超波束(HBF)用于波束锐化

介绍了一种波束锐化技术,即超波束形成技术（Hyper Beamforming,HBF）,可以在减小波束宽度的同时抑制旁瓣,提高目标检测性能及方位估计能力。该方法结合常规空间加权可以进一步降低旁瓣,从而可以利用较小规模的基阵尺寸,实现大基阵的检测性能。文中给出了恒定束宽HBF实现所需系数,并将该方法应用于CW及FM信号处理,给出了预期的仿真结果。     

用Transputer网络实现低频线阵波束形成

本文简要介绍了传输式ＲＩＳＣ结构处理器Ｔｒａｎｓｐｕｔｅｒ及并行Ｑｃｃａｍ语言，并举例说明了用该系列的通用处理器Ｔ８００构成网络实现大孔径直线阵多波束形成的一种方法。     

基于等效孔径相等的体积阵波束形成

利用自适应方法对"米"字形体积阵进行波束形成,发现在预成的多波束中奇偶号波束效果差别较大,影响了检测性能、定位精度.从预成波束方向阵等效孔径入手,解释了奇偶号波束不一致的原因,通过改变预成波束方向、构造阵等效孔径相等措施来改进体积阵波束形成.计算机仿真、水池实测验证了这种方法可以有效地提高体积阵波束形成的整体效果.     

基于线性预测的虚拟阵元波束形成

常规波束形成方法在信号频率一定的情况下,为提高其分辨率,需要增大基阵孔径,这在实际工程运用中受到了一定的限制。文中提出了基于线性预测技术的虚拟阵元波束形成方法。该方法在有限尺度基阵的情况下,运用线性预测技术,根据已知阵元的接收数据,估计虚拟阵元上的接收数据,使基阵孔径在虚拟的意义上得到了扩大,从而实现了高指向性窄波束,提高了基阵的指向性指数。仿真实验表明了方法的有效性。     

矢量水听器线阵波束图

本文研究了矢量水听器均匀线阵的采用不同方式进行波束形成的波束图,并和声压阵相应的量进行了比较.结果表明,相对于声压阵,在不增加阵孔径的前提下,利用矢量水听器阵获得的波束主瓣更窄,旁瓣更低.利用声压和振速的联合处理,去除了声压线阵方位估计中的左右舷模糊.     

细线阵的频域自适应波束形成

常规波束形成技术包括时域波束形成技术和频域波束形成技术.由于实际空间采样的声阵是有限孔径的，常规波束形成的空间波束谱估计具有加窗效应，所以分辨力不高，另外由于它是固定旁瓣响应，所以不能有效的抑制强干扰。为克服常规波束形成的不足，本文利用广义旁瓣干扰抵消自适应阵处理的原理，仿真研究了自适应波束形成分辨力与抗干扰能力的提高.     

基于二阶锥规划的稳健低旁瓣自适应波束形成

针对水下成像时圆弧阵常规波束旁瓣级较高,当存在强干扰时容易带来较多虚警的缺点,提出一种基于二阶锥规划的稳健低旁瓣自适应波束形成方法。该方法通过对波束旁瓣进行优化设计,可以将波束旁瓣级进行严格控制,并进一步结合协方差矩阵重构法,使波束形成器的稳健性得到提高,最后将该波束优化问题转化为二阶锥规划问题进行求解。计算机仿真结果表明,相较于其他算法来说,文中算法在波束旁瓣级得到严格控制的同时,可以在存在各类失配的情况下获得更高的输出信干噪比,稳健性更高。水池实验进一步验证了该方法的有效性,该研究成果可以在声呐成像领域应用。     

基于阵列响应向量估计的宽容体积阵波束形成

0引言波束形成作为声纳系统的重要组成部分,直接影响着整体性能。通过优化波束形成权系数,为系统提高检测概率,精确定位、跟踪、识别搭建了良好的平台。然而这些优化设计都是在理想阵列流形的假设下进行的,权系数对误差比较敏感,如果实际的阵列流形发生失配时优化波束的效果将急剧降低。在体积阵中产生阵流形失配的原因有很多,     

