MEMS Sensor Navigation Drift Correction via WLAN Feedback
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Solution Overview
Problem
Current pedestrian navigation systems face challenges in providing accurate and continuous navigation solutions in challenging areas like urban canyons and indoors due to GPS signal degradations, and existing MEMS-based solutions suffer from navigation errors that increase over time, requiring additional aiding sources like WLAN and magnetometers.
Innovation Solution
The integration of MEMS sensors with WLAN using a combination of INS and PDR mechanizations, motion constraints like NHC, ZUPT, and ZARU, and loosely-coupled (LC) or tightly-coupled (TC) integrations to enhance navigation performance, reducing drift and error accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If INS mechanization is used to calculate PVA by integrating raw data from accelerometers and gyroscopes, then 3D PVA information can be provided, but navigation errors increase rapidly with time due to drift characteristics of MEMS sensors
Solution Approach 1:
The system implements feedback by continuously comparing INS-calculated position with WLAN-measured position and using the difference to correct INS drift. The Kalman filter uses WLAN position updates to adjust and reset the INS error states, creating a closed-loop feedback mechanism that prevents error accumulation while maintaining 3D PVA capability.
Solution Approach 2:
WLAN positioning acts as an intermediary that mediates between the INS system and the final navigation solution. Instead of directly using INS output, the system introduces WLAN position measurements as an intermediate correction layer that bridges INS drift and accurate positioning, enabling long-term reliable navigation.
2Measurement precision
If PDR is used to reduce accumulated speed of navigation errors, then position solution accuracy is improved, but the system still drifts with time and requires additional aiding sources
Solution Approach 1:
The system merges PDR and WLAN positioning methods into a unified navigation solution. PDR provides continuous position estimates between WLAN updates, while WLAN measurements periodically correct PDR drift. This combination allows the system to maintain accuracy benefits of PDR while eliminating its time-dependent drift through WLAN aid.
Solution Approach 2:
The system applies periodic correction by using WLAN position measurements at discrete intervals to reset and correct PDR accumulated errors. Between WLAN updates, PDR operates independently; when WLAN becomes available, it periodically recalibrates the PDR solution, preventing long-term drift while maintaining continuous navigation.
3Ease of manufacture
If WLAN positioning is used as the main aiding resource for MEMS sensors in challenging areas, then special hardware designs and complicated infrastructure requirements are avoided, but WLAN infrastructure must be available in the environment
Solution Approach 1:
The system dynamically adapts its operation mode based on WLAN availability. When WLAN infrastructure is present, the system switches to aided mode using WLAN for position correction. When WLAN is unavailable, it operates in standalone mode using only INS/PDR. This dynamic behavior allows the system to simplify hardware requirements while adapting to different environmental conditions.
Solution Approach 2:
The system changes its operational parameters (aiding source selection, filter configuration, update rates) based on environmental conditions. When WLAN is available, it enables WLAN-aided navigation with corresponding parameter adjustments. When WLAN is absent, it transitions to unaided INS/PDR mode with different parameters, allowing the same hardware to operate effectively in both WLAN-rich and WLAN-poor environments.
Data Source
AI summary
Methods, systems and apparatuses for enhanced Microelectromechanical (MEMS)-based navigation in a mobile device are disclosed. In an embodiment, a method includes receiving navigation data from one or more navigation sensors on board the mobile device. The method may also include calculating, using a processing device, position, velocity, and attitude (PVA) values in response to the navigation data using an Inertial Navigation System (INS) mechanization. Additionally, the method may include calculating, using the processing device, Pedestrian Dead Reckoning (PDR) values in response to the navigation data. Also, the method may include determining, using the processing device, one or more navigation values in response to a combination of the PVA values calculated by the INS mechanization and the PDR values.