A Fast Acquisition Method for Multi-Rate Communication Signals under Low Signal-to-Noise Ratio

Abstract

The present invention belongs to the technical field of communication signal detection, and discloses a method for quickly capturing multi-rate communication signals under low signal-to-noise ratio, mainly solving problems such as slow signal detection speed, single method for selecting statistics, and decreased capture ability in a low signal-to-noise ratio environment when performing time-domain serial capture on signals with different code rates. The implementation steps are as follows: (1) pre-group the possible range of the signal code rate; (2) sample and quantize the received signal; (3) calculate the zero-delay slice of the cyclic correlation of the received signal to estimate the carrier frequency of the signal; (4) intercept the processed signal, slide and select multiple delay slices for accumulation, construct a statistic adapted to the change of the code rate, and perform a rough estimation of the signal code rate; (5) according to the rough estimation result of the code rate, select the best slice of the cyclic correlation to obtain the accurate value of the code rate. The present invention has the advantages of high flexibility and real-time performance, fast capture speed, high capture accuracy, etc., can quickly capture signals in a low signal-to-noise ratio complex electromagnetic environment, and is beneficial to the establishment of a fast communication link.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication signal detection, and further relates to a method for quickly capturing multi-rate communication signals under low signal-to-noise ratio. Background Art

[0002] Intercepting digital communication signals and estimating their parameters is an important link in establishing a fast communication link. The code rate is an important parameter of digital communication signals. Quickly capturing and accurately estimating the code rate of a signal is an important prerequisite for the effective implementation of subsequent steps such as modulation mode identification and signal blind demodulation.

[0003] Chongqing University of Posts and Telecommunications disclosed a method for estimating BOC signal parameters based on cyclic autocorrelation in its patent document "Method for Estimating BOC Signal Parameters Based on Cyclic Autocorrelation" (Patent Application No.: CN201510881642.1, Publication No.: CN105553635A). The specific steps of this method are as follows: segment the sampled BOC received signal with a certain length; calculate the cyclic autocorrelation function of each segment and take the absolute value; accumulate and average the cyclic autocorrelation functions to obtain the average cyclic autocorrelation function of the BOC signal; search for the maximum spectral peak on the cross-section of the average cyclic autocorrelation function with delay τ = 0, and the corresponding cyclic frequency is the carrier frequency to be estimated; extract the cross-section of the average cyclic autocorrelation function with τ ≠ 0, search for the two spectral peaks with different amplitudes and the closest to the zero frequency on this cross-section, and the pseudo-code rate and sub-carrier rate can be estimated according to their corresponding cyclic frequencies. This method can accurately estimate the pseudo-code rate, sub-carrier rate and carrier frequency of the BOC signal. Implementing cyclic autocorrelation by accumulation and averaging can also effectively reduce noise. However, there are still deficiencies. Its parameter estimation method makes the calculation of high-code-rate signals redundant. Just arbitrarily selecting a non-zero slice not only has poor stability but also ignores the influence of the appropriate τ on the parameter estimation accuracy, and it cannot guarantee the universality of this method for multi-rate communication signals.

[0004] Xidian University discloses a direct-sequence spread-spectrum (DSSS) signal detection method based on GPU parallelism in its patent document "A DSSS Signal Detection Method Based on GPU Parallelism" (Patent Application No.: 202110379795.1, Publication No.: CN113300737A). The specific steps of this method are as follows: constructing N time-delay slices of the received signal using the consistent estimator of the cyclic autocorrelation function; splicing the N slices into a multi-time-delay slice vector and constructing a cyclic statistic; obtaining the result of the cyclic statistic through GPU multi-thread parallel computing; normalizing the cyclic statistic; determining whether there is a DSSS signal by detecting whether the amplitude of the normalized cyclic statistic at zero cyclic frequency is 1; if so, obtaining the cyclic frequency corresponding to the second-largest spectral line and calculating the carrier frequency estimation result. This method detects the existence of DSSS signals using the cyclostationary property while performing carrier frequency estimation, and uses GPU multi-threads to execute the cyclic statistic calculation kernel function simultaneously. The slice selection scheme of this method cannot be changed according to the signal code rate, and it cannot effectively improve the detection speed for multi-rate communication signals, resulting in a problem of computational redundancy. Summary of the Invention

[0005] The purpose of the present invention is to propose a fast acquisition method for multi-rate communication signals under low signal-to-noise ratio in view of the defects existing in the above-mentioned prior art.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A fast acquisition method for multi-rate communication signals under low signal-to-noise ratio, specifically including the following steps:

[0008] Step 1: Determine the range of the code rate of the received signal, divide the code rates into multiple groups from large to small, and calculate the symbol width T of the midpoint of all groups. di ;

[0009] Step 2: Sample the entire received signal, and perform quantization processing on the real and imaginary parts of the signal to obtain N-point data x(n).

[0010] Step 3: Calculate the cyclic correlation zero-time-delay slice R x (α, 0) of the signal x(n), detect whether the signal exists, and estimate the carrier frequency f c ;

[0011] Step 4: If it is determined in Step 3 that the signal exists, then intercept the signal, slide and select n0 different time-delay slices for rough code rate estimation to obtain the rough code rate estimation result where n0 is the number of slices selected by sliding, which is a set value;

[0012] Step 5: Calculate the received signal at the delay time of For the cycle below, search for two non-zero peaks of the corresponding slice, and take the average of the coordinates of the two non-zero peaks to obtain the exact value of the code rate; where F s is the sampling frequency.

[0013] Further, in step 1, for each group i, it satisfies where floor is the floor operation, f i and f i+1 are two adjacent endpoint code rates.

[0014] Further, in step 4, perform a rough estimation of the code rate to obtain the rough estimation result of the code rate The specific implementation process is as follows:

[0015] Step 401, i is the counter of the loop process, and the initial value is set to 1; when 1 ≤ i ≤ n0, intercept the first N i points of data of x(n), perform low-pass filtering with a cut-off frequency of f i + f c and accumulate the signal in the delay time τ1 to τ i , τ i = T di / 2 for the cycle correlation to obtain the statistic and search for two non-zero peaks, read their coordinates and take the average to obtain Transfer to step (3); where N i is a set value;

[0016] Step 402, when i > n0, accumulate the signal in the delay time τ i = T di / 2 for the cycle correlation, calculate the statistic and search for two non-zero peaks, read their coordinates and take the average to obtain Transfer to step (3);

[0017] Step 403, if then consider as the rough estimation result of the code rate and enter step 5; otherwise, let i = i + 1 and return to step 401 or 402.

[0018] Further, in step 4, the calculation method of the cycle correlation is:

[0019]

[0020] In the i-th execution of steps 401 and 402, it is obtained only by compensating for the delay multiplication of the last N i - N i-1 points of data.

[0021] The present invention has the following advantages:

[0022] First, multiple low-pass filters are added to the signal processing flow of the present invention, improving the anti-noise performance in a low signal-to-noise ratio environment.

[0023] Second, the present invention performs adaptive duration detection for signals with different code rates, that is, short detection time is used for high-rate signals and long detection time is used for low-rate signals, thereby ensuring a high correct recognition rate for various rate signals and improving the detection probability at the same time.

[0024] Third, the present invention first pre-groups the possible range of the code rate from high to low, slides to select multiple slices, and constructs a detection statistic with adaptive characteristics. Finally, based on the rough estimate value, the optimal slice is selected for accurate estimation, further improving the accuracy of detection.

[0025] Fourth, in the i-th step of the code rate rough estimation process of the present invention can be obtained only by delaying and multiplying the post-calculated N i -N i-1 point data, further reducing the waste of computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart for the implementation of the present invention;

[0027] Figure 2 is a schematic diagram of the code rate pre-grouping of the present invention;

[0028] Figure 3 is a flowchart of the code rate rough estimation of the present invention;

[0029] Figure 4 is a performance graph of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following details the embodiments of the present invention, and the examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention.

[0031] The following further describes the embodiments of the present invention.

[0032] The present invention provides a method for quickly capturing multi-rate communication signals under low signal-to-noise ratio, referring to Figure 1 , specifically including the following steps:

[0033] Step 1: Predetermine the possible range of the code rate of the received signal and group it. As Figure 2 shown, the steps are as follows:

[0034] (101) Assume that the code rate of the signal to be detected is distributed in the range of the interval [f M+1 , f1], and it is divided into M groups. Here, equal-interval division can be adopted, or the low-code-rate range can be subdivided according to engineering needs to improve the estimation accuracy. Finally, M + 1 endpoint code rates are obtained, which are f1, f2, f3... f M , f M+1 in descending order.

[0035] (102) Calculate the T di value of the midpoint of all groups. Use the formula T di = floor(F s / f di ) to calculate the symbol width corresponding to the midpoint code rate f di of the i-th (1 ≤ i ≤ M) group. Among them, F s is the sampling frequency, and floor means rounding down. Since this method analyzes and processes the sampled digital signal, the number of points corresponding to the symbol width in the analog time domain at the F s sampling frequency is calculated here.

[0036] Step 2: Sample the received signal at the frequency F s , and perform quantization processing on the real and imaginary parts of the signal to obtain N-point data x(n).

[0037] Step 3: Calculate the slice R x (α, 0) of the cyclic correlation of the signal x(n) with τ = 0, and detect whether the signal exists and estimate the carrier frequency f c .

[0038] If x(t) is a discrete cyclostationary random process, its autocorrelation function is a periodic function of time t, so it can be expanded into a Fourier series:

[0039]

[0040] In the formula, R x (α, τ) is the cyclic correlation of x(t), α is called the cyclic frequency, and A is the set of all non-zero cyclic frequencies.

[0041] For the finite-length N-point observation data x(n), the consistent estimate of the cyclic correlation is:

[0042]

[0043] It can be seen from the above formula that in practice, the cyclic correlation of x(t) is quickly obtained by performing FFT on the signal after delay multiplication. Since the R x(α,0) slice has peaks only at zero frequency and double carrier frequency. Therefore, by detecting the non-zero peaks of the zero-delay slice, it is judged whether the signal exists, and the carrier frequency estimation value is extracted.

[0044] Step 4: If the signal is judged to exist in Step 3, intercept the signal, slide and select n0 different delay slices, and perform the Figure 3 coarse code rate estimation process as shown to obtain the coarse code rate estimation result The specific steps are as follows:

[0045] (401) i is the counter of the loop process, and the initial value is set to 1; when 1≤i≤n0, the operation in the i-th step is: intercept the first N i points of data of x(n), and perform low-pass filtering with a cut-off frequency of f i +f c Accumulate the cyclic correlation of the signal at the delay times τ1~τ i (τ i =T di / 2), to obtain the statistic Search for two non-zero peaks, read the coordinates of the two and take the average to obtain Transfer to step (3); where N i is a set value;

[0046] Here, n0 is the number of slices selected by each slide. The larger n0 is, the better the stability of this method, but the computational complexity will also increase significantly.

[0047] (402) When i>n0, the changed part of the operation in the i-th step is: accumulate the cyclic correlation of the signal at the delay time (τ i =T di / 2), calculate the statistic ensures that the selected n0 groups always contain the true code rate of the signal, search for two non-zero peaks, read the coordinates of the two and take the average to obtain Transfer to step (3);

[0048] (403) If then it is considered that is the coarse code rate estimation result, and proceed to Step 5; otherwise, let i = i + 1, and return to step (401) or (402).

[0049] The data interception and sliding selection of slices in Step 4 ensure that high code rate signals have relatively fast detection and recognition capabilities; on the contrary, for low code rate signals, the detection time is appropriately increased, that is, the detection speed is reduced. It can be further simplified that the in the i-th step can be calculated only by supplementing the last N i -N i-1It is obtained by delaying and multiplying the point data, reducing the waste of computing resources.

[0050] The cyclic correlation of the PSK signal is:

[0051]

[0052] σ a 2 is the variance of the signal complex envelope, and T d is the symbol width of the signal. It can be seen from the above formula that when the delay is not 0, R x (α,τ) takes non-zero values at all its cyclic frequencies, and the cyclic frequencies corresponding to these spectral lines are integer multiples of the symbol rate of the signal. Therefore, the symbol rate estimation can be obtained by searching for the cyclic frequency closest to the zero frequency.

[0053] The characteristics of the cyclic correlation obtained with different τ are: when τ = T d / 2, the relative intensity of the spectral line corresponding to the symbol rate is the largest, which is the best slice for symbol rate estimation. When τ > T d , there are no obvious discrete spectral lines corresponding to the symbol rate in the cyclic autocorrelation.

[0054] From the above analysis, it can be seen that in order to ensure the slices selected for each group in step 1, obvious discrete spectral lines can appear for all signals with symbol rates that meet , all 1 ≤ i ≤ M should satisfy: τ i is less than or equal to the symbol width corresponding to f i+1 , that is F s is the sampling frequency.

[0055] Step 5: Calculate the cyclic correlation of the signal at the delay time of , search for the two non-zero peaks of the corresponding section, and read the coordinates of the two and then take the average to obtain the accurate value of the symbol rate.

[0056] Figure 4 This is the symbol rate recognition effect of the BPSK signal under different bit signal-to-noise ratios for the symbol rate estimation process described in the present invention. Five different symbol rate situations are set. It can be seen from the figure that when the bit signal-to-noise ratio is higher than 1 dB, the recognition probability of the BPSK signal symbol rate is as high as 100%.

[0057] In summary, the present invention discloses a method for quickly capturing multi-rate communication signals under low signal-to-noise ratio. The signal code rate range is pre-grouped, and an adaptive detection scheme with computational complexity and detection statistic varying with the signal code rate is designed. This method overcomes the deficiencies of existing methods, such as the inability to achieve effective detection under low signal-to-noise ratio, computational redundancy for high-rate signals, and a single strategy for constructing statistics. It should be noted that without departing from the principle of the present invention, any modifications and changes made under the inspiration of the present invention are within the protection scope of the present invention.

Claims

1. A method for quickly capturing multi-rate communication signals under low signal-to-noise ratio, characterized in that, Specifically, it includes the following steps: Step 1: Determine the range of the received signal code rate, divide the code rate into multiple groups from large to small, and calculate the symbol width T at the midpoint of all groups. di ; where, for each group i, it satisfies F s is the sampling frequency, floor is the floor operation, f i and f i+1 are two adjacent endpoint code rates. Step 2: Sample the entire received signal, and perform quantization processing on the real part and the imaginary part of the signal respectively to obtain N-point data x(n); Step 3: Calculate the zero-delay slice R of the cyclic correlation of the signal x(n) x (α, 0), detect whether the signal exists, and estimate the carrier frequency f c , where α is the cyclic frequency; Step 4: If the signal is determined to exist in Step 3, intercept the signal, slide and select n0 different time-delay slices, perform a rough code rate estimation, and obtain the rough code rate estimation result. where n0 is the number of slices selected by sliding, which is a set value; Step 5: Calculate the cyclic correlation of the received signal at a delay time of Search for two non-zero peaks in the corresponding slice, and take the average of the coordinates of the two non-zero peaks to obtain the exact value of the code rate; where F s is the sampling frequency; Among them, in step 4, a rough code rate estimation is performed to obtain the rough code rate estimation result The specific implementation process is as follows: Step 401: Let \(i\) be the count value of the loop process, with the initial value set to 1. When \(1\leq i\leq n_0\), intercept the first \(N\) data points of \(x(n)\), perform low-pass filtering with a cut-off frequency of \(f\) i + \(f\) i + \(f\) c , accumulate the signal for cyclic correlation at delay times \(\tau_1\) to \(\tau\) i , \(\tau\) i = \(T\) di / 2, obtain the statistic and search for two non-zero peaks. After reading their coordinates, take the average to obtain Transfer to Step 403; where \(N\) i is a set value; Step 402, when i > n0, the accumulated signal is cyclically correlated at the delay time τ i = T di / 2, calculate the statistic and search for two non-zero peaks, after reading their coordinates, take the average to obtain Proceed to Step 403; Step 403, if is considered as the rough estimation result of the code rate, and proceed to Step 5; otherwise, let i = i + 1, and return to Step 401 or 402.

2. The fast acquisition method for multi-rate communication signals under low signal-to-noise ratio according to claim 1, characterized in that In Step 4, the calculation method of cyclic correlation is as follows: When the i-th execution in Steps 401 and 402 is performed, it is obtained only by complementarily calculating the delayed multiplication of the last N i -N i-1 point data.

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