Why GPS fails indoors and how AIEZ with Kalman filtering solves it
GPS positioning is designed for direct satellite signals that reach the receiver via line of sight. Indoors, these signals are attenuated and reflected off walls, ceilings, and metallic objects, causing errors due to multipath propagation, loss of satellite visibility, and Non-Line-Of-Sight conditions. The usual result is an unstable signal, position jumps, and drift that make GPS unusable for many indoor applications such as navigation in warehouses, shopping centers, or traceability in factories.
To resolve these limitations, complementary positioning systems are used, such as Pedestrian Dead Reckoning (PDR), which combine inertial sensors (IMU), magnetometers, and fusion algorithms. In this context, the Advanced IEZ (AIEZ) solution appears, which improves PDR accuracy through intelligent magnetic field detection, adaptive selection of heading algorithms, and optimized Kalman filtering.
The Quasi-static Magnetic field Detection (QMD) method detects when the magnetometer is measuring a magnetic field representative of the environment without dynamic disturbances. When QMD determines that the magnetic reading is clean or stable, the system uses the EC algorithm as a priority to leverage the magnetic reference and calibrate the heading. If QMD detects magnetic disturbances or non-ideal conditions, AIEZ activates the Heuristic heading Drift Reduction (HDR), which applies heuristic rules and drift corrections based on step patterns and inertial estimates. The ability to switch between EC and HDR depending on magnetic field quality reduces systematic errors and improves robustness against magnets, metallic structures, or environmental changes.
These capabilities are integrated into the IEZ framework as a sensory trust zone architecture: zones where the magnetic reference is reliable, zones where inertial integration dominates, and mixed zones. AIEZ adds adaptive Kalman filtering that fuses IMU readings, magnetometer data, step estimates, and QMD detections to produce continuous position and heading with less drift. Kalman filtering reduces noise, corrects error accumulation, and allows incorporating external measurements when available, such as Bluetooth or Wi-Fi reference points, achieving a hybrid system that overcomes GPS limitations indoors.
Practical advantages of AIEZ with Kalman filtering: greater indoor accuracy, reduced heading drift, automatic switching between EC and HDR strategies based on magnetic quality, resilience to electromagnetic interference, and the possibility of integration with cloud services for synchronization and storage. This makes AIEZ-based solutions ideal for industrial applications, logistics, and real-time location services.
Q2BSTUDIO brings expertise in the design and implementation of these solutions. We are a custom software and application development company, specialists in artificial intelligence, cybersecurity, and AWS and Azure cloud services. We design custom software that incorporates business intelligence and visualization services with Power BI, AI agents to automate processes, and AI-based applications for businesses. Our teams integrate positioning solutions like AIEZ with secure and scalable cloud architectures, ensuring both accuracy and cybersecurity compliance.
If you need an indoor location solution, a mobile application with advanced PDR, or consulting in artificial intelligence and cloud services, Q2BSTUDIO develops custom projects that combine custom software, custom applications, artificial intelligence, AI agents, business intelligence services, Power BI, and cybersecurity. Contact us to evaluate your case and design an implementation that uses AIEZ and Kalman filtering to solve positioning problems where GPS fails.
