Automatic signal modulation and classification method and system based on K-means

A classification method and classification system technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of increasing the difficulty of classification, not paying attention to the expansion of feature extraction methods, and not considering the various differences of signals

Inactive Publication Date: 2019-11-12
HOHAI UNIV
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Problems solved by technology

[0005] (3) Multiple differences between signals are not considered, multiple different features are extracted, and multi-signal classification is realized based on only one feature, which increases the difficulty of classification
[0006] (4) The feature extraction method is relatively simple, and there is no focus on the expansion of feature extraction methods

Method used

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  • Automatic signal modulation and classification method and system based on K-means
  • Automatic signal modulation and classification method and system based on K-means
  • Automatic signal modulation and classification method and system based on K-means

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Embodiment

[0143] Computer simulation is used to verify the recognition performance of the multi-signal classification algorithm. In order to generate data samples based on the ergodicity of parameters, signals under different parameter conditions are generated in the training and testing stages, and feature extraction is performed to achieve classification. In order to prevent too much sample size caused by too many parameter changes, and incomplete sample coverage caused by too few parameter changes, this paper mainly considers the changes in parameters such as signal-to-noise ratio, bandwidth, and symbol rate that have a greater impact on classification. Parameters such as sampling frequency and carrier frequency that have little influence remain unchanged.

[0144] Generate SF, LFM, BPSK, QPSK, 16QAM, and 2FSK signals according to the parameter settings shown in Table 1, and implement the first-level classification based on the naive Bayesian algorithm and the standard deviation of t...

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Abstract

The invention discloses an automatic signal modulation and classification method and system based on K-means. Modulation characteristics of the radar and communication signals are extracted by using signal processing algorithms such as time-frequency analysis, instantaneous autocorrelation and Fourier transform, and layer-by-layer classification of the radar and communication signals of differentmodulation types is realized based on a naive Bayes and K-means machine learning algorithm. Simulation results show that the multi-signal classification algorithm network can effectively realize signal classification of six different modulation types.

Description

technical field [0001] The invention belongs to the technical field of radar and electronics, and in particular relates to a K-means-based automatic signal modulation classification method and system. Background technique [0002] Cognitive radar needs to realize the perception of the external electromagnetic environment, including the classification and identification of other radar and communication signals detected. At present, the following problems exist in the field of classification and identification of modulation types of radiation source signals: [0003] (1) When setting parameters to generate signals, the influence of some parameters on the classification performance of the algorithm is not considered, resulting in a small sample size. [0004] (2) Only one type of modulation signal is considered, but the actual radar emitter signal has multiple modulation types. [0005] (3) Multiple differences between signals are not considered, multiple different features a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/12G06F18/23213
Inventor 王峰杨晨璐
Owner HOHAI UNIV
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