CN112699777A - Blind signal modulation type identification method based on convolutional neural network - Google Patents

Blind signal modulation type identification method based on convolutional neural network Download PDF

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CN112699777A
CN112699777A CN202011585710.7A CN202011585710A CN112699777A CN 112699777 A CN112699777 A CN 112699777A CN 202011585710 A CN202011585710 A CN 202011585710A CN 112699777 A CN112699777 A CN 112699777A
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signal
neural network
convolutional neural
histogram
phase difference
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杜鸿
文成玉
夏金森
代茂
王金霞
何再芝
熊航
廖聪慧
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Chengdu University of Information Technology
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract

The invention provides a blind signal modulation type identification method based on a convolutional neural network, which uses a phase difference histogram and an amplitude histogram calculated by three different sampling rates as signal characteristic parameters, classifies the signal characteristic parameters through a convolutional neural network classifier so as to obtain a signal modulation type, and has strong immunity to frequency offset and inaccurate bandwidth due to the fact that the texture of the phase difference histogram is insensitive to the frequency offset and the inaccurate bandwidth, so that the blind signal modulation type identification method is suitable for blind signal modulation type identification in a complex electromagnetic environment.

Description

Blind signal modulation type identification method based on convolutional neural network
Technical Field
The invention belongs to the field of radio communication, and particularly relates to a blind signal modulation type identification method based on a convolutional neural network.
Background
In the field of radio signal monitoring, modulation identification technology is more faced with blind signal identification, i.e. the conditions of unknown signal carrier frequency, unknown signal bandwidth and unknown signal symbol rate. In a complex electromagnetic environment, it is difficult to accurately measure the carrier frequency and bandwidth of the blind signal. From the viewpoint of engineering practicability, the modulation identification technology is required to have the immunity capability of frequency deviation and inaccurate bandwidth. When using modulated identification products provided by some well-known manufacturers, in the face of a blind signal, the operator often has to use several sets of carrier frequencies and bandwidths to identify each product separately to confirm the authenticity of the identification.
In the traditional modulation type identification method based on instantaneous parameters and high-order cumulant, the characteristic parameters are sensitive to the frequency deviation of a receiver and the bandwidth of a filter, and the performance in practical engineering is difficult to meet the application requirement. In the modulation type identification method based on the constellation diagram, an accurate signal symbol rate needs to be obtained and sampling rate conversion is carried out on sampling data, and besides high calculation cost, the accurate symbol rate cannot be obtained under the condition of low carrier-to-noise ratio, so that the application of the modulation type identification method is limited. In a modulation type recognition algorithm using a convolutional neural network, training data sets are often generated by a simulation means, the inherent characteristics of the data sets are frequency offset-free, and a trained model is difficult to adapt to an actual engineering application environment; some modulation identification methods directly using IQ data as input data of a neural network have the problem of being sensitive to frequency offset, interference and carrier-to-noise ratio.
Disclosure of Invention
In order to solve the problems, the invention provides a blind signal modulation type identification method based on a convolutional neural network, which takes three groups of signal phase difference histograms with different sampling rates and one group of amplitude histograms as input characteristic parameters of the convolutional neural network, and uses a radio frequency receiver to collect signal characteristic parameters under the conditions of different modulation types, carrier-to-noise ratios, symbol frequencies and frequency offsets as a training data set to train the convolutional neural network, so that the immunity of an identification model to frequency offsets and inaccurate bandwidths is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a blind signal modulation type identification method based on a convolutional neural network comprises the following steps:
s1, the radio frequency receiver uses the sampling rate FSCollecting time domain 0 intermediate frequency complex signal IQ stream, dividing IQ stream into M sampling numberBLKIQ block S ofBLKCalculating the IQ block SBLKAnd estimating the signal bandwidth
Figure 100002_DEST_PATH_IMAGE001
Using a cut-off frequency of
Figure 170574DEST_PATH_IMAGE001
To IQ block SBLKPerforming filtering to obtain filtered IQ block
Figure DEST_PATH_IMAGE002
；
S2 according to the sampling rate FSSum signal bandwidth estimation
Figure 651497DEST_PATH_IMAGE001
Calculating three IQ data extraction factors E4、E2And E1；
