research-article

Public Access

DroneScale: drone load estimation via remote passive RF sensing

Authors:

Vimal Kakaraparthi,

Nikshep Umamahesh,

Yeswanth Guddeti,

Dinesh Bharadia,

Tam VuAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 326 - 339

https://doi.org/10.1145/3384419.3430778

Published: 16 November 2020 Publication History

Abstract

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters.

References

[1]

9news. CU Boulder police investigating drone that flew over Folsom Field during game. https://www.denverpost.com/2017/10/19/drone-over-folsom-field/. October 19, 2017.

[2]

9news. How Many Drones Are Smuggling Drugs Across the U.S. Southern Border? https://www.airspacemag.com/fight-today/narcodrones-180974934/. July 02, 2019.

[3]

AJC News. Georgia man admits he tried to use drone to drop pot into prison. https://tinyurl.com/ybaft7k4. July 01, 2020.

[4]

C. Aker and S. Kalkan. Using deep networks for drone detection. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1--6. IEEE, 2017.

[5]

Amazon. Precision Steel Calibration Weight. https://tinyurl.com/ydfulrac. July 01, 2020.

[6]

Analog Dialogue. Understanding and Eliminating 1/f Noise. https://tinyurl.com/yb8h52d8. July 02, 2019.

[7]

M. Z. Anwar, Z. Kaleem, and A. Jamalipour. Machine learning inspired sound-based amateur drone detection for public safety applications. IEEE Transactions on Vehicular Technology, 68(3):2526--2534, 2019.

[8]

ARDRONIS. This Counter-Drone System Will Safely Hijack and Capture Rogue Drones. https://tinyurl.com/yapeqh22. July 01, 2020.

[9]

AUDS. Anti-UAV Defense System (AUDS). https://tinyurl.com/ybqckm6v. July 01, 2020.

[10]

Autel Robotics. Autel Robotics EVO. https://auteldrones.com/products/evo. July 01, 2020.

[11]

A. Bernardini, F. Mangiatordi, E. Pallotti, and L. Capodiferro. Drone detection by acoustic signature identification. Electronic Imaging, 2017(10):60--64, 2017.

[12]

B. Brannon and A. Barlow. Aperture uncertainty and adc system performance. Applications Note AN-501. Analog Devices, Inc.(September), 2000.

[13]

L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pages 108--122, 2013.

[14]

J. Busset, F. Perrodin, P. Wellig, B. Ott, K. Heutschi, T. Rühl, and T. Nussbaumer. Detection and tracking of drones using advanced acoustic cameras. In Unmanned/Unattended Sensors and Sensor Networks XI; and Advanced Free-Space Optical Communication Techniques and Applications, volume 9647, page 96470F. International Society for Optics and Photonics, 2015.

[15]

G. Cai, B. M. Chen, K. Peng, M. Dong, and T. H. Lee. Modeling and control system design for a uav helicopter. In 2006 14th Mediterranean Conference on Control and Automation, pages 1--6. IEEE, 2006.

[16]

G. Cai, B. M. Chen, K. Peng, M. Dong, and T. H. Lee. Modeling and control of the yaw channel of a uav helicopter. IEEE transactions on industrial electronics, 55(9):3426--3434, 2008.

[17]

E. E. Case, A. M. Zelnio, and B. D. Rigling. Low-cost acoustic array for small uav detection and tracking. In 2008 IEEE National Aerospace and Electronics Conference, pages 110--113. IEEE, 2008.

[18]

L. Chen. Curse of Dimensionality, pages 545--546. Springer US, Boston, MA, 2009.

[19]

F. Christnacher, S. Hengy, M. Laurenzis, A. Matwyschuk, P. Naz, S. Schertzer, and G. Schmitt. Optical and acoustical uav detection. In Electro-Optical Remote Sensing X, volume 9988, page 99880B. International Society for Optics and Photonics, 2016.

[20]

M. Clifford. Fundamental principles behind the sigma-delta adc topology: Part 1. Technical Article for Analog Devices, Inc, pages 1--4, 2016.

[21]

A. Coluccia, M. Ghenescu, T. Piatrik, G. De Cubber, A. Schumann, L. Sommer, J. Klatte, T. Schuchert, J. Beyerer, M. Farhadi, et al. Drone-vs-bird detection challenge at ieee avss 2017. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1--6. IEEE, 2017.

[22]

H. M. Corporation. The impact of clock generator performance on data converters.

[23]

Daily News. Mother-daughter team busted for using drone to smuggle contraband into prison. https://tinyurl.com/yavv4chn. July 01, 2020.

[24]

Dedrone. Dedrone: Complete Airspace Security Solution. https://www.dedrone.com/. July 01, 2020.

[25]

Dedrone. Worldwide Drone Incidents. https://tinyurl.com/y9d8vfh3. July 01, 2020.

[26]

A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1):1--22, 1977.

[27]

I. C. Dikmen, A. Arisoy, and H. Temeltas. Attitude control of a quadrotor. In 2009 4th International Conference on Recent Advances in Space Technologies, June 2009.

[28]

DJI. DJI Mavic 2 Zoom. https://tinyurl.com/y98ev3q5. July 01, 2020.

[29]

DJI. DJI Phantom 4 Pro. https://www.dji.com/phantom-4-pro. July 01, 2020.

[30]

DroneDetector. Protect Yourself From Air, Ground, and Water-Based Threats. http://dronedetector.com/. July 01, 2020.

[31]

DroneShield. DroneShield: Detect. Assess. Respond. https://www.droneshield.com/. July 01, 2020.

[32]

J. Drozdowicz, M. Wielgo, P. Samczynski, K. Kulpa, J. Krzonkalla, M. Mordzonek, M. Bryl, and Z. Jakielaszek. 35 ghz fmcw drone detection system. In 2016 17th International Radar Symposium (IRS), pages 1--4. IEEE, 2016.

[33]

FAA. FAA: Drones and Weapons, A Dangerous Mix. https://tinyurl.com/y8c227s5. July 01, 2020.

[34]

FalconShield. Falcon Shield: Automatic radio-controlled drone identiication solution. https://tinyurl.com/yaeed9gk. July 01, 2020.

[35]

Fintan Corrigan. How Do Drones Work And What Is Drone Technology. https://www.dronezon.com/learn-about-drones-quadcopters/what-is-drone-technology-or-how-does-drone-technology-work/. July 02, 2019.

[36]

S. R. Ganti and Y. Kim. Implementation of detection and tracking mechanism for small uas. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pages 1254--1260. IEEE, 2016.

[37]

Y. Guddeti, R. Subbaraman, M. Khazraee, A. Schulman, and D. Bharadia. SweepSense: Sensing 5 GHz in 5 Milliseconds with Low-cost Radios. pages 317--330, 2019.

[38]

L. Hauzenberger and E. Holmberg Ohlsson. Drone detection using audio analysis. 2015.

[39]

A. Herschfelt, C. R. Birtcher, R. M. Gutierrez, Y. Rong, H. Yu, C. A. Balanis, and D. W. Bliss. Consumer-grade drone radar cross-section and micro-doppler phenomenology. In 2017 IEEE Radar Conference (RadarConf), pages 0981--0985. IEEE, 2017.

[40]

F. Hoffmann, M. Ritchie, F. Fioranelli, A. Charlish, and H. Griffiths. Micro-doppler based detection and tracking of uavs with multistatic radar. In 2016 IEEE Radar Conference (RadarConf), pages 1--6. IEEE, 2016.

[41]

G. Hoffmann, H. Huang, S. Waslander, and C. Tomlin. Quadrotor helicopter flight dynamics and control: Theory and experiment. In AIAA guidance, navigation and control conference and exhibit, page 6461, 2007.

[42]

A. Hommes, A. Shoykhetbrod, D. Noetel, S. Stanko, M. Laurenzis, S. Hengy, and F. Christnacher. Detection of acoustic, electro-optical and radar signatures of small unmanned aerial vehicles. In Target and Background Signatures II, volume 9997, page 999701. International Society for Optics and Photonics, 2016.

[43]

J. Hua, Z. Xiong, J. Lowey, E. Suh, and E. R. Dougherty. Optimal number of features as a function of sample size for various classification rules. Bioinformatics, 21(8):1509--1515, 2005.

Digital Library

[44]

Ian Beavers. Noise Spectral Density: A New ADC Metric? https://www.analog.com/en/technical-articles/noise-spectral-density.html.

[45]

S. Jeon, J.-W. Shin, Y.-J. Lee, W.-H. Kim, Y. Kwon, and H.-Y. Yang. Empirical study of drone sound detection in real-life environment with deep neural networks. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1858--1862. IEEE, 2017.

