Brief paper: Risk-sensitive filtering for jump Markov linear systems
Automatica (Journal of IFAC)
A novel interacting multiple model algorithm
Signal Processing
IMM-based lane-change prediction in highways with low-cost GPS/INS
IEEE Transactions on Intelligent Transportation Systems
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Performance evaluation of a simplified bi-level MHT algorithm in tracking maneuvering targets
ARP '07 The Fourth IASTED International Conference on Antennas, Radar and Wave Propagation
A novel interacting multiple model algorithm based on multi-sensor optimal information fusion rule
ACC'09 Proceedings of the 2009 conference on American Control Conference
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Research on tracking of maneuvering multi-target based on bionics for IRST system
The Journal of Supercomputing
Qos Enhancement in Wireless VoIP Networks Using Interactive Multiple Model Based Kalman Filter
Wireless Personal Communications: An International Journal
Hi-index | 35.69 |
Computing the optimal conditional mean state estimate for a jump Markov linear system requires exponential complexity, and hence, practical filtering algorithms are necessarily suboptimal. In the target tracking literature, suboptimal multiple-model filtering algorithms, such as the interacting multiple model (IMM) method and generalized pseudo-Bayesian (GPB) schemes, are widely used for state estimation of such systems. We derive a reweighted interacting multiple model algorithm. Although the IMM algorithm is an approximation of the conditional mean state estimator, our algorithm is a recursive implementation of a maximum a posteriori (MAP) state sequence estimator. This MAP estimator is an instance of a previous version of the EM algorithm known as the alternating expectation conditional maximization (AECM) algorithm. Computer simulations indicate that the proposed reweighted IMM algorithm is a competitive alternative to the popular IMM algorithm and GPB methods