Radar signal classification method based on QMFB and convolutional neural network

A convolutional neural network and radar signal technology, applied in the field of electronic countermeasures, can solve problems such as unsatisfactory results and inability to meet the actual needs of electronic countermeasures, and achieve the effect of improving classification efficiency and recognition rate, accuracy and stability

Inactive Publication Date: 2019-01-01
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0006] The problem to be solved by the present invention is that: there are many types of LPI radar signals, and many types have common signal characteristics, with o

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  • Radar signal classification method based on QMFB and convolutional neural network
  • Radar signal classification method based on QMFB and convolutional neural network
  • Radar signal classification method based on QMFB and convolutional neural network

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[0027] The technical solutions in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described examples are only some examples of the present invention, not all examples. Based on the examples in the present invention, all other examples obtained by those skilled in the art without creative efforts belong to the protection scope of the present invention.

[0028] Such as figure 2 As shown, the main steps of this embodiment include: the first step, LPI radar simulation signal generation; the second step, QMFB processing signal data; the third step, constructing a new CNN; the fourth step, using the generated simulation data set to train the new CNN ; The fifth step, the signal classification result output. The specific implementation steps are as follows:

[0029] Step 1, LPI radar simulation signal generation:

[0030] Step 1.1: Under 7 signal-to-noise ratios of -6dB, -4dB, -2dB, 0dB, 2dB, 4dB,...

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Abstract

A modern radar generally uses a low probability intercept (LPI) radar signal to detect a target so that LPI signal classification is needed in radar countermeasures. Currently, a traditional artificial characteristic extraction algorithm is used to classify, and during practical application, the classification correct rate of the algorithm is not ideal. The invention provides a classification method based on the combination of a quadrature mirror filter group and a convolutional neural network. The method comprises the following steps of firstly, carrying out QMFB processing on an interceptedradar signal to obtain radar layered time frequency images; then, based on the novel convolutional neural network, classifying the radar layered time frequency images; and finally, outputting a LPI radar signal classification result. In the invention, based on the convolutional neural network, a plurality of LPI signal characteristics can be automatically extracted, and compared with the traditional algorithm, the method of the invention has improved classification efficiency and an improved identification correct rate.

Description

technical field [0001] The invention is a technology in the field of electronic countermeasures, in particular a method for sorting LPI radar signals based on a convolutional neural network. Background technique [0002] Modern radars are increasingly using low probability of intercept (Low Probability Intercept, LPI) radar signals to detect targets. LPI signals can greatly improve the survival and detection capabilities of modern radars in modern electronic warfare. Used in various types of radar. The LPI radar signal is a special radar waveform signal that prevents non-cooperative radar reconnaissance receivers from intercepting and detecting the signal type. In order to jam and suppress radars with LPI signals, it is necessary to classify the intercepted LPI radar signals to provide decision-making basis for electronic jamming and suppression measures. Therefore, it is an important content in the current research of radar countermeasures to classify the intercepted rada...

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Application Information

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IPC IPC(8): G01S7/38G01S7/02G06K9/00
CPCG01S7/021G01S7/38G06F2218/06G06F2218/08G06F2218/12
Inventor 郭磊林滋宜王秋然张克乐
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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