雷达系统对抗智能噪声干扰器的 ECCM 战略

IF 4.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Dario Benvenuti;Pia Addabbo;Gaetano Giunta;Goffredo Foglia;Danilo Orlando
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引用次数: 0

摘要

在本文中,我们要解决的问题是,如何探测一种不会迅速发射所有可用功率,而是逐渐增加发射功率的类噪声干扰器(NLJ)。这种控制策略会使传统的电子对抗措施无法揭示被测窗口中存在的噪声功率不连续。因此,受到攻击的雷达系统会变成盲区,需要专家操作员做出反应。为了应对这种情况,我们在设计阶段就假设了 NLJ 功率变化的特定模型,从而设计出两种创新的 NLJ 检测架构。第一个模型基于观测时间内的线性变化规律,而在第二个模型中,NLJ 功率在被测窗口内经历无约束波动。在这些假设下,我们对广义似然比检验进行了特别修改,将未知参数替换为通过迭代程序获得的合适估计值。利用合成数据进行的性能分析表明,与传统方法相比,建议的检测器更加有效和优越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ECCM Strategies for Radar Systems Against Smart Noise-Like Jammers
In this paper, we address the problem of detecting a Noise-Like Jammer (NLJ) that does not quickly transmit all the available power but it gradually increases the transmitted power. This control strategy would prevent conventional electronic counter-countermeasures from revealing the presence of a noise power discontinuity in the window under test. As a consequence, the radar system under attack becomes blind requiring a reaction by an expert operator. In order to face such a situation, we devise two innovative NLJ detection architectures by assuming at the design stage specific models for the NLJ power variation. The first model is based on a linear variation law over the observation time, whereas in the second model, the NLJ power experiences unconstrained fluctuations in the window under test. Under these hypotheses, we resort to ad hoc modifications of the generalized likelihood ratio test, where the unknown parameters are replaced by suitable estimates obtained through iterative procedures. The performance analysis, carried out using synthetic data, shows the effectiveness and superiority of the proposed detectors over the conventional approach.
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来源期刊
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing 工程技术-工程:电子与电气
CiteScore
11.20
自引率
9.30%
发文量
310
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
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