论文标题

多个相干信号的自适应雷达检测和分类算法

Adaptive Radar Detection and Classification Algorithms for Multiple Coherent Signals

论文作者

Han, Sudan, Yan, Linjie, Zhang, Yuxuan, Addabbo, Pia, Hao, Chengpeng, Orlando, Danilo

论文摘要

在本文中,我们解决了在存在连贯(或完全相关)信号的情况下目标检测的问题,这可能是由于智能干扰器的多径传播效应或电子攻击所致。为此,我们将手头的问题提出为多种假设检验,除了传统的雷达替代假设外,它还包含其他假设,这些假设涉及存在未知数的干扰信号。在这种情况下,并利用了模型订单选择规则的分类功能,我们设计了受惩罚的基于可能的可能比率的检测体系结构,这些检测体系结构可以作为副产品,假设有效。此外,我们提出了一个次优步骤,以估计多个相干信号到达的角度,以确保(至少对于所考虑的参数)与详尽的搜索几乎相同的性能。最后,对模拟数据进行的性能评估与常规雷达检测器进行了比较,强调了所提出的架构可以从检测和正确分类的概率方面提供令人满意的性能。

In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based detection architectures that can establish, as a byproduct, which hypothesis is in force. Moreover, we propose a suboptimum procedure to estimate the angles of arrival of multiple coherent signals ensuring (at least for the considered parameters) almost the same performance as the exhaustive search. Finally, the performance assessment, conducted over simulated data and in comparison with conventional radar detectors, highlights that the proposed architectures can provide satisfactory performance in terms of probability of detection and correct classification.

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