Sea Clutter Reduction and Target Enhancement by Neural Networks in a Marine Radar System

Vicen-Bueno, R.; Carrasco, Ruben; Rosa-Zurera, Manuel; Borge, José Carlos Nieto

doi:10.3390/s90301913

Cited by 38 publications

(27 citation statements)

References 18 publications

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“…(6) could then be replaced with an equivalent problem, as proposed in this paper, which would be expressed in the following form (Yang at all 2002; Zhong, 1997): (14) and which would have an iterative solution:…”

Section: Cofactor Matrix Vmentioning

confidence: 99%

Improvement in Accuracy of Determining a Vessel’s Position with the Use of Neural Networks Ana Robust M-Estimation

Czaplewski

Wąż

2017

Polish Maritime Research

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Section: Cofactor Matrix Vmentioning

confidence: 99%

Improvement in Accuracy of Determining a Vessel’s Position with the Use of Neural Networks Ana Robust M-Estimation

Czaplewski

Wąż

2017

Polish Maritime Research

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“…Background subtraction techniques can be classified mainly into three categories first, non-linear or pattern matching methods 19,20 that use fuzzy set theory, genetic algorithm (GA) and neural …”

Section: Background Subtraction Techniques and Implementationmentioning

confidence: 99%

Critical Analysis of Background Subtraction Techniques on Real GPR Data

et al. 2017

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Ground penetrating radar (GPR) is used to detect the underground buried objects for civil as well as defence applications under varying conditions of soil moisture content. The capability of detection depends upon soil moisture, target characteristics and subsurface characteristics, which are mainly responsible for contaminating the GPR images with clutter. Researchers earlier have used averaging, mean, median, Eigen values, etc. for subtracting the background from GPR images. To analyse the background subtraction or clutter reduction problems, in this paper, we have experimentally reviewed background subtraction techniques with or without target conditions to enhance the target detection under variable soil moisture content. Indigenously developed GPR has been used to collect the data for different soil conditions and several background subtraction signal processing techniques were critically reviewed like, mean, median, singular value decomposition (SVD), principal component analysis (PCA), independent component analysis (ICA) and training methods. The signal to clutter ratio (SCR) measurement has been used for performance evaluation of each technique. The relative merits and demerits of each technique has also been analysed. The background subtraction techniques have been appliedto experimental GPR data and it is observed that in comparison of mean, SVD, median, ICA, PCA, the training method shows the highest SCR with buried target. Finally, this review helps to select the comparatively better background subtraction technique to enhance the detection capability in GPR.

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“…Researchers made much effort on enhancing target or reducing clutter [35], which is an important preprocessing procedure for automatic target tracking. In fact, tracking a specific target in an image sequence can be viewed as searching a target in clutter.…”

Section: Image Measures Based On "Clutter" Theorymentioning

confidence: 99%

Image Sequence Measures for Automatic Target Tracking

Diao¹,

Mao²,

Zheng³

et al. 2012

PIER

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Abstract-In the field of automatic target recognition and tracking, traditional image metrics focus on single images, ignoring the sequence information of multiple images. We show that measures extracted from image sequences are highly relevant concerning the performances of automatic target tracking algorithms. To compensate the current lack of image sequence characterization systems from the perspective of the target tracking difficulties, this paper proposes three new metrics for measuring image sequences: inter-frame change degree of texture, inter-frame change degree of target size and inter-frame change degree of target location. All are based on the fact that inter-frame change is the main cause interfering with target tracking in an image sequence. As image sequences are an important type of data in the field of automatic target recognition and tracking, it can be concluded that the work in this paper is a necessary supplement for the existing image measurement systems. Experimental results reported show that the proposed metrics are valid and useful.

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Sea Clutter Reduction and Target Enhancement by Neural Networks in a Marine Radar System

Cited by 38 publications

References 18 publications

Improvement in Accuracy of Determining a Vessel’s Position with the Use of Neural Networks Ana Robust M-Estimation

Improvement in Accuracy of Determining a Vessel’s Position with the Use of Neural Networks Ana Robust M-Estimation

Critical Analysis of Background Subtraction Techniques on Real GPR Data

Image Sequence Measures for Automatic Target Tracking

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