Vol.119, No.2, 2019, pp.281-293, doi:10.32604/cmes.2019.04421
OPEN ACCESS
ARTICLE
RAIM Algorithm Based on Fuzzy Clustering Analysis
  • Shouzhou Gu1,*, Jinzhong Bei1, Chuang Shi2, Yaming Dang1, Zuoya Zheng4, Congcong Cui5
Chinese Academic of Surveying and Mapping, Beijing,100830, China.
GNSS Research Center, Wuhan University, Wuhan, 430079, China.
College of Geomatics, Shandong University of Science and Technology, Shandong Qingdao, 266590, China.
China Academy of Electronics and Information Technology, Beijing, 100041, China.
Beijing Xinxing Huaan Intelligence Technology Co., Ltd ., Beijing, 100160, China.
* Corresponding Author: Jinzhong Bei. Email: goldheal@casm.ac.cn.
(This article belongs to this Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract
With the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means (FCM). And on the condition of single or two gross errors, the performance of hard or fuzzy clustering analysis were compared. As the results of the experiments, the fuzzy clustering method based on FCM principle could detect multiple gross error effectively, also achieved the quality control of single point positioning and ensured better reliability results.
Keywords
Integrity, RAIM, FCM, single point positioning
Cite This Article
Gu, S., Bei, J., Shi, C., Dang, Y., Zheng, Z. et al. (2019). RAIM Algorithm Based on Fuzzy Clustering Analysis. CMES-Computer Modeling in Engineering & Sciences, 119(2), 281–293.