基于通用FLAC的模糊自适应UKF算法及其应用

A Fussy Adaptive UKF Algorithm Based on the Generic FLAC and Its Application

针对量测噪声方差统计值未知的非线性UKF(Unscented Kalman Filter)滤波问题,提出了一种基于通用FLAC(FussyLogic Adaptive Controller)的模糊自适应UKF算法。在标准的非线性UKF算法基础上,以残差的实际方差与理论方差的比值作为FLAC的输入,使FLAC对滤波模型的依赖性减弱,强化了模糊自适应UKF方法的通用性;在对未知的量测噪声方差阵进行动态调节的过程中设置了指数调节参数,可不同程度地放大或缩小方差阵调节的幅度,使算法的调节速度和精度得到控制。将算法应用于GPS/DR(Dead-Reckoning)组合导航系统中,仿真结果表明了该算法的有效性。

To consider the problem of non-linear UKF filtering under the circumstance of unknown covari- ance statistic of the measurement noise, a fussy adaptive UKF algorithm was presented based on the generic FLAC. On the foundation of standard UKF algorithm, the ratio of the theoretical value to the practical ones of the innovation＇s covariance was taken as the input of the FLAC to reduce the dependency of FLAC on the filter model,the generality of the fussy adaptive UKF algorithm was intensified; an exponential adjusting parameter was set in the procedure of adjusting the unknown covariance of measurement noise dynami-cally,the adjusted range could be amplified or minified to a different extent, so the adjusting velocity and precision of the algorithm was controlled. The algorithm was used in the GPS/DR integrated navigation system, simulation results show the efficiency of the algorithm.