This project will leverage artificial neural networks to automatically build various components of particle filters.

该项目将利用人工神经网络自动构建粒子滤波器的各种组件。

基本信息

批准号：
2841890
负责人：
金额：
--
依托单位：
University of Surrey
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-2841890
关键词：
project will leverage artificial neural

项目摘要

Particle filters are a family of flexible algorithms using Bayesian Monte Carlo methods to sequentially update the probability distribution of internal states of a dynamic system from noisy measurements. The methods are statistically consistent and are widely used in applications such as computer vision, target tracking, finance, navigation, and robotics. A particular challenge for particle filters is the need to specify the dynamic and measurement models that simulate state dynamics and their relation to measurements. This becomes non-trivial for practitioners when dealing with complex environments. This project will leverage artificial neural networks to automatically build various components of particle filters. Replacing heuristic models in particle filters with data-driven ones would make them an extremely powerful tool in applications central in the digitisation and the digital NMI agenda such as autonomous driving under different road conditions. The proposed differentiable particle filter (DPF) framework lies on the intersection of dynamic systems and robust AI, attempting toanswer the fundamental question of how we analyse dynamic systems based on observed data, rather than through an analytic, heuristic approach. It links not only closely to current research activities on Bayesian inference for complex systems, but also NMS activities such as cross theme CAV project. Project outcomes are expected to be applicable to a wide range of applications in the digital sector

粒子滤波器是一系列灵活的算法，使用贝叶斯蒙特卡罗方法根据噪声测量顺序更新动态系统内部状态的概率分布。这些方法在统计上是一致的，广泛应用于计算机视觉、目标跟踪、金融、导航和机器人等应用。粒子滤波器面临的一个特殊挑战是需要指定模拟状态动力学及其与测量的关系的动态和测量模型。在处理复杂环境时，这对于从业者来说并非易事。该项目将利用人工神经网络自动构建粒子滤波器的各种组件。用数据驱动的模型取代粒子滤波器中的启发式模型将使它们成为数字化和数字 NMI 议程核心应用程序中极其强大的工具，例如不同道路条件下的自动驾驶。所提出的可微粒子滤波器（DPF）框架基于动态系统和鲁棒人工智能的交叉点，试图回答我们如何基于观察到的数据分析动态系统的基本问题，而不是通过分析、启发式方法。它不仅与当前复杂系统贝叶斯推理的研究活动密切相关，而且与跨主题 CAV 项目等 NMS 活动密切相关。项目成果预计将适用于数字领域的广泛应用