Sea clutter suppression using neural network

Authors

  • Ravi Kumar Kandagatla Department of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, India
  • Vineela Malapati Department of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, India
  • Krishnaveni Cherukuri Department of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, India
  • Bharadwaj Sindhe Department of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, India
  • Vijaya Kumar Padarti Department of Electronics and Communication Engineering, Aditya University, Surampalem, India
  • Bala Krishna Thammileti Department of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, India

DOI:

https://doi.org/10.33175/mtr.2025.272334

Keywords:

Feed Forward Neural Network; Principal Component Analysis; SVM; Coastal line; Targets; SNR; SSIM

Abstract

Sea clutter suppression is a critical task in radar systems to enhance target detection performance in complex naval environments and at coastlines. This paper discusses the use of neural networks for marine clutter suppression and coastal surveillance radar clutter suppression. Effective maritime clutter suppression is made possible by the Feed Forward Neural Network (FFNN) and Principal Component Analysis (PCA) based clutter reduction method, which uses neural network deep learning capabilities to automatically identify and extract features and patterns from raw radar data. Support Vector Machine (SVM) is utilized for clutter suppression along the shoreline. To train and test the network model, a sizable collection of radar measurements, including clutter and target echoes, is gathered. After pre-processing, the gathered data is used in a specially created model, which uses its underlying patterns to distinguish between target echoes and clutter. Then, clutter in real-time radar signals is suppressed using the learned neural network models, improving the detection of targets on the sea and at the coastline. Performance measures Structural Similarity (SSIM) and Signal to Noise Ratio (SNR) shows that the proposed method provides improved clutter reduction.

Highlights

  • Maritime clutter suppression is required for better visibility of targets
  • Neural Networks provides better clutter suppression
  • Clutter reduction using PPI images helps in Surveillance applications
  • Objective performance measures SNR and SSI are used for performance analysis
  • Better training and good dataset helps in improved clutter reduction for neural network approaches

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Published

2024-07-09

Issue

Vol. 7 No. 1 (2025): January - March

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