A fundamental problem in signal processing is the estimation of unknown parameters or
functions from noisy observations. Important examples include localization of objects in …

Multiobject tracking provides situational awareness that enables new applications for
modern convenience, public safety, and homeland security. This paper presents a factor …

By approximating posterior distributions with weighted samples, particle filters (PFs) provide
an efficient mechanism for solving non-linear sequential state estimation problems. While …

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 …

In this work, a novel sequential Monte Carlo filter is introduced which aims at an efficient
sampling of the state space. Particles are pushed forward from the prediction to the posterior …

Cooperative localization (CL) is an important technology for innovative services such as
location-aware communication networks, modern convenience, and public safety. We …

Target state estimation is a key technology for unmanned underwater vehicles (UUVs) to
achieve target tracking, collision avoiding, formation control, and other tasks. Compared with …

Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and
financial networks. Many classical statistical models often fall short in handling the …

Accurate and reliable estimation of generator's dynamic state vectors in real time are critical
to the monitoring and control of power systems. A robust Cubature Kalman Filter (RCKF) …

Localization and tracking of marine animals can reveal key insights into their behaviors
underwater that would otherwise remain unexplored. A promising nonintrusive approach to …