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Related Experiment Video

Updated: May 24, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

FPGA-based real-time embedded system for RISS/GPS integrated navigation.

Walid Farid Abdelfatah¹, Jacques Georgy, Umar Iqbal

¹Trusted Positioning Inc., Calgary, AB T2L 2K7, Canada. wabdelfatah@trustedpositioning.com

Sensors (Basel, Switzerland)

|February 28, 2012

Summary

Integrating a reduced inertial sensor system (RISS) with GPS offers reliable navigation, especially in GPS-denied areas. This study presents a low-cost, real-time embedded system for fused positioning solutions.

Keywords:

FPGA Global Positioning System Kalman filter embedded systems inertial sensors land vehicle navigation soft-core

Related Experiment Videos

Last Updated: May 24, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Area of Science:

Navigation Systems Engineering
Robotics and Autonomous Systems
Sensor Fusion

Background:

Standalone GPS navigation is unreliable in challenging environments like urban canyons and tunnels.
Multi-sensor integration, particularly with Reduced Inertial Sensor Systems (RISS) and GPS, enhances navigation accuracy and consistency.
Kalman filtering is a key technique for fusing data from diverse sensors.

Purpose of the Study:

To bridge the gap between navigation algorithm development and practical implementation.
To create a low-cost, real-time embedded system for data-fused positioning.
To demonstrate the feasibility of real-time navigation solution computation.

Main Methods:

Integration of a 2D RISS (gyroscope, odometer/wheel encoders) with a GPS receiver.
Utilizing a Kalman filter for data fusion and navigation solution computation.
Development of a real-time embedded system with a soft-core processor on an FPGA for sensor synchronization and computation.

Main Results:

The developed system successfully synchronizes measurements from GPS, gyroscope, and odometer.
Real-time computation of a fused navigation solution is achieved.
The system provides a more consistent and reliable navigation solution compared to standalone GPS.

Conclusions:

The low-cost, real-time embedded navigation system effectively integrates multi-sensor data for enhanced positioning.
FPGA-based implementation offers flexibility and computational power for real-time Kalman filtering.
This approach significantly improves navigation reliability in GPS-challenged environments.