一种带有色量测噪声的非线性系统辨识方法

利用最大似然判据,本文提出了一种带有色量测噪声的非线性系统辨识方法.首先,利用量测差分方法将有色量测噪声白色化,获得新的量测方程,从而将带有色量测噪声的非线性系统辨识问题转化成带白色量测噪声和一步延迟状态的非线性系统辨识问题.其次,利用期望最大化(Expectation maximization,EM)算法提出了一种新的基于最大似然估计的非线性系统辨识方法,该算法由期望步骤(Expectation step,E-step)和最大化步骤(Maximization step,M-step)两部分组成.在期望步骤中,基于当前估计的参数并利用带有色量测噪声的高斯近似滤波器和平滑器,近似计算完整的对数似然函数的期望.在最大化步骤中,近似计算的似然函数期望值被最大化,并且通过解析更新获得噪声参数估计,通过Newton更新方法获得模型参数的估计.最后,数值仿真验证了本文提出算法的有效性.

时变块对角参数不确定线性系统的鲁棒H∞滤波

该文考虑具有时变块对角参数不确定性的线性连续系统的鲁棒H∞滤波问题，即设计线性状态估计器，使得对所有可允的时变块对角参数扰动．估计系统二次稳定且估计误差满足预先给定的H∞干扰衰减的约束．基于关于干扰衰减二次稳定的概念，将一个不确定系统的鲁棒H∞滤波问题转化为一个标准的确定性系统的H∞滤波问题。匿文利用Riccati代数方程方法解决了上述问题，并导出了状态估计器存在的充分必要条件及合乎要求的估计器的表达式。

DESIGN OF NONLINEAR OBSERVER FOR NONLINEAR SYSTEM BASED ON RBF NEURAL NETWORKS

A new type of nonlinear observer for nonlinear systems is presented. Instead of approximating thc cntire nonlinear system with the neural network （NN）, only the un-modeled part left over after the lincarization is approximated. Compared with the conventional linear observer, the observer provides more accurate estimation of the state. The state estimation error is proved to asymptotically approach zero with the Lyapunov method. The simulation result shows that the proposed observer scheme is effective and has a potential application ability in the fault detection and identification （FDI）, and the state estimation.

State estimation of connected vehicles using a nonlinear ensemble filter

The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.

Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation

The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.