公开(公告)号：US20210095965A1

公开(公告)日：2021-04-01

申请号：US17013750

申请日：2020-09-07

Applicant: Harbin Engineering University

Inventor： Lin ZHAO ,  Zihang PENG ,  Jicheng DING ,  Kun WANG ,  Yaguo BAI ,  Yongchao ZHANG ,  Renlong WANG

IPC: G01C21/16 ,  G01S19/47 ,  G06N3/08 ,  G06N5/04

Abstract: The disclosure relates to 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. 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.