Developing Novel Bayesian Track Before Detect Approaches for Maritime Big Data Challenges

在检测方法之前开发新颖的贝叶斯轨迹应对海事大数据挑战

• 批准号: 2889729
• 金额: --
• 依托单位: University of Liverpool
• 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-2889729
• 关键词: Developing; Novel; Bayesian; Track; Before

This project aims to research novel signal processing methods to separate low observable moving targets from a sea clutter background in static and moving maritime surveillance radar, which frequently see sea clutter return peak amplitudes that are far greater than those from small targets. By observing scenes for 10s to 100s of seconds, multi-scan processing allows detection and tracking of targets 10 to 20dB 'sub-clutter', even within the clutter spectrum. This project will study improvements achievable by extracting further detail from the radar data. In particular, techniques such as track before detect to extract sub-clutter moving targets from within the clutter spectrum is a key area of interest for this project. Sea surface analysis could be exploited in the tuning of such algorithms, for example, to approach the problem as one of detecting anomalies in a scene that deviate from expected reflectivity behaviour in a wave field. The relationship between performance improvements, the processing load and level of complexity of algorithms or interacting algorithm set, is to be explored under the project. Previous research has identified novel effective Monte Carlo algorithms which utilise gradient-based MCMC proposals which can provide useful enhancements to target extraction performance in scenarios that will be determined early in the project. The scope of the project is expected to also include characterisation of radar sensors including those produced by Hensoldt UK, deep exploration of the characteristics of sea-clutter, and of targets of interest, and exploration of computational techniques and machine learning to leverage the above mentioned computational paradigm with sensor data.The project may be complemented by research outcomes from associated academic partners, part-funded by Hensoldt, and Hensoldt staff, as well as liaison with Oceanographic Research and technology community, a focussed data collection campaign commencing autumn 2023, a PhD at the Cranfield Shrivenham campus since early 2023 and context from a user community coordinated by Hensoldt UK.

该项目旨在研究新型信号处理方法，以在静态和移动海事监视雷达中将低可观测移动目标从海杂波背景中分离出来，这些雷达经常会看到海杂波返回峰值幅度远大于小目标的幅度。通过观察场景10秒到100秒，多扫描处理允许检测和跟踪目标10到20分贝的“子杂波”，甚至在杂波频谱内。该项目将研究通过从雷达数据中提取更多细节可以实现的改进。特别是，技术，如跟踪检测到从杂波频谱中提取子杂波运动目标是本项目的一个关键领域的兴趣。海面分析可以用于调整这种算法，例如，将问题作为检测场景中偏离波场中预期反射率行为的异常之一来处理。将在该项目下探讨性能改进、处理负荷和算法或相互作用的算法集的复杂程度之间的关系。以前的研究已经确定了新的有效的蒙特卡罗算法，利用基于梯度的MCMC建议，可以提供有用的增强目标提取性能的情况下，将在项目的早期确定。该项目的范围预计还将包括雷达传感器的表征，包括由Hensoldt UK生产的雷达传感器，对海杂波特性和感兴趣目标的深入探索，以及对计算技术和机器学习的探索，以利用上述传感器数据的计算范例。该项目可能会得到相关学术合作伙伴的研究成果的补充，由Hensoldt提供部分资金，和Hensoldt员工，以及与海洋研究和技术社区的联络，2023年秋季开始的集中数据收集活动，自2023年初以来在克兰菲尔德Shrivenham校园的博士学位以及由Hensoldt UK协调的用户社区的背景。