摘要
编队飞行中需要确定主机和从机之间的相对位置关系,就是要解决导航定位问题。针对无人机在编队飞行过程中由于机动性较大,惯性元器件测量容易出现偏差,进而影响系统的运动状态方程的情况,或者是在系统噪声与观测噪声的统计特性不能够准确得到的情况,提出了一种新的模糊自适应滤波方法。根据实时得到的量测新息的实际方差与理论方差的差值和量测新息的均值,按照判定条件选择适合的滤波方法,然后由设计的模糊推理系统在线实时调整系统噪声和量测噪声矩阵,或是调整状态误差协方差阵即强跟踪滤波,使无人机编队飞行即使在恶劣的环境下依然保持确定的队形不变。仿真结果表明,该算法具有较好的自适应效果。
In UAV formation flight, the relative position of the lead aircraft and follower needs to determined, which is to solve the navigation problem. Since the UAV has great maneuverability during formation flight, the inertial measurement components prone to having bias that may affect the motion state equations of the system. Sometimes statistical properties of the system noise and observation noise can not be obtained accurately. To solve the problems, an improved adaptive filtering method was proposed. According to the difference of the actual covariance of real-time measurement and the theoretical covariance, and the mean of the measurement values, an appropriate filtering method was selected based on the conditions. Then, the designed fuzzy inference systems was used to adjust the system noise matrix and the measurement noise matrix, or adjust the state error covariance matrix, thus the UAV formation flight could keep the original formation even under harsh environment. Simulation results show that the algorithm has fine adaptability.
出处
《电光与控制》
北大核心
2012年第10期87-91,共5页
Electronics Optics & Control
