低信噪比条件下正弦信号幅度盲估计

研究了电子侦察信号处理中正弦信号幅度盲估计问题。提出了一种基于相关累加及线性回归的正弦信号幅度盲估计算法。先对接收到的信号进行频率估计,建立参考信号,将接收信号与参考信号相关后变换到基带并作累加,后将相关累加曲线进行最小二乘线性回归,以回归得到的直线斜率值作为信号幅度的估计值。仿真结果表明,当信噪比大于[-3 dB]时,方法的估计均方根误差小于1.1倍克拉美罗限,可在低信噪比条件下,实现正弦信号幅度的盲估计。     

离散实正弦信号参数估计的Cramer Rao方差下限

研究了高斯白噪声背景下根据离散现测值估计实正弦信号的参数问题。分析了实正弦信号幅度、频率和相位估计的方差下限．并讨论了实正弦信号参数的最大似然(ML)估计。给出了观测数据长度N较大时实正弦信号参数估计的Cramer Rao方差下限的计算公式。指出了实正弦信号参数估计方差下限与观测数据长度N、信号频率及初相有关，当N较大时实信号参数估计的方差下限约为对应的复信号的参数估计的方差下限的2倍。在不同的信噪比下进行了计算机模拟．模拟结果与本文分析结果吻合。     

基于快速傅里叶变换的正弦信号频率高精度估计算法

为了进一步提高加性高斯白噪声背景中正弦信号的频率估计精度,提出了一种新的基于插值快速傅里叶变换(FFT)的正弦信号频率估计算法.首先,对N点正弦采样序列进行等长度时域补零延长,再进行 2N 点FFT; 然后, 搜索幅度最大离散谱线位置得到频率粗估计值; 最后, 采用幅度最大谱线以及原信号的离散时间傅里叶变换(DTFT)在幅度最大谱线左右两侧的两点抽样值进行精估计.仿真结果表明,当信号实际频率位于FFT两条离散谱线之间任意位置时,所提算法的频率估计均方根误差均接近克拉美罗下限,具有较好的一致性,估计精度高于Candan算法、Fang算法、三谱线合理结合(RCTSL)算法和Aboutanios算法, 且信噪比阈值较低,估计性能优于现有频率估计算法.     

一种新的复正弦信号二维频率估计算法

如何从遥感图像中实时而有效的估计出二维频率一直是一个难题,提出了一种二维复正弦信号的相似最大似然频率估计迭代算法.通过建立最大似然二维频率估计信号模型,并且结合最大似然估计价值函数,给出了两种新的频率估计价值函数;针对所提及的算法进行了均值和方差分析,分析结果显示该算法是近似无偏估计的,并且满足Cramer-Rao边界条件;最后对所提出的算法进行了仿真,并给出了其在干涉合成孔径雷达(INSAR)去平地相位中的应用.结果表明:算法具有较高的频率估计精度和较小的计算量.     

基于频率敏感三维自组织映射的视差估计算法

提出基于频率敏感三维自组织映射的立体视频视差估计算法,视差预测采用基于亮度分类频率敏感三维自组织映射(Classified Frequency Sensitive Three-Dimensional Self-Organizing Map,CFS-3DSOM)的视差模式识别(Disparity pattern recognition,DPR)算法。其基本思想是对视差图像的低亮度区域和高亮度区域分别进行预测,在训练模式库时引入频率敏感方法。实验表明,与传统基于块的视差估计算法相比,CFS-3DSOM-DPR算法的视差预测图像的平均峰值信噪比提高了0.78~1.78dB,时间减少了70%。     

基于自适应陷波滤波器的频率和幅值估计

估计正弦信号的频率和幅值以实现准确信号跟随具有广泛的应用. 本文采用三维自适应陷波滤波器分析正弦信号, 提出了非归一化和归一化两种频率估计方法, 两种算法都具有圆形周期轨道, 能够获得信号的频率和幅值的准确估计以及正弦跟踪. 用Lyapunov定理和平均方法证明积分流形的存在性和一致渐近稳定性. 归一化算法改进了非归一化的收敛速度受制于信号幅值的缺点. 分析了估计器的带宽参数和频率自适应增益参数对频率跟踪暂态速度和稳态精度以及噪声特性的影响. 通过仿真证实了算法的有效性.     

