1. Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration
- Author
-
György Cserey, Ahmed Hameed Reja, Tamás Zsedrovits, Sameir A. Aziez, and Nawar Al-Hemeary
- Subjects
TK7800-8360 ,Computer Networks and Communications ,Computer science ,Real-time computing ,02 engineering and technology ,01 natural sciences ,k-nearest neighbors algorithm ,Data prediction ,0202 electrical engineering, electronic engineering, information engineering ,Data synchronization ,Experimental work ,Electrical and Electronic Engineering ,Divergence (statistics) ,Blocking (radio) ,020208 electrical & electronic engineering ,010401 analytical chemistry ,Kalman filter ,KNN predictor algorithm ,0104 chemical sciences ,ultra-tight GNSS/INS ,Hardware and Architecture ,Control and Systems Engineering ,GNSS applications ,Signal Processing ,Electronics - Abstract
The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.
- Published
- 2021