Methods for phased array calibration based on cnn-lstm using power measurement
US12320921B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 10, 2024 |
| Grant date | Jun 3, 2025 |
| Priority date | — |
| Expiry date | Dec 10, 2044 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D30/70
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present disclosure provide a method for phased array calibration based on CNN-LSTM using power measurement, comprising: establishing a phased array calibration signal model, and utilizing a program to conveniently obtain a large amount of data for training a neural network without the need for actual measurements; converting and preprocessing the generated data, and saving as a training dataset in the form of feature data and labels; establishing a CNN-LSTM network, and inputting the training data with labels into the CNN-LSTM network for training until the CNN-LSTM network converges to obtain the final calibration model; measuring the phased array to be measured to obtain feature data, obtaining a calibration result of the phased array by inputting the feature data into the calibration model obtained from the training. The method is designed to solve problems of low calibration accuracy, low measurement efficiency, and high instrumentation requirements of the existing phased array calibration processes, and the proposed calibration method has a very high calibration efficiency, and the number of measurements required is much lower than that of all current power …
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.