新的OFDM系统载波频偏盲估计算法

针对现有频偏估计算法复杂度高的问题,提出一种新的基于恒模信号的盲估计算法。假定信道变化缓慢,紧邻OFDM符号将产生相同信道响应,通过衡量它们之间的幅度差异,推导出一种目标函数,采用曲线拟合法对目标函数进行拟合并求得最小值时,即得到最接近真实值的CFO估计值。仿真结果表明,所提算法与现有PDE-F[1]算法和PDE-T[2]算法相比,提高了估计精度,降低了复杂度,尤其在窄带噪声和接收信号增益不断变化的环境下,该算法具有更好的适用性和稳定性。     

科氏质量流量计相位差的一种高精度估计方法

科氏质量流量计是一种用于直接测量质量流量的流量计,其测量精度易受环境噪声的影响。为提高其精度,提出了一种高斯噪声背景下的科氏质量流量计相位差估计新方法。当传感器信号频率在较小范围内缓慢波动时,通过锁相环实现整周期采样。采用离散傅里叶变换计算两路传感器信号的相位差,并实现了219点离散傅里叶变换,以获得精确的相位估计值。与快速傅里叶变换相比,所提出方法具有计算量小、计算速度快等优点,大大降低了数据处理容量。仿真结果表明:当信噪比高于0dB时,该方法的相位估计误差小于0.12°。     

基于薛定谔方程的随机滤波算法

针对一步预测问题,本文提出一种新的自适应滤波算法,该算法通过神经网络来调制薛定谔方程的势场函数.这种算法就是所谓的量子递归神经网络(RQNN),它可以过滤嵌入在真实信号中的非平稳噪声且不需要信号和噪声的任何先验信息.本文通过RQNN与RLS算法的仿真结果比较,表明:RQNN在过滤嵌入在直流信号,正弦信号,阶梯信号和语言信号中的高斯平稳噪声,高斯非平稳噪声或非高斯平稳噪声更准确和有更好的自适应性.实验结果表明:RQNN在过滤正弦信号中的高斯噪声时,输出信噪比相对于输入信噪比提高了20dB,这比RLS滤波器高10dB.     

Extended state space recursive least squares

A new extended state space recursive least squares (ESSRLS) algorithm is proposed for state estimation of nonlinear systems. It is based on state space recursive least squares (SSRLS) approach and uses first order linearization of the system. It inherits the capability of obtaining state estimate without knowledge of process and measurement noise covariance matrices (Q and R respectively). The proposed approach is considered to provide new design option for scenarios where noise statistics and system dynamics vary. ESSRLS is initialized using delayed recursion method and a forgetting factor λ is employed to optimize the performance. The selection of λ can be problem specific as shown through experimental validations. However a value closer to and less than unity is generally recommended. Theoretical bases are validated by applying this algorithm to problems of tracking a non-conservative oscillator, a damped system with amplitude death and a signal modeled by mixture of Gaussian kernels. Simulation results show an MSE performance gain of 20 dB and 23 dB over extended Kalman filter (EKF) and unscented Kalman filter (UKF) while tracking van der Pol oscillator without knowledge about noise variances. The computational complexity of ESSRLS falls within that of EKF and UKF.     

一种在低信噪比下的时变信道估计算法

基于时分复用导频的信道估计方案易受到噪声的影响,文章在分析多个OFDM符号信道多普勒频移扩展的低通特性后,提出了一种滑动窗口频域消噪的算法,降低了噪声对信道估计的影响。仿真结果表明,在低信噪比下信道估计的MSE性能大约有1—2dB提高,除了增加了硬件FTT运算外,没有增加计算复杂性。     

One‐bit in‐phase observation for direct learning‐based digital predistortion with modified frequency‐domain delay estimation and alignment

This article proposes a novel digital predistortion (DPD) implementation method for RF power amplifiers. The new approach adopts only one 1‐bit comparator in the feedback path to observe the in‐phase (I) or the quadrature (Q) component of the error signal between the input and the output signals. To this end, the theoretical derivation of the in‐phase observation based on direct learning architecture (DLA) DPD is first given in this article, by combining the existing 1‐bit method and the low‐cost in‐phase observation. To facilitate the delay estimation and alignment, a modified iterative frequency‐domain delay estimation is presented, which only acquires either I or Q components of the output signal to achieve satisfied delay estimation. Experimental results show that the proposed DPD method decreased the normalized mean square error (NMSE) and the adjacent channel power ratio (ACPR) to less than ?42 and ?51 dB, respectively, which indicates that the proposed DPD system can achieve comparable performance as the existing DPD identification techniques with lower implementation complexity.     

Semiparametric estimation in perturbed long memory series

The estimation of the memory parameter in perturbed long memory (LM) series has recently attracted attention. This has been mainly motivated by the adequacy of LM signal plus noise processes to model the behaviour of many financial and economic time series. In this context frequency domain semiparametric techniques are natural choices for the estimation of the memory parameter of the persistent signal. A new extension of the log periodogram regression that explicitly accounts for the added noise is proposed and its properties are compared with other existing techniques. A reduction of the asymptotic bias and a faster convergence are achieved because a larger bandwidth is permitted. Monte Carlo results confirm the bias reduction in finite samples. An application to a series of returns of the Spanish Ibex35 stock index is finally included.     

