Affiliations: [a]
Federal Institute of Norte de Minas Gerais, Campus Montes Claros, Montes Claros, MG, Brazil
| [b] Department of Computer Science, UNIMONTES, Av. Rui Braga, sn, Vila Mauricéia, Montes Claros, MG, Brazil
| [c] Department of Electronics Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil
| [d] Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais - Av. Antônio Carlos, Belo Horizonte, MG, Brazil
Correspondence:
[*]
Corresponding author. Marcos Flávio Silveira Vasconcelos D’Angelo, Department of Computer Science, UNIMONTES, Av. Rui Braga, sn, Vila Mauricéia, 39401-089 Montes Claros, MG, Brazil. E-mail: marcos.dangelo@unimontes.br.
Abstract: The traditional Interacting Multiple Model (IMM) filters usually consider that the Transition Probability Matrix (TPM) is known, however, when the IMM is associated with time-varying or inaccurate transition probabilities the estimation of system states may not be predicted adequately. The main methodological contribution of this paper is an approach based on the IMM filter and retention models to determine the TPM adaptively and automatically with relatively low computational cost and no need for complex operations or storing the measurement history. The proposed method is compared to the traditional IMM filter, IMM with Bayesian Network (BNs) and a state-of-the-art Adaptive TPM-based parallel IMM (ATPM-PIMM) algorithm. The experiments were carried out in an artificial numerical example as well as in two real-world health monitoring applications: the PRONOSTIA platform and the Li-ion batteries data set provided by NASA. The Retention Interacting Multiple Model (R-IMM) results indicate that a better prediction performance can be obtained when the TPM is not properly adjusted or not precisely known.
Keywords: Adaptive systems, dynamic systems, filtering techniques, markov models, system state estimation
