Previous Abstract Return to Session C6 Next Abstract

Session C6: Terrestrial Signals of Opportunity-Based Navigation Systems

A Joint TOA and DOA Approach for Opportunistic Navigation with Beamformed 5G Signals
Shaghayegh Shahcheraghi and Zak (Zaher) M. Kassas, The Ohio State University

Over the past decade, the tremendous potential of signals of opportunity (SOPs) as a promising candidate for navigation has been unveiled [1]. SOPs include cellular [2–5], low Earth orbit (LEO) satellite signals [6], FM radio [7], and digital television [8]. Among the aforementioned SOPs, the most accurate navigation results have been demonstrated with cellular signals [9]. Cellular signals have attracted a lot of attention due to their desirable characteristics, such as abundance, geometric and spectral diversity, and large bandwidth. The recent research has shown the potential of cellular SOP for providing a meter-level accuracy for ground vehicle navigation [10] and sub-meter-level accuracy for unmanned aerial vehicles (UAV) navigation [11].
Different techniques have been studied for cellular-based navigation. Using time-of-arrival (TOA) estimates from fifth-generation (5G) signals, experimental results showed meter-level navigation accuracy [12]. The direction-of-departure (DOD) and user equipment (UE)’s position were estimated using multiple base stations in [13], resulting in sub-meter-level position accuracy. A positioning method was proposed in [14], where the direction-of-arrival (DOA) and TOA of the received signal were used to localize a UE. In [15], estimation of signal parameters via rotational invariant techniques (ESPRIT) was used to estimate the DOA and DOD of the signal. In [16], a long-term evolution (LTE)-based receiver was proposed, where DOA and TOA were jointly tracked to navigate the UE.
In the recent research, most of the DOA-based positioning methods and beamforming have focused on mmWave signals, characterized by highly directive beams [17–19]. However, since signal propagation at sub-6 GHz differs from that at mmWave, alternative methods are needed to be applied to 5G signals operating at frequencies below 6 GHz. Another category of DOA-based research explored in the literature is network-based methods, where all processing occurs on the network side using uplink signals [20, 21]. In [22], TOA and azimuth DOA of received sub-6 GHz 5G signals were estimated at the user, beamforming was implemented, and joint TOA and azimuth DOA were exploited to localize a stationary receiver.
In [22], we introduced a new algorithm for joint azimuth and elevation DOA estimation of downlink 5G signals, where the DOA was estimated through delay-Doppler processing. Using the estimated DOA, beamforming was implemented. Additionally, experimental results were presented to assess the performance of the proposed receiver in both stationary and moving scenarios, showing a carrier-to-noise-ratio (C/N0) improvement of 7 dB in the stationary scenario and about 6 dB in the moving scenario, compared to a non-beamformed 5G framework. The stationary receiver was localized with a final 2D error of 10.6 m using beamformed 5G signals. In the mobile scenario, the framework achieved a position root mean-squared error (RMSE) of 5.4 m, showing a reduction of 2.7 m in RMSE compared to a non-beamformed 5G framework [22].
In [22], joint TOA and DOA were leveraged for localizing a stationary receiver, while TOA alone was used for navigating a moving ground vehicle. In this paper, we will extend this approach by utilizing both TOA and DOA for the navigation of the moving vehicle, investigating the impact of adding DOA on navigation accuracy. Specifically, using a custom antenna array, we design a receiver for estimating DOA from downlink 5G signals, followed by an analysis of the DOA estimation accuracy. Additionally, we explore how the structure of the antenna array influences beamforming and DOA estimation. Finally, we assess how incorporating DOA alongside TOA affects the overall navigation performance.


