Elman neural network assisting tight-integrated navigation method without GNSS signals
US11821729B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 7, 2020 |
| Grant date | Nov 21, 2023 |
| Priority date | — |
| Expiry date | Sep 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/046
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A tight-integrated navigation method assisted by Elman neural network when GNSS signals are blocked based on the tight-integrated navigation system model of the INS and GNSS, where the dynamic Elman neural network prediction model is used to train the inertial navigation error model and the GNSS compensation model, so as to solve the problem of tight-integrated navigation when the GNSS signals are blocked. When the GNSS signals are blocked, the trained neural network is used to predict the output error of GNSS and compensate the output of inertial navigation, so that the error will not diverge sharply, and the system can continue to work in the integrated navigation mode. The low-cost tight-integrated navigation module is used, and the collected information is preprocessed to form the sample data for training the neural network to train the Elman neural network model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.