Abstract
This work presents a real-time implementation of a cognitive radar system that predicts and avoids interference using a stochastic model of radio frequency (RF) activity. Next-generation radar/radio systems must sense, predict, and avoid interference as the spectrum grows more crowded. The tested cognitive radar monitors the RF environment to estimate the stochastic model parameters followed by a prediction and avoidance stage. An alternating renewal process models RF activity with random busy and idle time distributions, which are used to obtain interference probabilities. These interference probabilities determine a radar transmit bandwidth and center frequency to avoid colliding with other emitters in the environment. The approach is evaluated in terms of collisions and missed opportunities on a set of simulated and real measured RF spectra. Additionally, this paper outlines the effects and complexity of utilizing different distributions, parameters, and modes of operation for the implemented radar system. The results suggest that this approach accurately predicts and avoids RF interference with a degradation in performance as model variance increases.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE Radar Conference, RadarConf 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728116792 |
| DOIs | |
| State | Published - Apr 2019 |
| Event | 2019 IEEE Radar Conference, RadarConf 2019 - Boston, United States Duration: Apr 22 2019 → Apr 26 2019 |
Publication series
| Name | 2019 IEEE Radar Conference, RadarConf 2019 |
|---|
Conference
| Conference | 2019 IEEE Radar Conference, RadarConf 2019 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 4/22/19 → 4/26/19 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Signal Processing
- Instrumentation
Cite this
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A stochastic model for prediction and avoidance of RF interference to cognitive radars. / Kovarskiy, Jacob A.; Narayanan, Ram M.; Martone, Anthony F.; Sherbondy, Kelly D.
2019 IEEE Radar Conference, RadarConf 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8835523 (2019 IEEE Radar Conference, RadarConf 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A stochastic model for prediction and avoidance of RF interference to cognitive radars
AU - Kovarskiy, Jacob A.
AU - Narayanan, Ram M.
AU - Martone, Anthony F.
AU - Sherbondy, Kelly D.
PY - 2019/4
Y1 - 2019/4
N2 - This work presents a real-time implementation of a cognitive radar system that predicts and avoids interference using a stochastic model of radio frequency (RF) activity. Next-generation radar/radio systems must sense, predict, and avoid interference as the spectrum grows more crowded. The tested cognitive radar monitors the RF environment to estimate the stochastic model parameters followed by a prediction and avoidance stage. An alternating renewal process models RF activity with random busy and idle time distributions, which are used to obtain interference probabilities. These interference probabilities determine a radar transmit bandwidth and center frequency to avoid colliding with other emitters in the environment. The approach is evaluated in terms of collisions and missed opportunities on a set of simulated and real measured RF spectra. Additionally, this paper outlines the effects and complexity of utilizing different distributions, parameters, and modes of operation for the implemented radar system. The results suggest that this approach accurately predicts and avoids RF interference with a degradation in performance as model variance increases.
AB - This work presents a real-time implementation of a cognitive radar system that predicts and avoids interference using a stochastic model of radio frequency (RF) activity. Next-generation radar/radio systems must sense, predict, and avoid interference as the spectrum grows more crowded. The tested cognitive radar monitors the RF environment to estimate the stochastic model parameters followed by a prediction and avoidance stage. An alternating renewal process models RF activity with random busy and idle time distributions, which are used to obtain interference probabilities. These interference probabilities determine a radar transmit bandwidth and center frequency to avoid colliding with other emitters in the environment. The approach is evaluated in terms of collisions and missed opportunities on a set of simulated and real measured RF spectra. Additionally, this paper outlines the effects and complexity of utilizing different distributions, parameters, and modes of operation for the implemented radar system. The results suggest that this approach accurately predicts and avoids RF interference with a degradation in performance as model variance increases.
UR - http://www.scopus.com/inward/record.url?scp=85072622241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072622241&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2019.8835523
DO - 10.1109/RADAR.2019.8835523
M3 - Conference contribution
AN - SCOPUS:85072622241
T3 - 2019 IEEE Radar Conference, RadarConf 2019
BT - 2019 IEEE Radar Conference, RadarConf 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -