CN116582400A - L1 carrier recovery method and device based on short-time pulse interference and intelligent terminal - Google Patents
L1 carrier recovery method and device based on short-time pulse interference and intelligent terminal Download PDFInfo
- Publication number
- CN116582400A CN116582400A CN202310487539.3A CN202310487539A CN116582400A CN 116582400 A CN116582400 A CN 116582400A CN 202310487539 A CN202310487539 A CN 202310487539A CN 116582400 A CN116582400 A CN 116582400A
- Authority
- CN
- China
- Prior art keywords
- vector
- formula
- sparse
- carrier recovery
- determining
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000011084 recovery Methods 0.000 title claims abstract description 38
- 239000013598 vector Substances 0.000 claims abstract description 164
- 238000005070 sampling Methods 0.000 claims abstract description 52
- 238000012545 processing Methods 0.000 claims abstract description 20
- 239000011159 matrix material Substances 0.000 claims description 31
- 125000004122 cyclic group Chemical group 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 7
- 108010076504 Protein Sorting Signals Proteins 0.000 claims description 6
- 238000005311 autocorrelation function Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 102100029469 WD repeat and HMG-box DNA-binding protein 1 Human genes 0.000 description 1
- 101710097421 WD repeat and HMG-box DNA-binding protein 1 Proteins 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2657—Carrier synchronisation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2668—Details of algorithms
- H04L27/2681—Details of algorithms characterised by constraints
- H04L27/2688—Resistance to perturbation, e.g. noise, interference or fading
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The application discloses an L1 carrier recovery method and device based on short-time pulse interference and an intelligent terminal, wherein the L1 carrier recovery method based on short-time pulse interference comprises the following steps: acquiring an LI carrier signal; converting the LI carrier signal into a discrete signal according to a preset sampling rate; determining a CA vector according to the discrete signals; processing the CA vector by a compressed sensing method to obtain a sparse vector; and determining the frequency information and the phase information of the LI carrier signal according to the sparse vector. The application can recover the phase information of the original L1 carrier signal when the original L1 carrier signal is interfered by short-time pulse.
Description
Technical Field
The application relates to the technical field of satellite communication, in particular to an L1 carrier recovery method and device based on short-time pulse interference and an intelligent terminal.
Background
The ship-mounted global navigation satellite system receiver receives an L1 carrier signal from a satellite, wherein the L1 carrier signal refers to a signal transmitted by an L1 frequency band in a satellite navigation system, the L1 frequency band refers to a first frequency band in navigation signals, and navigation information such as corresponding distance, speed, time and the like is obtained by demodulating and processing the L1 carrier signal. However, when short-time strong pulse electromagnetic interference is encountered, such as solar black burst, solar storm, lightning, small nuclear explosion, etc., the signal phase is interfered, which affects the reception of navigation information.
In the prior art, by researching Unique (UW) words in maritime satellite global broadband network (BGAN) signal frames and adopting data-assisted frequency offset estimation and decision-oriented phase offset estimation algorithm, carrier recovery of the actually received maritime satellite BGAN signal is completed, and the method can only be used for recovering carrier signals influenced by Gaussian white noise and cannot be used for recovering carrier signals influenced by short-time large pulses.
Disclosure of Invention
The application mainly aims to provide an L1 carrier recovery method and device based on short-time pulse interference and an intelligent terminal, and aims to solve the technical problems that in the prior art, only carrier signals influenced by Gaussian white noise can be recovered and carrier signals influenced by short-time large pulses cannot be recovered.
In order to achieve the above object, a first aspect of the present application provides an L1 carrier recovery method based on glitches, where the L1 carrier recovery method based on glitches includes: acquiring an LI carrier signal; converting the LI carrier signal into a discrete signal according to a preset sampling rate; determining a CA vector according to the discrete signals; processing the CA vector by a compressed sensing method to obtain a sparse vector; and determining the frequency information and the phase information of the LI carrier signal according to the sparse vector.
Optionally, the determining the CA vector from the discrete signal includes: determining a CA vector according to the discrete signal, the first formula, the second formula and the third formula;
the first formula is as follows:
{s(n)}，n＝0，1，2，…，N-1，
wherein s (n) represents the nth byte of the discrete signal;
the second formula is:
R s (n，v)＝E{s(n)s * (n+v)}，
wherein ,Rs (N, v) represents an autocorrelation function of the discrete signal, v represents a discrete time delay of the discrete signal sequence, v=0, ±1, …, ± (N-1), and E represents a set formed by multiplying bits by bits;
the third formula is:
wherein ,Rs (ζ, v) represents the cyclic autocorrelation vector function of the discrete signal, and ζ represents the cyclic frequency;
searching a peak value of the cyclic autocorrelation vector function, taking the peak value as a sampling starting point, and taking one sampling point every other code element to obtain a CA code sequence;
a set of a plurality of consecutive CA code sequences is determined as a CA vector.
Optionally, the processing the CA vector by the compressed sensing method to obtain a sparse vector includes:
storing the CA vector in x (n);
according to a group of base psi T ＝[ψ 1 ，ψ 2 ，…，ψ N ]And a fourth formula determines a linear combined expression of x (n) above;
the fourth formula is:
wherein y represents an Mx1 order observation vector,representing an M N-dimensional matrix,>a represents an MXN-order sensing matrix, M represents the measurement dimension, N represents the number of samples of a complete CA vector, and M is less than N, ψ represents the NXN-order transformation momentsAn array, α represents an n×1-dimensional coefficient vector;
and calculating the sparse solution of the linear combination expression to obtain the sparse vector.
Optionally, the calculating the sparse solution of the linear combination expression to obtain the sparse vector includes:
establishing a compressed sampling model of the CA vector according to a first sampling model, wherein the first sampling model is as follows:
wherein ,representing the first sampling model, A representing a compressed sensing matrix, r representing an estimated value of the sparse vector of the CA vector, e representing impulse noise satisfying Alpha stable distribution;
determining an estimated value of the sparse vector of the CA vector through a least square method and a fifth formula, and obtaining a final estimated value until a first condition is met or the iteration number is larger than a preset iteration number;
the first condition is as follows:
arg min r ||y-Ar+ε|| Δ +λ||r|| 1 ，
wherein ε is greater than 0 and each component approaches 0 indefinitely, Δ represents optimization using the L1 norm and associated entropy norm combination constraint, L1 norm represents the sum of the absolute values of the values of all the elements of the vector;
determining an observation vector according to the final estimated value;
and calculating the sparse vector according to the observation vector.
The second aspect of the embodiment of the present application provides an L1 carrier recovery apparatus based on glitches, where the L1 carrier recovery apparatus based on glitches includes: the acquisition module is used for acquiring the LI carrier signal; the sampling module is used for converting the LI carrier signal into a discrete signal according to a preset sampling rate; the calculation module is used for determining a CA vector according to the discrete signals; the processing module is used for processing the CA vector through a compressed sensing method to obtain a sparse vector; and the recovery module is used for determining the frequency information and the phase information of the LI carrier signal according to the sparse vector.
Optionally, the above calculation module is further configured to: determining a CA vector according to the discrete signal, the first formula, the second formula and the third formula;
the first formula is as follows:
{s(n)}，n＝0，1，2，…，N-1，
wherein s (n) represents the nth byte of the discrete signal;
the second formula is:
R s (n，v)＝E{s(n)s * (n+v)}，
wherein ,Rs (N, v) represents an autocorrelation function of the discrete signal, v represents a discrete time delay of the discrete signal sequence, v=0, ±1, …, ± (N-1), and E represents a set formed by multiplying bits by bits;
the third formula is:
wherein ,Rs (ζ, v) represents the cyclic autocorrelation vector function of the discrete signal, and ζ represents the cyclic frequency;
searching a peak value of the cyclic autocorrelation vector function, taking the peak value as a sampling starting point, and taking one sampling point every other code element to obtain a CA code sequence;
a set of a plurality of consecutive CA code sequences is determined as a CA vector.
Optionally, the processing module is further configured to:
storing the CA vector in x (n); according to a group of base psi T ＝[ψ 1 ，ψ 2 ，…，ψ N ]And a fourth formula determines a linear combined expression of x (n) above;
the fourth formula is:
wherein y represents an Mx1 order observation vector,representing an M N-dimensional matrix,>a represents an MXN-order sensing matrix, M represents a measurement dimension, N represents the number of samples of a complete CA vector, M is smaller than N, psi represents an NXN-order transformation matrix, and alpha represents an NX1-dimensional coefficient vector;
and calculating the sparse solution of the linear combination expression to obtain the sparse vector.
Optionally, the processing module is further configured to: establishing a compressed sampling model of the CA vector according to a first sampling model, wherein the first sampling model is as follows:
wherein ,representing the first sampling model, A representing a compressed sensing matrix, r representing an estimated value of the sparse vector of the CA vector, e representing impulse noise satisfying Alpha stable distribution;
determining an estimated value of the sparse vector of the CA vector through a least square method and a fifth formula, and obtaining a final estimated value until a first condition is met or the iteration number is larger than a preset iteration number;
the first condition is as follows:
arg min r ||y-Ar+ε|| Δ +λ||r|| 1 ，
wherein ε is greater than 0 and each component approaches 0 indefinitely, Δ represents optimization using the L1 norm and associated entropy norm combination constraint, L1 norm represents the sum of the absolute values of the values of all the elements of the vector;
determining an observation vector according to the final estimated value;
and calculating the sparse vector according to the observation vector.
The third aspect of the present application provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and an L1 carrier recovery program based on glitches stored in the memory and capable of running on the processor, and the steps of the L1 carrier recovery method based on glitches are implemented as described above when the L1 carrier recovery program based on glitches is executed by the processor.
A fourth aspect of the application embodiments provides a computer readable storage medium, on which an L1 carrier recovery program based on glitches is stored, which when executed by a processor implements the steps of the L1 carrier recovery method based on glitches as described above.
From the above, the present application provides an L1 carrier recovery method based on short-time pulse interference, where the L1 carrier recovery method based on short-time pulse interference includes: acquiring an LI carrier signal; converting the LI carrier signal into a discrete signal according to a preset sampling rate; determining a CA vector according to the discrete signals; processing the CA vector by a compressed sensing method to obtain a sparse vector; and determining the frequency information and the phase information of the LI carrier signal according to the sparse vector. The application determines the CA vector according to the discrete signals of the L1 carrier signal, processes the CA vector by a compressed sensing method, estimates the frequency and the phase, and can recover the phase information of the original L1 carrier signal when the original L1 carrier signal is interfered by short-time pulse.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an embodiment of an L1 carrier recovery method based on short-time pulse interference according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an embodiment of an L1 carrier recovery apparatus based on short-time pulse interference according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an embodiment of an intelligent device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted in context as "when …" or "upon" or "in response to a determination" or "in response to detection. Similarly, the phrase "if a condition or event described is determined" or "if a condition or event described is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a condition or event described" or "in response to detection of a condition or event described".
The following description of the embodiments of the present application will be made more fully hereinafter with reference to the accompanying drawings, in which embodiments of the application are shown, it being evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
In order to solve the problems in the prior art, an embodiment of the present application provides an L1 carrier recovery method based on short-time pulse interference, referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of an L1 carrier recovery method based on short-time pulse interference, which includes the following steps:
step S10, acquiring an LI carrier signal;
step S20, the LI carrier signal is converted into discrete signals according to a preset sampling rate.
It should be noted that, the L1 carrier signal is collected into an 8-bit digital discrete signal at a sampling rate of 3GSps data by an Analog-to-digital converter (ADC) chip, and stored in a memory for cyclic refresh, and the 8-bit digital discrete signal is stored in an on-chip memory of the receiver, and is written with 1024 mbytes of data in one cycle, that is, the L1 carrier signal within about 0.36s is converted into a discrete signal represented by bytes every cycle.
Step S30, determining the CA vector according to the discrete signals.
It should be noted that, in this embodiment, the CA vector is a CA vector of a GPS carrier signal, specifically, a C/a code (Coarse/Acquisition code) sequence in a GPS system, which is a pseudorandom code sequence with a length of 1023, and is composed of 1 and-1 alternately.
The CA code sequence is a modulated signal in GPS for modulating navigation data onto a carrier wave and is also one of the key reference signals for the receiver to receive, track and locate GPS signals.
The CA code sequence is transmitted by a GPS satellite, and the receiver can extract the CA code sequence by demodulating a received GPS signal. The CA code sequence is transmitted in the GPS signal at a rate of 1.023MHz, with each chip being 1ms in length. Because the CA code sequence is a pseudo-random code sequence, the CA code sequence has good randomness and uniqueness, and can be used for the purposes of Doppler effect elimination, signal synchronization, tracking and the like, and is one of important reference signals in a GPS system.
In some embodiments, step S30 further comprises:
determining a CA vector according to the discrete signal, the first formula, the second formula and the third formula;
the first formula is as follows:
{s(n)}，n＝0，1，2，…，N-1，
wherein { s (n) } represents a sequence representation of the discrete signal, s (n) represents an nth byte of the discrete signal;
the second formula is:
R s (n，v)＝E{s(n)s * (n+v)}，
wherein ,Rs (N, v) represents an autocorrelation function of the discrete signal, v represents a discrete time delay of the discrete signal sequence, v=0, ±1, …, ± (N-1), E represents a set formed by multiplying bits by bits, and since the L1 carrier signal is a cyclostationary signal, its autocorrelation is a periodic function of a time variable;
the third formula is:
wherein ,Rs (ζ, v) represents the cyclic autocorrelation vector function of the discrete signal, and ζ represents the cyclic frequency; f (f) 0 For carrier frequency, f c For symbol rate, T c Is the chip time width, f s For a CA sampling frequency, k is any integer,represents the initial phase, n 0 ＝1。
The peak value of the cyclic autocorrelation vector function is searched, the peak value is taken as a sampling start point, every other code element is taken as a sampling point, a CA code sequence is obtained, and a set of a plurality of continuous CA code sequences is determined as a CA vector.
It should be noted that, the peak value of the cyclic autocorrelation vector function is a periodic peak value of 1023 symbols in sequence length.
And for the found peak value, determining the peak value position, taking the peak value position as a reference point, and taking one sampling point from the reference point every other code element to obtain 1023 sampling points, namely the CA code sequence.
Since the frequency of the GPS signal is high, high-speed calculation is required when the autocorrelation operation is performed, and the calculation is performed by performing Fast Fourier Transform (FFT) by the DSP to satisfy the real-time performance of the calculation.
When the GPS signal is affected by multipath effects, in order to avoid the occurrence of a plurality of peaks, the CA code sequence is extracted from the one nearest to v=0.
The L1 carrier signal is a BPSK signal, and its CA vector has spectral components at only a few fixed frequency points, and is highly sparse. And sequentially arranging calculation results of the CA vector function Rs to obtain a CA vector x (n).
In some embodiments, the first N points are taken from s (N), their CA vectors are calculated and stored in x (N).
Step S40, processing the CA vector by a compressed sensing method to obtain a sparse vector;
in some embodiments, step S40 includes:
storing the CA vector in x (n);
according to a group of base psi T ＝[ψ 1 ，ψ 2 ，…，ψ N ]And a fourth formula determining a linear combination expression of the above x (N), x (N) =αψ, α representing an n×1-dimensional coefficient vector, ψ representing an n×n-order transformation matrix; when the signal x contains at most b non-zero coefficients alpha and k < N on a certain basis, x is called b sparse signals, psi is the sparse basis of x, and the non-zero coefficient position of alpha is a signal support set;
projecting a signal x onto a set of measurement vectors ψ T ＝[ψ 1 ，ψ 2 ，…，ψ N ]M is the dimension of the measured value, M is less than N, the measured value of x is obtained,expressed in matrix form:
wherein y represents an Mx1 order observation vector,representing an M N-dimensional matrix,>a represents an mxn order sensing matrix, M represents the measurement dimension, N represents the number of samples of one complete CA vector, and M is less than N, ψ represents an nxn order transformation matrix, and α represents an nx1 dimensional coefficient vector.
Gaussian random matrices are the most widely used observation matrices in compressed sensing.A random Gaussian matrix of order MxN, each of whichThe elements being independently subject to a gaussian distribution with a mean of 0 and a variance of 1/M, i.e
Psi is a fast fourier transform matrix, converting the x-signal into the frequency domain r. Namely:
x＝ψr，
where r is a sparse representation of the signal. The frequency information can be recovered from r using the method of VAN NEE (1991).
And calculating the sparse solution of the linear combination expression to obtain the sparse vector.
In some embodiments, calculating the sparse solution of the linear combination expression to obtain the sparse vector includes: establishing a compressed sampling model of the CA vector according to a first sampling model, wherein the first sampling model is as follows:
wherein ,representing the first sampling model, wherein A represents a compressed sensing matrix, which is an MxN-order sensing matrix, r represents an estimated value of a sparse vector of the CA vector, and e represents impulse noise satisfying Alpha stable distribution;
determining a r=r_est e Rn by least squares such that y-ar| 2 Minimum, A.epsilon.R m*n Is a full rank matrix;
determining a least squares solution by a fifth formula, wherein the fifth formula is as follows:
r_est＝(A T A) -1 A T y，
and covering the found r_est with the original r, and continuing to solve, wherein the process is one-time iterative reconstruction.
Continuously performing iterative reconstruction until the first condition r is met or the iteration number is greater than the preset iteration number, and obtaining a final estimated value;
it should be noted that, when r satisfies the first condition, it means that the calculation result that r satisfies the first condition is minimum, that is, r is continuously modified until r satisfies the content in the formula is minimum;
the first condition is as follows:
arg min r ||y-Ar+ε|| Δ +λ||r|| 1 ，
wherein epsilon is greater than 0, each component is infinitely close to 0, and is used for ensuring that y=Ar cannot take negative numerical values in the algorithm iteration process, delta represents optimization adopting combination constraint of L1 norm and related entropy norm, and L1 norm represents the sum of absolute values of numerical values of all elements of the vector;
determining the final r when Ar is very close to y and the sum of coefficients in r is at the same time minimal;
determining an observation vector y_est according to the final estimated value;
the estimation method is a convex relaxation algorithm (Gradient Projection for Sparse Reconstruction GPSR);
the sparse vector x_est is calculated from the observation vector.
Step S50, determining the frequency information and the phase information of the LI carrier signal according to the sparse vector.
In some embodiments, the magnitude of the sparse vector x_est is binarized, using the square wave envelope as the recovered phase information.
From the above, the application uses the sparsity of the cyclic autocorrelation vector of the phase-coded signal and the phase-coded signal parameter estimation technique based on compressed sensing (Compressed sensing, CS), so that the phase information of the original L1 carrier signal can be recovered when the original L1 signal is interfered by short-time large pulses.
In agreement with the above, the following is an L1 carrier recovery apparatus for implementing the above glitch, specifically as follows: referring to fig. 2, an embodiment of an L1 carrier recovery apparatus based on glitch provided in an embodiment of the present application is a schematic diagram, where the L1 carrier recovery apparatus based on glitch includes:
an acquisition module 201, configured to acquire an LI carrier signal;
the sampling module 202 is configured to convert the LI carrier signal into a discrete signal according to a preset sampling rate;
a calculation module 203 for determining a CA vector from the discrete signals;
the processing module 204 is configured to process the CA vector by a compressed sensing method to obtain a sparse vector;
the recovery module 205 is configured to determine the frequency information and the phase information of the LI carrier signal according to the sparse vector.
In some embodiments, the computing module 203 is further configured to:
determining a CA vector according to the discrete signal, the first formula, the second formula and the third formula;
the first formula is as follows:
{s(n)}，n＝0，1，2，…，N-1，
wherein s (n) represents the nth byte of the discrete signal;
the second formula is:
R s (n，v)＝E{s(n)s * (n+v)}，
wherein ,Rs (N, v) represents an autocorrelation function of the discrete signal, v represents a discrete time delay of the discrete signal sequence, v=0, ±1, …, ± (N-1), and E represents a set formed by multiplying bits by bits;
the third formula is:
wherein ,Rs (ζ, v) represents the cyclic autocorrelation vector function of the discrete signal, and ζ represents the cyclic frequency;
searching a peak value of the cyclic autocorrelation vector function, taking the peak value as a sampling starting point, and taking one sampling point every other code element to obtain a CA code sequence;
a set of a plurality of consecutive CA code sequences is determined as a CA vector.
In some embodiments, the processing module 204 is further configured to:
storing the CA vector in x (n);
according to a group of base psi T ＝[ψ 1 ，ψ 2 ，…，ψ N ]And a fourth formula determines a linear combined expression of x (n) above;
the fourth formula is:
wherein y represents an Mx1 order observation vector,representing an M N-dimensional matrix,>a represents an MXN-order sensing matrix, M represents a measurement dimension, N represents the number of samples of a complete CA vector, M is smaller than N, psi represents an NXN-order transformation matrix, and alpha represents an NX1-dimensional coefficient vector;
gaussian random matrices are the most widely used observation matrices in compressed sensing.A random Gaussian matrix of order MxN, wherein each element independently obeys a Gaussian distribution with a mean of 0 and a variance of 1/M, i.e
Psi is a fast fourier transform matrix, converting the x-signal into the frequency domain r. Namely:
x＝ψr，
where r is a sparse representation of the signal. The frequency information can be recovered from r using the method of VAN NEE (1991).
And calculating the sparse solution of the linear combination expression to obtain the sparse vector.
In some embodiments, the processing module is further configured to:
establishing a compressed sampling model of the CA vector according to a first sampling model, wherein the first sampling model is as follows:
wherein ,representing the first sampling model, A representing a compressed sensing matrix, r representing an estimated value of the sparse vector of the CA vector, e representing impulse noise satisfying Alpha stable distribution;
determining an estimated value of the sparse vector of the CA vector through a least square method and a fifth formula, and obtaining a final estimated value until a first condition is met or the iteration number is larger than a preset iteration number;
the first condition is as follows:
arg min r ||y-Ar+ε|| Δ +λ||r|| 1 ，
wherein ε is greater than 0 and each component approaches 0 indefinitely, Δ represents optimization using the L1 norm and associated entropy norm combination constraint, L1 norm represents the sum of the absolute values of the values of all the elements of the vector;
determining an observation vector according to the final estimated value;
the estimation method is a convex relaxation algorithm (Gradient Projection for Sparse Reconstruction GPSR);
and calculating the sparse vector according to the observation vector.
In accordance with the foregoing, please refer to fig. 3, which is a schematic structural diagram of an embodiment of an intelligent device according to an embodiment of the present application. As shown, the system includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the program includes instructions for performing the following steps:
acquiring an LI carrier signal;
converting the LI carrier signal into a discrete signal according to a preset sampling rate;
determining a CA vector from the discrete signals;
processing the CA vector by a compressed sensing method to obtain a sparse vector;
and determining the frequency information and the phase information of the LI carrier signal according to the sparse vector.
The embodiment of the application also provides a computer storage medium, wherein the computer storage medium can store an L1 carrier recovery program based on the glitch, and the program can comprise part or all of the steps of any one of the L1 carrier recovery methods based on the glitch described in the embodiment of the L1 carrier recovery method based on the glitch when being executed.
Although the application is described herein in connection with the embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus (device), or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. A computer program may be stored/distributed on a suitable medium supplied together with or as part of other hardware, but may also take other forms, such as via the Internet or other wired or wireless telecommunication systems.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable information prompting device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable information prompting device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable information reminder device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable information-bearing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer implemented process such that the instructions which execute on the computer or other programmable device provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The L1 carrier recovery method based on the short-time pulse interference is characterized by comprising the following steps of:
acquiring an LI carrier signal;
converting the LI carrier signal into a discrete signal according to a preset sampling rate;
determining a CA vector from the discrete signals;
processing the CA vector by a compressed sensing method to obtain a sparse vector;
and determining the frequency information and the phase information of the LI carrier signal according to the sparse vector.
2. The glitch-based L1 carrier recovery method of claim 1, in which said determining a CA vector from said discrete signals comprises:
determining a CA vector according to the discrete signal, the first formula, the second formula and the third formula;
the first formula is:
{()}，n＝0，1，2，…，N-1，
wherein s (n) represents an nth byte of the discrete signal;
the second formula is:
R s (n，v)＝{s(n)s * (+v)}，
wherein ,Rs (N, v) represents an autocorrelation function of the discrete signal, v represents a discrete time delay of the discrete signal sequence, and v=0, ±1, …, ± (N-1), E represents a set formed after a bit-by-bit multiplication operation;
the third formula is:
wherein ,Rs (ζ, v) represents a cyclic autocorrelation vector function of the discrete signal, ζ representing a cyclic frequency;
searching a peak value of the cyclic autocorrelation vector function, taking the peak value as a sampling starting point, and taking a sampling point every other code element to obtain a CA code sequence;
a set of a plurality of consecutive CA code sequences is determined as the CA vector.
3. The L1 carrier recovery method based on glitch of claim 1 in which said processing said CA vectors by compressed sensing method to obtain sparse vectors comprises:
storing the CA vector in x (n);
according to a group of base psi T ＝ψ 1 ，ψ 2 ，…，ψ N ]And a fourth formula determines a linear combined expression of the x (n);
the fourth formula is:
wherein y represents an Mx1 order observation vector,representing an M N-dimensional matrix,>a represents an MXN-order sensing matrix, M represents a measurement dimension, N represents the number of samples of a complete CA vector, M is smaller than N, psi represents an NXN-order transformation matrix, and alpha represents an NX1-dimensional coefficient vector;
and calculating the sparse solution of the linear combination expression to obtain the sparse vector.
4. The L1 carrier recovery method based on glitch of claim 3 in which said calculating a sparse solution of said linear combined expression to obtain said sparse vector comprises:
establishing a compressed sampling model of the CA vector according to a first sampling model, wherein the first sampling model is as follows:
wherein ,representing the first sampling model, wherein A represents a compressed sensing matrix, r represents an estimated value of a sparse vector of the CA vector, and e represents impulse noise meeting Alpha stable distribution;
determining an estimated value of the sparse vector of the CA vector through a least square method and a fifth formula iteration until a first condition is met or the iteration number is larger than a preset iteration number, and obtaining a final estimated value;
the first condition is:
argmin r ||y-Ar+ε|| Δ +||r|| 1 ，
wherein ε is greater than 0 and each component approaches 0 indefinitely, Δ represents optimization using the L1 norm and associated entropy norm combination constraint, L1 norm represents the sum of the absolute values of the values of all the elements of the vector;
determining an observation vector according to the final estimated value;
and calculating the sparse vector according to the observation vector.
5. An L1 carrier recovery apparatus based on glitches, wherein the L1 carrier recovery apparatus based on glitches includes:
the acquisition module is used for acquiring the LI carrier signal;
the sampling module is used for converting the LI carrier signal into a discrete signal according to a preset sampling rate;
a calculation module for determining a CA vector from the discrete signals;
the processing module is used for processing the CA vector through a compressed sensing method to obtain a sparse vector;
and the recovery module is used for determining the frequency information and the phase information of the LI carrier signal according to the sparse vector.
6. The glitch-based L1 carrier recovery apparatus of claim 5, wherein said calculation module is further configured to:
determining a CA vector according to the discrete signal, the first formula, the second formula and the third formula;
the first formula is:
{()}，n＝0，1，2，…，-1，
wherein s (n) represents an nth byte of the discrete signal;
the second formula is:
R s (n，v)＝{s(n)s * (+v)}，
wherein ,Rs (N, v) represents an autocorrelation function of the discrete signal, v represents a discrete time delay of the discrete signal sequence, and v=0, ±1, …, ± (N-1), E represents a set formed after a bit-by-bit multiplication operation;
the third formula is:
wherein ,Rs (ζ, v) represents a cyclic autocorrelation vector function of the discrete signal, ζ representing a cyclic frequency;
searching a peak value of the cyclic autocorrelation vector function, taking the peak value as a sampling starting point, and taking a sampling point every other code element to obtain a CA code sequence;
a set of a plurality of consecutive CA code sequences is determined as the CA vector.
7. The glitch-based L1 carrier recovery apparatus of claim 5, wherein said processing module is further configured to:
storing the CA vector in x (n);
according to a group of base psi T ＝ψ 1 ，ψ 2 ，…，ψ N ]And a fourth formula determines a linear combined expression of the x (n);
the fourth formula is:
wherein y represents an Mx1 order observation vector,representing an M N-dimensional matrix,>a represents an MXN-order sensing matrix, M represents a measurement dimension, N represents the number of samples of a complete CA vector, M is smaller than N, psi represents an NXN-order transformation matrix, and alpha represents an NX1-dimensional coefficient vector;
and calculating the sparse solution of the linear combination expression to obtain the sparse vector.
8. The glitch-based L1 carrier recovery apparatus of claim 7, wherein said processing module is further configured to:
establishing a compressed sampling model of the CA vector according to a first sampling model, wherein the first sampling model is as follows:
wherein ,representing the first sampling model, wherein A represents a compressed sensing matrix, r represents an estimated value of a sparse vector of the CA vector, and e represents impulse noise meeting Alpha stable distribution;
determining an estimated value of the sparse vector of the CA vector through a least square method and a fifth formula iteration until a first condition is met or the iteration number is larger than a preset iteration number, and obtaining a final estimated value;
the first condition is:
argmin r ||y-Ar+ε|| Δ +||r|| 1 ，
wherein ε is greater than 0 and each component approaches 0 indefinitely, Δ represents optimization using the L1 norm and associated entropy norm combination constraint, L1 norm represents the sum of the absolute values of the values of all the elements of the vector;
determining an observation vector according to the final estimated value;
and calculating the sparse vector according to the observation vector.
9. An intelligent terminal, characterized in that the intelligent terminal comprises a memory, a processor and a glitch-based L1 carrier recovery program stored on the memory and executable on the processor, the glitch-based L1 carrier recovery program implementing the steps of the glitch-based L1 carrier recovery method according to any of claims 1-4 when executed by the processor.
10. A computer readable storage medium, wherein the computer readable storage medium has stored thereon an L1 carrier recovery program based on glitches, the L1 carrier recovery program based on glitches implementing the steps of the L1 carrier recovery method based on glitches according to any one of claims 1-4 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310487539.3A CN116582400A (en) | 2023-04-28 | 2023-04-28 | L1 carrier recovery method and device based on short-time pulse interference and intelligent terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310487539.3A CN116582400A (en) | 2023-04-28 | 2023-04-28 | L1 carrier recovery method and device based on short-time pulse interference and intelligent terminal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116582400A true CN116582400A (en) | 2023-08-11 |
Family
ID=87544616
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310487539.3A Pending CN116582400A (en) | 2023-04-28 | 2023-04-28 | L1 carrier recovery method and device based on short-time pulse interference and intelligent terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116582400A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117978271A (en) * | 2024-04-02 | 2024-05-03 | 浙江大学 | Optical fiber communication strong interference suppression method, system, equipment and storage medium |
-
2023
- 2023-04-28 CN CN202310487539.3A patent/CN116582400A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117978271A (en) * | 2024-04-02 | 2024-05-03 | 浙江大学 | Optical fiber communication strong interference suppression method, system, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106646546B (en) | A kind of satellite-signal multidimensional quick capturing method and system | |
| RU2360263C2 (en) | Device and method of high-speed detection of gps signals | |
| Wu et al. | Adaptive sparsity matching pursuit algorithm for sparse reconstruction | |
| CN116582400A (en) | L1 carrier recovery method and device based on short-time pulse interference and intelligent terminal | |
| CN106209701B (en) | MFSK signal code rate-estimation method and device under Alpha Stable distritation noise circumstance | |
| Wang et al. | Doppler estimation and timing synchronization of underwater acoustic communication based on hyperbolic frequency modulation signal | |
| CN102307054B (en) | Direct sequence spread spectrum signal acquisition method | |
| CN112444830A (en) | Compressed sensing GNSS signal capturing method based on singular value decomposition | |
| CN110708267B (en) | Method for determining frequency offset information estimation value | |
| Shen et al. | Chip rate and pseudo‐noise sequence estimation for direct sequence spread spectrum signals | |
| CN107465639B (en) | Multi-path delay synchronous decision demodulation method based on short-time discrete Fourier transform | |
| CN113746771A (en) | GMSK overlapping communication capturing method based on sparse Fourier transform | |
| CN110048976B (en) | Intermediate frequency-oriented blind despreading method and device for short code direct spread spectrum signal array | |
| CN111624632B (en) | Satellite navigation signal capturing method and device | |
| CN111679123A (en) | Symbol edge and frequency estimation method and system suitable for multi-mode modulation system | |
| CN113972951B (en) | Comb-shaped searching method for low signal-to-noise ratio and high dynamic signal carrier Doppler frequency offset | |
| CN115296698A (en) | High dynamic satellite communication system signal capturing method | |
| CN104297767A (en) | Self-adaptation repeated resampling navigation satellite capturing method and achieving system thereof | |
| CN109302364A (en) | A kind of frequency deviation estimating method for NB_IoT system | |
| CN102307164B (en) | digital frequency estimation method and system | |
| JPWO2009096005A1 (en) | Code conversion apparatus, receiver and code conversion method | |
| Li et al. | Low complexity acquisition of GPS signals | |
| CN109633707B (en) | Variable coefficient matched filtering rapid capturing method based on pre-averaging processing | |
| CN112764063A (en) | Method for realizing capture processing and receiver | |
| Sembiring et al. | Performance analysis of discrete hartley transform based ofdm modulator and demodulator |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |