Abstract

This paper proposes a technique for recognizing anthropogenic location objects on an underlying surface that is based on measuring the complete polarization scattering matrix (PSM) of a radiolocation scene. The intelligent analysis of a PSM implies the generation of adaptive robust estimates for trends and covariance matrices, as well as reflected non-stationary non-Gaussian signals. Based on the results of the digital computer simulation experiments and field measurements of the radiolocation scene’s PSM, machine learning algorithms for clustering and classifying the elements of an underlying surface and location object are verified. The possibility of using neural network architecture in the form of a support vector machine for real-time implementation of these algorithms is substantiated.

中文翻译：

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通过极化散射矩阵的智能分析识别下表面上的人为3D对象

摘要

本文提出了一种技术，该技术基于对无线电定位场景的完整极化散射矩阵（PSM）进行测量，从而识别下层表面上的人为定位对象。PSM的智能分析意味着生成趋势和协方差矩阵以及反射的非平稳非高斯信号的自适应鲁棒估计。基于数字计算机仿真实验的结果以及对无线电定位场景的PSM进行的现场测量，验证了用于对底层表面和位置对象的元素进行聚类和分类的机器学习算法。证实了使用支持向量机形式的神经网络体系结构实时实施这些算法的可能性。