TY - GEN
T1 - Source Localization with Spatially Distributed Active and Passive Sensors
AU - Liang, Yifan
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We consider solving the source localization problem by exploiting measurements collected from both active and passive sensors. We first briefly review some existing least squares approaches that utilize only one type of measurement (active or passive), and further establish a hybrid objective function that includes active and passive measurements simultaneously in the sense of squared least squares. We propose two different methods, Newton's method and the semidefinite relaxation method, to efficiently solve the optimization problem. Simulation results indicate that the source location estimates given by the proposed hybrid methods are superior to the peer methods that only utilize one type of measurement. The performance difference between Newton's method and the semidefinite relaxation method is also investigated.
AB - We consider solving the source localization problem by exploiting measurements collected from both active and passive sensors. We first briefly review some existing least squares approaches that utilize only one type of measurement (active or passive), and further establish a hybrid objective function that includes active and passive measurements simultaneously in the sense of squared least squares. We propose two different methods, Newton's method and the semidefinite relaxation method, to efficiently solve the optimization problem. Simulation results indicate that the source location estimates given by the proposed hybrid methods are superior to the peer methods that only utilize one type of measurement. The performance difference between Newton's method and the semidefinite relaxation method is also investigated.
KW - active and passive sensors
KW - hybrid measurements
KW - least squares
KW - nonconvex optimization
KW - source localization
UR - http://www.scopus.com/inward/record.url?scp=85190626321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190626321&partnerID=8YFLogxK
U2 - 10.1109/CISS59072.2024.10480159
DO - 10.1109/CISS59072.2024.10480159
M3 - Conference contribution
AN - SCOPUS:85190626321
T3 - 2024 58th Annual Conference on Information Sciences and Systems, CISS 2024
BT - 2024 58th Annual Conference on Information Sciences and Systems, CISS 2024
T2 - 58th Annual Conference on Information Sciences and Systems, CISS 2024
Y2 - 13 March 2024 through 15 March 2024
ER -