BLE Proximity Determination Using RSSI Graph Positioning
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Solution Overview
Problem
Existing proximity determination methods for mobile devices, such as those using Bluetooth Low Energy (BLE), are labor-intensive and prone to accuracy loss due to changes in the radio environment, requiring frequent recalibration and are unreliable in varying conditions like being in a hand or bag.
Innovation Solution
A method and system that uses a two-dimensional graph based on beacon measurements and device measurements to determine the most probable position of a mobile device relative to proximity determiners, utilizing a probability indicator to optimize proximity determination without reliance on physical maps, allowing for automatic adaptation to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprinting is used to increase positioning precision, then measurement precision is improved, but device complexity increases due to labor-intensive setup and calibration requirements
Solution Approach 1:
The system performs automatic environmental mapping and fingerprinting without requiring manual setup or calibration. The proximity determiners autonomously scan the environment, identify other determiners, and build the graph structure automatically, eliminating the need for labor-intensive traditional fingerprinting procedures while maintaining high positioning precision
Solution Approach 2:
The system performs environmental mapping and graph construction in advance during an initialization phase, creating a ready-to-use proximity map before actual proximity determination begins. This preliminary action stores the spatial relationships between determiners, so that during operation, the system can quickly determine proximity without repeated calibration
2Ease of operation
If traditional RSSI threshold comparison is used for proximity determination, then ease of operation is improved, but reliability deteriorates due to great variation in RSSI measurements
Solution Approach 1:
Instead of relying on a single RSSI value from one proximity determiner, the system incorporates measurements from multiple determiners and uses graph distance metrics. By adding spatial relationship dimensions to the determination process, the system achieves more reliable proximity detection that is not sensitive to variations in individual RSSI measurements
Solution Approach 2:
The system uses the graph structure to provide contextual feedback about the mobile device's position relative to multiple proximity determiners. By comparing the pattern of RSSI measurements across the graph rather than relying on a single threshold, the system achieves more reliable and context-aware proximity determination
3Measurement precision
If fingerprinting is used for positioning, then measurement precision is improved, but loss of time increases due to frequent recalibration requirements
Solution Approach 1:
The system dynamically updates the graph structure and proximity determiner positions as new determiners are added or removed from the environment. Rather than requiring complete recalibration, the system adaptively adjusts to environmental changes in real-time, maintaining positioning precision without time-consuming re-calibration cycles
Solution Approach 2:
The system performs environmental mapping and graph construction in advance during an initialization phase, creating a ready-to-use proximity map before actual proximity determination begins. This preliminary action stores the spatial relationships between determiners, so that during operation, the system can quickly determine proximity without repeated calibration
4Ease of operation
If BLE technology with extended range is used for electronic keys, then ease of operation is improved, but reliability worsens due to inability to accurately determine proximity
Solution Approach 1:
The system divides the proximity determination task into multiple independent measurements from several proximity determiners distributed in the environment. By segmenting the determination process across multiple nodes and combining their measurements through graph analysis, the system achieves accurate proximity detection even with extended-range BLE technology that provides more signal coverage
Data Source
AI summary
It is provided a method for enabling determination of proximity of a mobile device (2a, 2b) to a selected proximity determiner (3a). The method comprises the steps of: determining a base set of proximity determiners (3b-e) whereby an enlarged set of proximity determiners is defined as the selected proximity determiner and the base set of proximity determiners; receiving beacon measurements of signal strength of other proximity determiners in the enlarged set of proximity determiners; generating a two-dimensional graph based on the beacon measurements; receiving respective device measurements indicating signal strength of a signal from the mobile device at each proximity determiner in the enlarged set of proximity determiners; finding an optimum in a space defined by the two-dimensional graph; and determining the most probable position of the mobile device in the graph based on the optimum.