A Hybrid Model and Learning-Based Adaptive Navigation Filter

The fusion between an inertial navigation system and global navigation satellite systems is regularly used in many platforms, such as drones, land vehicles, and marine vessels. The fusion is commonly carried out in a model-based extended Kalman filter framework. One of the critical parameters of the...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 11
Main Authors: Or, Barak, Klein, Itzik
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Adaptive algorithm
Artificial neural networks
Covariance
deep neural network (DNN)
Drone vehicles
Extended Kalman filter
Global navigation satellite system
global navigation satellite system (GNSS)
inertial measurement unit (IMU)
Inertial navigation
inertial navigation system (INS)
Inertial sensing devices
Kalman Filter
Kalman filters
Machine learning
machine learning (ML)
Navigation
Navigation satellites
Navigation systems
Noise measurement
Position errors
quadcopter
supervised learning (SL)
unmanned autonomous vehicles
Vehicle dynamics
vehicle tracking
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:The fusion between an inertial navigation system and global navigation satellite systems is regularly used in many platforms, such as drones, land vehicles, and marine vessels. The fusion is commonly carried out in a model-based extended Kalman filter framework. One of the critical parameters of the filter is the process noise covariance. It is responsible for the real-time solution accuracy, as it considers both vehicle dynamics uncertainty and the inertial sensors quality. In most situations, the process noise covariance is assumed to be constant. Yet, due to vehicle dynamics and sensor measurement variations throughout the trajectory, the process noise covariance is subject to change. To cope with such situations, several adaptive model-based Kalman filters were suggested in the literature. In this article, we propose a hybrid model and learning-based adaptive navigation filter. We rely on the model-based Kalman filter and design a deep neural network (DNN) model to tune the momentary system noise covariance, based only on the inertial sensor readings. Once the process noise covariance is learned, it is plugged into the well-established model-based Kalman filter. After deriving the proposed hybrid framework, field experiment results using a quadrotor are presented and a comparison to model-based adaptive approaches is given. We show that the proposed method obtained an improvement of 25% in the position error. Furthermore, the proposed hybrid learning method can be used in any navigation filter and also in any relevant estimation problem.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3197775