Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.

Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

Source localization with minimum variance distortionless response for spherical microphone arrays

To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response （MVDR） to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio （SNR）.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

Robust Design for Generalized Minimum Variance Controllers

In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance causedby the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robast design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.

A Geometric Approach to Conditioning and the Search for Minimum Variance Unbiased Estimators

Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that this technique resides in the structure of an inner product space. The technique uses conditioning of an unbiased estimator on a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.

SIMULATION STUDY OF GENERALIZED MINIMUM VARIANCE CONTROL FOR AN EXTRACTION TURBINE

In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.

Nonlinear total least-squares variance component estimation for GM(1,1)model

The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.

基于未知风荷载作用下拉索振动位移的拉索MR阻尼器失效破坏的原位识别方法

MR阻尼器在长期服役过程中可能由于各种因素导致阻尼器失效破坏。现有的对MR阻尼器的性能识别多为离线方法,需将阻尼器从斜拉索上拆卸下来后进行人工检测。该文提出一种基于斜拉索在未知风荷载作用下振动位移,对在役失效无模型的MR阻尼器作用力进行原位在线识别的方法。首先,建立模态坐标下斜拉索-阻尼器系统运动方程及观测方程,并进行模态截断以减少未知变量的数量。然后,将模态风荷载建模为随机游走过程并加入系统的增广状态中,并视MR阻尼器的作用力视为作用在斜拉索上的‘附加未知输入’,采用最近提出的无直接反馈的平滑最小方差无偏Bayesian滤波,对结构增广状态和MR阻尼器作用力进行识别。通过识别数值模拟的MR阻尼器三种典型的失效破坏模式,验证所提方法的有效性。

基于SRM模型的宽带MVDR快速算法研究

为满足潜艇等水下航行器宽带自适应波束形成算法的更高实时性要求,在经典的MVDR(Minimum Variance Distortionless Response)时域解析信号模型的基础上,设计一种基于SRM(Spiked Random Matrix)的协方差逆运算的快速估计算法。首先对宽带信号进行时域解析信号的构造,依据基于随机矩阵理论,然后通过对快拍信号的协方差矩阵的特征值进行调整和处理,实现精确的协方差矩阵的逆运算估计,避免了传统的复杂求逆运算,尤其适合多传感器和快拍样本受限的环境。仿真实验和海试数据证明,提出的算法波束指向性能更优,信号处理复杂度更低,随着阵元间距数的增加,性能获得明显的提升。

含未知输入非线性系统的扩展平方根容积卡尔曼滤波算法

针对工程实际应用中存在的未知输入会导致经典的非线性滤波器状态估计精度下降甚至滤波发散的问题,提出了一种基于最小方差无偏估计(minimum variance unbiased estimation,MVUE)准则的扩展平方根容积卡尔曼滤波(extended square-root cubature Kalman filter,ESRCKF)算法。首先,结合上一时刻未知输入估计值对状态一步预测值进行修正,得到含未知输入条件下的状态预测值。其次,设计新息并采用加权最小二乘(weighted least squares,WLS)法获取当前时刻未知输入的无偏估计。最后,通过最小化协方差矩阵的迹,同时采用拉格朗日乘子法和舒尔补引理得到系统状态的最小方差无偏估计。仿真结果表明,相比于现有的非线性滤波算法,ESRCKF算法提高了在处理含未知输入非线性系统时的状态估计精度,并能同时实现系统状态和未知输入的最优估计,验证了该算法的有效性。

基于改进最小方差波束成形的心磁信号的源重建

由于冠心病患者的心磁图在信噪比较低时,最小方差波束成形(MV)方法的源重建效果不理想,提出一种基于改进最小方差波束成形(IMV)方法,在MV方法的基础上,使用导联矩阵和磁场信号二阶特征矩阵组成约束矩阵,构造新的空间滤波器的权矩阵,从而降低输出噪声功率增益。利用点扩散函数理论比较IMV方法和MV方法的空间谱估计的单源分辨率;利用3个电流偶极子产生36通道磁场仿真数据,采用IMV方法和MV方法在低信噪比下对仿真数据进行源重建;最后对3名冠心病患者的36通道心磁图的R波峰和T波峰时刻数据进行源重建。结果表明,IMV方法的单源分辨率更高,对仿真数据及冠心病患者心磁数据的重建精度更好。

基于改进最小方差无失真响应角度谱算法的气体泄漏定位方法

针对噪声环境下微小气体泄漏难以准确定位的问题,提出了一种基于改进最小方差无失真响应角度谱算法的气体泄漏定位方法。该算法通过引入信噪比追踪加权的方式,提取受噪声影响较小且单个声源能量占优的时频支撑域,并通过Softplus激活函数自适应地调整不同频率分量对角度谱函数的贡献,增加泄漏声源占优的时频域权重;此外,引入基于时频稀疏性的分频带处理,使各子频带内存在一个主导声源能量占优,抑制低频段噪声能量的积累同时避免高频混叠现象。通过软件仿真计算以及实验验证算法的性能,结果表明改进最小方差无失真响应角度谱算法可以实现气体泄漏源的精准定位,定位结果的最大误差在3.5°以内。相比传统算法,该方法在低信噪比和低采样点数下有更高的稳定性、抗噪能力及准确率,可为气体泄漏定位的实际应用提供一定的参考价值。

