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Automatic target detection and speed estimationusing forward scatter radar sensor

机译:利用前向散射雷达传感器进行自动目标检测和速度估算

摘要

Forward Scatter Radar (FSR) is a subclass of the bistatic radar, where the received target signal occurs mainly due to the direct path signal shadowing by the target body. Employing a separate deployed transmitter and receiver at considerable distance, the FSR can achieve a number of advantages, such as enhanced radar cross section, inherent detection ability of stealth target, reasonably low complexity design of system, more than the conventional monostatic radar. All of these features are attractive to the modern remote sensing systems. udThis thesis presents the research results of the detection and speed estimation of the ground target in FSR, which is a vital procedure for automatic targets classification. The hardware was designed and assembled by the Microwave Integrated Systems Laboratory (MISL), University of Birmingham. The experimental data used in this thesis have been collected from real field environments at multiple locations and from various targets. The complex automatic target detection and speed estimation algorithm were integrated to achieve higher accuracy. udThe main problem investigated in this research and the appropriate results are dedicated to automatic target speed estimation in complex FSR operational scenario. The improved and originally proposed algorithms are discussed and shown throughout the chapters in great detail. The measurements are implemented in large load of work and the database is created for the validation of these algorithms.
机译:前向散射雷达(FSR)是双基地雷达的子类,其中接收到的目标信号的出现主要是由于目标主体对直接路径信号的遮蔽。通过在相当远的距离上分开部署发射器和接收器,FSR可以实现许多优势,例如,比传统的单基地雷达具有更多的雷达横截面,隐身目标的固有检测能力,系统复杂性较低的设计。所有这些功能都对现代遥感系统具有吸引力。 ud本文介绍了FSR中地面目标的检测和速度估计的研究结果,这是自动目标分类的重要过程。该硬件是由伯明翰大学的微波集成系统实验室(MISL)设计和组装的。本文所使用的实验数据是从多个地点的实际环境和各种目标中收集的。集成了复杂的自动目标检测和速度估计算法,以实现更高的精度。 ud本研究中研究的主要问题和适当的结果专用于复杂FSR操作场景中的自动目标速度估计。在本章中将详细讨论并展示改进的算法和最初提出的算法。这些测量工作量很大,并且为验证这些算法而创建了数据库。

著录项

  • 作者

    Xu Chunyang;

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 English
  • 中图分类

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