基于导向矢量失配估计的鲁棒波束域自适应波束形成算法

针对目标导向矢量与波束指向失配导致自适应波束形成输出信干噪比下降问题,研究了一种基于导向矢量失配估计的鲁棒波束域自适应波束形成算法。该算法首先在信号与噪声子空间内,基于阵列主瓣宽度自适应迭代估计待检测目标导向矢量的失配误差,进而基于目标估计导向矢量计算波束域ADBF权值。仿真实验表明,算法在较低信干噪比条件下即能准确估计目标真实导向矢量,进而避免ADBF由于导向矢量失配引起的输出信干噪比损失。鲁棒波束域ADBF算法收敛速度快,易于工程实施。     

Robust adaptive phase‐only beamforming algorithm for interference suppression

The adaptive phase‐only beamforming technique is very important for suppressing interferences in phased array radar. However, similar to the conventional beamforming technique, which features variable magnitudes and phases, the performance of phase‐only beamforming is severely degraded by steering direction and array geometry errors. No studies on this problem are available. In this paper, the problem of adequate robustness for adaptive phase‐only beamformers is investigated, and a robust phase‐only beamforming algorithm based on semidefinite relaxation is proposed. This algorithm can suppress interferences by minimizing the array output power and maintain the desired signal without distortion. Robust adaptive phase‐only weighting is first converted into a non‐convex quadratic optimization problem and then into a convex optimization problem by the semidefinite relaxation technique, which can be solved easily. Experimental results demonstrate that the new robust adaptive phase‐only beamforming algorithm can significantly reduce performance degradation caused by various array errors. Copyright © 2014 John Wiley & Sons, Ltd.     

基于空间平滑差分的鲁棒自适应波束形成

针对当采样协方差矩阵中包含期望信号时,模型失配会使MVDR(Minimum Variance Distortionless Response)自适应波束形成算法性能大幅下降的问题,本文提出一种基于空间平滑差分算法(Spatial Smoothing Difference Algorithm, SSDA)的鲁棒自适应波束形成算法。该算法首先通过空间平滑差分算法获得源数目先验知识,再对MVDR空间功率谱进行波峰搜索得到期望信号,最后再从采样协方差矩阵中去除期望信号协方差矩阵。仿真结果表明,在高信噪比输入的情况下,改进后的算法在去除期望信号后其输出信干噪比有了很大提升,比MVDR算法提高了2～3倍。     

一种基于加权多循环频率的盲波束形成算法

由于单循环频率盲波束形成算法仅利用了循环平稳信号的某个典型的循环频率,其性能对循环频率的选择有较大依赖性,只要循环频率存在偏差会导致整个算法失效,因此基于单循环频率的算法在实际应用中存在局限性。针对该问题,为了充分利用信号的多循环频率信息,消除循环频率偏差对算法的影响,研究并首次提出了一种加权的多循环频率盲波束形成算法,给出了多循环频率的最优加权组合方案,该方法采用迭代的方法寻找最优循环频率加权矢量。仿真实验和理论分析表明,在存在循环频率偏差的情况下,本文提出的算法性能优于基于单循环频率算法。     

一种抗差自适应UKF算法及其在GNSS/SINS组合导航系统的应用

GNSS/SINS组合导航系统标准UKF算法缺乏对量测噪声方差及系统状态异常的自适应调节能力,进而影响了组合导航系统的滤波精度。为了解决上述问题,提出了一种抗差自适应UKF算法。首先,该算法引入变分贝叶斯估计原理以实时估计量测噪声方差;然后,基于滤波器预测残差,构建了自适应因子以降低系统状态异常时对导航解的影响;最后,将该算法应用于GNSS/SINS组合导航系统中,仿真结果表明,当量测噪声统计特性发生变化时,相对于标准UKF算法及抗差UKF算法,在整个仿真时段内,本文算法可提高位置精度分别为51.2%及9.3%,同时可以降低系统模型异常扰动和滤波器初值偏差对导航解的影响。实验结果表明本文算法具有较强的自适应性及抗差性,可提升复杂环境下组合导航系统的精度。     

A new approach to model reference adaptive control

A new approach to model reference adaptive control, based on a combination of direct and indirect control methods, is introduced in this paper. The controller structure is identical to that used in the direct method, but the algorithm used to update the controller parameters depends both on the output error as in direct control and on the plant parameter estimates as in indirect control. The global stability of the overall system is assured by the existence of a Lyapunov function. In the ideal case discussed here, the combined approach results in improved transient response with smaller amplitude of the control input as compared to the constituent methods.     

