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Analyse des Signatures de Cibles à l'aide du Radar HF – VHF Multifréquence et Multipolarisation MOSAR

机译:分析de signature deCiblesàl'aidedu Radar HF - VHFmultifréquenceetmultipolarisation mOsaR

摘要

This thesis is devoted to the analysis of flying target signatures at low frequencies, by means of a HF-VHF multifrequency and multipolarisation radar called MOSAR. Target recognition capabilities have been assessed by studying target radar cross-section (RCS) behaviour in the HF-VHF frequency band where targets cannot be steathly.Firstly, the MOSAR radar abilities are described as well as the improvements and changes introduced for an optimal operation. Then, measurement campaigns are analysed and emphasise is put on the difficulty to associate a signature to a given target without knowledge of it flight route.Secondly, the aircraft modelling possibilities are studied to better characterise RCS of targets at the operating frequencies. A commercial aircraft model has been designed and evaluated by comparison with measurements made in an anechoïc chamber. This allows the study of the influence of the flight route on aircraft RCS signature, and the results confirm the observations.Finally, target classification possibilities are considered. Two classification techniques are investigated : the nearest neighbour and the multilayer perceptron neural network (MLP). It is shown that the set of features and the number of frequencies used by the radar must be correctly chosen to minimise the probability of misclassification. Likewise, the use of these methods with MOSAR experimental data is discussed.
机译:本文致力于借助称为MOSAR的HF-VHF多频多极化雷达对低频飞行目标信号进行分析。通过研究无法隐身目标的HF-VHF频带中的目标雷达横截面(RCS)行为,评估了目标识别能力。首先,描述了MOSAR雷达的能力以及为实现最佳操作而引入的改进和变化。然后,对测量活动进行了分析,并着重指出了在不知道其飞行路线的情况下将签名与给定目标相关联的难度。其次,研究了飞机建模的可能性,以更好地表征工作频率下目标的RCS。通过与消声室中的测量结果进行比较,设计并评估了商用飞机模型。这样就可以研究飞行路线对飞机RCS签名的影响,结果证实了观察结果。最后考虑了目标分类的可能性。研究了两种分类技术:最近邻居和多层感知器神经网络(MLP)。结果表明,必须正确选择雷达使用的特征集和频率数,以最大程度地减少误分类的可能性。同样,讨论了将这些方法与MOSAR实验数据一起使用。

著录项

  • 作者

    David Arnaud;

  • 作者单位
  • 年度 1999
  • 总页数
  • 原文格式 PDF
  • 正文语种 fr
  • 中图分类

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