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RSS FeedsRemote Sensing, Vol. 14, Pages 5937: Marine Extended Target Tracking for Scanning Radar Data Using Correlation Filter and Bayes Filter Jointly (Remote Sensing)

 
 

23 november 2022 17:33:28

 
Remote Sensing, Vol. 14, Pages 5937: Marine Extended Target Tracking for Scanning Radar Data Using Correlation Filter and Bayes Filter Jointly (Remote Sensing)
 


As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR).


 
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Remote Sensing, Vol. 14, Pages 5936: Satellite Remote Sensing Identification of Discolored Standing Trees for Pine Wilt Disease Based on Semi-Supervised Deep Learning (Remote Sensing)
Remote Sensing, Vol. 14, Pages 5938: Correction: Ramillien et al. An Innovative Slepian Approach to Invert GRACE KBRR for Localized Hydrological Information at the Sub-Basin Scale. Remote Sens. 2021, 13, 1824 (Remote Sensing)
 
 
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