Indoor Location Aggregation Using Channel State Information
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Solution Overview
Problem
Existing indoor location estimation models struggle to provide accurate location estimation in enterprise settings due to system restrictions related to security and hardware capabilities, while also requiring the aggregation of multiple models to optimize performance based on varying environmental conditions.
Innovation Solution
A processor-implemented method and system that aggregates Channel State Information (CSI) from access points with multiple Location Estimation Models (LEMs) to identify candidate sub-regions and determine the actual location of user devices, using an accuracy map to select the most accurate LEM based on environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single location estimation model is used, then the system complexity is low, but the location estimation accuracy deteriorates under varying environmental conditions
Solution Approach 1:
The patent combines multiple location estimation models (LEMs) into a unified framework that aggregates their outputs. Each LEM processes CSI data independently, and their results are merged through a selection mechanism that chooses the most accurate estimate based on environmental conditions, thereby improving overall accuracy while managing complexity through structured integration.
Solution Approach 2:
The system dynamically selects which location estimation model to use based on real-time environmental conditions and accuracy maps. Instead of using a fixed single model, the system adapts its behavior by switching between different LEMs depending on the current sub-region and signal characteristics, optimizing accuracy for varying conditions.
2Measurement precision
If advanced techniques such as beamforming or multipath signal consideration are used, then location estimation accuracy is improved, but system restrictions due to security or system capabilities worsen
Solution Approach 1:
The patent creates a universal location estimation framework that can accommodate multiple different LEMs within a single system. This multi-functional architecture allows the system to work with various estimation techniques (including advanced ones like beamforming and multipath consideration) while maintaining compatibility with different enterprise security and capability restrictions, as each LEM can be independently configured and selected based on what the enterprise environment permits.
3Measurement precision
If multiple Location Estimation Models are aggregated, then location estimation accuracy is improved, but the computational processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing accuracy maps for different sub-regions and signal conditions before actual location estimation is needed. These accuracy maps store pre-analyzed performance characteristics of each LEM under various conditions, allowing the system to quickly select the appropriate model without performing extensive real-time computations, thus reducing processing time while maintaining accuracy.
Data Source
AI summary
Indoor localization, which estimates the location of a wireless device using Wi-Fi/Zigbee/Bluetooth, is increasingly important for Industry 4.0 applications, such as tracking of robots and large-scale inventory management. Existing approaches have certain practical issues related to enterprise hardware as specific data required by them is not always available due to security and other reasons. Present disclosure provides system and method that implement indoor Location Aggregator Model, which combines multiple estimation location models to provide localization to wireless/user devices while being compliant to enterprise environments. More specifically, channel state information is used for estimating position and identification of candidate sub-region within a region. Further, at least one Location Estimation Model is identified based on the identified candidate sub-region by all LEMS and an accuracy map. The identified LEM is then used for determining an actual location of a user device.