Multiple model estimation by hybrid grid

This paper considers the problem of state estimation for a hybrid system with Markovian switching parameters which belong to a continuous space. A hybrid grid multiple model (HGMM) estimator is presented. The total model set for HGMM is the combination of a fixed coarse grid and an adaptive fine grid. Three practical algorithms in this scheme are developed. These algorithms are used for state estimation in maneuvering target tracking. Simulation results demonstrate that the HGMM estimator outperforms the corresponding fixed structure multiple model (FSMM) at a negligible extra computational cost.

[1]  X. R. Li,et al.  Multiple-model estimation with variable structure. III. Model-group switching algorithm , 1999 .

[2]  Youmin Zhang,et al.  Multiple-model estimation with variable structure. V. Likely-model set algorithm , 2000, IEEE Trans. Aerosp. Electron. Syst..

[3]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[4]  X. Rong Li,et al.  Multiple-Model Estimation with Variable Structure—Part II: Model-Set Adaptation , 2000 .

[5]  X. Rong Li,et al.  General model-set design methods for multiple-model approach , 2005, IEEE Transactions on Automatic Control.

[6]  X. Rong Li,et al.  Mode-Set Adaptation in Multiple-Model Estimators for Hybrid Systems , 1992, 1992 American Control Conference.

[7]  X. R. Li,et al.  Chapter 10 Engineer ’ s Guide to Variable-Structure Multiple-Model Estimation for Tracking , 2022 .

[8]  Amir Averbuch,et al.  Radar target tracking-Viterbi versus IMM , 1991 .

[9]  Jeffery R. Layne,et al.  Monopulse radar tracking using an adaptive interacting multiple-model method with extended Kalman filters , 1998, Defense, Security, and Sensing.

[10]  D. P. Atherton,et al.  Adaptive interacting multiple model algorithm for tracking a manoeuvring target , 1995 .

[11]  D. Atherton,et al.  An investigation of the SFIMM algorithm for tracking manoeuvring targets , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[12]  X. Rong Li,et al.  Multiple-model estimation with variable structure- part VI: expected-mode augmentation , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[13]  X. Rong Li,et al.  Hybrid Estimation Techniques , 1996 .

[14]  X. Rong Li,et al.  Model-set sequence-conditioned estimation for variable-structure MM estimation , 1998, Defense, Security, and Sensing.

[15]  X. R. Li,et al.  Multiple-model estimation with variable structure. IV. Design and evaluation of model-group switching algorithm , 1999 .

[16]  W. D. Blair,et al.  Benchmark Problem for Radar Resource Allocation and Tracking Maneuvering Targets in the Presence of ECM , 1996 .

[17]  Peter Maybeck,et al.  Investigation of moving-bank multiple model adaptive algorithms , 1985, 1985 24th IEEE Conference on Decision and Control.

[18]  X. R. Li,et al.  Multiple-model estimation with variable structure: some theoretical considerations , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.