Tightly-coupled (TC) fusion of Inertial Measurement Units (IMUs) with Global Navigation Satellite Systems (GNSSs) is a common technique that provides high-rate positioning even under GNSS interruptions. In order to provide accurate positioning, errors of IMU and GNSS must be modelled and estimated by filtering techniques such as Extended Kalman Filter (EKF). Due to nonlinearity and stochastic characteristics of IMU and GNSS system and measurement models, robust filter design has been a challenge. Conventional design techniques use mission-specific fixed models and trial-and-error noise parameter tuning to design IμGNSS filters. These conventional techniques are inflexible and do not always lead to accurate designs as there are no ways to verify the filter ability to estimate sensors errors accurately. To address this challenge, this paper presents a flexible design framework and a systematic procedure for TC IμGNSS fusion. The framework utilizes symbolic engines to represent and linearize system and measurement models. Symbolic engines are flexible in new models and fusion algorithms development. In order to evaluate the estimation of sensors errors, an Inverse-Kinematics module is developed to generate error-free sensors measurements which can be contaminated by known errors. The filter parameters are tuned using Genetic Algorithms and the performance is evaluated based on the accuracy of estimating all states including the added known errors. The framework has been used to develop a quaternion-based EKF design and verified on real raw IμGNSS data. The results showed that the developed framework greatly reduces efforts to design robust and accurate fusion systems for TC IμGNSS integration.

Additional Metadata
Keywords Extended Kalman filter (EKF), Sensor fusion, tightly-coupled GNSS/IMU
Persistent URL dx.doi.org/10.1109/JSEN.2019.2935324
Journal IEEE Sensors Journal
Kourabbaslou, S.S. (Soroush Sheikhpour), Zhang, A. (Alan), & Atia, M. (2019). A Novel Design Framework for Tightly Coupled IμGNSS Sensor Fusion Using Inverse-Kinematics, Symbolic Engines, and Genetic Algorithms. IEEE Sensors Journal, 19(23), 11424–11436. doi:10.1109/JSEN.2019.2935324