RF Signal Association for Unauthorized Device Identification
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Solution Overview
Problem
Current systems lack an effective method to identify and associate multiple digital frequencies emitted by personal electronic devices for authentication and surveillance purposes, particularly in transactional environments, which is crucial for security and personalized services.
Innovation Solution
A computerized system and method that senses and associates RF signals, such as Bluetooth, Wi-Fi, and cellular signals, to generate unique identifiers for device and individual authentication, using sensors and cameras to triangulate device locations and correlate signals over time, enabling identification and authentication in transactional environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple RF signals are monitored and associated to generate unique identifiers, then identification accuracy and authentication reliability are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the identification process by monitoring different types of RF signals (Bluetooth, Wi-Fi, cellular) separately through dedicated sensors, then processes each signal type independently before associating them. This modular approach improves reliability through comprehensive signal coverage while managing system complexity by organizing the monitoring function into distinct, manageable components.
Solution Approach 2:
The system implements a nested structure where multiple RF signal monitoring layers are combined - Bluetooth signals, Wi-Fi signals, and cellular signals are monitored at different levels and nested within a unified identification framework. Each signal type provides a layer of identification data that is integrated into the overall unique identifier generation process, enhancing reliability through multi-layer verification while organizing complexity in a hierarchical manner.
2Measurement precision
If RF signals are associated over multiple time points to generate persistent identifiers, then identification accuracy is improved, but time consumption and processing duration increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and pre-processing RF signals in the background before authentication is actually needed. Signal patterns are tracked and stored over time intervals, so when identification is required, the association process can quickly reference pre-analyzed data rather than starting from scratch, thereby improving identification accuracy while minimizing real-time processing delays.
Solution Approach 2:
The system implements periodic monitoring of RF signals at defined time intervals, collecting signal data systematically over multiple periods. This periodic approach allows the system to build up a temporal pattern of signal associations that improves identification accuracy, while the structured timing prevents excessive processing by limiting data collection to necessary periodic intervals rather than continuous monitoring.
3Reliability
If multiple sensors and cameras are deployed to triangulate device locations, then surveillance capability and identification reliability are improved, but device complexity and cost increase
Solution Approach 1:
The system merges multiple sensing modalities - RF signal monitoring from Bluetooth, Wi-Fi, and cellular sensors, along with camera-based visual identification - into a unified surveillance and identification framework. By combining these different detection methods, the system achieves enhanced identification reliability through multi-modal verification while managing complexity through integrated processing that correlates data across sensor types rather than treating them as separate systems.
Solution Approach 2:
The system implements multi-functional sensors and processing units that can handle multiple types of RF signals and identification data streams through a single integrated architecture. The sensor array and processing system are designed to be universal, capable of monitoring various signal types and performing multiple functions (signal detection, location triangulation, identifier generation) through shared resources, thereby improving surveillance capability while avoiding the complexity of entirely separate systems for each function.
Data Source
AI summary
A computerized non-visible signal based surveillance system including a computerized subsystem for associating RF signals in a transactional environment, the computerized subsystem including a sensor configured to sense a plurality of RF signals emitted from within a predetermined range of the sensor at points in time and a computerized signal associator configured to receive outputs from the sensor and provide an output which associates at least some of the plurality of RF signals and generate a unique RF signal-based identifier and an alert generating subsystem configured to employ the unique RF signal-based identifier for identifying an unauthorized person or an unauthorized device based at least in part on the unique RF signal-based identifier. There is even further provided a surveillance method including associating RF signals and ascertaining a presence of an unauthorized person or an unauthorized device based at least in part on the unique RF signal-based identifier.