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Open AccessLetter

Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM

1
College of Engineering, Ocean University of China, Qingdao 266100, China
2
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B3P4, Canada
3
Laboratory of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 272; https://doi.org/10.3390/rs12020272
Received: 28 November 2019 / Revised: 9 January 2020 / Accepted: 11 January 2020 / Published: 14 January 2020
(This article belongs to the Special Issue Bistatic HF Radar)
High frequency surface wave radar (HFSWR) plays an important role in marine surveillance on account of its ability to provide wide-range early warning detection. However, vessel target track breakages are common in large-scale marine monitoring, which limits the continuous tracking ability of HFSWR. The following are the possible reasons for track fracture: highly maneuverable vessels, dense channels, target occlusion, strong clutter/interference, long sampling intervals, and low detection probabilities. To solve this problem, we propose a long-term continuous tracking method for multiple targets with stereoscopic HFSWR based on an interacting multiple model extended Kalman filter (IMMEKF) combined with an extreme learning machine (ELM). When the trajectory obtained by IMMEKF breaks, a new section of the track will start on the basis of the observation data. For multiple-target tracking, a number of broken tracks can be obtained by IMMEKF tracking. Then the ELM classifies the segments from the same vessel by extracting different features including average velocity, average curvature, ratio of the arc length to the chord length, and wavelet coefficient. Both the simulation and the field experiment results validate the method presented here, showing that this method can achieve long-term continuous tracking for multiple vessels, with an average correct track segment association rate of over 91.2%, which is better than the tracking performance of conventional algorithms, especially when the vessels are in dense channels and strong clutter/interference area. View Full-Text
Keywords: HFSWR; target tracking; interacting multiple model; extended Kalman filter; track association; extreme learning machine HFSWR; target tracking; interacting multiple model; extended Kalman filter; track association; extreme learning machine
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MDPI and ACS Style

Zhang, L.; Mao, D.; Niu, J.; Wu, Q.M.J.; Ji, Y. Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM. Remote Sens. 2020, 12, 272.

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