A Method of Extracting and Filtering Electromagnetic Spectrum Noise Based on Chi-Square Test

Abstract

The invention provides an electromagnetic spectrum noise extraction and filtering algorithm based on a chi-square test. The algorithm determines noise distribution parameters through quantitative and qualitative multi-standards, can process and distinguish these singular samples, and the processing accuracy is the same as that of normal samples. That is, after the signal data is input to the external device, noise extraction and filtering are realized through this algorithm, which provides data processing preparation for the subsequent separate output and storage of noise and signal data, and provides technical support for the monitoring and analysis of signal time domain and frequency spectrum.

Description

technical field [0001] The invention relates to algorithms related to theories of sample statistics, parameter estimation, noise extraction and filtering in complex signals, and in particular to an electromagnetic spectrum noise extraction and filtering algorithm based on a chi-square test. Background technique [0002] With the rapid development of electronic technology, the electromagnetic spectrum environment is becoming more and more complex, and the monitoring and management requirements for some civil electromagnetic spectrum are also getting higher and higher. In complex environments, it is difficult to predict the parameters of signals that will be received in the future. In the absence of prior information, both signal and noise have random uncertainty. Non-civilian systems have higher signal discrimination requirements and faster processing speed requirements, and a quick inspection and separation of these signals is required. [0003] In radio monitoring, the ti...

AI Technical Summary

Problems solved by technology

The problem with processing such signals is that the only known condition is that white noise obeys a Gaussian distribution in the time domain or frequency domain, so it is very difficult for the algorithm to achieve noise extraction and filtering at the same time

Some existing methods can only extract the mean value only with large errors, complex calculations, and cannot obtain the variance of the specific random distribution of noise

Some filtering algorithms cannot extract noise information while filtering

The existence of singular samples will cause great deviations in the processing results of quantitative algorithms, and existing algorithms cannot distinguish and deal with singular samples

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