Device Localization via Learning Model Signal Prediction
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Solution Overview
Problem
Traditional RF localization methods face challenges in environments with obstructions and changing conditions, such as inside buildings, where GPS signals are unavailable, and require frequent site surveys to maintain accuracy, which is hindered by structural changes and temporary environmental alterations.
Innovation Solution
A learning model-based system that uses multiple wireless reference points to process communication signals from user devices, creating a device model for localization without the need for fingerprinting or site surveying, adapting to changes by recognizing patterns in signal patterns and predicting future signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional RF localization methods use fingerprinting or site surveying to improve localization accuracy, then measurement precision is improved, but device complexity and time consumption increase due to frequent re-surveying requirements
Solution Approach 1:
The system employs machine learning models that automatically adapt to environmental changes without requiring manual re-surveying. The models self-update by processing ongoing localization data, eliminating the need for frequent fingerprinting operations and reducing system complexity while maintaining accuracy.
Solution Approach 2:
The patent pre-trains machine learning models with initial fingerprinting data, but the models are designed to automatically adapt to structural changes and environmental variations without requiring subsequent manual re-surveying. This preliminary action combined with automated adaptation reduces the need for repeated site surveys.
2Measurement precision
If traditional RF localization methods conduct frequent site surveys to maintain accuracy in changing environments, then localization accuracy is improved, but time consumption increases
Solution Approach 1:
The machine learning models continuously self-update by processing localization data from user devices, automatically adapting to environmental changes without requiring time-consuming manual re-surveying operations. This eliminates the need for frequent site surveys while maintaining accuracy.
Solution Approach 2:
The system performs continuous localization using the machine learning models, which continuously learn and adapt from incoming data. This continuous operation replaces the discontinuous, time-consuming periodic re-surveying process with an ongoing automated adaptation mechanism.
3Measurement precision
If the number of reference points is increased to improve localization accuracy, then measurement precision is improved, but device complexity and system cost increase
Solution Approach 1:
The patent changes the fundamental parameter from the number of reference points to the sophistication of the machine learning model. Instead of adding more hardware reference points, the system uses advanced algorithms that can extract more information from the same reference points, achieving higher accuracy without increasing system complexity.
Solution Approach 2:
The patent replaces the mechanical approach of adding more physical reference points with an intelligent software-based machine learning system. This substitution allows the system to achieve improved localization accuracy through algorithmic processing rather than through increased hardware deployment.
4Measurement precision
If traditional systems perform fingerprinting operations to adapt to structural changes, then localization accuracy is improved, but productivity decreases due to operational disruptions
Solution Approach 1:
The machine learning models automatically detect and adapt to structural changes by processing ongoing localization data, eliminating the need for manual fingerprinting operations that disrupt productivity. The system self-corrects without requiring operational stoppages or re-surveying activities.
Solution Approach 2:
The system maintains continuous localization operations without interruption for fingerprinting. The machine learning models continuously learn from incoming data, ensuring that localization accuracy is maintained even as the environment changes, without requiring disruptive re-surveying operations.
Data Source
AI summary
Methods and systems of localizing a device are presented. In an example method, a communication signal from a device is received by a wireless reference point during a period of time. A sequence of values is generated from the communication signal, as received by the wireless reference point, during the period of time. The sequence of values is supplied to a learning model configured to generate an output based on past values of the sequence of values and at least one predicted future value of the sequence of values. The current location of the device is estimated during the period of time based on the output of the learning model.