基于无迹卡尔曼滤波的双选信道估计方法

针对高速移动场景中由于信道存在时频域选择性衰落(双选衰落)导致正交频分复用(orthogonal frequency division multiplexing, OFDM)通信系统受到子载波间干扰而使通信质量下降的问题,采用基扩展信道模型,在消除子载波间干扰(inter carrier interference, ICI)影响的同时节约信道估计算法的空间复杂度。针对基扩展模型(basis expansion model, BEM)下的非线性信道状态空间模型,采用无迹卡尔曼滤波(unscented Kalman filter, UKF)方法进一步跟踪信道响应提升信道估计精度。仿真分析表明,本研究所提方法能够有效提升信道估计的精度,相对于传统算法具有更好的误码率性能和鲁棒性。     

A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Stock market prediction is of great interest to stock traders and investors due to high profit in trading the stocks. A successful stock buying/selling generally occurs near price trend turning point. Thus the prediction of stock market indices and its analysis are important to ascertain whether the next day's closing price would increase or decrease. This paper, therefore, presents a simple IIR filter based dynamic neural network (DNN) and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian stocks, namely the Bombay stock exchange (BSE), the IBM stock market, RIL stock market, and Oracle stock market. The weights of the dynamic neural information system are adjusted by four different learning strategies that include gradient calculation, unscented Kalman filter (UKF), differential evolution (DE), and a hybrid technique (DEUKF) by alternately executing the DE and UKF for a few generations. To improve the performance of both the UKF and DE algorithms, adaptation of certain parameters in both these algorithms has been presented in this paper. After predicting the stock price indices one day to one week ahead time horizon, the stock market trend has been analyzed using several important technical indicators like the moving average (MA), stochastic oscillators like K and D parameters, WMS%R (William indicator), etc. Extensive computer simulations are carried out with the four learning strategies for prediction of stock indices and the up or down trends of the indices. From the results it is observed that significant accuracy is achieved using the hybrid DEUKF algorithm in comparison to others that include only DE, UKF, and gradient descent technique in chronological order. Comparisons with some of the widely used neural networks (NNs) are also presented in the paper.     

Critical evaluation of non-linear filter configurations for the state estimation of Continuous Stirred Tank Reactor

A systematic approach has been attempted to design a non-linear observer to estimate the states of a non-linear system. The neural network based state filtering algorithm proposed by A.G. Parlos et al. has been used to estimate the state variables, concentration and temperature in the Continuous Stirred Tank Reactor (CSTR) process. (CSTR) is a typical chemical reactor system with complex nonlinear dynamics characteristics. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration. In this work, the comparison of the performances of an extended Kalman filter (EKF), unscented Kalman filter (UKF) and neural network (NN) based state filter for CSTR that rely solely on concentration estimation of CSTR via measured reactor temperature has been done. The performances of these three filters are analyzed in simulation with Gaussian noise source under various operating conditions and model uncertainties.     

基于滤波方法的瓦塞克模型估计研究

将扩展卡尔曼滤波和无损卡尔曼滤波应用于Vasicek模型的参数估计上,并通过对两种滤波方法的估计效果、运算速度等方面进行对比,探讨了EKF和UKF的特点、适用范围以及性能优劣,并得出虽然UKF的计算量要大于EKF,但UKF无论在估计效果上还是在稳定性和鲁棒性上,均明显优于EKF。因此,UKF在利率期限结构模型估计方面具有巨大的应用潜力。     

密集回波环境下地面机动目标跟踪的UKF-VSMM-PDA算法

对地面机动目标进行跟踪,除了利用量测信息外,利用地形特征等信息也可提高跟踪性能。但这些非正态信息却可能导致较高的非高斯概率密度,文中将机动和多回波同时考虑,并充分利用了地形特征等信息,提出了UKF—VSMM—PDA算法。对密集回波环境下地面机动目标进行跟踪,仿真结果证明了该算法的优越性能。     

