Redundant measurement-based second order mutual difference adaptive Kalman filter

Jiang, Lanyue; Zhang, Hai

doi:10.1016/j.automatica.2018.11.037

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…Multimodal, that is, multimodal biometrics, refers to the integration or fusion of two or more biometric technologies, making use of the unique advantages of its multiple biometric technology, combined with data fusion technology to make the authentication and identification process more accurate and safe [1,2]. e main difference from the traditional single biometric method is that multimodal biometric technology can collect different biometrics (such as fingerprint, finger vein, face, and iris image) through an independent or integrated collector of multiple acquisition methods and identify and authenticate by analyzing and judging the eigenvalues of multiple biometric methods [1,3]. Multimodal discourse refers to the use of hearing, vision, touch, and other senses to communicate through language, image, sound, action, and other means and symbolic resources.…”

Section: Multimodal Technologymentioning

confidence: 99%

Study on Automatic Evaluation Method of Spoken English Based on Multimodal Discourse Analysis Theory

Ren

2021

Security and Communication Networks

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Aiming at the low confidence of traditional spoken English automatic evaluation methods, this study designs an automatic evaluation method of spoken English based on multimodal discourse analysis theory. This evaluation method uses sound sensors to collect spoken English pronunciation signals, decomposes the spoken English speech signals by multilayer wavelet feature scale transform, and carries out adaptive filter detection and spectrum analysis on spoken English speech signals according to the results of feature decomposition. Based on multimodal discourse analysis theory, this evaluation method can extract the automatic evaluation features of spoken English and automatically recognize the speech quality according to the results. The experimental results show that, compared with the control group, the designed evaluation method has obvious advantages in confidence evaluation and can solve the problem of low confidence of traditional oral automatic evaluation methods.

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Section: Multimodal Technologymentioning

confidence: 99%

Study on Automatic Evaluation Method of Spoken English Based on Multimodal Discourse Analysis Theory

Ren

2021

Security and Communication Networks

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“…With the absence of the redundant measurements, the RMNCE method would be infeasible. In this situation, if the computational power permits, an alternative scheme named improved second order mutual difference estimation can be applied to deal with the single measurement noise covariance estimation problem [40].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

Zhang

Jiang

et al. 2019

Sensors

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The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.

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“…Remark 1. In (9), according to the change information of estimated b R at the current moment and the last moment, the forgetting factor ρ at the next moment can be obtained through the mapping function g Á ð Þ. The method can realize the purpose of the adaptive adjustment ρ based on the estimation of measurement noise covariance.…”

Section: Modified Vbakf Algorithmmentioning

confidence: 99%

An enhanced adaptive Kalman filtering for linear systems with inaccurate noise statistics

Chen

2022

Asian Journal of Control

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This paper investigates the simultaneous state and noise covariance estimation for linear systems with inaccurate noise statistics. An enhanced adaptive Kalman filtering (EAKF) based on dynamic recursive nominal covariance estimation (DNRCE) and modified variational Bayesian (VB) inference is presented. The EAKF realizes the concurrently estimation of state and noise covariance matrices by introducing a nominal parameter in the traditional recursive covariance estimation and designing a new adaptive forgotten factor for the dynamic model of the estimated information propagation. The simulation of a target tracking example shows that, compared with the existing filters, the proposed filter has good adaptive performance for inaccurate and time‐varying noise covariance matrices.

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Redundant measurement-based second order mutual difference adaptive Kalman filter

Cited by 17 publications

References 19 publications

Study on Automatic Evaluation Method of Spoken English Based on Multimodal Discourse Analysis Theory

Study on Automatic Evaluation Method of Spoken English Based on Multimodal Discourse Analysis Theory

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

An enhanced adaptive Kalman filtering for linear systems with inaccurate noise statistics

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