Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart particle filtering for 3D hand tracking
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Signal Processing
Cramer-Rao bound for nonlinear filtering with Pd<1 and itsapplication to target tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Complexity analysis of the marginalized particle filter
IEEE Transactions on Signal Processing
Target Tracking by Particle Filtering in Binary Sensor Networks
IEEE Transactions on Signal Processing
A Basic Convergence Result for Particle Filtering
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Visual Tracking in High-Dimensional State Space by Appearance-Guided Particle Filtering
IEEE Transactions on Image Processing
Saturated Particle Filter: Almost sure convergence and improved resampling
Automatica (Journal of IFAC)
Hi-index | 35.69 |
In this correspondence, an improvement on resampling algorithm (also called the systematic resampling algorithm) of particle filters is presented. First, the resampling algorithm is analyzed from a new viewpoint and its defects are demonstrated. Then some exquisite work is introduced in order to overcome these defects such as comparing the weights of particles by stages and constructing the new particles based on quasi-Monte Carlo method, from which an exquisite resampling (ER) algorithm is derived. Compared to the resampling algorithm, the proposed algorithm can maintain the diversity of particles thus avoid the sample impoverishment in particle filters, and can obtain the same estimation accuracy through less number of sample particles. These advantages are finally verified by simulations of non-stationary growth model and a re-entry ballistic object tracking.