Maritime space understanding using multiple radar systems by iterative image reconstuction algorithm

Syoji Kobashi, Hokuto Mita, Kazuya Nakagawa, Kohji Nishiyama, Hitoshi Maeno, Kei Kuramoto, Y. Hata
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引用次数: 1

Abstract

Understanding a space where autonomous robots work is an open problem. In case of maritime space, it is also very important. For this purpose, marine radar has been used to acquire images around the maritime vehicles. However, radar images are easily distorted by signal attenuation with distance, blurring due to antenna directivity, reflection by obstructs, etc. There are some conventional methods to improve the image quality. However, there is a limitation due to the acquisition mechanism of radar systems. To overcome the limitation, this study shows a novel approach, which uses multiple radar images to understand the maritime space. The method estimates radar cross section (RCS) from multiple radar images by iterative image reconstruction algorithm. Performance of the proposed method is validated using actual radar images taken by marine radar system equipped on a ship. The experimental results showed that the proposed method presents a maritime space map with high image quality and without distance attenuation, sidelobe diffusion.
基于迭代图像重建算法的多雷达系统海洋空间理解
理解自主机器人工作的空间是一个悬而未决的问题。就海洋空间而言,这也非常重要。为此目的,已使用船用雷达来获取海上车辆周围的图像。然而,雷达图像容易因信号随距离衰减、天线方向性模糊、障碍物反射等原因而失真。有一些传统的方法可以提高图像质量。然而,由于雷达系统的捕获机制,存在一定的局限性。为了克服这一限制,本研究提出了一种利用多幅雷达图像来理解海洋空间的新方法。该方法采用迭代图像重建算法从多幅雷达图像中估计雷达截面(RCS)。利用舰船雷达系统拍摄的实际雷达图像验证了该方法的有效性。实验结果表明,该方法能得到高质量的海洋空间地图,且无距离衰减和旁瓣扩散。
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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