Список литературы

2518-1092

Research result. Information technologies

2518-1092

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

3903

ARTIFICIAL INTELLIGENCE AND DECISION MAKING

<strong>NEURAL INFERENCE OF OBJECT LOCALIZATION FROM DISCONTINUOUS TIME-OF-ARRIVAL SEQUENCES</strong>

Minina

Anna Valerievna

Minina

Anna Valerievna

minina.annette@gmail.com

Nikulin

Rostislav Ruslanovich

Nikulin

Rostislav Ruslanovich

1470970@bsuedu.ru

Sidorenko

Igor Alexandrovich

Sidorenko

Igor Alexandrovich

2025

10300

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.

positioningTDoALSTMblackoutneural networkslocalizationnavigationcoordinate regression

Список литературы

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.