Correlating Cellular and WLAN Identifiers via Geospatial Tracking
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Solution Overview
Problem
Existing methods face challenges in correlating cellular and WLAN communication terminals to track individuals effectively, especially in scenarios where hostile users attempt to prevent tracking by disrupting communication networks.
Innovation Solution
A system and method that receive location coordinates from cellular and WLAN networks to identify correlations between identifiers, using geographical and temporal correlations to associate cellular and WLAN identifiers, even in sporadic WLAN coverage, and employing negative correlations to disqualify non-matching identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location coordinates from cellular and WLAN networks are used to correlate identifiers, then tracking accuracy is improved, but system complexity increases due to integrating multiple network systems
Solution Approach 1:
The system divides the tracking task into separate cellular network component and WLAN network component, each processed independently before being correlated. This segmentation allows each subsystem to maintain simplicity while the integrated system achieves high tracking accuracy through combining multiple identifier sources.
Solution Approach 2:
A correlation system acts as an intermediary between cellular and WLAN networks, receiving location coordinates from both networks and processing the correlation of identifiers. This intermediary approach manages system complexity by centralizing the integration logic while allowing individual network systems to remain independent and simple.
2Reliability
If geographical and temporal correlation methods are used to associate identifiers, then correlation reliability is improved, but processing time increases due to analyzing multiple data points
Solution Approach 1:
The system performs preliminary filtering of WLAN identifiers by defining a group of candidates based on proximity to the cellular identifier at a given time. This preliminary action reduces the number of identifiers that require full geographical and temporal correlation analysis, thereby maintaining high correlation reliability while reducing overall processing time.
Solution Approach 2:
The system applies full geographical and temporal correlation analysis only to a selected subset of candidate identifiers rather than all WLAN identifiers. This partial action approach maintains high reliability for the correlated identifiers while significantly reducing processing time by excluding non-candidate identifiers from extensive analysis.
3Measurement precision
If candidate filtering based on proximity is applied, then identification accuracy is improved, but computational load increases due to continuous position comparisons
Solution Approach 1:
The system defines a group of candidate WLAN identifiers that are located in the vicinity of the cellular identifier at a given time, rather than comparing all WLAN identifiers. This partial action maintains high identification accuracy for relevant identifiers while reducing computational load by excluding distant identifiers from comparison.
Solution Approach 2:
The system applies proximity-based filtering locally at each time point, defining candidate groups based on local spatial relationships between cellular and WLAN identifiers. This local quality approach improves identification accuracy for nearby identifiers while reducing overall computational load by limiting comparisons to local neighborhoods rather than global sets.
Data Source
AI summary
Methods and systems for tracking mobile communication terminals based on their identifiers. The disclosed techniques identify cellular terminals and Wireless Local Area Network (WLAN) terminals that are likely to be carried by the same individual, or cellular and WLAN identifiers that belong to the same multi-mode terminal. A correlation system is connected to a cellular network and to a WLAN. The system receives location coordinates of cellular identifiers used by mobile terminals in the cellular network, and location coordinates of WLAN identifiers used by mobile terminals in the WLAN. Based on the location coordinates, the system is able to construct routes that are traversed by the terminals having the various cellular and WLAN identifiers. The system attempts to find correlations in time and space between the routes.