基于自适应 UKF 微型航姿系统噪声在线估计

针对先验噪声与系统真实噪声不符引起标准无迹卡尔曼(unscented Kalman filter,UKF)性能退化的情况,提出一种应用于非线性时变状态和参数联合估计的自适应UKF(adaptive unscented Kalman filter,AUKF)算法.根据新的协方差矩阵与相应估计值之间存在的误差,构建成本函数.采用梯度下降法进行在线预估,对噪声的协方差进行在线更新并反馈给标准的UKF.实验和仿真分析表明,与标准UKF相比,自适应UKF算法在精度上有较大的提高.对于时变噪声协方差不确定时,自适应UKF噪声在线估计的鲁棒性得到明显改善,验证了自适应UKF噪声在线估计模型的准确性和可行性.

Online noise estimation of mini-AHRS based on adaptive UKF algorithm

Considering that the prior noise of a normal unscented Kalman filter does not agree with its real behavior,an adaptive unscented Kalman filter algorithm applied to nonlinear joint estimation of both time-varying states and parameters is proposed. Firstly, a cost function is built based on the error between the covariance matrices of innovation and their corresponding estimations. Then the gradient descent method for online forecast is used. Finally, the noise covariance is online updated, the updated covariance feedback to the standard UKF. Experimental and simulation analysis indicates that adaptive UKF provides higher estimation precision than the nomal UKF algorithm. For time-varying noise covariance is uncertain,adaptive UKF online noise estimation robustness is improved significantly, and the accuracy and feasibility of online adaptive UKF noise estimation model is verified.