An Enhanced, Real-Time, Low-Cost GNSS/INS Integrated Navigation Algorithm and Its Platform Design

The integration of the global navigation satellite system (GNSS) and the inertial navigation system (INS) is a well-established method for achieving accurate positioning, especially in applications involving unmanned aerial vehicles (UAVs). UAVs are increasingly used across various fields, yet they...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 25; číslo 7; s. 2119
Hlavní autoři: Wang, Pengcheng, Gao, Yuting, Zhao, Qingzhi, Wang, Yalong, Zhou, Feng, Zhang, Dengxiong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 27.03.2025
MDPI
Témata:
Accuracy
Algorithms
Artificial satellites
Data collection
Drone aircraft
Electronics in navigation
Evaluation
global navigation satellite system (GNSS)
Inertial navigation
Inertial navigation (Aeronautics)
inertial navigation system (INS)
integrated navigation
Kalman filters
Navigation systems
Open source software
Public domain
Public software
real-time
Sensors
Software upgrading
Unmanned aerial vehicles
unmanned aerial vehicles (UAVs)
Vehicles
Velocity
China
unmanned aerial vehicles (UAVs)
inertial navigation system (INS)
global navigation satellite system (GNSS)
integrated navigation
real-time
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:The integration of the global navigation satellite system (GNSS) and the inertial navigation system (INS) is a well-established method for achieving accurate positioning, especially in applications involving unmanned aerial vehicles (UAVs). UAVs are increasingly used across various fields, yet they face challenges such as the need for real-time processing and the impact of low-quality measurements from cost-effective devices. To address these challenges, we propose a velocity-constrained, enhanced, real-time, low-cost, GNSS/INS integrated navigation algorithm and design an algorithmic platform based on the open-source software KF_GINS. The algorithm supports loosely coupled integration of GNSS position data and raw inertial measurement unit (IMU) data, utilizing a 4G data transmission unit (DTU) for real-time data transmission and performing loosely coupled computations on the received data. Subsequently, we successfully applied this algorithm to low-cost integrated navigation devices, such as UAVs. We tested the algorithm platform using one set of vehicle-mounted data and six UAV datasets. Experimental results indicate that the algorithm platform effectively performs computations under various conditions, improving single-point positioning (SPP) accuracy by up to 15.38% horizontally and 6.78% vertically. These findings demonstrate the algorithm platform’s capability to significantly enhance the accuracy and stability of integrated navigation positioning for UAVs.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25072119