Adaptive autoregressive modeling of non-stationary vibration signals under distinct gear states. Part 1: modeling

Non-parametric time-frequency techniques are increasingly developed and employed to process non-stationary vibration signals of rotating machinery in a great deal of condition monitoring literature. However, their capacity to reveal power variations in the time-frequency space as precisely as possible becomes a hard constraint when the aim is that of monitoring the occurrence of mechanical faults. Therefore, for an early diagnosis, it is imperative to utilize methods with high temporal resolution, aiming at detecting spectral variations occurring in a very short time. This paper proposes three new adaptive parametric models transformed from time-varying vector-autoregressive model with their parameters estimated by means of noise-adaptive Kalman filter, extended Kalman filter and modified extended Kalman filter, respectively, on the basis of different assumptions. The performance analysis of the proposed adaptive parametric models is demonstrated using numerically generated non-stationary test signals. The results suggest that the proposed models possess appealing advantages in processing non-stationary signals and thus are able to provide reliable time-frequency domain information for condition monitoring.     

Hartree-fock approximation for inverse many-body problems

A new method is presented to reconstruct the potential of a quantum mechanical many-body system from observational data, combining a nonparametric Bayesian approach with a Hartree-Fock approximation. A priori information is implemented as a stochastic process, defined on the space of potentials. The method is computationally feasible and provides a general framework to treat inverse problems for quantum mechanical many-body systems.     

Control of epileptiform spikes based on nonlinear unscented Kalman filter

A new control strategy based on nonlinear unscented Kalman filter(UKF) is proposed for a neural mass model that serves as a model for simulating real epileptiform stereo-electroencephalographic (SEEG) signals. The UKF is used as an observer to estimate the state from the noisy measurement because it has been proved to be effective for state estimation of nonlinear systems. A UKF controller is constructed via the estimated state and is illustrated to be effective for epileptiform spikes suppression of aforementioned model by numerical simulations.     

基于改进雁群PSO算法的模糊自适应扩展卡尔曼滤波的SLAM算法

针对扩展卡尔曼滤波同时定位与地图创建算法中难以建立准确的先验噪声模型的问题, 提出一种基于改进雁群粒子群算法的模糊自适应卡尔曼滤波算法. 利用分数阶微积分改进粒子进化速度, 利用混沌来改进粒子的初始化和发生早熟时的处理. 改进后的雁群粒子群算法在收敛速度与避免早熟方面有了很大改进, 并将改进的雁群粒子群算法用于模糊自适应扩展卡尔曼滤波同时定位与地图创建算法的训练, 并与用雁群粒子群算法训练的模糊自适应扩展卡尔曼滤波同时定位与地图创建算法进行对比, 其在定位与构图方面有很大的提高. 关键词： 同时定位与地图创建 雁群粒子群算法 分数阶微积分 混沌     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

Reparameterization for statistical state estimation applied to differential equations

The ensemble Kalman filter is a widely applied data assimilation technique useful for improving the forecast of computational models. The main computational cost of the ensemble Kalman filter comes from the numerical integration of each ensemble member forward in time. When the computational model involves a partial differential equation, the degrees of freedom of the solution in the discretization of the spatial domain are oftentimes used for the representation of the state of the system, and the filter is applied to this state vector. We propose a method of approximating the state of a partial differential equation in a representation space developed separately from the numerical method. This representation space represents a reparameterization of the state vector and can be chosen to retain desirable physical features of the solutions. We apply the ensemble Kalman filter to this representation of the state, and numerically demonstrate that acceptable results are obtained with substantially smaller ensemble sizes.     

Multi-parameter identification from scalar time series generated by a Malkus-Lorenz water wheel

We address the issue of multi-parameter estimation from scalar outputs of chaotic systems, using the dynamics of a Malkus water wheel and simulations of the corresponding Lorenz-equations model as an example. We discuss and compare two estimators: one is based on a globally convergent adaptive observer and the second is an extended Kalman filter (EKF). Both estimators can identify all three unknown parameters of the model. We find that the estimated parameter values are in agreement with those obtained from direct measurements on the experimental system. In addition, we explore the question of how to distinguish the impact of noise from those of model imperfections by investigating a model generalization and the use of uncertainty estimates provided by the extended Kalman filter. Although we are able to exclude asymmetric inflow as a possible unmodeled effect, our results indicate that the Lorenz-equations do not perfectly describe the water wheel dynamics.     

Estimating the state of large spatio-temporally chaotic systems

We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points.     

Nonparametric identification of asymmetric nonlinear vibration systems with the Hilbert transform

The objective of the paper is presenting a simple and more accurate technique for precise identification of nonlinear elastic force functions acting in asymmetric vibration systems. The identification procedure based on the Hilbert transform is a nonparametric one; it does not require a priori information about the system structure or its parameters. The examples of the identification of asymmetric classic vibration nonlinear models – the Helmholtz and the double-well Duffing oscillators – are investigated.     

方位和径向速度联合的浅海目标运动分析方法

提出了一种利用目标方位和径向速度联合的浅海目标运动分析方法。该方法克服了传统纯方位目标运动分析方法要求观测站机动的局限性。将浅海传播条件下径向速度的宽带和窄带估计结果与目标方位相结合,利用卡尔曼滤波原理得到了一种目标跟踪定位方法。仿真结果表明,在观测站非机动和低信噪比情况下,该方法利用单个观测站即能够对宽带或窄带的运动目标进行准确地跟踪定位。利用1996年5月的实验数据对算法性能进行了验证,估计结果与真实目标距离符合较好,距离跟踪误差在10%以内。      

Ternary-material lithium-ion battery SOC estimation under various ambient temperature

Ambient temperature produces great effects on battery state-of-charge (SOC) estimation, due to the unstable estimation algorithm, the weakened traceability of battery model, and variable model parameters at various temperatures, especially lower temperatures. The widely used method based on the equivalent circuit model (ECM) offline in using different algorithm, like current integral, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), can obtain an accurate SOC estimation at room temperature, but it is difficult to guarantee the high precision at lower temperatures. To address this problem, the battery model is investigated at different temperature, and an offset item is proposed to develop the observer equation in the estimated model. Then, the square root of the Sigma points Kalman filter (SR-UKF) is applied, and on the basis of the individual model parameter-temperature table and the developed model, the high accuracy of SOC estimation is achieved. Additionally, considering the burden of original parameter modification (all model parameters modified) at various temperature which will increase the product cost and computational complexity of the battery management system (BMS), the relationship between individual model parameter and the error of SOC estimation is built, which is helpful for the simplification of parameter modification. The results indicate that the proposed method based on the developed estimated model and the simplified parameter modification can achieve an accurate, stable, and efficient SOC estimation.     

Multi-rate sensor fusion-based adaptive discrete finite-time synergetic control for flexible-joint mechanical systems

This paper proposes an adaptive discrete finite-time synergetic control （ADFTSC） scheme based on a multi-rate sensor fusion estimator for flexible-joint mechanical systems in the presence of unmeasured states and dynamic uncertainties. Multi-rate sensors are employed to observe the system states which cannot be directly obtained by encoders due to the existence of joint flexibilities. By using an extended Kalman filter （EKF）, the finite-time synergetic controller is designed based on a sensor fusion estimator which estimates states and parameters of the mechanical system with multi-rate measurements. The proposed controller can guarantee the finite-time convergence of tracking errors by the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach.     

Kalman filtering for reliable estimation of BBB permeability

INTRODUCTION: The blood-brain barrier (BBB) plays an important role in the pathophysiology of a number of central nervous system disorders. In the past, a number of laboratory techniques have been proposed to quantify permeability coefficient ki, an important index of barrier function. Recently, magnetic resonance imaging (MRI) has been used to estimate ki based on graphical plot technique. The MR technique was found to be in good agreement with the gold standard, quantitative autoradiography (QAR). However, a reduced image signal-to-noise ratio, among other factors such as partial volume effects, did not allow reliable estimation of permeability coefficients. This proof-of-principle study proposes the use of Kalman filter as a filtering technique for a reliable estimation of permeability coefficients. The results are compared to those obtained using the Wiener filter technique. MATERIALS AND METHODS: MRI experiments were performed in Wistar rats (N=2) using a 4.7-T Bruker Biospec MR system (Bruker Biospin, Billerica, MA). After acquiring localizer images, T2-weighted diffusion-weighted imaging images were acquired. Finally, a rapid T1 mapping protocol was implemented to acquire one pre-gadolinium diethylenetriamine pentaacetic acid baseline data set followed by postinjection data sets at 3-min intervals for 45 min. Data were postprocessed with and without the application of Kalman and Wiener filters to obtain an estimate of ki. RESULTS AND DISCUSSION: Comparing T1 maps, Patlak plots and permeability maps with and without the Kalman filtering presented several interesting observations. Kalman-filtered Patlak plots, compared to nonfiltered plots, showed that discrete data points on the plot were closer to the line fit. The number of time points used for the construction of the graphical plot had no effect on permeability coefficient estimates when the Kalman filter was used. A box-and-whiskers plot showed longer Y-error bars for nonfiltered and Wiener data compared to Kalman-filtered data. These observations suggest that it may be possible to obtain reliable permeability coefficient estimates in a short study time by applying the Kalman filter to the data. Future work involves investigating the application of this filter on a large-sample-size animal study and evaluating the role of partial volume effects on BBB permeability estimation.     

