This paper develops novel covariance and square‐root factor formulations of a consider‐neglect Kalman filter for navigation applications. The proposed filter partitions system parameters into three distinct categories: those to be estimated by the filter, those whose contribution to the system are considered without being explicitly estimated, and those with sufficiently low effect on the system such that their contribution can be neglected altogether. Discussion on appropriate selection of parameters to be considered and neglected is provided with specific attention given to descent‐to‐landing navigation. Monte Carlo simulations and analysis are performed to assess the performance of the developed square‐root consider‐neglect filter in a descent‐to‐landing navigation scenario.

中文翻译：

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考虑和忽略参数的递归过滤器的实现和分析的模块化框架

本文为导航应用开发了一种可忽略不计的卡尔曼滤波器的新颖协方差和平方根公式。所提出的滤波器将系统参数划分为三个不同的类别：由滤波器估计的参数，没有显式估计而考虑对系统的贡献的参数以及对系统影响足够低的参数，以使它们的贡献可以完全忽略。论参数适当选择要考虑的，而忽略配备给后裔到着陆导航特别注意。进行了蒙特卡洛模拟和分析，以评估已开发的平方根考虑忽略滤波器在下降到着陆的导航场景中的性能。