A dynamic target following method and system using an adaptive Kalman filter
By adjusting the parameters of the traditional Kalman filter using an adaptive Kalman filter, the problem of poor robustness of the traditional Kalman filter in non-line-of-sight conditions is solved, thereby improving the robustness and reliability of the following system.
CN117472096BActive Publication Date: 2026-06-30ANHUI UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ANHUI UNIV
- Filing Date
- 2023-10-31
- Publication Date
- 2026-06-30
AI Technical Summary
Technical Problem
Traditional Kalman filters are prone to losing targets during brief non-line-of-sight situations, have poor robustness, and cannot effectively handle abnormal measurement noise.
Method used
An adaptive Kalman filter is used to adaptively adjust the covariance matrix of predicted noise and measured noise by setting signal quality threshold and velocity threshold, and to correct the filtering effect by combining the detection results of sensor array.
Benefits of technology
It improves the robustness of the tracking system in complex environments, effectively eliminates the influence of abnormal measurements, and enhances the reliability and stability of the tracking system.
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Smart Images
Figure CN117472096B_ABST
Abstract
This invention belongs to the field of motion control and navigation technology, specifically relating to a dynamic target following method, system, and apparatus using an adaptive Kalman filter. It includes the following steps: S1: Constructing a conventional Kalman filter. S2: Setting the parameter adjustment thresholds for the adaptive Kalman filter, including signal quality indicators and motion state indicators. S3: Designing an adaptive Kalman filter that supports adaptive adjustment of the covariance matrix Q of the prediction noise in conjunction with the parameter adjustment thresholds. k The covariance matrix N of the measurement noise k S4: Real-time acquisition of the relative angle and distance of the target at the current moment. S5: Correction of the detection results using an adaptive Kalman filter. S6: Adjustment of the speed and rotation angle of the following platform based on the correction results to achieve real-time tracking of the target. This invention solves the problem that traditional Kalman filter-based tracking systems are prone to target loss and have poor robustness under brief abnormal conditions.
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