Uncooperative RF Direction Finding with I/Q Data | Proceedings of the 2020 the 4th International Conference on Information System and Data Mining (2024)

1. Samith Abeywickrama, Lahiru Jayasinghe, Hua Fu, Subashini Nissanka, and Chau Yuen. 2018. RF-based direction finding of UAVs using DNN. In 2018 IEEE International Conference on Communication Systems (ICCS). IEEE, 157--161.Google ScholarCross Ref
2. Victor Bahl and Venkat Padmanabhan. 2000. RADAR: An In-Building RF-based User Location and Tracking System. https://www.microsoft.com/en-us/research/publication/radar-an-in-building-rf-based-user-location-and-tracking-system/Google Scholar
3. Arunkumar Byravan and Dieter Fox. 2017. Se3-nets: Learning rigid body motion using deep neural networks. In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 173--180.Google ScholarDigital Library
4. Direction Finders. 2017. Introduction into Theory of Direction Finding. (2017). http://telekomunikacije.etf.bg.ac.rs/predmeti/ot3tm2/nastava/df.pdf [Online; accessed 28-Feb-2017].Google Scholar
5. P.J.D. Gething. 1978. Radio Direction-finding: And the Resolution of Multi-component Wave-fields. Peter Peregrinus. https://books.google.com/books?id=BCcIAQAAIAAJGoogle Scholar
6. GNU Radio Website. accessed August 2016. (accessed August 2016). http://www.gnuradio.orgGoogle Scholar
7. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.Google ScholarDigital Library
8. Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 6645--6649.Google ScholarCross Ref
9. Wenchao Huang, Yan Xiong, Xiang-Yang Li, Hao Lin, Xufei Mao, Panlong Yang, Yunhao Liu, and Xingfu Wang. 2015. Swadloon: Direction Finding and Indoor Localization Using Acoustic Signal by Shaking Smartphones. IEEE Transactions on Mobile Computing 14, 10 (Oct. 2015), 2145--2157. https://doi.org/10.1109/TMC.2014.2377717Google ScholarDigital Library
10. Seigo Ito and Nobuo Kawaguchi. 2006. Orientation Estimation Method using Divergence of Signal Strength Distribution. In Third International Conference on Networked Sensing Systems. 180-187.Google Scholar
11. Johan Kirkhorn. 1999. Introduction to IQ-demodulation of RF-data. IFBT, NTNU 15 (1999).Google Scholar
12. Frederick August Kolster and Francis Winkley Dunmore. 1922. The radio direction finder and its application to navigation. Washington. ISBN: 978-1-333-95286-0.Google Scholar
13. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.Google Scholar
14. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436.Google Scholar
15. Jin Liu, Yi Pan, Min Li, Ziyue Chen, Lu Tang, Chengqian Lu, and Jianxin Wang. 2018. Applications of deep learning to MRI images: A survey. Big Data Mining and Analytics1, 1 (2018), 1--18.Google Scholar
16. J. Moell and T.N. Curlee. 1987. Transmitter Hunting: Radio Direction Finding Simplified. McGraw-Hill Education. https://books.google.com/books?id=RfzF2-fHJ6MCGoogle Scholar
17. Tathagata Mukherjee, Michael Duckett, Piyush Kumar, Jared Devin Paquet, Daniel Rodriguez, Mallory Haulcomb, Kevin George, and Eduardo Pasiliao. 2017. RSSI-based supervised learning for uncooperative direction-finding. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 216--227.Google ScholarCross Ref
18. Tathagata Mukherjee, Piyush Kumar, Debdeep Pati, Erik Blasch, Eduardo Pasiliao, and Liqin Xu. 2019. LoSI: Large scale location inference through FM signal integration and estimation. Big Data Mining and Analytics 2, 4 (2019), 319--348.Google ScholarCross Ref
19. Kevin P Murphy. 2012. Machine learning: a probabilistic perspective. MIT press.Google ScholarDigital Library
20. Timothy J. O'Shea, Johnathan Corgan, and T. Charles Clancy. 2016. Convolutional Radio Modulation Recognition Networks. In Engineering Applications of Neural Networks. 213--226.Google Scholar
21. T. O'Shea and J. Hoydis. 2017. An Introduction to Deep Learning for the Physical Layer. IEEE Transactions on Cognitive Communications and Networking 3, 4 (2017), 563--575.Google ScholarCross Ref
22. Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, et al. 2015. Deep face recognition.. In bmvc, Vol. 1. 6.Google Scholar
23. Boaz Porat and Benjamin Friedlander. 1991. Direction finding algorithms based on high-order statistics. IEEE Transactions on Signal Processing 39, 9 (1991), 2016--2024.Google ScholarDigital Library