References:
[1] J. Raquet et al., “Position, navigation, and timing technologies in the 21st century,” J. Morton, F. van Diggelen, J. Spilker, Jr., and B. Parkinson, Eds. Wiley-IEEE, 2021, vol. 2, Part D: Position, Navigation, and Timing Using Radio Signals-of-Opportunity, ch. 35–43, pp. 1115–1412.
[2] A. Xhafa, J. del Peral-Rosado, J. L´opez-Salcedo, and G. Seco-Granados, “Evaluation of 5G positioning performance based on UTDoA, AoA and base-station selective exclu- sion,” Sensors, vol. 22, no. 1, pp. 101–118, 2021.
[3] A. Abdallah and Z. Kassas, “Multipath mitigation via synthetic aperture beamforming for indoor and deep urban navigation,” IEEE Transactions on Vehicular Technology, vol. 70, no. 9, pp. 8838–8853, September 2021.
[4] P. Wang and Y. Morton, “Impact analysis of inter-cell interference in cellular networks for navigation applications,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 1, pp. 685–694, 2023.
[5] Z. Kassas, A. Abdallah, S. Shahcheraghi, J. Khalife, C. Lee, J. Jurado, S. Wachtel, J. Duede, Z. Hoeffner, T. Hulsey, R. Quirarte, and R. Tay, “I can hear you loud and clear: GNSS-less aircraft navigation with terrestrial cellular signals of opportunity,” IEEE Transactions on Aerospace and Electronic Systems, 2024.
[6] J. Kang, P. Eberechukwu N, J. Lee, H. Wymeersch, and S. S. Kim, “Fundamental performance bounds for carrier phase positioning in LEO-PNT systems,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2024, pp. 13 496–13 500.
[7] S. Zheng, J. Hu, L. Zhang, K. Qiu, J. Chen, P. Qi, Z. Zhao, and X. Yang, “FM-based positioning via deep learning,” IEEE Journal on Selected Areas in Communications, vol. 42, no. 9, pp. 2568–2584, September 2024.
[8] Z. Jiao, L. Chen, X. Lu, Z. Liu, X. Zhou, Y. Zhuang, and G. Guo, “Carrier phase ranging with DTMB signals for urban pedestrian localization and GNSS aiding,” Remote Sensing, vol. 15, no. 2, pp. 423–446, 2023.
[9] L. Italiano, B. Tedeschini, M. Brambilla, H. Huang, M. Nicoli, and H. Wymeersch, “A tutorial on 5G positioning,” IEEE Communications Surveys & Tutorials, pp. 1–48, 2024, accepted.
[10] I. Lapin, G. Granados, J. Samson, O. Renaudin, F. Zanier, and L. Ries, “STARE: Real-time software receiver for LTE and 5G NR positioning and signal monitoring,” in Proceedings of Workshop on Satellite Navigation Technology, April 2022, pp. 1–11.
[11] J. Del Peral-Rosado, A. Yildirim, S. Schlotzer, P. Nolle, S. Razavi, S. Parsawa, R. Mund- lamuri, F. Kaltenberger, N. Sirola, S. Garlaschi, L. Canzian, J. Talvitie, I. Lapin, and D. Flachs, “First field trial results of hybrid positioning with dedicated 5G terrestrial and UAV-based non-terrestrial networks,” in Proceedings of ION GNSS Conference, September 2023, pp. 1598–1605.
[12] A. Abdallah, K. Shamaei, and Z. Kassas, “Assessing real 5G signals for opportunistic navigation,” in Proceedings of ION GNSS Conference, 2020, pp. 2548–2559.
[13] E. Rastorgueva-Foi, M. Costa, M. Koivisto, K. Leppanen, and M. Valkama, “User posi- tioning in mmw 5G networks using beam-RSRP measurements and Kalman filtering,” in Proceedings of International Conference on Information Fusion, July 2018, pp. 1–7.
[14] A. Fascista, A. Coluccia, H. Wymeersch, and G. Seco-Granados, “Millimeter-wave downlink positioning with a single-antenna receiver,” IEEE Transactions on Wireless Communications, vol. 18, no. 9, pp. 4479–4490, 2019.
[15] W. Ma, C. Qi, and G. Li, “High-resolution channel estimation for frequency-selective mmwave massive MIMO systems,” IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3517–3529, 2020.
[16] K. Shamaei and Z. Kassas, “A joint TOA and DOA acquisition and tracking approach for positioning with LTE signals,” IEEE Transactions on Signal Processing, vol. 69, pp. 2689–2705, 2021.
[17] A. Bindle, T. Gulati, and N. Kumar, “A detailed introduction of different beamforming techniques used in 5G,” International Journal of Communication Systems, vol. 34, no. 5, p. e4718, 2021.
[18] J. Palacios, G. Bielsa, P. Casari, and J. Widmer, “Single-and multiple-access point in- door localization for millimeter-wave networks,” IEEE Transactions on Wireless Com- munications, vol. 18, no. 3, pp. 1927–1942, 2019.
[19] D. Yacong, “Channel estimation for massive MIMO systems based on sparse representa- tion and sparse signal recovery,” Ph.D. dissertation, University of California, San Diego, 2018.
[20] M. Pan, S. Liu, P. Liu, W. Qi, Y. Huang, W. Zheng, Q. Wu, and M. Gardill, “In situ calibration of antenna arrays for positioning with 5G networks,” IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 10, pp. 4600–4613, 2023.
[21] M. Koivisto, M. Costa, J. Werner, K. Heiska, J. Talvitie, K. Leppanen, V. Koivunen, and M. Valkama, “Joint device positioning and clock synchronization in 5G ultra-dense networks,” IEEE Transactions on Wireless Communications, vol. 16, no. 5, pp. 2866– 2881, May 2017.
[22] S. Shahcheraghi, J. Kuric, and Z. M. Kassas, “Opportunistic positioning with beam- formed 5G signals,” in Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), 2024, pp. 2522– 2533.

Previous Abstract Return to Session C6 Next Abstract