CN113162652A - Method and module for detecting frequency of spread spectrum signal - Google Patents
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Abstract
The invention discloses a method for detecting frequency of spread spectrum signals, which comprises the following steps: receiving a spread spectrum signal to be detected, wherein the spread spectrum signal to be detected comprises modulation information; carrying out square operation on the spread spectrum signal to be detected to eliminate the modulation signal and obtain a frequency estimation signal; sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal, and carrying out narrow-band filtering on the frequency-mixed signal; the DFT operation is performed on the filtered signal function to detect the carrier frequency offset. The frequency domain processing is directly carried out on the signal correlation value by the spread spectrum signal frequency detection method, the modulation phase ambiguity is eliminated, a frequency discriminator is not needed, only a DFT algorithm is needed to carry out frequency spectrum analysis, and the frequency discrimination range is greatly expanded. In addition, the DFT algorithm only needs to use little resource consumption, and the frequency discrimination resolution can be greatly improved.
Description
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a method and a module for detecting frequency of a spread spectrum signal.
Background
In the conventional spread spectrum communication system and satellite navigation receiving application, due to the influence of doppler shift and local carrier error, a received signal may generate slow frequency drift, so that the received signal may have phase drift, which may have adverse effect on the communication system using coherent demodulation, and thus the receiving performance may be degraded. In an all-digital receiver, a local oscillation signal of a digital down-conversion (DDC) is limited, and digital system resources, working frequency and the like cannot slide in a larger range, so that the tracking bandwidth of a loop cannot be large for optimizing the performance and designing a larger carrier loop equivalent bandwidth.
Thus, a corresponding estimation and compensation of the carrier frequency is required before accurate phase tracking. Conventional frequency estimation algorithms fall into two categories, open-loop estimation algorithms and closed-loop estimation algorithms. The traditional closed-loop estimation algorithm is simple to implement, low in calculation requirement and short in acquisition time, but the tracking range is small, the acquisition performance can be close to the best at high signal-to-noise ratio, and at low signal-to-noise ratio, a symbol error caused by noise is transmitted to a feedback loop through a decision device, so that the loop performance is rapidly deteriorated. As a first estimation parameter for demodulation, such performance is clearly not satisfactory.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a method and a module for detecting a frequency of a spread spectrum signal. The technical problem to be solved by the invention is realized by the following technical scheme:
a method of frequency detection of a spread spectrum signal, comprising:
receiving a spread spectrum signal to be detected, wherein the spread spectrum signal to be detected comprises modulation information;
carrying out square operation on the spread spectrum signal to be detected to eliminate the modulation signal and obtain a frequency estimation signal;
sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal, and carrying out narrow-band filtering on the frequency-mixed signal;
the DFT operation is performed on the filtered signal function to detect the carrier frequency offset.
In a specific embodiment, the expression of the spread spectrum signal to be detected is:
r(t)＝A*P(t)*ej(2π(f+Δf)t+θ)+n(t)，
wherein A is signal power, P (t) is shaping pulse shape, f is carrier frequency, Δ f is carrier frequency offset, θ is modulation information phase of current symbol,
n (t) is noise.
In one embodiment, the frequency estimation signal expression is:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)，
wherein, a is signal power, f is carrier frequency, Δ f is carrier frequency offset, and n (t) is noise.
In one embodiment, the frequency of the mixed signal is: f2 is 2 × F2-fs/2, where F2 is the frequency of the signal of interest before mixing and fs is the system frequency.
The invention also provides a spread spectrum signal frequency detection module, which can be independently arranged and can be integrated into a system to be used as a functional module of the system, and the module comprises:
the system comprises a signal receiving unit, a signal processing unit and a signal processing unit, wherein the signal receiving unit is used for receiving a spread spectrum signal to be detected, and the spread spectrum signal to be detected comprises modulation information;
the signal modulation unit is used for carrying out square operation on the spread spectrum signal to be detected so as to eliminate the modulation signal and obtain a frequency estimation signal;
the filtering unit is used for sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal and carrying out narrow-band filtering on the frequency-mixed signal;
and the detection unit is used for performing DFT operation on the filtered signal function so as to detect the carrier frequency deviation.
In a specific embodiment, the expression of the spread spectrum signal to be detected is:
r(t)＝A*P(t)*ej(2π(f+Δf)t+θ)+n(t)，
wherein A is signal power, P (t) is shaping pulse shape, f is carrier frequency, Δ f is carrier frequency offset, θ is modulation information phase of current symbol,
n (t) is noise.
In one embodiment, the frequency estimation signal expression is:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)，
wherein, a is signal power, f is carrier frequency, Δ f is carrier frequency offset, and n (t) is noise.
