发表机构
Lahore University of Management Sciences; COMSATS University Islamabad（拉合尔管理科学大学; 科曼萨茨伊斯兰堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多传感器数据同时含异常值导致状态估计不准确的问题，提出基于MAP原理和新型NIR检验的EKF，提升估计精度与计算效率。
AI 中文摘要
在真实世界条件下部署的传感器，由于模型不确定性、周围环境变化和/或数据丢失，经常产生被异常值污染的测量值。因此，管理这些异常值对于状态估计至关重要，以避免不准确的估计和结果可靠性的降低。为了解决这个问题，我们引入了一种基于最大后验（MAP）原理的新型扩展卡尔曼滤波器（EKF），适用于异常值同时在多个维度出现的情景。为了在滤波过程中检测异常值，我们引入了归一化创新比率（NIR）检验的一种新变体，并将其嵌入到EKF框架中。我们的方法提高了状态估计过程的估计精度和计算效率，即使当来自多个传感器的数据同时包含异常值时也是如此。
英文摘要
Sensors deployed in real-world conditions often produce measurements corrupted by outliers due to model uncertainties, changes in the surrounding environment, and/or data loss. As a result, managing these outliers becomes crucial for state estimation to avoid inaccurate estimations and a reduction in the reliability of results. To address this issue, we introduce a novel form of extended Kalman filter (EKF) based on the maximum a posteriori (MAP) principle for scenarios where outliers simultaneously occur in multiple dimensions. For detecting outliers during the filtering process, we introduce a novel variant of the normalized innovation ratio (NIR) test and embed it within the EKF framework. Our approach enhances the estimation accuracy and computational efficiency of state estimation process even when data from several sensors simultaneously contain outliers.
Journal refIEEE Sensors Letters, vol. 8, no. 10, Oct. 2024, Art. no. 7004804
DOI：10.1109/LSENS.2024.3452205