24. RHO-THETA. 2017. RT-500-M. (2017). https://www.rhotheta.com/products/rt_500_m [Online; accessed 28-Feb-2017].Google Scholar
25. Debashri Roy, Tathagata Mukherjee, Mainak Chatterjee, and Eduardo Pasiliao. 2019. Detection of Rogue RF Transmitters Using Generative Adversarial Nets. In IEEE WCNC.Google Scholar
26. Debashri Roy, Tathagata Mukherjee, Mainak Chatterjee, and Eduardo Pasiliao. 2019. Primary User Activity Prediction in DSA Networks using Recurrent Structures. In IEEE Dyspan.Google Scholar
27. Richard Roy and Thomas Kailath. 1989. ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on acoustics, speech, and signal processing 37, 7 (1989), 984--995.Google ScholarCross Ref
28. Hirokazu Satoh, Seigo Ito, and Nobuo Kawaguchi. 2005. Position estimation of wireless access point using directional antennas. In International Symposium on Location-and Context-Awareness. Springer, 144--156.Google ScholarDigital Library
29. Ralph Schmidt. 1986. Multiple emitter location and signal parameter estimation. IEEE transactions on antennas and propagation 34, 3 (1986), 276--280.Google ScholarCross Ref
30. Richard Socher, Yoshua Bengio, and Christopher D Manning. 2012. Deep learning for NLP (without magic). In Tutorial Abstracts of ACL 2012. Association for Computational Linguistics, 5--5.Google ScholarDigital Library
31. Wikipedia. 2016. Direction finding --- Wikipedia, The Free Encyclopedia. (2016). https://en.wikipedia.org/wiki/Direction_finding [Online; accessed 20-Dec-2016].Google Scholar
32. Wikipedia. 2017. LoJack --- Wikipedia, The Free Encyclopedia. (2017). https://en.wikipedia.org/wiki/LoJack [Online; accessed 28-Feb-2017].Google Scholar
33. Wikipedia. 2017. National Distress System --- Wikipedia, The Free Encyclopedia. (2017). https://en.wikipedia.org/wiki/National_Distress_System [Online; accessed 28-Feb-2017].Google Scholar
34. Wikipedia. 2017. Rescue 21 --- Wikipedia, The Free Encyclopedia. (2017). https://en.wikipedia.org/wiki/Rescue_21 [Online; accessed 28-Feb-2017].Google Scholar
35. Lauren J Wong, William C Headley, and Alan J Michaels. 2018. Emitter Identification Using CNN IQ Imbalance Estimators. arXiv preprint arXiv:1808.02369 (2018).Google Scholar
36. Zeng Yu, Tianrui Li, Ning Yu, Xun Gong, Ke Chen, and Yi Pan. 2017. Three-stream convolutional networks for video-based person re-identification. arXiv preprint arXiv:1712.01652 (2017).Google Scholar
37. Liu Zhuo, Shi Dan, Gao Yougang, Shen Yaqin, Bi Junjian, and Tan Zhiliang. 2014. The distinction among electromagnetic radiation source models based on directivity with support vector machines. In Electromagnetic Compatibility, Tokyo (EMC'14/Tokyo), 2014 International Symposium on. IEEE, 617--620.Google Scholar

Index Terms

1. Uncooperative RF Direction Finding with I/Q Data

   1. Computing methodologies

      1. Machine learning

         1. Machine learning approaches

            1. Neural networks

• Nested inter-antenna spacing constrained sparse arrays for direction finding

  Abstract

  In the design of a sparse array with large uniform degree of freedom (uDOF) for direction finding, the minimum inter-antenna spacing is usually constrained to be no more than half-wavelength. This is not attractive for reducing antenna ...

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• Fourth-order direction finding in antenna arrays with partial channel gain/phase calibration

  Highlights

  • The problem of direction finding in partly calibrated uniform linear array (ULA) system with channel gain/phase mismatch is considered.

  Abstract

  In this paper, we consider the problem of direction finding in partly calibrated uniform linear array (ULA) system with channel gain/phase mismatch, by taking advantage of the high-order statistics (HOS) of the array observations. In ...

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• Evaluation of Transmit Diversity in MIMO-Radar Direction Finding

  It has been recently shown that multiple-input multiple-output (MIMO) antenna systems have the potential to dramatically improve the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation ...

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