The task of localizing single and multiple concurrent speakers in a reverberant environment with background noise poses several problems. One of the major problems is the severe corruption of the frame-wise localization estimates. To improve the overall localization accuracy, we propose a particle filter based tracking algorithm using the recently proposed Multiband Joint PositionPitch (M-PoPi)...

We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will t...

This paper presents an integrated method by using optical flow and kernel particle filter (KPF) to detect and track moving targets in omnidirectional vision. According to the circle character in omnidirectional image, the algorithms of optical flow fields and kernel particle filter are improved based on the polar coordinates at the omnidirectional center. The edge of a motion object can be dete...

Particle filter (PF) is widely used in mobile robot localization, since it is suitable for the nonlinear nonGaussian system. Localization based on PF, However, degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot looses its diversity. One of the main reasons for loosing particle diversity is sample impoverishment. It occurs when likeliho...

Aiming at the weight degeneracy phenomena in particle filter algorithm, the improved particle filtering algorithm based on neural network was presented. BP neural network and generalized regression neural network (GRNN) are adapted to improve resampling algorithm and importance probability density function. This algorithm utilizes the nonlinear mapping function of BP neural network. First of al...

In this paper, we propose a simplified particle probability hypothesis density (PHD) filter and its hardware implementation for multiple-target tracking (MTT). In the proposed algorithm, the update step of particle PHD filter is simplified and the time-varying number of measurements is arranged in combination series/parallel mode. This may result in fixed hardware architecture and therefore pre...

We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will t...

The leader node particle filter is a partially distributed approach to tracking in a sensor network, in which the node performing the particle filter computations (the leader node) changes over time. The primary advantage is that the position of the leader node can follow the target, improving the efficiency of data collection. When the leader node changes, the particle filter must be communica...

Degradation is a major problem for the particle filter algorithm. Although the conventional resampling method can solve the degradation problem, it will make the particles lose diversity. In this paper, we proposed a weights selected particle filter (WSPF) algorithm for adaptive multi-user detection (MUD) in synchronous code division multiple access (CDMA) system. This algorithm assumes the num...

Simultaneous Localization and Mapping is the process by which a robot is able to place itself in, and map out its environment. This twofold solution has proved to be a breakthrough in the quest to attain an autonomous robot. However the solution pertains to only a small set of spaces. To improve upon this, efforts are being made to cover not just localization, but also global localization and t...