基于自适应观测器的鲁棒失磁故障检测方法

针对永磁同步电机存在失磁问题,提出了一种基于自适应状态观测器的失磁故障实时检测方法。首先,通过选择磁场同步旋转坐标系下定子电流为状态变量,建立了嵌入式永磁同步电机失磁故障的数学模型。然后,针对电机参数变化而导致传统滑模观测器无法准确检测失磁故障问题,提出将模型参考自适应与滑模变结构控制相结合的方法,设计了自适应滑模观测器,借助Lyapunov稳定性理论证明观测器的稳定性,推导出待辨识参数的自适应律,依据滑模变结构等值控制原理,建立了估计永磁体磁链算式。最后,通过仿真验证,证明了所提方法的可行性和有效性。     

感应电机的神经网络鲁棒自适应动态面控制

针对感应电机的高阶、强耦合的非线性,设计一种具有自适应性和鲁棒性的动态面控制律,以实现对感应电机的控制。首先通过变量代换,将五阶感应电机模型变为块三角形式;接着采用神经网络逼近,将模型转化为含有结构不确定性和参数不确定性的参数化模型。对于感应电机系统中的参数不确定性,在动态面控制方法的基础上设计参数自适应律,在线估计其未知参数;对于感应电机系统中的结构不确定性,设计鲁棒控制律,使得电机控制系统具有鲁棒性。最后通过仿真实现,以验证所提出的控制方法的有效性与实用性。     

Robust adaptive divided difference filter based on forgetting factors and its applications

This paper proposes robust and adaptive divided difference filters (RADDFs) based on forgetting factors which are robust to dynamic systems with biases or uncertainties. The RADDFs are founded on the principle of covariance matching. The robustness of RADDFs is reflected in that it amplifies the innovation covariance to compensate the effect of dynamic biases or uncertainties. The forgetting factor is adjusted adaptively. Then, the scalar forgetting factor is further extended to multiple forgetting factors. The proposed RADDFs are illustrated by Mars entry navigation system with atmospheric density uncertainty, lift over drag ratio uncertainty, and ballistic coefficient uncertainty. To validate the filter performance by multiple forgetting factors, a typical tight coupling nonlinear system with abrupt biases is used.     

基于LMS算法的粉尘静电信号处理

基于课题组自主研发的颗粒物检测系统,根据静电感应的原理获得了大量含有随机噪声的粉尘信号.采用自适应噪声对消的方法对粉尘静电感应信号进行提取,并提出一种新的变步长最小均方误差(least mean square,LMS)自适应算法来修改滤波器系数.引入新的步长因子和误差的非线性关系,使算法具有更好的稳态性能,对新算法的机理和参数进行深入分析,更好地提高了低信噪比下算法的收敛速率并保证了稳态时的性能.将该算法用于粉尘静电感应信号的滤波处理,仿真结果证明该算法能很好地滤除随机噪声.     

Robust adaptive steering control for unmanned surface vehicle with unknown control direction and input saturation

A robust adaptive steering control method is proposed to solve the control problem of the unmanned surface vehicle (USV) with uncertainties, unknown control direction, and input saturation. In the controller design process, the adaptive fuzzy system is incorporated into dynamic surface control (DSC) to approximate the uncertainty term induced by external environmental disturbances and model parameters. Then, the Nussbaum function is used to eliminate the requirement for a priori knowledge of the control direction. Besides, to handle the input saturation, the adaptive fuzzy DSC is extended by a second‐order nonlinear filter and antisaturation auxiliary function to compensate for the magnitude and rate saturation of the rudder. All signals of the closed‐loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov theorem and the Lemma of Nussbaum gain, and the course error can converge to a small neighborhood of zero through choosing design parameters appropriately. Finally, simulation results and comprehensive comparisons are shown for the USV course system, which is demonstrative of the proposed controller's effectiveness and robustness.     

An efficient adaptive algorithm for edge detection based on the likelihood ratio test

The edge detection problem in blurred and noisy 2‐D signals is dealt with. An adaptive signal processing algorithm is proposed which marks edge points according to an hypothesis test which compares the likelihoods of two models describing the local signal behaviour in the two cases of absence/presence of an edge. The two models are identified by a regularized least squares estimation algorithm, obtaining a numerically efficient procedure, quite robust with respect to additive noise and blurr perturbation. No global thresholding or data prefiltering is required. Copyright © 2002 John Wiley & Sons, Ltd.     

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

In this article, the problems of modeling and controlling are investigated for a suspension cable system of a helicopter with input saturation, system parameter uncertainties, and external disturbances by using the boundary control method. In accordance with the Hamilton's principle, the model of the suspension cable system of a helicopter is established by using a set of partial differential and ordinary differential equations. Considering nonsymmetric saturation constraint, the auxiliary systems are designed to handle with the effect of input saturation. Considering Lyapunov's direct method and the designed auxiliary systems, two robust adaptive boundary controllers are provided by the actuators at the helicopter and the box. Under the proposed controllers, the error between the bottom payload and the target location and the vibration range are uniformly ultimately bounded. Moreover, they will converge to a small neighborhood of zero by selecting the suitable parameters. Meanwhile, to guarantee the validity of the proposed adaptive boundary control laws, some sufficient conditions are raised. Simulation results are provided to verify that the effectiveness of the designed controllers in this paper.     