高斯色噪声下的子阵平滑主模式抑制波束形成

实际海洋波导是一种具有空-时相关性的衰落信道,且其驻留的海洋环境背景噪声场呈现非均匀各向异性的特性,噪声场时空相关性并非δ函数,大孔径声呐的探测性能会受到很大影响。将背景噪声建模为有色高斯随机过程,设计基于子阵平滑的主模式抑制(Dominant Mode Rejection, DMR)波束形成算法,以实现小快拍数条件下具有去相关作用的自适应处理,较好地提高了大孔径阵声呐在高斯色噪声环境下的探测能力。模拟仿真结果表明,该方法具有良好的抑制高斯色噪声的能力。     

An extended nonparametric homogeneously weighted moving average sign control chart

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted moving average (DHWMA) control chart based on the sign statistic is proposed for monitoring the location parameter of an unknown and continuous distribution. The performance of the proposed chart is measured through the run-length distribution and its associated characteristics by performing Monte Carlo simulations. The DHWMA sign chart is compared with other nonparametric sign charts, such as the homogeneously weighted moving average, generally weighted moving average (GWMA), double GWMA, and triple exponentially weighted moving average sign charts, as well as the traditional DHWMA chart. The results indicate that the proposed chart performs just as well as and in some cases better than its competitors, especially for small shifts. Finally, two examples are provided to show the application and implementation of the proposed chart.     

A nonparametric triple exponentially weighted moving average sign control chart

Nonparametric control charts are used in process monitoring when there is insufficient information about the form of the underlying distribution. In this article, we propose a triple exponentially weighted moving average (TEWMA) control chart based on the sign statistic for monitoring the location parameter of an unknown continuous distribution. The run-length characteristics of the proposed chart are evaluated performing Monte Carlo simulations. We also compare its statistical performance with existing nonparametric sign charts, such as the cumulative sum (CUSUM), exponentially weighted moving average (EWMA), generally weighted moving average (GWMA), and double exponentially weighted moving average (DEWMA) sign charts as well as the parametric TEWMA-$\overline{X}$ chart. The results show that the TEWMA sign chart is superior to its competitors, especially for small shifts. Moreover, two examples are given to demonstrate the application of the new scheme.     

On moving average control charts and their conditional average run lengths

Moving average control charts have been presented in the Quality Control literature in the past 75 years; however, their conditional average run lengths have not been obtained. The objective of this article is to derive the autocorrelation function between two moving averages, and then make application of the bivariate normal distribution to compute the conditional type II error probability at the future time, t +1, given that a manufacturing process is in statistical control at the present time t. Our Tables 3 through 8 show that the values of Shewhart's average run length and the corresponding conditional first-order moving average run lengths are almost the same after one standard deviation shift from the target of a normal process mean. Our conclusion section 6 describes that the comparisons of the two average run lengths are not on a valid statistical basis.     

Exponentially weighted moving average control charts based on the moving average statistic and lnS2 for monitoring a Weibull process with subgroups

In this paper, we propose 2 new exponentially weighted moving average (EWMA) control charts based on the moving average (MA) statistic and lnS2 to monitor the process mean and variability of a Weibull process with subgroups. The inverse error function is used to transform the Weibull‐distributed data to a standard normal distribution. The Markov chain approach is used to derive the average run length (ARL). Subsequently, the performances of the proposed charts with other existing control charts are provided. The comparison shows that the EWMA‐MA outperforms the and EWMA‐ control charts for monitoring the process mean of ARL values. The comparison also shows that the EWMA‐lnS2 outperforms the S2 and S2‐MA control charts for monitoring the process variability of ARL value. Two examples are used to illustrate the application of the proposed control charts.     

