2015
DOI: 10.1109/tsp.2014.2385039
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Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems

Abstract: In this contribution, we present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of particle filters which makes the method applicable to a wide range of non-linear models. To exploit the inherent structure of JMNLS, we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is marginalized out analytically. This results in an efficient implementation of the algorithm and reduces the estimation error v… Show more

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Cited by 65 publications
(49 citation statements)
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“…2. It can be observed that the M-BCRB using N = 1000 and importance density chosen as in [33] is the tightest bound in this setting, and the performance of the RBPF (same filter as used in M-BCRB) is superior to the performance of the IMM-EKF, as it can better handle the non-linearity in the process model. Again, the M-BCRB is tighter than the J-BCRB according to (7).…”
Section: Performance Evaluationmentioning
confidence: 96%
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“…2. It can be observed that the M-BCRB using N = 1000 and importance density chosen as in [33] is the tightest bound in this setting, and the performance of the RBPF (same filter as used in M-BCRB) is superior to the performance of the IMM-EKF, as it can better handle the non-linearity in the process model. Again, the M-BCRB is tighter than the J-BCRB according to (7).…”
Section: Performance Evaluationmentioning
confidence: 96%
“…In the following, we propose to use particle filters to approximate p(x k , r k |z 1:k ) that make use of the inherent structure of JMNLS and JMLGS by a technique known as Rao-Blackwellization [30]- [33]. This generally leads to an improved performance over a standard particle filter as the asymptotic variance is reduced [34], [35].…”
Section: B M-bcrbmentioning
confidence: 99%
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“…where the first density can be evaluated analytically using conditional Hidden Markov Model (HMM) filters, and the density p(x s,1:t |Y s,1:t ) can be approximated using particle filters [17]. This technique is known as Rao-Blackwellization and generally can lead to a reduction in variance of the estimated parameters.…”
Section: ) Generalmentioning
confidence: 99%