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Use of MeVMaNS-Statistic as a Basis for the Panoramic Detection Threshold Estimation


A.B. Tokarev

Detection of signals is one of the basic, classical problems of radio engineering. Its relevance is explained by the variety of situations and the optimization criteria, corresponding to detection implementation. Present work analyses the features of signals detection, applyed to the radio monitoring systems and servers. Radio monitoring systems are oriented to detect arbitrary radio emissions, so the main used way of detection is the threshold energy method. With a panoramic detection of signals it is required to adapt the detection threshold to the noise level in the analyzed frequency range. It is expedient to use maximum noise samples of the received broadband radiation discrete spectrum for the threshold estimation. Before signals detection is finished, the locations of signals and noise on the frequency axis are unknown. That's why during calculation of the threshold we need to search the areas on the frequency axis, where the probability of signals existence is minimal. MeVMaNS-statistics – is the mean value of the maximum noise spectrum samples. If the frequency axis usage can reach 50...70 %, it is recommended to choose the window width for the MeVMaNS-statistics calculation equal to 18 samples. To achieve the best balance between browsing speed range and quality detection it is expedient to use the radio monitoring hardware with the frequency resolution not less than 4 spectrum samples per each radio channel. If the size of the spectrum array will be equal or more than 1000 samples, it will be possible to correctly implement the detection of signals for the values of frequency axis usage up to 50 %. The threshold calculation, based on MeVMaNS-statistic, allows you to increase the stability of performance with inexactly known parameters of the formation of discrete spectrum, however, the presented single-pass algorithm do not guarantee the constancy of the false detection probability. To improve the quality of detection it is necessary to explore more flexible double-pass algorithm.
June 24, 2020
May 29, 2020

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