Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Proceedings of the 3rd international symposium on Information processing in sensor networks
Adaptive mobile positioning in WCDMA networks
EURASIP Journal on Wireless Communications and Networking
Automatica (Journal of IFAC)
Adaptive methods for sequential importance sampling with application to state space models
Statistics and Computing
Time series analysis of hybrid neurophysiological data and application of mutual information
Journal of Computational Neuroscience
IEEE Transactions on Communications
Enhanced importance sampling: unscented auxiliary particle filtering for visual tracking
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
State estimation for Markovian Jump Linear Systems with bounded disturbances
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
We present an efficient particle filtering method to perform optimal estimation in jump Markov (nonlinear) systems (JMSs). Such processes consist of a mixture of heterogeneous models and possess a natural hierarchical structure. We take advantage of these specificities in order to develop a generic filtering methodology for these models. The method relies on an original and nontrivial combination of techniques that have been presented recently in the filtering literature, namely, the auxiliary particle filter and the unscented transform. This algorithm is applied to the complex problem of time-varying autoregressive estimation with an unknown time-varying model order. More precisely, we develop an attractive and original probabilistic model that relies on a flexible pole representation that easily lends itself to interpretations. We show that this problem can be formulated as a JMS and that the associated filtering problem can be efficiently addressed using the generic methodology developed in this paper. Simulations demonstrate the performance of our method compared to standard particle filtering techniques.