IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
In-car positioning and navigation technologies: a survey
IEEE Transactions on Intelligent Transportation Systems
Hierarchical and conditional combination of belief functions induced by visual tracking
International Journal of Approximate Reasoning
Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots
Mathematics and Computers in Simulation
Modeling the stochastic drift of a MEMS-based gyroscope in gyro/odometer/GPS integrated navigation
IEEE Transactions on Intelligent Transportation Systems
Localization with non-unique landmark observations
RoboCup 2010
Nonlinear Kalman Filters and Particle Filters for integrated navigation of unmanned aerial vehicles
Robotics and Autonomous Systems
Object tracking in the presence of occlusions using multiple cameras: A sensor network approach
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 35.68 |
This paper concerns the sequential estimation of a hidden state vector from noisy observations delivered by several sensors. Different from the standard framework, we assume here that the sensors may switch autonomously between different sensor states, that is, between different observation models. This includes sensor failure or sensor functioning conditions change. In our model, sensor states are represented by discrete latent variables, whose prior probabilities are Markovian. We propose a family of efficient particle filters, for both synchronous and asynchronous sensor observations as well as for important special cases. Moreover, we discuss connections with previous works. Lastly, we study thoroughly a wheel land vehicle positioning problem where the GPS information may be unreliable because of multipath/masking effects