Parallel Processing for Novel Navigation

本项目的重点是探索GPU和多线程CPU的能力，以增强用于惯性导航系统的粒子过滤器。GPS干扰技术正变得越来越复杂，因此对新的导航解决方案的需求越来越大。目前，惯性导航系统对加速度测量误差过于敏感，这可能会导致位置测量中的漂移，使位置在很长一段时间内不准确。粒子滤波的使用为缓解这些挑战和增强惯性导航系统的稳健性提供了一个很有前途的解决方案。该项目旨在最大限度地利用现代处理硬件(GPU、多线程CPU等)来提高多传感器定位-导航和定时(PNT)系统的精度和健壮性。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。将传感器处理的并行实现用于PNT应用的潜力巨大。有些方面，如惯性导航处理的顺序性质，不适合并行化，但其他方面更兼容。例如，使用粒子过滤器(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，利物浦大学是这一领域的优秀中心。利物浦最近的工作表明，可以并行传输(在线)粒子过滤器，以获得多个处理器的全部好处。博士项目的主要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.