In this paper we extend the L proof of Hu et al. (2008) from bootstrap type of particle filters to particle filters with general importance distributions. The result essentially shows that with general importance distributions the particle filter converges provided that the importance weights are bounded. By numerical simulations we also show that this condition is often also a practical requir...

In this paper we extend the L proof of Hu et al. (2008) from bootstrap type of particle filters to particle filters with general importance distributions. The result essentially shows that with general importance distributions the particle filter converges provided that the importance weights are bounded. By numerical simulations we also show that this condition is often also a practical requir...

Nowadays, the necessity of safeguarded environments is stronger than ever. The defence of public areas against terroristic threats requires intelligent security assistance systems that comprise state-of-the-art surveillance technology to localize persons with hazardous materials. The recent progress in the detection of hazardous materials by a new generation of chemical sensors leads to an incr...

The paper proposes Gaussian mixture particle probability hypothesis density filter(PHD) algorithm ,which can effectively solve the problem that the object number is changing or unknown, based on particle PHD filter. This algorithm calculates the object number and state by recursive procedure, avoiding the uncertainty of target state estimation caused by particle sampling and clustering. Gaussia...

Particle filter-based approaches have proven to be capable of efficiently solving the self-localization problem in RoboCup scenarios and are therefore applied by many participating teams. Nevertheless, they require a proper parametrization – for sensor models and dynamic models as well as for the configuration of the algorithm – to operate reliably. In this paper, we present an approach for opt...

Tracking a varying number of moving acoustic sources is a challenging problem with many potential applications. Particle Þlters provide a possible solution, however as the number of sources varies, spurious estimates arise leading to highly inaccurate tracking results. In this paper we propose an algorithm for tracking a varying number of acoustic sources based on the particle Þltering algorith...

In this paper, we develop a method for tracking markless human pose in monocular video human motion. The number of required particles will grow exponentially when particle filter is applied to high dimensional tracking problems such as tracking human body poses, and particle filter with partitioned sampling is adopted to deal with this problem. We design a 2D human body model with constraints, ...

To solve the problem of particle degeneracy and sample impoverishment in conventional particle filter, we propose the weight approaching particle filter(WAPF) to increase the particle diversity before resampling step for adaptive multi-user detection (MUD) in synchronous code division multiple access (CDMA) system.. In the resampling step, particles are classified into two groups according to t...

In this paper, we present a way to track multiple maneuvering targets with varying time-frequency signatures. A particle filter is used to track targets that have constant speeds with changing heading directions. The target motion dynamics help the particle filter achieve an angular resolution otherwise not possible by the conventional beamforming techniques. Moreover, the particle filter has a...