Target decomposition and polarimetric radar applied to concealed threat detection

Dean O'Reilly, N. Bowring, N. Rezgui, D. Andrews
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引用次数: 3

Abstract

Target decomposition is of interest to the security and defence community as it enables data sets to be reduced to their principal identifying components as a pre-processor to running pattern recognition algorithms. An investigation into the application of target decomposition theory for concealed threat detection is presented. A validation study has been conducted in which the scattering of an EM wave by a number of primitive radar calibration targets has been simulated using an EM FEA solver. A knife has then been illuminated with quad polar radar again in a simulation conducted using an EM FEA solver. The validation of the target decomposition algorithm is achieved by analysing the calibration targets with well-known scattering mechanisms. The decomposition of more complex targets such as those encountered in Concealed Threat Detection (CTD) scenarios are then analysed. The decomposition is performed as prescribed by Cloude et al and the scattering of the illuminating wave due to the target is mapped onto a 3D space detailing polarimetric entropy (H), anisotropy (A) and alpha-angle (α). The decomposition of these complex scattering mechanisms is then used to classify the data. Both theoretical and simulated data sets are used to validate this technique.
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目标分解与极化雷达在隐蔽威胁探测中的应用
目标分解是安全和防御社区感兴趣的,因为它可以将数据集简化为其主要识别组件,作为运行模式识别算法的预处理程序。研究了目标分解理论在隐蔽威胁检测中的应用。利用有限元求解器模拟了一组原始雷达标定目标对电磁波的散射,并进行了验证研究。然后,在使用EM FEA求解器进行的模拟中,再次使用四极雷达照射刀。通过对已知散射机制的标定目标进行分析,验证了目标分解算法的有效性。然后分析了在隐蔽威胁检测(CTD)场景中遇到的更复杂目标的分解。按照cloud等人的规定进行分解,并将目标引起的照明波散射映射到详细描述极化熵(H)、各向异性(a)和α角(α)的3D空间中。然后利用这些复杂散射机制的分解来对数据进行分类。理论和模拟数据集都用于验证该技术。
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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