The particle filter is an effective image denoising technique. An important issue with the application of the particle filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an estimation of the noise level. The second contribution is an improved particle filter (Rao-Blackwellized Partic...

This paper proposes a new radar tracking filter named Noise-estimate Particle Filter (NPF). Kalman filter and particle filter are popular filtering techniques for target tracking. The tracking performance of the Kalman filter severely depends on the setting of several parameters such as system noise and observation noise. However, it is an open problem how to choose proper parameters for variou...

This paper analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution. Both problems are tackled in this paper. We believe we have largely solved the first problem and have reduced the...

Modern parallel computing devices such as the graphics processing unit (GPU) have gained significant traction in scientific computing, and are particularly well-suited to dataparallel algorithms such as the particle filter. Of the components of the particle filter, the resampling step is the most difficult to implement well on such devices, as it often requires a collective operation, such as a...

Visual tracking in mobile robots have to track various target objects in fast processing, but existing state-ofthe-art methods only use specific image feature which only suitable for certain target objects. In this paper, we proposed new approach without depend on specific feature. By using deep learning, we can learn essential features of many of the objects and scenes found in the real world....

The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper outlines the marginalized particle filter and very briefly hint at possible generalizations, giving rise to a larger family of marginalized nonlinear filters. Furthermore, we an...

We revisit the Bayesian online inference problems for the linear dynamic systems (LDS) under nonGaussian environment. The noises can naturally be non-Gaussian (skewed and/or heavy tailed) or to accommodate spurious observations, noises can be modeled as heavy tailed. However, at the cost of such noise robustness, the performance may degrade when such spurious observations are absent. Therefore,...