Sequential Monte Carlo probability hypothesis density (SMC-PHD) filtering is a popular
method used recently for audio-visual (AV) multi-speaker tracking. However, due to the …

A key challenge when designing particle filters in high-dimensional state spaces is the
construction of a proposal distribution that is close to the posterior distribution. Recent …

The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been
shown to be promising for audio-visual multi-speaker tracking. Recently, the zero diffusion …

Abstract Sequential Monte Carlo probability hypothesis density (SMC-PHD) filtering has
been recently exploited for audio-visual (AV) based tracking of multiple speakers, where …

Target tracking is a challenging task and generally no analytical solution is available,
especially for the multi-target tracking systems. To address this problem, probability …

Particle filters (PFs) are algorithms that approximate the so-called filtering distributions in
complex state-space models. We present a unified view on PFs as importance sampling with …

Particle flow (PF) is a method originally proposed for single target tracking, and used
recently to address the weight degeneracy problem of the sequential Monte Carlo …

Particle flow filters, introduced in a series of papers by Daum and Huang, are an attractive
alternative to particle filters for filtering tasks in high-dimensional spaces or with very …

Computational Bayesian inference has numerous applications in many branches of signal
processing and machine learning. Bayesian techniques allow for principled modeling of …

The estimation of the state of dynamic systems using measurements is a frequently arising
challenge. State estimation is used in applications like robotics, industrial manufacturing …