Method of Complex Envelope Processing for Signal Edges Detection
DOI:
https://doi.org/10.20535/RADAP.2023.92.54-59Keywords:
signal edges, complex envelope, moving average, threshold, signal structure, signal-to-noise ratioAbstract
Problem statement. The need of information processing automation in modern radio monitoring systems stimulates development of flexible methods for signal detection and its parameters estimation in time domain. A priori uncertainty of signal time-frequency structure complicates the automatic determination of signals edges. Purpose. The purpose of the article is subsequent automation of radio frequency spectrum analysis process by developing and implementing a method for determining signals time edges under conditions of a known noise power and signal-to-noise ratio. Method. To determine signal time edges in given frequency channel, square of signals’ complex envelope is first calculated, smoothed with moving average window and compared with threshold. Threshold is calculated as a quantile of gamma distribution using Wilson-Hilferty approximation of χ2 distribution quantiles for a given probability of false alarm. An analytical expression is obtained for calculation length of moving average window depending on signal-to-noise ratio. An algorithm has been developed for determining signals’ time parameters and filtering them by duration. Unknown noise power value in frequency channel can be replaced by its estimate under the assumption that frequency channel is not constantly occupied and noise level is estimated on signal-free time intervals. Conclusions. Proposed method makes it possible to automatically determine edges of signal with an arbitrary structure at signal-to-noise ratio values from -6 dB. Adjustable length of moving average window makes it possible to reduce the error in determining signal time parameters by 2-4 times with an increase in the signal-to-noise ratio compared to a fixed window length. Prospects for further research in this direction should be focused on development and implementation of methods for detection signal edges under conditions of an unknown noise level.
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