Application of the Three State Kalman Filtering for Moving Vehicle Tracking
Application of the Three State Kalman Filtering for Moving Vehicle Tracking
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DOI：
10.1109/tla.2016.7530397
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发表时间：
2016-05-01
影响因子：
1.3
通讯作者：
Rivera, C. A.
中科院分区：
文献类型：
--
作者：
Olivera, R.;Olivera, R.;Rivera, C. A.
三态卡尔曼滤波器（KF）应用于运动车辆三状态（位置、速度、加速度）的最优估计；该问题的建模类似于存在加性高斯白噪声 (AWGN) 的线性时不变 (LTI) 系统。针对不同的过程加速噪声（协方差），对稳态滤波器参数进行了仿真和分析。我们表明，如果加速噪声和测量噪声较低，KF 估计会产生最小均方误差 (MSE)。
The three-state Kalman filter (KF) is applied in the optimal estimation of three state (position, velocity and acceleration) in a moving vehicle; the problem is modeled like linear time invariant (LTI) system in presence of additive white Gaussian noise (AWGN). The steady-state filter parameters have been simulated and analyzed for different process acceleration noise (covariance). We show that KF estimation produce minimum mean square error (MSE) if acceleration noise and measurement noise are lower.