TY - GEN
T1 - Emission source localization and sensor registration using RSS measurements
AU - Wang, Qi
AU - Duan, Zhansheng
AU - Rong Li, X.
N1 - Publisher Copyright:
© 2017 International Society of Information Fusion (ISIF).
PY - 2017/8/11
Y1 - 2017/8/11
N2 - Emission source localization and sensor registration using received signal strength (RSS) measurements is investigated. Previous studies for RSS localization assume that the sensors receiving signals are bias free, which is not the case in practice. This issue is taken into consideration in this paper for the localization problem. To avoid non-convexity of the global optimization problem for the traditional maximum likelihood (ML) or least squares (LS) estimation, we present novel semidefinite programming methods, linear least squares (LLS) and constrained LS (CLS) methods by approximating and linearizing the original model. The methods are divided into two types: URSS and DRSS. The former estimates the source location and sensor bias simultaneously while the latter estimates the sensor bias after localizing the source. Numerical examples show that our proposed methods have good performance. Some of them are close to the Cramer-Rao Lower Bound (CRLB).
AB - Emission source localization and sensor registration using received signal strength (RSS) measurements is investigated. Previous studies for RSS localization assume that the sensors receiving signals are bias free, which is not the case in practice. This issue is taken into consideration in this paper for the localization problem. To avoid non-convexity of the global optimization problem for the traditional maximum likelihood (ML) or least squares (LS) estimation, we present novel semidefinite programming methods, linear least squares (LLS) and constrained LS (CLS) methods by approximating and linearizing the original model. The methods are divided into two types: URSS and DRSS. The former estimates the source location and sensor bias simultaneously while the latter estimates the sensor bias after localizing the source. Numerical examples show that our proposed methods have good performance. Some of them are close to the Cramer-Rao Lower Bound (CRLB).
KW - Constrained LS (CLS)
KW - Linear Least squares (LLS)
KW - Received signal strength (RSS)
KW - Semidefinite programming (SDP)
KW - Sensor bias
KW - Source localization
UR - https://www.scopus.com/pages/publications/85029427208
U2 - 10.23919/ICIF.2017.8009625
DO - 10.23919/ICIF.2017.8009625
M3 - 会议稿件
AN - SCOPUS:85029427208
T3 - 20th International Conference on Information Fusion, Fusion 2017 - Proceedings
BT - 20th International Conference on Information Fusion, Fusion 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference on Information Fusion, Fusion 2017
Y2 - 10 July 2017 through 13 July 2017
ER -