基于辅助阵元的阵列误差有源校正算法

阵列互耦、幅相误差和阵元位置误差的综合影响会严重影响超分辨率算法的性能,为此,针对上述3种阵列误差的综合影响,给出了一类基于辅助阵元的有源校正算法.首先,将3种阵列误差的综合影响等效为一种依赖方位变化的幅相误差,并通过一种基于辅助阵元的自校正方法得到了关于3种阵列误差的非线性最小二乘优化模型;然后,针对阵列误差矩阵可能出现的不同模型,给出了相应的求解算法;接着,针对算法的参数估计唯一性给出了较为细致的分析,并对算法做了进一步改进;最后,通过仿真实验验证了新算法的有效性.     

A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method.     

Joint direction of arrival estimation and array calibration for coprime MIMO radar

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.     

一种经典阵元幅相误差、阵元间互耦自校正方法的仿真分析

阵元幅相误差以及阵元间互耦的存在会使大多数高分辨DOA估计算法的性能恶化或失效。因此,阵列误差的校正技术在实际工程应用中具有重要的意义。本文基于一种经典自校正算法进行仿真分析,针对较多文章在理论上分析说明此方法对于均匀线阵的局限性,本文主要考查在实际应用中,在一定条件下此方法同样适用于均匀线阵。     

Array errors active calibration algorithm and its improvement

The combined effects of mutual coupling, gain and phase errors, and sensor position errors have great negative impact on the direction-finding performance of the MUSIC algorithm. This paper investigates the calibration of the array errors induced by the three errors. Based on the array errors calibration algorithm (algorithm 1) presented by See, two improved array error-calibrating algorithms are presented. One (algorithm 2) uses the sparseness of mutual coupling matrix, and the other (algorithm 3) utilizes...     

Array calibration of angularly dependent gain and phase uncertainties with carry-on instrumental sensors

Array calibration with angularly dependent gain and phase uncertainties has long been a difficult problem. Although many array calibration methods have been reported extensively in the literature, they almost all assumed an angularly independent model for array uncertainties. Few calibration methods have been developed for the angularly dependent array uncertainties. A novel and efficient auto-calibration method for angularly dependent gain and phase uncertainties is proposed in this paper, which is called ISM (Instrumental Sensors Method). With the help of a few well-calibrated instrumental sensors, the ISM is able to achieve favorable and unambiguous direction-of-arrivals (DOAs) estimate and the corresponding angularly dependent gain and phase estimate simultaneously, even in the case of multiple non-disjoint sources. Since the mutual coupling and sensor position errors can all be described as angularly dependent gain/phase uncertainties, the ISM proposed still works in the presence of a combination of     

基于均匀十字阵的幅相误差自校正算法

针对实际应用中普遍存在的幅相误差扰动问题,结合子空间类波达方向估计算法,基于均匀十字阵列,提出了一种幅相误差的自校正算法。均匀十字阵列可分解为两个相互垂直的均匀线性阵列,利用均匀线阵接收数据协方差矩阵的结构特点对幅相误差进行初步估计,计算出波达方向的初始估计值,通过迭代方法得到更精确的估计值,自校正方法不需要任何参数初始值,实现比较简单。仿真实验验证了算法具有良好的误差校正效果,能够比较准确地估计出信源的波达方向角和阵元的幅相误差值。     

均匀线阵中幅相及位置误差的快速校正方法

着重研究了针对均匀线阵中由各阵元幅度相位不一致性及位置误差的综合影响引起的阵列流形误差的校正问题。该方法利用单信号源(可以为事先设置的校正源或某目标源)，无须准确知道信号源的波迭方向，只须在校正过程中将阵列天线以已知角度旋转两次．即可对各阵元的幅度、相位及位置因子作较精确的估计，从而估计出综合误差存在情况下的阵列流形，并可同时估计信号源的波达方向。该方法无需迭代．计算简单快速．且具有较高的估计精度。计算机模拟实验结果表明了本文方法的有效性。     

基于任务的故障检测机制的研究

磁盘阵列故障检测机制是保证磁盘阵列可靠性的核心技术;为了能够及时检测系统故障,提出一种基于任务的故障检测机制;该机制根据磁盘阵列任务特征,将任务分为三类,并描述任务间的依赖关系;当故障发生时,该机制能够及时主动地检测出错任务,并根据任务间依赖关系划分恢复组,将故障影响控制在恢复组内;计算机仿真结果显示,采用该故障检测机制的故障处理系统不仅故障检出率高,而且对磁盘阵列控制器系统读写速率影响小,能够显著地提高磁盘阵列系统可靠性。     

Robustness analysis of nearfield subband beamformers in the presence of microphone gain and phase errors

Subband beamforming has found many applications in microphone array processing field, due to its advantages over the fullband counterpart. In this paper, the performance of nearfield subband beamformers for arbitrary arrays in the presence of microphone gain and phase errors is studied from the perspective of statistical analysis. Through the bias and variance analysis of array response, some insightful properties on the robustness of nearfield subband beamformers have been derived. It is shown that the robustness of nearfield subband beamformers is dependent on the source-to-array distance, i.e., the robustness will deteriorate when the source is close to microphone arrays. Moreover, the variation in sound speed, i.e., the temperature in homogeneous environments, has little effect on the robustness performance of subband beamformers. The theoretical results are further verified by several numerical examples on nearfield subband beamformers.     

L型阵列通道不一致及阵元位置误差的联合校正方法

针对L型阵列中通道不一致和阵元位置误差同时存在的情况,利用辅助阵列和校正源,提出一种L型阵列通道不一致和阵元位置误差的解耦合估计方法.该方法计算量小,不需要误差参数的任何先验知识,且校正源的位置可以未知.理论分析和仿真结果表明,提出的方法能很好地解决L型阵列中通道不一致和阵元位置误差的联合校正问题,且两种误差的估计精度高.     

稳健的全阵波束形成方法

由于相干干扰和角度估计误差影响波束形成性能,对此提出一种稳健的波束形成方法。采用加权空间平滑算法去除相干性;对期望信号施加约束,保证一定角度估计误差范围内算法的稳健性;进行全阵波束形成变换,避免阵列孔径损失。仿真结果表明,在相干干扰环境中,该算法的方向图在期望信号方向有明显主瓣,有利于接收期望信号;在一定的角度估计误差范围内,该算法可保持比较高的且比较平坦的输出信干噪比;随着输入信噪比的增加,输出信干噪比几乎线性增大。     

中文文本自动校对综述

文本校对在新闻发布、书刊出版、语音输入、汉字识别等领域有着极其重要的应用价值,是自然语言处理领域中的一个重要研究方向。该文对中文文本自动校对技术进行了系统性的梳理,将中文文本的错误类型分为拼写错误、语法错误和语义错误,并对这三类错误的校对方法进行了梳理,对中文文本自动校对的数据集和评价方法进行了总结,最后展望了中文文本自动校对技术的未来发展。     

均匀圆阵列天线仿真系统设计

均匀圆阵列天线以其优越的系统性能在通信、雷达探测、导航制导等许多领域得到了广泛的应用。该文建立了均匀圆阵列天线的系统模型,在此模型基础上对天线的系统性能和各种误差的影响进行了仿真分析,并针对接收信号的不同类型选择了合适的算法,进而完成了波达角估计（DOA）和输出特性的仿真分析。文章中还介绍了用MATLAB实现均匀圆阵列天线（UCA）仿真系统的思路、方法及实现方案,并建立了对均匀圆阵列天线进行全面分析的模拟仿真平台,为实验室研究提供了方便的工具。     

C程序数组越界的运行时验证技术研究与实现

缓存区溢出能引起非常严重的安全问题,对网络和分布式系统（如机群,网格,P2P系统等）构成严重威胁。数组越界在缓存区溢出中占据重要位置,如何检测数组越界错误是一个重要且极具意义的课题。针对该课题,给出一种对C语言数组越界进行运行时验证的方法。分析了数组越界的错误类型,根据这些类型分别研究了数组越界的运行时验证的思想;设计了基于程序插桩进行数组越界动态检测的算法,给出了该方法基于开源编译器Clang的具体实现;用实验证明了该方法是切实可行并且有效的。     

