无人机组合导航系统的自适应算法研究

无人机组合导航滤波器的设计需要考虑器件和外部环境不稳定带来的影响。同时在飞行过程中也面临着组合导航系统噪声和量测噪声统计特性不确定问题,从而导致滤波精度低、稳定性差,还有可能发散。采用常规卡尔曼滤波无法解决此问题。为此研究了一种基于UKF的自适应卡尔曼滤波算法。在系统噪声统计特性未知时,此算法能自动平衡状态信息与观测信息在滤波结果中的权比,实时调整状态向量和观测向量的协方差,从而提高系统的性能。仿真结果显示,使用自适应UKF算法与普通的UKF算法相比,可以获得更优的导航精度和稳定性。

Adaptive Filter Research Based on Fine Alignment of SINS

It is essential to consider the affect of circumstance and stability of apparatus when designing a practical filter for UAV integrated navigation, meanwhile which also faced with the uncertain problems of system noise and measurement noise statistical characteristics of combined navigation in the course of the flight, Leads to filter low accuracy, poor stability, and may diverge, and using conventional kalman filter cannot solve this problem. So a kind of adaptive filtering algorithm based on the UKF is proposed, when the system noise statistical characteristics is unknown, adaptive UKF algorithm can balance automatically the right of the state information and observation information in the filtering result, so that to real-time adjust the covariance of the state vector and observation vector, thereby improving system performance. The simulation results show: Compared with normal UKF algorithm, the adaptive UKF algorithm can obtained a better navigation accuracy and rapidity.