基于非线性最优滤波的GPS导航系统平滑交互多模型

M. Malleswaran, V. Vaidehi, H. Ramesh, P. Malin Bruntha
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引用次数: 1

摘要

介绍了一种用于GPS导航系统的交互式多模型无气味双滤波平滑算法(IMM-UTFS)。Unscented卡尔曼滤波器(UKF)通过Unscented变换来传播其状态估计和协方差,而不需要任何线性化。交互多模型(IMM)算法通过将多个匹配不同运动模型的并行滤波器的单个估计组合在一起得到其估计。本文在IMM算法中采用Unscented双滤波平滑算法,提高了导航估计精度。仿真结果表明,与UKF等传统滤波器和IMM-UKF等多模型滤波器相比,IMM-UTFS能够提高整体导航精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non linear optimum filter based smoothing Interacting Multiple Model for GPS navigation system
An Interacting Multiple Model Unscented Two Filter Smoother (IMM-UTFS) approach for GPS navigation system is introduced in this paper. The Unscented Kalman Filter (UKF) propagates its state estimate and covariance through unscented transform without any need of linearization. The Interacting Multiple Model (IMM) algorithm obtains its estimate by combining the individual estimate from a number of parallel filters matched to different motion models of the vehicle. This paper adopts the Unscented Two Filter Smoother to the IMM algorithm to increase the navigation estimation accuracy. The dynamic behavior of the vehicle is analyzed and the simulation results show that IMM-UTFS can improve overall navigation accuracy as compared to traditional filters like UKF and multiple model filters like IMM-UKF.
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