CN103780462B - Satellite communication signals Modulation Identification method based on Higher Order Cumulants and spectrum signature - Google Patents

Satellite communication signals Modulation Identification method based on Higher Order Cumulants and spectrum signature Download PDF

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CN103780462B
CN103780462B CN201410029764.3A CN201410029764A CN103780462B CN 103780462 B CN103780462 B CN 103780462B CN 201410029764 A CN201410029764 A CN 201410029764A CN 103780462 B CN103780462 B CN 103780462B
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spectral peak
spectrum
power spectrum
satellite communication
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CN103780462A (en
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张更新
马兆宇
李悦
边东明
谢智东
胡婧
孙谦
李永强
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PLA University of Science and Technology
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Abstract

The present invention discloses a kind of satellite communication signals Modulation Identification method based on Higher Order Cumulants and spectrum signature, comprise the steps: signal bandpass filtering, estimating carrier frequency, estimate symbol speed, obtain Higher Order Cumulants parameter, APSK or 16QAM signal identification, obtain quadratic power spectrum spectral peak number, BPSK or msk signal identification, acquisition biquadratic is composed, and obtains biquadratic spectrum spectral peak number, π/4DQPSK signal identification, obtain base band quadratic power spectrum spectral peak number, 6PSK, 8PSK, OQPSK, QPSK signal identification.Use the satellite communication signals Modulation Identification method of the present invention, satellite communication signals modulation system can be realized that total blindness identifies, algorithm is simple, be easily achieved, accuracy rate high.

Description

Satellite communication signals Modulation Identification method based on Higher Order Cumulants and spectrum signature
Technical field
The invention belongs to satellite communication and digital signal processing technique field, particularly a kind of based on Higher Order Cumulants and spectrum The satellite communication signals Modulation Identification method of feature.
Background technology
Conventional satellite communication signals include BPSK (Binary Phase Shift Keying binary phase shift keying), QPSK(Quadrature Phase Shift Keying quaternary phase-shift keying (PSK)), OQPSK(Offset QPSK offset the quaternary Phase-shift keying (PSK)), π/4DQPSK(π/4Difference Quadrature Phase Shift Keying π/4 differential quadrature phase Move keying), 6PSK(6Phase Shift Keying six phase place phase-shift keying (PSK)), 8PSK(8Phase Shift Keying eight enters Phase-shift keying (PSK) processed), MSK(Minimum Shift Keying minimum shift keying), 16QAM(16Quadrature Amplitude Modulation16 quadrature amplitude modulation), APSK(Amplitude Phase Shift Keying amplitude phase shift Keying) signal.In the case of non-cooperative communication, effectively monitor and identify signal of communication, having ten in civil and military field Divide and be widely applied value.As a example by satellite communication, at civil area, signal cognition is used for monitoring in satellite network user's whether work Making in legal running parameter, satellite repeater resource is occupied and is usurped by detection disabled user simultaneously；At its military neck Territory, signal cognition accurately is to realize space electromagnetic countermeasure and obtain the precondition of information superiority.
In the document of the relevant Modulation Identification the most delivered, signal modulate method substantially can be divided: base In the statistical pattern recognition method that maximum likelihood hypothesis testing method and the feature based of decision-making theory extract.Maximum likelihood is assumed Detection method is the test problem of the many hypothesis of a kind of likelihood, is characterized in by observing signal waveform to be identified, it is assumed that for certain The modulation system of a kind of candidate, then judges to determine its modulation system by similarity.Such method is typically for certain class The statistical property of concrete modulated signal is analyzed and obtains certain decision rule, thus is only applicable to the knowledge of such modulated signal Not, identification range is narrow, and algorithm is complicated, is difficult to realize.Method based on statistical-simulation spectrometry includes based on instantaneous temporal signatures Method, method based on signal statistics feature and method etc. based on transform domain feature.
For this specific communication scene of satellite communication, these methods there is also some shortcomings: 1, used characteristic parameter Extraction generally require more priori, it is difficult to accomplish total blindness's Modulation Identification；2, do not account for raised cosine to shape signal The impact of characteristic of division, satellite communication signals generally individually uses raised cosine to shape so that the decline of algorithm discrimination was even lost efficacy；3、 Identification kind is few, the restricted application of algorithm, is formed without the identification system for satellite communication signals.4, algorithm is complicated, It is difficult to real-time Modulation Mode Recognition.
