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Radar Signal Processing

Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing


In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data.

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Authors:
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi
Submitted On:
4 December 2018 - 11:04pm
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GlobalSIP2018_talk.pdf

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[1] Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3840. Accessed: Jul. 19, 2019.
@article{3840-18,
url = {http://sigport.org/3840},
author = {Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi },
publisher = {IEEE SigPort},
title = {Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing},
year = {2018} }
TY - EJOUR
T1 - Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing
AU - Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3840
ER -
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. IEEE SigPort. http://sigport.org/3840
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, 2018. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. Available at: http://sigport.org/3840.
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing." Web.
1. Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3840

DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND


In the 3.5 GHz Citizens Broadband Radio Service (CBRS), 100 MHz of spectrum will be shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. This paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of matched-filter detectors for these sensors.

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Authors:
Michael Souryal, Wen-Bin Yang
Submitted On:
19 November 2018 - 1:36pm
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globalSIP_DETECTION OF INCUMBENT RADAR IN CBRS BAND.pdf

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[1] Michael Souryal, Wen-Bin Yang, "DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3686. Accessed: Jul. 19, 2019.
@article{3686-18,
url = {http://sigport.org/3686},
author = {Michael Souryal; Wen-Bin Yang },
publisher = {IEEE SigPort},
title = {DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND},
year = {2018} }
TY - EJOUR
T1 - DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND
AU - Michael Souryal; Wen-Bin Yang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3686
ER -
Michael Souryal, Wen-Bin Yang. (2018). DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND. IEEE SigPort. http://sigport.org/3686
Michael Souryal, Wen-Bin Yang, 2018. DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND. Available at: http://sigport.org/3686.
Michael Souryal, Wen-Bin Yang. (2018). "DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND." Web.
1. Michael Souryal, Wen-Bin Yang. DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3686

Hidden Markov Model-based Gesture Recognition with FMCW Radar


In this paper we present experimental results for the development
of a gesture recognition system using a 77 GHz FMCW
radar system. We measure the micro-Doppler signature of a
gesturing hand to construct an energy distribution in velocity
space over time. A gesturing hand is fundamentally a dynamical
system with unobservable “state” (i.e. the name of the gesture)
which determines the sequence of associated observable
velocity-energy distributions, so a Hidden Markov Model is
used to for gesture recognition, a more tailored approach than

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Authors:
Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali
Submitted On:
6 December 2016 - 12:06pm
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20161208_globalsip_1330.pdf

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[1] Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali, "Hidden Markov Model-based Gesture Recognition with FMCW Radar", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1366. Accessed: Jul. 19, 2019.
@article{1366-16,
url = {http://sigport.org/1366},
author = {Greg Malysa; Dan Wang; Lorin Netsch; Murtaza Ali },
publisher = {IEEE SigPort},
title = {Hidden Markov Model-based Gesture Recognition with FMCW Radar},
year = {2016} }
TY - EJOUR
T1 - Hidden Markov Model-based Gesture Recognition with FMCW Radar
AU - Greg Malysa; Dan Wang; Lorin Netsch; Murtaza Ali
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1366
ER -
Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali. (2016). Hidden Markov Model-based Gesture Recognition with FMCW Radar. IEEE SigPort. http://sigport.org/1366
Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali, 2016. Hidden Markov Model-based Gesture Recognition with FMCW Radar. Available at: http://sigport.org/1366.
Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali. (2016). "Hidden Markov Model-based Gesture Recognition with FMCW Radar." Web.
1. Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali. Hidden Markov Model-based Gesture Recognition with FMCW Radar [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1366