CN110149290B - Frequency offset rough estimation method suitable for low signal-to-noise ratio high dynamic environment - Google Patents
Frequency offset rough estimation method suitable for low signal-to-noise ratio high dynamic environment Download PDFInfo
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Abstract
A frequency deviation rough estimation method suitable for a low signal-to-noise ratio high dynamic environment belongs to the technical field of communication. The invention obtains the amplitude-frequency characteristic through the preset shaping factor of the shaping filter in the communication system, then obtains the amplitude-frequency characteristic for the signal received by the receiver, and after the two complete the correlation operation, obtains the frequency corresponding to the point with the maximum correlation, and the frequency value is the result of the rough estimation of the frequency offset. The method is a frequency offset blind estimation method, can be generally used as the first stage of receiver baseband signal processing, can greatly reduce the residual frequency offset of a received signal after rough frequency offset estimation and compensation, and fully simplifies the subsequent complexity of realization. Compared with the prior art, the frequency offset rough estimation method has the advantages of less required prerequisites, low adaptive signal-to-noise ratio, large dynamic range, very wide application scene and very good use value.
Description
Technical Field
The invention relates to a frequency offset rough estimation method suitable for a low signal-to-noise ratio high dynamic environment, and belongs to the technical field of communication.
Background
The satellite mobile communication has the characteristics of high dynamic and low signal-to-noise ratio, which causes the satellite communication signal to have large Doppler frequency offset and high-order frequency change rate, and brings difficulty to the synchronization and demodulation of the signal. In satellite mobile communication, due to rapid relative motion between a satellite and a communication terminal, large Doppler frequency offset is brought; at the same time, the symbol rate in mobile satellite communications tends to be low, subject to satellite and terminal capabilities. The combined result of the two results leads to that in satellite mobile communication, the Doppler frequency offset can often reach dozens of times or even hundreds of times of the symbol rate, and meanwhile, the serious frequency change rate exists.
Synchronization of signals can be divided into carrier synchronization and bit timing synchronization. In a typical receiver, bit timing synchronization of a received signal is usually first completed, and then carrier synchronization (including frequency synchronization and phase synchronization) is further completed based on the bit timing synchronization. However, this is premised on that the received signal only has a small frequency offset (generally, the frequency offset is required to be limited within 10% of the symbol rate), and if the frequency offset is further increased, the performance of bit timing synchronization is seriously deteriorated, even the bit timing synchronization cannot be completed.
Aiming at the characteristics of a high dynamic environment, the carrier synchronization of the signals can be divided into two parts: coarse synchronization and fine synchronization. Coarse synchronization, also referred to as parameter acquisition, initially estimates and compensates the frequency offset of a received signal in a high dynamic environment, cancels the frequency offset or sends a parameter estimation value to a tracker to help the tracker to quickly enter a locked state, and the latter enters the tracker to perform higher-precision estimation (including carrier frequency, phase and bit timing estimation) on the basis of the former.
Currently, the commonly used frequency offset estimation methods mainly include a data-aided mode and a non-data-aided mode. The data-aided mode usually utilizes pilot frequency (or physical frame header, unique word, etc.) to realize the estimation of frequency offset, but in the application context of the scene, the frame structure adopts the mode of inserting pilot frequency at intervals to deal with the problem of rotor wing shielding, which causes the speed of known data to be greatly reduced, and then the range of frequency offset estimation carried out by the data-aided mode is completely exceeded by combining the high dynamic application environment of the data-aided mode. For the conventional blind estimation method, the estimation function usually cannot work normally under such a low signal-to-noise ratio.
Disclosure of Invention
The technical problem solved by the invention is as follows: the method overcomes the defects of the prior art, provides a frequency offset rough estimation method suitable for a low signal-to-noise ratio high dynamic environment, completes initial frequency offset estimation under an extremely low signal-to-noise ratio and a high dynamic scene, can greatly reduce residual frequency offset of a received signal after rough frequency offset estimation and compensation, and fully simplifies subsequent implementation complexity. The method can be generally used as a first stage of receiver baseband signal processing, and is a frequency offset blind estimation method.
The technical solution of the invention is as follows: a frequency offset rough estimation method adapting to a low signal-to-noise ratio high dynamic environment comprises the following steps:
obtaining a first amplitude-frequency characteristic of the shaping filter by using a shaping factor of the shaping filter preset in a communication system;
obtaining a second amplitude-frequency characteristic of a signal received by the receiver;
and calculating the correlation values of the two amplitude-frequency characteristics, wherein the frequency offset at the maximum correlation value is the coarse frequency offset estimation result.
Further, the method for obtaining the first amplitude-frequency characteristic by using the shaping factor of the shaping filter predetermined in the communication system includes:
obtaining time domain impact response h of a shaping filter by using shaping factor rf and prior parameterrcos；
For the time domain impulse response hrcosObtaining corresponding frequency domain characteristic h by solving Fourier transformrcos_fft；
For the frequency domain characteristic hrcos_fftTaking the mold, and cutting to obtain a first amplitude-frequency characteristic hrcos_fft_abs_window。
Further, the a priori parameters include a sampling multiple N of the receiver and a length L of a time domain impulse responsesymbol。
Further, the signal received by the receiver is a baseband signal after down-conversion and variable-rate filtering.
Further, the sampling rate R of the variable rate filteringbFrom a sampling multiple N and a symbol rate RsAnd Rb ═ N × Rs.
Further, the length of the first amplitude-frequency characteristic after the truncation is LwindowSaid L iswindowDetermined by the sampling multiple N and the shaping factor rf.
Further, said Lwindow＝NFFTN (1+ rf); wherein N isFFTThe number of points of the fourier transform.
Further, the method for obtaining the second amplitude-frequency characteristic from the signal received by the receiver comprises:
selecting an access window for receiving a signal;
selecting the step of sliding the access window;
calculating the frequency domain characteristic X of the received signal in each step access windowdata_fft；
For the frequency domain characteristic Xdata_fftObtaining a second amplitude-frequency characteristic X of the signal by taking the modeldata_fft_abs。
Further, the method for calculating the correlation value of the two amplitude-frequency characteristics comprises the following steps: the convolution of the two amplitude-frequency characteristics is calculated.
Further, the result of the coarse frequency offset estimation is
Ffreq_esti＝(Nindex-NFFT/2)/NFFT×Rb(ii) a Wherein N isindexThe frequency offset at which the correlation value is largest.
Compared with the prior art, the invention has the advantages that:
(1) the method mainly solves the problem of initial frequency offset estimation in a large dynamic environment, needs less prior information (only needs the forming factor of a forming filter), has a large adaptive dynamic range (the normalized frequency offset can reach 50 percent), and can be widely applied.
(2) The invention is also suitable for the environment with low signal-to-noise ratio, and can achieve good rough estimation effect under very low signal-to-noise ratio.
(3) The invention can be generally used as the first stage of baseband signal processing of a receiver, can greatly reduce the residual frequency offset of a received signal after rough frequency offset estimation and compensation, and fully simplifies the complexity of subsequent realization.
Drawings
Fig. 1 is a composition of a transmission signal of the communication system;
FIG. 2 is a block diagram of an implementation of the present invention;
FIG. 3 simulated signal amplitude-frequency characteristics;
figure 4 illustrates the amplitude-frequency characteristics of the simulated shaped filter;
FIG. 5 shows the result of the calculation of the amplitude-frequency characteristic of the simulated signal and the amplitude-frequency characteristic of the shaping filter;
fig. 6 shows the statistical result of the residual frequency difference after multiple times of simulated frequency offset coarse estimation and compensation.
Detailed Description
A frequency offset rough estimation method adapting to a low signal-to-noise ratio high dynamic environment comprises the following steps:
(1) obtaining the amplitude-frequency characteristic of the communication system by using the preset shaping factor of the shaping filter;
(2) the amplitude-frequency characteristic of a signal received by a receiver is obtained;
(3) and calculating the correlation value of the two, wherein the frequency offset at the position with the maximum correlation value is the frequency offset rough estimation result.
The shaping factor of the shaping filter predetermined in the communication system is known information common to the communication system, and both the transmitter and the receiver know the information.
The step (1) of obtaining the amplitude-frequency characteristic by using the shaping factor of the shaping filter predetermined in the communication system includes:
(1) obtaining time domain impact response h of shaping filter by utilizing shaping factor rfrcos；
(2) Fourier transform (N) is obtained for the time domain impulse responseFFTPoint FFT) to obtain the corresponding frequency domain characteristic hrcos_fft；
(3) The frequency domain characteristic is modeled to obtain a corresponding amplitude-frequency characteristic hrcos_fft_abs。
In practical implementation, the time domain impulse response of the shaping filter is obtained by using the shaping factor, and the sampling multiple N (number of sampling points per symbol) and the length L of the time domain impulse response are combined when the receiver processes the time domain impulse responsesymbolThe combination of the three can uniquely determine the time domain impact response h of the filterrcos。
The signal received by the receiver is a baseband signal after down-conversion and variable-rate filtering, and the specific sampling rate RbDependent on the sampling multiple NAnd symbol rate RsI.e., Rb ═ N × Rs.
The amplitude-frequency characteristic of the shaping filter needs to be intercepted by a certain length LwindowThe length value needs to consider a sampling multiple N and a formation factor rf, and specifically includes:
intercepting the amplitude-frequency characteristic of the formed filter according to a formula 1, wherein the interception length is Lwindow：
Lwindow＝NFFTN (1+ rf) (equation 1)
hrcos_fft_absThe intercepted signal is hrcos_fft_abs_window。
The step (2) of obtaining the amplitude-frequency characteristic of the signal received by the receiver comprises the following steps:
(1) selecting a received signal access window LdataIn the case of burst signals, the length of the access window is preferably greater than the length L of a burstburst；
(2) Selecting the step of each sliding of the calculation window, the step value LstepSelecting a suggestion smaller than the difference between the calculation window length and the burst length;
selecting the step length as L according to equation 2step：
Lstep<Ldata-Lburst(formula 2)
(3) Each time the signal X is received in the windowdataFrequency domain characteristic X ofdata_fft；
(4) The frequency domain characteristic is modeled to obtain an amplitude-frequency characteristic X of the signaldata_fft_abs；
The step (3) of calculating the correlation value of the two comprises the following steps:
(1) the window length of the correlation calculation is the number N of the whole FFT pointsFFT；
(2) The correlation calculation is a correlation in the frequency domain;
(3) one of the signals of the correlation calculation is hrcos_fft_abs_windowThe other signal being Xdata_fft_abs。
The serial number N of the position with the maximum correlation valueindexThe corresponding frequency deviation is the estimation of the coarse frequency deviationValue Ffreq_estiSpecifically, the method comprises the following steps:
Ffreq_esti＝(Nindex-NFFT/2)/NFFT×Rb(formula 3)
The principle formula used is derived as follows:
for the transmit signal composition shown in fig. 1:
the source x (t) after the action of the shaping filter yields w (t):
w(t)＝x(t)*hrcos(t) (equation 4)
After introducing the frequency offset, there are:
and adding Gaussian noise to obtain a final output signal:
the frequency domain form is:
Z(jω)＝[X(jω)·Hr cos(jω)]*[2πδ(ω-ω0)]+N0/2 (equation 7)
This signal is the output of the transmitter and is also the receiver input signal R (j ω), so:
R(jω)＝Z(jω)＝[X(jω)·Hr cos(jω)]*[2πδ(ω-ω0)]+N0/2 (formula 8)
Taking the amplitude-frequency characteristic of R (j omega), and carrying out convolution operation on the amplitude-frequency characteristic of the transmitter shaping filter coefficient, namely:
and | a × B | ═ a | × | B |, so:
because | A + B | is less than or equal to | A | + | B |, therefore:
when F (j ω) is the maximum value, ω is found, and whether both F' (j ω) and N (j ω) are the maximum value or not can be determined.
First, F' (j ω):
let omega*-ω0＝ωΔThen there are:
Hr costhe distribution of (j ω) is even symmetric, i.e.:
the meaning of the above formula is that X (j omega) and H are calculatedr cos(j ω) and Hr cos{j[ω-ω']The shadow area of the overlapping area of the integral of the amplitude-frequency characteristics of the three components over the whole frequency is 0 when the shadow area of the overlapping area is the maximum.
And ω' ═ ω - ω0Therefore: omega-omega0。
That is, the value of ω at the time when the shadow area of the overlap region is maximum is the frequency offset value at that time.
Let ω be ω0Analysis is made whether N (j ω) can take the maximum value at this time:
it can be seen that the actual meaning of N (j ω)To be Hr cos(j ω) after shifting ω in the frequency domain to obtain the amplitude-frequency characteristic, integrating the amplitude-frequency characteristic in the whole frequency domain to obtain the shadow area, wherein the value is a constant.
Thus, there are
Namely, the frequency at the moment when the shadow area is maximum is the frequency offset value of the current system.
By utilizing the theory, the rough estimation of the frequency offset can be realized.
The specific implementation steps of the invention are carried out according to fig. 2.
The method comprises the following steps: obtaining the frequency domain characteristics of the input signal and the shaping filter impulse response
Assuming that the number of sampling points of the input signal is M, the FFT operation is performed on the input signal, and the number of FFT points is N in generalfftMore than or equal to 2M; obtaining a frequency domain characteristic X (j ω) ═ F (X (t)) of the input signal;
similarly, FFT operation is performed on the impulse response h (t) of the shaping filter, with the number of FFT points being NfftThe frequency domain characteristic H (j ω) of the shaped filter impulse response is obtained as F (H (t)).
Step two: obtaining amplitude-frequency characteristics of input signal and amplitude-frequency characteristics of impulse response of shaping filter
The amplitude-frequency characteristic | X (j ω) | ═ abs (| F (X (t)) |) of the input signal is obtained.
Obtaining the amplitude-frequency characteristic of the shaped filter impulse response: h (j ω) | ═ abs (| F (H (t)) |).
Step three: and (4) convolving the amplitude-frequency characteristic of the input signal and the amplitude-frequency characteristic of the impulse response of the shaping filter, and solving the maximum value of the convolution result.
max(|X(jω)|*|H(jω)|)＝max[abs(|F(h(t))|)*abs(|F(x(t))|)]
Step four: according to the FFT point serial number N when the convolution result is maximumindexObtaining the frequency deviation estimated value at the moment, wherein the sampling rate RbDependent on the sampling multiple N and the symbol rate RsI.e., Rb ═ N × Rs.
Ffreq_esti＝(Nindex-Nfft/2)/Nfft×Rb
The following steps are performed according to the above procedure to obtain a coarse frequency offset estimation result of the system.
Inputting: the systematic symbol rate is 3.65ksps, SNR is 2dB, the shaping factor is 0.35, and the number of FFT points is 9920.
After QPSK modulation is performed on input baseband data, frequency offset-1.5 kHz (normalized frequency offset 40%) is added, then noise is added, and simulation is performed according to the method to obtain the amplitude-frequency characteristic of the signal as shown in figure 3.
The amplitude-frequency characteristics of the shaping filter obtained by using the shaping factor are shown in fig. 4.
The results obtained by performing correlation calculation on the amplitude-frequency characteristics of the two signals are shown in fig. 5.
Taking the FFT point number corresponding to the maximum value in fig. 5, or the frequency offset value at this time, that is, the estimation result.
The simulation of the frequency offset rough estimation method suitable for the low signal-to-noise ratio and high dynamic environment is completed 100 times, and after rough frequency offset is estimated and compensated, the statistical result of the residual frequency offset is obtained as shown in fig. 6.
Those skilled in the art will appreciate that those matters not described in detail in the present specification are well known in the art.
Claims (8)
1. A frequency offset rough estimation method adapting to a low signal-to-noise ratio high dynamic environment is characterized by comprising the following steps:
obtaining a first amplitude-frequency characteristic of the shaping filter by using a shaping factor of the shaping filter preset in a communication system;
obtaining a second amplitude-frequency characteristic of a signal received by the receiver;
calculating correlation values of the two amplitude-frequency characteristics, wherein the frequency offset at the maximum correlation value is a frequency offset rough estimation result;
the method for obtaining the first amplitude-frequency characteristic by using the shaping factor of the shaping filter preset in the communication system comprises the following steps:
by usingObtaining time domain impact response h of the shaping filter by using shaping factor rf and prior parameterrcos；
For the time domain impulse response hrcosObtaining corresponding frequency domain characteristic h by solving Fourier transformrcos_fft；
For the frequency domain characteristic hrcos_fftTaking the mold, and cutting to obtain a first amplitude-frequency characteristic hrcos_fft_abs_window；
The method for solving the second amplitude-frequency characteristic of the signal received by the receiver comprises the following steps:
selecting an access window for receiving a signal;
selecting the step of sliding the access window;
calculating the frequency domain characteristic X of the received signal in each step access windowdata_fft；
For the frequency domain characteristic Xdata_fftObtaining a second amplitude-frequency characteristic X of the signal by taking the modeldata_fft_abs。
2. The method of claim 1, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: the prior parameters comprise a sampling multiple N of the receiver and a length L of a time domain impact responsesymbol。
3. The method of claim 1, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: the signal received by the receiver is a baseband signal after down-conversion and variable-rate filtering.
4. The method of claim 3, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: sampling rate R of the variable rate filteringbFrom a sampling multiple N and a symbol rate RsIs determined, and Rb＝N*Rs。
5. The method of claim 1, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: after cutting outThe first amplitude-frequency characteristic has a length LwindowSaid L iswindowDetermined by the sampling multiple N and the shaping factor rf.
6. The method of claim 5, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: said Lwindow＝NFFTN (1+ rf); wherein N isFFTThe number of points of the fourier transform.
7. The method of claim 1, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: the method for calculating the correlation values of the two amplitude-frequency characteristics comprises the following steps: the convolution of the two amplitude-frequency characteristics is calculated.
8. The method of claim 1, wherein the coarse frequency offset estimation method is adapted to a low signal-to-noise ratio and a high dynamic environment, and comprises: the result of the coarse frequency offset estimation is
Ffreq_esti＝(Nindex-NFFT/2)/NFFT×Rb(ii) a Wherein N isindexIs the frequency offset at which the correlation value is maximum, RbIs the sampling rate of the variable rate filtering.
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