Summary of the invention
It is an object of the invention to provide a kind of satellite communication signals Modulation Identification based on Higher Order Cumulants and spectrum signature Method, can realize total blindness's Modulation Identification, algorithm is simple, be easily achieved, accuracy rate is high.
The technical solution realizing the object of the invention is: a kind of satellite communication based on Higher Order Cumulants and spectrum signature is believed Number Modulation Identification method, comprises the steps:
10) signal bandpass filtering: receive pending signal data, and it is carried out bandpass filtering；
20) estimating carrier frequency: filtered signal is carried out segment processing, calculates the power spectrum of every segment signal, and right Power spectrum is smoothed, and utilizes frequency algorithm placed in the middle to estimate the carrier frequency of signal；
30) estimate symbol speed: filtered signal carries out a square process, calculates its quadratic power spectrum, detects quadratic power The baseband spectrum line structure of spectrum, utilizes the character rate of the line structure characteristic estimating signal of character rate；
40) obtain Higher Order Cumulants parameter: calculate Higher Order Cumulants, obtain Higher Order Cumulants parameter, provide threshold value；
50) APSK or 16QAM signal identification: if comparing according to signal Higher Order Cumulants parameter and threshold value, identify APSK or 16QAM signal, then identify that process terminates, and otherwise continues；
60) quadratic power spectrum spectral peak number is obtained: according to the line structure property calculation signal quadratic power of signal quadratic power spectrum The spectral peak number of spectrum；
70) BPSK or msk signal identification: if identifying BPSK or msk signal identification according to quadratic power spectral peak number, then Identification process terminates, and otherwise continues；
80) biquadratic spectrum is obtained: the signal after square process is carried out the high-pass filtering that cut-off frequency is carrier frequency, so After again carry out square processing, be calculated the biquadratic spectrum of signal；
90) biquadratic spectrum spectral peak number is obtained: according to the line structure property calculation signal biquadratic of signal biquadratic spectrum The spectral peak number of spectrum；
100) π/4DQPSK signal identification: if identifying π/4DQPSK signal according to biquadratic spectral peak number, then identify Process terminates, and otherwise continues；
110) base band quadratic power spectrum spectral peak number is obtained: according to the baseband spectrum line structure characteristic of signal quadratic power spectrum, detection The ratio of spectral line peak value and average in base band quadratic power spectrum band limits, then adjudicates as spectral peak if greater than a certain thresholding, finally It is calculated the baseband spectrum peak number mesh of signal quadratic power spectrum；
120) 6PSK, 8PSK, OQPSK, QPSK signal identification: according to biquadratic spectrum spectral peak number and base band quadratic power spectrum spectrum Peak number mesh, finally identifies 6PSK, 8PSK, OQPSK, QPSK signal.
The present invention compared with prior art, its remarkable advantage:
1, being suitable for satellite communication channel feature: satellite communication channel is awgn channel, signal shaping generally individually uses liter remaining String shapes, and the present invention has taken into full account that raised cosine shapes signal Higher Order Cumulants and the impact of spectrum signature, is suitable for satellite communication The feature of channel；
2, identify without priori total blindness: the present invention does not relies on priori, including rolloff-factor or modulation index, Signal to noise ratio, accurately carrier frequency, carrier phase, symbol synchronization etc., accomplished total blindness's Modulation Mode Recognition；
3, the modulation system kind identified is many: establish the conventional satellite communication signals Modulation Mode Recognition collection of comparatively perfect Closing, conventional satellite communication signals includes BPSK, QPSK, OQPSK, π/4DQPSK, 6PSK, 8PSK, MSK, 16QAM, APSK letter Number；
4, implementation complexity is low: the present invention proposes feature extraction based on base band quadratic power spectrum, eliminates tradition envelope The calculating of spectrum, quadratic power spectrum, biquadratic spectrum all use unified spectral peak decision method simultaneously, simplify decision rule, reduce The complexity that algorithm realizes, can complete the quick Real time identification of modulation system；
5, recognition accuracy is high: invention introduces Line enhancement algorithm, makes spectral line characteristic become apparent from, the spectral peak of proposition Decision method is more practical, uses self-adaptive decision threshold simultaneously, improves Low SNR modulated mode identification accurate Rate.
With detailed description of the invention the present invention done below in conjunction with the accompanying drawings and further illustrate.
Accompanying drawing explanation
Fig. 1 is the flow chart of present invention satellite communication signals based on Higher Order Cumulants and spectrum signature Modulation Identification method.
Fig. 2 is the flow chart of estimating carrier frequency step in Fig. 1.
Fig. 3 is the flow chart of estimate symbol rate step in Fig. 1.
Fig. 4 is the flow chart obtaining Higher Order Cumulants parameter step in Fig. 1.
Fig. 5 is the flow chart obtaining quadratic power spectrum spectral peak number step in Fig. 1.
Fig. 6 is the flow chart obtaining biquadratic spectrum spectral peak number step in Fig. 1.
Fig. 7 A, 7B are that power spectrum is smooth front and back contrasts schematic diagram.
Fig. 8 A, 8B are contrast schematic diagrams before and after bpsk signal square spectrum Line enhancement.
Fig. 9 is simulation result figure of the present invention.
Figure 10 is various modulation system higher order cumulants value tables.
With detailed description of the invention, the present invention is described in further detail below in conjunction with the accompanying drawings.
Detailed description of the invention
As it is shown in figure 1, present invention satellite communication signals based on Higher Order Cumulants and spectrum signature Modulation Identification method, including Following steps:
10) signal bandpass filtering: receive pending signal data, and it is carried out bandpass filtering；
20) estimating carrier frequency: filtered signal is carried out segment processing, calculates the power spectrum of every segment signal, and right Power spectrum is smoothed, and utilizes frequency algorithm placed in the middle to estimate the carrier frequency of signal；
As in figure 2 it is shown, described estimating carrier frequency (20) step includes:
21) signal subsection: filtered signal data is divided into L section, and calculates the power spectrum X of every segment signal datai；
22) seek smooth power spectrum: multiple power spectrum superpositions, be averaging, obtain smooth power spectrum
23) effective bandwidth is intercepted: intercept smooth power spectrum effective bandwidth according to the Welch algorithm simplified；
Described intercepting effective bandwidth (23) step, particularly as follows: start search from smooth power spectrum two ends, finds out all of Minimum point, finds the minimum point of peak value both sides by arranging adaptive threshold, retains the frequency component between minimum point, Remaining component zero setting.
24) carrier frequency is obtained: utilize frequency algorithm placed in the middle to estimate signal(-) carrier frequency.
Described acquisition carrier frequency (24) step particularly as follows:
The carrier frequency of signal is
f c = Σ i = 1 N / 2 i | X ( i ) | 2 Σ i = 1 N / 2 | X ( i ) | 2 × f s N - - - ( 1 ) ,
Wherein, X (i) is the power spectrum of signal, and N is total sampling number, fsIt it is sample frequency.
30) estimate symbol speed: filtered signal carries out a square process, calculates its quadratic power spectrum, detects quadratic power The baseband spectrum line structure of spectrum, utilizes the character rate of the line structure characteristic estimating signal of character rate；
As it is shown on figure 3, described estimate symbol speed (30) step includes:
31) quadratic power spectrum calculates: filtered signal carries out a square process, calculates its quadratic power spectrum；
32) Line enhancement: each sample value to quadratic power spectrum pin, compares it and all sample points in adjacent domain The difference of meansigma methods, thus realize the Line enhancement of quadratic power spectrum；
Described Line enhancement (32) step particularly as follows:
Env _ new ( k ) = Env ( k ) Σ n = 1 L w ( n ) Env ( k + n ) , k ∈ ( 0 , N / 2 ] - - - ( 2 ) ,
Wherein, N is total sampling number,For the weighted average of signal envelope, filter length L × N, w (n) are mean filter weights,
The main thought of Line enhancement is aiming at square each sample value of spectrum, compares it all with in adjacent domain The difference of the meansigma methods of sample point, carries out maximum value search to the result after comparing the most again so that it is determined that character rate information.
33) symbol rate estimation: compose spectral line peak value according to the line structure Characteristics Detection base band quadratic power of character rate Position, if peak value and the ratio of average are more than decision threshold in search bandwidth, then the frequency values that spectrum peak position is corresponding is symbol Number speed.
40) obtain Higher Order Cumulants parameter: calculate Higher Order Cumulants, obtain Higher Order Cumulants parameter, provide threshold value；
As shown in Figure 4, described acquisition Higher Order Cumulants parameter (40) step includes:
41) Higher Order Cumulants is calculated: the fourth order cumulant C of signal calculated40、C42With six rank cumulants C63；
42) Higher Order Cumulants parameter is obtained: measured Higher Order Cumulants parameter by higher order cumulants
F1=| C63|, (4)
F 2 = | C 42 | | C 40 | ;
43) threshold value is provided: Higher Order Cumulants parameter F1, F2 according to raised cosine shaped signal are special with the change of signal to noise ratio Point, provides threshold value th1, th2.
50) APSK or 16QAM signal identification: if comparing according to signal Higher Order Cumulants parameter and threshold value, identify APSK or 16QAM signal, then identify that process terminates, and otherwise continues；
60) quadratic power spectrum spectral peak number is obtained: according to the line structure property calculation signal quadratic power of signal quadratic power spectrum The spectral peak number of spectrum；
As it is shown in figure 5, described acquisition quadratic power spectrum spectral peak number (60) step particularly as follows:
61) detection bandwidth sets: the line structure feature of detection quadratic power spectrum, sets monitoring bandwidth B2；
62) maximum time maximum value search: with twice carrier frequency 2FcCentered by certain band limits (2F of frequencyc-B2, 2Fc+B2Search maximum and time maximum in)；
63) spectral peak judgement: calculate the ratio of maximum and average and time maximum and average respectively, if greater than a certain door Limit then judgement is a spectral peak；
64) spectral peak number calculates: collects and obtains total spectral peak number C2.
70) BPSK or msk signal identification: if identifying BPSK or msk signal identification according to quadratic power spectral peak number, then Identification process terminates, and otherwise continues；
80) biquadratic spectrum is obtained: the signal after square process is carried out the high-pass filtering that cut-off frequency is carrier frequency, so After again carry out square processing, be calculated the biquadratic spectrum of signal；
As shown in Figure 6, described acquisition biquadratic spectrum spectral peak number (80) step includes:
81) detection bandwidth sets: the line structure feature of detection biquadratic spectrum, sets monitoring bandwidth B4；
82) maximum time maximum value search: with four times of carrier frequency 4FcCentered by certain band limits (4F of frequencyc-B4, 4Fc+B4Search maximum and time maximum in)；
83) spectral peak judgement: calculate the ratio of maximum and average and time maximum and average respectively, if greater than a certain door Limit then judgement is a spectral peak；
Described spectral peak judgement (83) step particularly as follows:
Extract n power spectrum discrete spectrum blob detection parameter as follows
C n = Max ( spec n ( i ) ) 1 i 2 - i 1 ( Σ i = i 1 i = i 2 spec n ( i ) - Max ( spec n ( i ) ) ) , i ∈ [ i 1 , i 2 ] - - - ( 5 ) ,
Wherein,Wherein n is the exponent number of higher-order spectrum, specnFor signal n power Spectrum, fcFor carrier frequency, B is detection bandwidth, and N is that Fourier transformation is counted, fsFor sample frequency.
Actual raised cosine shapes and often there is adjacent one another are or close maximum at the spectral peak of satellite communication signals with secondary Maximum, if pressing peak-to-average force ratio threshold judgement, then can be mistaken for two spectral peak lines, for avoiding this situation to occur, arranges spectral peak Interval determination point dn, if rule out two spectral line intervals are less than spectral peak spaced points, then shows really one spectral peak line, no Then, it is two spectral peak lines.
84) spectral peak number calculates: collects and obtains total spectral peak number C4.
90) biquadratic spectrum spectral peak number is obtained: according to the line structure property calculation signal biquadratic of signal biquadratic spectrum The spectral peak number of spectrum；
100) π/4DQPSK signal identification: if identifying π/4DQPSK signal according to biquadratic spectral peak number, then identify Process terminates, and otherwise continues；
If C4=2, then this signal is π/4DQPSK signal, terminates flow process.
110) base band quadratic power spectrum spectral peak number is obtained: according to the baseband spectrum line structure characteristic of signal quadratic power spectrum, detection The ratio of spectral line peak value and average in base band quadratic power spectrum band limits, then adjudicates as spectral peak if greater than a certain thresholding, finally It is calculated the baseband spectrum peak number mesh of signal quadratic power spectrum；
The baseband spectrum peak number mesh C2_bd of signal quadratic power spectrum.
120) 6PSK, 8PSK, OQPSK, QPSK signal identification: according to biquadratic spectrum spectral peak number and base band quadratic power spectrum spectrum Peak number mesh, finally identifies 6PSK, 8PSK, OQPSK, QPSK signal.
As C4=0, if C2_bd=0, then this signal is 6PSK signal, if C2_bd=1, then this signal is 8PSK letter Number, terminate flow process；As C4=1, if C2_bd=0, then this signal is OQPSK signal, if C2_bd=1, then this signal is QPSK signal, terminates flow process.
The present invention is directed to this application scenarios of satellite communication, take into full account satellite channel feature, by using higher order cumulants The method that amount and spectrum signature combine, constructs the satellite communication signals identification set of a comparatively perfect, by introducing power Spectrum smoothing and Line enhancement algorithm, improve the accuracy rate of parameter estimation and Modulation Mode Recognition, it is achieved that is not having priori Accurate Real time identification to conventional satellite communication signals in the case of knowledge.
Fig. 7 gives that the power spectrum of the present invention is smooth front and back contrasts situation, and Fig. 8 gives frequency after employing Line enhancement method Spectrum improvement situation.Fig. 9 gives employing Modulation Mode Recognition simulation result based on Higher Order Cumulants and spectrum signature.Emulation data Being 400 emulation symbols, the raised cosine roll off factor is 0.35.It can be seen from the results that 6PSK and APSK signal is big in signal to noise ratio When 8dB, recognition accuracy reaches more than 95%, and in addition to 6PSK and APSK signal, other signal is when signal to noise ratio is more than 5dB, identifies Accuracy rate is more than 95%, and BPSK, MSK and π/4DQPSK signal recognition accuracy when signal to noise ratio is more than 1dB reaches more than 95%, Demonstrate the feasibility of the method.
The invention provides a kind of satellite communication signals Modulation Mode Recognition method based on Higher Order Cumulants and spectrum signature, It should be pointed out that, for those skilled in the art, under the premise without departing from the principles of the invention, it is also possible to do Going out some improvements and modifications, these improvements and modifications also should be regarded as protection scope of the present invention.It addition, it is the clearest and the most definite in the present embodiment Each ingredient all can use prior art to be realized.

Claims (10)

1. a satellite communication signals Modulation Identification method based on Higher Order Cumulants and spectrum signature, it is characterised in that include as Lower step:
10) signal bandpass filtering: receive pending modulated signal data, and it is carried out bandpass filtering；
20) estimating carrier frequency: filtered signal is carried out segment processing, calculates the power spectrum of every segment signal, and to power Spectrum is smoothed, and utilizes frequency algorithm placed in the middle to estimate the carrier frequency of signal；
30) estimate symbol speed: filtered signal carries out a square process, calculates its quadratic power spectrum, detection quadratic power spectrum Baseband spectrum line structure, utilizes the character rate of the line structure characteristic estimating signal of character rate；
40) obtain Higher Order Cumulants parameter: calculate Higher Order Cumulants, obtain Higher Order Cumulants parameter, provide threshold value；
50) APSK or 16QAM signal identification: if comparing according to signal Higher Order Cumulants parameter and threshold value, identify APSK Or 16QAM signal, then identify that process terminates, and otherwise continues；
60) quadratic power spectrum spectral peak number is obtained: compose according to the line structure property calculation signal quadratic power of signal quadratic power spectrum Spectral peak number；
70) BPSK or msk signal identification: if identifying BPSK or msk signal according to quadratic power spectral peak number, then identify process Terminate, otherwise continue；
80) biquadratic spectrum is obtained: the signal after square process is carried out the high-pass filtering that cut-off frequency is carrier frequency, the most again Secondary carry out square processes, and is calculated the biquadratic spectrum of signal；
90) biquadratic spectrum spectral peak number is obtained: compose according to the line structure property calculation signal biquadratic of signal biquadratic spectrum Spectral peak number；
100) π/4DQPSK signal identification: if identifying π/4DQPSK signal according to biquadratic spectral peak number, then identify process Terminate, otherwise continue；
110) base band quadratic power spectrum spectral peak number is obtained: according to the baseband spectrum line structure characteristic of signal quadratic power spectrum, detect base band The ratio of spectral line peak value and average in quadratic power spectrum band limits, then adjudicates as spectral peak if greater than a certain thresholding, finally calculates Obtain the baseband spectrum peak number mesh of signal quadratic power spectrum；
120) 6PSK, 8PSK, OQPSK, QPSK signal identification: according to biquadratic spectrum spectral peak number and base band quadratic power spectrum spectral peak number Mesh, finally identifies 6PSK, 8PSK, OQPSK, QPSK signal.
Satellite communication signals Modulation Identification method the most according to claim 1, it is characterised in that described estimating carrier frequency (20) step includes:
21) signal subsection: filtered signal data is divided into L section, and calculates the power spectrum X of every segment signal datai；
22) seek smooth power spectrum: multiple power spectrum superpositions, be averaging, obtain smooth power spectrum
23) effective bandwidth is intercepted: intercept algorithm according to the Welch power spectrum simplified and intercept smooth power spectrum effective bandwidth；
24) carrier frequency is obtained: utilize frequency algorithm placed in the middle to estimate signal(-) carrier frequency.
Satellite communication signals Modulation Identification method the most according to claim 2, it is characterised in that described intercepting effective bandwidth (23) first step particularly as follows: start search from smooth power spectrum two ends, finds out all of minimum point, adaptive by arranging Answer thresholding to find the minimum point of peak value both sides, then retain the frequency component between minimum point, remaining component zero setting.
Satellite communication signals Modulation Identification method the most according to claim 2, it is characterised in that described acquisition carrier frequency (24) step particularly as follows:
The carrier frequency of signal is
f c = Σ i = 1 N / 2 i | X ( i ) | 2 Σ i = 1 N / 2 | X ( i ) | 2 × f s N - - - ( 1 ) ,
Wherein, X (i) is the power spectrum of signal, and N is total sampling number, fsIt it is sample frequency.
Satellite communication signals Modulation Identification method the most according to claim 1, it is characterised in that described estimate symbol speed (30) step includes:
31) quadratic power spectrum calculates: filtered signal carries out a square process, calculates its quadratic power spectrum；
32) Line enhancement: each sample value to quadratic power spectrum, compares it and the meansigma methods of all sample points in adjacent domain Difference, thus realize quadratic power spectrum Line enhancement；
33) symbol rate estimation: compose the position of spectral line peak value according to the line structure Characteristics Detection base band quadratic power of character rate, If peak value and the ratio of average are more than decision threshold in search bandwidth, then the frequency values that spectrum peak position is corresponding is symbol speed Rate.
Satellite communication signals Modulation Identification method the most according to claim 5, it is characterised in that described Line enhancement (32) Step particularly as follows:
E n v _ n e w ( k ) = E n v ( k ) Σ n = 1 L w ( n ) E n v ( k + n ) , k ∈ ( 0 , N / 2 ] - - - ( 2 ) ,
Wherein, N is total sampling number,For the weighted average of signal envelope, filter length L × N, w N () is mean filter weights,
Satellite communication signals Modulation Identification method the most according to claim 1, it is characterised in that described acquisition higher order cumulants Amount parameter (40) step includes:
41) Higher Order Cumulants is calculated: the fourth order cumulant C of signal calculated40、C42With six rank cumulants C63；
42) Higher Order Cumulants parameter is obtained: measured Higher Order Cumulants parameter by higher order cumulants
F1=| C63|, (4)
F 2 = | C 42 | | C 40 | ;
43) threshold value is provided: according to Higher Order Cumulants parameter F1 of raised cosine shaped signal, F2 with the Variation Features of signal to noise ratio, Provide threshold value th1, th2.
Satellite communication signals Modulation Identification method the most according to claim 1, it is characterised in that described acquisition quadratic power is composed Spectral peak number (60) step particularly as follows:
61) detection bandwidth sets: the line structure feature of detection quadratic power spectrum, sets monitoring bandwidth B2；
62) maximum time maximum value search: with twice carrier frequency 2FcCentered by certain band limits (2F of frequencyc-B2,2Fc+ B2Search maximum and time maximum in)；
63) spectral peak judgement: calculate the ratio of maximum and average and time maximum and average respectively, if greater than a certain thresholding then Judgement is a spectral peak；
64) spectral peak number calculates: collects and obtains total spectral peak number C2.
Satellite communication signals Modulation Identification method the most according to claim 1, it is characterised in that described acquisition biquadratic is composed Spectral peak number (80) step includes:
81) detection bandwidth sets: the line structure feature of detection biquadratic spectrum, sets monitoring bandwidth B4；
82) maximum time maximum value search: with four times of carrier frequency 4FcCentered by certain band limits (4F of frequencyc-B4,4Fc+ B4Search maximum and time maximum in)；
83) spectral peak judgement: calculate the ratio of maximum and average and time maximum and average respectively, if greater than a certain thresholding then Judgement is a spectral peak；
84) spectral peak number calculates: collects and obtains total spectral peak number C4.
Satellite communication signals Modulation Identification method the most according to claim 9, it is characterised in that described spectral peak is adjudicated (83) step particularly as follows:
Extract n power spectrum discrete spectrum blob detection parameter as follows
C n = M a x ( spec n ( i ) ) 1 i 2 - i 1 ( Σ i = i 1 i = i 2 spec n ( i ) - M a x ( spec n ( i ) ) ) , i ∈ [ i 1 , i 2 ] - - - ( 5 ) ,
Wherein,Wherein n is the exponent number of higher-order spectrum, specnCompose for signal n power, fc For carrier frequency, B is detection bandwidth, and N is that Fourier transformation is counted, fsFor sample frequency, actual raised cosine shapes satellite communication Adjacent one another are or close maximum and time maximum is often there is, if pressing peak-to-average force ratio threshold judgement, then at the spectral peak of signal Two spectral peak lines can be mistaken for, for avoiding this situation to occur, spectral peak interval determination point dn is set, if rule out two spectrums Line interval less than spectral peak spaced points, then shows really one spectral peak line, otherwise, is two spectral peak lines.
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CN112769724B (en) * 2021-04-07 2021-07-02 广州慧睿思通科技股份有限公司 Method, device and equipment for identifying signal type of interphone and storage medium
CN114422016B (en) * 2021-12-28 2024-01-02 成都爱科特科技发展有限公司 PCMA signal identification method
CN114915525A (en) * 2022-04-01 2022-08-16 北京鹏鹄物宇科技发展有限公司 Signal detection method and system based on maximum likelihood ratio multi-antenna modulation recognition

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7428270B1 (en) * 1999-02-15 2008-09-23 Christian Dubuc Method and system for detecting and classifying the modulation of unknown analog and digital telecommunications signals
CN101674270A (en) * 2009-10-16 2010-03-17 电子科技大学 Identification method of low signal-to-noise ratio phase-shift keying modulating signal
CN102571230A (en) * 2011-12-22 2012-07-11 中国人民解放军总参谋部第六十三研究所 Distributed collaborative signal identification method based on blind estimation of higher order statistics and signal to noise ratio
CN103441974A (en) * 2013-08-26 2013-12-11 东南大学 Modulation and recognition device and method for union high-order statistic and spectral peak features

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7428270B1 (en) * 1999-02-15 2008-09-23 Christian Dubuc Method and system for detecting and classifying the modulation of unknown analog and digital telecommunications signals
CN101674270A (en) * 2009-10-16 2010-03-17 电子科技大学 Identification method of low signal-to-noise ratio phase-shift keying modulating signal
CN102571230A (en) * 2011-12-22 2012-07-11 中国人民解放军总参谋部第六十三研究所 Distributed collaborative signal identification method based on blind estimation of higher order statistics and signal to noise ratio
CN103441974A (en) * 2013-08-26 2013-12-11 东南大学 Modulation and recognition device and method for union high-order statistic and spectral peak features

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Higher-order cyclic cumulants for high order modulation classification;Octavia A.Dbbre,等;《MILCOM 2013 - 2013 IEEE Military Communications Conference》;20131120;全文 *
Software-defined radio based automatic blind hierarchical modulation detector via second-order cyclostationary analysis and fourth-order cumulant;Yang Qu，等;《Military Communications Conference, 2003. MILCOM "03. 2003 IEEE (Volume:1 )》;20031016;全文 *
卫星通信常用调制方式的自动识别;范海波、杨志俊、曹志刚;《通信学报》;20040131;第25卷(第1期);全文 *
通信信号调制识别方法简析;马兆宇、边东明、张更新;《军事通信技术》;20120930;第33卷(第3期);全文 *

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