A Hybrid Machine Learning Signal Classification Method Based on PCA Dimensionality Reduction

A machine learning and signal classification technology, applied in the field of communication, can solve the problems of low degree of automation, poor classification effect, insufficient adaptability to unknown situations, etc., and achieve the effect of good classification effect and high degree of automation

Active Publication Date: 2021-11-30
HOHAI UNIV
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AI Technical Summary

Problems solved by technology

Conventional intrapulse analysis methods use threshold judgment based on manual experience, which is not highly automated and has insufficient adaptability to unknown situations. Due to limited training samples, manual experience thresholds may have poor classification results.

Method used

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  • A Hybrid Machine Learning Signal Classification Method Based on PCA Dimensionality Reduction
  • A Hybrid Machine Learning Signal Classification Method Based on PCA Dimensionality Reduction
  • A Hybrid Machine Learning Signal Classification Method Based on PCA Dimensionality Reduction

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Experimental program
Comparison scheme
Effect test

Embodiment

[0137] The classification and recognition performance of multi-class radar and communication signals is verified by MATLAB simulation, including two stages of training and testing.

[0138] In the training phase, the signal-to-noise ratio of the four signals is set to 20dB, and the symbol width is 10 -6 s, the number of sampling points is 5000, the sampling frequency is 100MHz, the carrier frequency is 20MHz, the chirp bandwidth is 10MHz, the time width is 50us, and the frequencies of 2FSK are 10MHz and 20MHz respectively.

[0139] In the first level of training, the four signals are trained using the method based on instantaneous autocorrelation. The instantaneous autocorrelation processing results of the four signals are as follows: Figure 4 , 5 , 6, and 7. After the instantaneous autocorrelation loop processing, the number of zero-crossing points of the four signals is extracted 50 times each, and the results are as follows Figure 8 As shown, it can be seen that the nu...

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Abstract

The invention discloses a hybrid machine learning signal classification method based on PCA dimensionality reduction, comprising the following steps: Step 1, for linear frequency modulation signal LFM, binary phase shift keying BPSK signal, binary frequency shift keying 2FSK and four-phase For phase-shift keying QPSK signals, according to the instantaneous autocorrelation classification method, set the zero-crossing threshold and standard deviation threshold to separate the LFM signal, QPSK signal from other signals; step 2, the second-level classification, for the remaining signal BPSK The signal and the 2FSK signal adopt three characteristics of normalized amplitude duty cycle, normalized central instantaneous phase absolute value variance and normalized central instantaneous frequency absolute value variance, and use principal component analysis PCA algorithm to realize feature dimensionality reduction; steps 3. The objective function of the optimal classification is obtained by using the SVM classifier, and the distinction between BPSK and 2FSK signals is realized. The invention adopts the machine learning technology to realize classification, has high degree of automation and good classification effect.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a hybrid machine learning signal classification method based on PCA dimensionality reduction. Background technique [0002] Radar and electronic warfare systems need to automatically learn to recognize the electromagnetic signals they detect. The intra-pulse characteristics of radar signals are important parameters for electromagnetic signal sorting and identification in radar and electronic warfare systems. Therefore, in order to reliably sort and identify radar signals, it is necessary to analyze the intra-pulse features of radar signals. Conventional intrapulse analysis methods use threshold judgment based on manual experience, which is not highly automated and has insufficient adaptability to unknown situations. Due to limited training samples, manual experience thresholds may have poor classification results. Contents of the invention [0003] In view of the above-...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/12G06F18/2135
Inventor 王峰黄珊珊
Owner HOHAI UNIV
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