We review some advances of the particle filtering (PF) algorithm that have been achieved in
the last decade in the context of target tracking, with regard to either a single target or …

摘要本文梳理了粒子滤波理论基本内容, 发展脉络和最新研究进展, 特别是对其在多目标跟踪
应用中的一系列难点问题与主流解决思路进行了详细分析和报道. 常规粒子滤波研究重点主要 …

Agent-based modelling is a valuable approach for systems whose behaviour is driven by the
interactions between distinct entities. They have shown particular promise as a means of …

Measurements to estimate parameters of a model are commonplace in the physical
sciences, where the traditional approach to automation is to use a sequence of preselected …

Particle Filters (PFs) are Sequential Monte Carlo methods which are widely used to solve
filtering problems of dynamic models under Non-Linear Non-Gaussian noise. Modern PF …

Resampling is a well-known statistical algorithm that is commonly applied in the context of
Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian …

Particle Filters (PFs) are commonly used Sequential Monte Carlo (SMC) algorithms to
process a never-ending stream of measurements relating to a nonlinear non-Gaussian state …

Aiming at the problems of low signal precision and uncertain sparsity in traditional structural
damage identification methods, a structural damage identification method based on an …

Forecasting of ocean drift trajectories are important for many applications, including search
and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting …

Each factory and manufacturing plant needs a flexible and reliable in-plant resource supply
to serve production processes efficiently. Manufacturing systems are composed of several …