DOI: 10.18413/2518-1092-2025-10-3-0-4

NEURAL INFERENCE OF OBJECT LOCALIZATION FROM DISCONTINUOUS TIME-OF-ARRIVAL SEQUENCES

Anna Valerievna Minina
Rostislav Ruslanovich Nikulin
Igor Alexandrovich Sidorenko

The article addresses the problem of recovering the coordinates of a moving object in conditions of temporary signal loss or degradation (blackout), typical for complex radio environments. A method based on a Long Short-Term Memory (LSTM) recurrent neural network is proposed for interpolating and predicting positions using sequences of Time Difference of Arrival (TDoA) measurements from three fixed anchors. The developed model was trained on synthetic trajectories simulating object movement and demonstrates high robustness to missing data, with a median error of less than 30 meters even under significant signal degradation. A comparative analysis with alternative methods, including GRU, TCN, and Kalman Filter, confirms the superior performance of the LSTM architecture in unstable environments with limited measurements. The results indicate the proposed approach is promising for real-time applications in autonomous navigation and positioning systems.

Keywords: positioning, TDoA, LSTM, blackout, neural networks, localization, navigation, coordinate regression.

Number of views: 64 (view statistics)

Количество скачиваний: 163

Full text (PDF)To articles list

Information for citation:

Minina A.V., Nikulin R.R., Sidorenko I.A. Neural Inference of Object Localization from Discontinuous Time-of-arrival Sequences // Research result. Information technologies. – T.10, №3, 2025. – P. 45-54. DOI: 10.18413/2518-1092-2025-10-3-0-4

User comments
Reference lists

While nobody left any comments to this publication.
You can be first.

Seysenbayev B. I., Esmagulova A. M. Positioning Algorithms in Wireless Networks. – Almaty: KazNTU, 2020. – 123 p.
Goodfellow I., Bengio Y., Courville A. Deep Learning. – Moscow: Williams, 2018. – 720 p. (Translated from English)
Liu H., Darabi H., Banerjee P., Liu J. Survey of wireless indoor positioning techniques and systems // IEEE Transactions on Systems, Man, and Cybernetics. – 2007. – Vol. 37, No. 6. – P. 1067–1080.
Hochreiter S., Schmidhuber J. Long short-term memory // Neural Computation. – 1997. – Vol. 9. – No. 8. – P. 1735–1780.
Gers F. A., Schmidhuber J., Cummins F. Learning to forget: Continual prediction with LSTM // Neural Computation. – 2000. – Vol. 12. – No. 10. – P. 2451–2471.
Graves A. Supervised sequence labelling with recurrent neural networks. – Springer, 2012. – 150 p.
Cho K. et al. Learning phrase representations using RNN encoder–decoder for statistical machine translation // arXiv preprint arXiv:1406.1078, 2014.
Kingma D., Ba J. Adam: A method for stochastic optimization // Proceedings of ICLR, 2015.
Zhang Z. et al. TDOA-based localization using LSTM networks in multipath environments // Sensors. – 2021. – Vol. 21. – No. 11. – P. 3851.
Mazuelas S. et al. Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks // IEEE Journal on Selected Areas in Communications. – 2009. – Vol. 27. – No. 6. – P. 1091–1102.
Alarifi A. et al. Ultra wideband indoor positioning technologies: Analysis and recent advances // Sensors. – 2016. – Vol. 16. – No. 5. – P. 707.
Ioffe S., Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift // Proceedings of ICML, 2015.
LeCun Y., Bengio Y., Hinton G. Deep learning // Nature. – 2015. – Vol. 521. – P. 436–444.
Yassin M. et al. Recent advances in indoor localization: A survey on theoretical approaches and applications // IEEE Communications Surveys & Tutorials. – 2017. – Vol. 19. – No. 2. – P. 1327–1346.
Zhao M., Adib F., Katabi D. Emotion recognition using wireless signals // Communications of the ACM. – 2018. – Vol. 61. – No. 9. – P. 91–100.

All journals

Send article

Research result. Information technologies is included in the scientific database of the RINTs (license agreement No. 765-12/2014 dated 08.12.2014).

Журнал включен в перечень рецензируемых научных изданий, рекомендуемых ВАК

The journal is indexed by the following scientific databases and platforms

Research Result. Research result. Information technologies (ISSN 2518-1092)

The journal materials and website are licensed under Creative Commons «Attribution» 4.0 International.

The Founder: Federal State Autonomous Educational Institution of Higher Education "Belgorod National Research University"The Founder’s address: 85 Pobedy Street, Belgorod, the Belgorod region, 308015, Russia

The Publisher: Federal State Autonomous Educational Institution of HigherEducation "Belgorod National Research University" The Founder’s address:85 Pobedy Street, Belgorod, the Belgorod region, 308015, Russia

Editors Office: chief editor Chernomorets Andrey Alekseevich, e-mail: RR_IT@bsuedu.ru, phone: +7 (4722) 30-13-92.

Registered by the Federal Service for Supervision of Communications, Information Technology and Mass Media (Roskomnadzor)

Certificate

Charter of the editorial board of the mass media "Research result. Information technologies"

Info letter (Russian)

Order No. 636-OD dated 30.06.2023 "On approval of the Charters of the editorial boards of the mass media of scientific journals of Belgorod State National Research University"

Order No. 1097-OD from 15.11.2023 "On approval of the Regulations for the publication of scientific journals of Belgorod State National Research University"

Have questions?
You can write to us:

✉ Executive Secretary

✉ Site administration

✉ Content manager