Advanced Target discrimination using fingerprinting based on High Resolution Range and micro-Doppler profiles

使用基于高分辨率范围和微多普勒轮廓的指纹进行高级目标辨别

• 批准号: 2435716
• 金额: --
• 依托单位: University of Strathclyde
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2435716
• 关键词: Advanced; Target; discrimination; using; fingerprinting

Ground-based airborne imaging radar systems are frequently required to provide enhanced situational awareness information beyond target detection and tracking. In particular, target recognition is an important challenging task that modern radars are frequently requested to provide. Understanding the characteristics of targets, including profiles, polarimetric responses and micro-motions is fundamental to be able to characterise and discriminate cooperative and non-cooperative targets such as vehicles, ballistic missiles and ships. Novel radar systems are now able to provide enriched target information, such as High Resolution Range Profiles (HRRPs) and micro-Doppler (MD) signatures, however there has been much less research reported on how to exploit and maximise the benefits of these systems for the target recognition task. This project will develop models and algorithms to exploit and assess the capabilities of what we will call " a target's fingerprint" extracted from both HRRP and MD to perform advanced target recognition.Micro-motion characteristics of different targets will be utilised in order to extract unique signatures leading to accurate discrimination between a number of classes. Of particular interest is the derivation of realistic motion models of targets and the way that different motion and observation parameters, e.g. manoeuvring and observation angle, affect the received radar returns. Individual characteristics in range and Doppler domain will be combined and used to generate both detailed, image-like representations and more abstract feature vectors through novel signal processing approaches.Aim: Develop models, signal processing solutions and systems (e.g waveforms) in order to discriminate targets by exploiting a fusion of High Resolution Range and micro-Doppler profiles.Objectives:- Understand the principles of High Resolution Range and Micro-Doppler profiles and signature extraction;- Develop joint models for Range-Doppler representation of targets;- Develop supervised and unsupervised (Deep Learning) frameworks for target fingerprinting;- Develop algorithms for target fingerprinting based target tracking in multi-target scenario;- Develop algorithms for target behaviour prediction based on fingerprinting and tracking (patterns of life); - Acquire experimental data in controlled environment using radar sensors available at Strathclyde; - Validate models and algorithms on lab data; - Validate models and algorithms on data provided by Leonardo;

地面机载成像雷达系统经常需要提供目标检测和跟踪之外的增强态势感知信息。特别是，目标识别是现代雷达经常被要求提供的一项重要且具有挑战性的任务。了解目标的特征，包括轮廓、偏振响应和微运动，对于能够表征和区分车辆、弹道导弹和舰船等合作和非合作目标至关重要。新型雷达系统现在能够提供丰富的目标信息，例如高分辨率距离剖面 (HRRP) 和微多普勒 (MD) 特征，但是关于如何利用这些系统执行目标识别任务并最大限度地发挥其优势的研究报告却少得多。该项目将开发模型和算法，以利用和评估从 HRRP 和 MD 中提取的“目标指纹”的能力，以执行高级目标识别。将利用不同目标的微运动特征来提取独特的签名，从而准确区分多个类别。特别令人感兴趣的是目标的真实运动模型的推导以及不同运动和观察参数的方式，例如。机动和观察角度会影响接收到的雷达回波。范围和多普勒域中的各个特征将被组合并用于通过新颖的信号处理方法生成详细的、类似图像的表示和更抽象的特征向量。目标：开发模型、信号处理解决方案和系统（例如波形），以便通过利用高分辨率范围和微多普勒轮廓的融合来区分目标。目标：-了解高分辨率范围和微多普勒轮廓的原理和 签名提取；- 开发目标距离-多普勒表示的联合模型；- 开发用于目标指纹识别的有监督和无监督（深度学习）框架；- 开发多目标场景中基于目标指纹识别的目标跟踪算法；- 开发基于指纹识别和跟踪（生命模式）的目标行为预测算法； - 使用斯特拉斯克莱德提供的雷达传感器在受控环境中获取实验数据； - 根据实验室数据验证模型和算法； - 根据莱昂纳多提供的数据验证模型和算法；