基于改进UKF的加油机位姿预测方法

针对无人机自主空中加油保持阶段加油机位姿跟踪精度不高的问题,提出了一种改进UKF（无损卡尔曼滤波）预测方法。建立了视觉导航系统模型,利用Harris算法检测角点,并用RANSAC（随机序列一致性）算法进行角点匹配。将历史预测数据引入当前时刻UKF预测值,并通过匹配角点所得姿态观测值对改进UKF预测值进行修正,从而实现加油机姿态的高精度预测。仿真结果表明,改进UKF在遭遇突发强干扰时姿态预测性能明显优于标准UKF,所预测误差小于5.8%,满足空中加油精度要求。该算法避免了强干扰引发的预测出错,有效抑制了突发干扰。     

噪声自适应拟合补偿的鲁棒性声纹识别算法

目前的声纹识别系统在安静环境下性能已经很高,但处在复杂噪声背景时,由于训练和应用环境的不同会导致系统性能急剧下降。为解决这一问题,从i-vector空间降噪思想出发,通过偏最小二乘算法直接推导含噪i-vector与纯净i-vector之间的关系,并采用自编码器衡量未知噪声类型的含噪i-vector与已知噪声类型的含噪i-vector之间的相似性,提出一种i-vector空间噪声自适应的鲁棒性声纹识别算法(IPLS-AE)。实验结果显示,相对典型的最大后验概率估计算法(IMAP),提出的IPLS-AE算法对各信噪比与各类型的噪声都有较好的补偿性能,等错误率和最小检测代价函数对已知噪声最高有31.3%和26.8%的相对改善,对未知噪声最高有28.3%和25.2%的相对改善。结果说明,IPLS-AE算法能可靠地识别并补偿噪声,提升系统的鲁棒性。     

改进的CS-UKF加速度方差自适应跟踪算法

针对基于当前统计模型的状态噪声协方差阵中的加速度方差调整方法对一般机动目标、非机动目标跟踪精度差的问题,研究其改进方法;在建立机动目标当前统计模型离散状态方程和雷达导引头离散观测方程的基础上;利用雷达导引头测量信息和位置预测值之间的扰动对加速度方差进行调整,提出了改进的加速度方差自适应调整无迹卡尔曼滤波跟踪算法;数字仿真验证了该算法对非机动目标、一般机动目标以及高机动目标均具有良好的跟踪效果。     

基于UKF的自适应野值剔除算法

在基于RSSI信号的无线定位系统中,由于外界环境中各种存在随机干扰噪声,使得采样到的RSSI信号往往具有非线性、不稳定等特点,很大程度上偏离了其真实值,它直接影响了定位系统的定位精度。为了提取RSSI信号的真实值,提出一种基于UKF的自适应野值剔除算法,它根据信息来修正其预测值和增益,可以实时对动态观测数据中的野值进行检测和剔除。并通过与UKF算法对比仿真实验,matlab仿真结果表明,使用改进算法对RSSI信号进行滤波处理,提取的RSSI信号值更接近其真实值,收敛速度更快,误差更小,稳定性更好, 可以有效地剔除野值信号,并抑制了野值信号对滤波的影响。     

基于自适应EKF的弱GNSS信号跟踪算法

弱GNSS信号跟踪技术是卫星导航接收机关键技术之一,跟踪技术的好坏将直接影响卫星导航接收机在弱信号条件下的跟踪性能;在动态环境和先验信息不充分的情况下,由于扩展卡尔曼滤波(EKF)的固定设计使其不能满足要求,针对此不足引入一种自适应扩展卡尔曼滤波(AEKF)的信号跟踪算法;该自适应滤波算法能够实时监测残差或滤波器新息的动态变化,来修正观测噪声方差和状态噪声方差,以此调整滤波器增益,观测值和控制预测值在滤波结果中的权重;理论分析和结果表明,该算法能够充分利用观测信号的统计特性,克服了传统EKF算法不足,获得更好的跟踪性能。     

改进的自适应衰减卡尔曼滤波算法

SINS/GPS组合导航系统的融合算法主要是卡尔曼滤波,卡尔曼滤波实现最优估计的前提是系统的模型和随机噪声信息必须准确已知。实际情况下,大部分系统的模型和随机噪声信息不完全可知,这可能会导致滤波器估计精度下降。针对这一问题,根据求解遗传因子的方法不同对传统的自适应衰减卡尔曼滤波进行改进,提出一种改进的自适应衰减卡尔曼滤波。改进后的算法分别适用于系统噪声统计模型不准确可知和量测噪声统计模型不准确可知两种情况,分别对应于两种滤波算法,并且二者具有统一的滤波框架。仿真结果表明,改进的自适应衰减卡尔曼滤波比卡尔曼滤波精度较高,有效解决了因为噪声模型不准确导致的精度下降问题。     

