Fingerprinting Location Database Building for RTLS
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Solution Overview
Problem
Existing real-time location systems (RTLS) face inefficiencies in building and updating location databases due to varying signal intensities affected by environmental conditions, leading to increased time and cost, especially in wide spaces, and errors from non-identical tag reception sensitivities.
Innovation Solution
A finger printing-based location tracking system that uses beacon apparatuses to transmit signals to tags, which measure and transmit signal intensities to a server for database building, where signal intensities exceeding a set maximum value are equally divided among beacon apparatuses, and a beacon count is incremented, allowing for independent estimation of tag positions and moving paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a specific sample-oriented tag is used to establish a database, then location data can be collected, but errors occur due to non-identical reception sensitivities of all tags
Solution Approach 1:
The system allows tags to autonomously determine their own locations by comparing received signal strengths against the database without requiring centralized calibration or adjustment for each tag's reception sensitivity, enabling self-service operation despite hardware variations
Solution Approach 2:
The system changes the approach from calibrating each tag's reception sensitivity to using relative signal strength comparisons, where the actual location is determined by which access point signals are strongest, making the system invariant to absolute reception sensitivity differences
2Area of stationary object
If data is gathered in a wide space environment, then comprehensive location coverage is achieved, but time and cost extend proportionally
Solution Approach 1:
The system performs preliminary actions by pre-establishing the location database during an initial site survey phase, allowing subsequent location tracking to occur rapidly without repeated database creation, thus reducing time loss in operational phases
Solution Approach 2:
The location database serves multiple functions: it enables both initial location determination and continuous tracking, supports various tag types without re-calibration, and can be used across different environmental conditions, reducing the need for separate database creation efforts
3Adaptability or versatility
If signal intensity database is built using specific sample tags, then location estimation is possible, but the database must be rebuilt due to seasonal or atmospheric variations
Solution Approach 1:
Instead of adapting the database to environmental changes by rebuilding it, the system inverts the approach by using the existing database as a reference and adjusting interpretations of signal strengths to account for environmental variations, avoiding repeated database creation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the time and cost of database creation and updates by allowing for autonomous database building and accurate location tracking, even in varying environmental conditions, by effectively distributing signal intensities and tracking tag movements across multiple beacon apparatuses.
Implementation Method 1
a tag at any position for measuring each signal intensity of beacon signals received from surrounding at least one beacon apparatus
Data Source
AI summary
Disclosed is a location tracking system and the method thereof for storing location information. The location tracking system includes at least one beacon apparatus arranged to transmit a beacon signal to a tag; a tag at any position respectively measuring a signal intensity of beacon signals received from surrounding at least one beacon apparatus, and transmitting a measured per-beacon signal intensity to a specific beacon apparatus; and a server arranged to determine if at least any one of signal intensities exceeds a set maximum value by analyzing a per-beacon signal intensity input from the specific beacon apparatus, and store a location-based signal intensity into a database by equally dividing a distance between corresponding beacon apparatuses and then distributing a measured signal intensity into a divided each position in multiple cases the signal intensity exceeds a set maximum value.


