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Radar signal modulation type classification method based on hybrid network

A technology of radar signal and modulation type, applied in the field of radar signal detection, can solve the problems of low efficiency, achieve the effect of simplifying the training model, reducing the probability of false alarm and missing alarm, and reducing the time

Pending Publication Date: 2021-05-28
HOHAI UNIV
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  • Claims
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the low efficiency of the radar signal modulation type classification method in the prior art, and provide a kind of radar signal modulation type classification method based on the hybrid network. The technical scheme is as follows:

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  • Radar signal modulation type classification method based on hybrid network
  • Radar signal modulation type classification method based on hybrid network
  • Radar signal modulation type classification method based on hybrid network

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Embodiment 1

[0051] Such as figure 1 As shown, a hybrid network-based classification method for radar signal modulation types includes the following steps:

[0052] The radar signal is input into a pre-built modulation type classification model; the radar signal involved in this embodiment includes SF, BPSK, LFM, OFDM, NLFM and stepped frequency signals.

[0053]Construct data samples: Generate SF, BPSK, LFM, OFDM, NLFM and step frequency signals according to the parameter settings in Table 1, and construct 3000 data samples in total. This embodiment mainly changes the parameters of the symbol rate (CR), signal bandwidth (BW), signal-to-noise ratio (SNR), frequency difference (FD), etc., without changing the sampling frequency (FS), time width (TW) and Carrier frequency (CF) and other parameters, thereby increasing the complexity of data samples, close to real scene applications. The parameter setting method in this embodiment can generate data samples based on the ergodicity of various ...

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Abstract

The invention discloses a radar signal modulation type classification method based on a hybrid network. The method comprises the following steps: inputting a radar signal into a pre-constructed modulation type classification model fused with a deep learning hybrid network; and enabling the modulation type classification model to output a fuzzy function feature plane, and classifying the radar signals based on the fuzzy function feature plane. The modulation type classification model comprises the following steps: extracting an SF signal in a radar signal; extracting a BPSK signal in the radar signal; constructing a feature plane of the extracted unclassified radar signals; inputting the feature plane into a fusion deep learning hybrid network, and obtaining a fuzzy function diagram of unclassified radar signals; and based on the fuzzy function diagram, classifying the extracted unclassified radar signals. The classification method has good classification characteristics, the sample size of deep learning algorithm training is reduced, the training model is simplified, and the training data size is reduced, so that the training complexity is reduced.

Description

technical field [0001] The invention relates to a radar signal modulation type classification method, in particular to a radar signal modulation type classification method based on a hybrid network, belonging to the technical field of radar signal detection. Background technique [0002] With the rapid development of modern radio technology, a variety of radar radiation sources with variable parameters and complex forms are widely used, making it more and more difficult to accurately analyze the characteristics of radar signals. The parameters of the radar signal are designed manually. In the traditional signal recognition method, the feature extraction and selection links are generally designed by manual experience. Some signals have significant and reliable features. frequency, which can be used to achieve accurate classification of signals. However, these features can only achieve good results in some specific objects, and do not have certain universality. For complexly...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41
CPCG01S7/417G01S7/418
Inventor 王峰杨晨璐
Owner HOHAI UNIV
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