Analysis of parallelizable resampling algorithms for particle filtering
Signal Processing
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
Particle filtering based likelihood ratio approach to faultdiagnosis in nonlinear stochastic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Models of computation for reactive control of autonomous mobile robots
Expert Systems with Applications: An International Journal
Improved ultra wideband-based tracking of twin-receiver automated guided vehicles
Integrated Computer-Aided Engineering - Anniversary Volume: Celebrating 20 Years of Excellence
Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems
Integrated Computer-Aided Engineering
| Hi-index | 0.00 |
Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots' state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.