Design of Luenberger Observers for a Class of Hybrid Linear Systems
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Proceedings of the 3rd international symposium on Information processing in sensor networks
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
Automatica (Journal of IFAC)
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
On state estimation of discrete-time Markov jump linear systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
| Hi-index | 35.69 |
Jump Markov linear systems (JMLSs) are linear systems whose parameters evolve with time according to a finite state Markov chain. Given a set of observations, our aim is to estimate the states of the finite state Markov chain and the continuous (in space) states of the linear system. In this paper, we present original deterministic and stochastic iterative algorithms for optimal state estimation of JMLSs. The first stochastic algorithm yields minimum mean square error (MMSE) estimates of the finite state space Markov chain and of the continuous state of the JMLS. A deterministic and a stochastic algorithm are given to obtain the marginal maximum a posteriori (MMAP) sequence estimate of the finite state Markov chain. Finally, a deterministic and a stochastic algorithm are derived to obtain the MMAP sequence estimate of the continuous state of the JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problem of deconvolution of Bernoulli-Gaussian (BG) processes and the problem of tracking a maneuvering target are addressed