基于甚高频的语音增强算法研究

甚高频通信在民航中有广泛应用,但是它极易受到各种噪声的干扰,传统方法去噪效果差且没有消除固有干扰,提出了一种基于小波分析的自适应卡尔曼滤波算法.该算法在小波分析中提出了新阈值函数,在自适应卡尔曼滤波算法中增加了调节窗口长度的自适应因子,以此来调节滤波增益,可以有效地避免滤波发散.随机选择了某一时段4种不同频率的甚高频语...     

Design and performance analysis of LMS algorithm based adaptive filter embedded with CFAR detector under non‐homogeneous clutter scenarios

The paper presents performance analysis of least‐mean‐square algorithm based adaptive filter embedded with constant false alarm rate (CFAR) detector for the purpose of better detection of target under non‐homogeneous clutter environment in radar application. The objective of this paper is to develop a method by redesigning the radar detector in such a way to emphasize the target response and de‐emphasize the clutter response. The hardware implementation using pipeline technique for the adaptive filter reveals its capability to support high sampling frequency, which is an ardent necessity for high performance radar. The moderate area‐delay‐product and low power consumption have made it suitable for hardware realization for such application. The extensive MATLAB simulation of proposed design shows remarkable improvement of detection performance in terms of signal‐to‐noise ratio of 17 dB considering probability of detection at 0.8 over the generic cell averaging CFAR (CA‐CFAR). Copyright © 2015 John Wiley & Sons, Ltd.     

基于自适应模拟退火遗传算法的多目标最优潮流

采用自适应遗传算法来确定基本遗传算法的交叉率和变异率,保证遗传算法的收敛性。同时引入模拟退火法思想,通过拉伸目标函数的适应度使优秀个体在产生后代时具有明显的优势,从而加速寻优的过程,形成一种新的算法:自适应模拟退火遗传算法。应用该算法进行电力系统多目标最优潮流计算,IEEE30试验系统计算结果表明了该算法的灵活性和有效性。     

Robust adaptive sliding mode control for uncertain delta operator systems

In this paper, a robust adaptive sliding mode controller is presented for delta operator systems with mismatched uncertainties and exogenous disturbances. The parameters of the delta operator system are taken for norm‐bounded uncertainties. The exogenous disturbance is also assumed to be bounded. After the statement of a sufficient condition for the existence of linear sliding surface based on linear matrix inequality technique, a robust reaching motion control method for delta operator systems is presented. Afterwards, an adaptive sliding mode controller for delta operator systems is designed. A bridge between the robust adaptive sliding mode control and the delta operator system framework is made. Numerical example is given to illustrate the effectiveness of the developed techniques. Copyright © 2009 John Wiley & Sons, Ltd.     

LCMV分频的改进维纳滤波后置波束形成算法

针对封闭环境中语音信号受到混响影响,提出了LCMV分频的改进维纳滤波后置波束形成算法。该算法通过计算麦克风阵列接收到含混响信号的短时傅里叶变换得到频域阵列信号,对频域阵列信号分频处理,将分频的信号进行线性约束最小方差波束形成滤波处理,该波束滤波根据每个频段上混响时间不同的特性对频域阵列信号进行分频处理后,将波束形成算法分别应用到高低频中,以提高混响抑制的精度;再由频域阵列信号的组合功率谱进行维纳后置滤波以抑制混响,由麦克风阵列接收到混响信号的直达波和反射波之间不相关性及麦克风阵列接收信号的空间信息解决维纳滤波器的精确估计问题;最后由逆短时傅里叶变换恢复出时域信号。仿真结果表明,该算法对混响抑制具有明显的改善;且在混响时间600 ms条件下语音增强系统的PESQ值提高了0.26。     

Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo

Linear time‐invariant systems play significant role in the control field. A number of methods have been published for identification of the deterministic part of a process. However, identification of the stochastic part has had much less attention. This paper deals with estimation of covariance matrices of the noise entering a linear system. The process and measurement noise covariance matrices are tuning parameters of the Kalman filter, and they affect the quality of the state estimation. The noise covariance matrices are generally not known, and their estimation from the measured data is a challenging task. This paper introduces a method for estimation of the noise covariance matrices using Bayesian approach along with Monte Carlo numerical methods. Performance of the approach is tested on various systems and noise properties. The second part of the paper compares Monte Carlo approach with the recently published methods. The speed of convergence is compared with the Cramér–Rao bounds. Copyright © 2012 John Wiley & Sons, Ltd.     

接受-拒绝算法的贝叶斯不确定度评定

针对贝叶斯不确定度评定中获取测量模型后验分布困难的问题,给出一种基于接受-拒绝采样思想实现贝叶斯测量不确定度评定的方法。面向线性/非线性测量模型,先利用贝叶斯假设或蒙特卡洛法获得被测量的先验信息,再基于接受-拒绝采样获得被测量的接受采样点形成后验分布,对被测量进行统计推断得到测量不确定度评定结果。通过规范示例和实际测量评定实例,验证了采用接受-拒绝算法的贝叶斯不确定度评定方法相较于传统GUM和MCM评定方法,能够得到可靠评定结果,且获取贝叶斯后验分布过程简便,在无/有历史信息条件下测量不确定度评定应用中具有可行性和实用性。