A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration

This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyr...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 24; no. 22; p. 7331
Main Authors Tavares Jr, Adalberto J. A., Oliveira, Neusa M. F.
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 16.11.2024
MDPI
Subjects
Accelerometers
Analysis
Artificial satellites
Attitudes
Communications equipment
Electronics in navigation
Estimates
Inertial navigation
Inertial navigation (Aeronautics)
inertial sensor error model
INS/GNSS integration
Kalman filter tuning
Methods
Monte Carlo method
Navigation systems
Sensors
Simulation
Simulation methods
Velocity
United States
United Kingdom
New Jersey
Kalman filter tuning
inertial sensor error model
INS/GNSS integration
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Summary:This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria’s commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24227331