Visual features and Deep Maxout Network‐based moving target detection using radar signals under sea clutter

2021 ◽  
Author(s):  
Tirumala Krishna Battula ◽  
Jaya Eppili
Keyword(s):  
Target Detection ◽  
Visual Features ◽  
Moving Target ◽  
Sea Clutter ◽  
Moving Target Detection ◽  
Radar Signals

scholarly journals A New Maritime Moving Target Detection and Tracking Method for Airborne Forward-looking Scanning Radar

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1586
Author(s):  
Weibo Huo ◽  
Jifang Pei ◽  
Yulin Huang ◽  
Qian Zhang ◽  
Jianyu Yang
Keyword(s):  
Likelihood Ratio ◽  
Target Detection ◽  
Moving Target ◽  
Sea Clutter ◽  
Moving Target Detection ◽  
Tracking Method ◽  
Ratio Calculation ◽  
Detection And Tracking ◽  
Spectral Residual ◽  
Forward Looking

Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking.

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