University of Oulu

M. Cheng, M. R. K. Aziz and T. Matsumoto, "Integrated Factor Graph Algorithm for DOA-Based Geolocation and Tracking," in IEEE Access, vol. 8, pp. 49989-49998, 2020, doi: 10.1109/ACCESS.2020.2979510

Integrated factor graph algorithm for DOA-based geolocation and tracking

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Author: Cheng, Meng1; Aziz, Muhammad Reza Kahar2; Matsumoto, Tad1,3
Organizations: 1School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi 923-1211, Japan
2Department of Electrical Engineering, Institut Teknologi Sumatera (ITERA), Lampung Selatan 35365, Indonesia
3Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-05-08


This paper proposes a new position tracking algorithm by integrating extended Kalman filter (EKF) and direction-of-arrival (DOA)-based geolocation into one factor graph (FG) framework. A distributed sensor network is assumed for detecting an anonymous target, where the process and observation equations in the state space model (SSM) are unknown. Importantly, the predicted state information can be utilized not only for filtering, but also for enhancing the observation process. To be specific, by taking the prediction into account as the a priori, a new FG scheme is proposed for GEolocation, denoted by FG-GE. The benefits are two-fold, compared with the conventional geolocation scheme which does not rely on the a priori information. First of all, significant performance improvement can be observed, in terms of the root mean square error (RMSE), when severe sensing errors are suddenly encountered. Furthermore, the proposed FG-GE can achieve dramatic reduction of computational complexity. In addition, this paper also proposes the use of a predicted Cramer-Rao lower bound (P-CRLB) to dynamically estimate the observation error variance, which demonstrates more robust tracking performance than that with only fixed average variance approximation.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 8
Pages: 49989 - 49998
DOI: 10.1109/ACCESS.2020.2979510
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Hitachi, Ltd., and in part by the Hitachi Kokusai Electric, Inc.
Copyright information: © The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see