Rohde & Schwarz has patented a signal analyzer that can receive and digitize signals, detect trigger events, store segments in memory, and analyze for anomalies like glitches. The display shows stored segments, detected anomalies, and timestamps. Detection is based on deviations from known signal spectrum and cyclical signal shape. GlobalData’s report on Rohde & Schwarz gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Rohde & Schwarz, Wireless network optimization was a key innovation area identified from patents. Rohde & Schwarz's grant share as of January 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
Signal analyzer for detecting and analyzing signal anomalies
A recently granted patent (Publication Number: US11885832B2) discloses a signal analyzer designed to receive and digitize signals, particularly radio frequency (RF) signals. The analyzer includes processors that can detect trigger events in the digitized signal based on preset signal shapes, store segments of the signal in memory upon detection of trigger events, and analyze stored segments for anomalies such as glitches. The display of the analyzer can show a list of stored segments, detected anomalies, and timestamps of these anomalies. The processors are further configured to detect anomalies by comparing the digitized signal to a known signal spectrum and cyclical signal shape.
Moreover, the patent claims describe the use of machine learning algorithms, including trained neural networks, to enhance anomaly detection in the stored segments of the digitized signal. The method outlined in the patent involves receiving and digitizing the signal, detecting trigger events based on signal shape, storing segments in memory, analyzing for anomalies, and displaying results. The anomalies are identified by deviations from known signal spectra and cyclical shapes, with the option to determine trigger types and parameters using neural networks. Additionally, the method includes detecting anomalies based on frequent changes in signal levels above or below a threshold value, providing a comprehensive approach to signal analysis and anomaly detection in RF signals.
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