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Multi-Feature Maximum Likelihood Association with Space-borne SAR, HFSWR and AIS

Published online by Cambridge University Press:  20 October 2016

Hui Zhang
Affiliation:
(College of Computer Science, Inner Mongolia University, Hohhot 010021, China)
Yongxin Liu
Affiliation:
(College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China)
Yonggang Ji
Affiliation:
(First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China)
Linglin Wang
Affiliation:
(College of Computer Science, Inner Mongolia University, Hohhot 010021, China)
Jie Zhang
Affiliation:
(First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China)
Corresponding
E-mail address:

Abstract

Ship surveillance is important in maritime management. Space-borne Synthetic Aperture Radar (SAR), High Frequency Surface Wave Radar (HFSWR) and the Automatic Identification System (AIS) are three main sensors for the ship surveillance of large maritime areas. Fusion of these sensors' measurements can produce an accurate ship image distribution in a surveillance area. Data association is fundamental to data fusion. A Maximum Likelihood (ML) association algorithm with multi-feature improvements is proposed to increase detection accuracy and reduce false alarms. The tested features are position, size, heading and velocity. First, the ship measurement model is established. Then, the problem of data association for SAR, HFSWR and AIS is formulated as a multi-dimensional assignment problem. In the data assignment process, Jonker-Volgenant-Castanon (JVC) and Lagrangian relaxation algorithms are applied. Simulation results show that the algorithm proposed here can improve the association accuracy compared with the Nearest Neighbour (NN) and the position-only ML algorithms, using the additional features of length and velocity. Real data experiments illustrate that the algorithm can enhance target identification and reduce false alarms.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

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