Rong Dandi, Wang Yi
Nanjing Research Institute of Electronics Technology, Nanjing 210039, China.
Sensors (Basel). 2024 Sep 20;24(18):6094. doi: 10.3390/s24186094.
The spatial target motion model exhibits a high degree of nonlinearity. This leads to the fact that it is easy to diverge when the conventional Kalman filter is used to track the spatial target. The Unscented Kalman filter can be a good solution to this problem. This is because it conveys the statistical properties of the state vector by selecting sampling points to be mapped through the nonlinear model. In practice, however, the measurement noise is often time-varying or unknown. In this case, the filtering accuracy of the Unscented Kalman filter will be reduced. In order to reduce the influence of time-varying measurement noise on the spatial target tracking, while accurately representing the a posteriori mean and covariance of the spatial target state vector, this paper proposes an adaptive noise factor method based on the Unscented Kalman filter to adaptively adjust the covariance matrix of the measurement noise. In this paper, numerical simulations are performed using measurement models from a space-based infrared satellite and a ground-based radar. It is experimentally demonstrated that the adaptive noise factor method can adapt to time-varying measurement noise and thus improve the accuracy of spatial target tracking compared to the Unscented Kalman filter.
空间目标运动模型具有高度非线性。这导致在使用传统卡尔曼滤波器跟踪空间目标时很容易发散。无迹卡尔曼滤波器可以很好地解决这个问题。这是因为它通过选择要通过非线性模型进行映射的采样点来传递状态向量的统计特性。然而，在实际中，测量噪声往往是时变的或未知的。在这种情况下，无迹卡尔曼滤波器的滤波精度将会降低。为了减少时变测量噪声对空间目标跟踪的影响，同时准确表示空间目标状态向量的后验均值和协方差，本文提出一种基于无迹卡尔曼滤波器的自适应噪声因子方法，以自适应调整测量噪声的协方差矩阵。本文使用天基红外卫星和地基雷达的测量模型进行了数值模拟。实验证明，与无迹卡尔曼滤波器相比，自适应噪声因子方法能够适应时变测量噪声，从而提高空间目标跟踪的精度。
