Source Separation for Electronic Surveillance

源分离是电子监视系统中的一个关键的早期处理阶段，其中需要检测、分离和识别多个被错误拦截的传输以发现可能的威胁（例如脉冲和连续波雷达、导航系统等）。当要检测和分离的信号在时间和频率上重叠时，这可以证明是一个具有挑战性的信号处理任务，不能通过简单的滤波或波束形成来解决。最近稀疏表示已经成为解决源分离问题的一个非常强大的技术，特别是在欠定的情况下（即当目标源比传感器少时），包括单通道源分离的困难情况。稀疏表示通常利用被截获信号的性质的先验知识来创建“非线性”分离算法，其性能大大超过传统滤波技术。此外，在某些情况下，他们也可以适应学习的结构ofsignals被观察到实现分离在一个完全blind manner.本项目的目的是开发新的算法，基于稀疏表示能够检测，分离和分类的个别EM信号，在时间和频率上重叠。此外，计算效率将通过借用压缩传感理论的最新思想来追求.

Source separation is a critical early processing stage in electronic surveillance systems where the multiple simultaneouslyintercepted transmissions need to be detected, separated and identified for possible threats (e.g. pulsed and continuouswave radar, navigation systems, etc.). When the signals to be detected and separated overlap in time and frequency thiscan prove a challenging signal processing task that cannot be solved through simple filtering or beamforming.Recently sparse representations have emerged as a very powerful technique for solving source separation problems,particularly in underdetermined scenarios (i.e when there are fewer target sources than sensors), including the difficultcase of single channel source separation. Sparse representations usually exploit prior knowledge of the nature of thesignals to be intercepted to create 'nonlinear' separation algorithms that substantially surpass the performance oftraditional filtering techniques. Furthermore, in certain circumstances, they can also be adapted to learn the structure ofthe signals being observed to achieve the separation in a totally blind manner.The aim of this project is to develop new algorithms based around sparse representations capable of detection,separation and classification of individual EM signals that overlap in time and frequency. In addition computationalefficiency will be pursued by borrowing recent ideas from compressed sensing theory.