Multiple model estimation: A convex model formulation

Hallouzi, Redouane; Verhaegen, Michel; Kanev, Stoyan

doi:10.1002/acs.1034

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…The concept of the GMBM has been a subject of significant research interest, and many extensions of the method and analyses have been published. The novel contributions are often focused on the extension of the method for nonlinear systems or on the design of the weighting mechanisms of the filters associated with the particular models . Also, combinations of the mixture‐model methods and the correlation methods can be found in the literature .…”

Section: Feedback Noise CM Estimation Methodsmentioning

confidence: 99%

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Duník

Straka

Kost

et al. 2017

Adaptive Control & Signal

118

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This paper deals with the estimation of the noise covariance matrices of systems described by state-space models. Stress is laid on the systematic survey and classification of both the recursive and batch processing methods proposed in the literature with a special focus on the correlation methods. Besides the correlation methods, representatives of other groups are introduced also with respect to their basic idea, estimate properties, assumptions and possible extensions, and user-defined parameters. Common and dual properties of the methods are highlighted, and a simulation comparison using exemplary MATLAB implementations of the methods is provided. KEYWORDSadaptive systems, noise covariance matrix, state estimation, state-space model, system identification INTRODUCTIONKnowledge of a system model is a key prerequisite for many state estimation, signal processing, fault detection, and optimal control problems. The model is often designed to be consistent with random behaviour of the system quantities and properties of the measurements. While the deterministic part of the model often arises from mathematical modelling on the basis of physical, chemical, or biological laws governing the behaviour of the system, the statistics of the stochastic part are often difficult to find by the modelling and have to be identified using the measured data. Incorrect description of the noise statistics may result in significant worsening of estimation, signal processing, detection, or control quality or even in a failure of the underlying algorithms.In the last 5 decades, therefore, a significant research interest has been focused on a design of the methods for the estimation of the properties of the stochastic part of the model. The attention has been devoted to both the input-output models 1-5 and the state-space (SS) models 6-14 and both recursive and batch processing methods. This paper focuses on the methods estimating the properties of the stochastic part of the system described by an SS model discrete in time. In particular, the methods estimating the covariance matrices (CMs) † of noises in the state and measurement equation from a sequence of measured data are of interest. The methods are further denoted as the noise CM estimation methods.

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Section: Feedback Noise CM Estimation Methodsmentioning

confidence: 99%

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Duník

Straka

Kost

et al. 2017

Adaptive Control & Signal

118

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“…Due to inherent estimate errors and measurement noises, the maneuver at the initial stage may not be detected in time, so the hypothesis test always report an alarm after the maneuver has occurred for a period, that is, the performance of MTT will be seriously affected by the maneuver detection delays or even failures in the DSF. The MMF designs a set of motion models to cover all possible maneuvering patterns, and the overall estimation output is a certain combination of the outputs based on each individual model 11‐13 . Since the maneuvering behaviors of noncooperative targets are usually not available a priori, it is difficult to design the model set and the Markov transition probability matrix (TPM) by the fact that the design of the model set and the TPM needs the target type and intent, respectively.…”

Section: Introductionmentioning

confidence: 99%

Recursive Bayesian inference and learning for target tracking with unknown maneuvers

Liang

2022

Adaptive Control & Signal

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This paper addresses the problem of target tracking under completely unknown maneuvering behavior to cope with the complicated target maneuvers. The actual but unknown motion model is formulated as a linear stochastic model with unknown transition matrix and modeling error covariance. Considering the diversity and time-varying of maneuvers, both the transition matrix and covariance are random. Accordingly, the tracker design amounts to recursively updating the joint posterior of target state and unknown parameters (i.e., the transition matrix and covariance). Since it is difficult to obtain the analytical joint posterior, a Bayesian inference and learning scheme is proposed in the sequential Monte Carlo (SMC) framework to alternatively update the state posterior and the unknown parameters posterior. Particularly, it is revealed that the state prediction density used in the SMC obeys a Student's t-distribution, thus ensuring the effective sampling of particle trajectories. Finally, a maneuvering benchmark shows that the proposed tracker can significantly reduce the peak tracking error, which is one of the most important performance indices for preventing tracking failure.

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“…Thus, it is difficult to model this system both in the normal state and in the different degraded modes with a unique model. Multiple model (MM) approaches have been developed to cope with this issue (see [ 25 , 26 ]). They consist in implementing different observers in parallel; each observer corresponds to a specific system state (normal or degraded).…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles

Judalet¹,

Glaser

Gruyer³

et al. 2018

Sensors

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The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

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Multiple model estimation: A convex model formulation

Cited by 11 publications

References 29 publications

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Recursive Bayesian inference and learning for target tracking with unknown maneuvers

Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles

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