Signal Analyzer Anomaly Detection via ML Background Analysis
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Solution Overview
Problem
Existing signal analysis tools, such as oscilloscopes, face challenges in efficiently detecting and storing signal anomalies like glitches, as setting a suitable trigger can be difficult and requires user intervention, often causing interruptions during normal work.
Innovation Solution
A signal analyzer with a digitizing unit, trigger detection unit, acquisition unit, and anomaly search unit that detects and stores signal anomalies in the background using machine learning algorithms, allowing for later analysis without precise trigger settings, and displaying detected anomalies for user access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If traditional trigger function is used to detect signal anomalies, then anomaly detection capability is improved, but user work efficiency deteriorates due to frequent interruptions and difficulty in setting suitable triggers
Solution Approach 1:
The anomaly search unit automatically analyzes stored signal segments to detect anomalies without requiring user intervention. The system performs background analysis of acquired signal segments, automatically identifying anomalies and presenting results to the user, thereby eliminating the need for users to manually search for anomalies and interrupting their workflow minimally.
Solution Approach 2:
The acquisition unit stores signal segments in advance based on trigger events before anomaly analysis is performed. By pre-acquiring and storing relevant signal segments in memory, the system prepares data for subsequent anomaly detection, enabling efficient analysis without requiring users to wait or interrupt their work.
2Measurement precision
If precise trigger settings are used to detect specific anomalies, then detection accuracy is improved, but ease of operation deteriorates due to difficulty in configuring appropriate triggers
Solution Approach 1:
The anomaly search unit automatically performs comprehensive analysis of stored signal segments without requiring users to manually configure precise trigger settings. The system independently identifies various anomaly types through automated algorithms, eliminating the complexity of trigger configuration while maintaining high detection accuracy across multiple anomaly types.
Solution Approach 2:
The anomaly search unit is designed to detect multiple types of signal anomalies simultaneously using a single automated analysis process. Rather than requiring separate trigger configurations for different anomaly types, the universal anomaly detection capability analyzes all stored segments for various anomaly patterns, simplifying operation while maintaining precision.
3Adaptability or versatility
If multiple trigger configurations are loaded simultaneously to increase detection coverage, then anomaly detection coverage is improved, but device complexity increases
Solution Approach 1:
The anomaly search unit automatically performs comprehensive analysis of all stored signal segments without requiring users to load or manage multiple trigger configurations. The automated system independently determines which segments contain anomalies, providing broad detection coverage while eliminating the complexity of managing multiple trigger settings.
Data Source
AI summary
The invention relates to a signal analyzer, comprising a signal receiving unit configured to receive a signal, in particular a radio frequency (RF) signal, a digitizing unit configured to digitize the received signal, and a trigger detection unit configured to detect a trigger event in the digitized signal. The signal analyzer further comprises an acquisition unit configured to store a segment of the digitized signal in a memory of the signal analyzer if the trigger detection unit detects the trigger event in the digitized signal, and an anomaly search unit configured to analyze the stored segment of the digitized signal in order to detect signal anomalies, in particular glitches, in the stored segment of the digitized signal.