摘要
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,甚至有可能发散,传统常规卡尔曼滤波无法解决上述问题。提出一种根据极大似然准则的自适应卡尔曼滤波算法,利用滤波残差的均值和方差不断对卡尔曼滤波的状态噪声方差阵和测量噪声方差阵进行实时修正,提高滤波器对模型不确定性和噪声变化的适应能力和鲁棒性。仿真表明,所提出的组合导航滤波器能够满足无人机导航任务的要求,并且具有很好的导航精度和稳定性。
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 faces with the uncertain problems of system noise and measurement noise statistical characteristics of combined navigation in the course of the flight, which leads to low ac- curacy of filter, poor stability, and diverging. Using conventional kalman filter can not solve this problem. So an a- daptive kalman filtering algorithm based on the maximum likelihood criterion was proposed, which can correct con- stantly state noise variance array and measurement noise variance array of kalman filter by using the mean and vari- ance of filter residual in real time, and improve the filter model uncertainty and the noise of the ability to adapt to changes and robustness. The simulation shows that the proposed integrated navigation filter can satisfy the require- ments of the UAV navigation task, and has good navigation accuracy and stability.
出处
《计算机仿真》
CSCD
北大核心
2012年第7期51-54,64,共5页
Computer Simulation
关键词
无人机
自适应滤波
组合导航
UAV
Self-adaptive filter
Integrated navigation