KF光谱优选的木材抗弯强度预测方法

木材抗弯强度是评价木材力学性质的重要指标,其快速准确预测具有工程应用价值和科学意义。重点研究了使用近红外光谱分析光谱特征优选的卡尔曼滤波(KF)方法进行PLS建模,完成木材抗弯强度的预测。试验用126个蒙古栎无疵试样,依据国家标准《木材物理力学性质试验方法》测量抗弯强度得到力学真值;在900～1 700 nm波段进行近红外光谱采集,一阶导数与S-G卷积结合进行光谱预处理;然后,将光谱及抗弯力学样本视为动态系统,光谱冗余波长视为噪声信号,通过KF迭代得到系数矩阵和标准方差,并运用二者比值实现特征优选;最后建立蒙古栎的偏最小二乘(PLS)抗弯强度近红外模型。结果表明,经过KF优选后,光谱变量数由117减小到18个,预测模型的相关系数ｒ=0.81、预测误差均方根RMSEP=6.59;为了进一步验证方法有效性,与无信息变量消除法(UVE)、连续投影方法(SPA)特征选择方法进行了对比,KF特征优选后的预测相关系数r分别提高了0.05和0.16,预测误差均方根RMSEP降低了2.33和7.66,采用KF特征选择建立的模型预测结果最佳。KF作为特征方法可有效选择近红外光谱特征波长,降低模型维度,提高模型的适用性与准确性。     

基于光谱技术的Bipls算法结合CARS算法的苹果可溶性固形物含量检测

可溶性固形物含量是判断苹果内部品质的重要参考属性之一。利用高光谱技术获取苹果感兴趣区域的反射光谱,以S-G平滑(Savitzky-Golay smoothing)和直接正交信号校正(Direct orthogonal signal correction, DOSC)算法对光谱数据进行梯度预处理后,用后向区间偏最小二乘法(Bipls)优选出3,5,6,7,8,9,13,14,15,16,17,18,19,20,21,23等16个子区间,共计177个波长。结合竞争自适应重加权采样算法(CARS)再作进一步筛选,提取出449.6,512.9,544.8,547.2,594.3,596.8,928.2 nm等7个特征波长,利用偏最小二乘算法(PLS)建立基于特征波长的可溶性固形物含量检测模型,所得模型评价为R_c=0.906 2,RMSEC为0.482 2,R_p=0.871 6,RMSEP为0.614 0。该算法模型预测性能同Bipls和Bipls-SPA模型相比更为优异,证明了Bipls结合CARS算法在提高苹果可溶性固体物含量检测精度方面的有效性。     

基于UKF的恒星相机高精度姿态确定方法

为进一步提升姿态确定的精度和稳定性,首先分析了恒星相机和陀螺联合定姿的基本原理,然后选用误差四元数作为状态变量,推导了基于无迹卡尔曼滤波的恒星相机和陀螺联合定姿的算法.针对恒星相机、陀螺敏感器精度要求高的特点,仿真了多种精度的恒星相机数据和陀螺数据进行无迹卡尔曼滤波定姿试验,并与扩展卡尔曼滤波定姿试验结果进行了比较.试验表明无迹卡尔曼滤波定姿方法有效可靠、适用性强,可以有效提高恒星相机的姿态确定精度,三轴精度较扩展卡尔曼滤波算法提高约10%到20%.     

基于自适应EKF的摄像机标定优化方法

针对扩展卡尔曼滤波算法在摄像机标定优化应用中,滤波精度较大程度地依赖于噪声协方差矩阵的准确性这一问题,提出了一种基于自适应扩展卡尔曼滤波算法的摄像机标定优化方法。以所检测到的二维棋盘格标靶上特征点的图像坐标作为自适应扩展卡尔曼滤波算法的观测量,摄像机的内、外参数作为状态量,将观测图像上的特征点进行逐点滤波运算,过程和观测噪声协方差矩阵在迭代过程中随着观测值和预测值之间新息的变化而更新,从而优化对应的摄像机参数。实验结果表明,经本文算法优化后获得的摄像机内、外参数具有较小的重投影误差,USB相机和工业相机的标定结果较张正友标定法分别提升了61.17%和12.17%,所提算法较无迹卡尔曼滤波算法和扩展卡尔曼滤波算法在噪声环境下具有更高的标定精度和更好的鲁棒性。     

