姿态稳定在整个四旋翼飞行器系统中起到了关键的作用,但由于模型具有高度非线性,捷联惯导测量单元中陀螺仪积分误差大,加速度在时变环境误差大等问题。为了得到更加精准的姿态角,在扩展卡尔曼滤波(EKF)的基础上研究姿态性能,要解决扩展卡尔曼滤波算法中的预测误差P的发散和过程噪声矩阵Q和测量噪声矩阵R的设置的问题,提出了UD分解滤波结合自适应过程噪声矩阵Q和测量噪声矩阵R调整。经过仿真和实际测试与互补滤波和常规EKF算法对比,提出的方法在估计精度和收敛速度都明显好于互补滤波算法和卡尔曼滤波算法,能够在时变的环境下得到更加精准稳定的姿态角。 Attitude stability plays a key role in the whole quadrotor system. However, due to the high degree of non-linearity of the model, gyroscope integral errors in SINS measurement units and large errors in time-varying environmental errors occur. In order to obtain a more accurate attitude angle, we study attitude performance based on Extended Kalman Filter (EKF), and we must solve the problem of the dispersion of prediction error P and the setting of process noise matrix Q and measurement noise matrix R in Extended Kalman Filter Problem, proposed UD decomposition filter combined with adaptive process noise matrix Q and measurement noise matrix R adjustment. After the simulation and actual testing and complementary filtering and conventional EKF algorithm comparison, the proposed method in the estimation accuracy and convergence speed are significantly better than the complementary filtering algorithm and Kalman filter algorithm, can get more accurate and stable attitude angle in a time-varying environment .