Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

By Aboelmagd Noureldin, Tashfeen B. Karamat, Mark D. Eberts, Ahmed El-Shafie. IEEE Transactions on Vehicular Technology, 2009

369 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/661fe4500d13a31f769109a81fc992e0afd700b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Xiang-Gen Xia
2. Yuan Zhuang

In Artificial Intelligence

1. Yuan Zhuang
2. Chunqi Wang
3. Ahmed Tamtaoui
4. Ikram Belhajem

Citations in the corpus

• An Improved Robust Low Cost Approach for Real Time Vehicle Positioning in a Smart City (2016, background): “In their work, Georgy et al. [5] compare the performance of the KF and the Particle Filter (PF) used as vehicle state estimators combining GPS with the vehicle odometer and low cost MEMS-based inertial sensors; they conclude according to empirical results that the PF outperforms KF in case of GPS ma”
• Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation (2020, background): “In the SINS, the positioning error may increase rapidly with time due to the cumulative error of accelerometer measurement [24].”