[46]

W. Kester. Which adc architecture is right for your application. In EDA Tech Forum, volume 2, pages 22--25, 2005.

[47]

E. Kuantama, O. G. Moldovan, I. Ţarcă, T. Vesselényi, and R. Ţarcă. Analysis of quadcopter propeller vibration based on laser vibrometer. Journal of Low Frequency Noise, Vibration and Active Control, page 1461348419866292, 2019.

[48]

D. Lee and M. Lee. Low flicker noise, odd-phase master lo active mixer using a low switching frequency scheme. IEEE Journal of Solid-State Circuits, 50(10):2281--2293, 2015.

[49]

C. Liang, N. Cao, X. Lu, and Y. Ye. Uav detection using continuous wave radar. pages 1--5, 09 2018.

[50]

H. Liu, Z. Wei, Y. Chen, J. Pan, L. Lin, and Y. Ren. Drone detection based on an audio-assisted camera array. In 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), pages 402--406. IEEE, 2017.

[51]

LORD. IMU MicroStrain LORD 3DM-GX5-25. http://tinyurl.com/yxm8bngy. July 01, 2020.

[52]

F. M. Mahmoud and M. Abdelghany. Low flicker-noise rf cmos gilbert-cell mixer for 2.4 ghz wireless communication systems. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pages 1158--1161. IEEE, 2016.

[53]

R. Mahony, V. Kumar, and P. Corke. Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE Robotics and Automation magazine, 19(3):20--32, 2012.

[54]

J. Mezei and A. Molnár. Drone sound detection by correlation. In 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), pages 509--518. IEEE, 2016.

[55]

P. J. Moreno, P. P. Ho, and N. Vasconcelos. A kullback-leibler divergence based kernel for svm classification in multimedia applications. In Advances in neural information processing systems, pages 1385--1392, 2004.

Digital Library

[56]

T. Müller. Robust drone detection for day/night counter-uav with static vis and swir cameras. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, volume 10190, page 1019018. International Society for Optics and Photonics, 2017.

[57]

News 5. Drone spotted dropping items into Cuyahoga County jail. https://tinyurl.com/y89db3z2. July 01, 2020.

[58]

P. Nguyen, T. Kim, J. Miao, D. Hesselius, E. Kenneally, D. Massey, E. Frew, R. Han, and T. Vu. Towards RF-based Localization of a Drone and Its Controller. In Proceedings of the 5th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet'19, pages 21--26, Seoul, Republic of Korea, June 2019. Association for Computing Machinery.

Digital Library

[59]

P. Nguyen, H. Truong, M. Ravindranathan, A. Nguyen, R. Han, and T. Vu. Matthan: Drone presence detection by identifying physical signatures in the drone's rf communication. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pages 211--224. ACM, 2017.

Digital Library

[60]

Parrot. Parrot - ANAFI 4K Quadcopter. https://www.parrot.com/us/drones/anafi. July 01, 2020.

[61]

Parrot. Parrot Bebop 2. https://www.parrot.com/us/drones/parrot-bebop-2. July 01, 2020.

[62]

P. Petrides, C. Kyrkou, P. Kolios, T. Theocharides, and C. Panayiotou. Towards a holistic performance evaluation framework for drone-based object detection. In 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pages 1785--1793. IEEE, 2017.

[63]

Planet Analog. SIGNAL CHAIN BASICS (Part 104): Understanding noise in ADCs. https://www.planetanalog.com/signal-chain-basics-part-104-understanding-noise-in-adcs/.

[64]

G. J. Pottie and W. J. Kaiser. Principles of embedded networked systems design. Cambridge University Press, 2005.

Digital Library

[65]

Q. Pu, S. Gupta, S. Gollakota, and S. Patel. Whole-home gesture recognition using wireless signals. In Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, MobiCom '13, pages 27--38, New York, NY, USA, 2013. Association for Computing Machinery.

Digital Library

[66]

S. Rahman and D. A. Robertson. Radar micro-Doppler signatures of drones and birds at K-band and W-band. Scientific Reports, 8(1):17396, Nov. 2018. Number: 1 Publisher: Nature Publishing Group.

[67]

S. Ramesh, T. Pathier, and J. Han. SoundUAV: Towards Delivery Drone Authentication via Acoustic Noise Fingerprinting. In Proceedings of the 5th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet'19, Seoul, Republic of Korea, June 2019. Association for Computing Machinery.