S3, according to the extraction factor E4、E2And E1Calculating a signal phase difference histogram and an amplitude histogram;
s4, in the training stage of the convolutional neural network classifier model, changing the modulation type, the carrier-to-noise ratio, the symbol rate and the carrier frequency offset of a signal source, acquiring an IQ block, and calculating and recording the phase difference histogram and the amplitude histogram of S3 as a training and verification data set for training the convolutional neural network classifier model;
s5, in the application stage of the convolutional neural network classifier model, aiming at the IQ block of the signal to be identified, calculating the phase difference histogram and the amplitude histogram of S3, and identifying the modulation type of the signal by using the convolutional neural network classifier model.
Further, the decimation factor E of S24、E2And E1The specific calculation steps are as follows:
Figure 100002_DEST_PATH_IMAGE003
，
Figure DEST_PATH_IMAGE004
，
Figure 100002_DEST_PATH_IMAGE005
。
further, the step of specifically calculating the phase difference histogram and the amplitude histogram in S3 is as follows:
s31, using the decimation factor E4、E2And E1To IQ blocks respectively
Figure 493551DEST_PATH_IMAGE002
Integer extraction is carried out to obtain three down-sampling rate IQ blocks SBLK4、SBLK2And SBLK1；
S32 for IQ block SBLK4、SBLK2And SBLK1Respectively calculate its phase array Sp4、Sp2And Sp1Calculating a phase difference array Spd4、Spd2And Spd1Wherein, in the step (A),
Spd4(k)= Sp4(k + 1) - Sp4 (k)，
Spd2(k)= Sp2(k + 1) - Sp2(k)，
Spd1(k)= Sp1(k + 1) - Sp1(k)，
k belongs to (0, …, N-2) as a data point index in the phase array, and N is the actual sampling point number of each IQ block;
s33, phase difference array Spd4、Spd2And Spd1The value is translated to 0-359 interval from +/-180 interval and rounded, and distribution histogram H of three phase difference arrays is calculatedpd4、Hpd2And Hpd1And at the same time,
put Hpd4(k4) Is (H)pd4(178)+Hpd4(182))/2，k4∈(179，…，181)，
Put Hpd2(k2) Is (H)pd2(177)+Hpd2(183))/2，k2∈(178，…，182)，
Put Hpd1(k1) Is (H)pd1(176)+Hpd1(184))/2，k1∈(177，…，183)，
H is to bepd4、Hpd2And Hpd1As a phase profile characteristic of the signal;
s34, IQ block SBLK2The amplitude distribution interval of (1) is normalized to 0-359, rounded, and an amplitude histogram H is calculatedmagAs an amplitude distribution characteristic of the signal.
The invention has the beneficial effects that:
1) in a sampling rate interval with the bandwidth of 1-4 times, phase difference histograms of different types of modulation signals are remarkably changed, and special texture characteristics exist, and the amplitude histogram characteristics are combined, so that the method can identify various common types of analog and digital modulation signals such as AM, FM, 2/4FSK, MSK/GMSK, 2/4/8PSK, 16/64QAM and the like;
2) when the carrier wave has frequency deviation, the texture of the phase difference histogram also deviates on a phase axis along with the frequency deviation, but the texture characteristics are not changed, the influence of the bandwidth of a filter on the texture characteristics is small, the sensitivity of the amplitude histogram to the frequency deviation and the bandwidth of the filter is low, and the characteristic parameter calculation does not depend on the symbol rate, so that the method has strong immunity to the frequency deviation and the inaccurate bandwidth and can still work in a complex electromagnetic environment with low carrier-to-noise ratio and strong channel interference;
3) the signal feature calculation process of the method is low in complexity, and the method has the characteristic of being easy to realize in engineering by combining with a mature pre-trained or modified convolutional neural network classifier.
Drawings
FIG. 1 is a flow chart of a blind signal modulation type identification method based on a convolutional neural network;
Detailed Description
The following describes in detail embodiments of the system according to the present invention with reference to examples.
In an embodiment, the radio frequency receiver uses the USRP B210, the modulation signal source uses the E4432B, and the IQ block sampling number M is setBLK=16384, receiver and signal source center frequency 1 GHz.
In the training phase of the convolutional neural network model, automatic training data acquisition software controls the modulation type, the symbol rate and the output level of a signal source through a control interface of E4432B, wherein the modulation type is respectively set to be 2FSK, 4FSK, MSK, GMSK, BPSK, QPSK, 8PSK, 16QAM and 64QAM, the symbol rate uses Bd = (6.0, 8.4, 10.8, 13.2, 15.6, 46.8) ksps, and the output level of the signal source is set to be (-60, -69, -75, -81, -87, -90, -93, -96) dBm;
acquisition software controlled receiver sampling rate FSGreater than 4 times the modulation signal bit rate BbSetting B210 gain G =50dB, setting receiver frequency offset dF = (± 0.2B)b，±0.1Bb，0)×BbTwo IQ blocks are collected for each parameter combination, and 5280 IQ blocks are collected for 9 kinds of digital modulation;
for analog modulation signals FM and AM, 240 narrowband FM IQ blocks are collected by using an analog interphone as a signal source, 240 wideband FM IQ blocks are recorded by using FM broadcasting stations with different frequency points as the signal source, 240 IQ data blocks are recorded by using an airport tower with a voice signal section and a navigation station as the signal source, and 240 IQ data blocks are recorded for the signal source by using AM modulation output of E4432B.
Through the signal sample collection process, the data set of 11 modulation types comprises 6240 IQ blocks, a phase difference histogram and an amplitude histogram of each IQ block are calculated and recorded to serve as a signal characteristic parameter data set, and the specific steps of characteristic parameter calculation and convolutional neural network classifier training are as follows:
t1, calculating fast Fourier transform magnitude spectra and estimating signal bandwidth for each IQ data block
Figure 888760DEST_PATH_IMAGE001
Using a cut-off frequency of
Figure 765449DEST_PATH_IMAGE001
To IQ block SBLKPerforming filtering to obtain filtered IQ block
Figure 587911DEST_PATH_IMAGE002
；
T2 according to the sampling rate FSSum signal bandwidth estimation
Figure 854945DEST_PATH_IMAGE001
Calculating three IQ data extraction factors E4、E2And E1The specific calculation steps are as follows:
Figure 53845DEST_PATH_IMAGE003
，
Figure 785041DEST_PATH_IMAGE004
，
Figure 778404DEST_PATH_IMAGE005
；
t3, according to the decimation factor E4、E2And E1Calculating a phase difference histogram and an amplitude histogram of the signal, the specific calculation steps including:
r31, using decimation factor E4、E2And E1To IQ blocks respectively
Figure 532734DEST_PATH_IMAGE002
Integer extraction is carried out to obtain three down-sampling rate IQ blocks SBLK4、SBLK2And SBLK1；
T32 for IQ block SBLK4、SBLK2And SBLK1Respectively calculate its phase array Sp4、Sp2And Sp1Calculating a phase difference array Spd4、Spd2And Spd1Wherein, in the step (A),
Spd4(k)= Sp4(k + 1) - Sp4 (k)，
Spd2(k)= Sp2(k + 1) - Sp2(k) ，
Spd1(k)= Sp1(k + 1) - Sp1(k)，
k belongs to (0, N-2) and is a data point subscript in the phase array, and N is the actual sampling point number of each IQ block;
t33 phase difference array Spd4、Spd2And Spd1The values are translated to 0-359 from +/-180 intervals and rounded, and distribution histograms H of the three phase difference arrays are calculatedpd4、Hpd2And Hpd1And at the same time,
put Hpd4(k4) Is (H)pd4(178)+Hpd4(182))/2，k4∈(179，…，181)，
Put Hpd2(k2) Is (H)pd2(177)+Hpd2(183))/2，k2∈(178，…，182)，
Put Hpd1(k1) Is (H)pd1(176)+Hpd1(184))/2，k1∈(177，…，183)，
H is to bepd4、Hpd2And Hpd1As a phase profile characteristic of the signal;
t34, IQ block S to reduce sampling rateBLK2The amplitude distribution interval is normalized to 0-359 interval and rounded, and an amplitude distribution histogram H is calculatedmagAs an amplitude distribution characteristic of the signal.
T4, in this embodiment, the convolutional neural network classifier uses a modified inclusion v3 model, and inputs a grayscale image of 150 × 180 specification, and the specific implementation steps of the training are as follows:
t41, drawing the three phase difference histograms and the three amplitude histograms into the same single-channel image matrix by using OpenCV according to Hpd4、Hpd2、Hpd1And HmagThe image files are sequentially arranged from top to bottom in equal intervals, each parameter area in the image matrix is separated by 5 pixels, the image matrix is stored as image files in 11 folders, and 70% of the image files are randomly selected as a training set, 20% of the image files are selected as a verification set, and 10% of the image files are selected as a test set;
t42, setting the classifier class list as AM, FM, 2FSK, 4FSK, MSK, GMSK, BPSK, QPSK, 8PSK, 16QAM and 64QAM, performing transfer training on the pre-training model, and recording the trained model for modulation identification.
After the convolutional neural network classifier model training is completed, the blind signal modulation recognition method specifically comprises the following steps:
r1, frequency point CF and rough bandwidth B designated according to identification task commandWThe RF receiver sets the center frequency to CF at a sampling rate FS>4×BWCollecting time domain 0 intermediate frequency complex signal IQ stream, dividing IQ stream into M sampling numberBLKIQ block S ofBLKCalculating the IQ block SBLKAnd estimating the signal bandwidth
Figure 269746DEST_PATH_IMAGE001
Using a cut-off frequency of
Figure 793131DEST_PATH_IMAGE001
To IQ block SBLKPerforming filtering to obtain filtered IQ block
Figure 222975DEST_PATH_IMAGE002
；
R2 according to sampling rate FSSum signal bandwidth estimation
Figure 48890DEST_PATH_IMAGE001
Calculating three IQ data extraction factors E4、E2And E1；
R3 according to the decimation factor E4、E2And E1Calculating a phase difference histogram H of a signalpd4、Hpd2、Hpd1Sum amplitude histogram Hmag；
R4, drawing the three phase difference histograms and the three amplitude histograms into the same single-channel image matrix by using OpenCV according to Hpd4、Hpd2、Hpd1And HmagThe image matrix is sequentially arranged from top to bottom in an equal way, each parameter area in the image matrix is separated by 5 pixels, RGB three channels are set to have the same pixel value, the image matrix is transmitted to a classifier to be used as input, and the recognition result and the reliability are obtained after the prediction of the classifier.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art without departing from the spirit and principle of the present application, and any modifications, equivalents, improvements, etc. made therein are intended to be included within the scope of the present application. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (3)

1. A blind signal modulation type identification method based on a convolutional neural network is characterized by comprising the following steps:
s1, the radio frequency receiver uses the sampling rate FSCollecting time domain 0 intermediate frequency complex signal IQ stream, dividing IQ stream into M sampling numberBLKIQ block S ofBLKCalculating the IQ block SBLKBy fast Fourier transforming the magnitude spectrum, estimating the signal bandwidth from the magnitude spectrum
Figure DEST_PATH_IMAGE001
Using a cut-off frequency of
Figure 233792DEST_PATH_IMAGE001
To IQ block SBLKPerforming filtering to obtain filtered IQ block
Figure 5439DEST_PATH_IMAGE002
；
S2 according to the sampling rate FSSum signal bandwidth estimation
Figure 956077DEST_PATH_IMAGE001
Calculating three IQ data extraction factors E4、E2And E1；
S3, according to the extraction factor E4、E2And E1Calculating a signal phase difference histogram and an amplitude histogram;
s4, in the training stage of the convolutional neural network classifier model, changing the modulation type, the carrier-to-noise ratio, the symbol rate and the carrier frequency offset of a signal source, and calculating and recording the phase difference histogram and the amplitude histogram of S3 as a training and verification data set for training the convolutional neural network classifier model;
and S5, in the application stage of the convolutional neural network classifier model, aiming at the signal to be identified, calculating the phase difference histogram and the amplitude histogram of S3, and identifying the modulation type of the signal by using the convolutional neural network classifier model.
2. The blind signal modulation type identification method based on convolutional neural network as claimed in claim 1, wherein S2 said extracting factor E4、E2And E1The specific calculation steps are as follows:
Figure DEST_PATH_IMAGE003
，
Figure 838583DEST_PATH_IMAGE004
，
Figure DEST_PATH_IMAGE005
。
3. the blind signal modulation type identification method based on the convolutional neural network as claimed in claim 1, wherein the step of calculating the phase difference histogram and the amplitude histogram specifically in S3 is as follows:
s31, using the decimation factor E4、E2And E1To IQ block S respectivelyBLKInteger extraction is carried out to obtain three down-sampling rate IQ blocks SBLK4、SBLK2And SBLK1；
S32 for IQ block S with reduced sampling rateBLK4、SBLK2And SBLK1Respectively calculate its phase array Sp4、Sp2And Sp1Calculating a phase difference array Spd4、Spd2And Spd1Wherein, in the step (A),
Spd4(k)= Sp4(k + 1) - Sp4 (k)，
Spd2(k)= Sp2(k + 1) - Sp2(k)，
Spd1(k)= Sp1(k + 1) - Sp1(k)，
k belongs to (0, …, N-2) and is a data point subscript in the phase array, and N is the actual sampling point number of each data block;
s33, phase difference array Spd4、Spd2And Spd1The value is translated to 0-359 interval from +/-180 interval and rounded, and distribution histogram H of three phase difference arrays is calculatedpd4、Hpd2And Hpd1And at the same time,
put Hpd4(k4) Is (H)pd4(178)+Hpd4(182))/2，k4∈(179，…，181)，
Put Hpd2(k2) Is (H)pd2(177)+Hpd2(183))/2，k2∈(178，…，182)，
Put Hpd1(k1) Is (H)pd1(176)+Hpd1(184))/2，k1∈(177，…，183)，
H is to bepd4、Hpd2And Hpd1Phase distribution as a signalPerforming sign;
s34, IQ block S for reducing sampling rateBLK2The amplitude distribution interval of (1) is normalized to 0-359, rounded, and an amplitude histogram H is calculatedmagAs an amplitude distribution characteristic of the signal.
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CN115277324B (en) * 2022-07-25 2023-11-10 电信科学技术第五研究所有限公司 FSK signal identification method based on convolutional neural network
CN117155748A (en) * 2023-10-27 2023-12-01 四川轻化工大学 Modulation format identification method based on multidimensional amplitude distribution characteristics
CN117155748B (en) * 2023-10-27 2024-01-23 四川轻化工大学 Modulation format identification method based on multidimensional amplitude distribution characteristics

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