Research on MCMC particle filter algorithm based on effective particles

Dynamic contour tracking of medical images based on improved particle filter;

Application of Gaussian mixture particle filter in initial alignment for strapdown inertial navigation system on stationary base;

Performance analysis and improvement of particle filter algorithm;

Amplitude information based particle filtering for passive tracking of maneuvering target;

Algorithm and Simulation of the Particle Filtering Based on Terrain-Aided Navigation for the Intermediate Guidance of Cruise Missile;

AdaBoost tracker embedded in particle filtering;

Improved particle filters for MIMO demodulation;

The performance of particle filters in robot localization is improved through the use of a robust adaptive particle filter.

Resampling is a critically operation to solve degeneracy problem with particle filters.

posterior probability density(PDF) function,while Particle Filter(PF) uses random particles.

,Extended Kalman Filter(EKF),Unscented Kalman Filter(UKF),and Particle Filter(PF),are studied here.

Aiming at the singleness of implementing the regularized filter in interacting multiple model algorithm, an improved algorithm is proposed, in which Kalman Filter(KF) is used to match the linear part of the system and Particle Filter(PF) is used to match the non-linear part of the system, the degree of occlusion is determined according to the match extent.

Non-rigid target tracking based on genetic algorithm and particle filter;

Indoor mobile robot self-location based on particle filter;

An improved unscented particle filter for target tracking in sensor networks;