首页> 外文会议>Conference on Signal Processing, Sensor Fusion, and Target Recognition XIII; 20040413-20040415; Orlando,FL; US >High-accuracy, low-ambiguity emitter classification using an advanced Dempster-Shafer algorithm
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High-accuracy, low-ambiguity emitter classification using an advanced Dempster-Shafer algorithm

机译:使用先进的Dempster-Shafer算法进行高精度,低歧义的发射器分类

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High-accuracy, low-ambiguity emitter classification based on ESM signals is critical to the safety and effectiveness of military platforms. Many previous ESM classification techniques involved comparison of either the average observed value or the observed limits of ESM parameters with the expected limits contained in an emitter library. Signal parameters considered typically include radio frequency (RF), pulse repetition interval (PRI), and pulse width (PW). These simple library comparison techniques generally yield ambiguous results because of the high density of emitters in key regions of the parameter space (X-band). This problem is likely to be exacerbated as military platforms are more frequently called upon to conduct operations in littoral waters, where high densities of airborne, sea borne, and land based emitters greatly increase signal clutter. A key deficiency of the simple techniques is that by focusing only on parameter averages or limits, they fail to take advantage of much information contained in the observed signals. In this paper we describe a Dempster-Shafer technique that exploits a set of hierarchical parameter trees to provide a detailed description of signal behavior. This technique provides a significant reduction in ambiguity particularly for agile emitters whose signals provide much information for the algorithm to utilize.
机译:基于ESM信号的高精度,低歧义辐射源分类对军事平台的安全性和有效性至关重要。许多以前的ESM分类技术都涉及将ESM参数的平均观测值或观测极限与包含在发射器库中的预期极限进行比较。通常考虑的信号参数包括射频(RF),脉冲重复间隔(PRI)和脉冲宽度(PW)。这些简单的库比较技术通常会产生模棱两可的结果,因为参数空间(X波段)的关键区域中的发射器密度很高。随着越来越多地要求军事平台在沿海水域进行作战,在沿海水域,高密度的空中,海上和陆基发射源会大大增加信号的混乱程度,这一问题可能会加剧。简单技术的一个关键缺陷是,仅关注参数的平均值或极限,它们就无法利用观察到的信号中包含的许多信息。在本文中,我们描述了一种Dempster-Shafer技术,该技术利用一组分层的参数树来提供信号行为的详细描述。这项技术可显着降低歧义度,尤其是对于其信号为算法使用提供大量信息的敏捷发射器而言。

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