Small sample positioning algorithm based on electromagnetic flying time-space inseparable characteristics
By utilizing propagation axis segment measurement, data normalization, and noise augmentation in the electromagnetic flying ring localization algorithm, combined with random forest and neural network, the problems of small sample size and large number of classifications are solved, thus improving the accuracy and applicability of the localization algorithm.
CN116577723BActive Publication Date: 2026-03-17YANGTZE DELTA REGION INST OF UNIV OF ELECTRONICS SCI & TECH OF CHINE (HUZHOU)
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Patent Information
- Application Number
- CN202310450790.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-04-21
- Publication Date
- 2026-03-17
- Estimated Expiration
- 2043-04-21
AI Technical Summary
Technical Problem
Existing electromagnetic ring localization algorithms suffer from low accuracy and poor fitting performance when the number of samples is small and the number of categories is large.
Method used
By selecting line segments on the propagation axis for measurement, the data is normalized and decomposed along the r-axis and z-axis for data augmentation and noise processing. The model parameters are then optimized by combining random forest and neural network machine learning methods for training and testing.
Benefits of technology
The accuracy and efficiency of the electromagnetic ring positioning algorithm have been improved, making it suitable for small-sample applications such as drones and autonomous driving.
✦ Generated by Eureka AI based on patent content.
Abstract
The application provides a small sample positioning algorithm based on electromagnetic flying ring space-time inseparable characteristics, and is used for solving the technical problems that the existing technology needs more training amount to have higher accuracy and that too many classification numbers result in low sample fitting effect in the background art.The application discloses a small sample positioning algorithm based on electromagnetic flying ring space-time inseparable characteristics, and comprises the following steps: step one: selecting a line segment on a half space plane along a propagation axis as a boundary to measure, and obtaining space spectrum data on the line segment; step two: for the data detected by the electromagnetic flying ring, after normalization, the data is decomposed according to the detection sequence, r-axis and z-axis, so that the number of categories is reduced and the number of samples is increased; step three: the data is subjected to noise addition and expansion, the sample amount is increased, and the generalization ability is enhanced, training noise and test noise with different variances are set to carry out combination experiments, and relatively accurate noise matching results are obtained.
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