A nonparametric double generally weighted moving average signed-rank control chart for monitoring process location

Most control charts have been developed based on the actual distribution of the quality characteristic of interest. However, in many applications, there is a lack of knowledge about the process distribution. Therefore, in recent years, nonparametric (or distribution-free) control charts have been introduced for monitoring the process location or scale parameter. In this article, a nonparametric double generally weighted moving average control chart based on the signed-rank statistic (referred as DGWMA-SR chart) is proposed for monitoring the location parameter. We provide the exact approach to compute the run-length distribution, and through an extensive simulation study, we compare the performance of the proposed chart with existing nonparametric charts, such as the exponentially weighted moving average signed-rank (EWMA-SR), the generally weighted moving average signed-rank (GWMA-SR), the double exponentially weighted moving average signed-rank (DEWMA-SR), and the double generally weighted moving average sign (DGWMA-SN) charts, as well as the parametric DGWMA- $\overline{X}$ chart for subgroup averages. The simulation results show that the DGWMA-SR chart (with suitable parameters) is more sensitive than the other competing charts for small shifts in the location parameter and performs as well as the other nonparametric charts for larger shifts. Finally, two examples are given to illustrate the application of the proposed chart.     

A hybrid homogeneously weighted moving average control chart for process monitoring

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ₁ and λ₂ . The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data.     

Dynamic analysis of an axially moving sandwich beam with viscoelastic core

A development of the beam model of the axially moving sandwich continua with elastic faces and the core characterized by viscoelastic properties is presented in this paper. Two-parameter Kelvin–Voigt rheological model is used to describe material properties of the core. The Galerkin method is used to solve the governing partial differential equation. Dynamic analysis of the composite with two aluminum facings and a polyurethane core is carried out. The effect of the transport speed, the core thickness and the internal damping of the core material on the dynamic behavior of the system is investigated in undercrtitical and supercritical range of transport speed.     

Nonparametric triple exponentially weighted moving average signed-rank control chart for monitoring shifts in the process location

Nonparametric (or distribution-free) control charts are used for monitoring processes where there is a lack of knowledge about the underlying distribution. In this article, a triple exponentially weighted moving average control chart based on the signed-rank statistic (referred as TEWMA-SR chart) is proposed for monitoring shifts in the location parameter of an unknown, but continuous and symmetric, distribution. The run-length characteristics of the proposed chart are evaluated performing Monte Carlo simulations. A comparison study with other existing nonparametric control charts based on the signed-rank statistic, the TEWMA sign chart, and the parametric TEWMA-$\overline{X}$ chart indicates that the proposed chart is more effective in detecting small shifts, while it is comparable with the other charts for moderate and large shifts. Finally, two illustrative examples are provided to demonstrate the application of the proposed chart.     

移动质量作用下轴向运动悬臂梁振动特性分析

将弹炮发射系统简化为移动质量作用下的轴向运动悬臂梁系统,推导了轴向运动梁的振动方程,采用修正的Galerkin法离散求解该偏微分方程,得到以模态坐标表示的二阶时变常微分方程组,通过Newmark-β法对方程组进行了求解。计算结果表明,移动质量载荷主要使梁的一阶模态受到激励,移动质量的大小和运动速度对悬臂梁的振动响应影响较大,在移动质量作用下梁的伸缩运动都处于不稳定状态;在移动质量脱离悬臂梁后,梁的轴向收缩运动使得梁的瞬时振动频率不断减小,振动位移逐渐衰减,而振动速度逐渐增大,梁的运动处于不稳定状态,伸展时梁的自由振动规律相反。     

A moving average S control chart for monitoring process variability

An efficient alternative to the S control chart for detecting shifts of small magnitude in the process variability using a moving average based on the sample standard deviation s statistic is proposed. Control limit factors are derived for the chart for different values of sample size and span w. The performance of the moving average S chart is compared to the S chart in terms of average run length. The result shows that the performance of moving average S chart for varying values of w outweigh those of the S chart for small and moderate shifts in process variability.