Signal Analyzer for Periodic and Random Interference Detection
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Solution Overview
Problem
Current methods for detecting anomalies in electrical signals are inefficient due to the random and ephemeral nature of signal distortions, requiring large amounts of memory and processing power, making it difficult to detect and compensate for periodic and random interference in real-time.
Innovation Solution
A signal analyzer equipped with a divergence detector for periodic interference and an information detector for random events, using statistical analysis and output circuitry to compensate for anomalies, employing techniques like spectrogram computation, Kullback-Leibler divergence metric, and information content analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods capture large sets of sample data to detect random distortions, then detection capability improves, but memory requirements and processing power increase unreasonably
Solution Approach 1:
The patent segments the signal analysis into two distinct detectors: a divergence detector for periodic interference and an information detector for random events. This segmentation allows each detector to specialize in specific types of anomalies, improving detection efficiency without requiring large memory resources. The divergence detector uses sequential probability ratio tests on segmented signal portions, while the information detector analyzes information content changes, both operating with minimal memory footprint compared to traditional comprehensive capture methods.
Solution Approach 2:
The patent performs preliminary classification of interference types before detailed analysis. By first determining whether interference is periodic or random using the divergence detector, the system can then apply the appropriate detection method (information detector for random events). This preliminary action avoids the need to process and store all possible signal variations, significantly reducing memory requirements while maintaining high detection capability.
2Measurement precision
If traditional methods analyze large amounts of captured data, then anomaly detection accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent divides signal analysis into specialized segments: periodic interference detection using divergence measures and random event detection using information content analysis. Each segment processes only relevant data types, avoiding the computational burden of analyzing all signal characteristics comprehensively. This segmented approach maintains high detection accuracy while significantly reducing processing time compared to traditional methods that must evaluate all possible anomalies in captured datasets.
Solution Approach 2:
The patent changes the analytical parameters used for detection based on the type of interference identified. For periodic interference, it uses divergence measures (KL-divergence) that efficiently detect deviations from expected periodic patterns. For random events, it uses information content metrics that measure unexpected information in the signal. These parameter changes enable accurate detection with minimal computational effort for each specific anomaly type.
3Reliability
If the system compensates for both periodic and random interference, then signal reliability improves, but device complexity increases
Solution Approach 1:
The patent segments the compensation function into two specialized detectors: a divergence detector for periodic interference compensation and an information detector for random event compensation. Each detector is optimized for its specific interference type, using straightforward algorithms (sequential probability ratio tests for divergence, information content calculation for random events). This segmentation achieves comprehensive compensation for both interference types while keeping individual detector structures simple and manageable.
Solution Approach 2:
The patent creates a universal anomaly detection framework that handles both periodic and random interference through a common architectural structure. The output circuitry receives inputs from both detectors and provides unified compensation, making the system multi-functional without requiring separate complex processing paths. This universal approach improves signal reliability across different interference types while avoiding the complexity of entirely separate compensation systems.
Data Source
AI summary
A signal analyzer includes a divergence detector for detecting periodic interference in a signal, an information detector for detecting a random event in the signal, and output circuitry for providing compensation for the periodic interference and the random event.