Recursive Extended Finite-Memory Positioning Algorithm for Robust UAV Positioning in GNSS-Denied Environment

초록

Recently, the operational domain of uncrewed aerial vehicles (UAVs) has expanded from outdoor environments to indoor spaces such as factories, power plants, and tunnels. In these environments, where the Global Navigation Satellite Systems (GNSS) is unavailable, and the risk of collision with surrounding structures is high, a positioning system with high accuracy and fast update rates is essential. In this article, we propose a novel 3-D positioning algorithm for UAVs operating in a GNSS-denied environment. The proposed algorithm is based on sensor fusion of the inertial measurement unit (IMU) and ultra-wideband-based wireless sensor network (UWB-WSN). The proposed algorithm, referred to as the recursive extended finite-memory positioning (REFMP), features a unique finite-memory (FM) structure that estimates the current position using only a limited set of recent information. This FM structure endows the algorithm with robustness against measurement model errors, linearization errors, and uncertainties in initial position information. We evaluated the performance of the proposed algorithm through both simulations and real-world experiments under various challenging conditions, including UAV collisions with walls, aggressive maneuvers inducing sensor measurement errors, and scenarios with uncertain initial position information. By comparing the proposed algorithm with state-of-the-art UAV positioning algorithms based on IMU and sensor fusion, we demonstrate the superior positioning accuracy and robustness of REFMP under harsh conditions.

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