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

在静态和运动海上监视雷达中，海杂波回波峰值幅值往往远大于小目标，本项目旨在研究一种新的信号处理方法，将低可观测运动目标与海杂波背景分离。通过观察场景10秒到100秒，多次扫描处理允许检测和跟踪目标10到20dB的“次杂波”，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来实现的改进。其中，先跟踪后检测从杂波频谱中提取亚杂波运动目标的技术是本课题研究的重点。例如，海面分析可以用于调整这类算法，以解决在波场中偏离预期反射率行为的场景中检测异常的问题。性能改进、处理负载和算法或交互算法集的复杂程度之间的关系，将在该项目下进行探索。先前的研究已经确定了新的有效的蒙特卡罗算法，该算法利用基于梯度的MCMC建议，可以在项目早期确定的场景中提供有用的增强目标提取性能。该项目的范围预计还包括雷达传感器的特征，包括Hensoldt英国公司生产的雷达传感器，深入探索海杂波特征和感兴趣的目标，以及探索计算技术和机器学习，以利用上述传感器数据的计算范式。该项目可能会得到Hensoldt和Hensoldt员工共同资助的相关学术合作伙伴的研究成果的补充，以及与海洋学研究和技术界的联系，从2023年秋季开始的重点数据收集活动，从2023年初开始在克兰菲尔德Shrivenham校区的博士学位，以及Hensoldt英国协调的用户社区的背景。

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.