基于极大似然准则与滚动时域估计的自适应UKF算法

针对无迹卡尔曼滤波(unscented Kalman filter, UKF)在系统噪声统计特性未知或不准确的情况下滤波精度降低甚至发散的问题,提出一种基于极大似然准则与滚动时域估计的自适应UKF算法。首先根据极大似然准则构造关于系统噪声统计的估计模型;然后引入滚动时域策略对所提模型进行优化;最后采用序列二次规划方法求取噪声统计的估计值,得到带有噪声统计估计器的自适应UKF。提出的算法可以实现系统噪声统计的在线估计,克服了标准UKF的缺陷。通过惯性导航/全球定位系统(inertial navigation system/global positioning system, INS/GPS)组合导航系统中的应用实例,验证了提出算法的有效性。

Adaptive UKF based on maximum likelihood principle and receding horizon estimation

The filtering performance of unscented Kalman filter(UKF) would be degraded or even divergent due to unknown or inaccurate system noise statistics. An adaptive UKF based on maximum likelihood principle and receding horizon estimation is presented to address this problem. An estimation model of system noise statistics is constructed according to the maximum likelihood principle. Then, the receding horizon strategy is employed to optimize the above model. Eventually, the sequential quadratic programming is applied to calculate the estimation of noise statistics and the adaptive UKF with a noise statistics estimator can be obtained. It can realize online estimation of system noise statistics and overcome the defect of standard UKF. The performance of the proposed adaptive UKF is verified through the application examples in inertial navigation system/global positioning system integrated navigation system.