CN109120562A - One kind is added up matched MFSK signal frequency estimation method based on frequency spectrum - Google Patents
One kind is added up matched MFSK signal frequency estimation method based on frequency spectrum Download PDFInfo
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- CN109120562A CN109120562A CN201810883533.7A CN201810883533A CN109120562A CN 109120562 A CN109120562 A CN 109120562A CN 201810883533 A CN201810883533 A CN 201810883533A CN 109120562 A CN109120562 A CN 109120562A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0014—Carrier regulation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/10—Frequency-modulated carrier systems, i.e. using frequency-shift keying
- H04L27/106—M-ary FSK
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
- H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0014—Carrier regulation
- H04L2027/0024—Carrier regulation at the receiver end
- H04L2027/0026—Correction of carrier offset
- H04L2027/0032—Correction of carrier offset at baseband and passband
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Abstract
The invention belongs to signal processing technology field, it is related to a kind of adding up matched MFSK signal frequency estimation method based on frequency spectrum.The present invention receives signal subsection to non-partner first and does Fast Fourier Transform (FFT) (FFT), and modulus obtains frequency spectrum.Then the corresponding addition of first half element to every section, the data after obtaining accumulation process.Next different intervals, starting point are pressed in the data after accumulation process, continuously take m sections (m is modulation system), this m sections of corresponding element obtains element of the maximum value of vector as matrix M corresponding position after being added.The last position according to matrix M maximum value, finds out MFSK frequency modulation(PFM) parameter.The invention can effectively estimate MFSK signal modulation frequency, calculation amount is greatly reduced due to not needing time frequency analysis compared to based on estimation methods such as wavelet transformation, Short Time Fourier Transforms, and it is of less demanding to data length is received, it is still with good performance under low signal-to-noise ratio.
Description
Technical field
The invention belongs to signal processing technology field, it is related to a kind of adding up matched MFSK signal frequency estimation based on frequency spectrum
Method.
Background technique
Multi system frequency shift key control (MFSK) have good anti-multipath delay character, be widely used in recent years shortwave,
Ultrashort wave radio set and underwater sound communication channel.Carrying out accurate modulation parameter blind estimate to such signal is that non-cooperation recipient is complete
At an important content in the necessary links and spectrum monitoring and communication countermeasure of Signal Matching identification and blind demodulation.At present
Have numerous studies to the blind Receiver Problem of MFSK signal, including Modulation Identification, the symbol rate to signal, symbol synchronization information, with
And the signal demodulation on these Research foundations, but it is few for the document of frequency information direct estimation.Current main method
Have based on mountain clustering, based on time frequency analysis, based on the estimation method of all-digital phase-locked loop.
Method based on mountain clustering mainly estimates cluster centre as MFSK signal after obtaining signal spectrum
Modulating frequency.But artificially give some parameters such as clusters number initial cluster center to clustering algorithm needs in advance, and is not having
In the case where having priori knowledge, artificially determine these parameters be it is very subjective with it is difficult.And the effect under Low SNR
It is bad.
Method based on time frequency analysis obtains MFSK signal time-frequency by wavelet transformation or Short Time Fourier Transform (STFT)
Change waveform, obtains symbol jumping moment pulse after carrying out differential transformation, extract its timed-branching estimating code element rate and carry out
Then symbol synchronization obtains carrier frequency information.But the local frequencies in signal are extracted by wavelet transformation and change the moment to obtain
The process of character rate and jumping moment error under low signal-to-noise ratio is larger, and noiseproof feature is bad, while wavelet transform dimension pair
As a result it is affected.Short Time Fourier Transform is a kind of one of very common time frequency analyzing tool, is converted by Rational choice
Parameter can obtain signal time-frequency figure, to estimate the relevant parameter of signal.But due to Heisenberg uncertainty principle
Limitation, there are the contradictions between time frequency resolution for Short Time Fourier Transform, cause Parameter Estimation Precision by time-frequency figure resolution ratio shadow
Sound is larger, and the parameter of Short Time Fourier Transform is not easy to choose.Under blind condition of acceptance, since signal parameter is unknown, time-frequency figure ginseng
Several determinations is also more difficult.
Compared to time frequency analysis, data volume needed for the method based on all-digital phase-locked loop is smaller, and this method is based on improved number
Word phaselocked loop (enhanced phase-lock loop, EPLL) track locked signal variation, provide signal amplitude, frequency and
The tracking result of instantaneous phase.But this method is mainly for the demodulation of MFSK signal, to the estimation of frequency rely primarily on frequency with
Track signal, is difficult to realize in non-cooperative communication, can not be widely applied.
Summary of the invention
The technical problems to be solved by the invention are exactly that a kind of MFSK signal frequency information is provided in blind received situation
The method accurately estimated.The present invention mainly by the frequency spectrum of signal, using the fixed characteristic of MFSK signal modulation frequency increment into
Row analysis.The method present invention compared to time frequency analysis does not need time-frequency figure；It is more accurate compared to mountain clustering, noiseproof feature
More preferably, the still evaluated error very little under low signal-to-noise ratio.
The technical solution adopted by the present invention are as follows:
One kind is added up matched MFSK signal frequency estimation method based on frequency spectrum, is received signal subsection to non-cooperation and is done quickly
Fourier transformation (FFT) obtains frequency spectrum and adds up, and using the characteristic that modulation intervals are fixed, MFSK tune is pressed after frequency spectrum accumulation process
System processed scans for.If frequency interval matches, MFSK signal spectrum energy peak is corresponding, and accumulated value is maximum, and
The interior cumulative maximum value in an interval of the search starting point before beginning modulation frequency is all identical.Specific method the following steps are included:
S1, set certain non-partner receive signal asWherein N is signal length.Determine segments
By dataIt is divided into K sections, wherein L is the length of every segment data,It indicates to be rounded downwards, the data vector after being segmented
si
si={ y(i-1)L+1, y(i-1)L+2..., yiL, i=1,2 ..., K；
S2, to siFFT transform is done, frequency spectrum w is obtainedi；
S3, to { wi, the first half element of i=1 ..., K are cumulative according to formula (1), vector spec is obtained,
S4, setting search variables Δ f=1；
S5, according to formula (2) operation and determine matrix PΔfThe all elements of (f, t), wherein f=1,2 ..., L/2-M
Δ f, t=1,2 ..., Δ f, M are known modulation system；
S6, matrix P is found out according to formula (3)ΔfThe maximum value of every a line, and be stored in matrix M (Δ f, f),
M (Δ f, f)=maxPΔf(f :) formula (3)
S7, judgementIf it is, Δ f=Δ f+1, and repeat S5, S6；If it is not, then entering step
S8；
Column locations (g corresponding to maximum value in S8, search M (Δ f, f)d, hd), d=1 ..., D are estimated by formula (4)
M modulating frequency of MFSK signal is obtained,
Wherein m=0 ..., M-1.
Beneficial effects of the present invention are, in the case where just knowing that the other situation of MFSK class signal, carry out intelligence to signal spectrum and search
Rope realizes that modulating frequency is accurately estimated, and estimation procedure does not need time-frequency map analysis, greatly reduces calculation amount and parameter shadow
It rings, it is smaller by SNR influence；Accurately priori knowledge is provided for subsequent demodulation.
Detailed description of the invention
Fig. 1 present invention realizes process flow diagram flow chart；
Different state of signal-to-noise lower frequency estimation normalization root-mean-square errors in Fig. 2 embodiment of the present invention 1；
Different state of signal-to-noise lower frequency estimation normalization root-mean-square errors in Fig. 3 embodiment of the present invention 2.
Specific embodiment
Below in conjunction with drawings and examples, technical solution of the present invention is further described.
Embodiment 1
The purpose of the present embodiment be under different signal-to-noise ratio scenes to different 4FSK signal center frequencies and frequency increment into
Accurate 4FSK signal frequency information estimation may be implemented in row estimation, verifying inventive method.Signal length is emulated in the present embodiment
For N=62500, sample rate Fs=6250Hz, symbol, chip rate 125Baud, the centre frequency of signal modulation is randomly generated
For f0=2000Hz, data frequency difference Af=300Hz, with the stepping 1 from -5dB to 10dB of interior signal-to-noise ratio.According to the side of invention
Formula:
S1, reception signal are { y1, y2..., yN, K=10 sections are splitted data into, every segment length L=6250 is obtained
S2, to every segment data siFFT transform is done, w is obtainedi, i=1,2 ..., 10；
S3, to { wi, the first half element of i=1 ..., K add up according to the following formula, vector spec is obtained,
S4, setting search variables Δ f=1；
S5, M=4 length is continuously taken to be the vector section of Δ f, according to the following formula operation and determination since f on spec
Matrix PΔfThe all elements of (f, t), wherein f=1,2 ..., 3125-4 Δ f, t=1,2 ..., Δ f；
S6, matrix P is found out according to the following formulaΔfThe maximum value of every a line, and be stored in matrix M (Δ f, f),
M (Δ f, f)=maxPΔf(f :)
S7, judgementIf it is, Δ f=Δ f+1, and repeat S5, S6；If it is not, then into
Step S8；
Column locations (g corresponding to maximum value in S8, search M (Δ f, f)d, hd), d=1 ..., D are estimated by following formula
To the frequency increment and center frequency estimation value of 4FSK signal,
100 Monte Carlo experiments are repeated under each signal-to-noise ratio, pass through the normalization of frequency increment and center frequency estimation
Root-mean-square error (NRMSE) measures its performance:
It obtains frequency increment and center frequency estimation normalization root-mean-square error is as shown in Figure 2.
