Method, device, debugging system and equipment for optimizing millimeter wave radar filtering parameters
By comparing the actual and theoretical information covariance matrices calculated in millimeter-wave radar, the filtering parameters were optimized, solving the adaptation problem of hardware filters in different application scenarios and improving trajectory tracking accuracy and perception reliability.
CN122430804APending Publication Date: 2026-07-21GREE ELECTRIC APPLIANCE INC OF ZHUHAI
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GREE ELECTRIC APPLIANCE INC OF ZHUHAI
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-21
AI Technical Summary
Technical Problem
Existing millimeter-wave radar hardware filters use fixed parameters, making it difficult to adapt to different application scenarios. This results in limited trajectory tracking accuracy and affects radar debugging performance and sensing reliability.
Method used
By receiving the filtered innovation sequence uploaded by millimeter-wave radar, the actual innovation covariance matrix is calculated and compared with the theoretical innovation covariance matrix of the Kalman filter. Based on the comparison results, the filtering parameters are optimized to achieve online adaptive adjustment.
Benefits of technology
This solves the adaptation problem of hardware filters in different application scenarios, improving trajectory tracking accuracy and perception reliability.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN122430804A_ABST
Abstract
The application relates to a millimeter wave radar filtering parameter optimization method and device, a debugging system and equipment. The application receives a filtering innovation sequence output by a radar hardware end Kalman filter, calculates an actual innovation covariance matrix based on a sliding window and compares the actual innovation covariance matrix with a theoretical innovation covariance matrix, and optimizes filtering parameters adaptively according to a comparison result and updates the filtering parameters in real time, thereby solving the technical problems that a fixed parameter hardware filter is difficult to adapt to different application scenarios and trajectory tracking precision is limited.
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