Electromagnetic Wave Discrimination via Amplitude Histogram Clustering
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Solution Overview
Problem
Existing electromagnetic wave discrimination devices struggle to accurately classify electromagnetic waves in complex communication environments without prior information, leading to difficulties in identifying interfering signals and are not suitable for mobile facilities due to large system configurations.
Innovation Solution
An electromagnetic wave discrimination device that samples communication signals at predetermined times, calculates amplitude feature quantities, determines degrees of similarity, classifies signals into clusters, and discriminates between them based on these similarities, allowing for accurate identification of interfering signals without prior environmental information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional amplitude probability distribution (ADP) is used as feature quantity to estimate bit error rate or throughput, then electromagnetic wave discrimination can be performed, but the system requires prior information about modulation system and signal strength which limits adaptability to changing electromagnetic environments
Solution Approach 1:
The patent transforms the electromagnetic wave discrimination approach by changing from using ADP (amplitude probability distribution) to using amplitude histogram as the feature quantity. This parameter change eliminates the need for prior information about modulation systems and signal strengths, enabling the system to adapt to varying electromagnetic environments while maintaining discrimination accuracy through cluster analysis of amplitude distribution patterns
2Ease of operation
If single parameter estimation (waveform peak value or mean value) is used for waveform characterization, then measurement simplicity is maintained, but accurate discrimination of complicated electromagnetic environments becomes difficult
Solution Approach 1:
The patent transitions from single-parameter estimation to multi-dimensional amplitude histogram analysis. By constructing histograms that capture the distribution of amplitude values across multiple bins, the system gains dimensional richness in the feature space, enabling accurate discrimination of complex electromagnetic environments while maintaining operational simplicity through automated cluster analysis
3Measurement precision
If comprehensive waveform analysis is performed to achieve accurate electromagnetic environment discrimination, then measurement precision improves, but system configuration becomes large scale and unsuitable for mobile facilities
Solution Approach 1:
The patent extracts only the essential amplitude distribution characteristics from the electromagnetic waves by constructing amplitude histograms, discarding unnecessary waveform details. This extraction approach maintains high discrimination accuracy by focusing on the most informative features (amplitude distribution patterns) while significantly reducing system complexity and enabling deployment in mobile facilities
Data Source
AI summary
An electromagnetic wave discrimination device according to the present invention discriminates electromagnetic waves, and includes an acquisition section, a feature quantity calculation section, a similarity calculation section, a classification section, a discrimination section, an output section, and a storage section. The acquisition section receives communication signals of a predetermined frequency, and samples waveform data of the communication signals every predetermined time to obtain sample data for each predetermined time. The feature quantity calculation section calculates amplitude feature quantities for each predetermined time, based on the sampling data for each predetermined time. The similarity calculation section calculates degrees of similarity with respect to the amplitude feature quantities for each predetermined time. The classification section classifies the communication signals for each predetermined time into clusters to obtain cluster analysis results of the communication signals for each predetermined time, based on the degrees of similarity. The discrimination section discriminates communication signals constituting one cluster for each of the clusters to obtain discrimination results, based on the cluster analysis results of the communication signals for each predetermined time. The output section outputs the discrimination results. The storage section stores the amplitude feature quantities, the degrees of similarity, the cluster analysis results, and the discrimination results.