Multiple-model estimation with variable structure- Part VI: expected-mode augmentation

Li, X. Rong; Jilkov, Vesselin P.; Ru, Jifeng

doi:10.1109/taes.2005.1541435

Cited by 89 publications

(11 citation statements)

References 26 publications

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“…To address the problem of the model uncertainty, IMM-VB needs a lot of models to improve the algorithm performance. This has two obvious defects [13]. First, the computation load grows with the increase of the number of the models.…”

Section: Model-set Adaptationmentioning

confidence: 99%

“…whereμ j k|k−1 is the normalization coefficient. Based on the model set M k , the output of each filter is merged in the fusion stage [13]. Therefore, we aim to approximate the sum term in (18) by a single one, that is…”

Section: Model-set Conditioned Estimation Based Variational Bayesian mentioning

confidence: 99%

“…Different implementations for MSA approaches have been proposed. For instance, the expected mode augmentation (EMA) [13], the likely model set (LMS) [14], the minimal sub model set (MSS) [15], and the best model augmentation (BMA) [16].…”

Section: Introductionmentioning

confidence: 99%

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Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Chen

Gao

2020

EURASIP J. Adv. Signal Process.

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The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information divergence, as a criterion, is to choose the best match model that has the minimum divergence between candidate models and true mode. Subsequently, the model-conditioned estimation based on variational Bayesian approximation is proposed to estimate system state and measurement noise variances. To deal with the coupled noise intractability, the moments matching technique is used to obtain the mixed statistics of measurement noise at the fusion stage. The proposed algorithm is compared with the interacting multiple models (IMM) algorithm and the variational Bayesian-interacting multiple models (IMM-VB) algorithm via two scenarios for maneuvering target tracking, and simulation results show that the MSA-VB has improved estimation and tracking performance.

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Section: Model-set Adaptationmentioning

confidence: 99%

Section: Model-set Conditioned Estimation Based Variational Bayesian mentioning

confidence: 99%

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Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Chen

Gao

2020

EURASIP J. Adv. Signal Process.

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“…Increasing performance while decreasing the overall number of constraints may be achieved by combining at least two filters, one from each category [113][114][115][116][117][118][119][120][121][122][123]. It may also be accomplished by the use of many models that are fused together, as in an interacting multiple model (IMM) [124][125][126][127][128][129][130][131][132][133][134][135]. The research paper aims to investigate the application of the sliding innovation filter (SIF) and the interacting multiple model (IMM) strategy in aeronautical actuator systems.…”

Section: Introductionmentioning

confidence: 99%

Estimating the complex maneuvering of a UGV using IMM-SIF

Al-Shabi

Obaideen

Gadsden

2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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Estimating the position of a unmanned ground vehicle (UGV) that is navigating a complex road is a challenging task. Numerous algorithms have been developed to estimate the maneuvering status of the UGV. In this study, a newly developed filtering technique called the sliding innovation filter (SIF) is combined with multiple model technique to improve the estimation accuracy. The SIF uses the measured states as a discontinuous hyperplane to constrain the estimates to stay close to it. By combining the benefits of both methods, the proposed filter minimizes chatter during position estimation when the UGV is maneuvering. The effectiveness of the proposed method is evaluated on a UGV navigating an S-shaped road, and the results are compared to those obtained using the standard SIF.

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“…For the FSMM methods, if the model set used does not match the set of actual models of target movement, the performance can be degraded. To cope with this problem, several VSMM methods have been proposed, such as likely mode set (LMS) filter [9], expected-mode augmentation (EMA) filter [10], equivalent-model augmentation (EqMA) filter [11], and hybrid grid multiple model (HGMM) filter [12]. These filters use different model-set adaptation schemes to adjust the model set.…”

Section: Introductionmentioning

confidence: 99%

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

Li¹,

Sun²

2019

Sensors

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In maneuvering target tracking applications, the performance of the traditional interacting multiple model (IMM) filter deteriorates seriously under heavy-tailed measurement noises which are induced by outliers. A robust IMM filter utilizing Student’s t-distribution is proposed to handle the heavy-tailed measurement noises in this paper. The measurement noises are treated as Student’s t-distribution, whose degrees of freedom (dof) and scale matrix are assumed to be governed by gamma and inverse Wishart distributions, respectively. The mixing distributions of the target state, dof, and scale matrix are achieved through the interacting strategy of IMM filter. These mixing distributions are used for the initialization of time prediction. The posterior distributions of the target state, dof, and scale matrix conditioned on each mode are obtained by employing variational Bayesian approach. Then, the target state, dof, and scale matrix parameters are jointly estimated. A variational method is also given to estimate the mode probability. The unscented transform is utilized to solve the nonlinear estimation problem. Simulation results show that the proposed filter improves the estimation accuracy of target state and mode probability over existing filters under heavy-tailed measurement noises.

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Multiple-model estimation with variable structure- Part VI: expected-mode augmentation

Cited by 89 publications

References 26 publications

Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Estimating the complex maneuvering of a UGV using IMM-SIF

Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

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