自适应高阶无迹增量卡尔曼滤波算法

Adaptive High Order Unscented Incremental Kalman Filtering

在工程实际中,由于环境因素的影响、测量设备的不稳定性、模型和参数的选取不当等往往会对量测方程带来未知的系统误差。针对这一问题,提出了一种自适应高阶无迹增量卡尔曼滤波算法。首先,利用增量建模技术建立增量量测方程。其次,将其与高阶无迹卡尔曼滤波器相结合,并引入自适应加权因子对滤波发散进行抑制,发展出一种自适应增量滤波算法。计算机仿真实验表明,新算法能够成功消除这种未知的系统误差,提高估计精度和稳定性,具备良好的应用前景。

In actual engineering,there usually are unknown system errors to the measurement equation due to the effect of environmental factors,the instability of measurement devices and improper models and parameters.To solve this problem,an adaptive high order unscented incremental Kalman filter is put forward.Firstly,incremental measurement equation is established by incremental modeling technology.Then,combining with high order unscented Kalman filter,an adaptive incremental filtering algorithm is developed by introducing an adaptive weighted factor which can restrain the filtering divergence.Computer simulations show that the proposed algorithm can successfully eliminate the unknown system error.Meanwhile,the novel method can improve the estimation accuracy and stability,and has good application prospect.