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

粒子滤波是一种灵活的算法，利用贝叶斯蒙特卡罗方法从噪声测量中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，并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。粒子滤波器的一个特殊挑战是需要指定模拟状态动力学的动态和测量模型及其与测量的关系。对于处理复杂环境的从业者来说，这一点非常重要。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。用数据驱动的模型取代粒子过滤器中的启发式模型，将使它们成为数字化和数字NMI议程（如不同路况下的自动驾驶）中核心应用的极其强大的工具。所提出的可微粒子滤波（DPF）框架建立在动态系统和鲁棒人工智能的交叉点上，试图回答我们如何基于观察数据分析动态系统的基本问题，而不是通过分析的启发式方法。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。预计项目成果将适用于数字领域的广泛应用

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