目标对象雷达微动信号仿真方法及目标对象智能检测方法
By constructing a parameterized model library and generating high-fidelity radar micro-motion signals using a hybrid electromagnetic simulation algorithm, and combining this with deep learning for logistics security inspection, the accuracy and safety issues of exotic pet detection in logistics security inspection have been solved, achieving efficient and accurate exotic pet identification and non-contact detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA JILIANG UNIV
- Filing Date
- 2026-05-06
- Publication Date
- 2026-07-17
AI Technical Summary
Existing logistics security inspection technologies suffer from insufficient detection accuracy when detecting exotic pets, inability to distinguish exotic pets from non-target objects, the need for opening boxes for inspection, and increased biosafety risks. Furthermore, traditional radar signal simulation methods have low fidelity and are difficult to generate high-quality training data.
A parametric model library is constructed, and a high-fidelity radar micro-motion signal is generated using a hybrid electromagnetic simulation algorithm. Intelligent detection is performed by combining deep learning. The library includes target object model library, envelope environment model library, and radar sensor model library. Multi-scenario and diverse radar micro-motion signal datasets are generated through SBR-PO hybrid physical simulation.
It enables the generation of high-fidelity radar micro-motion signal data without real-world testing, reducing the training cost of detection algorithms, improving detection accuracy and robustness, accurately identifying exotic pets in packages, and improving security inspection efficiency and safety through non-contact detection.
Smart Images
Figure CN122131268B_ABST