应用联邦自适应UKF的卫星多传感器数据融合

在卫星自主导航系统中，一方面，系统状态模型存在难以准确建模的问题，要求信息融合算法具有一定的自适应性；另一方面，系统的量测模型通常具有较强的非线性，又要求信息融合算法在强非线性下保持较高的精度和鲁棒性. 针对以上两个问题，本文提出了基于星敏感器、红外地平仪、磁强计、雷达高度计、紫外敏感器的多信息联邦自适应UKF组合导航方案，该方案将多个导航传感器提供的信息在联邦滤波器里融合，并采用自适应UKF算法构建联邦滤波器的子滤波器. 采用这种方案，可有效组织并充分利用导航传感器提供的导航信息，并且系统模型具有一定的自适应性. 数字仿真结果表明，与传统的联邦卡尔曼滤波方法相比，该方法更适合于非线性较强、系统模型参数不准确的场合，有效提高了导航精度.

Multi-sensor Data Fusion for Satellite Based on Federate Adaptive Unscented Kalman Filter

Generally speaking, in a satellite autonomous navigation system, it is not easy to build a state model of the practical system, which requires the information fusion algorithm having some self-adaptive capability. However, due to nonlinearity in the system measurement model, the information fusion algorithm must maintain high accuracy and robustness in a strongly nonlinear circumstance. To this end, an advanced federal adaptive unscented Kalman filter(UKF) method is proposed based on star sensor, infrared horizon sensor, agnetometer, radar altimeter and ultraviolet sensor. This method combines information from multiple navigation sensors in the federated filter, and uses an adaptive UKF algorithm to build the local filter. With this method, information coming from navigation sensors can be effectively organized and fully utilized, and the system model possesses adaptability. Numerical simulation using the proposed method is compared to that only using a conventional federated Kalman filter. The results show that the proposed method is more suitable for systems that are highly nonlinear or have inaccurat parameters, and can make navigation more accurate.