A Hybrid Model and Learning-Based Adaptive Navigation Filter

TL;DR

This paper introduces a hybrid navigation filter combining model-based Kalman filtering with a deep learning approach to adaptively tune process noise covariance, significantly improving position accuracy in drone navigation.

Contribution

It presents a novel hybrid model and learning-based adaptive filter that dynamically adjusts process noise covariance using sensor data, enhancing navigation accuracy.

Findings

Achieved 25% reduction in position error with the proposed method.

Demonstrated the hybrid approach's effectiveness on a quadrotor platform.

Showed the method's applicability to various navigation filters and estimation problems.

Abstract

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 is covariance 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 paper, 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 model to tune the momentary system noise covariance matrix, 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.