A Slide Window Variational Adaptive Kalman Filter

Huang, Yulong; Zhu, Fengchi; Jia, Guangle; Zhang, Yonggang

doi:10.1109/tcsii.2020.2995714

Cited by 52 publications

(46 citation statements)

References 22 publications

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“…Simulation study of a bench-marking target tracking example [23] is presented in this section for comparing the performance assessment of the proposed algorithm with the existed algorithms; the CKF, ACKF, AFCKF-P adaption approaches. The nonlinear system and measurement models for target tracking example can be expressed as follows [6], [22].…”

Section: Simulation Resultsmentioning

confidence: 99%

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Narasimhappa

2021

Preprint

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This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, Pk − based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, R * k is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, Q * k and measurement noise covariance matrices by combining the innovation and residual vector in the AFCKF algorithm. It can provide reliable state estimation in the presence of unknown noise statistics. Bench-marking target tracking example is consider to show the performance improvement of the developed algorithms. As compared with existing adaptive approaches, the proposed fading algorithm can provide better estimation results.

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Section: Simulation Resultsmentioning

confidence: 99%

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Narasimhappa

2021

Preprint

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“…and linear measurements Following a Bayesian approach, the process and measurement noise covariances are regarded as random matrices to be estimated together with the state trajectory. For the resulting adaptive estimation problem, although there have been some variational adaptive filters proposed in (Huang et al, 2018) and (Huang et al, 2020), for such filters there is no available proof of stability. Motivated by this, this paper aims to propose a novel adaptive filter for unknown PNCM and MNCM that ensures mean-square boundedness of the estimation error.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…where E denotes expectation. Both VB and VB slidingwindow filters of (Huang et al, 2018) and (Huang et al, 2020) have been derived via VB inference in factorized form.…”

Section: Idea Of Variational Bayes Inferencementioning

confidence: 99%

“…The approach of Huang et al (2018) has been extended and applied also in nonlinear, multi-sensor, and multimodal settings (Dong et al, 2021a,b;Youn et al, 2020). As an improvement, a variational Bayes sliding window Kalman filter (VB Sliding Window) is proposed in (Huang et al, 2020) by imposing an approximation on the smoothing posterior probability density function (PDF) of the sliding window states. However, the performance of this filter is affected by the window length and, more importantly, for both adaptive filters in (Huang et al, 2018) and (Huang et al, 2020) no theoretical guarantee of stability of the estimation error has been proved.…”

Section: Introductionmentioning

confidence: 99%

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A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability

Dong¹,

Battistelli²,

Chisci³

et al. 2021

Preprint

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This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while preserving the stability guarantee of the Kalman filter. To this end, the full information estimation problem over a finite interval is firstly addressed. Then, a novel adaptive variational Bayesian (VB) moving horizon estimation (MHE) method is proposed, exploiting VB inference, MHE and Monte Carlo integration with importance sampling for joint estimation of the unknown process and measurement noise covariances, along with the state trajectory over a moving window of fixed length. Further, it is proved that the proposed adaptive VB MHE filter ensures mean-square boundedness of the estimation error with any number of importance samples and VB iterations, as well as for any window length. Finally, simulation results on a target tracking example demonstrate the effectiveness of the VB MHE filter with enhanced estimation accuracy and convergence properties compared to the conventional non-adaptive Kalman filter and other existing adaptive filters.

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“…A novel adaptive Kalman filter based on Variational Bayesian method and Gauss-Inverse-Wishart mixture distribution was proposed for the linear system filtering problem with unknown system state and observed noise covariance matrix [ 32 ]. Reference [ 33 ] further improved the above filter based on the approximation of slide Window State Vectors based on the work in reference [ 32 ]. Nonlinear filtering under cross-correlation noise has become an important branch of filtering under non-standard noise.…”

Section: Introductionmentioning

confidence: 99%

Adaptive federated filter for multi-sensor nonlinear system with cross-correlated noises

Wang

Yang

2021

PLoS ONE

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This paper presents an adaptive approach to the federated filter for multi-sensor nonlinear systems with cross-correlations between process noise and local measurement noise. The adaptive Gaussian filter is used as the local filter of the federated filter for the first time, which overcomes the performance degradation caused by the cross-correlated noises. Two kinds of adaptive federated filters are proposed, one uses a de-correlation framework as local filter, and the subfilter of the other one is defined as a Gaussian filter with correlated noises at the same-epoch, and much effort is made to verify the theoretical equivalence of the two algorithms in the nonlinear fusion system. Simulation results show that the proposed algorithms are superior to the traditional federated filter and Gaussian filter with same-paced correlated noises, and the equivalence between the proposed algorithms and high degree cubature federated filter is also demonstrated.

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A Slide Window Variational Adaptive Kalman Filter

Cited by 52 publications

References 22 publications

A new state estimation approach-Adaptive Fading Cubature Kalman filter

A new state estimation approach-Adaptive Fading Cubature Kalman filter

A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability

Adaptive federated filter for multi-sensor nonlinear system with cross-correlated noises

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