Deep Learning Location Estimation Using Multi-Carrier Mobile Data
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Solution Overview
Problem
GPS-based location estimation technologies face significant errors and inaccuracies in indoor and densely populated urban areas due to signal blocking, signal strength weakening, and multipath signal transmission, which can hinder quick and accurate location provision in emergency situations.
Innovation Solution
A method and apparatus for estimating a location using mobile communication data from multiple telecommunications companies, utilizing a deep learning model to collect, process, and integrate data from various carriers, predicting missing data, and enhancing location estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based location estimation is used, then location accuracy is improved outdoors, but location accuracy deteriorates in indoor and densely populated areas due to signal blocking and multipath transmission
Solution Approach 1:
The patent introduces mobile communication data as an intermediary to supplement GPS location estimation. When GPS signals are blocked or degraded in indoor and densely populated areas, the system uses mobile communication data from multiple carriers as an alternative source to estimate location, thereby maintaining accuracy despite harmful environmental factors
Solution Approach 2:
The patent combines multiple data sources (GPS data and mobile communication data from multiple carriers) to create a composite location estimation system. This composite approach leverages the strengths of each data source while compensating for their individual weaknesses, particularly in challenging environments where GPS alone is insufficient
2Speed
If GPS-based location estimation is used, then location information can be provided quickly, but initial location determination takes a long time
Solution Approach 1:
The patent performs preliminary actions by collecting and preprocessing mobile communication data from multiple carriers in advance. This pre-collected data is ready for immediate use when location estimation is needed, eliminating the time delay associated with initial data collection and enabling faster initial location determination
3Measurement precision
If mobile communication data from multiple carriers is integrated, then location estimation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the mobile communication data processing into distinct components: data collection from multiple carriers, data preprocessing, feature extraction, and location estimation. This segmentation allows each component to be optimized independently and simplifies the overall processing pipeline while maintaining high location estimation accuracy
Data Source
AI summary
The present disclosure relates to a method and apparatus for estimating a location using mobile communication data based on deep learning. A method for estimating a location based on mobile communication data according to an embodiment of the present disclosure may comprise: collecting data from a plurality of mobile communication companies; learning a prediction model based on the collected data of the plurality of mobile communication companies; generating data of one or more other mobile communication companies by inputting data of a specific mobile communication company among the plurality of mobile communication companies into the learned prediction model; and estimating a location of a user based on the data of the specific mobile communication company and the generated data of one or more other mobile communication companies.