Radar signal identification and positioning method based on MobileNet model transfer learning

A radar signal, transfer learning technology, applied in radio wave measurement systems, instruments, etc., can solve the problems of lack of interpretability, inability to intuitively analyze the working principle, lack of trust in deep learning models and intelligent systems, etc., to improve explainability. properties and transparency, improving convergence speed and generalization performance, and improving computational efficiency

Pending Publication Date: 2022-02-08
HARBIN ENG UNIV
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Problems solved by technology

[0004] Although the current convolutional neural network has made unprecedented breakthroughs in many fields, deep learning models are often regarded as black boxes because they lack proper in

Method used

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  • Radar signal identification and positioning method based on MobileNet model transfer learning
  • Radar signal identification and positioning method based on MobileNet model transfer learning
  • Radar signal identification and positioning method based on MobileNet model transfer learning

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Embodiment 1

[0089] refer to figure 1 , is a flow chart of the radar signal modulation identification and localization algorithm system based on the MobileNet transfer learning model and gradient-weighted class activation mapping.

[0090] Step 1: Refer to figure 2 , the radar signal is converted into a two-dimensional time-frequency image through time-frequency analysis through Choi-Williams time-frequency distribution, and a training set and a test set are generated. The debugging methods of radar signals include: linear frequency modulation (LFM), BPSK, Frank code, Costas code, P1 code, P2 code, P3 code, P4 code signal;

[0091] Step 2: Refer to image 3 , build a depthwise separable convolution module by using spatial convolution and channel convolution;

[0092]Step 2-1: Depthwise separable convolution consists of two layers: depthwise convolution and channel convolution. Use depthwise convolution to apply a single convolution on each input channel (input depth). Then, channel c...

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Abstract

The invention belongs to the technical field of radar signal modulation mode identification, and particularly relates to a radar signal identification and positioning method based on MobileNet model transfer learning. According to the method, based on MobileNet model transfer learning and gradient weighted class activation mapping, the network model is built by using depth separable convolution, so that model parameters can be effectively reduced, and the calculation efficiency of the model is improved; transfer learning training is carried out by loading the pre-training model in the training process, so that the convergence speed and generalization performance of the model can be improved; and meanwhile, a prediction result of the network model is visualized by adopting a gradient weighted category activation mapping method, so that the interpretability and transparency of the deep learning model are improved.

Description

technical field [0001] The invention belongs to the technical field of radar signal modulation mode identification, and in particular relates to a radar signal identification and positioning method based on MobileNet model transfer learning. Background technique [0002] Traditional radar signal recognition algorithms use manually extracted features to classify signals, such as high-order cumulants, cyclostationary features, distribution distances, probability distances, spectral correlations, autocorrelation functions, and time-frequency image features. These features work well for specific radar signals, but the method is computationally complex, has high development costs, and lacks flexibility and universality. In recent years, with the continuous improvement of computer hardware level, deep learning has achieved remarkable results in computer vision and other fields because of its excellent performance. At the same time, deep learning has also produced a large number o...

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Application Information

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IPC IPC(8): G01S7/41
CPCG01S7/418
Inventor 司伟建骆家冀邓志安张春杰
Owner HARBIN ENG UNIV
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