Terminal Location Measurement via RSSI Weighted Fusion
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Solution Overview
Problem
Current user proximity sensing techniques, such as those based on Bluetooth Low Energy (BLE) and Received Signal Strength Indicator (RSSI), face challenges with high initial costs and low accuracy in measuring user location, necessitating a more precise and reliable method for location measurement in IoT environments.
Innovation Solution
A method involving the measurement of RSSI values from multiple electronic devices, application of various algorithms to determine preliminary locations, application of weights to these locations, and integration with sensor data to calculate a final location, enhancing accuracy without requiring additional devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bluetooth Low Energy (BLE) technique is used for user proximity sensing, then the detection of user proximity is improved, but the initial cost increases due to requiring additional BLE devices
Solution Approach 1:
The patent uses Wi-Fi signals as an intermediary to perform location measurement without requiring dedicated proximity sensing devices. The Wi-Fi signals from existing access points serve as the measurement medium, eliminating the need for additional BLE devices while maintaining location detection capability
Solution Approach 2:
The patent makes existing Wi-Fi infrastructure serve multiple functions: both providing network connectivity and enabling location measurement. By utilizing the dual functionality of Wi-Fi for communication and positioning, the system avoids the need for separate dedicated sensing devices
2Device complexity
If RSSI-based location estimation is used, then additional devices are not required, but the accuracy of location measurement deteriorates due to irregular accuracy depending on terminal locations
Solution Approach 1:
The patent merges multiple location estimation algorithms together, combining their results through weighted fusion. This integration of multiple algorithms compensates for the weaknesses of individual methods and produces more accurate and stable location estimates across different terminal positions
Solution Approach 2:
The patent dynamically adjusts the weights assigned to different location estimation algorithms based on their performance characteristics and environmental conditions. By changing the parameter weights adaptively, the system optimizes measurement accuracy for different terminal locations and signal conditions
3Measurement precision
If multiple algorithms are applied to RSSI values to extract preliminary locations, then the accuracy of location determination is improved, but the complexity of the measurement process increases
Solution Approach 1:
The system performs self-optimization by automatically selecting and weighting algorithms based on their performance. The weighted fusion mechanism autonomously handles the complexity of multiple algorithms by assigning appropriate weights to each, reducing the need for manual configuration and simplifying the overall process
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 allows for precise user location determination, improving accuracy and reliability while eliminating the need for additional hardware, thereby providing a cost-effective solution for IoT applications.
Implementation Method 1
measuring received signal strength indicator (RSSI) values of signals received from a plurality of electronic devices deployed in a specific space
Data Source
AI summary
A user terminal measures a location thereof. In a method for measuring a terminal location, the terminal measures received signal strength indicator (RSSI) values of signals received from a plurality of electronic devices deployed in a space. Then the terminal extracts a preliminary location of the terminal with respect to each of a plurality of predetermined algorithms by applying the plurality of algorithms to the measured RSSI values, identifies a first estimated location of the terminal by applying a predetermined weight to each preliminary location, identifies a second estimated location of the terminal using an output of at least one sensor, and determines a final location of the terminal, based on the first and second estimated locations.