Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar

Arjuna Madanayake; Gihan J. Mendis; Viduneth Ariyarathna; Sravan Pulipati; Tharindu Randeny; Shubhendu Bhardwaj; Xin Wang; Soumyajit Mandal; Jin Wei

doi:10.1109/RFID49298.2020.9244873

Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar

Arjuna Madanayake
, Gihan J. Mendis
, Viduneth Ariyarathna
, Sravan Pulipati
, Tharindu Randeny
, Shubhendu Bhardwaj
, Xin Wang
, Soumyajit Mandal
, Jin Wei

Electrical Engineering

Florida International University
Purdue University
University of Akron
Stony Brook University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper describes hardware, signal processing, and machine learning methods for Doppler radar-based accurate and robust detection of micro unmanned aerial systems (UAS). Typical detection accuracy of ∼98% was obtained in over-the-air tests with a 2.4 GHz continuous wave (CW) radar and a variety of commercially-available micro-UAS devices. Several methods are described for further improving detection performance, including multi-beam synthesis with uniform circular arrays to provide 360° azimuthal sensitivity; dielectric lens antennas and focal plane arrays at mm-wave frequencies (28 GHz) for improved spatial resolution; and polyspectra-based feature extraction methods for improved modeling of nonlinear phase modulation processes within the measured Doppler signatures.

Original language	English
Title of host publication	2020 IEEE International Conference on RFID, RFID 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728155760
DOIs	https://doi.org/10.1109/RFID49298.2020.9244873
State	Published - Sep 28 2020
Event	2020 IEEE International Conference on RFID, RFID 2020 - Orlando, United States Duration: Sep 28 2020 → Oct 16 2020

Publication series

Name	2020 IEEE International Conference on RFID, RFID 2020

Conference

Conference	2020 IEEE International Conference on RFID, RFID 2020
Country/Territory	United States
City	Orlando
Period	09/28/20 → 10/16/20

Access to Document

10.1109/RFID49298.2020.9244873

Cite this

Madanayake, A., Mendis, G. J., Ariyarathna, V., Pulipati, S., Randeny, T., Bhardwaj, S., Wang, X., Mandal, S., & Wei, J. (2020). Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar. In 2020 IEEE International Conference on RFID, RFID 2020 Article 9244873 (2020 IEEE International Conference on RFID, RFID 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RFID49298.2020.9244873

@inproceedings{1826b7c963c941cbae4c0f67c86ef71f,

title = "Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar",

abstract = "This paper describes hardware, signal processing, and machine learning methods for Doppler radar-based accurate and robust detection of micro unmanned aerial systems (UAS). Typical detection accuracy of ∼98\% was obtained in over-the-air tests with a 2.4 GHz continuous wave (CW) radar and a variety of commercially-available micro-UAS devices. Several methods are described for further improving detection performance, including multi-beam synthesis with uniform circular arrays to provide 360° azimuthal sensitivity; dielectric lens antennas and focal plane arrays at mm-wave frequencies (28 GHz) for improved spatial resolution; and polyspectra-based feature extraction methods for improved modeling of nonlinear phase modulation processes within the measured Doppler signatures.",

author = "Arjuna Madanayake and Mendis, \{Gihan J.\} and Viduneth Ariyarathna and Sravan Pulipati and Tharindu Randeny and Shubhendu Bhardwaj and Xin Wang and Soumyajit Mandal and Jin Wei",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on RFID, RFID 2020 ; Conference date: 28-09-2020 Through 16-10-2020",

year = "2020",

month = sep,

day = "28",

doi = "10.1109/RFID49298.2020.9244873",

language = "English",

series = "2020 IEEE International Conference on RFID, RFID 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE International Conference on RFID, RFID 2020",

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Madanayake, A, Mendis, GJ, Ariyarathna, V, Pulipati, S, Randeny, T, Bhardwaj, S, Wang, X, Mandal, S & Wei, J 2020, Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar. in 2020 IEEE International Conference on RFID, RFID 2020., 9244873, 2020 IEEE International Conference on RFID, RFID 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on RFID, RFID 2020, Orlando, United States, 09/28/20. https://doi.org/10.1109/RFID49298.2020.9244873

Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar. / Madanayake, Arjuna; Mendis, Gihan J.; Ariyarathna, Viduneth et al.
2020 IEEE International Conference on RFID, RFID 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9244873 (2020 IEEE International Conference on RFID, RFID 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar

AU - Madanayake, Arjuna

AU - Mendis, Gihan J.

AU - Ariyarathna, Viduneth

AU - Pulipati, Sravan

AU - Randeny, Tharindu

AU - Bhardwaj, Shubhendu

AU - Wang, Xin

AU - Mandal, Soumyajit

AU - Wei, Jin

PY - 2020/9/28

Y1 - 2020/9/28

N2 - This paper describes hardware, signal processing, and machine learning methods for Doppler radar-based accurate and robust detection of micro unmanned aerial systems (UAS). Typical detection accuracy of ∼98% was obtained in over-the-air tests with a 2.4 GHz continuous wave (CW) radar and a variety of commercially-available micro-UAS devices. Several methods are described for further improving detection performance, including multi-beam synthesis with uniform circular arrays to provide 360° azimuthal sensitivity; dielectric lens antennas and focal plane arrays at mm-wave frequencies (28 GHz) for improved spatial resolution; and polyspectra-based feature extraction methods for improved modeling of nonlinear phase modulation processes within the measured Doppler signatures.

AB - This paper describes hardware, signal processing, and machine learning methods for Doppler radar-based accurate and robust detection of micro unmanned aerial systems (UAS). Typical detection accuracy of ∼98% was obtained in over-the-air tests with a 2.4 GHz continuous wave (CW) radar and a variety of commercially-available micro-UAS devices. Several methods are described for further improving detection performance, including multi-beam synthesis with uniform circular arrays to provide 360° azimuthal sensitivity; dielectric lens antennas and focal plane arrays at mm-wave frequencies (28 GHz) for improved spatial resolution; and polyspectra-based feature extraction methods for improved modeling of nonlinear phase modulation processes within the measured Doppler signatures.

UR - https://www.scopus.com/pages/publications/85097260012

U2 - 10.1109/RFID49298.2020.9244873

DO - 10.1109/RFID49298.2020.9244873

M3 - Conference contribution

AN - SCOPUS:85097260012

T3 - 2020 IEEE International Conference on RFID, RFID 2020

BT - 2020 IEEE International Conference on RFID, RFID 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on RFID, RFID 2020

Y2 - 28 September 2020 through 16 October 2020

ER -

Madanayake A, Mendis GJ, Ariyarathna V, Pulipati S, Randeny T, Bhardwaj S et al. Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar. In 2020 IEEE International Conference on RFID, RFID 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9244873. (2020 IEEE International Conference on RFID, RFID 2020). doi: 10.1109/RFID49298.2020.9244873

Physics-Aware Processing of Rotational Micro-Doppler Signatures for DBN-Based UAS Classification Radar

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