Receiver autonomous integrity monitoring algorithm based on robust extended kalman filter

2016 ◽  
Author(s):  
Zhen Li ◽  
Dan Song ◽  
Fei Niu ◽  
Chengdong Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Integrity Monitoring ◽  
Receiver Autonomous Integrity Monitoring ◽  
Monitoring Algorithm

scholarly journals Analyses of the sensitivity of multi-constellation advanced receiver autonomous integrity monitoring vertical protection level availability to error parameters and a failure model over China

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401877619 ◽  
Author(s):  
Xueen Zheng ◽  
Ye Liu ◽  
Guochao Fan ◽  
Jing Zhao ◽  
Chengdong Xu
Keyword(s):  
Satellite System ◽  
Protection Level ◽  
Integrity Monitoring ◽  
System Service ◽  
Service Stations ◽  
Receiver Autonomous Integrity Monitoring ◽  
Global Navigation Satellite ◽  
Range Error ◽  
Monitoring Algorithm ◽  
Advanced Receiver

The availability of advanced receiver autonomous integrity monitoring for vertical guidance down to altitudes of 200 ft (LPV-200) is discussed using real satellite orbit/ephemeris data collected at eight international global navigation satellite system service stations across China. Analyses were conducted for the availability of multi-constellation advanced receiver autonomous integrity monitoring and multi-fault advanced receiver autonomous integrity monitoring, and the sensitivity of availability in response to changes in error model parameters (i.e. user range accuracy, user range error, Bias-Nom and Bias-Max) was used to compute the vertical protection level. The results demonstrated that advanced receiver autonomous integrity monitoring availability based on multiple constellations met the requirements of LPV-200 despite multiple-fault detections that reduced the availability of the advanced receiver autonomous integrity monitoring algorithm; the advanced receiver autonomous integrity monitoring availability thresholds of the user range error and Bias-Nom used for accuracy were more relevant to geographic information than the user range accuracy and Bias-Max used for integrity at the eight international global navigation satellite system service stations. Finally, the possibility of using the advanced receiver autonomous integrity monitoring algorithm for a Category III navigation standard is discussed using two sets of predicted errors, revealing that the algorithm could be used in 79% of China.

A measurement domain receiver autonomous integrity monitoring algorithm

GPS Solutions ◽  
2005 ◽  
Vol 10 (2) ◽  
pp. 85-96 ◽  
Author(s):  
Shaojun Feng ◽  
Washington Y. Ochieng ◽  
David Walsh ◽  
Rigas Ioannides
Keyword(s):  
Integrity Monitoring ◽  
Measurement Domain ◽  
Receiver Autonomous Integrity Monitoring ◽  
Monitoring Algorithm

scholarly journals An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Haiyun Yao ◽  
Hong Shu ◽  
Hongxing Sun ◽  
B. G. Mousa ◽  
Zhenghang Jiao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Unscented Kalman Filter ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Integrity Monitoring ◽  
Observation Vector ◽  
Indoor Positioning System ◽  
Monitoring Algorithm

AbstractIndoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

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