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凹障碍超宽带SAR图像特征分析 被引量:2

Study on ultra-wideband SAR image feature of negative obstacle
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摘要 野外环境下的凹障碍感知一直是地面无人作战平台环境感知面临的难题,长期以来常规传感器,例如立体视觉、红外相机和激光雷达,都没有取得好的效果。超宽带合成孔径雷达作为一种全天时、全天候的高分辨率雷达,在目标感知方面得到了广泛的运用。基于超宽带合成孔径雷达感知凹障碍是一种有效的感知手段,阐述了凹障碍的雷达成像几何,利用MATLAB模拟仿真合成孔径雷达数据获得了凹障碍图像,分析得出了凹障碍在雷达图像表现出由阴影区和光亮区紧密相连的特征,并通过实测数据成像获得的凹障碍图像结果,对凹障碍雷达图像特征进行了进一步的验证。 Negative obstacle sensing is one of the most difficult problems for unmanned ground vehicle in unstructured environments.The regular obstacle sensors,such as stereo vision,infrared detector and ladar,have their limited performances in unconstructed environments.Ultra-wideband SAR(synthetic aperture radar)sensors have the ability to operate in all weather,all lighting and foliage covered conditions,which have been received widely.Sensing negative obstacle by ultra-wideband SAR for unmanned ground vehicle was an effective way.Image geometry of negative obstacle was expounded.The simulation image of negative obstacle was obtained by simulation based on MATLAB,and the conclusion that the image feature of negative obstacle is the shadow area next to shine area is obtained.Moreover,a real data experiment is presented and the experimental result proves the same conclusion again.
作者 蒋志彪 王建 宋千 周智敏 JIANG Zhibiao;WANG Jian;SONG Qian;ZHOU Zhimin(College of Electronic Science,National University of Defense Technology,Changsha 410073,China)
出处 《国防科技大学学报》 EI CAS CSCD 北大核心 2017年第6期160-164,共5页 Journal of National University of Defense Technology
基金 国家自然科学基金资助项目(61372163)
关键词 合成孔径雷达 凹障碍 后向投影 negative obstacle synthetic aperture radar back projection
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  • 1Desouza G N, Kak A C. Vision for mobile robot navigation: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(2): 237-267.
  • 2Pomerleau D. Neural Network Vision for Robot Driving. Massachusetts: MIT Press, 1995.
  • 3Coombs D, Herman M, Hong T H, Nashman M. Real-time obstacle avoidance using central flow divergence and peripheral flow. IEEE Transactions on Robotics and Automation, 1998, 14(1): 49-59.
  • 4Ulrich I, Nourbakhsh I. Appearance-based obstacle detection with monocular color vision. In: Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence. Austin, USA: AAAI Press, 2000. 866-871.
  • 5Saxena A, Chung S H, Ng A Y. 3-D depth reconstruction from a single still image. International Journal of Computer Vision, 2008, 76(1): 53-69.
  • 6Klarquist W N, Geisler W S. Maximum likelihood depth from defocus for active vision. In: Proceedings of the International Conference on Intelligent Robots and Systems. Washington D. C., USA: IEEE, 1995. 374-379.
  • 7Rajagopalan A N, Chaudhuri S, Mudenagudi U. Depth estimation and image restoration using defocused stereo pairs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1521-1525.
  • 8Bellutta P, Manduchi R, Matthies L, Owens K, Rankin A. Terrain perception for DEMO III. In: Proceedings of IEEE Conference on Intelligent Vehicles Symposium. Dearborn, USA: IEEE, 2000. 3-8.
  • 9Rankin A, Huertas A, Matthies L. Evaluation of stereo vision obstacle detection algorithms for off-road autonomous navigation. AUVSI Unmanned Systems North America. Pasadena, USA: Jet Propulsion Laboratory, 2005.
  • 10Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J. Stanley, the robot that won the DARPA grand challenge. Journal of Robotics Systems, 2006, 23(9): 661-692.

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