基于广义误差熵于粉尘环境下的无迹卡尔曼滤波方法
By employing the unscented Kalman filter method with generalized error entropy in a dusty environment and using the minimum generalized error entropy criterion for state estimation, the problem of slow convergence speed and large error in robot target state estimation in a dusty environment is solved, achieving faster convergence and lower average error.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- UNIV OF ELECTRONICS SCI & TECH OF CHINA
- Filing Date
- 2023-10-30
- Publication Date
- 2026-07-17
AI Technical Summary
In dusty environments, existing unscented Kalman filter algorithms for nonlinear non-Gaussian systems for robot target state estimation have slow convergence speeds and large errors, making it difficult to effectively handle estimation errors caused by non-Gaussian perturbations.
An unscented Kalman filter method based on generalized error entropy is adopted. By setting the generalized Gaussian kernel function and Kalman gain, time and measurement updates are performed, and the state estimation is performed using the minimum generalized error entropy criterion to reduce dust noise interference.
It improves the convergence speed of unscented Kalman filtering, reduces the average error of target state estimation, and is suitable for nonlinear non-Gaussian robot systems.
Smart Images
Figure CN117335771B_ABST