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Maximum likelihood modulation recognition method based on feature vectors in multi-sensor reception

A technology of eigenvector and modulation identification, applied in modulation type identification, modulation carrier system, transmission system, etc., can solve the problems of low reliability and low recognition accuracy, and achieve the effect of improving receiving gain

Active Publication Date: 2017-02-22
THE PLA INFORMATION ENG UNIV
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Problems solved by technology

[0005] The present invention aims at the problems of low recognition accuracy and low reliability of signal modulation modes in the application fields such as communication signal monitoring in a non-cooperative receiving environment, and proposes a maximum likelihood modulation recognition method based on eigenvectors in multi-sensor reception

Method used

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  • Maximum likelihood modulation recognition method based on feature vectors in multi-sensor reception
  • Maximum likelihood modulation recognition method based on feature vectors in multi-sensor reception
  • Maximum likelihood modulation recognition method based on feature vectors in multi-sensor reception

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Embodiment 1

[0030] Example 1: Combining Figure 1-Figure 4 , the present invention aims at the problem of using eigenvectors to construct likelihood functions for maximum likelihood modulation recognition in the case of multi-sensor non-cooperative reception. Taking the set of signal modulation types {BPSK, QPSK, 8PSK} to be identified as an example, when the number of sensors is greater than 5, the number of symbols is greater than 300, and the signal-to-noise ratio is greater than 2dB, the recognition rate can reach 100%.

[0031] The flow of the maximum likelihood modulation recognition method based on eigenvectors in multi-sensor reception, such as figure 1 shown, including the following steps:

[0032] Step 1: Each sensor sub-node estimates the signal-to-noise ratio according to the received signal, extracts the recognition features, and constructs the recognition feature vector (F 1 , F 2 ,...,F N ); Step 2: The master node constructs a likelihood function based on the recogniti...

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Abstract

The invention belongs to the technical field of communication signal modulation recognition in a multi-sensor non-cooperative reception environment and particularly relates to a maximum likelihood modulation recognition method based on feature vectors in multi-sensor reception. The method comprises the following steps that step 1.each sensor sub-node estimates a signal-to-noise ratio, extracts modulation recognition features and configures recognition feature vectors according to respective receiving signals; step 2.a likelihood function based on the recognition feature vectors is configured on a major node; and step 3.maximum likelihood judgement is carried out in a to-be-recognized signal modulation type set at the major node according to the configured likelihood function, so that a recognition result is obtained. The method adopts the multi-sensor reception signals, extracts the recognition feature vectors and designs a classifier, so that the problems of accuracy and reliability of the communication signal modulation recognition under the band conditions in a mobile communication, short-wave communication or underwater acoustic communication process are solved.

Description

technical field [0001] The invention belongs to the technical field of identification of communication signal modulation modes in a multi-sensor non-cooperative receiving environment, and in particular relates to a maximum likelihood modulation identification method based on eigenvectors in multi-sensor reception. Background technique [0002] In the process of mobile communication, short-wave communication or underwater acoustic communication, due to the multipath effect, the receiving point receives the vector superposition of multiple path signals, and the quality of the signal received by different receiving points is different. Traditional single-sensor reception cannot take advantage of signal differences at different locations, while multi-sensor reception can use different locations to obtain signals with different reception qualities, and improve the quality of received signals through data fusion. Therefore, multi-sensor reception can improve signal-to-noise ratio ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L1/00H04L27/00
CPCH04L1/0041H04L1/0046H04L1/0054H04L27/0012
Inventor 王彬岳强彭华汪洋孙亮黄焱马金全邱钊洋姚祺
Owner THE PLA INFORMATION ENG UNIV
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