Client-Based Tuning Parameter Storage for Positioning Accuracy
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Solution Overview
Problem
Variations in sensor readings across different mobile devices, due to factors like antenna size, interference, firmware, and hardware differences, lead to inaccuracies in location estimation services.
Innovation Solution
A method involving the determination and compensation of device-specific biases in radio sensor readings through the exchange of fingerprint information and tuning parameters between mobile devices and servers, enabling iterative convergence of these parameters to actual biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device-specific tuning parameters are stored on the server, then measurement accuracy across different devices is improved, but server storage requirements and data management complexity increase
Solution Approach 1:
The patent extracts device-specific tuning parameters from the server environment and stores them locally in the mobile terminal's memory. This extraction reduces server storage requirements while maintaining the ability to compensate for device-specific measurement biases, thereby improving location accuracy without increasing server data burden.
Solution Approach 2:
The mobile terminal is enabled to autonomously store and manage its own device-specific tuning parameters locally. The terminal can retrieve these parameters when needed for measurement compensation without requiring continuous server intervention, making the system self-sufficient and reducing server storage requirements.
2Quantity of substance
If device-specific tuning parameters are stored locally in the mobile terminal, then server storage requirements are reduced, but the complexity of parameter distribution and updates increases
Solution Approach 1:
The patent implements a feedback mechanism where the mobile terminal sends its device identifier to the server, which then retrieves and returns the appropriate tuning parameters specific to that device. This automated feedback loop simplifies parameter distribution and updates, reducing the perceived complexity for the user while maintaining accurate device-specific compensation.
3Measurement precision
If tuning parameters are iteratively updated, then measurement accuracy converges to actual bias, but the number of communication exchanges between client and server increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing device-specific tuning parameters on the server before they are needed. When the mobile terminal requests parameters, it receives pre-computed values that have already converged to accurate bias compensation, eliminating the need for multiple iterative communication exchanges and reducing time loss.
Data Source
AI summary
Inter-alia, a method is disclosed comprising: determining at least one fingerprint information indicative of at least one identifier of a cellular and/or a non-cellular radio network, wherein based on the identifier at least one cell of the cellular radio network and/or at least one node of the non-cellular radio network is identifiable; sending the at least one fingerprint information, wherein the at least one fingerprint information comprises or is accompanied by a first set of tuning parameters in case such a set of tuning parameters is stored in a memory; receiving a second set of tuning parameters in response to the sending of the at least one fingerprint information, wherein the first and/or the second set of tuning parameters respectively comprise a correction value for compensating a bias for the at least one fingerprint information; and storing the received second set of tuning parameters, wherein the first set of tuning parameters is updated with the second set of tuning parameters enabling to iteratively converge the set of tuning parameters to the actual bias. It is further disclosed an according apparatus, computer program and system.