Kalman Filtering Versus Voting: Which Strategy is Best for Multi-Sensor Localization?

Lan, Tianxiang; Geffert, Arne; Dodinoiu, Andreas; Becker, Ulrik

doi:10.23919/enc48637.2020.9317318

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the estimation method based on analytical model is represented by Kalman filter and its extension method [10], [11]. In reference [12], the application of two out of three voting and Kalman filter in train integrated positioning system is analyzed, and various sensors are divided into three groups, each group includes a GNSS receiver and an inertial navigation IMU unit (or an axle speed sensor).…”

mentioning

confidence: 99%

The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

Wang,

Gao,

Cao

et al. 2024

Chinese J. Elect.

View full text Add to dashboard Cite

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar, the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

show abstract

mentioning

confidence: 99%