适用于任意几何结构平面阵列的无网格DOA估计算法

由于在网格失配情况下依然具备良好的估计性能,无网格DOA估计算法近年来受到了广泛的关注与研究,其中又以基于原子范数最小化(ANM)的DOA估计算法最具代表性。随着可分离ANM(DANM)算法的提出,为ANM理论在2维DOA估计领域中的应用提供了可能。不过传统的DANM算法以及其后续的一系列改进算法都只适用于均匀矩形阵列(URA)或稀疏矩形阵列(SRA),无法适用于具有任意几何结构的平面阵列。针对上述问题,该文提出一种适用于任意几何结构平面阵列的无网格DOA估计算法,即B-DANM算法。该算法利用一类贝塞尔函数对实际平面天线阵列接收信号的协方差数据进行展开,从而获得适用于任意几何结构平面阵列的DANM算法框架,然后再通过求解半定规划问题、Toeplitz矩阵的Vandermonde分解以及估计参数配对、角度变换等过程来得到最终的DOA估计结果。仿真实验验证了,在任意几何结构平面阵列的测向系统中,B-DANM算法相比于传统的2维DOA估计算法在精度、分辨力等方面的优势。     

基于压缩感知的循环平稳特征检测方法

在认知无线电中,传统的循环平稳特征检测技术为了达到理想的感知效果,需要大量的数据采样点,导致其感知过程复杂度大,感知时间长.针对此问题,提出了一种基于压缩感知的改进循环平稳特征检测方法,该算法利用信号循环自相关函数(Cyclic Autocorrelation Function,CAF)的稀疏特性,基于分段平均的时变自相关函数估计值,通过压缩感知技术重构二维CAF矩阵,再根据重构结果实现循环平稳特征检测.该方法不仅可有效降低计算复杂度和检测时间,而且提高了二维CAF的估计精度.仿真结果表明该方法的检测性能优于基于经典CAF估计的循环平稳特征检测技术.     

可重构智能表面辅助的毫米波信道估计算法

针对可重构智能表面(RIS)辅助的毫米波通信中信道状态信息难以获取问题,该文给RIS的部分器件配备射频链,以分开估计基站(BS)/用户(UE)到RIS之间的信道。根据该结构,提出一种低复杂度的信道估计算法。该算法首先采用解耦原子范数最小化(ANM)方法将信道的离开角和到达角的2维角度估计问题转化为两个1维的角度估计的半正定规划(SDP)问题;其次,利用交替方向乘子算法(ADMM)对该SDP问题进行求解,采用动量梯度下降法对信道矩阵参数进行更新以避免矩阵求逆运算,并通过对迭代步长和信道矩阵参数的联合优化以获得更加精准的信道估计值;最后利用信号的2维角度和信道矩阵参数得到路径增益估计。仿真结果表明,该算法达到了优良的信道估计性能,且在确保信道估计性能的系统参数设置下,该算法的复杂度较低。     

布尔函数的自相关原点矩

本文给出布尔函数的自相关原点矩这一概念，讨论布尔函数密码学特征在自相关原点矩上的数值表现，得到了平衡布尔函数、仿射函数、Bent函数以及部分Bent函数的自相关原点矩的数值特征。     

自适应时延估计中的信号自相关函数估计

应用自适应滤波方法可以估计未知源信号的观察信号间的时延。但是只有知道观察信号的信噪比或者其中源信号分量的自相关函数或功率,才能确定时延估计的方差。本文根据被动检测时延估计的信号模型和自适应滤波原理分析指出,采用自适应滤波结果直接计算的相关分量的自相关函数及功率是一个有偏估计。应用自适用滤波器参数,可以得到一个常数修正因子,修正该有偏估计,得到自相关函数的近似无偏估计。不同条件下的估计实例表明,通过修正显著提高了信号功率估计的准确性。     

偏振模色散自相关函数的实验测量

采用宽光源和光谱分析仪测量偏振模色散自相关函数,来替代传统的可调谐激光器和偏振计的测量方法,得到了与理论分布曲线基本吻合的结果,这种方法大大缩短了测量时间,使得测量结果更接近光纤中偏振模色散的真实情况.     

