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2 results on '"Bai, Mingming"'

1. Statistical Similarity Measure-Based Adaptive Outlier-Robust State Estimator With Applications.

Author

Bai, Mingming, Huang, Yulong, Zhang, Yonggang, and Chambers, Jonathon

Subjects
*RANDOM matrices, *COST functions, *GAUSSIAN distribution, *PROBABILITY density function, *SYMMETRIC matrices
Abstract

This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. Two SSMs are first proposed to evaluate the similarities between a pair of positive definite random matrices and between a pair of weighted random vectors, respectively. The AORSE is developed by maximizing a hybrid SSMs based cost function, wherein the posterior density function of the hidden state is assumed as a Gaussian distribution with the posterior covariance being approximately determined in a heuristic way. Simulation and experimental examples of moving-target tracking demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Published
2022
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2. A Novel Progressive Gaussian Approximate Filter for Tightly Coupled GNSS/INS Integration.

Author

Bai, Mingming, Huang, Yulong, Zhang, Yonggang, and Jia, Guangle

Subjects
*GLOBAL Positioning System, *INERTIAL navigation systems, *FILTERS & filtration, *NOISE measurement, *COVARIANCE matrices
Abstract

In this article, we focus on addressing the nonlinear filtering problem with large prior uncertainty but high measurement accuracy, which may be encountered in the application of tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integration. Although the existing methods, such as progressive Gaussian approximate filter (PGAF), can address this problem, it has poor estimation accuracy. To improve the estimation accuracy of PGAF, the step sizes as well as the measurement noise covariance matrix (MNCM) are jointly estimated based on the variational Bayesian approach, from which a novel PGAF with variable step size is developed. Tightly coupled GNSS/INS integration simulations illustrate that the proposed filter outperforms the existing methods both in estimation accuracy and rate of convergence. [ABSTRACT FROM AUTHOR]

Published
2020
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