Recurrent Neural Network based Sensor Fusion Algorithm for Alternative Position, Navigation and Timing

Jeon, Byoung-Ju; Petrunin, Ivan; Tsourdos, Antonios

doi:10.1109/dasc52595.2021.9594472

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…AI instead can bring new horizons if proper training data is provided. RNNs have been used in fusion frameworks as described in [17,18], combing INS and GNSS data. Some of the disadvantages in using such framework, is its high computational cost and its difficulty in storing data for long term [19].…”

Section: Figure 1 Nlos Multipath Environmentmentioning

confidence: 99%

GNSS/INS/VO fusion using Gated Recurrent Unit in GNSS denied environments

Negru

Geragersian

Petrunin

et al. 2023

AIAA SCITECH 2023 Forum

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Urban air mobility is a growing market, which will bring new ways to travel and to deliver items covering urban and suburban areas, at relatively low altitudes. To guarantee a safe and robust navigation, Unmanned Aerial Vehicles should be able to overcome all the navigational constraints. The paper is analyzing a novel sensor fusion framework with the aim to obtain a stable flight in a degraded GNSS environment. The sensor fusion framework is combining data coming from a GNSS receiver, an IMU and an optical camera under a loosely coupled scheme. A Federated Filter approach is implemented with the integration of two GRUs blocks. The first GRU is used to increase the accuracy in time of the INS, giving as output a more reliable position that it is fused, with the position information coming from, the GNSS receiver, and the developed Visual Odometry algorithm. Further, a master GRU block is used to select the best position information. The data is collected using a hardware in the loop setup, using AirSim, Pixhawk and Spirent GSS7000 hardware. As validation, the framework is tested, on a virtual UAV, performing a delivery mission on Cranfield university campus. Results showed that the developed fusion framework, can be used for short GNSS outages.

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Section: Figure 1 Nlos Multipath Environmentmentioning

confidence: 99%