In one embodiment, the frequency of the mixed signal is: f2 is 2 × F2-fs/2, where F2 is the frequency of the signal of interest before mixing and fs is the system frequency.
The invention has the beneficial effects that:
the frequency domain processing is directly carried out on the signal correlation value by the spread spectrum signal frequency detection method, the modulation phase ambiguity is eliminated, a frequency discriminator is not needed, only a DFT algorithm is needed to carry out frequency spectrum analysis, and the frequency discrimination range is greatly expanded. In addition, the DFT algorithm only needs to use little resource consumption, and the frequency discrimination resolution can be greatly improved.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting a frequency of a spread spectrum signal according to an embodiment of the present invention;
fig. 2 is an output of a simulation example of the embodiment of the present invention when the signal-to-noise ratio Δ f is +7.5 KHz;
fig. 3 is an output of a simulation example of an embodiment of the present invention when the snr is high and Δ f ═ 7.5 KHz;
fig. 4 shows an output when the signal-to-noise ratio is-1 dB and Δ f is +7.5KHz in a simulation example according to an embodiment of the present invention;
fig. 5 shows an output when the signal-to-noise ratio is-1 dB and Δ f is-7.5 KHz in a simulation example according to an embodiment of the present invention;
fig. 6 is a block diagram of a spread spectrum signal frequency detection module according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for detecting a frequency of a spread spectrum signal according to an embodiment of the present invention, including:
receiving a spread spectrum signal to be detected, wherein the spread spectrum signal to be detected comprises modulation information;
carrying out square operation on the spread spectrum signal to be detected to eliminate the modulation signal and obtain a frequency estimation signal;
sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal, and carrying out narrow-band filtering on the frequency-mixed signal;
the DFT operation is performed on the filtered signal function to detect the carrier frequency offset.
In a specific embodiment, the expression of the spread spectrum signal to be detected is:
r(t)＝A*P(t)*ej(2π(f+Δf)t+θ)+n(t)，
wherein A is signal power, P (t) is shaping pulse shape, f is carrier frequency, Δ f is carrier frequency offset, θ is modulation information phase of current symbol,
n (t) is noise.
To more clearly illustrate the solution of the present embodiment, the following exemplary derivation is given:
assuming that the shape of the molding pulse is an ideal rectangle, p (t) is 1, and the above equation is:
r(t)＝A*ej(2π(f+Δf)t+θ)+n(t)
squaring r (t) to obtain:
r2(t)＝A2*ej(2π(2*f+2*Δf)t+2*θ)+n2(t)
since θ ∈ (0, π), then 2 ∈ (0,2 π), from the periodicity, the above equation can be transformed into:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)
the above formula shows that: the integration through the square operation eliminates the correlation value and eliminates the modulation information, and the output signal of the integration eliminates the 2 frequency multiplication component [2 x f, 2 x delta f ] of the carrier and the frequency deviation, thereby converting the carrier frequency deviation estimation problem of the modulation signal into the frequency estimation of the dot frequency component.
Further, the frequency estimation signal expression is:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)，
wherein, a is signal power, f is carrier frequency, Δ f is carrier frequency offset, and n (t) is noise.
In one embodiment, the frequency of the mixed signal is: f2 is 2 × F2-fs/2, where F2 is the frequency of the signal of interest before mixing and fs is the system frequency.
The smaller the sampling rate, the smaller the original signal center frequency and bandwidth that can be recovered without aliasing after sampling. Therefore, the frequency estimation signal needs to be down-converted to move it to the vicinity of the baseband (low frequency). The lower the frequency after down-conversion is, the smaller the sampling frequency can be used, the finer the frequency resolution is, or the same resolution is maintained, the smaller the number of points of DFT (N is smaller) can be used, thereby reducing the operation amount of DFT and saving precious hardware resources.
The digital domain mixing and filtering needs a large amount of multiplication and addition operations, particularly, a multiplier is a precious and limited hardware resource, and the fs/2 frequency conversion without multiplication is a mixing method without multiplication to realize the frequency conversion of fixed frequency points.
For a set of discrete sequences:
A(n)＝ejπn＝cos(πn)＝(-1)n
a (n) includes (1, -1), and a (n) is (1, -1, 1, -1, … …) alternately. When a (n) is used as the mixing sequence multiplied by the time-domain signal sequence, which is equivalent to multiplying this signal sequence by the cosine sampling sequence cos (n), the frequency of a (n) is fs/2 because its cosine value repeats every two points. Although the (-1) n sequence is multiplied by the input signal sequence, in a digital signal processor (such as an FPGA), the multiplication by 1 or-1 is only simple to keep the original value or to invert (the inversion and the addition of one to the complement) and multiplication is not actually used, so that the shifting of the input signal in the frequency shift is simply completed.
After mixing, the present application does not perform spectrum analysis by using the existing FFT method, but performs analysis by using DFT (discrete fourier transform).
Specifically, for a finite long sequence x (N) with a length of N, its discrete fourier transform is:
in the formula, WN＝e-j*2π/N。
If the formula is directly used for spectrum analysis, N × N complex multiplications and N (N-1) complex additions are required, which results in a huge amount of operations and is difficult to implement. A general solution is to use an optimized FFT kernel for near real-time processing. The FFT implementation method divides the whole [0: Fs/2] spectrum range into N regions at equal intervals for analysis, and because the frequency of the signal in the engineering implementation has a known frequency range, when the signal is subjected to spectrum analysis, only the frequency band where the signal possibly exists needs to be analyzed, so that the frequency analysis formula is changed into:
the spectrum analysis realized by the above formula is the DFT method. The calculation amount of DFT analysis is L times of complex multiplication and L (N-1) times of complex addition, and the smaller the target analysis frequency interval [ L: L-1] is, the smaller the calculation amount is.
The DFT method has variable parameters: spectral analysis interval, spectral analysis precision. When the algorithm initially works, in a larger analysis interval, a signal which possibly exists is rapidly detected by a larger frequency stepping value (poorer analysis precision); when the existence of the signal is detected, the suspected signal is set as the center, and fine search is carried out in a smaller range around the frequency center in a smaller frequency step mode to obtain accurate signal frequency.
The present embodiment is described with reference to a specific example in which the system frequency fs is 62MHz, and the signal frequency of interest f2 is 2 × (15.58-Δf) At MHz, the f2 bandwidth is 40KHz, as known from the carrier tracking bandwidth (+ -10 KHz).
According to the sampling theorem, the minimum sampling frequency of f2 can be effectively recovered to be 80KHz (2 x f2), namely the resampling frequency fs/D after extraction is more than 80KHz when the Zoom-DFT calculation is carried out, 775 times of extraction (62MHz/80KHz) can be carried out at the maximum, which is an ideal condition for completely converting the signal to the baseband. When using the fs/2 frequency conversion method without multiplication, f2 is multiplied by a cosine sequence with frequency (fs/2 ═ 31) MHz, then f2 after mixing becomes:
F2＝2*(15.58-Δf)－fs/2＝31.16-2*Δf-31＝0.16-2*Δf(MHz)，
the above formula shows that the center frequency of the mixed F4 is 0.16MHz and the bandwidth is 40 KHz.
In the specific implementation of carrier frequency offset estimation, the 3dB bandwidth of the narrow-band filter is set to 40KHz, the decimation factor D is 40, and the DFT point number N is 2048, so that a spectrogram with a resolution of 756.8Hz (62M/(40 × 2048)) can be obtained. The peak detection output is 0.16 MHz-2-ΔfThe spectrogram contains 2 ×ΔfComponent, so that the output frequency offset can be actually detectedΔfThe resolution of (1) is 756.8 Hz/2-378.4 Hz, i.e. the frequency offset estimation algorithm can accurately distinguish the carrier frequency deviation of 378.4 Hz.
To more clearly show the advantages of this embodiment, the simulation is implemented in the FPGA. Please refer to fig. 2-5.
In fig. 2 and 3, no noise is actively added to the input signal, the signal-to-noise ratio is high, the DFT output spectrum is pure, the peak is obvious, and the amplitude is high. The abscissa is the DFT output sequence number, one step represents the frequency offset of the signal carrier 4 af, and the ordinate is the frequency amplitude. The frequency offset of 7.5KHz brings 20 serial number offsets, which is accurate consistent with a resolution of about 378.4Hz for af in the above example analysis.
In fig. 4 and 5, after the signal is mixed with noise with a certain signal-to-noise ratio, the DFT output peak is obviously decreased, and the bottom noise is obviously increased.
When the signal-to-noise ratio of the signal is equal to-1 dB, the module can still accurately estimate the carrier frequency offset, and the stability is good.
The method of the embodiment directly carries out frequency domain processing on the signal correlation value, eliminates the modulation phase ambiguity, does not need to use a frequency discriminator, only needs to use a DFT algorithm to carry out spectrum analysis, and greatly expands the frequency discrimination range. In addition, the DFT algorithm only needs to use little resource consumption, and the frequency discrimination resolution can be greatly improved.
Referring to fig. 6, fig. 6 is a block diagram of a spread spectrum signal frequency detection module provided in the present invention, where the module may be independently installed or integrated into a system as a functional module of the system, and includes:
the system comprises a signal receiving unit, a signal processing unit and a signal processing unit, wherein the signal receiving unit is used for receiving a spread spectrum signal to be detected, and the spread spectrum signal to be detected comprises modulation information;
the signal modulation unit is used for carrying out square operation on the spread spectrum signal to be detected so as to eliminate the modulation signal and obtain a frequency estimation signal;
the filtering unit is used for sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal and carrying out narrow-band filtering on the frequency-mixed signal;
and the detection unit is used for performing DFT operation on the filtered signal function so as to detect the carrier frequency deviation.
In a specific embodiment, the expression of the spread spectrum signal to be detected is:
r(t)＝A*P(t)*ej(2π(f+Δf)t+θ)+n(t)，
wherein A is signal power, P (t) is shaping pulse shape, f is carrier frequency, Δ f is carrier frequency offset, θ is modulation information phase of current symbol,
n (t) is noise.
In one embodiment, the frequency estimation signal expression is:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)，
wherein, a is signal power, f is carrier frequency, Δ f is carrier frequency offset, and n (t) is noise.
In one embodiment, the frequency of the mixed signal is: f2 is 2 × F2-fs/2, where F2 is the frequency of the signal of interest before mixing and fs is the system frequency.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, module (apparatus, device), or computer program product. Accordingly, this application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "module" or "system. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. A computer program stored/distributed on a suitable medium supplied together with or as part of other hardware, may also take other distributed forms, such as via the Internet or other wired or wireless telecommunication systems.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (8)
1. A method for frequency detection of a spread spectrum signal, comprising:
receiving a spread spectrum signal to be detected, wherein the spread spectrum signal to be detected comprises modulation information;
carrying out square operation on the spread spectrum signal to be detected to eliminate the modulation signal and obtain a frequency estimation signal;
sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal, and carrying out narrow-band filtering on the frequency-mixed signal;
the DFT operation is performed on the filtered signal function to detect the carrier frequency offset.
2. The method for detecting the frequency of a spread spectrum signal according to claim 1, wherein the expression of the spread spectrum signal to be detected is:
r(t)＝A*P(t)*ej(2π(f+Δf)t+θ)+n(t)，
wherein A is signal power, P (t) is shaping pulse shape, f is carrier frequency, Δ f is carrier frequency offset, θ is modulation information phase of current symbol,
n (t) is noise.
3. The method for frequency detection of a spread spectrum signal according to claim 1, wherein said frequency estimation signal expression is:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)，
wherein, a is signal power, f is carrier frequency, Δ f is carrier frequency offset, and n (t) is noise.
4. The method for detecting the frequency of a spread spectrum signal according to claim 1, wherein the frequency of the mixed signal is: f2 is 2 × F2-fs/2, where F2 is the frequency of the signal of interest before mixing and fs is the system frequency.
5. A spread spectrum signal frequency detection module, comprising:
the system comprises a signal receiving unit, a signal processing unit and a signal processing unit, wherein the signal receiving unit is used for receiving a spread spectrum signal to be detected, and the spread spectrum signal to be detected comprises modulation information;
the signal modulation unit is used for carrying out square operation on the spread spectrum signal to be detected so as to eliminate the modulation signal and obtain a frequency estimation signal;
the filtering unit is used for sequentially carrying out down-conversion and frequency mixing on the frequency estimation signal and carrying out narrow-band filtering on the frequency-mixed signal;
and the detection unit is used for performing DFT operation on the filtered signal function so as to detect the carrier frequency deviation.
6. The spread-spectrum signal frequency detection module according to claim 5, wherein the expression of the spread-spectrum signal to be detected is:
r(t)＝A*P(t)*ej(2π(f+Δf)t+θ)+n(t)，
wherein A is signal power, P (t) is shaping pulse shape, f is carrier frequency, Δ f is carrier frequency offset, θ is modulation information phase of current symbol,
n (t) is noise.
7. The spread spectrum signal frequency detection module of claim 5, wherein the frequency estimation signal expression is:
r2'(t)＝A2*ej(2π(2*f+2*Δf)t)+n2(t)，
wherein, a is signal power, f is carrier frequency, Δ f is carrier frequency offset, and n (t) is noise.
8. The spread spectrum signal frequency detection module of claim 5, wherein the mixed signal frequency is: f2 is 2 × F2-fs/2, where F2 is the frequency of the signal of interest before mixing and fs is the system frequency.
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