CN110048976B

CN110048976B - Intermediate frequency-oriented blind despreading method and device for short code direct spread spectrum signal array

Info

Publication number: CN110048976B
Application number: CN201910167622.6A
Authority: CN
Inventors: 邱钊洋; 彭华; 李天昀; 王彬; 谢云飞
Original assignee: Information Engineering University of PLA Strategic Support Force
Current assignee: Information Engineering University of PLA Strategic Support Force
Priority date: 2019-03-06
Filing date: 2019-03-06
Publication date: 2021-07-02
Anticipated expiration: 2039-03-06
Also published as: CN110048976A

Abstract

The invention belongs to the technical field of signal processing, and particularly relates to a short code direct spread signal array blind despreading method and device for intermediate frequency, wherein the method comprises the following steps: performing characteristic value decomposition on the received intermediate frequency signal to obtain a complex intermediate frequency pseudo code waveform combined by two orthogonal carriers, wherein the complex intermediate frequency pseudo code waveform and the intermediate frequency signal contain the same intermediate frequency; constructing a vector representation of complex domain complex correlation by utilizing the correlation between a complex intermediate frequency pseudo code waveform and an intermediate frequency signal, converting an initial phase of periodic change on the waveform into constellation frequency offset, and simultaneously reserving amplitude information of an information code body; correcting the frequency offset and the phase offset of the constellation diagram to obtain a stable decision constellation diagram; and carrying out demapping according to the stable decision constellation diagram to obtain a signal sending sequence. The invention realizes blind despreading of spread spectrum signals with low complexity and high reliability, does not need any prior information, is suitable for the full-blind analysis of DS/BPSK signals, greatly reduces the complexity, improves the anti-noise performance, has excellent performance and is more suitable for engineering practice.

Description

Intermediate frequency-oriented blind despreading method and device for short code direct spread spectrum signal array

Technical Field

The invention belongs to the technical field of signal processing, and particularly relates to a short code direct spread spectrum signal array blind despreading method and device for an intermediate frequency.

Background

Direct sequence spread spectrum signals (DSSS) are widely used in satellites, ultrashort waves, and in underwater acoustic communication channels and various commercial communication systems due to their very strong characteristics of noise immunity and multipath delay resistance. The despreading of the signals is a necessary link for a non-cooperative receiver to finish signal system clarification and blind demodulation, and is also an important content in spectrum monitoring and communication countermeasure. At present, most of the analysis and research on such signals focus on the pseudo code sequence estimation problem, and assume that the signals acquire synchronization or have small residual frequency offset, but the frequency offset is unpredictable and difficult to estimate under the non-cooperative receiving condition, so there is an urgent practical need for researching a signal blind despreading method suitable for any frequency offset.

Eigenvalue decomposition methods are a class of classical methods used to reconstruct the pseudo-code waveform of the direct-spread signal. By carrying out eigenvalue decomposition on the periodic segment autocorrelation matrix, extracting the principal eigenvector to reconstruct the pseudo code waveform, but under the condition of step loss, the two principal eigenvectors can generate combined phase ambiguity, and simultaneously, the waveform reconstruction has larger deviation due to inaccurate estimation of the step loss time under low signal-to-noise ratio. The method adopts a double-period segmented calculation base band autocorrelation matrix, extracts a main eigenvector to reconstruct a pseudo code waveform after eigenvalue decomposition (EVD), overcomes the phase ambiguity problem, but performs matrix operation in a complex field, and simultaneously increases the matrix eigenvalue decomposition scale by two times, so that the calculated amount of the algorithm and the required storage space are greatly increased, the data amount is not effectively utilized to cause performance degradation, and the requirements of engineering practice are difficult to meet.

Disclosure of Invention

Therefore, the invention provides the intermediate-frequency-oriented short code direct spread signal array blind despreading method and device, which greatly reduce the calculation amount, have excellent performance and are more suitable for engineering practice.

According to the design scheme provided by the invention, the method for blind despreading the intermediate-frequency-oriented short code direct spread signal array comprises the following contents:

A) performing characteristic value decomposition on the received intermediate frequency signal to obtain a complex intermediate frequency pseudo code waveform combined by two orthogonal carriers, wherein the complex intermediate frequency pseudo code waveform and the intermediate frequency signal contain the same intermediate frequency;

B) constructing a vector representation of complex domain complex correlation by utilizing the correlation between a complex intermediate frequency pseudo code waveform and an intermediate frequency signal, converting an initial phase of periodic change on the waveform into constellation frequency offset, and simultaneously reserving amplitude information of an information code body; correcting the frequency offset and the phase offset of the constellation diagram to obtain a stable decision constellation diagram;

C) and carrying out demapping according to the stable decision constellation diagram to obtain a signal sending sequence.

An intermediate frequency-oriented blind despreading device for an array of short code direct spread spectrum signals comprises: a decomposition module, a construction module, and a demapping module, wherein,

the decomposition module is used for decomposing the characteristic value of the received intermediate frequency signal to obtain a complex intermediate frequency pseudo code waveform combined by two paths of orthogonal carriers, and the complex intermediate frequency pseudo code waveform and the intermediate frequency signal contain the same intermediate frequency;

the construction module is used for constructing vector representation of complex field complex correlation by utilizing the correlation between a complex intermediate frequency pseudo code waveform and an intermediate frequency signal, converting an initial phase of periodic variation on the waveform into constellation diagram frequency offset, and simultaneously reserving amplitude information of an information code body; correcting the frequency offset and the phase offset of the constellation diagram to obtain a stable decision constellation;

and the demapping module is used for performing demapping according to the stable decision constellation to obtain a signal sending sequence.

The invention has the beneficial effects that:

the invention overcomes the adverse effect that the modulation parameters such as low signal-to-noise ratio spread spectrum signal carrier wave, symbol rate and the like are difficult to estimate under the non-cooperative receiving condition, realizes the blind despreading of the spread spectrum signal with low complexity and high reliability, extracts the pseudo code sequence by the constitution and the utilization of the medium-frequency real signal characteristic value vector and simultaneously utilizes the intermediate result without any prior information, is suitable for the blind analysis of the DSSS/BPSK signal, greatly reduces the complexity compared with the traditional algorithm, improves the anti-noise performance, has certain reference significance on the parameter estimation and demodulation problems of the spread spectrum signals of other systems, and has important guiding significance on the development of the blind despreading technology in the signal processing process.

Description of the drawings:

FIG. 1 is a flow chart of a blind despreading method in an embodiment;

FIG. 2 is a characteristic value decomposition diagram in the example;

FIG. 3 is a schematic diagram of constellation diagram acquisition in an embodiment;

FIG. 4 is a graph of the mean value of the envelope of the cross-correlation coefficient with noise and the time of step-out in the embodiment;

FIG. 5 is a schematic diagram of the intermediate frequency oriented digital blind despreading in the embodiment;

FIG. 6 is a schematic block diagram of an embodiment of an intermediate frequency digital blind despreading method for short code direct spread spectrum signals;

FIG. 7 is a diagram illustrating a blind despreading apparatus according to an embodiment;

FIG. 8 is a schematic diagram of an exploded module of an embodiment;

FIG. 9 is a schematic diagram of a construction module in an embodiment;

FIG. 10 is a schematic diagram of the performance of out-of-step time estimation in a simulation experiment;

FIG. 11 is a variation curve of the out-of-step time estimation error with carrier frequency variation in a simulation experiment;

FIG. 12 is a schematic diagram illustrating comparison of error rate performance in a simulation experiment;

fig. 13 is a variation curve of the bit error rate with the frequency offset in the simulation experiment.

The specific implementation mode is as follows:

in order to make the objects, technical solutions and advantages of the present invention clearer and more obvious, the present invention is further described in detail below with reference to the accompanying drawings and technical solutions.

Direct Sequence Spread Spectrum (DSSS) signals are widely used in various civilian communication systems. Under the non-cooperative receiving condition, the frequency offset is often difficult to estimate due to unknown pseudo code sequence and extremely low signal-to-noise ratio. Most of the existing methods extract a main eigenvector to reconstruct a pseudo code waveform based on a baseband segmentation correlation matrix eigenvalue decomposition method, but the existing methods have the problems of high complexity, performance degradation under the condition of containing frequency offset and the like. For short code direct spread signals, the inversion of the phase, whether in baseband or at intermediate frequency, represents a jump in the information bits, so that the demodulation and despreading of such signals are independent of each other and can theoretically be performed simultaneously. The advantage of demodulating and despreading at intermediate frequency is that real number operation saves calculation amount, and the method can cope with residual frequency offset of any size. In the embodiment of the present invention, referring to fig. 1, a method for blind despreading an intermediate-frequency-oriented short code direct spread spectrum signal array is provided, which includes the following steps:

s101) carrying out characteristic value decomposition on the received intermediate frequency signal to obtain a complex intermediate frequency pseudo code waveform combined by two paths of orthogonal carriers, wherein the complex intermediate frequency pseudo code waveform and the intermediate frequency signal contain the same intermediate frequency;

s102) constructing a complex field complex correlation vector representation by utilizing the correlation between a complex intermediate frequency pseudo code waveform and an intermediate frequency signal, converting an initial phase of periodic variation on the waveform into a constellation diagram frequency offset, and simultaneously reserving amplitude information of an information code body; correcting the frequency offset and the phase offset of the constellation diagram to obtain a stable decision constellation diagram;

s103) carrying out demapping according to the stable decision constellation map to obtain a signal sending sequence.

The method comprises the steps of decomposing a characteristic value of an intermediate frequency DSSS real signal, absorbing carrier frequency into a characteristic vector, obtaining two modulation pseudo code waveforms which are mutually Hilbert transform pairs, further combining the modulation pseudo code waveforms to obtain a complex intermediate frequency pseudo code waveform, carrying out relevant demodulation and de-spreading on the complex intermediate frequency signal to obtain a relevant constellation, finally deducing the characteristic that constellation rotation is caused by different segment phase accumulation, and correcting to realize a totally blind short code DSSS-BPSK intermediate frequency signal de-spreading demodulation scheme.

And carrying out characteristic value decomposition on the received intermediate frequency signal to obtain two modulation pseudo code waveforms which are a Hilbert transform pair, and combining the two modulation pseudo code waveforms to obtain a complex intermediate frequency pseudo code waveform. In another embodiment of the present invention, referring to fig. 2, the eigenvalue decomposition process includes the following steps:

s1001) carrying out pseudo code waveform period estimation on the received intermediate frequency signal, and acquiring out-of-step time according to the pseudo code waveform period estimation;

s1002) carrying out periodic segmentation on the signal according to the step-out time to obtain an autocorrelation matrix; and performing singular value decomposition on the autocorrelation matrix, obtaining left singular eigenvectors corresponding to the two maximum eigenvalues, and reconstructing a complex intermediate frequency pseudo code waveform, wherein the two left singular eigenvectors are each other a Hilbert transform pair.

From the initial moment of the signal, carrying out periodic segmentation on the signal according to the pseudo code waveform period estimation to obtain the mean value of the absolute values of the cross correlation coefficients between any two sections of signals; the out-of-sync time is obtained by a shift search.

Using the correlation between the complex intermediate frequency pseudo code waveform and the intermediate frequency signal to construct a vector representation of complex domain complex correlation, as shown in fig. 3, another embodiment of the present invention includes the following contents:

s2001) correlating the complex intermediate frequency pseudo code waveform with the complex intermediate frequency signal to obtain a relevant constellation diagram;

s2002) carrying out frequency offset and phase offset correction on the relevant constellation diagram to obtain a synchronous BPSK binary phase shift keying constellation serving as a stable decision constellation diagram.

The method for decomposing the characteristic value of the baseband is a classic method for solving blind estimation of a pseudo code sequence of a direct sequence spread spectrum signal, and the received baseband signal is segmented according to periods on the assumption that the period of the pseudo code is known, and can be expressed as follows:

s_k＝m_kp₁+m_k+1p₂+n_k (1)

in the formula m_kAnd m_k+1Is a continuous two-bit information code word, the code words are distributed according to random uniform 01 and are independent and uncorrelated with each other, p₁Is a vector and represents (T)₀-T_x) A back segment of a spread spectrum waveform of a time length; p is a radical of₂Represents T_xFront section of a time-length spread spectrum waveform, T_xRepresenting the length of time from the header of the data to be processed to the header of the next information symbol, i.e. the out-of-step time, n_kIs a real gaussian noise.

The expectation of the autocorrelation matrix for a segment is given by the form:

R＝σ_m ²||p₁||²u₁u₁ ^T+σ_m ²||p₂||²u₂u₂ ^T+σ_n ²I (2)

wherein

From the equation (2), it can be obtained that the autocorrelation matrix includes two main eigenvalues, and the corresponding eigenvectors are two segments of the pseudo code waveform, so that the splicing of the pseudo code waveform can be further realized.

When the out-of-step time estimation is completed, the autocorrelation matrix is expressed as:

R＝σ_m ²||p||²uu^T+σ_n ²I (3)

at this time, the eigenvector corresponding to the maximum eigenvalue can be + -u₁The pseudo code sequence estimation can be realized according to the pseudo code waveform. The eigenvalue decomposition method utilizes the good autocorrelation characteristic and shift orthogonality of the m sequence, the noise resistance is excellent, and a plurality of algorithms developed later, such as a neural network method based on Hebbian criterion, etc., take the autocorrelation characteristic and the shift orthogonality as theoretical bases, however, the frequency offset of signals is required to be small enough by the baseband demodulation scheme, so that the tracking adjustment of a phase-locked loop can be realized. Under the actual non-cooperative receiving condition, the frequency offset is unpredictable, and the phase-locked loop can only work in a smaller frequency difference range, so that the phase-locked loop can not effectively deal with the situation without prior parameter information. In comparison, despreading under any frequency offset condition is a more reliable choice, and in intermediate frequency processing, because all the operations are real number domain operations, the calculation complexity can be greatly reduced, so that the intermediate frequency blind despreading scheme is an ideal scheme for a non-cooperative receiver.

The intermediate frequency signal processing requires that the period and the out-of-step time estimation are completed firstly, and for the pseudo code period estimation of the DSSS signal, a mature solution with good performance is provided, such as an accumulative autocorrelation method, a quadratic spectrum method, a correlation entropy method and the like. The performance of the quadratic spectrum method is more excellent, and the performance is not degraded when the quadratic spectrum method is applied to intermediate-frequency real signals based on frequency domain characteristics. The method mainly comprises the following steps of performing secondary Fourier transform on a power spectrum to obtain equally spaced spectrum peaks, wherein the intervals correspond to pseudo code periods, and the estimation principle can be expressed as follows:

the out-of-step time estimation problem can be determined by a correlation envelope, good estimation performance is obtained by the method on a base band due to good cross-correlation characteristics of a pseudo code waveform, and the applicability of the method to the intermediate frequency DSSS signal can be seen as follows.

For a short code signal, the medium frequency short code DSSS signal of one cycle can be expressed as:

x_k＝A_k(m_kp₁+m_k+1p₂)+n_k (5)

wherein:

wherein, w_cRepresenting the intermediate digital angular frequency, w_c＝2w_c＝2πf_c/f_s，A_kRepresenting an N-dimensional modulation matrix, N representing the period of the pseudo-code waveform, T_sIs a sampling period, f_cIs the carrier frequency and is,

is the carrier initial phase. At this time, for short code spreading, the cross-correlation coefficient of the two-segment periodic signal can be obtained:

with [ A ]_i ^TA_j]_kRepresents a diagonal matrix A_i ^TA_jKth diagonal element of (c):

[A_i ^TA_j]_k＝cos(w_c(iN+k)+φ₀)cos(w_c(iN+k)+φ₀+w_c(j-i)N) (8)

the absolute value of which is expected:

at this time:

let F (F)_c)＝E(|[A_i ^TA_j]_kI), when the number of information symbols is certain and enough, E (| [ A ]) in statistical meaning_i ^TA_j]_kI) will be independent of i, j, with the expectation of the modulus only being dependent on f_cIs related to the value of (A). m is_im_j,m_i+1m_j+1All can independently obtain +/-1 with equal probability, and the probabilities are all

According to pseudo code waveform energy definition:

the correlation coefficient modulus can be used to observe the change of the segment waveform correlation with the step-out time:

when f is_cAt a specific value, F (F)_c) Is a constant value epsilon ∈ (0, 1)]It can be seen that the presence of frequency offset does not affect the out-of-step time estimation, and the relationship between the correlation envelope and the out-of-step time is shown in the curve of fig. 4, whereby an accurate estimation of the out-of-step time can be obtained by a shift search.

At this time:

therefore, the cross-correlation envelope power measuring method is still effective when being popularized to the intermediate-frequency real signal, and the relation curve range of the cross-correlation coefficient envelope mean value and the step-out time follows with f_cThe specific value is (0, 1)]The shape of the curve is fixed, which is an important basis for detecting the step-out time under the condition of intermediate frequency.

The pseudo-code synchronized short code DSSS intermediate frequency model of the formula (5) is applied to the frequency offset matrix A_kThe development is carried out to obtain:

x_k＝m_kA_kp+n_k＝m_k(A_(k)cos-A_(k)sin)p+n_k (14)

wherein the diagonal matrix A_(k)cosAnd A_(k)sinThe nth element of (a) may be represented as:

angular frequency w and initial phase

Can be expressed as a function of carrier frequency and initial phase:

computing the autocorrelation matrix for the segment is expected to be available:

wherein, for each of the segments,

is constant and only influences the power size reflected on the autocorrelation matrix, and the eigenvalue decomposition of the constant influences the power size to obtain two amplitude sums

The main eigenvalue of the power mean correlation, as shown in FIG. 5(a), assumes u₁,u₂The waveforms are completely orthogonal and take into account

To discrete sampled values, such that

The power of the two is often different, so that the eigenvector obtained by decomposing the eigenvalue of the autocorrelation matrix corresponds to different eigenvalues, unitary ambiguity does not exist, and the following eigenvector is obtained:

wherein:

however, in practice u₁,u₂Complete orthogonality is not possible, especially for shorter code lengths, u₁,u₂The orthogonality condition of (a) is weakened, and considering the sufficient number of symbols,

and

will tend to be equal in statistical sense, resulting in unitary ambiguity problem, i.e. the feature vector space is expanded to a linear combination of two orthogonal bases, and the corresponding feature vectors can be expressed as:

during synchronization, the eigenvector obtained after the characteristic value decomposition of the autocorrelation matrix is an intermediate frequency pseudo code waveform comprising two paths of orthogonal carrier combinations, and the intermediate frequency pseudo code waveform has the same intermediate frequency as the intermediate frequency signal, so that intermediate frequency correlation is kept, and a theoretical basis is provided for a direct intermediate frequency blind despreading method.

The core of the if despreading problem is to use the correlation between the pseudo code waveform and the received signal waveform, and a vector form of correlation multiplication needs to be constructed. Due to the influence of the segmented initial phase, if correlation is carried out in a real number domain, the phase is converted into amplitude of periodic variation, confusion is generated between the phase and amplitude information of the information code, and the phase is difficult to strip, so that a complex correlation form is constructed in a complex number domain, the initial phase of the periodic variation on a waveform is converted into frequency deviation of a constellation diagram, and meanwhile, the amplitude information of the information code is kept.

Observed feature vector u₁',u₂' derived by singular value decomposition, satisfying the orthogonality condition (u)₁')^Tu₂' -0, when present:

according to the pseudo code waveform and the energy hypothesis of the feature vector:

the following can be obtained:

c₁ ²+c₂ ²＝c₃ ²+c₄ ² (24)

the vertical type (22) and (24) can obtain:

|c₁|＝|c₄|,|c₂|＝|c₃| (25)

at the same time u₁,u₂Hil each otherbert transform pair, hence u₁',u₂The relationship of' can be found in:

H(u₁')＝c₁u₂-c₂u₁＝±u₂' (26)

in equation (26), H [ · ] represents a Hilbert transform, two eigenvectors are Hilbert transform pairs, and the phase ambiguity of the transform pairs can be removed by correlation methods to consider the phase ambiguity of the eigenvectors, and the principle can be described as follows:

P＝[H(u₁')]^Tu₂' (27)

at this time, a complex intermediate frequency pseudo code waveform u is constructed_f＝u₁'+j*sign(P)u₂', there are:

according to u₁,u₂The relationship between:

u₁+ju₂＝ue^jwn (29)

obtaining a complex intermediate frequency pseudo code waveform:

constructing a complex intermediate frequency signal s (n) ═ s_f(n)+j*H[s_f(n)]. Wherein s (n) is further represented by

s_B(n) represents the baseband signal and is a cyclic repetition of the baseband pseudo-random waveform p (t).

At this time, the complex intermediate frequency pseudo code waveform is correlated with the complex intermediate frequency signal to obtain:

where N represents the number of pseudo-code waveform points, equal to the period, s^*(iN + j) represents the ith segment of the received signal. In combination with formula (16), and substituting u (n), s (n) yields:

it can be seen that the correlation values are represented in the form of BPSK constellation pattern with frequency offset in complex coordinate system, as shown in fig. 5 (b). Under the premise of unknown carrier frequency offset and initial phase, a series of frequency offset and phase offset blind estimation methods, such as a simpler and effective frequency multiplication method based on maximum likelihood, etc., can be used to correct the frequency offset and phase offset of the constellation to obtain a stable decision constellation, as shown in fig. 5 (c). Wherein the frequency doubling method^[13]The correction frequency offset and phase offset may be obtained according to equations (33) (34), where M ═ 2 for BPSK constellations:

wherein

Representing the carrier frequency of the constellation sequence,

and expressing the carrier phase, thereby realizing the correction of the frequency offset and the phase deviation of the constellation frequency offset, obtaining a stable constellation sequence, and finally carrying out demapping according to a stable BP constellation pattern to obtain a sending sequence. Fig. 5 shows the main processing results of the signals in an embodiment of the invention.

Compared with a baseband despreading scheme, the intermediate frequency blind despreading scheme does not need to recover a pseudo code sequence, directly adopts an intermediate frequency pseudo code waveform to perform related despreading demodulation, simultaneously converts a phase difference accumulated in a period into a frequency difference, and finally performs estimation elimination through a maximum likelihood method, so that the problems of complexity, error propagation and the like of a baseband algorithm are reduced while the modulation constellation is recovered, the performance of the algorithm is improved, the flow is shown in figure 6, and the short code direct spread signal intermediate frequency digital blind despreading flow comprises the following steps:

step 1: pseudo code waveform period estimation is carried out on the received intermediate frequency signal to obtain a period T₀；

Step 2: carrying out periodic segmentation on the signals from the signal starting time t equal to 0, and calculating the absolute value mean value M (t) of the cross correlation coefficient between any two sections of signals;

and step 3: let T be T + T_sAnd repeating the step 2 until T is T₀Obtaining an estimated value of the out-of-step time by using the formula (13);

and 4, step 4: from T_xPeriodically segmenting the signal, and calculating an autocorrelation matrix mean value R;

step 5, performing singular value decomposition on the R to obtain left singular vectors u corresponding to 2 maximum eigenvalues₁,u₂Reconstructing a complex intermediate frequency pseudo code waveform u_f＝u₁'+j*sign(P)u₂'；

Step 6, calculating the complex intermediate frequency signal waveform s' ═ s + jH(s), and carrying out correlation according to the formula (32) to obtain a correlation constellation;

and 7, carrying out frequency offset and phase offset elimination on the relevant constellation according to the formulas (33) and (34), namely obtaining a synchronous constellation, and judging a recoverable bit stream.

The method comprises the steps of decomposing characteristic values of intermediate-frequency DSSS real signals to obtain two modulation pseudo code waveforms which are mutually Hilbert transform pairs, combining the two modulation pseudo code waveforms to obtain complex intermediate-frequency pseudo code waveforms, carrying out correlation demodulation and de-spreading on the complex intermediate-frequency signals to obtain a correlation constellation, finally deducing the characteristic of constellation rotation caused by phase accumulation of different segments, and correcting by adopting a non-data-assisted maximum likelihood method to realize a blind short-code DSSS-BPSK intermediate-frequency signal de-spreading demodulation scheme.

Based on the above method, the present invention further provides an intermediate frequency-oriented short code direct sequence spread spectrum signal array blind despreading device, as shown in fig. 7, including: comprises the following steps: a decomposition module 101, a construction module 102 and a demapping module 103, wherein,

the decomposition module 101 is configured to perform eigenvalue decomposition on the received intermediate frequency signal to obtain a complex intermediate frequency pseudo code waveform of two orthogonal carrier combinations, where the complex intermediate frequency pseudo code waveform and the intermediate frequency signal have the same intermediate frequency;

the constructing module 102 is configured to construct a vector representation of complex-domain complex correlation by using correlation between a complex intermediate-frequency pseudo code waveform and an intermediate-frequency signal, convert an initial phase of periodic variation on the waveform into a constellation frequency offset, and simultaneously retain amplitude information of an information code body; correcting the frequency offset and the phase offset of the constellation diagram to obtain a stable decision constellation;

and a demapping module 103, configured to perform demapping according to the stable decision constellation to obtain a signal sending sequence.

As described above, referring to fig. 8, the decomposition module 101 includes: an estimation sub-module 1001 and a reconstruction sub-module 1002, wherein,

an estimation submodule 1001 configured to perform pseudo code waveform period estimation on a received intermediate frequency signal, and acquire out-of-step time according to the pseudo code waveform period estimation;

the reconstruction submodule 1002 is configured to perform periodic segmentation on the signal according to the step-out time to obtain an autocorrelation matrix; and performing singular value decomposition on the autocorrelation matrix, obtaining left singular eigenvectors corresponding to the two maximum eigenvalues, and reconstructing a complex intermediate frequency pseudo code waveform, wherein the two left singular eigenvectors are each other a Hilbert transform pair.

As described above, referring to fig. 9, the building block 102 includes: a constellation acquisition sub-module 2001 and a correction sub-module 2002, wherein,

a constellation acquiring submodule 2001, configured to correlate the complex intermediate frequency pseudo code waveform with the complex intermediate frequency signal to acquire a relevant constellation;

and a correction submodule 2002 for performing frequency offset and phase offset correction on the relevant constellation diagram to obtain a synchronized BPSK binary phase shift keying constellation serving as a stable decision constellation.

To further verify the effectiveness of the present invention, the following is further explained by specific simulation experimental data, and it should be noted that the signal-to-noise ratios adopted in the experimental conditions are all true signal-to-noise ratios of the information sequences without the influence of the spread spectrum gain and the over-sampling, which are defined as:

Es/N0＝SNR-10log₁₀(K)-10log₁₀(r) (35)

wherein, K represents the spread spectrum gain, the corresponding short code spread spectrum is the length of the pseudo code sequence, and r represents the over-sampling factor.

1) Time-out-of-synchronization estimation performance

Experiment one: DSSS/BPSK short code spread spectrum signal is selected in experiment, the length of pseudo code sequence is 63, root raised cosine forming is adopted, and the rate R of pseudo code is_b160kBd/s, 800kHz, simulated baseband signal and intermediate frequency F_cThe performance is estimated when the frequency is 236.3kHz, the number of information symbols is 200, the range of the test signal-to-noise ratio Es/N0 is 0-10dB, 500 monte carlo simulations are performed under each signal-to-noise ratio condition, the performance is compared with the performance estimated under the baseband frequency offset-free condition, and the experimental result corresponds to fig. 10 (a).

Experiment two: DSSS/BPSK short code spread spectrum signal is selected in experiment, the length of pseudo code sequence is 63, root raised cosine forming is adopted, and the rate R of pseudo code is_b160kBd/s, 800kHz as the sampling rate Fs, 50-500 as the number of information symbols, 5dB as the signal-to-noise ratio Es/N0, 500 Monte Carlo simulations are performed under each data size condition, comparison with the algorithm performance of the baseband without frequency offset is performed, and the experimental result corresponds to the graph (b) in FIG. 10.

As can be seen from fig. 10(a), when there is a frequency offset, the step time estimation algorithm used in the preprocessing step degrades the anti-noise performance compared to the baseband without carrier, but substantially keeps the same performance when it is greater than 6dB, so that the error performance within one sampling period can be achieved. Meanwhile, fig. 10(b) shows the relationship between the out-of-sync time estimation and the data amount under the condition of a fixed signal-to-noise ratio, and when the number of information symbols is greater than 200, the estimation performance is basically stable.

Experiment three: DSSS/BPSK short code spread spectrum signal is selected in experiment, the length of pseudo code sequence is 63, root raised cosine forming is adopted, and the rate R of pseudo code is_b160kBd/s, a sampling rate Fs of 800kHz, and F for the intermediate frequency range, taking into account the Nyquist theorem_cThe method comprises the steps of stepping 5kHz at 180-220 kHz, wherein the number of information symbols is 200, the signal-to-noise ratio is Es/N0 at 5dB, Monte Carlo simulation is performed 500 times under each signal-to-noise ratio condition, comparison is performed with the performance of an algorithm without frequency offset of a baseband, an experimental result corresponds to that shown in figure 11, the displayed algorithm has strong robustness on the numerical value of carrier frequency, the algorithm is suitable for actual blind receiving conditions, accurate out-of-step time estimation can be achieved under a small data scale, and a cushion is laid for a subsequent blind de-spreading algorithm.

2) Error code performance of blind despreading method

Experiment four: DSSS/BPSK short code spread spectrum signal is selected in experiment, the length of the pseudo code sequence is 63, raised cosine forming is adopted, and the pseudo code rate R is_b160kBd/s, a sampling rate Fs of 800kHz, and an intermediate frequency F_c236.3kHz, 200 information symbols and a test signal-to-noise ratio range of 0dB and 10dB]Stepping by 1dB, carrying out Monte Carlo simulation for 50000 times under each signal-to-noise ratio condition to carry out error rate statistics.

Experiment five: DSSS/BPSK short code spread spectrum signal is selected in experiment, the length of the pseudo code sequence is 63, raised cosine forming is adopted, and the pseudo code rate R is_b160kBd/s, a sampling rate Fs of 800kHz, and an intermediate frequency F_c236.3kHz, the number of information symbols is 50-500, the range of the test signal-to-noise ratio is Es/N0-5 dB, and the error rate statistics is carried out by conducting 50000 Monte Carlo simulations under the condition of each data volume.

The demodulation error rate under the fixed data volume is directly related to the pseudo code waveform estimation precision, so the error rate index can reflect the performance of the algorithm on the pseudo code waveform estimation under different signal-to-noise ratios of the fixed data volume. As can be seen from fig. 12(a), the technical scheme of the present invention is superior to the complex baseband autocorrelation matrix eigenvalue decomposition algorithm and the subspace tracking-based PASTD principal component extraction algorithm in terms of noise immunity, and compared with the complex baseband autocorrelation matrix eigenvalue decomposition algorithm, the error rate is reduced by about 50% under the same condition, and approaches the theoretical limit. Fig. 12(b) shows a variation curve of the algorithm estimation performance with the data volume under the condition of a fixed signal-to-noise ratio, which corresponds to the performance of the intermediate frequency pseudo code waveform estimation, and it can be seen from the graph that the performance of the CB-SVD algorithm based on the complex baseband matrix decomposition is obviously degraded when the data volume is small, and is limited by the signal-to-noise ratio of the training data, and the performance of the method based on the subspace projection is basically stable with the increase of the data volume, and is difficult to be improved. According to the technical scheme, the performance is continuously improved along with the increase of the data volume, the symbol number is 200, the symbol number basically tends to be stable, and the convergence rate and the steady-state error have obvious advantages. 4) And (5) analyzing the complexity of the algorithm.

Experiment six: DSSS/BPSK short code spread spectrum signal is selected in experiment, the length of the pseudo code sequence is 63, raised cosine forming is adopted, and the pseudo code rate R is_b160kBd/s, a sampling rate Fs of 800kHz, and F for the intermediate frequency range, taking into account the Nyquist theorem_cThe method comprises the steps of stepping 5kHz at 180-220 kHz, counting 200 information symbols, carrying out Monte Carlo simulation 50000 times to carry out error rate statistics, wherein the signal-to-noise ratio is Es/N0 at 5 dB. Fig. 13 shows that the error code performance of the blind despreading scheme of the present invention varies with the intermediate frequency, and it can be seen that the blind despreading scheme of the present invention has stable performance in a wider range of intermediate frequency, is insensitive to frequency values, and is very suitable for blind receiving conditions.

As shown in table 1, in the autocorrelation matrix calculation process of the technical solution in the embodiment of the present invention, the intermediate-frequency real signal with one time period is used for calculation, and compared with the autocorrelation matrix calculated by the complex signal with two times period, the calculated amount is 1/64, and meanwhile, the complexity of the eigenvalue decomposition of the real matrix is O (N)³) Where N is the matrix dimension, the amount of computation becomes 2 when complex matrix decomposition is performed³Twice as many periods as 8 times, the calculated amount becomes 2 again³The complexity of the decomposition of the real autocorrelation matrix in one time period is 1/64 of the original SVD algorithm, which indicates the absolute advantage of the algorithm of the invention in the aspect of computational efficiency.

TABLE 1 Algorithm complexity comparison (number of real multiplications)

Unless specifically stated otherwise, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

Based on the foregoing method, an embodiment of the present invention further provides a server, including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method described above.

Based on the above method, the embodiment of the present invention further provides a computer readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the above method.

The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An intermediate frequency-oriented blind despreading method for an array of short code direct spread spectrum signals, comprising:

2. The blind despreading method for an intermediate-frequency-oriented short-code direct-spread signal array according to claim 1, wherein in A), the eigenvalue decomposition is performed on the received intermediate-frequency signal to obtain two modulation pseudo-code waveforms which are a Hilbert transform pair each other, and the two modulation pseudo-code waveforms are combined to obtain a complex intermediate-frequency pseudo-code waveform.

3. The method for blind despreading of an array of intermediate-frequency-oriented short-code direct-spread signals according to claim 1, wherein the eigenvalue decomposition in A) comprises the following steps:

A1) carrying out pseudo code waveform period estimation on the received intermediate frequency signal, and acquiring out-of-step time according to the pseudo code waveform period estimation;

A2) carrying out periodic segmentation on the signal according to the step-out time to obtain an autocorrelation matrix; and performing singular value decomposition on the autocorrelation matrix, obtaining left singular eigenvectors corresponding to the two maximum eigenvalues, and reconstructing a complex intermediate frequency pseudo code waveform, wherein the two left singular eigenvectors are each other a Hilbert transform pair.

4. The blind despreading method for the intermediate-frequency-oriented short-code direct-spread signal array according to claim 3, wherein in A1), from the signal starting time, the signal is periodically segmented according to the pseudo-code waveform period estimation, and the mean value of the absolute values of the cross-correlation coefficients between any two segments of signals is obtained; the out-of-sync time is obtained by a shift search.

5. The method for blind despreading of an array of intermediate-frequency-oriented short-code direct-spread signals according to claim 1, wherein B) comprises the following steps:

B1) correlating the complex intermediate frequency pseudo code waveform with a complex intermediate frequency signal to obtain a relevant constellation diagram;

B2) and carrying out frequency offset and phase offset correction on the relevant constellation diagram to obtain a synchronous BPSK binary phase shift keying constellation serving as a stable decision constellation diagram.

6. The blind despreading method for an intermediate-frequency-oriented short code direct spread signal array according to claim 1 or 5, characterized in that the frequency offset and the phase offset of the constellation are corrected by using frequency offset and phase offset blind estimation to obtain a stable decision constellation.

7. An intermediate frequency-oriented blind despreading device for an array of short code direct spread spectrum signals, comprising: a decomposition module, a construction module, and a demapping module, wherein,

8. The apparatus according to claim 7, wherein the decomposition module comprises: an estimation sub-module and a reconstruction sub-module, wherein,

the estimation submodule is used for carrying out pseudo code waveform period estimation on the received intermediate frequency signal and acquiring out-of-step time according to the pseudo code waveform period estimation;

the reconstruction submodule is used for carrying out periodic segmentation on the signal according to the out-of-step time to obtain an autocorrelation matrix; and performing singular value decomposition on the autocorrelation matrix, obtaining left singular eigenvectors corresponding to the two maximum eigenvalues, and reconstructing a complex intermediate frequency pseudo code waveform, wherein the two left singular eigenvectors are each other a Hilbert transform pair.

9. The apparatus according to claim 7, wherein the building block comprises: a constellation acquisition sub-module and a correction sub-module, wherein,

the constellation acquisition submodule is used for correlating the complex intermediate frequency pseudo code waveform with a complex intermediate frequency signal to acquire a relevant constellation diagram;

and the correction submodule is used for carrying out frequency offset and phase offset correction on the relevant constellation diagram to obtain a synchronous BPSK binary phase shift keying constellation serving as a stable decision constellation.

10. The blind despreading apparatus for the intermediate frequency-oriented short code direct spread signal array according to claim 7 or 9, wherein the frequency offset and the phase offset of the constellation are corrected by using the blind estimation of the frequency offset and the phase offset to obtain a stable decision constellation.