一种基于内点罚函数和梯度迭代的频偏估计方法

为了克服现有技术的不足,提出了基于内点罚函数和梯度迭代的频偏估计方法。即利用频偏估计的方差与信噪比和相关训练段长度的关系式,在不缩小频偏估计范围的条件下,建立最小化频偏估计精度的方差的最优化函数问题,由于是非线性优化问题,采用内点惩罚函数法, 在效用函数收敛于由频偏估计范围确定的相关长度的可行域之内时,通过梯度迭代,得到一个局部最小值,即最优相关训练段长度,实现相关训练段长度自适应信噪比的变化而调整,提高频偏估计的抗噪性和精度。     

基于模糊量化和2 bit深度像素的运动估计算法

提出了一种2 bit深度像素的运动估计算法。首先,将像素深度的降采样过程形式化为区间分划和区间映射2个步骤,其中前者为多对一映射,决定着运动估计性能,后者为一一映射;其次,提出一种非均匀量化方法求解区间分划的3个初始阈值,并利用隶属度函数对初始阈值细化,从而克服信号噪声等因素导致的初始阈值周围像素值的误匹配;再次,讨论了适用于2 bit深度像素运动估计的误差度量准则,进而提出了基于模糊量化和2 bit深度像素的运动估计算法;最后,借助信号自相关函数,建立比特深度转换误差—运动向量精度模型来估计该算法所能达到的预测精度。实验结果证明,对于多种类型的视频序列,尤其是场景细节和物体运动比较复杂者,该算法始终能保持较高的估计精度,运动补偿的平均峰值信噪比较之传统2 bit深度像素的运动估计提高0.27 dB。     

水下声源信号频段确定技术

相干函数在频域上反映了2个信号的相关性,根据它们来估计信号的频带、设定滤波器的截止频率,对接收信号进行滤波,提高信噪比,从而减小时延估计误差.通过仿真,结果证明这种方法对低信噪比情况下的信号具有较高时延估计精度.     

基于高阶累积量和多相滤波器联合处理的PRBC-LFM参数提取

针对传统算法在进行伪码-线性调频信号参数提取时存在的条件限制和在低信噪比下提取精度不高的问题,文章提出了一种利用多相滤波器组和高阶累积量相结合进行参数的提取方法,该方法能够完成伪码-线性调频信号（PRBC-LFM）参数在高斯噪声下的提取。首先通过多相滤波器组能快速完成信号在频域上的均匀划分,接着对子带信号进行三阶累积量的短时估计,从而达到了抑制高斯噪声和实现在低信噪比下进行信号参数的提取的目的。测试结果表明,在低信噪比下使用该方法进行参数估计时精度很高。     

乘积型指数滤波器及其在多目标时延估计中的应用

指数滤波器是一类新构造出来的输出信噪比和目标时延分辨力随指数变化的滤波器,该滤波器在损失一定输出信噪比的前提下可以有效提高目标时延分辨力,从而提高目标时延估计精度,但仅采用单个指数滤波器仍存在输出信噪比和目标时延分辨力均达不到实际需求的情况。在乘积型高阶模糊函数乘积运算的启发下,在指数滤波器的基础上提出了一种新的乘积型指数滤波器,并分析了该乘积型指数滤波器的输出信噪比及目标时延分辨力等性能。仿真实验表明,所提的乘积型指数滤波器在低信噪比情况下可以更有效提高多目标时延估计精度,且算法简单易于实现,适用于背景复杂的多目标参数估计任务。     

低信噪比条件下宽带欠定信号高精度DOA估计

为提高低信噪比条件下宽带欠定信号DOA估计精度,该文提出基于网格失配迭代最小化稀疏学习的宽带DOA估计方法。该方法首先对频域协方差矩阵进行矢量化处理实现虚拟阵列扩展,将欠定信号转换为超定信号。其次利用线性变换滤除含有噪声项的虚拟阵元,并对协方差估计误差进行了白化处理,抑制了信号中的干扰项。最后建立了包含不同频点联合稀疏参数和网格失配参数的贝叶斯层次架构,推导了联合稀疏参数、网格失配参数的最小稀疏表达式并进行了迭代学习。较传统方法,该方法不依赖任何先验信息,更好地抑制了虚拟阵元中的噪声和干扰,降低了网格失配对DOA估计的影响,在低信噪比条件下具有更高的DOA估计精度和分辨率。仿真实验验证了该方法的有效性。     

CDMA信号码片内多径时延估计算法

针对CDMA信号的特点,提出了低信噪比条件下估计延迟差小于一个码片周期的多径信号时延的有效算法,该文首先建立了一个能够有效抑制噪声影响的多径功率延迟函数,然后推导了建立在此函数基础上的高分辨率时延估计算法,给出了算法的具体步骤,分析了算法的性能,最后通过对比仿真和性能仿真证明了该方法具有低信噪比条件下高频谱分辨率的特点。     

