TY - GEN
T1 - Simultaneous localization of multiple jammers and receivers using probability hypothesis density
AU - Bhamidipati, Sriramya
AU - Gao, Grace Xingxin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/5
Y1 - 2018/6/5
N2 - Now-a-days, the availability of low-cost jammers in the commercial market is increasing. Due to this, there has been a rising risk of multiple jammers, not just one. However, it is challenging to locate multiple jammers because the traditional way of jammer localization via multilateration only works for one jammer. In addition, during attack the positioning capability of the receivers is compromised due to their inability to track the GPS signals. We propose our Simultaneous Localization of Multiple Jammers and Receivers (SLMR) algorithm by utilizing the signal power received at a network of receivers. Our algorithm not only locates multiple jammers, but also utilizes the jammers as additional navigation signals for positioning the receivers. In particular, we design a non-linear Gaussian Mixture Probability Hypothesis Density Filter over a graphical framework, which is optimized using Levenberg-Marquardt minimizer. Under the presence of multiple simulated jammers, we validate that our proposed SLMR algorithm is able to simultaneously locate multiple jammers and receivers, even though the number of jammers is unknown.
AB - Now-a-days, the availability of low-cost jammers in the commercial market is increasing. Due to this, there has been a rising risk of multiple jammers, not just one. However, it is challenging to locate multiple jammers because the traditional way of jammer localization via multilateration only works for one jammer. In addition, during attack the positioning capability of the receivers is compromised due to their inability to track the GPS signals. We propose our Simultaneous Localization of Multiple Jammers and Receivers (SLMR) algorithm by utilizing the signal power received at a network of receivers. Our algorithm not only locates multiple jammers, but also utilizes the jammers as additional navigation signals for positioning the receivers. In particular, we design a non-linear Gaussian Mixture Probability Hypothesis Density Filter over a graphical framework, which is optimized using Levenberg-Marquardt minimizer. Under the presence of multiple simulated jammers, we validate that our proposed SLMR algorithm is able to simultaneously locate multiple jammers and receivers, even though the number of jammers is unknown.
UR - http://www.scopus.com/inward/record.url?scp=85048893379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048893379&partnerID=8YFLogxK
U2 - 10.1109/PLANS.2018.8373472
DO - 10.1109/PLANS.2018.8373472
M3 - Conference contribution
AN - SCOPUS:85048893379
T3 - 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings
SP - 940
EP - 944
BT - 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018
Y2 - 23 April 2018 through 26 April 2018
ER -