一种新的卫星钟差Kalman滤波噪声协方差估计方法

采用Kalman滤波方法进行钟差参数计算和预报时, 需确定Kalman滤波噪声协方差矩阵. 针对这一问题, 提出了一种新的卫星钟差Kalman滤波噪声协方差估计方法, 通过建立新息的相关函数序列与未知的噪声参数间的线性函数模型, 采用最小二乘法进行噪声参数估计. 采用精密钟差数据进行钟差参数估计和预报分析, 结果表明, 该方法具有较好的收敛性, 并与顾及随机噪声模型的开窗分类因子自适应抗差估计方法进行对比分析, 验证了新方法的正确性和有效性.     

Robust output synchronization of phase planar systems

We propose a technique to synchronize, under the master/slave synchronization scheme, two planar systems represented by phase state variables; we name them phase planar systems. The coupling signal has a discontinuous term that produces a closed-loop system having good characteristics of robustness with respect to bounded disturbances and parametric variations, and guarantees exponential convergence to the synchronization state. In general, the coupling signal needs the full state vector of both systems, but because we assume that only the system outputs are available, we include a robust observer. This observer also guarantees exponential convergence to the state of the plant in spite of the existence of bounded disturbances and parametric variations; this characteristic facilitates the stability analysis of the closed-loop system. The performance of the synchronization technique is illustrated with experimental results.     

Estimation-based approach for underwater image restoration

For underwater image restoration, modeling of PSF relies on a priori knowledge of the inherent optical properties under water. But full knowledge-based restoration could be corrupted by the complexity and instability of the application environments. Concerned with representation of the backscattering noise of underwater image, this Letter proposes a simplified multilayer transfer model to formulate both the point spread function and the statistics of backscattering noise explicitly. Consequently, the parameters for image restoration are estimated from in situ measurement of the backscattering background without a priori knowledge of the inherent optical properties. The robust Wiener filter is applied to implement the restoration. Experimental results are presented.     

Sequential Parameter Estimation Using Modal Dispersion Curves in Shallow Water

Existing sequential parameter estimation methods use the acoustic pressure of a line array as observations. The modal dispersion curves are employed to estimate the sound speed profile(SSP) and geoacoustic parameters based on the ensemble Kalman filter. The warping transform is implemented to the signals received by a single hydrophone to obtain the dispersion curves. The experimental data are collected at a range-independent shallow water site in the South China Sea. The results indicate that the SSPs are well estimated and the geoacoustic parameters are also well determined. Comparisons of the observed and estimated modal dispersion curves show good agreement.     

光电跟踪系统的共轴跟踪控制技术研究

为了提高光电跟踪系统的跟踪精度,对其粗跟踪环节的共轴跟踪控制技术进行了研究。介绍了共轴跟踪控制的基本原理,分析了常用非线性卡尔曼滤波算法,并仿真比较了无迹卡尔曼滤波(UKF)和容积卡尔曼滤波(CKF)的位置和速度的预测精度。在此基础上综合UKF和CKF的优点,设计了对延迟的合成位置信号进行处理的双并联滤波器,实现了光电跟踪系统的共轴跟踪控制。利用实测数据仿真实验表明,光电跟踪系统的跟踪精度明显提高。     

基于预估测量值的EKF在手臂测姿中的应用

针对载体线性加速度以及周围局部磁干扰对姿态测量精度的影响,基于已有的惯性测量单元,设计了一个基于四元数的实时估计手臂姿态的扩展卡尔曼滤波器(EKF)。提出利用四元数引入加速计和磁强计的预估测量值构造自适应测量噪声协方差阵的方法,结合QUEST算法,来判定姿态角解算对陀螺仪、加速计和磁强计输出信息的依赖程度,以此来提高测量精度。文末通过实验仿真对该方法进行了验证,并对实验结果和电磁跟踪系统采集到的数据进行了比较,结果表明,本文提出的方法能显著提高手臂姿态测量精度,可有效满足应用要求。     

基于卡尔曼滤波的低复杂度去混响算法*

在电话会议、智能音箱等应用场景下,传声器往往处在声源的远场。混响信号的存在会掩蔽后续到达的直达声信号,降低传声器接收信号的语音质量,以及语音识别系统的准确识别率。多通道线性预测算法是一种经典的盲去混响算法,但该算法往往具有较高的计算复杂度。本文提出了一种简化的卡尔曼滤波更新算法,通过对角化卡尔曼滤波器状态向量误差协方差矩阵,降低了自适应多通道线性预测去混响算法的复杂度。通过与现有分块对角简化算法对比发现,本文提出的简化算法在保证语音质量的同时,进一步降低了原卡尔曼滤波算法的复杂度。