A Method for Predicting Inertial Navigation System Positioning Errors Using a Back Propagation Neural Network Based on a Particle Swarm Optimization Algorithm

In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization–Back Propagation Neural Network (PSO-BPNN) to replace the GPS...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3722
Main Authors: Wang, Yabo, Jiao, Ruihan, Wei, Tingxiao, Guo, Zhaoxing, Ben, Yueyang
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 07.06.2024
MDPI
Subjects:
Accuracy
Algorithms
Artificial intelligence
backpropagation neural network
Fuzzy logic
GPS denial
GPS/SINS integrated navigation system
Kalman filters
Navigation systems
Neural networks
Optimization
particle swarm optimization
Propagation
Velocity
GPS/SINS integrated navigation system
backpropagation neural network
particle swarm optimization
GPS denial
ISSN:1424-8220, 1424-8220
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization–Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s24123722