Embodiment 2
The purpose of the present embodiment be under different signal-to-noise ratio scenes to 2FSK, 8FSK signal center frequency and frequency increment into
Row estimation, verifying inventive method estimate performance.It is N=40000, sample rate Fs=that signal length is emulated in the present embodiment
8000Hz, is randomly generated symbol, chip rate 160Baud, and the centre frequency of signal modulation is f0=2000Hz, data frequency are poor
Value △ f=300Hz, with interior signal-to-noise ratio from 0dB to 10dB stepping 1.100 tests are done according to the identical mode of embodiment 1, are obtained
It is as shown in Figure 3 to normalize RMS estimation error.
The result shows that the mentioned method of the present invention can be with accurate estimation MFSK signal frequency information, especially in low signal-to-noise ratio
Accurate estimation still may be implemented.To 4FSK signal, in 1dB, frequency increment estimation normalization root-mean-square error is less than -16dB,
Centre frequency normalization estimation root-mean-square error is less than -24%.To 8FSK signal, in 2dB, frequency increment estimation normalization is square
Root error is less than -16dB, and center frequency estimation normalizes root-mean-square error and is less than -22dB, and more to 2FSK signal estimation performance
It is good.The time-frequency figure for not doing signal illustrates that this method can solve the estimation of MFSK frequency modulation(PFM) parameter directly on frequency domain and ask
Topic.
Claims (1)
1. one kind is added up matched multi system frequency shift key control MFSK signal frequency estimation method based on frequency spectrum, known signal class is set
Not, which comprises the following steps:
S1, set non-partner receive signal asWherein N is signal length；Determine segmentsBy dataIt is divided into K sections, wherein L is the length of every segment data,It indicates to be rounded downwards, the data vector s after being segmentedi:
si={ y(i-1)L+1,y(i-1)L+2,…,yiL, i=1,2 ..., K；
S2, to siFFT transform is done, frequency spectrum w is obtainedi；
S3, to { wi, the first half element of i=1 ..., K are cumulative according to formula 1, obtain vector spec:
S4, setting search variables △ f=1；
S5, matrix P is determined according to formula 2△fThe all elements of (f, t), wherein f=1,2 ..., L/2-M △ f, t=1,2 ...,
△ f, M are known modulation system；
S6, matrix P is found out according to formula 3△fThe maximum value of every a line, and be stored in matrix M (△ f, f):
M (△ f, f)=maxP△f(f :) (formula 3)
S7, judgementIt is whether true, if it is, △ f=△ f+1, and repeat step S5, S6；If it is not, then
Enter step S8；
Column locations (g corresponding to maximum value in S8, search M (△ f, f)d,hd), d=1 ..., D are obtained by the estimation of formula 4
M modulating frequency of MFSK signal,
Wherein m=0 ..., M-1.
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| CN112543162A (en) * | 2020-11-12 | 2021-03-23 | 重庆邮电大学 | Short wave communication time-frequency joint synchronization method based on Costas sequence |
| CN114036458A (en) * | 2021-10-22 | 2022-02-11 | 哈尔滨工程大学 | Non-cooperative underwater acoustic signal time frequency information acquisition method |
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Cited By (4)
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| CN112543162A (en) * | 2020-11-12 | 2021-03-23 | 重庆邮电大学 | Short wave communication time-frequency joint synchronization method based on Costas sequence |
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| CN114036458A (en) * | 2021-10-22 | 2022-02-11 | 哈尔滨工程大学 | Non-cooperative underwater acoustic signal time frequency information acquisition method |
| CN114036458B (en) * | 2021-10-22 | 2024-04-30 | 哈尔滨工程大学 | Non-cooperative underwater acoustic signal time-frequency information acquisition method |
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