Radar radiation source identification method based on one-dimensional CNN and LSTM
An identification method and radiation source technology, applied in the field of radar radiation source identification and signal processing, can solve the problems of low identification accuracy and complex calculation, and achieve the effects of high identification rate, reduced complexity, and high real-time performance.
Pending Publication Date: 2020-12-22
XIDIAN UNIV
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[0006] The purpose of the present invention is to address the deficiencies in the prior art above, to propose a radar radiation source identification method based on a one-dimensional CNN and LSTM network, to solve the problems of complex calculation and low recognition accuracy of the existing radar radiation source identification method
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[0027] The present invention will be described in further detail below in conjunction with the accompanying drawings.
[0028] Refer to attached figure 1 , to further describe in detail the specific steps of the present invention.
[0029] Step 1, construct the CNN local feature extraction module.
[0030] Build a 6-layer CNN local feature extraction module, and its structure is as follows: first convolutional layer → first pooling layer → second convolutional layer → second pooling layer → third convolutional layer → third pooling Floor.
[0031] Set the number of convolution kernels in the first to third convolutional layers to 32, 32, and 64 respectively, and the size of the convolution kernels to 4×1, 3×1, 3×1 respectively, and the step size is set to 1. The activation function is the eLU function. The first to third pooling layers all adopt the maximum pooling method. The size of the pooling area core is set to 5×1, 4×1, 4×1 respectively, and the step size is set to 4....
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The invention discloses a one-dimensional CNN and LSTM radar radiation source identification method. The method comprises the following steps: (1) constructing a CNN local feature extraction module; (2) constructing an LSTM global feature extraction module; (3) connecting the local feature extraction module and the global feature extraction module to form a radar radiation source identification network; (4) generating a training set; (5) training a radar radiation source identification network; and (6) identifying the radar radiation source identification sample. The radar radiation source identification network constructed by the invention can directly perform feature extraction on the radar radiation source time domain signal without dimension transformation, and has good real-time performance. Meanwhile, the CNN module and the LSTM module are adopted to extract the local features and the global features of the signals, feature extraction is more sufficient, the recognition rate is increased, and the method has better real-time performance and anti-noise performance.
Description
technical field [0001] The present invention belongs to the technical field of signal processing, and further relates to a radar radiation source based on a one-dimensional convolutional neural network CNN (Convolution Neural Network) and a long-short-term memory network LSTM (LongShort-Term Memory) in the technical field of radar radiation source identification recognition methods. The invention can be used in electronic intelligence reconnaissance, electronic support and threat warning systems to identify received radar radiation source signals. Background technique [0002] Radar radiation source identification technology is a crucial part of electronic warfare, the core of electronic support and the key technology in radar countermeasure system. The radiation source identification technology provides the basis for the next step of situation estimation, threat estimation and decision-making adjustment by obtaining information such as the system, use, and model of the tar...
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Login to View More IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/045G06F2218/12G06F2218/08
Inventor 武斌殷雪凤李鹏张葵王钊荆泽寰袁士博
Owner XIDIAN UNIV