Connected region labeling-based frequency hopping signal dynamic clustering extraction method

Abstract

The invention discloses a connected region labeling-based frequency hopping signal dynamic clustering extraction method. The method comprises the following steps that: a received signal model is constructed; a morphological method is adopted to perform de-noising and segmentation processing on received signals; connected region labeling is performed on a processed image; the lasting durations of connected region labeling signals and labels corresponding to the lasting durations of the connected region labeling signals are dynamically clustered; secondary dynamic clustering is performed on the appearing time points of the connected region labeling signals and the labels corresponding to the appearing time points of the connected region labeling signals according to a dynamical clustering result; frequency hopping signal detection and extraction are performed according to a dynamical clustering result; and frequency hopping signal verification and quality evaluation extraction are performed on the extracted signals, and a frequency hopping energy diagram can be obtained. With the connected region labeling-based frequency hopping signal based dynamic clustering extraction method adopted, accurate extraction of the energy diagram of required frequency hopping signals can be realized when low-signal noise ratio strong interference exists, and different-time duration interference of which the period is identical with a hopping period exists, or another kind of frequency hopping signals of which the period is identical with the period of required frequency hopping signals exist.

Description

[0001] technology neighborhood [0002] The invention belongs to the field of communication technology, in particular to a method for dynamic clustering and extraction of frequency hopping signals based on connected area marks. Background technique [0003] Frequency hopping communication is widely used in modern military affairs. As a third party of non-cooperative communication, it is particularly critical to extract frequency hopping signals. However, there are interferences of various lengths and strong power noise in the electromagnetic environment. Therefore, in the How to accurately extract the frequency hopping signal is one of the research focuses of modern communication theory when the signal-to-noise ratio is low and when there is interference with the same period and period hopping. [0004] The frequency of the frequency hopping signal keeps changing randomly over time. For the research on frequency hopping signal extraction, in recent years, most extraction metho...

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Problems solved by technology

[0004] The frequency of the frequency hopping signal keeps changing randomly over time. For the research on frequency hopping signal extraction, in recent years, most extraction methods only remove burst and fixed-frequency interference based on the duration, and frequency sweep also removes it again based on the bandwidth. , and the signal-to-noise ratio of these methods to achieve correct extraction is relatively high, considering the extraction when only one frequency hopping signal is included, and the interference with the same period as the hopping period cannot be removed during extraction, and the morphological denoising is of great importance to The uneven part generated at the edge of the fixed-frequency signal and the different burst signals generated in the same line after processing, and even the fixed-frequency interference with power fluctuations are added. The traditional method counts line by line, and cannot be completely removed according to the length of time. , and the duration threshold needs to know the approximate hopping period, which will seriously affect the extraction quality of the frequency hopping signal, and there will still be interference that cannot be removed, and the extraction in the existing literature is only to obtain the binary image of the frequency hopping signal, while To obtain the frequency hopping signal energy map requires a high signal-to-noise ratio, and the denoising effect is not ideal

Moreover, the clustering mentioned in the literature does not implement dynamic clustering, that is, it needs to know several types of signals in advance, and after clustering, it only shows that the extracted feature quantities belong to the same category, and does not track the specific signal corresponding to the feature quantity , so it does not mean that this class is all frequency hopping signals, and the extracted signals are not verified for the required frequency hopping signals, and the extraction quality evaluation is not given. In the second clustering, this paper fully considered Therefore, it is difficult to know how many types of signals are contained in the signal received in a complex environment, which contains interference with the same period as the hopping period, and contains more than two frequency hopping signals with close hopping periods at low In the case of strong signal-to-noise ratio interference, the existing methods cannot accurately extract the energy map of any frequency hopping signal

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