无字典的超分辨率多径稀疏信道估计方法

使用带网格的观测字典进行稀疏信道估计是近年来常用的多径稀疏信道估计方法,而网格的存在使得这种方法存在估计精度较差的问题,尤其在网格间距较大时,这种方法的劣势更加明显。本文针对这个问题,抛弃了传统的观测字典,采用连续压缩感知方法,结合线性调频训练序列的使用,提出了更加精确的多径稀疏信道估计方法。这种方法避免了网格化带来的误差,实现了高精度、高分辨率的估计。本文首先对此进行了理论阐述,进而在两种不同的多径稀疏信道模型下进行了仿真试验,并从估计精度、计算效率等方面与其他稀疏估计算法进行了对比。仿真结果证明,采用本文提出的方法进行多径稀疏信道估计时,相比其他算法可以更加精确地估计出信道冲激响应。      

m值“复合”逻辑函数的Chrestenson循环谱、自相关函数及应用

该文利用反演公式求得了m值“复合”逻辑函数的Chrestenson循环谱的计算公式，并由此得到了m值“复合”逻辑函数的自相关函数的计算公式，进而运用这两个公式，给出了m值“复合”逻辑函数具备平衡性、相关免疫性的条件，并对m值“复合”逻辑函数的自相关函数及其性质进行了分析；此外该文还得到有限个m值“复合”逻辑函数的非零线性和函数的Chrestenson循环谱的计算公式。     

基于自相关函数的数字示波周期测量的实现

随着数字信号处理能力的提高,数字示波器发展为测量幅度和频率的极其有用的电子测量仪器。通过对自相关函数增加数字示波FM周期测量的改进算法增加示波器的测量功能,提高仪器处理能力,有效地保证采样分辨率、峰值、周期、频率以及上升时间等参数的测量都获得很好品质。     

基于变分模型的块压缩感知重构算法

为了提高现有块压缩感知重构算法的性能,提出了基于全变分和混合变分模型的块压缩感知（简称BCS-TV和BCS-MV）算法。该方法以块为单位进行图像采样,以自然图像正则项的稀疏性为先验条件,通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM),在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比,该算法的PSNR约提高1.5 dB,SSIM约提高0.05,运行速度较稳定,特别适合具有固定传输时延的多媒体数据处理场合。     

Consistent estimation of the cyclic autocorrelation

The cyclic autocorrelation is often used to describe nonstationary random processes. The authors investigate the conditions under which the cyclic autocorrelation can be estimated consistently in mean square for discrete time Gaussian processes. They extend and generalize results of Hurd (1989) and refine results of Boyles and Gardner (1983). They derive necessary and sufficient conditions for consistency in mean square of an estimator, which are in the form of a single sum of autocorrelation coefficients, in the form of a double sum of autocorrelation coefficients, in the bifrequency domain and in terms of the average spectrum. They also discuss the rate of convergence for this estimator     

Range estimation from random array phase measurements

Estimation of the location of a source of radiation from antenna-array phase measurements is considered. A least-squares technique for estimation is analysed for performance.     

基于循环自相关的正弦调频信号参数估计新方法

为解决现有的正弦调频(SFM)信号参数估计方法运算复杂度高、受信噪比限制等问题,提出了一种基于循环自相关的SFM信号参数估计新方法。首先分析了SFM信号循环自相关函数特征,推导了信号调制频率的估计表达式;然后对信号延时相乘以去除其正弦调制特性,得到单频信号并估计信号载频。最后,利用信号频率调制的周期性,对下变频至零频的信号进行周期累加以减少噪声影响,通过对累加后的信号进行瞬时频率计算得到调制指数估计值。仿真表明,信噪比(SNR)大于6 dB时,各参数估计值的均方根误差小于-18 dB。该算法计算量较小,为同等条件下利用卡森准则(CR)方法的16%,便于工程实现。     

CW autocorrelation measurements of picosecond laser pulses

Completely general and novel expressions are presented fornth-order fast or slow correlation functions, with or without background contributions, from which more specializednth- and second-order auto-correlation functions are derived. A straightforward method for obtaining CW autocorrelation measurements of picosecond pulses is then described which employs an audio loudspeaker driven at 30 Hz in one arm of the correlator to permit autocorrelation display at this frequency. Results of the application of this device to measurements of the picosecond pulses from a CW synchronously mode-locked Rhodamine 6G dye laser are presented.     

Blind channel estimation via combining autocorrelation and blind phase estimation

Symbol spaced blind channel estimation methods are presented which can essentially use the results of any existing blind equalization method to provide a blind channel estimate of the channel. Blind equalizer's task is reduced to only phase equalization (or identification) as the channel autocorrelation is used to obtain the amplitude response of the channel. Hence, when coupled with simple algorithms such as the constant modulus algorithm (CMA) these methods at baud rate processing provide alternatives to blind channel estimation algorithms that use explicit higher order statistics (HOS) or second-order statistics (subspace) based fractionally-spaced/multichannel algorithms. The proposed methods use finite impulse response (FIR) filter linear receiver equalizer or matched filter receiver based infinite impulse response+FIR linear cascade equalizer configurations to obtain blind channel estimates. It is shown that the utilization of channel autocorrelation information together with blind phase identification of the CMA is very effective to obtain blind channel estimation. The idea of combining estimated channel autocorrelation with blind phase estimation can further be extended to improve the HOS based blind channel estimators in a way that the quality of estimates are improved.     

A two-stage autocorrelation method for frequency estimation of sinusoidal signal

A two-stage autocorrelation method is proposed for frequency estimation of a complex sinusoidal signal in complex additive white Gaussian noise. In the first stage, a function about signal frequency is constructed via an autocorrelation procedure. By the Least-Square (LS) principle, the signal frequency is estimated from the function. In the second stage, the estimated error of signal frequency in the first stage is defined. Applying the Taylor series, a function about the estimated error of signal frequency is conducted from the autocorrelation function. Applying the LS estimation once more, the estimate of estimated error of frequency in the first stage is obtained. Then the fine estimate of signal frequency is obtained. Simulation results show that the performance of proposed method can approach the Cramer–Rao lower bound in the whole frequency range.     

A closed-form expanded autocorrelation method for frequency estimation of a sinusoid

A closed-form expanded autocorrelation method for real single-tone frequency estimation is proposed. Firstly, the modified covariance (MC) method based on multiple autocorrelation lags is applied to provide a coarse frequency estimate. Then, a closed-form adjustment term based on a least square cost function is derived to get the fine frequency estimate. Simulation results show that the performance of the proposed algorithm, when compared with several existing closed-form time-domain estimators, is closer to the Cramer-Rao bound (CRB). Moreover, the proposed method has lower computation complexity than other autocorrelation-based approaches, which also use multiple autocorrelation lags.     

Parameter estimation for random amplitude chirp signals

We consider the problem of estimating the parameters of a chirp signal observed in multiplicative noise, i.e., whose amplitude is randomly time-varying. Two methods for solving this problem are presented. First, an unstructured nonlinear least-squares approach (NLS) is proposed. It is shown that by minimizing the NLS criterion with respect to all samples of the time-varying amplitude, the problem reduces to a two-dimensional (2-D) maximization problem. A theoretical analysis of the NLS estimator is presented, and an expression for its asymptotic variance is derived. It is shown that the NLS estimator has a variance that is very close to the Cramer-Rao bound. The second approach combines the principles behind the high-order ambiguity function (HBF) and the NLS approach. It provides a computationally simpler but suboptimum estimator. A statistical analysis of the HAF-based estimator is also carried out, and closed-form expressions are derived for the asymptotic variance of the HAF estimators based on the data and on the squared data. Numerical examples attest to the validity of the theoretical analyzes and establish a comparison between the two proposed methods     

Higher order spectral estimation for random fields

This paper is concerned with the nonparametric estimation of the higher order cumulant spectra of vector-valued stationary random fields onZ ^d by smoothing the periodograms, whereZ is the space of integers and the dimensiond1. We derive the asymptotic cumulant properties of the spectral estimates, and consider an application to multidimensional nonlinear systems identification. Numerical examples with simulated data are provided.     

Comparison of autocorrelation and cross-correlation methods forsignal-selective TDOA estimation

Two algorithms for signal-selective time-difference-of-arrival estimation are compared in terms of their implementation and their mean-squared errors (MSEs). A tradeoff between ease of implementation and MSE performance is shown to exist. It is shown that MSE is independent of the distance between sensors for both algorithms