A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices 论文
2017IEEE Transactions on Automatic Control引用 664
Target Tracking and Data Fusion in Sensor NetworksGaussian Processes and Bayesian InferenceControl Systems and Identification
详细信息
- 发表期刊/会议
- IEEE Transactions on Automatic Control
- 发表日期
- 2017-09-05
- 发表年份
- 2017
关键词
Target Tracking and Data Fusion in Sensor NetworksGaussian Processes and Bayesian InferenceControl Systems and Identification
摘要
In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement noise covariance matrices are inferred based on the VB approach. Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.