My research project

University roles and responsibilities

  • Computer Science PGR Representative (2022/23)



    Wenhan Li, Xiongjie Chen, Wenwu Wang, Victor Elvira, Yunpeng Li (2023)Differentiable Bootstrap Particle Filters for Regime-Switching Models, In: 2023 IEEE Statistical Signal Processing Workshop (SSP)2023-pp. 200-204 IEEE

    Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world applications, both the state dynamics and measurements can switch between a set of candidate models. For instance, in target tracking, vehicles can idle, move through traffic, or cruise on motorways, and measurements are collected in different geographical or weather conditions. This paper proposes a new differentiable particle filter for regime-switching state-space models. The method can learn a set of unknown candidate dynamic and measurement models and track the state posteriors. We evaluate the performance of the novel algorithm in relevant models, showing its great performance compared to other competitive algorithms.