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Enhanced motion and sizing of bank in moving-bank MMAE

机译：移动银行MMAE中银行的增强运动和大小

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摘要

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. A new online parameter discretization method is developed based on the probabilities associated with the generalized chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This new algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.
机译：这项研究的重点是提供面对潜在的重大参数变化（例如系统组件故障）时生成精确参数估计值的方法。标准的多模型自适应估计（MMAE）算法使用一组Kalman滤波器，每个滤波器都基于系统的不同模型。 MMAE中的参数离散化是指选择由元素Kalman滤波器假定的参数值，并且动态重新声明这种离散化会产生移动库MMAE。一种新的在线参数离散化方法是基于与MMAE中元素卡尔曼滤波器的残差信息形成的广义卡方随机变量相关的概率而开发的。该新算法通过对飞机导航系统进行计算机仿真来验证，该系统在尝试成功成功降落飞机时会受到干扰/干扰。

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