Advanced Electronic Surveillance DSP and ML Technique

电磁环境（EME）正变得越来越拥挤和有争议。雷达和通信系统的设计者都在开发更复杂、更难以探测的方法。因此，电子监视（ES）面临着一个日益复杂的运行环境。随着雷达和通信系统自适应能力的不断提高，对ES系统的要求也越来越高。最近，有人试图将机器学习（ML）的进步引入对EME的理解。一些工作已经将ML应用于通信中的调制识别，而其他人则试图从其射频发射（RF）中对单个雷达发射机进行分类。ES可以利用这些技术，使操作员更好地了解周围复杂的EME。考虑到低信噪比、拥挤和有争议的环境，对现实世界应用问题的学术研究很少。机器学习在电磁环境中的应用还存在许多有待解决的研究问题。这些问题包括：-如何在有限的先验信息或标记能力下收集训练数据。o一般来说，标签是一项重大挑战，也是一项艰巨的任务。该博士学位为该领域的最佳实践开发了方法。-如何预处理射频数据以从ML算法中获得最佳性能，例如，最佳归一化技术以应对不同的功率水平，并使用时频变换来提供更多信息。-如何处理特定的ES挑战，如转瞬即逝的信号，例如，只有几个信号/类的例子可用于ML技术。o错误的数据标签和不完整的数据集；拥堵的射频场景；干扰和多路径问题，特定于ES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调查。当将算法移植到硬件上时，现实世界中存在哪些限制？这些问题存在于ES中的许多问题中，ML可以解决这些问题。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。通过这种方法，博士生将能够利用伦敦大学学院开发的渗透时频变换进行预处理，以及目前伦敦大学学院RFSoC的工作，创建用于测试和部署算法的数据集。

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.