Radio frequency interference suppression and classification method based on convolutional neural network

Abstract

The invention discloses a radio frequency interference suppression and classification method based on a convolutional neural network. The method comprises: initializing a transmission signal parameter, a target signal parameter and an interference signal parameter of an SAR system, calculating a target signal and an interference signal, superposing the interference signal on the target signal to obtain affected echo signals, and marking an interference type of the interference signal in each echo signal; carrying out discrete Fourier transform on the echo signals to obtain frequency domain forms of the echo signals, and carrying out classification based on a proportion to obtain a training set and a testing set randomly; inputting the training set into a convolutional neural network VGG16for training to obtain a test network; and inputting the testing set in the test network, and verifying a classification result of the testing set by the test network. Therefore, the classification and recognition of the interference signals by the echo signals are realized under the circumstances that the synthetic aperture radar works normally and the key parameters such as the signal parameter,the imaging range and the resolution are not changed.

Description

technical field [0001] The invention relates to the technical field of synthetic aperture radar signal processing, in particular to a convolutional neural network-based radio frequency suppression interference classification method. Background technique [0002] Synthetic Aperture Radar (SAR) is an active high-resolution microwave imaging system with all-day and all-weather working capabilities. Compared with other shorter-wavelength SAR systems, low-frequency (such as L, P-band) SAR systems have stronger penetrating capabilities, and can obtain target information covered by vegetation and buried on the shallow surface. Therefore, such systems have great advantages in applications such as battlefield reconnaissance, forest monitoring, and resource surveying. However, SAR systems are often interfered by wireless communication signals, closed-circuit television network signals, and wireless signals emitted by other radar systems. This type of interference is generally called ...

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Problems solved by technology

[0004] The problem with the above-mentioned interference classification method is that the interference classification must be completed by setting a reasonable threshold, which makes the signal processing system achieve better classification results only when it has accurate prior information; however, in practice, first However, the lack of experimental information and the inevitable systematic error will make the accuracy of this method difficult to meet the requirements.

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