论文标题
部分可观测时空混沌系统的无模型预测
Nonlinear Kalman Filter Using Cramer Rao Bound
论文作者
论文摘要
本文研究了动态系统的最佳状态估计，其传输函数可以是非线性，并且输入噪声可以是任意分布的。我们的算法与常规的扩展卡尔曼滤波器（EKF）和粒子滤波器（PF）有所不同，因为它不仅估计了状态矢量，而且还估计了Cramer-Rao结合（CRB）（CRB），它是精确指标。该算法结合了状态估计，CRB和传入的新测量，根据最大似然（ML）标准更新状态估计。为了说明所提出的自动驾驶方法的有效性，我们将其应用于基于距离和多普勒偏移的嘈杂测量值的车辆的位置和速度。仿真结果表明，所提出的算法比标准EKF和PF更准确地达到估计值。
This paper studies the optimal state estimation for a dynamic system, whose transfer function can be nonlinear and the input noise can be of arbitrary distribution. Our algorithm differs from the conventional extended Kalman filter (EKF) and the particle filter (PF) in that it estimates not only the state vector but also the Cramer-Rao bound (CRB), which serves as an accuracy indicator. Combining the state estimation, the CRB, and the incoming new measurement, the algorithm updates the state estimation according to the maximum likelihood (ML) criterion. To illustrate the effectiveness of the proposed method for autonomous driving, we apply it to estimate the position and velocity of a vehicle based on the noisy measurements of distance and Doppler offset. Simulation results show that the proposed algorithm can achieve estimation significantly more accurate than the standard EKF and the PF.
