Multiple-stage adaptive architectures are conceived to face with the problem of target detection buried in noise, clutter, and intentional interference. First, a scenario where the radar system is under the electronic attack of noiselike interferers is considered. In this context, two sets of training samples are jointly exploited to devise a novel two-step estimation procedure of the interference covariance matrix. Then, this estimate is plugged in the adaptive matched filter to mitigate the deleterious effects of the noiselike jammers on radar sensitivity. Besides, a second scenario, which extends the former by including the presence of coherent jammers, is addressed. Specifically, the sparse nature of data is brought to light and the compressive sensing paradigm is applied to estimate target response and coherent jammers amplitudes. The likelihood ratio test, where the unknown parameters are replaced by previous estimates, is designed and assessed. Remarkably, the sparse approach allows for echo classification and estimation of both angles of arrival and number of the interfering sources. The performance analysis, conducted resorting to simulated data, highlights the effectiveness of the newly proposed architectures also in comparison with suitable competing architectures (when they exist).

中文翻译：

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针对类噪声和/或相干干扰的新 ECCM 技术

多级自适应架构旨在解决隐藏在噪声、杂波和故意干扰中的目标检测问题。首先，考虑雷达系统受到类噪声干扰的电子攻击的情况。在这种情况下，联合利用两组训练样本来设计干扰协方差矩阵的新的两步估计程序。然后，将该估计值插入自适应匹配滤波器以减轻类噪声干扰器对雷达灵敏度的有害影响。此外，还解决了第二种情况，它通过包括相干干扰器的存在来扩展前者。具体来说，揭示了数据的稀疏性质，并应用压缩传感范式来估计目标响应和相干干扰器幅度。设计和评估似然比检验，其中未知参数由先前的估计值代替。值得注意的是，稀疏方法允许对到达角和干扰源数量进行回声分类和估计。借助模拟数据进行的性能分析突出了新提出的架构与合适的竞争架构（如果存在）相比的有效性。