基于MIT规则的自适应Unscented卡尔曼滤波及其在旋翼飞行机器人容错控制的应用

提出一种新颖的基于MIT规则的自适应Unscented卡尔曼滤波(Unscented Kalman filter,UKF)算法,用来进行参数以及状态的联合估计.针对旋翼飞行机器人执行器提出一种执行器健康因子(Actuator health coefficients,AHCs)的故障模型结构,应用自适应UKF对AHCs参数进行在线估计,将联合估计的状态以及故障参数引入基于模型的反馈线性化控制结构,组成完整的容错控制系统.提出的自适应UKF算法以及容错控制结构经过中科院沈阳自动化研究所ServoHeli-20旋翼无人智能平台数学模型进行仿真试验验证,效果良好.     

最佳自适应无迹卡尔曼滤波算法应用研究

在无迹卡尔曼滤波的实际应用中,前一时刻状态参数估值异常误差和动力学模型异常误差通过状态转移方程影响预测状态信息,从而直接影响滤波解的精度.将最佳自适应因子引入无迹卡尔曼滤波中,得到最佳自适应无迹卡尔曼滤波,通过最佳自适应因子来调节预测状态向量的协方差矩阵,即调整预测状态信息对滤波解的贡献,从而可以控制前一时刻状态参数估...     

捷联惯导/天文导航/合成孔径雷达组合导航系统

设计了适用于巡航导弹的捷联惯性/天文导航/合成孔径雷达(SINS/CNS/SAR)组合导航系统,针对巡航导弹对导航系统高精度和自主性的要求以及其匀速、等高状态飞行的特性,突出工程实用性,对SINS/CNS/SAR组合导航采用非线性建模,非线性的无迹卡尔曼滤波(UKF)方法进行各子滤波器的滤波,非线性的模型和滤波方法更符合实际,因而提高了系统精度,而子滤波器之间的信息融合过程选择基于信息分配因子实时调整的联邦滤波器,大大增强了系统的容错性和实时性。提出了一种新的可观测度计算方法,简化了可观测度的计算。数字和半实物仿真实验表明组合导航系统对于巡航导弹高空长航的飞行环境具有很强适用性,该组合导航系统不但适用于巡航导弹,也适用于无人机等其他一些长航时飞行器,具有重大的工程应用价值。     

基于双层修正无迹卡尔曼的水下节点定位算法* .txt

摘要：针对水下无线传感网络中运动节点定位精度低的问题,提出了一种新的基于双层修正无迹卡尔曼的水下节点定位算法(DLMUKF)。该算法利用下层无迹卡尔曼滤波算法对节点状态进行预测,根据各信标节点的测距传播时延对预测的节点状态进行修正。运用上层无迹卡尔曼滤波算法对修正后的状态进行新的预测与修正。仿真实验中,DLMUKF算法的平均定位误差约为传统多边定位算法的15%,约为基于无迹卡尔曼滤波（UKF）定位算法的16%,受节点运动时间与速度的影响最小。通过实验证明DLMUKF算法能更充分利用实际距离值,可以有效减小运动节点的定位误差。 .txt     

基于UKF的稳定平台导引头被动测距算法研究

本文的主要目的是为了解决主动寻的空空导弹被动测距的问题.由于很多导弹采用的是稳定平台的导引头,使得现有的只测角被动测距的方法不能直接进行应用.本文通过对稳定平台导引头结构和导弹常用坐标系之间关系的分析,得到了合适的测量方程和状态方程,由于状态方程是一个比较复杂的非线性方程,本文采用了UKF的方法,并给出了得到下一个时刻采样点的算法.对本文提出的算法利用六自由度导弹模型进行了仿真,并针对常见格斗场景对UKF与EKF的性能进行了比较和分析.最后通过对脱靶量的比较表明了本文提出的算法有效的提高了导弹的导引精度.