摘要
建立了GPS/INS(Global Positioning System/InertialNavigation System)紧组合模式下的线性/非线性模型,针对SRUKF(Square Root Unscented Kalman Filter)不能与该模型达到最佳匹配的问题,提出一种基于混合滤波思想的SRUKF。将时间更新分为两个阶段:利用线性状态方程得到状态的一步预测并根据该值构造Sigma点,求取Sigma点加权和以实现对量测值的一步预测。新的算法省去了线性状态方程的UT(Unscented Transform)环节,在保证滤波精度的前提下,降低了计算量。将改进的算法应用于已建立的模型,仿真实验表明,相比于UKF(UnscentedKalman Filter)及SRUKF,该算法不仅能够有效获得导航参数的精确估计,还具有较强的实时性,发挥出了算法的最佳性能。
A linear/nonlinear model is established for the GPS/INS（Global Positioning System/Inertial Navigation System） tight integrated mode, for the model could not achieve the best match for the SRUKF（Square Root Unscented Kalman Filter）, improved SRUKF based on the hybrid filter is presented. The time update is divided into two phases： to use the linear equation of state to get the one step prediction, generating the Sigma points according to the value, to sum the weights of Sigma points in order to achieve the one step prediction of measured value. The new algorithm eliminates the need to unscented transform for the linear equation of state, under the premise of ensuring the precision of the filter, reducing the amount of computation. The improved algorithm is applied to the established model. The simulation results show that compared to UKF and SRUKF, the algorithm not only gets the accurate estimation of navigation parameters effectively, but also has a strong real-time perfor- mance, playing the best performance of the algorithm as well.
出处
《电子信息对抗技术》
2013年第2期24-29,共6页
Electronic Information Warfare Technology
