Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Particle Filter SLAM with High Dimensional Vehicle Model
Journal of Intelligent and Robotic Systems
Using the marginalised particle filter for real-time visual-inertial sensor fusion
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Stochastic modeling and particle filtering algorithms for tracking a frequency-hopped signal
IEEE Transactions on Signal Processing
In-car positioning and navigation technologies: a survey
IEEE Transactions on Intelligent Transportation Systems
Exploiting motion correlations in 3-D articulated human motion tracking
IEEE Transactions on Image Processing
Bayesian phase tracking for multiple pulse signals
Signal Processing
Particle filtering: the need for speed
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing
Journal of Signal Processing Systems
Fusing depth and video using rao-blackwellized particle filter
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Extensions of the SMC-PHD filters for jump Markov systems
Signal Processing
Marginalized particle filter for maneuvering target tracking application
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
A novel track maintenance algorithm for PHD/CPHD filter
Signal Processing
Automatica (Journal of IFAC)
Particle filter with multimode sampling strategy
Signal Processing
Expert Systems with Applications: An International Journal
Hi-index | 35.69 |
The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear state-space model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported.