CN115378462B - Method and system for capturing direct spread spectrum signal with large signal-to-noise ratio - Google Patents
Method and system for capturing direct spread spectrum signal with large signal-to-noise ratio Download PDFInfo
- Publication number
- CN115378462B CN115378462B CN202210994253.XA CN202210994253A CN115378462B CN 115378462 B CN115378462 B CN 115378462B CN 202210994253 A CN202210994253 A CN 202210994253A CN 115378462 B CN115378462 B CN 115378462B
- Authority
- CN
- China
- Prior art keywords
- signal
- sequence
- sparse
- pseudo code
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/69—Spread spectrum techniques
- H04B1/707—Spread spectrum techniques using direct sequence modulation
- H04B1/7073—Synchronisation aspects
- H04B1/7075—Synchronisation aspects with code phase acquisition
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Complex Calculations (AREA)
Abstract
The application relates to a high-efficiency large signal-to-noise ratio direct spread spectrum signal capturing method and a system, which comprises the steps of firstly obtaining a sparse pseudo code and a local carrier signal sequence of the current iteration, wherein the sparse pseudo code is obtained by processing according to the local pseudo code and a preset sparse factor; then stripping the carrier wave in the received direct spread spectrum signal through the current iterated local carrier wave signal sequence to obtain a corresponding low-frequency signal; then, carrying out sparsification treatment on the low-frequency signal according to the sparsification factor to obtain a sparsification signal, calculating according to the frequency domain form of the sparsification pseudo code and the sparsification signal to obtain a related sequence, carrying out time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain a rearranged signal, and carrying out sparsification reconstruction on the rearranged signal to obtain a reconstructed sequence; and finally, judging whether the current iteration is successfully captured according to the reconstruction sequence, and outputting a capturing result if the current iteration is successfully captured. The application can give consideration to the capture speed and the capture probability.
Description
Technical Field
The application relates to the technical field of wireless communication, in particular to a method and a system for capturing a direct spread spectrum signal with a large signal-to-noise ratio.
Background
The direct spread spectrum signal receiver structure is shown in fig. 1, and is composed of a radio frequency front end, a baseband signal processing module and an electric text interpretation module, and has the functions of down-conversion sampling, digital demodulation and spread spectrum and electric text interpretation and decoding. The acquisition of the received signal is particularly critical, and is a primary technology in a baseband processing link, and the task is to estimate the carrier Doppler frequency shift and pseudo code phase delay of the received signal within a limited time so as to smoothly enter the next link. Compared with the general signal, the direct spread spectrum signal applied to the closed loop test environment has the advantages of large bandwidth, interference resistance and low interception, and also has the characteristic of large signal to noise ratio, so that the capturing of the signal has higher sensitivity, and the industry is focused on how to increase the capturing speed.
At present, two main types of methods for improving the capture speed of the direct spread spectrum signal with a large signal-to-noise ratio are adopted, and one type is a traditional non-downsampling method, including a parallel frequency capture algorithm, a parallel code phase capture algorithm, an improved algorithm thereof and the like; the other type is a downsampling method, which comprises a coherent downsampling strategy, an average method, a time-frequency joint acquisition algorithm and the like. In the traditional non-downsampling capturing method, a parallel code phase capturing algorithm is the most method in engineering practical application, and a two-dimensional searching process is changed into a one-dimensional searching process by utilizing Fourier transformation, so that the capturing efficiency is greatly improved, however, multiple Fourier transformation is a bottleneck that the capturing speed is difficult to break through. For the down-sampling capture algorithm, the down-sampling capture algorithm uses the strong correlation of the pseudo codes in the spread spectrum signal to carry out down-sampling processing on the signal, shortens the correlation time of the pseudo codes, realizes the improvement of the capture speed, but introduces the pseudo code phase estimation ambiguity, and greatly reduces the capture probability.
Disclosure of Invention
Based on this, it is necessary to provide a method and a system for capturing a direct spread spectrum signal with a large signal-to-noise ratio, which can achieve both capturing speed and capturing probability.
A method of large signal-to-noise ratio direct spread spectrum signal acquisition, the method comprising:
acquiring a sparse pseudo code and a local carrier signal sequence of a current iteration; the sparse pseudo code is obtained by processing according to a local pseudo code and a preset sparse factor;
stripping the carrier wave in the received direct spread spectrum signal through the current iterated local carrier wave signal sequence to obtain a corresponding low-frequency signal;
performing sparsification processing on the low-frequency signal according to the sparsification factor to obtain a sparsified signal, calculating a correlation sequence according to a frequency domain form of the sparsification pseudo code and the sparsified signal, performing time domain rearrangement on the correlation sequence to separate time domain peaks in the correlation sequence to obtain a rearranged signal, and performing sparse reconstruction on the rearranged signal to obtain a reconstructed sequence;
and judging whether the current iteration successfully captures the direct spread spectrum signal according to the reconstruction sequence, and outputting a capturing result if the current iteration successfully captures the direct spread spectrum signal.
In one embodiment, whether the direct spread spectrum signal is successfully captured in the current iteration is determined according to the reconstruction sequence, if the direct spread spectrum signal is not successfully captured, the local carrier signal sequence is updated to obtain the local carrier signal sequence of the next iteration, so as to calculate the reconstruction sequence of the next iteration and capture the reconstruction sequence until the direct spread spectrum signal is successfully captured, and the iteration is terminated.
Preferably, updating the local carrier signal sequence to obtain the local carrier signal sequence of the next iteration includes:
obtaining a local carrier signal sequence p (N) = { p (0), p (1), …, p (N-1) } of the current iteration, wherein N represents the length of the local carrier signal sequence; the frequency of the local carrier signal sequence is f i -f r +λΔf, where f i Representing the intermediate frequency, f, of the received direct spread spectrum signal r Representing an estimated carrier doppler shift range, -f, of said direct spread signal r ,f r ]λ represents the number of the local carrier signal sequence of the current iteration, λ=0, 1,2, …,2f r With a number initial value of 0, Δf represents a Doppler searchRope stepping;
and adding 1 to the number lambda corresponding to the local carrier signal sequence of the current iteration, and updating the frequency of the local carrier signal sequence to obtain the local carrier signal sequence of the next iteration.
Preferably, the time domain rearrangement of the related sequence is performed to separate time domain peaks in the related sequence, so as to obtain a rearranged signal, and the sparse reconstruction of the rearranged signal is performed to obtain a reconstructed sequence, which includes:
s1, carrying out hash rearrangement on the related sequence by adopting a remainder function with a parameter sigma, so that time domain peaks in the related sequence are separated, and a rearranged signal is obtained; wherein the remainder function is:
H(m)＝X((σm)mod(N/k))
wherein H (m) represents a remainder function, X (m) represents a related sequence, m represents an mth sampling point, sigma is a random prime number, and modulo N is reversible, and the modulo inverse sigma exists 0 So that ((σ. σ) 0 ) mod (N/k))=1, mod is a remainder operation, k represents a sparse factor, and N represents the length of the correlation sequence;
s2, filtering the rearranged signals to obtain filtered signals, performing inverse fast Fourier transform on the filtered signals to obtain time domain forms of the filtered signals, and searching to obtain the largest 2N/k in the time domain forms of the filtered signals 2 Large value abscissa set corresponding to each value:
wherein z (m) represents the time domain form of the filtered signal;
s3, obtaining a corresponding original abscissa set before sparsification processing and hash rearrangement according to the large-value abscissa set and a pre-constructed hash mapping function:
h σ (m)＝[σm/k]
[0，N-1]→[0，N/k-1]
wherein J represents an original abscissa set comprising 2N/k original abscissa values, h σ (m) represents a hash-map function,is an upward rounding operation;
s4, executing S1-S3, circulating for preset times, and recording large-value abscissas which appear in an original abscissas set of each circulation to obtain a primary image set;
estimating the amplitude corresponding to each large-value abscissa in the original image set according to a pre-constructed offset mapping function and a hash mapping function to obtain a reconstruction sequence:
ξ σ (n)＝σn-k·h σ (n)
[0，N-1]→[-k/2，k/2]
wherein y' (n) represents the reconstruction sequence, n represents the nth sampling point of the reconstruction sequence, h σ (n) represents a Hash mapping function, ζ σ (n) denotes an offset mapping function, F denotes fourier transform, IFFT denotes inverse fast fourier transform, and I denotes a set of primary images.
Preferably, filtering the rearranged signal to obtain a filtered signal, and performing inverse fast fourier transform on the filtered signal to obtain a time domain form of the filtered signal, where the time domain form is:
z(m)＝IFFT[H(m)·T(m)]
where T (m) is a smoothing filter subject to parameters (epsilon, epsilon', mu, omega) representing the stop band cut-off factor, the pass band cut-off factor, the degree of ripple oscillation and the filter length of the smoothing filter in the frequency domain, respectively.
Preferably, the step of obtaining the thinned pseudo code includes:
obtaining a section of local pseudo code, and entering the local pseudo codeSampling the rows to obtain a local pseudo code reference sequence c (N) with the length of N; the period of the local pseudo code is T, and N=T.f is satisfied s Wherein f s Is the sampling frequency;
and carrying out sparsification processing on the local pseudo code reference sequence according to a preset sparsification factor to obtain a sparsified pseudo code:
wherein, c' (m) represents sparse pseudo code, and k is less than or equal to log 2 N is a positive integer divisible by N, and represents a sparseness factor.
Preferably, stripping the carrier wave in the received direct spread spectrum signal through the local carrier wave signal sequence of the current iteration to obtain a corresponding low frequency signal, which comprises:
the received direct spread spectrum signal is sampled at a sampling rate f s Sampling, and intercepting a signal with duration of T to obtain an intermediate frequency digital signal sequence;
multiplying the intermediate frequency digital signal sequence with the current iterated local carrier signal sequence to strip the carrier wave in the direct spread spectrum signal, so as to obtain a mixed frequency digital signal sequence;
and removing a sum frequency part in the mixed digital signal sequence by using a low-pass filter, wherein the reserved difference frequency part is a low-frequency signal.
Preferably, the calculating to obtain the correlation sequence according to the frequency domain form of the sparse pseudo code and the sparse signal includes:
performing fast Fourier transform on the sparse pseudo code and taking conjugation to obtain a frequency domain form of the sparse pseudo code;
and performing fast Fourier transform on the sparse signals, and multiplying the sparse signals with the frequency domain form of the sparse pseudo code to obtain a correlation sequence.
Preferably, determining whether the current iteration is successfully captured according to the reconstruction sequence, and if so, outputting a capturing result, including:
calculating the module value of the elements in the reconstruction sequence, and comparing the maximum module value in the module value with a preset detection threshold;
if the maximum modulus value exceeds the detection threshold, judging successful capture and outputting a capture result; the capturing result comprises pseudo code phase delay and carrier Doppler frequency shift of the received direct spread spectrum signal; and the position corresponding to the large-value abscissa corresponding to the maximum modulus is pseudo code phase delay, and the difference between the center frequency corresponding to the local carrier signal sequence and the intermediate frequency of the direct spread spectrum signal when the acquisition is successful is judged to be carrier Doppler frequency shift.
A large signal-to-noise ratio direct spread spectrum signal acquisition system, the system comprising:
the acquisition module is used for acquiring the sparse pseudo code and the local carrier signal sequence of the current iteration; the sparse pseudo code is obtained by processing according to a local pseudo code and a preset sparse factor;
the carrier stripping module is used for stripping the carrier in the received direct spread spectrum signal through the local carrier signal sequence of the current iteration to obtain a corresponding low-frequency signal;
the sparse reconstruction module is used for carrying out sparse processing on the low-frequency signals according to the sparse factors to obtain sparse signals, calculating to obtain a related sequence according to a frequency domain form of the sparse pseudo code and the sparse signals, carrying out time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain rearranged signals, and carrying out sparse reconstruction on the rearranged signals to obtain a reconstruction sequence;
and the acquisition judging module is used for judging whether the current iteration is successfully acquired according to the reconstruction sequence, and outputting an acquisition result if the current iteration is successfully acquired.
The method and the system for capturing the direct spread spectrum signal with the large signal-to-noise ratio firstly acquire the sparse pseudo code and the local carrier signal sequence of the current iteration, wherein the sparse pseudo code is processed according to the local pseudo code and a preset sparse factor; then stripping the carrier wave in the received direct spread spectrum signal through the current iterated local carrier wave signal sequence to obtain a corresponding low-frequency signal; then, carrying out sparsification treatment on the low-frequency signal according to the sparsification factor to obtain a sparsification signal, calculating according to the frequency domain form of the sparsification pseudo code and the sparsification signal to obtain a related sequence, carrying out time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain a rearranged signal, and carrying out sparsification reconstruction on the rearranged signal to obtain a reconstructed sequence; and finally, judging whether the current iteration is successfully captured according to the reconstruction sequence, and outputting a capturing result if the current iteration is successfully captured. According to the application, the sparse factor is introduced to carry out sparsification treatment, so that the complexity of subsequent operation is reduced, the original capturing result is recovered by applying a sparse reconstruction method, and the capturing time of the direct spread spectrum signal is obviously reduced; because the sparsification processing makes the time domain peak value of the related sequence concentrated and difficult to distinguish, the application further carries out time domain rearrangement, reduces the concentration degree of the time domain peak value, ensures the easy resolution of the time domain peak value and ensures the capturing probability of the subsequent direct spread spectrum signal. In summary, the application can give consideration to both the capture speed and the capture probability.
Drawings
Fig. 1 is a schematic diagram of a direct spread spectrum signal receiver;
FIG. 2 is a flow chart of a method for capturing a large signal-to-noise ratio direct spread spectrum signal in one embodiment;
FIG. 3 is an overall flow chart of the method in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In a general closed loop test scene, a direct spread spectrum signal simulator firstly spreads generated telegraph text information through pseudo codes, modulates the telegraph text information on a carrier wave to form a transmitting signal, and directly inputs the transmitting signal to a signal receiving end for testing. Taking a single direct spread spectrum signal with large signal-to-noise ratio without text bit inversion as an example, after the direct spread spectrum signal passes through a down converter, an intermediate frequency analog signal r (t) is received by a signal receiving end, and the expression is as shown in the formula (1):
wherein P represents the signal power reaching the signal acquisition module; d (·) represents a text information bit sequence; c (·) represents a pseudo code sequence of period T; τ represents the pseudo code delay; f (f) i Representing the intermediate frequency of the received signal; f (f) d Representing the carrier doppler shift; f (f) r Representing the upper limit of the carrier Doppler shift range; w (·) represents zero mean variance σ n 2 Is a gaussian white noise sequence of (c).
For the signal receiving end, the carrier doppler and pseudo code phase delay of the received signal are unknown and therefore are estimated first by the acquisition process.
In one embodiment, as shown in fig. 2, a method for capturing a large signal-to-noise ratio direct spread spectrum signal is provided, which includes the following steps:
step 202, acquiring a sparse pseudo code and a local carrier signal sequence of a current iteration.
The sparse pseudo code is obtained by processing the local pseudo code according to a preset sparse factor, namely the local pseudo code is subjected to sparse processing by adopting the sparse factor to obtain the sparse pseudo code. In the capturing process, the local pseudo code sequence after sampling and sparsification is stored in advance, so that unnecessary calculation load is avoided from being increased in each repeated generation.
And 204, stripping the carrier wave in the received direct spread spectrum signal through the local carrier wave signal sequence of the current iteration to obtain a corresponding low-frequency signal.
The carrier stripping means that a carrier is locally generated and multiplied by a received direct spread spectrum signal to obtain a mixed frequency digital signal containing sum frequency and difference frequency components, the mixed frequency digital signal is subjected to low-pass filter to remove the sum frequency components, and the difference frequency components are reserved, so that a low-frequency signal is obtained.
And 206, carrying out sparsification processing on the low-frequency signal according to the sparsification factor to obtain a sparsification signal, calculating according to the frequency domain form of the sparsification pseudo code and the sparsification signal to obtain a correlation sequence, carrying out time domain rearrangement on the correlation sequence to separate time domain peaks in the correlation sequence to obtain a rearranged signal, and carrying out sparsification reconstruction on the rearranged signal to obtain a reconstruction sequence.
In the capturing process, when the received spread spectrum signal carrier is matched with the local oscillator, the large signal-to-noise ratio characteristic can enable a capturing result to have an obvious peak value in the time domain amplitude response, the moment corresponding to the peak value is pseudo code phase delay to be estimated, and the amplitude corresponding to the rest moments is very small. Therefore, the capturing result of the direct spread spectrum signal with large signal-to-noise ratio has obvious sparse characteristic in the time domain amplitude response. By the inspired, the signal can be subjected to sparsification operation to reduce the operation complexity, and then the original capturing result is reconstructed, so that the high-efficiency capturing of the direct spread spectrum signal with a large signal-to-noise ratio is realized.
The sparsification process utilizes the time domain sparsity of the capturing result, and introduces a sparsity factor to process the signal, so that the subsequent calculation scale is reduced to improve the capturing speed. The peak values in the time domain of the correlated signals are concentrated and difficult to distinguish due to the thinning process, so that the concentrated peak values are uniformly distributed in the time domain for separation, and time domain rearrangement is needed.
Step 208, judging whether the current iteration is successfully captured according to the reconstruction sequence, and if so, outputting a capturing result.
The acquisition results are known to include carrier doppler and pseudo code phase delays of the received direct spread spectrum signal.
The method for capturing the direct spread spectrum signal with the large signal-to-noise ratio comprises the steps of firstly acquiring a sparse pseudo code and a local carrier signal sequence of a current iteration, wherein the sparse pseudo code is obtained by processing according to the local pseudo code and a preset sparse factor; then stripping the carrier wave in the received direct spread spectrum signal through the current iterated local carrier wave signal sequence to obtain a corresponding low-frequency signal; then, carrying out sparsification treatment on the low-frequency signal according to the sparsification factor to obtain a sparsification signal, calculating according to the frequency domain form of the sparsification pseudo code and the sparsification signal to obtain a related sequence, carrying out time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain a rearranged signal, and carrying out sparsification reconstruction on the rearranged signal to obtain a reconstructed sequence; and finally, judging whether the current iteration is successfully captured according to the reconstruction sequence, and outputting a capturing result if the current iteration is successfully captured. According to the method, the sparse factors are introduced to carry out sparsification treatment, so that the complexity of subsequent operation is reduced, an original capturing result is recovered by applying a sparse reconstruction method, and the capturing time of a direct spread spectrum signal is remarkably reduced; because the sparsification processing makes the time domain peak value of the related sequence concentrated and difficult to distinguish, the application further carries out time domain rearrangement, reduces the concentration degree of the time domain peak value, ensures the easy resolution of the time domain peak value and ensures the capturing probability of the subsequent direct spread spectrum signal. In summary, the method can give consideration to both the capturing speed and the capturing probability.
In one embodiment, whether the current iteration successfully captures the direct spread spectrum signal is determined according to the reconstruction sequence, if the current iteration does not successfully capture the direct spread spectrum signal is determined, the local carrier signal sequence is updated to obtain the local carrier signal sequence of the next iteration, so as to calculate and capture the reconstruction sequence of the next iteration, and the iteration is terminated until the current iteration successfully captures the direct spread spectrum signal.
In one embodiment, updating the local carrier signal sequence to obtain the local carrier signal sequence of the next iteration includes:
obtaining a local carrier signal sequence p (N) = { p (0), p (1),. The local carrier signal sequence p (N-1) }, where N represents the length of the local carrier signal sequence; the frequency of the local carrier signal sequence is f i -f r +λΔf, where f i Represents the intermediate frequency, f, of the received direct spread spectrum signal r Representing the range of carrier doppler shift of an estimated direct spread signal [ -f r ，f r ]λ represents the number of the local carrier signal sequence of the current iteration, λ=0, 1,2,.. r And/Δf, the initial value of the number is 0, and Δf represents the Doppler search step. The expression of the local carrier signal sequence is:
and adding 1 to the number lambda corresponding to the local carrier signal sequence of the current iteration, and updating the frequency of the local carrier signal sequence to obtain the local carrier signal sequence of the next iteration.
In one embodiment, the step of obtaining the sparse pseudocode comprises:
acquiring a section of local pseudo code, and sampling the local pseudo code to obtain a local pseudo code reference sequence c (N) with the length of N; wherein the period of the local pseudo code, i.e. the duration, is T and n=t·f is satisfied s ，f s For sampling frequency f s ≥2(f i +f r )；
According to a preset sparse factor, carrying out sparse processing on a local pseudo code reference sequence to obtain a sparse pseudo code:
wherein, c' (m) represents sparse pseudo code, and k is less than or equal to log 2 N is a positive integer divisible by N, and represents a sparseness factor.
In one embodiment, stripping the carrier in the received direct spread spectrum signal through the local carrier signal sequence of the current iteration to obtain a corresponding low frequency signal comprises:
since the incoming direct spread spectrum signal is an intermediate frequency analog signal, it needs to be sampled and converted into a digital signal for convenient processing. The method comprises the following specific steps:
the received direct spread spectrum signal is sampled at a sampling rate f s Sampling is carried out, a signal with the duration of T is intercepted, an intermediate frequency digital signal sequence is obtained, the intermediate frequency digital signal sequence is multiplied with a local carrier signal sequence of the current iteration to strip carriers in a direct spread spectrum signal, a mixed frequency digital signal sequence is obtained, a sum frequency part in the mixed frequency digital signal sequence is removed by a low-pass filter, and a reserved difference frequency part is a low-frequency signal. The method is as followsThe formula:
wherein s (n) represents a low frequency signal, LPF represents a low pass filter, r (n) represents an intermediate frequency digital signal sequence, p (n) represents a local carrier signal sequence of the current iteration, n τ ＝τ·f s Representing the position of the sampling point corresponding to the pseudo code phase delay, namely the pseudo code phase delay which needs to be estimated, w L (n) represents a low frequency noise sequence.
It should be noted that the local pseudo code, the received direct spread spectrum signal, the sampling frequency of the local carrier, the length of the generated signal, and the duration are all the same. The same sampling frequency is adopted to ensure the correctness of the signal point-to-point operation.
In one embodiment, the computing the correlation sequence from the frequency domain form of the sparse pseudo code and the sparse signal includes:
performing fast Fourier transform on the sparse pseudo code, taking conjugate to obtain a frequency domain form of the sparse pseudo code, performing fast Fourier transform on the sparse signal, and multiplying the frequency domain form of the sparse pseudo code to obtain a correlation sequence, namely completing despreading correlation operation, wherein the specific formula is as follows:
where X (m) represents the correlation sequence, s '(m) represents the sparse signal, and C' (m) represents the frequency domain form of the sparse pseudocode.
In one embodiment, performing time domain rearrangement on the correlation sequence to separate time domain peaks in the correlation sequence to obtain a rearranged signal, performing sparse reconstruction on the rearranged signal to obtain a reconstructed sequence, including:
s1, carrying out hash rearrangement on a related sequence by adopting a residual function with a parameter sigma, so that time domain peaks in the related sequence are separated, and a rearranged signal is obtained; wherein the remainder function is:
H(m)＝X((σm)mod(N/k)) (6)
wherein H (m) represents a remainder function, X (m) represents a related sequence, m represents an mth sampling point, sigma is a random prime number, and modulo N is reversible, and the modulo inverse sigma exists 0 So that ((σ. σ) 0 ) mod (N/k))=1, mod is a remainder operation, k represents a sparseness factor, and N represents the length of the correlation sequence.
It can be seen that the relation between the time domain form h (m) of the reordering function and the time domain form x (m) of the correlation sequence is shown as follows, which indicates that the purposes of time domain reordering and peak separation are achieved by the reordering operation on the frequency domain.
h(m)＝x((σ 0 m)mod(N/k) (7)
S2, filtering the rearranged signals to obtain filtered signals, performing inverse fast Fourier transform on the filtered signals to obtain time domain forms of the filtered signals, and searching to obtain the largest 2N/k in the time domain forms of the filtered signals 2 Large value abscissa set corresponding to each value:
M＝argmax m |z(m)| (8)
where z (m) represents the time domain form of the filtered signal.
In order to avoid spectrum leakage and subsequent peak extraction, the rearranged signal needs to be filtered and then transformed to the time domain, specifically as follows:
z(m)＝IFFT[H(m)·T(m)] (9)
where T (m) is a smoothing filter subject to parameters (epsilon, epsilon', mu, omega) representing the stop band cut-off factor, the pass band cut-off factor, the degree of ripple oscillation and the filter length of the smoothing filter in the frequency domain, respectively.
S3, obtaining a corresponding original abscissa set before sparsification processing and hash rearrangement according to the large-value abscissa set and a pre-constructed hash mapping function:
J＝{m∈[0，N-1]|h σ (m)∈argmax m |z(m)|} (10)
wherein J represents an original abscissa set comprising 2N/k original abscissa values, h σ (m) represents a hash-map function,is an upward rounding operation.
Since the resulting filtered sequence z (m) is a compressed and hashed rearranged result, it is necessary to reconstruct it to recover the original time sequence.
S4, executing S1-S3, cycling for preset times, recording large-value abscissas which appear in the original abscissas of each cycle, and obtaining a primary image set, namely performing positioning operation. Although the previous hash rearrangement operation can reduce the peak concentration in z (m) to make it as uniform as possible, it is unavoidable that some adjacent peaks cannot be separated, so-called hash collision phenomenon. In order to solve the hash collision problem, the process of reordering until positioning is repeated L times, and the large-value abscissa positions that occur in each set J are recorded to form the original image set I, since the original image positions that remain unchanged with a higher probability in each rearrangement correspond to the true large-value positions with a high probability.
Estimating the amplitude corresponding to each large-value abscissa in the original image set according to a pre-constructed offset mapping function and a hash mapping function to obtain a reconstruction sequence, namely performing estimation operation:
wherein y' (n) represents the reconstruction sequence, n represents the nth sampling point of the reconstruction sequence, h σ (n) represents a Hash mapping function, ζ σ (n) denotes an offset mapping function, F denotes fourier transform, IFFT denotes inverse fast fourier transform, and I denotes a set of primary images.
In one embodiment, determining whether the current iteration successfully captures according to the reconstruction sequence, and if so, outputting a capture result, including:
calculating the module value of the elements in the reconstruction sequence, and comparing the maximum module value in the module value with a preset detection threshold; if the maximum modulus value exceeds the detection threshold, judging successful capture and outputting a capture result; the acquisition result comprises pseudo code phase delay and carrier Doppler frequency shift of the received direct spread spectrum signal; the position corresponding to the large-value abscissa corresponding to the maximum modulus value is pseudo code phase delay, and the difference between the center frequency corresponding to the local carrier signal sequence and the intermediate frequency of the direct spread spectrum signal when the acquisition is successful is judged to be carrier Doppler frequency shift.
As shown in fig. 3, an overall flow chart of the present method is provided.
The implementation of the method is illustrated herein:
for a direct spread spectrum signal with a large signal-to-noise ratio, the input signal-to-noise ratio of the direct spread spectrum signal entering the capture module is set to be-10 dB, the telegraph text information is in a full 1 sequence, the spread spectrum pseudo code rate is 1.023MHz, the intermediate frequency is 1.5MHz, the pseudo code phase delay is random, and the carrier Doppler dynamic range is between-5 kHz and 5 kHz. The duration of the signal is 10ms, and the baseband processing module adopts a sparse reconstruction acquisition algorithm to estimate the pseudo code phase delay and the carrier Doppler frequency shift of the signal.
The sampling rate is 5MHz, the coherence time length T is 1ms, the Doppler search step is 100Hz, the circulation times L is 10, the selected sparse factor is 25, the signal is captured by the method provided by the application, the final pseudo code estimation error is less than 400 nanoseconds, the Doppler estimation error is less than 100Hz, the Doppler estimation error is within the capture error tolerance range, and the capture speed is improved by about 25% compared with the speed of the traditional parallel code phase capture algorithm. Therefore, the method provided by the application can realize the high-efficiency capturing of the direct spread spectrum signal with large signal-to-noise ratio.
It should be understood that, although the steps in the flowcharts of fig. 2 to 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
In one embodiment, a large signal-to-noise ratio direct spread spectrum signal acquisition system is provided, comprising:
the acquisition module is used for acquiring the sparse pseudo code and the local carrier signal sequence of the current iteration; the sparse pseudo code is obtained by processing according to the local pseudo code and a preset sparse factor;
the carrier stripping module is used for stripping the carrier in the received direct spread spectrum signal through the local carrier signal sequence of the current iteration to obtain a corresponding low-frequency signal;
the sparse reconstruction module is used for carrying out sparse processing on the low-frequency signals according to the sparse factors to obtain sparse signals, calculating the sparse signals according to the frequency domain form of the sparse pseudo code to obtain a related sequence, carrying out time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain rearranged signals, and carrying out sparse reconstruction on the rearranged signals to obtain a reconstructed sequence;
and the acquisition judging module is used for judging whether the current iteration is successfully acquired according to the reconstruction sequence, and outputting an acquisition result if the current iteration is successfully acquired.
According to the application, the sparse factor is introduced to carry out sparse processing on the mixed signals, so that the complexity of subsequent operation is reduced, and finally, the original capturing result is recovered by applying a sparse reconstruction method, thereby obviously reducing the capturing time of the signals. Compared with the classical parallel code phase acquisition algorithm, the system processing speed is improved efficiently. The application can greatly improve the working processing efficiency of the receiver and has great significance for improving the real-time performance of the system operation.
For a specific limitation of a large snr direct spread spectrum signal acquisition device, reference is made to the limitation of a large snr direct spread spectrum signal acquisition method hereinabove, and no further description is given here. The various modules in the large signal-to-noise ratio direct spread spectrum signal acquisition device can be implemented in whole or in part by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as local pseudo codes, local carriers and received direct spread spectrum signals. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a large signal-to-noise ratio direct spread spectrum signal acquisition method.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method of the above embodiments when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method of the above embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (9)
1. A method for capturing a large signal-to-noise ratio direct spread spectrum signal, the method comprising:
acquiring a sparse pseudo code and a local carrier signal sequence of a current iteration; the sparse pseudo code is obtained by processing according to a local pseudo code and a preset sparse factor;
stripping the carrier wave in the received direct spread spectrum signal through the current iterated local carrier wave signal sequence to obtain a corresponding low-frequency signal;
carrying out sparsification treatment on the low-frequency signal according to the sparsification factor to obtain a sparsification signal, and calculating according to the frequency domain form of the sparsification pseudo code and the sparsification signal to obtain a related sequence;
performing time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain rearranged signals, and performing sparse reconstruction on the rearranged signals to obtain a reconstructed sequence; the method comprises the following specific steps:
s1, carrying out hash rearrangement on the related sequence by adopting a remainder function with a parameter sigma, so that time domain peaks in the related sequence are separated, and a rearranged signal is obtained; wherein the remainder function is:
H(m)＝X((σm)mod(N/k))
wherein H (m) represents a remainder function, X (m) represents a related sequence, m represents an mth sampling point, sigma is a random prime number, and modulo N is reversible, and the modulo inverse sigma exists 0 So that ((σ. σ) 0 ) mod (N/k))=1, mod is a remainder operation, k represents a sparse factor, and N represents the length of the correlation sequence;
s2, filtering the rearranged signals to obtain filtered signals, performing inverse fast Fourier transform on the filtered signals to obtain time domain forms of the filtered signals, and searching to obtain the largest 2N/k in the time domain forms of the filtered signals 2 Large value abscissa set corresponding to each value:
wherein z (m) represents the time domain form of the filtered signal;
s3, obtaining a corresponding original abscissa set before sparsification processing and hash rearrangement according to the large-value abscissa set and a pre-constructed hash mapping function:
[0,N-1]→[0,N/k-1]
wherein J represents an original abscissa set comprising 2N/k original abscissa values, h σ (m) represents a hash-map function,is an upward rounding operation;
s4, executing S1-S3, circulating for preset times, and recording large-value abscissas which appear in an original abscissas set of each circulation to obtain a primary image set;
estimating the amplitude corresponding to each large-value abscissa in the original image set according to a pre-constructed offset mapping function and a hash mapping function to obtain a reconstruction sequence:
ξ σ (n)＝σn-k·h σ (n)
[0,N-1]→[-k/2,k/2]
wherein y' (n) represents the reconstructed sequence and n represents the reconstructed sequenceNth sampling point, h σ (n) represents a Hash mapping function, ζ σ (n) denotes an offset mapping function, F denotes fourier transform, IFFT denotes inverse fast fourier transform, and I denotes a set of primary images;
and judging whether the current iteration successfully captures the direct spread spectrum signal according to the reconstruction sequence, and outputting a capturing result if the current iteration successfully captures the direct spread spectrum signal.
2. The method of claim 1 wherein determining whether the current iteration successfully acquired the direct spread spectrum signal based on the reconstruction sequence, if not, updating the local carrier signal sequence to obtain the local carrier signal sequence for the next iteration for reconstruction sequence calculation and acquisition determination for the next iteration until successful acquisition, terminating the iteration.
3. The method of claim 2, wherein updating the local carrier signal sequence to obtain the local carrier signal sequence for the next iteration comprises:
obtaining a local carrier signal sequence p (N) = { p (0), p (1), …, p (N-1) } of the current iteration, wherein N represents the length of the local carrier signal sequence; the frequency of the local carrier signal sequence is f i -f r +λΔf, where f i Representing the intermediate frequency, f, of the received direct spread spectrum signal r Representing an estimated carrier doppler shift range, -f, of said direct spread signal r ,f r ]λ represents the number of the local carrier signal sequence of the current iteration, λ=0, 1,2, …,2f r The initial value of the number is 0, and the Deltaf represents Doppler search steps;
and adding 1 to the number lambda corresponding to the local carrier signal sequence of the current iteration, and updating the frequency of the local carrier signal sequence to obtain the local carrier signal sequence of the next iteration.
4. A method according to claim 3, wherein filtering the reordered signal to obtain a filtered signal and performing an inverse fast fourier transform on the filtered signal to obtain a time-domain version of the filtered signal comprises:
filtering the rearranged signal to obtain a filtered signal, and performing inverse fast fourier transform on the filtered signal to obtain a time domain form of the filtered signal, wherein the time domain form is as follows:
z(m)＝IFFT[H(m)·T(m)]
where T (m) is a smoothing filter subject to parameters (epsilon, epsilon', mu, omega) representing the stop band cut-off factor, the pass band cut-off factor, the degree of ripple oscillation and the filter length of the smoothing filter in the frequency domain, respectively.
5. The method of claim 1, wherein the step of obtaining sparse pseudocode comprises:
acquiring a section of local pseudo code, and sampling the local pseudo code to obtain a local pseudo code reference sequence c (N) with the length of N; the period of the local pseudo code is T, and N=T.f is satisfied s Wherein f s Is the sampling frequency;
and carrying out sparsification processing on the local pseudo code reference sequence according to a preset sparsification factor to obtain a sparsified pseudo code:
wherein, c' (m) represents sparse pseudo code, and k is less than or equal to log 2 N is a positive integer divisible by N, and represents a sparseness factor.
6. The method of claim 1, wherein stripping the carrier from the received direct spread spectrum signal through the current iteration of the local carrier signal sequence to obtain a corresponding low frequency signal comprises:
the received direct spread spectrum signal is sampled at a sampling rate f s Sampling, and intercepting a signal with duration of T to obtain an intermediate frequency digital signal sequence;
multiplying the intermediate frequency digital signal sequence with the current iterated local carrier signal sequence to strip the carrier wave in the direct spread spectrum signal, so as to obtain a mixed frequency digital signal sequence;
and removing a sum frequency part in the mixed digital signal sequence by using a low-pass filter, wherein the reserved difference frequency part is a low-frequency signal.
7. The method of claim 1, wherein computing a correlation sequence from the frequency domain form of the sparse pseudo code and the sparse signal comprises:
performing fast Fourier transform on the sparse pseudo code and taking conjugation to obtain a frequency domain form of the sparse pseudo code;
and performing fast Fourier transform on the sparse signals, and multiplying the sparse signals with the frequency domain form of the sparse pseudo code to obtain a correlation sequence.
8. The method of claim 4, wherein determining whether the current iteration successfully acquired based on the reconstruction sequence, and outputting an acquisition result if successfully acquired, comprises:
calculating the module value of the elements in the reconstruction sequence, and comparing the maximum module value in the module value with a preset detection threshold;
if the maximum modulus value exceeds the detection threshold, judging successful capture and outputting a capture result; the capturing result comprises pseudo code phase delay and carrier Doppler frequency shift of the received direct spread spectrum signal; and the position corresponding to the large-value abscissa corresponding to the maximum modulus is pseudo code phase delay, and the difference between the center frequency corresponding to the local carrier signal sequence and the intermediate frequency of the direct spread spectrum signal when the acquisition is successful is judged to be carrier Doppler frequency shift.
9. A large signal-to-noise ratio direct spread spectrum signal acquisition system, the system comprising:
the acquisition module is used for acquiring the sparse pseudo code and the local carrier signal sequence of the current iteration; the sparse pseudo code is obtained by processing according to a local pseudo code and a preset sparse factor;
the carrier stripping module is used for stripping the carrier in the received direct spread spectrum signal through the local carrier signal sequence of the current iteration to obtain a corresponding low-frequency signal;
the sparse reconstruction module is used for carrying out sparse processing on the low-frequency signals according to the sparse factors to obtain sparse signals, calculating to obtain a related sequence according to a frequency domain form of the sparse pseudo code and the sparse signals, carrying out time domain rearrangement on the related sequence to separate time domain peaks in the related sequence to obtain rearranged signals, and carrying out sparse reconstruction on the rearranged signals to obtain a reconstruction sequence; the method comprises the following specific steps:
s1, carrying out hash rearrangement on the related sequence by adopting a remainder function with a parameter sigma, so that time domain peaks in the related sequence are separated, and a rearranged signal is obtained; wherein the remainder function is:
H(m)＝X((σm)mod(N/k))
wherein H (m) represents a remainder function, X (m) represents a related sequence, m represents an mth sampling point, sigma is a random prime number, and modulo N is reversible, and the modulo inverse sigma exists 0 So that ((σ. σ) 0 ) mod (N/k))=1, mod is a remainder operation, k represents a sparse factor, and N represents the length of the correlation sequence;
s2, filtering the rearranged signals to obtain filtered signals, performing inverse fast Fourier transform on the filtered signals to obtain time domain forms of the filtered signals, and searching to obtain the largest 2N/k in the time domain forms of the filtered signals 2 Large value abscissa set corresponding to each value:
wherein z (m) represents the time domain form of the filtered signal;
s3, obtaining a corresponding original abscissa set before sparsification processing and hash rearrangement according to the large-value abscissa set and a pre-constructed hash mapping function:
h σ (m)＝[σm/k]
[0,N-1]→[0,N/k-1]
wherein J represents an original abscissa set comprising 2N/k original abscissa values, h σ (m) represents a hash-map function,is an upward rounding operation;
s4, executing S1-S3, circulating for preset times, and recording large-value abscissas which appear in an original abscissas set of each circulation to obtain a primary image set;
estimating the amplitude corresponding to each large-value abscissa in the original image set according to a pre-constructed offset mapping function and a hash mapping function to obtain a reconstruction sequence:
ξ σ (n)＝σn-k·h σ (n)
[0,N-1]→[-k/2,k/2]
wherein y' (n) represents the reconstruction sequence, n represents the nth sampling point of the reconstruction sequence, h σ (n) represents a Hash mapping function, ζ σ (n) denotes an offset mapping function, F denotes fourier transform, IFFT denotes inverse fast fourier transform, and I denotes a set of primary images;
and the acquisition judging module is used for judging whether the current iteration is successfully acquired according to the reconstruction sequence, and outputting an acquisition result if the current iteration is successfully acquired.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210994253.XA CN115378462B (en) | 2022-08-18 | 2022-08-18 | Method and system for capturing direct spread spectrum signal with large signal-to-noise ratio |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210994253.XA CN115378462B (en) | 2022-08-18 | 2022-08-18 | Method and system for capturing direct spread spectrum signal with large signal-to-noise ratio |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115378462A CN115378462A (en) | 2022-11-22 |
| CN115378462B true CN115378462B (en) | 2023-10-17 |
Family
ID=84065481
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210994253.XA Active CN115378462B (en) | 2022-08-18 | 2022-08-18 | Method and system for capturing direct spread spectrum signal with large signal-to-noise ratio |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115378462B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109558563A (en) * | 2017-09-26 | 2019-04-02 | 中国人民解放军信息工程大学 | Degree of rarefication adaptive sparse Fourier transform method, terminal and storage medium |
| CN112240956A (en) * | 2020-09-09 | 2021-01-19 | 湖北第二师范学院 | Weak signal capturing system and method for electronic communication network |
| CN113484834A (en) * | 2021-05-17 | 2021-10-08 | 西安电子科技大学 | Target detection method based on signal compression of millimeter wave radar |
| CN113746771A (en) * | 2021-09-24 | 2021-12-03 | 天津津航计算技术研究所 | GMSK overlapping communication capturing method based on sparse Fourier transform |
-
2022
- 2022-08-18 CN CN202210994253.XA patent/CN115378462B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109558563A (en) * | 2017-09-26 | 2019-04-02 | 中国人民解放军信息工程大学 | Degree of rarefication adaptive sparse Fourier transform method, terminal and storage medium |
| CN112240956A (en) * | 2020-09-09 | 2021-01-19 | 湖北第二师范学院 | Weak signal capturing system and method for electronic communication network |
| CN113484834A (en) * | 2021-05-17 | 2021-10-08 | 西安电子科技大学 | Target detection method based on signal compression of millimeter wave radar |
| CN113746771A (en) * | 2021-09-24 | 2021-12-03 | 天津津航计算技术研究所 | GMSK overlapping communication capturing method based on sparse Fourier transform |
Non-Patent Citations (3)
| Title |
|---|
| 信号处理中去噪算法的改进仿真;刘跃华;曾迎生;;计算机工程与应用(第04期);全文 * |
| 基于稀疏傅里叶变换的哈希映射宽带频谱感知算法;张煜培;赵知劲;郑仕链;;信号处理(第08期);全文 * |
| 稀疏傅里叶变换理论及研究进展;仲顺安;王雄;王卫江;刘箭言;;北京理工大学学报(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115378462A (en) | 2022-11-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104065397B (en) | Real-time synchronization catches method and the device of pseudo-code | |
| CN110166080B (en) | Coherent multi-carrier capturing method and device | |
| Lapini et al. | Blind speckle decorrelation for SAR image despeckling | |
| JPH05136631A (en) | Non-modulated signal detecting and frequency pull-in device | |
| CN113972929B (en) | Method for capturing spread spectrum signal under high dynamic Doppler | |
| CN117310763B (en) | Method and device for synchronizing pseudo code phase modulation-linear frequency modulation time division low-rail navigation signals | |
| CN116366092A (en) | Doppler capturing method, device and storage medium | |
| CN111934710A (en) | High-dynamic spread spectrum signal rapid acquisition algorithm | |
| CN114217329A (en) | Short code capturing method based on serial search | |
| CN110907958B (en) | Signal capturing method and device, computer equipment and storage medium | |
| CN115378462B (en) | Method and system for capturing direct spread spectrum signal with large signal-to-noise ratio | |
| CN115980798A (en) | Fast unambiguous signal capture algorithm | |
| CN111624632B (en) | Satellite navigation signal capturing method and device | |
| CN106411795B (en) | A kind of non-signal estimation method reconstructed under frame | |
| CN110958035A (en) | Frequency secondary capturing device and method based on triangular envelope fitting | |
| Brajović et al. | FHSS signal sparsification in the Hermite transform domain | |
| CN115061165B (en) | Navigation spread spectrum signal capturing method and system based on low-loss down-sampling strategy | |
| CN107132556B (en) | Multi-core Beidou software receiver signal parallel capturing method based on combined FFT | |
| CN102420635A (en) | Multipath search method and multipath searcher | |
| CN114584174B (en) | Signal capturing method based on frequency domain focusing and synthetic Fourier transform | |
| Choi et al. | An iterative deautoconvolution algorithm for nonnegative functions | |
| CN111641572A (en) | Noise power evaluation method and device and storage medium | |
| Tang et al. | Fast acquisition method using modified PCA with a sparse factor for burst DS spread-spectrum transmission | |
| CN112987041A (en) | Bit synchronization method and device under weak signal and computer storage medium | |
| CN111245473A (en) | Down-sampling-serial FFT-based high-dynamic low-signal-to-noise-ratio signal coarse capturing method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |