阅读说明：本技术 一种信号调制识别设备 (Signal modulation identification equipment ) 是由 阎文俊 陈亮辉 于 2019-10-09 设计创作，主要内容包括：本发明公开了一种信号调制识别设备,采用带通采样模式,采集宽带信号并完成模拟信号的数字化,采样速率低,能够最大可能的保证数据的有效位,宽带信号在宽带检测模块中经过短时傅立叶分析、平滑降噪、宽带降噪和门限检测后,通过频谱瞬时特征和频谱连续特征联合判证识别,将疑似信号从含有大量干扰和无用信号的宽带频谱中筛选出来,减轻了后端处理压力,有效提高了识别效率；通过数字下变频模块实现了数字正交变频和基带滤波功能；调制识别模块基于不同调制方式信号的频谱特征并辅以时域波形特征选取分类特征识别参数对宽带信号进行调制识别,有效提高了识别的准确性。(The invention discloses a signal modulation recognition device, which adopts a band-pass sampling mode to collect broadband signals and finish the digitization of analog signals, has low sampling rate and can ensure the effective bit of data to the maximum extent, after the broadband signals are subjected to short-time Fourier analysis, smooth noise reduction, broadband noise reduction and threshold detection in a broadband detection module, the suspected signals are screened from broadband frequency spectrums containing a large amount of interference and useless signals through the joint judgment and recognition of frequency spectrum instantaneous characteristics and frequency spectrum continuous characteristics, the back-end processing pressure is reduced, and the recognition efficiency is effectively improved; the digital orthogonal frequency conversion and baseband filtering functions are realized through the digital down-conversion module; the modulation identification module selects classification characteristic identification parameters to perform modulation identification on the broadband signals based on the frequency spectrum characteristics of the signals in different modulation modes and assisted by time domain waveform characteristics, so that the identification accuracy is effectively improved.)

1. A signal modulation recognition device is characterized by comprising a server, an acquisition card and a GPU card, wherein the acquisition card and the GPU card are connected with the server through a PCle bus;

the input broadband signal of the signal input interface is a radio frequency signal of an L waveband;

the acquisition card is used for acquiring broadband signals, completing broadband signal detection, acquiring the number of narrow-band signals in the broadband signals, the frequency point and bandwidth parameters of each signal, and transmitting the parameters to the server through a PCIe bus;

the server estimates the modulation rate of the narrow-band signals from the wide-band signals according to the frequency points and the bandwidth parameters of the narrow-band signals, completes digital down-conversion of multiple paths of narrow-band signals, dynamically adjusts the path number of the digital down-conversion according to the number of the narrow-band signals, and transmits the digital down-conversion to the GPU card through a PCIe bus;

the GPU card is used for processing the data of the multiple paths of narrow-band signals, identifying the modulation mode of the multiple paths of narrow-band signals and transmitting the identification result back to the server;

the network output interface is used for outputting the number of the narrow-band signals, the frequency points, the bandwidth parameters, the modulation rate and the modulation mode according to the network interface format.

2. The signal modulation identification device according to claim 1, wherein said acquisition card comprises a broadband acquisition module and a broadband detection module;

the broadband acquisition module acquires broadband signals and completes digitization of analog signals by adopting a band-pass sampling mode;

the broadband detection module is used for detecting and classifying the narrowband suspected signals in the broadband signals and acquiring the number of the narrowband signals in the broadband signals and calculating the frequency point and bandwidth parameters of each signal.

3. The signal modulation identification device according to claim 2, wherein the broadband detection module comprises a time-frequency analysis unit, an instantaneous spectrum buffer unit, an accumulated spectrum buffer unit, an adaptive threshold generation unit, a noise reduction processing unit, a spectrum instantaneous feature identification unit, a spectrum continuous feature identification unit and a joint judgment unit;

the time-frequency analysis unit adopts a short-time Fourier transform algorithm to perform high-resolution time-frequency analysis on the broadband signal, and obtains the frequency and time change relation of all signals in the broadband signal to obtain the current instantaneous frequency spectrum of the signal;

the instantaneous frequency spectrum caching unit is used for caching the instantaneous frequency spectrum obtained by the time-frequency analysis unit each time;

the accumulated frequency spectrum caching unit is used for caching all instantaneous frequency spectrums obtained by the time-frequency analysis unit within a set time to form a data matrix with a set time length and form a continuous frequency spectrum;

the adaptive threshold generating unit generates a decision threshold of the narrow-band signal by adopting an adaptive threshold generating algorithm based on sliding Gaussian window convolution, detects the wide-band signal by utilizing an automatic threshold and smoothes the instantaneous frequency spectrum;

the noise reduction processing unit is used for carrying out broadband noise reduction processing according to the continuity and regularity of the energy points detected in the continuous frequency spectrum on the time-frequency domain to eliminate discrete noise in the energy points;

the spectrum instantaneous feature identification unit is used for extracting and identifying spectrum instantaneous features;

the spectrum continuous characteristic identification unit is used for extracting and identifying spectrum continuous characteristics;

and the combined evidence judging unit analyzes the continuous spectrum characteristic change of the signal within set time according to the extracted instantaneous spectrum characteristic and the continuous spectrum characteristic of the signal, measures and calculates frequency intervals and time intervals of the change rule, and further classifies and judges the remaining suspected narrowband signals.

4. The signal modulation identification device of claim 1, wherein the server comprises a digital down-conversion module and a network encapsulation module;

the digital down-conversion module is used for realizing the functions of quadrature frequency conversion and baseband filtering of broadband signals;

and the network packaging module is used for packaging the data according to a network interface format and then outputting the data.

5. The apparatus for identifying signal modulation according to claim 4, wherein the digital down-conversion module comprises a quadrature frequency conversion unit, a decimation filtering unit and a digital resampling unit;

the orthogonal frequency conversion unit adopts a digital mixing orthogonal conversion method or a digital orthogonal conversion method based on a multiphase filter to realize the orthogonal frequency conversion of the broadband signal;

the decimation filtering unit is used for realizing the conversion of decimation filtering from 2 to 128 according to the filtering hierarchical structure of the conventional Digital Down Conversion (DDC) chip and by combining the speed of an FPGA and a resource design three-level structure;

the digital resampling unit adopts a Lagrange interpolation algorithm and carries out digital resampling on the broadband signal through a multiphase low-pass filtering structure.

6. The signal modulation identification device according to claim 1, wherein the GPU card comprises a plurality of modulation identification modules, and the modulation identification modules select classification feature identification parameters based on spectrum features of signals of different modulation modes and assisted by time domain waveform features, and perform modulation identification on broadband signals through a modulation identification algorithm.

Technical Field

The invention belongs to the technical field of communication equipment, and relates to signal modulation identification equipment.

Background

With the rapid development of modern communication technology, modulation schemes for automatically identifying communication signals are widely applied in the civil field and the military field, especially in the national security field, so modulation identification has become a very important research direction in the signal analysis and processing field.

Identification methods of modulated signals can be divided into two main categories: one is a decision theory method and the other is a statistical pattern recognition method. The former adopts hypothesis testing theory to solve the signal classification problem, and usually derives statistical test quantity based on the cost function minimization principle according to the statistical characteristics of the signal to form a decision criterion. The method is suitable for the identification of a specific type of modulation signals, and the identification range is narrow. The latter then typically divides the classification system into two subsystems: a feature extraction subsystem that extracts predefined features from the data; and the pattern recognition subsystem is used for recognizing the classification corresponding to the signal.

Under the condition of broadband receiving, the existing signal modulation recognition equipment cannot optimally receive a certain signal due to the existence of strong signal interference, so that the signal-to-noise ratio of the received signal is poor, great difficulty is brought to signal blind detection, weak signals are easily lost, meanwhile, the collected signal energy is very much, the back-end processing pressure is large, the recognition efficiency is low, in the existing modulation recognition algorithm, the classification characteristic recognition parameters are single, and the recognition accuracy is low.

Disclosure of Invention

The invention aims to provide a signal modulation recognition device which is high in recognition accuracy and high in recognition efficiency.

The purpose of the invention can be realized by the following technical scheme:

a signal modulation recognition device comprises a server, an acquisition card and a GPU card, wherein the acquisition card and the GPU card are connected with the server through a PCle bus;

the input broadband signal of the signal input interface is a radio frequency signal of an L waveband;

the acquisition card is used for acquiring broadband signals, completing broadband signal detection, acquiring the number of narrow-band signals in the broadband signals, the frequency point and bandwidth parameters of each signal, and transmitting the parameters to the server through a PCIe bus;

the server estimates the modulation rate of the narrow-band signals from the wide-band signals according to the frequency points and the bandwidth parameters of the narrow-band signals, completes digital down-conversion of multiple paths of narrow-band signals, dynamically adjusts the path number of the digital down-conversion according to the number of the narrow-band signals, and transmits the digital down-conversion to the GPU card through a PCIe bus;

the GPU card is used for processing the data of the multiple paths of narrow-band signals, identifying the modulation mode of the multiple paths of narrow-band signals and transmitting the identification result back to the server;

the network output interface is used for outputting the number of the narrow-band signals, the frequency points, the bandwidth parameters, the modulation rate and the modulation mode according to the network interface format.

Furthermore, the acquisition card comprises a broadband acquisition module and a broadband detection module.

The broadband acquisition module acquires broadband signals and completes digitization of analog signals by adopting a band-pass sampling mode;

the broadband detection module is used for detecting and classifying the narrowband suspected signals in the broadband signals and acquiring the number of the narrowband signals in the broadband signals and calculating the frequency point and bandwidth parameters of each signal.

Furthermore, the broadband detection module comprises a time-frequency analysis unit, an instantaneous spectrum cache unit, an accumulated spectrum cache unit, a self-adaptive threshold generation unit, a noise reduction processing unit, a spectrum instantaneous characteristic identification unit, a spectrum continuous characteristic identification unit and a joint judgment unit;

the time-frequency analysis unit adopts a short-time Fourier transform algorithm to perform high-resolution time-frequency analysis on the broadband signal, and obtains the frequency and time change relation of all signals in the broadband signal to obtain the current instantaneous frequency spectrum of the signal;

the instantaneous frequency spectrum caching unit is used for caching the instantaneous frequency spectrum obtained by the time-frequency analysis unit each time;

the accumulated frequency spectrum caching unit is used for caching all instantaneous frequency spectrums obtained by the time-frequency analysis unit within a set time to form a data matrix with a set time length and form a continuous frequency spectrum;

the adaptive threshold generating unit generates a decision threshold of the narrow-band signal by adopting an adaptive threshold generating algorithm based on sliding Gaussian window convolution, detects the wide-band signal by utilizing an automatic threshold and smoothes the instantaneous frequency spectrum;

the noise reduction processing unit is used for carrying out broadband noise reduction processing according to the continuity and regularity of the energy points detected in the continuous frequency spectrum on the time-frequency domain to eliminate discrete noise in the energy points;

the spectrum instantaneous feature identification unit is used for extracting and identifying spectrum instantaneous features;

the spectrum continuous characteristic identification unit is used for extracting and identifying spectrum continuous characteristics;

and the combined evidence judging unit analyzes the continuous spectrum characteristic change of the signal within set time according to the extracted instantaneous spectrum characteristic and the continuous spectrum characteristic of the signal, measures and calculates frequency intervals and time intervals of the change rule, and further classifies and judges the remaining suspected narrowband signals.

Further, the server comprises a digital down-conversion module and a network encapsulation module;

the digital down-conversion module is used for realizing the functions of quadrature frequency conversion and baseband filtering of broadband signals;

and the network packaging module is used for packaging the data according to a network interface format and then outputting the data.

Furthermore, the digital down-conversion module comprises a quadrature frequency conversion unit, a decimation filtering unit and a digital resampling unit;

the orthogonal frequency conversion unit adopts a digital mixing orthogonal conversion method or a digital orthogonal conversion method based on a multiphase filter to realize the orthogonal frequency conversion of the broadband signal;

the decimation filtering unit is used for realizing the conversion of decimation filtering from 2 to 128 according to the filtering hierarchical structure of the conventional Digital Down Conversion (DDC) chip and by combining the speed of an FPGA and a resource design three-level structure;

the digital resampling unit adopts a Lagrange interpolation algorithm and carries out digital resampling on the broadband signal through a multiphase low-pass filtering structure.

Furthermore, the GPU card comprises a plurality of groups of modulation identification modules, the modulation identification modules select classification characteristic identification parameters based on the frequency spectrum characteristics of signals in different modulation modes and assisted by time domain waveform characteristics, and the broadband signals are modulated and identified through a modulation identification algorithm.

The invention has the beneficial effects that: the signal modulation recognition equipment provided by the invention adopts a band-pass sampling mode to collect broadband signals and complete the digitization of analog signals, has low sampling rate and can ensure the effective bit of data to the maximum extent, and after the broadband signals are subjected to short-time Fourier analysis, smooth noise reduction, broadband noise reduction and threshold detection in a broadband detection module, suspected detection signals are screened out from a large amount of interference and useless signals in the broadband signals through the joint judgment and recognition of frequency spectrum instantaneous characteristics and frequency spectrum continuous characteristics, so that the back-end processing pressure is reduced, and the recognition efficiency is effectively improved; the functions of orthogonal frequency conversion and baseband filtering are realized in a digital circuit through a digital down-conversion module; the modulation identification module selects classification characteristic identification parameters based on the frequency spectrum characteristics of signals in different modulation modes and assisted by time domain waveform characteristics, and modulates and identifies broadband signals through a modulation identification algorithm, so that the identification accuracy is effectively improved.

Drawings

The invention is described in further detail below with reference to the figures and specific embodiments.

FIG. 1 is a schematic diagram of the system of the present invention.

Fig. 2 is a schematic diagram of a broadband detection module of the present invention.

Fig. 3 is a schematic diagram of a digital down conversion module of the present invention.

Fig. 4 is a flow chart of the modulation identification algorithm of the present invention.

Fig. 5 is a schematic diagram illustrating the modulation identification of the PM secondary modulation signal according to the present invention.

Fig. 6 is a schematic diagram illustrating identification of FM secondary modulation signal modulation according to the present invention.

Fig. 7 is a flow chart of a software implementation of the modulation identification module of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.

As shown in fig. 1, the present invention provides a signal modulation identification device, which includes a server, and an acquisition card and a GPU card connected to the server via a PCle bus, wherein the acquisition card is connected to a signal input interface, and the server is connected to a network output interface.

The input broadband signal of the signal input interface is a radio frequency signal of an L waveband.

The acquisition card is used for acquiring broadband signals, completing broadband signal detection, acquiring the number of narrowband signals in the broadband signals, the frequency point and bandwidth parameters of each signal, and transmitting the parameters to the server through the PCIe bus.

The server estimates the modulation rate of the narrow-band signals from the wide-band signals according to the frequency points and the bandwidth parameters of the narrow-band signals, completes digital down-conversion of multiple paths of narrow-band signals, dynamically adjusts the path number of the digital down-conversion according to the number of the narrow-band signals, and transmits the digital down-conversion to the GPU card through the PCIe bus.

The GPU card is used for processing the data of the multi-channel narrow-band signals, identifying the modulation mode of the multi-channel narrow-band signals and transmitting the identification result back to the server.

The network output interface is used for outputting the number of the narrow-band signals, the frequency point, the bandwidth parameter, the modulation rate and the modulation mode according to the network interface format.

Specifically, the acquisition card comprises a broadband acquisition module and a broadband detection module.

The broadband acquisition module acquires broadband signals and completes digitization of analog signals by adopting a band-pass sampling mode. Compared with the low-pass sampling mode, the bandpass sampling mode has a lower sampling rate, and can guarantee the valid bits of data to the maximum extent.

And the broadband detection module is used for detecting and classifying the narrowband suspected signals in the broadband signals and acquiring the number of the narrowband signals in the broadband signals and calculating the frequency point and bandwidth parameters of each signal.

As shown in fig. 2, the wideband detection module includes a time-frequency analysis unit, an instantaneous spectrum buffer unit, an accumulated spectrum buffer unit, an adaptive threshold generation unit, a noise reduction processing unit, a spectrum instantaneous feature identification unit, a spectrum continuous feature identification unit, and a joint evidence judgment unit.

And the time-frequency analysis unit is used for carrying out high-resolution time-frequency analysis on the broadband signal by adopting a short-time Fourier transform (STFT) algorithm to obtain the frequency and time change relation of all signals in the broadband signal so as to obtain the current instantaneous frequency spectrum of the signal.

Specifically, the short-time fourier transform algorithm is as follows:

according to the definition of the short-time fourier transform:

the frequency energy distribution is as follows:

SPEC_X(t,f)＝|STFT_X(t,f)|²

with the fixed window function fixed and the signal data sliding along the time axis, the definition of STFT can be equivalently written as:

when the time in the above formula is discretized, and t ═ mT and τ ═ nT are taken, the DTFT after windowing signal x (n) is expressed as:

since DFT is a frequency discretization of DTFT, willSubstituting the above equation to obtain discrete short-time Fourier transform:

the corresponding instantaneous spectrum is:

SPEC_X(n,k)＝|STFT_X(n,k)|²。

and the instantaneous spectrum buffer unit is used for buffering the instantaneous spectrum obtained by the time-frequency analysis unit each time, and the instantaneous spectrum represents the spectrum characteristics of the signal at a certain moment.

And the accumulated frequency spectrum caching unit is used for caching all instantaneous frequency spectrums obtained by the time-frequency analysis unit within a set time to form a data matrix with a set time length to form a continuous frequency spectrum, and the continuous frequency spectrum represents the stage change of the frequency spectrum characteristics of a signal within a period of time.

The adaptive threshold generation unit generates a judgment threshold of the narrow-band signal by adopting an adaptive threshold generation algorithm based on sliding Gaussian window convolution, detects the wide-band signal by utilizing an automatic threshold, smoothes the wide-band signal on an instantaneous frequency spectrum, filters most noise interference and can be used as an accurate basis for judging the narrow-band signal in the wide-band signal.

Specifically, the adaptive threshold generation algorithm is as follows:

after time-frequency analysis, the power spectrum Y (n) of the broadband signal can be obtained. Taking M point Gaussian discrete window sequences:

wherein the content of the first and second substances,

fs is the sampling frequency, N is the FFT window length, BW is the gaussian window equivalent bandwidth; considering that the detection signals are all narrow-band signals, α is 2 for best weighting, and g (m) is normalized:

the threshold is then:

in order to eliminate the influence of a strong signal with a large bandwidth on a nearby weak signal, a simple threshold limit needs to be applied to y (n), that is:

and a sequence eta ' (n) formed by convolving Y ' (n) and G ' (n) after threshold processing is the final actual background noise estimated value, and a fixed value of 1-2dB is added to the background noise estimated value to be used as a judgment threshold of the narrow-band signal.

The noise reduction processing unit can not filter continuous noise interference on a time axis after the automatic threshold is smoothed on the instantaneous frequency spectrum, the discrete noises can destroy the time domain characteristics of signals to cause false alarm and false alarm, and the discrete noises are randomly dispersed on a time-frequency space and are difficult to form the continuity and regularity of similar signals, so that broadband noise reduction processing can be performed according to the continuity and regularity of energy points detected in the continuous frequency spectrum on the time-frequency domain, and the discrete noises in the energy points are eliminated. After noise is further filtered by broadband noise reduction, good conditions are provided for next step of feature identification.

And the spectrum instantaneous feature identification unit is used for extracting and identifying spectrum instantaneous features. The instantaneous characteristics of the frequency spectrum refer to the signal bandwidth, the in-band energy distribution, the positions, the number, the size relation and the frequency intervals among spectral peaks of all signals at the current moment.

And the spectrum continuous characteristic identification unit is used for extracting and identifying spectrum continuous characteristics. The continuous frequency spectrum characteristics refer to the time length occupied by each signal in set time, the outconnection interval, the spectrum peak position, the bandwidth change and other characteristics, and a large number of useless signals such as isolated noise, broadcast signals, sweep signals and the like can be eliminated.

The joint evidence judging unit analyzes the continuous spectrum characteristic change of the signal in a set time according to the extracted instantaneous spectrum characteristic and the continuous spectrum characteristic of the signal, measures and calculates the frequency interval and the time interval of the change rule, further classifies and judges the remaining suspected narrowband signals, judges whether the signals at the moment have corresponding FSK modulation characteristic, PSK modulation characteristic, pilot frequency characteristic and synchronous head characteristic of link signals and the like, classifies the signals according to the characteristics, judges whether the signals are continuous modulation signals or link signals with synchronous heads, and then outputs the bandwidth parameters, carrier frequency and initial judgment signal types of the signals.

The server comprises a digital down-conversion module and a network encapsulation module.

And the digital down-conversion module is used for realizing the functions of quadrature frequency conversion and baseband filtering of the broadband signal.

As shown in fig. 3, the digital down-conversion module includes a quadrature frequency conversion unit, a decimation filtering unit, and a digital resampling unit.

And the orthogonal frequency conversion unit realizes the orthogonal frequency conversion of the broadband signal by adopting a digital mixing orthogonal conversion method or a digital orthogonal conversion method based on a polyphase filter.

And the decimation filtering unit is used for realizing the conversion of decimation filtering from 2 to 128 according to the filtering hierarchical structure of the conventional Digital Down Conversion (DDC) chip and by combining the speed and the resource design three-level structure of the FPGA.

And the digital resampling unit is used for digitally resampling the broadband signal through a multiphase low-pass filtering structure by adopting a Lagrange interpolation algorithm.

And the network packaging module is used for packaging the data according to a network interface format and then outputting the data.

The GPU card comprises a plurality of groups of modulation identification modules.

And the modulation identification module is used for selecting classification characteristic identification parameters based on the frequency spectrum characteristics of signals in different modulation modes and assisted by time domain waveform characteristics, and modulating and identifying the broadband signals through a modulation identification algorithm.

The classification characteristic identification parameters are selected as follows:

the carrier component signal class is identified by measuring the carrier component. In our signal set to be identified, AM signals, FSK signals (discontinuous phase) and secondary modulation signals externally modulated to PM have a single-frequency carrier component, and AM, PCM-BPSK-PM and PCM-QPSK-PM signals have a carrier component, 2FSK, 4FSK and PCM-2FSK-PM have 2, 4 and 5 carrier components, respectively.

Then, the signals in the residual signal set are classified into 3 categories by measuring the fluctuation of the signal envelope in various ways: amplitude modulated signals with significantly large envelope fluctuations, i.e., DSB and SSB signals; shaping PSK signals (including BPSK, QPSK, UQPSK, 8PSK, and OQPSK) that cause some degree of envelope fluctuation; FM (including PCM-FM and FM-FM) signals, MSK and FQPSK signals which are basically free of envelope fluctuation.

For DSB and SSB modulation signals, the DSB is further distinguished by the envelope thereof, and the DSB has larger envelope fluctuation; they can also be distinguished by their squared spectrum, which has strong single-frequency components at 2 times the carrier frequency, since the DSB signal has only pi-hopping to the carrier, whereas the SSB signal does not have this feature.

In the sub-class of the molded PSK signals, OQPSK signals are further distinguished through the degree of envelope fluctuation, and the OQPSK signals are obviously smaller in envelope fluctuation compared with other PSK signals; and BPSK, QPSK and 8PSK signals are distinguished by their spectrum characteristics after nonlinear transformation: BPSK and QPSK/8PSK can be distinguished by detecting whether a single-frequency component exists at a carrier frequency position which is 2 times of a signal square spectrum; the QPSK signal and the 8PSK signal are distinguished by detecting single-frequency components at carrier frequencies which are 4 times of a signal fourth power spectrum. The UQPSK signal has different values according to the balance factor, the signal characteristic is between BPSK and QPSK, the parameter value range in the project is closer to BPSK, and envelope parameters need to be used for distinguishing under higher signal-to-noise ratio.

In the FM, MSK and FQPSK subclasses, analog modulated FM (including PCM-FM and FM-FM) signals can be distinguished from digital modulated MSK/FQPSK by the detection of signal squared spectral single frequency components; MSK and FQPSK signals are distinguished at higher signal-to-noise ratios by detection of single-frequency component intensities at 4 carrier frequencies of the quadruplicate spectrum.

The modulation identification algorithm is shown in fig. 4, wherein N1 is the number of signal power spectrum single-frequency components; q2 is a single-frequency component amplitude value at 2 times carrier frequency of square spectrum; q2_ max is the maximum squared spectral amplitude; q4 is a single-frequency component amplitude value at a carrier frequency which is 4 times of a fourth power spectrum; gamma ray_maxNormalizing the maximum value of the instantaneous amplitude spectral density for the zero center; and R is an envelope characteristic parameter.

As shown in fig. 5 and 6, for the secondary modulation signals externally modulated to PM and FM, PM and FM demodulation are performed respectively, and then the internal modulation identification is performed according to the above process.

As shown in fig. 7, a software implementation flow of the modulation identification module is that a low-intermediate frequency real signal is received first, and direct spectrum analysis is performed on the low-intermediate frequency real signal to facilitate analysis of the number of carriers, so as to distinguish AM/PM/FSK signals first; then, performing down-conversion filtering on the real signal according to the carrier and bandwidth estimation values sent by other modules to obtain an IQ signal with approximate zero intermediate frequency, and extracting the IQ signal according to the bandwidth estimation values to obtain approximate oversampling multiples under various bandwidth conditions, so that the uniform processing of a back-plane spectrum analysis module is facilitated; the IQ signal can also conveniently carry out envelope calculation, the R parameter is directly obtained from the envelope, and the spectral analysis of the envelope is used for calculating the parameter. In the whole module, the secondary modulation submodule and the main module work simultaneously, and when the main module judges that the FM signal and the PM signal exist, the strobe signal SEL is output simultaneously to enable the identification results of the two submodules to be valid.

The signal modulation recognition equipment provided by the invention adopts a band-pass sampling mode to collect broadband signals and complete the digitization of analog signals, has low sampling rate and can ensure the effective bit of data to the maximum extent, and after the broadband signals are subjected to short-time Fourier analysis, smooth noise reduction, broadband noise reduction and threshold detection in a broadband detection module, suspected detection signals are screened out from a large amount of interference and useless signals in the broadband signals through the joint judgment and recognition of frequency spectrum instantaneous characteristics and frequency spectrum continuous characteristics, so that the back-end processing pressure is reduced, and the recognition efficiency is effectively improved; the functions of orthogonal frequency conversion and baseband filtering are realized in a digital circuit through a digital down-conversion module; the modulation identification module selects classification characteristic identification parameters based on the frequency spectrum characteristics of signals in different modulation modes and assisted by time domain waveform characteristics, and modulates and identifies broadband signals through a modulation identification algorithm, so that the identification accuracy is effectively improved.

In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.