Automated ship detection with image enhancement and feature extraction in FMCW marine radars

D. Yulian, R. Hidayat, H. A. Nugroho, A. Lestari, F. Prasaja
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引用次数: 4

Abstract

Automated ship detection process has been an essential need for modern Radar system to perform automatic target tracking. This automated process is more commonly found in pulse radars with high rate of Signal to Noise Ratio (SNR), not in Frequency Modulated Continuous Wave (FMCW) radars with very low rate of SNR. The process of automated ship detection with image enhancement and feature extraction in FMCW radars will be elaborated in this paper. The process of image enhancement is designed to split target from the noise and enhance the image of the target with very low SNR. The output of this process will be classified into several groups by utilizing object geographical, circularity and solidity data. From this process, it clearly shows that with image enhancement, the radar detecting capability in average increases by 390%, 140%, 112% and 62% for radar image within the radii of 2 Nautical Mile (NM), 4 NM, 10 NM and 20 NM. With image classification by geographical data and feature extraction, the ship images will be significantly distinguished from clutter with the accuracy of 90%.
基于图像增强和特征提取的FMCW船用雷达船舶自动检测
船舶自动探测过程已成为现代雷达系统实现目标自动跟踪的基本要求。这种自动化过程更常见于具有高信噪比(SNR)的脉冲雷达,而不是具有非常低信噪比的调频连续波(FMCW)雷达。本文阐述了FMCW雷达中基于图像增强和特征提取的船舶自动检测过程。图像增强的目的是将目标从噪声中分离出来,对信噪比很低的目标图像进行增强。这个过程的输出将被分为几个组,利用对象地理,圆度和固体数据。从这个过程中可以清楚地看出,通过图像增强,雷达对半径为2海里、4海里、10海里和20海里的雷达图像的探测能力平均提高了390%、140%、112%和62%。通过地理数据的图像分类和特征提取,将舰船图像与杂波区分开,准确率达到90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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