Advanced Electronic Surveillance DSP and ML Technique

先进的电子监控 DSP 和 ML 技术

• 批准号: 2884142
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884142
• 关键词: Advanced; Electronic; Surveillance; DSP; ML

The electromagnetic environment (EME) is becoming increasingly congested and contested. Designers of both radar and communications systems are developing methods that are both more complicated and increasingly harder to detect. Electronic Surveillance (ES) is therefore facing an increasingly complex environment to operate within. As radar and communications systems develop in their adaptability the requirements on ES systems increase significantly.Recently there have been attempts to bring the advances made in machine learning (ML) to the understanding of the EME. Several works have applied ML to modulation recognition in communications whilst others have attempted to classify individual radar transmitters from their radio frequency emissions (RF). ES could leverage these techniques to provide the operator with greater understanding of the complicated EME around them. Little academic research has been completed on real world applied problems considering low SNR, congested and contested environments.Many open research questions exist in the application of ML to the Electromagnetic Environment (EME). These include:- How to collect the training data with limited a priori information or ability to label it.o Labelling is a significant challenge in general and a non-trivial task to undertake. This PhD develop methods for best practice in this area.- How to pre-process RF data to get best performance from ML algorithms, e.g., best normalisation techniques to cope with varying power levels and the use of time-frequency transforms to provide more information.- How to deal with specific ES challenges such aso Fleeting signals, e.g., only a few examples of a signal/class are available to ML techniques.o Erroneous data labelling and incomplete datasetso Congested RF scenarioso Interference and multi-path problems specific to ES problems.- How to aggregate ML techniques across multiple distributed sensors- Benchmarking of ML techniques against traditional methods and defining when either methods break.- How to deal with multiple signal types - one model or multiple models. Cutting edge solutions may implement hierarchical modelling solutions which can be investigated within this research programme.- What real world limitations exist when porting algorithms onto hardwareThese questions are present across many of the problems within ES that ML could address. The PhD will therefore focus on a particular research area; detecting, counting and separating signals in a congested EME using ML. Through this the PhD student will be able to leverage pervious time-frequency transforms developed at UCL for pre-processing along with current UCL RFSoC work to create datasets for testing and to deploy algorithms on.

电磁环境（EME）变得越来越拥挤和有争议。雷达和通信系统的设计者正在开发更复杂、更难探测的方法。因此，电子监控（ES）面临着越来越复杂的运行环境。随着雷达和通信系统适应性的发展，对ES系统的要求显着增加。最近，人们试图将机器学习（ML）的进步带到对EME的理解中。一些工作已经将ML应用于通信中的调制识别，而另一些工作则试图从射频发射（RF）中对单个雷达发射机进行分类。ES可以利用这些技术为操作员提供对周围复杂EME的更好理解。针对低信噪比、拥挤和竞争环境下的真实的应用问题，学术界的研究还很少，ML在电磁环境中的应用还存在许多尚未解决的问题。这些问题包括：-如何收集先验信息有限的训练数据或对其进行标记的能力。o标记通常是一项重大挑战，也是一项重要的任务。这个博士开发在这一领域的最佳实践方法。如何预处理RF数据以获得ML算法的最佳性能，例如，最好的归一化技术，以科普不断变化的功率水平和使用时间频率变换，以提供更多的信息。如何应对特定的ES挑战，例如：信号/类的仅几个示例可用于ML技术。错误的数据标记和不完整的干扰拥塞RF干扰干扰和特定于ES问题的多径问题。如何跨多个分布式传感器聚合ML技术-将ML技术与传统方法进行基准测试，并定义任何一种方法何时失效。如何处理多种信号类型-一个模型或多个模型。尖端的解决方案可以实现分层建模解决方案，可以在本研究计划中进行研究。在将算法移植到硬件上时，存在哪些真实的限制这些问题存在于ML可以解决的ES中的许多问题中。因此，博士将专注于一个特定的研究领域;使用ML在拥挤的EME中检测，计数和分离信号。通过这一点，博士生将能够利用在UCL开发的先前的时频变换进行预处理，沿着当前的UCL RFSoC工作，以创建用于测试的数据集并部署算法。