Adaptive Indoor Positioning via Angle-Based Triangulation
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Solution Overview
Problem
Existing indoor positioning systems face challenges in accuracy due to dynamic environments, noise in radio signals, and unpredictable human movement, leading to unstable and approximate location tracking of mobile devices in facilities.
Innovation Solution
A system utilizing two or more sensors and a server with an adaptive supervised machine learning system to record and analyze radio signal strength from portable communication devices, determining location coordinates and initiating a learning cycle when network attributes change, thereby improving tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trilateration technique is used for tracking mobile devices, then the system implementation is simple, but the location accuracy is approximate and low
Solution Approach 1:
The patent changes the fundamental parameter used for tracking from distance-based trilateration to angle-based triangulation. By measuring angles of arrival (AOA) of radio signals using antenna arrays and calculating phase differences, the system achieves precise location determination through geometric triangulation rather than approximate distance estimation, thereby improving measurement precision while accepting increased system complexity.
2Reliability
If door counters are used to track user movement, then the counting function is provided, but the system fails when several people move in and out simultaneously
Solution Approach 1:
The patent transitions from one-dimensional door counter measurements to two-dimensional spatial tracking using angle-based triangulation. By determining precise (x, y) coordinates of devices through angular measurements from multiple access points, the system can simultaneously track multiple users passing through doorways without mutual interference, thereby improving reliability while maintaining high productivity through parallel tracking of multiple devices.
3Adaptability or versatility
If RSSI-based indoor positioning is implemented, then the system can track mobile devices using available radio signals, but the tracking accuracy is reduced due to dynamic environment, noise, and unpredictable human movement
Solution Approach 1:
The patent replaces the RSSI-based electromagnetic signal strength measurement system with an angle-based triangulation system. Instead of relying on signal strength which is affected by environmental factors, the system uses phase difference measurements across antenna arrays to determine angles of arrival, providing positioning accuracy that is independent of signal strength variations caused by walls, furniture, and human movement.
4Ease of manufacture
If components are attached to movable positions like shelves and racks, then the installation flexibility is improved, but the tracking stability is affected when remodeling occurs
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors and detects changes in the radio environment and physical layout. When remodeling or component relocation occurs, the system adapts by recalibrating its triangulation calculations based on updated signal characteristics, thereby maintaining tracking stability despite changes in physical configuration or component positions.
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
Enhances the accuracy of indoor positioning systems, especially in crowded facilities, by filtering out known devices and adapting to changes in the radio environment, providing precise visitor counting and tracking.
Implementation Method 1
Each of the two or more sensors are configured to record a radio signal strength corresponding to each of one or more radio signal probes from each of the one or more portable communication devices
Data Source
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AI summary
The present disclosure provides a method and system for tracking one or more portable communication devices in a radio communication network in a facility. The system comprises two or more sensors and a server. Each of the two or more sensors is configured to record a radio signal strength corresponding to each of one or more radio signal probes from each of the one or more portable communication devices, by utilizing a corresponding media access control address of each of the one or more portable communication devices. The server is configured to determine a location coordinate of each of the one or more portable communication devices using the recorded radio signal strengths and an adaptive supervised machine learning system. A learning cycle of the adaptive supervised machine learning system is configured to initiate when a variation in one or more technical attributes of the radio communication network in the facility is detected.