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。

• 批准号: 2841890
• 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

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

粒子过滤器是使用贝叶斯蒙特卡洛方法的柔性算法家族，可从噪声测量中顺序更新动态系统的内部状态的概率分布。这些方法在统计上是一致的，并且广泛用于计算机视觉，目标跟踪，金融，导航和机器人技术等应用程序。粒子过滤器的一个特殊挑战是需要指定模拟状态动力学及其与测量关系的动态和测量模型。在处理复杂的环境时，这对于从业者而言是不平凡的。该项目将利用人工神经网络自动构建粒子过滤器的各种组件。用数据驱动的模型在粒子过滤器中替换启发式模型将使它们成为数字化中心和数字NMI议程（例如在不同的道路条件下的自动驾驶）中中心的应用程序中非常强大的工具。所提出的可区分粒子滤波器（DPF）框架在于动态系统和鲁棒AI的交集，试图通过观察到的数据而不是通过分析性的启发式方法来分析我们如何基于观察到的数据分析动态系统的基本问题。它不仅将有关复杂系统的贝叶斯推论的当前研究活动链接，还与NMS活动（例如Cross Theme Cav项目）联系起来。预计项目结果将适用于数字领域的广泛应用