共查询到20条相似文献,搜索用时 500 毫秒
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多变量自校正滤波器和平滑器 总被引:2,自引:0,他引:2
本文用状态空间方法提出了多变量自回归滑动平均(ARMA)过程的自校正滤波器,运用
时间序列分析方法,提出了多变量ARMA过程的自校正滤波器和自校正平滑器,并且用这两
种方法提出的自校正滤波器是一致的,推广了对于单变量ARMA过程的Hagander和Wittenmark
的结果[3]. 相似文献
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基于稳态Kalman滤波器和射影理论,提出了统一和通用的时域Wiener状态滤波新方法,用它得到带非零均值相关噪声线性随机系统的渐近稳定的Wiener状态估值器和解耦Wiener状态估值器.它可统一处理状态滤波、预报和平滑问题.发现了Kalman滤波器和Wiener滤波器之间的变换关系,Wiener状态估值器可由Kalman估值器通过自回归滑动平均(ARMA)新息模型得到.一个仿真例子说明了其有效性. 相似文献
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吕永乐 《计算机工程与应用》2012,48(3):237-241
传统的自回归滑动平均模型(ARMA)和新近出现的函数系数自回归模型(FAR)不能满足非线性时间序列预测分析的准确度与运算速度要求,为了改进预测性能,研究提出了一种新的统计预测模型——多项式系数自回归模型(PCAR)。给出了PCAR模型的表示形式,详细探讨了PCAR模型的参数估计和阶次选择方法,在此基础上又提出了基于BIC准则的建模算法。同ARMA模型相比,PCAR模型扩大了适用对象范围,有效降低了模型选择误差;同FAR模型相比,它具有参数模型的特点,避免了系数函数局部线性回归估计所存在的不足;分析了PCAR模型与ARMA、FAR模型的等价条件。通过实验分析得出了PCAR模型较ARMA、FAR模型的单步预测准确度分别提高了99.65%和18.7%的结论,而且PCAR建模运算所需时间仅为FAR模型的0.2%。 相似文献
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卡尔曼滤波是一种根据时变随机信号的统计特性,对信号的未来值做出尽可能接近真值的一种估计方法,首先介绍了卡尔曼滤波原理,然后阐述了它在运动目标检测的应用。针对传统的固定值的卡尔曼滤波方法的缺陷,提出了自适应更新参数的卡尔曼滤波方法。通过与传统的卡尔曼滤波方法、帧差法、光流法和高斯混合模型方法的比较,证明了该方法的有效性。 相似文献
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Gerasimos Rigatos 《International journal of systems science》2016,47(9):2152-2168
The Derivative-free nonlinear Kalman Filter is used for developing a communication system that is based on a chaotic modulator such as the Duffing system. In the transmitter’s side, the source of information undergoes modulation (encryption) in which a chaotic signal generated by the Duffing system is the carrier. The modulated signal is transmitted through a communication channel and at the receiver’s side demodulation takes place, after exploiting the estimation provided about the state vector of the chaotic oscillator by the Derivative-free nonlinear Kalman Filter. Evaluation tests confirm that the proposed filtering method has improved performance over the Extended Kalman Filter and reduces significantly the rate of transmission errors. Moreover, it is shown that the proposed Derivative-free nonlinear Kalman Filter can work within a dual Kalman Filtering scheme, for performing simultaneously transmitter–receiver synchronisation and estimation of unknown coefficients of the communication channel. 相似文献
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Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots 总被引:1,自引:0,他引:1
Gerasimos G. Rigatos 《Mathematics and computers in simulation》2010,81(3):590-607
Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations. 相似文献
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Pires Danúbia Soares Serra Ginalber Luiz de Oliveira 《International Journal of Control, Automation and Systems》2019,17(3):793-800
International Journal of Control, Automation and Systems - A methodology based on the smart combination of evolving fuzzy clustering and OKID (Observer/ Kalman Filter Identification) algorithm, is... 相似文献
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结合全球定位系统(GPS)和航位推算(DR)两种定位方式的优点,构建了基于卡尔曼滤波的自适应联邦滤波算法,实现陆地GPS/DR组合定位系统的数据融合;针对DR子系统的强非线性和扩展卡尔曼滤波算法带来的较大线性化损失,并结合机动加速度均值自适应算法,设计了一种基于U-D分解的自适应迭代卡尔曼滤波算法,更有效的减少DR子系统线性化带来的误差损失,提高定位精度;与同仿真环境下,DR子系统采用扩展卡尔曼滤波方法作了比较,结果表明该信息融合算法能更有效解决DR子系统的线性化误差问题,整个系统数据融合精度更高. 相似文献
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Gerasimos G. Rigatos 《Robotics and Autonomous Systems》2012,60(7):978-995
The paper studies and compares nonlinear Kalman Filtering methods and Particle Filtering methods for estimating the state vector of Unmanned Aerial Vehicles (UAVs) through the fusion of sensor measurements. Next, the paper proposes the use of the estimated state vector in a control loop for autonomous navigation and trajectory tracking by the UAVs. The proposed nonlinear controller is derived according to the flatness-based control theory. The estimation of the UAV’s state vector is carried out with the use of (i) Extended Kalman Filtering (EKF), (ii) Sigma-Point Kalman Filtering (SPKF), (iii) Particle Filtering (PF), and (iv) a new nonlinear estimation method which is the Derivative-free nonlinear Kalman Filtering (DKF). The performance of the nonlinear control loop which is based on these nonlinear state estimation methods is evaluated through simulation tests. Comparing the aforementioned filtering methods in terms of estimation accuracy and computation speed, it is shown that the Sigma-Point Kalman Filtering is a reliable and computationally efficient approach to state estimation-based control, while Particle Filtering is well-suited to accommodate non-Gaussian measurements. Moreover, it is shown that the Derivative-free nonlinear Kalman Filter is faster than the rest of the nonlinear filters while also succeeding accurate, in terms of variance, state estimates. 相似文献
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SOPC Builder中IP构件的设计与实现 总被引:3,自引:0,他引:3
Altera公司的SOPC Builder是一个灵活、方便的系统设计工具,用来在可编程逻辑器件上快速搭建片上系统。有效的片上系统设计依赖于可复用的IP核。介绍了可编程片上系统中IP核总线接口的设计,并结合实例说明如何将用户逻辑包装成SOPC Builder的IP构件,以方便今后的复用。 相似文献
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针对复杂道路条件下车辆的导航问题,将全球定位系统(GPS)与车载终端传感器系统相结合,提出了基于多传感器系统的车辆精确定位模型,并针对扩展类卡尔曼滤波易产生突发性误差而导致的安全问题,采用基于Sigma点的无迹卡尔曼滤波器(UKF)传感器信息融合算法。根据实时的道路状况和车辆自身的运动状态给出符合要求的状态估值,实验与基于多项式扩展卡尔曼滤波车辆传感器信息融合算法在精度和效率方面进行了比较,结果表明,基于UKF传感器信息融合的算法在复杂路况下的估计精度和运行效率都有显著提高,能够根据当前的路线情况和车载传感器的反馈信息快速地估计出车辆的运动状态,实时计算出动态的车辆控制输入。 相似文献