Mobile Proximity Positioning Using RSSI Graph Fingerprints
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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 require frequent recalibration due to changes in the radio environment, leading to potential loss of accuracy.
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 relying on physical maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprinting-based positioning is used to improve positioning precision, then measurement precision is improved, but device complexity and ease of manufacture worsen due to labor-intensive setup and calibration requirements
Solution Approach 1:
The system performs self-calibration by automatically creating and updating fingerprints based on RSSI measurements from multiple devices without requiring manual intervention. The calibration process is automated through the server that collects measurements and generates fingerprints dynamically, eliminating the need for labor-intensive manual setup and recalibration.
Solution Approach 2:
The system performs preliminary calibration actions by pre-establishing fingerprints during initial device connections and continuously updating them in the background. This preliminary action ensures that positioning can proceed immediately without requiring manual calibration at the time of use, and the system proactively maintains accuracy through automated updates.
2Measurement precision
If frequent recalibration is performed to maintain positioning accuracy, then measurement precision is improved, but loss of time and productivity worsen due to labor-intensive re-calibration requirements
Solution Approach 1:
The system automatically performs recalibration without human intervention by collecting RSSI measurements from active devices and updating fingerprints in real-time. This self-service approach eliminates the time loss associated with manual recalibration while maintaining positioning accuracy through continuous background updates.
Solution Approach 2:
The calibration process continues uninterrupted in the background as devices naturally move through the environment and provide RSSI measurements. Instead of stopping operations for recalibration, the system continuously updates fingerprints using ongoing device activity, ensuring positioning accuracy without interrupting productivity.
3Ease of operation
If RSSI threshold comparison is used to simplify proximity determination, then ease of operation is improved, but measurement precision worsens due to great variation in RSSI measurements
Solution Approach 1:
The system uses feedback from multiple RSSI measurements from different devices to continuously refine fingerprints and improve proximity determination accuracy. By collecting and analyzing measurements from multiple sources, the system adapts to RSSI variations and maintains precise proximity detection while keeping the operation simple through automated processing.
Solution Approach 2:
The system combines multiple RSSI measurements from different proximity determiners and devices to create a more robust proximity determination. By merging multiple data points into composite fingerprints, the system compensates for individual measurement variations and achieves accurate proximity detection while maintaining operational simplicity.
Data Source
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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.