A real-time dynamic generation method and system for an adaptive signal search threshold

Abstract

The present disclosure provides a method and system for dynamically generating an adaptive signal search threshold in real time. Among them, the real-time dynamic generation method of the adaptive signal search threshold includes performing mobile weighted smoothing processing on the current segment of the spectrum trajectory data to obtain a smooth curve; searching for all peak points in the smooth curve, and sorting them according to their numerical values ​​to form a peak point sequence; Filter out the minimum and maximum values ​​of the peak point sequence, and then divide the filtered peak point sequence into noise sequence and signal sequence according to the first scale factor; calculate the estimated values ​​of noise and signal respectively; estimate the signal After making a difference between the value and the estimated value of the noise, it is multiplied by the second scale factor to obtain an adjustment value, and finally the adjustment value is superimposed on the estimated value of the noise to obtain the detection threshold value of the current segment spectrum trace data; The smooth curve corresponding to the current spectrum trace data is compared with the corresponding detection threshold value to generate a corresponding threshold curve.

Description

technical field [0001] The disclosure belongs to the field of spectrum monitoring, in particular to a method and system for real-time dynamic generation of an adaptive signal search threshold. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Searching the power spectrum can more intuitively reflect the distribution of the electromagnetic spectrum in a certain frequency band. Under certain conditions, the human eye can quickly and intuitively distinguish a large number of signals. But in the case of automatic signal search, it is not easy to set the appropriate decision threshold. Threshold selection will directly affect the effectiveness of signal search and the accuracy of parameter measurement. In order to adapt to the complex electromagnetic environment, the selection of the threshold should not only consider the adaptive change with ...

AI Technical Summary

Problems solved by technology

However, in a complex electromagnetic environment, it has serious shortcomings: in a complex electromagnetic environment, the surrounding electromagnetic environment changes rapidly, channel transmission interference is uncertain, and signal attenuation and other reasons will cause the received signal to be unstable. With different signal-to-noise ratios, the energy value will also decrease, which increases the difficulty of signal detection.

[0008] 2) When calculating the threshold of this section, although the average value of signal and noise is considered, and a weighting factor is used in the calculation, this puts forward higher requirements for the technical level and experience of the operator

And each signal is formed by a piece of continuous data, so there will be such a phenomenon: that is, a small value in the large signal will be larger than the peak point of the small signal, resulting in , the peak point of this small signal is not included in the calculation of the estimated value of the signal

As a result, the detection threshold is higher than this small signal, so that the subsequent signal search algorithm cannot detect the small signal, resulting in the problem of missing alarms

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