扩展卡尔曼滤波和不敏卡尔曼滤波在实时雷达回波反演大气波导中的应用

为了改善雷达回波反演大气波导(RFC)方面存在的单时次、单方位角反演的问题,提出利用扩展卡尔曼滤波和不敏卡尔曼滤波的反演算法对大气波导结构的多方位角实时跟踪反演. 在卡尔曼滤波方法中分别给出大气波导结构的参数化方程、观测方程、滤波算法的状态转移方程,最后导出滤波反演算法的迭代求解流程. 在大气波导结构不随时间变化和随时间变化的两种条件下,对扩展卡尔曼滤波和不敏卡尔曼滤波算法进行数值实验. 实验结果表明,不敏卡尔曼滤波更适用于RFC这高度非线性反演问题,它可能今后为大气波导结构多方位角实时跟踪反演的业务化运行提供理论基础与技术保证. 关键词： 大气波导 雷达回波 扩展卡尔曼滤波 不敏卡尔曼滤波     

Ensemble Kalman filter with the unscented transform

A modification scheme to the ensemble Kalman filter (EnKF) is introduced based on the concept of the unscented transform [S. Julier, J. Uhlmann, H. Durrant-Whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Trans. Automat. Control. 45 (2000) 477-482; S.J. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation, Proc. IEEE 92 (2004) 401-422], which therefore will be called the ensemble unscented Kalman filter (EnUKF) in this work. When the error distribution of the analysis is symmetric (not necessarily Gaussian), it can be shown that, compared with the ordinary EnKF, the EnUKF has more accurate estimations of the ensemble mean and covariance of the background by examining the multidimensional Taylor series expansion term by term. This implies that, the EnUKF may have better performance in state estimation than the ordinary EnKF in the sense that the deviations from the true states are smaller. For verification, some numerical experiments are conducted on a 40-dimensional system due to Lorenz and Emanuel [E.N. Lorenz, K.A. Emanuel, Optimal sites for supplementary weather observations: Simulation with a small model, J. Atmos. Sci. 55 (1998) 399-414]. Simulation results support our argument.     

Stochastic stability of the derivative unscented Kalman filter

This is the second of two consecutive papers focusing on the filtering algorithm for a nonlinear stochastic discretetime system with linear system state equation. The first paper established a derivative unscented Kalman filter(DUKF) to eliminate the redundant computational load of the unscented Kalman filter(UKF) due to the use of unscented transformation(UT) in the prediction process. The present paper studies the error behavior of the DUKF using the boundedness property of stochastic processes. It is proved that the estimation error of the DUKF remains bounded if the system satisfies certain conditions. Furthermore, it is shown that the design of the measurement noise covariance matrix plays an important role in improvement of the algorithm stability. The DUKF can be significantly stabilized by adding small quantities to the measurement noise covariance matrix in the presence of large initial error. Simulation results demonstrate the effectiveness of the proposed technique.     

基于UKF的多用户混沌通信

为克服信道噪声、系统参数误配及多用户干扰对混沌通信系统的影响,本文组合不同的状态空间模型并结合盲提取算法,提出了一种双无先导卡尔曼滤波器 (dual unscented Kalman filter, DUKF),以实现多用户的混沌通信.仿真结果表明,在多输入多输出信道的多用户通信环境下,该算法有较快的收敛速度,并能有效地实现多用户的混沌通信. 关键词： 混沌通信 多输入多输出 双无先导卡尔曼滤波器 盲提取     

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.     

Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking

The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n+2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.     

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

In order to improve the accuracy of the battery state of charge(SOC) estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.     

Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter

In recent years, many pose estimation algorithms were developed, and have been successfully applied to solve unmanned aerial vehicle (UAV) aerial refueling pose estimation problems. This paper mainly focuses on solving this problem under serious turbulences circumstance. The extended Kalman filter is a set of mathematical equations to estimate the state of a process, which is able to support estimations of past, present, and even future states. In reference to previous papers and some simulations, we build up the noise models of refueling boom and atmospheric turbulence. Then, an extend Kalman filter is adopted to solve the pose estimation problem in UAV aerial refueling with serious turbulences. The experimental results demonstrate the feasibility and effectiveness of our proposed approach.     

Comment on “Ensemble Kalman filter with the unscented transform”

The results of numerical experiments with the ensemble unscented Kalman filter and 40-dimensional model of Lorentz and Emanuel in Luo and Moroz (2009) [2] are inconclusive.