Abstract. In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The ef...
Sequential importance sampling (SIS), also known as particle filtering, has drawn increasing attention recently due to its superior performance in nonlinear and non-Gaussian dynam...
Yan Zhai, Mark B. Yeary, Joseph P. Havlicek, Jean-...
— Location tracking in wireless networks has many applications, including enhanced network performance. In this work we investigate the experimental use of “particle filter”...
Abstract— Particle Filters have been widely used as a powerful optimization tool for nonlinear, non-Gaussian dynamic models such as Simultaneous Localization and Mapping (SLAM) a...
We propose a multi-target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. The PHD filter is used to compen...
Emilio Maggio, Elisa Piccardo, Carlo S. Regazzoni,...