Abstract
In this paper a practical method for estimating the full kinematic state of a land-vehicle, along with sensors, low-cost inertial measuring unit (IMU), and Global Positioning System (GPS). However, this INS-GPS system requires in generally a robust architecture such as an Extended Kalman Filter (EKF) approach in direct configuration, by reason of its properties of extensive evaluations of nonlinear equations. In addition, a practical approach for controlling the Degree of Observability (DoO) in GPS-INS integrated systems is used in these tests. Other than that, traditional observability analysis is inadequate for a long navigation trajectories matrix that becomes very large, such that it rises computational difficulties. Two datasets are used to verify the efficacy of the proposed approach against the existing GPS-INS integration scheme. The first set is real road data collected from a higher grade IMU at each (0.01 s) that was combined with DGPS data at each (1 s) in order to obtain the assumed true solution for the trajectory. The second one is real test data collected during land-vehicle trajectory. The implementation consists of three main algorithms that as well namely: Strapdown (Dead Reckoning DR), DoO, and EKF algorithms. The results are shown, implementation of the both approaches based on EKF and concept of DoO in GPS/INS Integrated systems are enough robust for its use along with low-cost sensors.
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Dahmane, B., Lejdel, B., Harrats, F., Nassar, S., Hadj Abderrahmane, L. (2022). GPS/IMU in Direct Configuration Based on Extended Kalman Filter Controlled by Degree of Observability. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_14
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