RF Signal Detection with PDFT Derivatives for Intermittent Activity
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Solution Overview
Problem
Existing systems struggle to efficiently track and gain intelligence from the high magnitude and intermittent wireless activities across wide frequency ranges, particularly in 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 machine learning for signal classification, edge processing, and a modular architecture for real-time detection and management of RF environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional systems track wireless activities across wide frequency ranges, then coverage area is improved, but system complexity increases due to high magnitude and intermittent nature of signals
Solution Approach 1:
The system segments the wide frequency range into multiple sub-bands and processes signals in parallel across different frequency segments. This allows the system to maintain comprehensive frequency coverage while reducing the complexity of processing any single frequency band by dividing the overall task into manageable segments that can be handled independently.
Solution Approach 2:
The patent introduces a time-domain dimension to frequency-domain analysis by computing power distribution over time. This transforms the problem from purely frequency-based tracking to a multi-dimensional approach that incorporates temporal characteristics, enabling better signal identification and reducing complexity through dimensional transformation.
2Speed
If the system processes high magnitude wireless activities in real-time, then detection speed is improved, but computational resources are consumed
Solution Approach 1:
The system performs preliminary processing by computing the power distribution histogram and its derivatives before final signal detection. This pre-processing step organizes and structures the raw signal data, making subsequent detection operations more efficient and reducing the computational burden during real-time analysis.
Solution Approach 2:
The patent transforms signal parameters from raw frequency-domain values to power distribution histograms with temporal components. This parameter transformation creates a more compact and informative representation that enables faster detection while reducing computational requirements compared to processing original signal data directly.
3Measurement precision
If the system tracks intermittent signals with short durations, then detection accuracy is improved, but data volume increases
Solution Approach 1:
The system extracts only the essential features from raw signal data by computing power distribution histograms and their temporal derivatives. This extraction process filters out redundant information and retains only the critical characteristics needed for accurate signal detection, significantly reducing data volume while maintaining detection accuracy for intermittent signals.
Solution Approach 2:
The patent discards raw signal data after extracting meaningful patterns into power distribution representations. By recovering the essential information in a compressed form and discarding the original high-volume raw data, the system maintains detection accuracy for short-duration intermittent signals while dramatically reducing data storage and transmission requirements.
4Adaptability or versatility
If the system processes signals across multiple frequency bands, then detection capability is improved, but network traffic increases
Solution Approach 1:
The system segments frequency analysis into independent sub-bands processed in parallel, allowing multi-frequency detection capability while minimizing network traffic. Each frequency segment is processed locally and independently, enabling versatile detection across multiple bands without requiring continuous transmission of all raw data across the network.
Solution Approach 2:
The patent creates a compressed copy of frequency domain information in the form of power distribution histograms with temporal characteristics. This copied representation retains the essential detection capability across multiple frequency bands but reduces the data volume and network traffic required compared to transmitting original multi-frequency signal data.
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.