Power Distribution Signal Detection for Intermittent RF Activity
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Solution Overview
Problem
Existing systems struggle to track and gain intelligence from the high magnitude of intermittent wireless activities over wide frequency ranges, particularly in environments with social media and cellular applications, due to their short durations and complexity.
Innovation Solution
The system employs a PDFT processor that analyzes power distribution by frequency over time, using first and second derivatives to detect signals, and incorporates edge processing, machine learning, and modular architecture for real-time signal detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal detection methods are used to monitor wireless activities, then system simplicity is maintained, but the ability to track and gain intelligence from high magnitude intermittent activities degrades
Solution Approach 1:
The system segments the frequency spectrum into multiple bins and divides the time domain into discrete intervals, creating a structured approach to analyze intermittent signals. This segmentation enables the system to process high-magnitude activities across wide frequency ranges by breaking down the complex monitoring task into manageable frequency-time cells, resolving the contradiction between improved detection precision and system complexity.
Solution Approach 2:
The patent introduces a new dimensional approach by transforming the traditional time-frequency signal representation into a power distribution matrix that maps power levels across frequency bins over time intervals. This dimensional transformation enables the system to capture the magnitude and temporal characteristics of intermittent activities, enhancing detection capability while organizing complexity through structured data representation.
2Adaptability or versatility
If comprehensive monitoring of wide frequency ranges is implemented, then signal detection coverage is improved, but processing time and computational load increase
Solution Approach 1:
The system applies local quality analysis by examining power distribution characteristics within specific frequency bins and time intervals rather than processing the entire spectrum uniformly. This localized analysis identifies regions of interest where intermittent activities occur, enabling comprehensive frequency coverage while reducing processing time by focusing computational resources on active frequency regions rather than the entire wide frequency range.
Solution Approach 2:
The patent implements periodic action through its analysis of power distribution over discrete time intervals, capturing the temporal patterns of intermittent wireless activities. By organizing processing into periodic time slots and using the periodic nature of many wireless communications to identify signal characteristics, the system achieves comprehensive monitoring while managing computational load through time-division processing.
3Measurement precision
If detailed signal analysis is performed to gain intelligence from activities, then detection accuracy is improved, but network traffic and backhaul requirements increase
Solution Approach 1:
The system extracts key intelligence from detailed signal analysis by deriving high-level features such as power distribution patterns, temporal characteristics, and frequency occupancy metrics. This extraction process converts raw signal data into actionable intelligence while minimizing the amount of data that needs to be transmitted over the network, resolving the contradiction between detection accuracy and network bandwidth consumption.
Solution Approach 2:
The patent creates a simplified representation or copy of the complex signal environment in the form of a power distribution matrix that captures essential characteristics without requiring transmission of the complete raw signal data. This copying approach enables detailed analysis for gaining intelligence while reducing the substance (data volume) that must be moved over the network backbone.
4Speed
If real-time processing of intermittent activities is implemented, then situational awareness is improved, but computational resources and processing power requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing signal data into frequency-time bins and calculating power distribution characteristics before final detection and classification. This preliminary organization of data into structured representations enables faster real-time processing by reducing the complexity of subsequent analysis, achieving high-speed situational awareness while managing computational power requirements through efficient data preparation.
Data Source
AI summary
Systems, methods, and devices for automatic signal detection in an RF environment are disclosed. A sensor device in a nodal network comprises at least one RF receiver, a generator engine, and an analyzer engine. The at least one RF receiver measures power levels in the RF environment and generates FFT data based on power level data. The generator engine calculates a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of the FFT data. The analyzer engine creates a baseline based on statistical calculations of the power levels measured in the RF environment for a predetermined period of time, and identifies at least one signal based on the first derivative and the second derivative of the FFT data in at least one conflict situation from comparing live power distribution to the baseline of the RF environment.