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Residual‐based multi‐filter methodology for all‐source fault detection, exclusion, and performance monitoring
NAVIGATION ( IF 2.2 ) Pub Date : 2020-08-25 , DOI: 10.1002/navi.384
Juan Jurado 1 , John Raquet 2 , Christine M. Schubert Kabban 3 , Jonathon Gipson 4
Affiliation  

All‐source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all‐source systems. As part of an overall all‐source assured or resilient navigation objective, this research contributes a fault‐ and sensor‐agnostic fault detection and exclusion method that can provide the user with performance guarantees without constraining the statistical distribution of the fault. The proposed method is compared against normalized solution separation approaches using Monte‐Carlo simulations in a 2D non‐GPS navigation problem.

中文翻译:

基于残差的多过滤器方法,用于全源故障检测,排除和性能监控

在过去的十年中,随着可行的替代传感器技术的发展,全源导航变得越来越重要。但是,随着通知系统的传感器的数量和类型增加,由于传感器建模错误,信号干扰和未检测到的故障而损坏系统的可能性也随之增加。尽管对后者进行了广泛的研究,但大多数现有方法已将故障限制在偏差范围内,并且设计算法的前提是假设同时冗余,同步传感器具有有效的测量模型,而所有源系统都无法保证这些。作为总体上有来源保证或具有弹性的导航目标的一部分,该研究为故障和传感器不可知的故障检测和排除方法做出了贡献,该方法可以为用户提供性能保证,而不会限制故障的统计分布。在二维非GPS导航问题中,将所提出的方法与使用蒙特卡罗模拟的归一化解决方案分离方法进行了比较。
更新日期:2020-08-25
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