Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

Sameir A. Aziez,György Cserey,Tamás Zsedrovits,Ahmed Hameed Reja,Nawar Al-Hemeary

doi:10.3390/electronics10131513

Sameir A. Aziez, György Cserey + Show 3 more

Open Access

https://doi.org/10.3390/electronics10131513

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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.

Highlights

The INS and GNSS data used for the calculations were extracted from aircraft autopilot logs
GNSS/INS system: Scenario (I): This scenario compares the positions in three coordinates (X, Y, Z) for INS, GNSS data with k nearest neighbor (KNN) predictor; a-1 shows the drone trajectory in X-axis without using KNN predictor; a-2 shows the drone trajectory in X-axis by using the KNN predictor; b-1 shows the drone trajectory in Y-axis without using KNN predictor; b-2 shows the drone trajectory in Y-axis by using the KNN predictor; c-1 shows the drone trajectory in Z-axis without using KNN predictor; c-2 shows the drone trajectory in Z-axis by using the KNN predictor
INS and GNSS must be achieved to estimate the errors from the two systems simultaneously, this paper adds a new technique of using a predictor on GNSS output before integrating with the INS

Summary

Introduction

A predictor should be used to predict the in-between sampling instants of the GNSS receiver in order to synchronize with INS data before the integration process and overcome the GNSS receiver’s stopping time (blocking data) when the signal is lost for a few seconds [12]. Different prediction algorithms are used in the output of the GNSS or GPS receivers before integration with the INS data for synchronization purposes. The k nearest neighbor (KNN) predictor algorithm is used to predict in between sampling instants of GNSS receiver data based on actual GNSS data attributes. The KNN predictor algorithm is used at the GNSS receiver’s output to predict between samples instant of GNSS data for the synchronization purpose with INS.

Ksamples

Kalman

Simulation Results

Comparing

Conclusions