RF Spectrum Signal Clustering for Long-Duration Visualization
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Solution Overview
Problem
Long duration broadband RF spectrum signals are challenging to visualize due to large volumes of data and insufficient time resolution, making it impractical to represent short pulse signals effectively in spectrograms.
Innovation Solution
A system and method that processes RF spectrum signals by extracting representative feature vectors, grouping them into final clusters, determining signal occupancy, and generating visualizations based on signal occupancy within a set display time, reducing data volume and enhancing time resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete broadband RF spectrum signals are recorded and processed using traditional spectrogram methods, then signal visualization is achieved, but the large volume of data cannot be realistically processed and displayed quickly
Solution Approach 1:
The patent extracts representative feature vectors from RF spectrum signals, identifying and isolating key characteristics (frequency, bandwidth, intensity, pulse width) from the complete signal data. This extraction process removes unnecessary data while preserving essential signal information, enabling faster processing without sacrificing visualization quality
Solution Approach 2:
Instead of processing complete signal data, the patent creates simplified representations (feature vectors) that copy only the essential characteristics of the signals. These feature vectors serve as substitutes for the full signal data, maintaining visualization accuracy while dramatically reducing data volume for processing
2Duration of action of moving object
If long duration signals are displayed in spectrograms, then complete signal coverage is achieved, but time resolution becomes insufficient to visualize short pulse signals
Solution Approach 1:
The patent transitions from traditional 2D spectrogram representation (frequency vs. time) to a multi-dimensional feature space that includes frequency, bandwidth, intensity, and pulse width as separate dimensions. This dimensional transformation allows simultaneous visualization of both long-duration signal coverage and short pulse characteristics by representing signals in a higher-dimensional feature space rather than being constrained to a flat time-frequency plane
3Measurement precision
If relational databases are used to store and query extracted signal features, then long duration signals can be visualized, but the database speed limits throughput of data recording and analysis
Solution Approach 1:
The patent performs signal feature extraction and clustering operations in advance, before the actual visualization or analysis is needed. By pre-processing the signals and organizing them into clustered groups with representative feature vectors, the system eliminates the need for time-consuming real-time database queries during analysis, significantly improving throughput while maintaining analytical capability
Data Source
AI summary
A circuit for processing data for radio frequency (RF) spectrum signals includes an input interface; a controller includes a controller memory that stores instructions. A processor executes the instructions, causing a circuit to: obtain representative RF feature vectors of pre-sorted cluster information for the RF spectrum signals via the input interface; group the representative RF feature vectors of the pre-sorted cluster information for the RF spectrum signals into final clusters for the RF spectrum signals; compute representative RF feature vectors for the final clusters for the RF spectrum signals; determine signal occupancy of each final cluster within a corresponding set display time for each of the RF spectrum signals; and generate visualization data for each final cluster for the RF spectrum signals for a display in accordance with the signal occupancy within the corresponding set display time for the RF spectrum signals.