Enhanced Heading Correction Using an Extended Kalman Filter with IMU and Sparse Location Data

Hossein Shoushtari, Harald Sternberg

Peer Reviewed

Abstract:	Positioning in GNSS-challenged environments is a major hurdle in real-time applications such as augmented reality, robotics, and autonomous systems, where continuous and accurate localization is essential. The advent of powerful wireless-enabled devices equipped with multi-sensor systems, particularly inertial measurement units (IMUs), offers new possibilities for localization. However, inertial localization (IL) systems, while capable of providing relative displacements, suffer from drift over time, necessitating external corrections. This research focuses on enabling long-term navigation in environments with sparse correction signals. To address this challenge, we propose a novel heading correction method that efficiently integrates sparse positional data with IL results while minimizing the reliance on external support points. Since positional corrections are often uncertain, we propose an extended Kalman Filter (EKF) mechanism to autonomously compute heading corrections, refine pose estimates, and generate a quality assessment matrix. Experimental results in the case study on indoor positioning in a 5G-testbed demonstrate that the integrating IMU data with sparse positional updates supports real-life and real-time pedestrian navigation. We evaluate performance under varying positional update rates, ranging from one sparse update every 20 seconds to one every few minutes. Nevertheless, this method can be used for a variety of other applications in addition to pedestrian navigation.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	2694 - 2701
Cite this article:	Shoushtari, Hossein, Sternberg, Harald, "Enhanced Heading Correction Using an Extended Kalman Filter with IMU and Sparse Location Data," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 2694-2701. https://doi.org/10.33012/2025.20435
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