TY - GEN
T1 - Bayesian estimator based target localization in ship monitoring system using multiple compact high frequency surface wave radars
AU - Park, Sangwook
AU - Cho, Chul Jin
AU - Lee, Younglo
AU - Da Costa, Andrew
AU - Lee, Sang Ho
AU - Ko, Hanseok
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Recently, the usage of high frequency surface wave radars has been expanded to monitoring of ships within observable region because it can always observe wide target region with low power consumption. However, ship monitoring systems using high frequency radars suffer from the fact that a detection position is far away from true position. To resolve this problem, the proposed method determines final location by applying Bayesian estimator to detection results from each ship monitoring system in high frequency radar network. According to Bayesian theory, a posterior distribution is factorized into likelihood and prior distributions, and both distributions are modeled by using each detection results and auto-identification system data, respectively. Effectiveness of the proposed method is demonstrated through appropriate synthetic and real data. From the results, location accuracy can be improved when the proposed method is applied to location estimation.
AB - Recently, the usage of high frequency surface wave radars has been expanded to monitoring of ships within observable region because it can always observe wide target region with low power consumption. However, ship monitoring systems using high frequency radars suffer from the fact that a detection position is far away from true position. To resolve this problem, the proposed method determines final location by applying Bayesian estimator to detection results from each ship monitoring system in high frequency radar network. According to Bayesian theory, a posterior distribution is factorized into likelihood and prior distributions, and both distributions are modeled by using each detection results and auto-identification system data, respectively. Effectiveness of the proposed method is demonstrated through appropriate synthetic and real data. From the results, location accuracy can be improved when the proposed method is applied to location estimation.
KW - Compact HF radar network
KW - Distributed sensor network
KW - Fusion
KW - HF surface wave radar
KW - Ship detection
UR - http://www.scopus.com/inward/record.url?scp=85049971147&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049971147&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-90509-9_9
DO - 10.1007/978-3-319-90509-9_9
M3 - Conference contribution
AN - SCOPUS:85049971147
SN - 9783319905082
T3 - Lecture Notes in Electrical Engineering
SP - 157
EP - 167
BT - Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017
PB - Springer Verlag
T2 - 13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017
Y2 - 16 November 2017 through 22 November 2017
ER -