TY - GEN
T1 - Decentralized Direct Localization Based on Gauss-Newton Method in Multi-Sensor Networks
AU - Zhang, Guoxin
AU - Liang, Yunfei
AU - Wang, Cong
AU - Yi, Wei
AU - Ngo, Hien Quoc
AU - Matthaiou, Michail
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2024 ISIF.
PY - 2024
Y1 - 2024
N2 - Traditional centralized direct localization methods require the transmission of the complete baseband signal to the fusion center (FC) for target localization. Due to the limited communication bandwidth as well as energy required in transmission, this centralized framework is not suitable for largescale sensor networks. This paper proposes an information-driven decentralized direct localization framework. Firstly, a maximum-likelihood position estimator, based on the Gauss-Newton method, is derived. Then, a decentralized implementation framework is constructed. At its core, there is no dedicated FC while the sensors transmit information to their neighboring nodes only through single hops, achieving target localization through iterative processes based on the concept of consensus. Simulation results confirm the stability and robustness of the proposed method in different scenarios.
AB - Traditional centralized direct localization methods require the transmission of the complete baseband signal to the fusion center (FC) for target localization. Due to the limited communication bandwidth as well as energy required in transmission, this centralized framework is not suitable for largescale sensor networks. This paper proposes an information-driven decentralized direct localization framework. Firstly, a maximum-likelihood position estimator, based on the Gauss-Newton method, is derived. Then, a decentralized implementation framework is constructed. At its core, there is no dedicated FC while the sensors transmit information to their neighboring nodes only through single hops, achieving target localization through iterative processes based on the concept of consensus. Simulation results confirm the stability and robustness of the proposed method in different scenarios.
KW - Consensus
KW - decentralized estimation
KW - direct localization
KW - Gauss-Newton method
KW - maximum-likelihood estimation
UR - http://www.scopus.com/inward/record.url?scp=85207693364&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85207693364&partnerID=8YFLogxK
U2 - 10.23919/FUSION59988.2024.10706293
DO - 10.23919/FUSION59988.2024.10706293
M3 - Conference contribution
AN - SCOPUS:85207693364
T3 - FUSION 2024 - 27th International Conference on Information Fusion
BT - FUSION 2024 - 27th International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Information Fusion, FUSION 2024
Y2 - 7 July 2024 through 11 July 2024
ER -