空间粒子辐射引起存储单元多位比特纠错方案

介绍了汉明码检/纠错的原理,并以IDT公司开发的具有检测两位错误和纠正一位错误的IDT49C465检/纠错电路单元为基础,针对主存储器某地址单元遭遇高能粒子轰击导致两位或多位比特出错的情况,提出了解决这类错误的一种方案。此方案通过利用阵列移位寄存器组对存储区数据进行转置来实现,并对此方案的数据可靠性做了概率分析。     

基于内插阵列变换的扩展传播算子实值算法

针对内插阵列变换(VIA)思想在非圆信号波达方向(DOA)估计算法中的应用问题,提出一种基于内插阵列变换的扩展传播算子实值算法——VIA-EPM实值算法。利用真实阵列流型与虚拟阵列流型之间的变换矩阵,将真实阵列输出转换为虚拟阵列输出,再根据信号源为实数的特点,分别求取虚拟阵列输出的实部和虚部,将其串联组合,扩展阵列输出的维数,通过对扩展阵列输出矩阵进行分块并得出扩展传播算子,进而得到一种传播算子(PM)类的DOA估计算法。仿真实验表明：存在阵元位置误差的情况下,VIA-EPM实值算法通过对阵元位置校准数据进行内插阵列变换,取得与阵元位置校准的扩展传播算子实值算法(EPM实值算法)相当的估计性能,保持了阵列扩展能力、高估计精度以及高分辨力;并且在二维阵元位置误差情况下,其估计性能明显优于阵元位置未校准的EPM实值算法。结合VIA-EPM实值算法的计算复杂度分析可以看出：它同时获得了内插阵列变换技术以及信号非圆特性的优势;与复运算相比,其复杂度也相对降低。     

网络化高精度和高稳定性阵列电子胶带秤仪表

通过设计高灵敏秤体、提高称重传感器采样精度等提高电子胶带秤计量精度的传统方法很难补偿胶带张力引起的称重误差;而通过并行设置多个秤体、构成阵列秤的方式能够减小胶带张力对计量精度的影响。基于内力理论分析得出电子胶带秤首尾两端的胶带张力是造成称重误差的主要原因,且两端胶带张力占比随称重单元增多而变小,因此阵列电子胶带秤可改善胶带张力变化对计量精度的影响和稳定性。设计了以高性能ARM处理器STM32F767为控制核心的网络化高精度和高稳定性阵列电子胶带秤仪表,实现了8路称重阵列动态计量、逻辑控制、人机交互、掉电存储、工业控制与通信、互联网远程监控等功能。采用静态挂码方式对该仪表进行试验,结果表明该仪表的称重误差约为0.03%,且对胶带速度和载荷引起的张力变化不敏感,具有较高的计量精度和稳定性。     

基于微气体传感器阵列和神经网络的VOCs的辨别

六个由贵金属Au,Cu,Pt做添加剂的SnO2气体传感器构成了微气体传感器阵列.首先研究了这六只传感器对挥发性有机化合物(VOCs)敏感特性,本文中的VOCs 指VOCsmixture和甲醛(HCHO)气体,其中VOCsmixture是10 ppm甲苯、1 ppm丙酮、5 ppm α-派烯和10 ppm乙醇的混合气.然后采用BP神经网络对所获得的传感器信号进行了分析、识别.结果显示微气体传感器阵列与BP神经网络相结合不仅能有效地识别低浓度的单成分VOCsmixture和甲醛气体,而且也能有效地识别两元气体中的VOCsmixture和甲醛气体.     

基于IMU阵列的标定方法

微电子机械系统(MEMS)技术的发展使惯性传感器行业发生了革命性的变化,这使得生产惯性传感器阵列成为可能。然而,低成本的惯性测量系统会受到比例因子和轴失准误差的影响,从而造成位置和姿态估计的精度降低。在单个IMU校正的基础上,设计了一套基于IMU阵列的标定方法,该标定方法为了解决传统六面法在标定IMU阵列过程中方向激励不足的问题,设计了正20面的校正装置,该标定方法不仅能够估计出IMU阵列中单个IMU的比例因子、轴失准误差和偏置,还能估计出阵列中不同IMU之间的坐标轴对齐误差。通过把标定结果和官方所给的校正参数进行对比,可以得到经过本文所提的IMU阵列标定方法得到的标定结果能够达到工厂标定结果的百分之五十到百分之九十。