Signal Processing and Novel Technologies in Radar Systems

雷达系统中的信号处理和新技术

• 批准号: 2435933
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2435933
• 关键词: Signal; Processing; Novel; Technologies; Radar

Radar and communication technologies operating across the same frequencies leave each system susceptible tointerference from the other, resulting in performance degradation. Current approaches to mitigating interferenceplace restrictions on the characteristics of the radar system, which may limit their suitability to operate in a desired environment [1].Over the past decade a novel approach has being examined in which radar and communications are incorporatedinto the same platform, creating a dual-function radar communication system (DFRC). The radar architectureand modulation of radar pulses can be exploited by the DFRC system in order to communicate without impactingradar performance [2]. By integrating the functionality of traditionally separate systems, cost and power consumption can be reduced and the systems more efficiently managed.However, there are many questions concerning this novel technology that must be addressed. A theoretical framework must be developed for optimising the dual-function system to facilitate fast data transfer without compromise of radar functionality. Existing transmission waveforms are optimised to the single-function of the systememitting them, so the waveform of a dual-function system requires a novel design. Current research does notexamine the potential for radar constructive interference to be exploited in communication systems. Machinelearning offers an alternative approach to the development of complex mathematical models for spectrum sharing of radar and communications systems. For the deployment of this technology to be realised the research mustbe underpinned by experimental verification using real hardware.The PhD project outlined by Professor Ratnarajah offers the opportunity to research and provide a meaningfulcontribution to overcoming these challenges.

雷达和通信技术在相同的频率上工作，使每个系统容易受到其他系统的干扰，导致性能下降。现有的抗干扰方法对雷达系统的特性进行了限制，这可能会限制其在所需环境中的适用性[1]。在过去的十年中，一种新的方法被研究，其中雷达和通信被集成到同一平台中，创建了双功能雷达通信系统（DFRC）。DFRC系统可以利用雷达结构和雷达脉冲调制，以便在不影响雷达性能的情况下进行通信[2]。通过集成传统上独立系统的功能，可以降低成本和功耗，并更有效地管理系统。然而，有许多问题，关于这项新技术，必须加以解决。必须建立一个理论框架，优化双功能系统，以促进快速数据传输，而不损害雷达功能。现有的传输波形被优化为系统的单功能，因此双功能系统的波形需要新颖的设计。目前的研究并没有探讨雷达相长干扰在通信系统中的应用潜力。Machinelearning为雷达和通信系统频谱共享的复杂数学模型的开发提供了另一种方法。为了实现这项技术的部署，研究必须通过使用真实的硬件进行实验验证来支持。Ratnarajah教授概述的博士项目提供了研究的机会，并为克服这些挑战做出了有意义的贡献。