低信噪比下非数据辅助的OFDM系统信道阶数和噪声方差的估计

在低信噪比情况下,该文提出一种新的针对正交频分复用(OFDM)系统信道阶数和噪声方差的非数据辅助(NDA)估计算法。算法中应用了一种新的基于联合极大几何均值(MGM)的代价函数。新的代价函数不仅利用了循环前缀(CP)冗余性,同时也利用了信道记忆性。对比只利用了CP的方法,该算法可以在低信噪比情况下更准确地估计信道阶数和噪声方差。仿真结果表明,在低信噪比情况下,该算法针对信道阶数的估计得到约10 dB的信噪比增益;同时,对噪声方差的估计,该算法显著提高了估计精度,抑制了信噪比20 dB以下估计性能恶化的现象。     

认知无线电中OFDM信号信噪比盲估计

针对认知正交频分复用(OFDM,orthogonal frequency division multiplexing)系统中低信噪比多径信道下传统的OFDM信号信噪比盲估计算法的估计性能差,计算复杂度高的问题,提出一种新的OFDM信号信噪比盲估计方法,该方法首先利用自相关函数的特性粗略估计出信道阶数,确定循环前缀部分中不受符号间干扰的数据区间,然后根据选定区间的数据的自相关函数值估计接收信号的信号功率,最后利用循环前缀数据为部分有用数据的复制这一特性估计出噪声功率,从而估计出接收信号的信噪比。仿真实验结果表明,提出的方法无需任何先验信息,在低信噪比多径信道下具有良好的估计性能,且计算复杂度低,更适合于认知OFDM系统。     

一种新的多天线SC-FDE UWB系统信噪比估计方法

为辅助实现多天线单载波频域均衡超宽带系统相干检测,提出一种新的信噪比估计方法.该方法在不同发射天线传输满足空间复用条件的导频序列,接收端首先获得多天线子信道最小二乘估计,再根据信道共轭对称特性构造噪声采样矢量,然后应用克拉美罗定理获得噪声方差信息,最后联合二者计算系统的信噪比值.仿真结果表明:和传统方法不同,提出方法不仅在高信噪比端大大提高了估计精度,而且可以和简单易执行的最小二乘信道估计器同时使用.     

MIMO-OFDM系统中基于导频辅助的信道估计

该文对MIMO-OFDM系统中基于导频辅助的LMMSE信道估计算法进行了研究,导出了其估计均方差的下界。为降低算法复杂度,首先利用奇异值分解给出一种低阶近似的信道估计器结构;其次提出了一种基于最优导频设计的简化算法。该简化算法不仅降低了算法复杂度,且能有效地获得最优估计性能。最后文中给出了估计信道特性的方法。     

BLUE‐Based Channel Estimation Technique for Amplify and Forward Wireless Relay Networks

The best linear unbiased estimator (BLUE) is most suitable for practical application and can be determined with knowledge of only the first and second moments of the probability density function. Although the BLUE is an existing algorithm, it is still largely unexplored and has not yet been applied to channel estimation in amplify and forward (AF)‐based wireless relay networks (WRNs). In this paper, a BLUE‐based algorithm is proposed to estimate the overall channel impulse response between the source and destination of AF strategy‐based WRNs. Theoretical mean square error (MSE) performance for the BLUE is derived to show the accuracy of the proposed channel estimation algorithm. In addition, the Cramér‐Rao lower bound (CRLB) is derived to validate the MSE performance. The proposed BLUE channel estimation algorithm approaches the CRLB as the length of the training sequence and number of relays increases. Further, the BLUE performs better than the linear minimum MSE estimator due to the minimum variance characteristic exhibited by the BLUE, which happens to be a function of signal‐to‐noise ratio.     

一种基于LMS滤波的OFDM系统信道估计方法

提出了一种适用于OFDM系统的最小均方(LMS)滤波的信道估计算法,对发送序列中导频位置的信道响应进行LMS滤波,进一步得出所有子载波上的信道响应。仿真结果表明,该方法同基于离散傅里叶变换(DFT)的信道估计算法相比,改善了估计的均方误差(MSE)和误码率(BER)性能。     

Recursive maximum likelihood estimation of time‐varying carrier frequency offset for orthogonal frequency‐division multiplexing systems

