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Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration
- Source :
- Electronics, Vol 10, Iss 1513, p 1513 (2021), Electronics, Volume 10, Issue 13
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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.
- 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
Subjects
Details
- ISSN :
- 20799292
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Electronics
- Accession number :
- edsair.doi.dedup.....040bf2d1338a4ed3bcecf1504be9d13f