Normalising Flow-based Differentiable Particle Filters: Performance Evaluation in Linear Gaussian State-Space Models
Efficiently optimizing particle filters using normalizing flows for improved performance in state-space models.
Normalising Flow-based Differentiable Particle Filters: Performance Comparison
Normalising flow-based differentiable particle filters outperform traditional methods in parameter estimation and tracking performance.
Efficient Position Estimation with Multiple Update Particle Filter
The author proposes the Multiple Update Particle Filter as an efficient method to update particles in a particle filter when dealing with sharp-peaked likelihood functions from multiple observations. By leveraging prior knowledge of distribution spreads, this method enhances position estimation accuracy.
Analyzing Normalising Flow-based Differentiable Particle Filters
The authors introduce a novel normalizing flow-based differentiable particle filtering framework to enhance state estimation and model learning in complex environments, addressing limitations of existing methods.