A recursive maximum likelihood carrier frequency offset (CFO) estimator is proposed in this work, where redundancy information contained in the cyclic prefix of multiple consecutive orthogonal frequency‐division multiplexing (OFDM) symbols is exploited in an efficient recursive fashion. Because the estimator is based on multiple OFDM symbols, the time‐varying CFO must be considered. We investigate the effect of time‐varying CFO on the performance of the estimator and the trade‐off between fast tracking ability and low estimation variance. We show that, without channel noise, the mean squared error (MSE) of estimation due to CFO estimation variation increases approximately quadratically with n, where n is the number of OFDM symbols used for CFO estimation (estimation window size), whereas the MSE due to channel noise decreases proportionally to 1/n (approximately) if the CFO is constant. A closed‐form expression of the optimal estimation window size (approximately) is derived by minimizing the MSE caused by both time‐varying CFO and channel noise. For wireless systems with time‐varying rate of change for CFO, the proposed estimator can be implemented adaptively. In addition, typical optimal estimation window sizes for WiMAX, DVB‐SH and MediaFLO systems are evaluated as an example. Copyright © 2011 John Wiley & Sons, Ltd.     

Motion estimator with adaptive reduction of search points

A fast motion estimation algorithm of low power consumption is proposed. A motion estimator based on the proposed algorithm for H.263 and MPEG-4 video encoder of mobile multimedia applications is implemented by 0.13 /spl mu/m 1.2 V six-metal CMOS technology.     

物理层网络编码的符号时钟估计

现有的关于物理层网络编码(PNC)的研究多建立在时钟已完全同步的基础上,对PNC的符号时钟同步研究较少。而实际上符号时钟在PNC中是必不可少的。针对这一问题,该文提出一种新的基于正交训练序列适用于双向中继信道PNC的符号时钟估计方法。该方法根据最大似然估计准则,运用基于离散傅里叶变换(DFT)的插值算法来估计时钟误差。仿真结果表明,所提出的DFT插值算法性能优越,在信噪比(SNR)大于10 dB的条件下,系统的均方误差(MSE)性能比经典优选采样点法提升1个数量级,并且非常逼近修正克拉美罗界(MCRB)。     

一种改进的奇偶阵列结构运动估计器设计

提出了一种改进的奇偶阵列计算结构的运动估计器架构,该运动估计器利用了二维数据复用并能够实现全搜索法。设计了运动估计器的状态机控制逻辑,在其控制下,运动估计器的处理单元达到了100%的利用率。本运动估计器实现了高速、并行的运算,从而可以应用在高清视频的实时后处理等场合。     

Cramér-Rao Bound for SAGE-based Residual Frequency Offset Estimation Algorithm in OFDM Systems

The Cramér-Rao bound (CRB) is a powerful tool in estimation theory as it gives a performance lower bound for parameter estimation problems. In this paper, a much tighter CRB for Lee’s residual frequency offset (RFO) estimation method (IEEE Transactions on Communications 54:765, 2006) is first given. The tighter low bound is obtained by considering the ICI that affects the performance of space-alternating generalized expectation-maximization (SAGE) based RFO estimator. It can be concluded that the performance of SAGE based RFO estimation method decreases as the normalized RFO increases and increases with the increasing of signal-to-noise (SNR). Simulation results show that the proposed CRB of SAGE based RFO estimator is extremely tight. It approximates closely the MSE performance obtained by Monte Carlo simulation.     

On the Joint Synchronization of Clock Offset and Skew in RBS-Protocol

Motivated by the necessity of having a good clock synchronization amongst the nodes of wireless ad-hoc sensor networks, the joint maximum likelihood (JML) estimator for clock phase offset and skew under exponential noise model for reference broadcast synchronization (RBS) protocol is formulated and found via a direct algorithm. The Gibbs sampler is also proposed for joint clock phase offset and skew estimation and shown to provide superior performance relative to JML- estimator. Lower and upper bounds for the mean-square errors (MSE) of JML-estimator and Gibbs Sampler are introduced in terms of the MSE of the uniform minimum variance unbiased (UMVU) estimator and the conventional best linear unbiased estimator (BLUE), respectively.     

Linear expansions for frequency selective channels in OFDM

Modeling the frequency selective fading channels as random processes, we employ a linear expansion based on the Karhunen–Loeve (KL) series representation involving a complete set of orthogonal deterministic vectors with a corresponding uncorrelated random coefficients. Focusing on OFDM transmissions through frequency selective fading, this paper pursues a computationally efficient, pilot-aided linear minimum mean square error (MMSE) uncorrelated KL series expansion coefficients estimation algorithm. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Moreover, truncation in the linear expansion of channel is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also provide performance analysis results studying the influence of the effect of SNR and correlation mismatch on the estimator performance. Simulation results confirm our theoretical results and illustrate that the proposed algorithm is capable of tracking fast fading and improving performance.