Digital Library

[68]

M. Ringnér. What is principal component analysis? Nature biotechnology, 26(3):303--304, 2008.

[69]

M. Ritchie, F. Fioranelli, H. Griffiths, et al. Monostatic and bistatic radar measurements of birds and micro-drone. In 2016 IEEE Radar Conference (RadarConf), pages 1--5. IEEE, 2016.

[70]

Rong Zheng, Shuwu Zhang, and Bo Xu. Text-independent speaker identification using gmm-ubm and frame level likelihood normalization. In 2004 International Symposium on Chinese Spoken Language Processing, pages 289--292, 2004.

[71]

F. Sabatino. Quadrotor control: modeling, nonlinearcontrol design, and simulation. 2015.

[72]

A. Schroder, M. Renker, U. Aulenbacher, A. Murk, U. Boniger, R. Oechslin, and P. Wellig. Numerical and experimental radar cross section analysis of the quadrocopter dji phantom 2. In 2015 IEEE Radar Conference, pages 463--468. IEEE, 2015.

[73]

A. Schumann, L. Sommer, J. Klatte, T. Schuchert, and J. Beyerer. Deep cross-domain flying object classification for robust uav detection. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1--6. IEEE, 2017.

[74]

Y. Seo, B. Jang, and S. Im. Drone detection using convolutional neural networks with acoustic stft features. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1--6. IEEE, 2018.

[75]

X. Shi, C. Yang, W. Xie, C. Liang, Z. Shi, and J. Chen. Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges. IEEE Communications Magazine, 56(4):68--74, Apr. 2018.

[76]

D.-H. Shin, D.-H. Jung, D.-C. Kim, J.-W. Ham, and S.-O. Park. A distributed fmcw radar system based on fiber-optic links for small drone detection. IEEE Transactions on Instrumentation and Measurement, 66(2):340--347, 2016.

[77]

K. Stasiak, M. Ciesielski, A. Kurowska, and W. Przybysz. A study on using different kinds of continuous-wave radars operating in c-band for drone detection. In 2018 22nd International Microwave and Radar Conference (MIKON), pages 521--526. IEEE, 2018.

[78]

The New York Times. Gatwick Airport Shut Down by `Deliberate' Drone Incursions. https://tinyurl.com/y7quw3jc. July 01, 2020.

[79]

TJP. Explosive-laden drones targeted precision missile tech in Beirut - report. https://tinyurl.com/y7w7htf. July 01, 2020.

[80]

Tommy Neu. Why your high-speed ADC can never have enough SNR.

[81]

U. S. Customs and Border Protection. Yuma Sector Agents Intercept Narcotics Dropped From Drones. https://tinyurl.com/yboplk3o. July 01, 2020.

[82]

J. Verbeke and S. Debruyne. Vibration analysis of a uav multirotor frame. In Proceedings of ISMA 2016 International Conference on Noise and Vibration Engineering, pages 2401--2409, 2016.

[83]

www.droneflyers.com. Basics of Radio Frequencies for FPV Quadcopter Drones. https://tinyurl.com/y75pzn2g. July 02, 2019.

[84]

www.northwestgeorgianews.com. Report: Trio planned to use drone to to get tobacco, phones to inmate. https://tinyurl.com/y8hnamtj. July 01, 2020.

[85]

www.wpxi.com. Man accused of using drone to drop explosives on ex-girlfriend's property. https://tinyurl.com/y9x6jw2s. July 01, 2020.

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Index Terms

DroneScale: drone load estimation via remote passive RF sensing
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensors and actuators

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SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

852 pages

ISBN:9781450375900

DOI:10.1145/3384419

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Published: 16 November 2020

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November 16 - 19, 2020

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Abdelnasser HMcCann J(2024)Drone Propeller Speed Measurement: Case Study Using 5GHz RF and mmWave Radar2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)10.1109/VTC2024-Spring62846.2024.10683414(1-6)Online publication date: 24-Jun-2024
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Altherwy YRoman ANaqvi SAlsuhaibani AAkram T(2024)Remote Sensing Insights: Leveraging Advanced Machine Learning Models and Optimization for Enhanced Accuracy in Precision AgricultureIEEE Access10.1109/ACCESS.2024.345516912(132290-132302)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3455169
Al-lQubaydhi NAlenezi AAlanazi TSenyor AAlanezi NAlotaibi BAlotaibi MRazaque AHariri S(2024)Deep learning for unmanned aerial vehicles detection: A reviewComputer Science Review10.1016/j.cosrev.2023.10061451(100614)Online publication date: Feb-2024
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