基于扩展卡尔曼滤波的小型固定翼无人机姿态估计方法分析

Attitude Estimation of Fixed-wing UAVs with Extended Kalman Filter

随着小型固定翼无人机的发展,由于其成本低、体积小的特点受到越来越多的企业青睐,但是由于成本问题,导致大部分企业只能选择成本较低的传感器,因此,针对固定翼无人机的姿态估计变得困难。为了解决这个问题,需要研究出一种新的姿态估计方法。利用离散型扩展卡尔曼滤波原理,结合常用的惯性导航系统和卫星定位系统,对小型无人机姿态进行估计解算。仿真结果显示,该方法可以准确地估计出无人机的姿态信息,其最大误差不超过2°,通过与未进行滤波算法处理的结果相比较,大大地减小了姿态估计的误差,因此这种基于扩展卡尔曼滤波的姿态估计算法针对小型无人机的姿态估计是可靠的。

As developing of small fixed-wing Unmanned Aerial Vehicles （UAVs）, the global cost and size of the UAVs has been reduced. Therefore, a lower price sensor has been chosen on cost issue. This makes it difficult to estimate the attitude of fixed-wing UAVs. To solve this problem, developed an efficient method about attitude estimation. An Extended Kalman Filter （EKF） was used with Inertial Navigation System （INS） and satellite positioning system to estimate UAVs＇ attitude. Simulation results show that the attitude of the UAVs has been accurately estimated, with maximum error standard deviations rounding two degrees. Compared with attitude without EKF, this method has reduced deviation a lot. So, this attitude estimation method is reliable.