利用FFT相位差校正信号频率和初相估计的误差分析

对利用分段FFT的相位差估计正弦信号的频率和初相的误差进行了分析。首先分析了在FFT幅度最大处FFT相位噪声的统计特性，从而得到了在高斯白噪声背景下频率和初相估计均方根误差的计算公式，指出了频率和初相估计均方根误差与信噪比及FFT长度有关，讨论了信号实际频率与FFT离散谱线对应的频率的偏离程度对频率和初相估计均方根误差的影响，给出了计算机Monte Carlo模拟结果，模拟结果与本文得到的公式计算结果相吻合。     

低压电力基于PLC的信道估计算法仿真

研究优化低压电力信道问题,针对低压电力线信道噪声污染严重,由于信道环境恶劣,造成误码率高。为了解决上述问题,采用OFDM系统中对低压电力信道的信道估计算法。传统LS(最小二乘法估计)算法复杂度低但性能受到限制,受噪声影响大。在综合考虑性能和复杂度,提出了新的改进的方法,并针对FFT的时域插值算法的缺陷,采用改进的算法进行仿真。仿真结果表明,改进算法使误码率降低,且减小了噪声的影响,证明信道估计算法提高了信噪比和估计的精度。     

基于相位差分算法的参数估计研究

在数字信道化接收机后续精测频结构的基础上,讨论了相位差分算法在信号参数估计中的应用.首先从最小二乘估计模型人手分析了相对无模糊的相位差分算法,然后根据算法分别对加性高斯白噪声中的复正弦信号的频率估计方法和线性调频信号参数的联合估计方法进行了研究,最后给出了方法的最小均方误差分析及其数值结果.实验结果证明,方法能够有效、准确地估计出信号的参数,而且在较高信噪比条件下,其估计均方误差能达到Cramer-Rao下限.同时方法不含复杂运算,非常易于硬件实现.     

微弱正弦信号参数估计算法研究

该文提出了一种强噪声背景下微弱正弦信号频率、幅值与相位三个参数的估计算法.根据离散小波变换,对强噪声背景下采样序列进行预处理,构造N+2个新的序列.对该序列实施离散傅里叶变换,结合序列的构造方式得到了正弦信号频率,相位和幅值的估计算法.仿真实验表明,在不同类型的噪声背景下,该算法具有较高的估计精度与较强的鲁棒性.     

Direction of arrival estimation with antenna arrays based on fuzzy cerebellar model articulation controller neural network

Direction of arrival (DOA) estimation has been a challenging problem in many applications such as wireless communication, radar, sonar, and navigation. However, it is difficult to improve the angle resolution and reduce the computational complexity of super‐resolution methods. To solve these problems, the DOA estimation is viewed as a mapping problem, which can be modeled using a suitable artificial neural network trained with input‐output pairs. This article presents the use of a fuzzy cerebellar model articulation controller (FCMAC) neural network for the DOA estimation under a linear antenna array. The FCMAC neural network is a special feedforward neural network based on local approximation that can be adapted to solve the multidimensional nonlinear fitting problem. A new preprocessing scheme has been used in both training and test phase. It use magnitude and phase angles instead of the real and imaginary parts of the array covariance matrix to be the input of neural network. The proposed method avoids complex matrix eigen‐decomposition, such as multiple signal classification, and offers fast computation rate. The performance of FCMAC neural network is compared with the conventional subspace methods and the radial basis function neural network in the cases of noisy environment and coherent signal. Simulation results indicate that FCMAC neural network produces up to 61% lower error, 60% higher angle resolution, and 99% lower calculation time than other three methods, which indicates the superior performance of the proposed DOA estimation method under coherent signals and different noise levels.     

一种频率估计的倍频等长信号加权融合算法

提出一种新型加权融合算法.首先,根据倍频等长信号间频率的倍数生成倍频修正矩阵,对倍频等长信号频谱进行同频化处理,使之达到同频等长信号频谱的分析效果.然后,针对倍频等长信号的相位不连续问题,设计具有相位连续特性和噪声对消特性的相位补偿矩阵,对同频化的倍频等长信号频谱进行加权融合,使之近似达到与倍频等长信号长度相同的相位连续信号频谱的分析效果.最后,通过谱峰搜索加权融合频谱获得高精度的频率估计值.算法分析与仿真实验表明:与现有方法相比,本算法精度较高,计算量较小,抗噪性强,普适性好.     

一种低功耗直接数字频率合成方法的研究

介绍了一种低功耗的正弦输出直接数字频率合成(DDFS)的方法。本设计采用分段线性插值拟合正弦函数的思想实现相位码／幅度码的转换功能，避免了ROM的使用；此外，结合硬件优化的系统结构，达到了显著降低功耗和系统复杂度的目的。系统输出频率范围为DC到Nyquist采样频率，具有60dB的频谱纯度。