A Hybrid Model and Learning-Based Adaptive Navigation Filter

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 modelbased 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.

Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

“…Learn the optimal inertial time step in a navigation filter Hybrid approach Matrice 300 Quadrotor [35] Estimate adaptive process noise covariance using deep learning Hybrid approach Matrice 300 Quadrotor [36] 7 Dogs Classify ataxia using inertial sensors End-to-end machine learning…”

“…To that end, in the last fifty years different model-based adaptive filters were proposed. In [36], we presented a hybrid learning, adaptive navigation fusion to perform the fusion between INS and the aiding sensor. We leverage the well-established, model-based Kalman filter and construct a learning network to adaptively adjust the process noise covariance using only the inertial sensor measurements as input to the network.…”