车载毫米波雷达多径假目标分析与消除方法

针对城市道路环境下车载毫米波雷达存在的多径假目标干扰真实目标定位的问题,提出了一种多径假目标消除方法,以实现真实目标的准确定位。首先分析了简化的城市道路多径场景的电磁传播路径并建立回波模型,然后应用恒虚警率检测方法及最小方差无失真响应测角定位算法得到目标的初始定位结果,最后通过理论分析多径假目标空间位置分布规律并将初始定位点迹与多径假目标进行匹配消除,保留了真实目标定位点迹。实测试验结果表明,所提出的多径假目标消除方法能够有效消除假目标定位点,获得真实目标的定位结果。

改进爬行动物搜索算法的线阵波束形成研究

针对最小方差无畸变响应方法在已知先验信息的情况下抗干扰性能较差的问题,提出了基于改进爬行动物搜索的波束形成方向图控制算法。该算法通过设置合适的适应度函数,迭代寻找最优权值,并利用其实现对方向图的控制。首先采用Sobol序列对种群初始化方法进行改进,获得更均匀的种群初始分布;其次,改进进化因子提高该算法的收敛速度;最后,为了增强抗干扰性能,对适应度函数进行改进,使方向图旁瓣更低。仿真实验表明,相较于传统算法,该算法可以获得更深的零陷并将其准确的对准干扰来向,同时降低峰值旁瓣电平,提高了对干扰的抑制能力,使得元启发式算法在波束形成领域更具应用价值。

最小方差霍夫曼编码设计及应用研究

随着云存储和云计算的发展,大量数据被上传及存储在服务器端。针对传统霍夫曼编码设计存在码字长度分布不均匀、码方差偏大、“字符—概率—码字”唯一对应难,引起储存空间占用大、解码误码率高的问题,文章基于“极小量扰动”思想提出一种最小方差霍夫曼编码设计方法。仿真结果表明,该文设计的最小方差霍夫曼编码码字长度分布更均匀,码方差更小,且所得编码能与符号对应;进行文本压缩实验时,压缩率分别为69.6%、65.9%、49.3%,能有效提升编码质量,降低冗余度。

基于稀疏约束的最小方差无偏自适应荷载估计

提出了一种基于稀疏约束的最小方差无偏自适应荷载估计算法,该算法是对传统的最小方差无偏估计算法的改进。利用大部分结构荷载在空间稀疏的特点,通过PM(pseudo-measurement)技术对荷载向量施加了一个稀疏约束,最终无约束的最小二乘估计转换为了基于l 1范数的稀疏约束,在这样的改进下,传统算法在加速度观测下的荷载漂移问题被有效地解决,同时提高了算法荷载估计的鲁棒性。此外,对于噪声估计传统做法都是通过经验进行手动设置,在工程应用中极大不便。基于此,引入了自适应估计算法,实现了测量噪声协方差自适应估计。最后,通过1个10自由度弹簧阻尼系统和一个3层框架试验结构验证了算法的有效性。

基于MVC算法的机械爪控制器设计

传统机械手爪控制器多采用经典PID控制算法,尽管PID控制实施简单,但是很难保证手爪以最优的方式运动。基于最小方差控制(MVC)理论以手爪的波动方差为控制目标,通过求解丢番图方程获得控制器增益,实现手爪波动方差最小的控制效果。在实例仿真中与传统的PID算法进行比较,结果表明,MVC算法控制器有效地改善了动态性能,有较强的信号跟踪能力。

Improved minimum variance distortionless response spectrum method for efficient and robust non-uniform undersampled frequency identification in blade tip timing

The noncontact blade tip timing(BTT)measurement has been an attractive technology for blade health monitoring(BHM).However,the severe undersampled BTT signal causes a significant challenge for blade vibration parameter identification and fault feature extraction.This study proposes a novel method based on the minimum variance distortionless response(MVDR)of the direction of arrival(DoA)estimation for blade natural frequency estimation from the non-uniformly undersampled BTT signals.First,based on the similarity between the general data acquisition model for BTT and the antenna array model in DoA estimation,the circumferentially arranged probes on the casing can be regarded as a non-uniform linear array.Thus,BTT signal reconstruction is converted into the DoA estimation problem of the non-uniform linear array signal.Second,MVDR is employed to address the severe undersampling issue and recover the BTT undersampled signal.In particular,spatial smoothing is innovatively utilized to enhance the estimation of covariance matrix of the BTT signal to avoid ill-condition or singularity,while improving efficiency and robustness.Lastly,numerical simulation and experimental testing are employed to verify the validity of the proposed method.Monte Carlo simulation results suggest that the proposed method behaves better than conventional methods,especially under a lower signal-to-noise ratio condition.Experimental results indicate that the proposed method can effectively overcome the severe undersampling problem of BTT signal induced by physical limitations,and has a strong potential in the field of BHM.