Parallel Processing for Novel Navigation

这个项目的重点是探索gpu和多线程cpu的能力，以增强惯性导航系统中使用的粒子滤波器。GPS干扰技术正变得越来越复杂，因此对新型导航解决方案的需求日益增长。惯性导航系统目前对加速度测量误差过于敏感，这可能会导致位置测量的漂移，导致位置在很长一段时间内不准确。粒子滤波器的使用为缓解这些挑战和增强惯性导航系统的鲁棒性提供了一个有希望的解决方案。该项目旨在充分利用现代处理硬件（gpu，多线程cpu等）来提高多传感器定位导航和定时（PNT）系统的准确性和鲁棒性。它还将涵盖分布式处理方法、传感器融合、导航和定时系统，并将提供获得传感器硬件实时处理经验的机会。在PNT应用中使用传感器处理并行实现的潜力是巨大的。有些方面，如惯性导航处理的顺序性，不适合并行化，但其他方面则更兼容。例如，使用“粒子滤波器”（也称为顺序蒙特卡罗方法）进行传感器融合具有巨大的潜力，而利物浦大学是该领域的卓越中心。利物浦大学最近的工作已经证明，流（在线）粒子过滤器可以并行化，以获得多个处理器的全部好处。博士项目的关键目标将是使并行处理的全部好处可用于多感官PNT系统。

This project focuses on exploring the power of GPUs and multi-threading CPUs to enhance particle filters for use in inertial navigation systems. GPS jamming technology is becoming more sophisticated and therefore there is a growing need for novel navigation solutions. Inertial navigation systems are currently too sensitive to acceleration measurement errors, which can allow drift in the position measurement, rendering the position inaccurate over long durations of time. The use of a particle filter offers a promising solution to mitigate these challenges and enhance the robustness of inertial navigation systems. This project will be aimed at making best use of modern processing hardware (GPUs, multi-threading CPUs, etc) to improve the accuracy and robustness of a multi-sensor Positioning-Navigation and Timing (PNT) system. It will also cover distributed processing methods, sensor fusion, navigation and timing systems, and will provide opportunities to gain experience with real-time processing with sensor hardware.The potential for using parallel implementations of sensor processing for PNT applications is huge. Some aspects, such as the sequential nature of inertial navigation processing, are not amenable to parallelisation, but others are more compatible. For example, the use of 'particle filters' (also known as sequential Monte Carlo methods) for sensor fusion have great potential, and the University of Liverpool is a centre of excellence in this area. Recent work at Liverpool has demonstrated streaming (online) particle filters that can be parallelised to obtain the full benefit of multiple processors. The key objective of the PhD project will be to make the full benefits of parallel processing available to a multisensory PNT system.