVM)的自适应盲均衡器(ABSVME)。该方法根据信号的特征恢复思想,将LSSVM均衡器的输出进行过采样,构造具有时间去相关特性的代价函数,结合Kumar快速算法和静态迭代学习算法在线跟踪信道。通过仿真实验,并与传统恒模盲均衡器和最大似然序列估计均衡器进行比较,结果证明该方法具有优良的非线性均衡能力。     

Entropy minimization for supervised digital communications channelequalization

This paper investigates the application of error-entropy minimization algorithms to digital communications channel equalization. The pdf of the error between the training sequence and the output of the equalizer is estimated using the Parzen windowing method with a Gaussian kernel, and then, the Renyi's quadratic entropy is minimized using a gradient descent algorithm. By estimating Renyi's entropy over a short sliding window, an online training algorithm is also introduced. Moreover, for a linear equalizer, an orthogonality condition for the minimum entropy solution that leads to an alternative fixed-point iterative minimization method is derived. The performance of linear and nonlinear equalizers trained with entropy and mean square error (MSE) is compared. As expected, the results of training a linear equalizer are very similar for both criteria since, even if the input noise is non-Gaussian, the output filtered noise tends to be Gaussian. On the other hand, for nonlinear channels and using a multilayer perceptron (MLP) as the equalizer, differences between both criteria appear. Specifically, it is shown that the additional information used by the entropy criterion yields a faster convergence in comparison with the MSE