CN109975842B - Wavelet transform-based Beidou satellite signal high-precision blind capturing method - Google Patents

Wavelet transform-based Beidou satellite signal high-precision blind capturing method Download PDF

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CN109975842B
CN109975842B CN201910161390.3A CN201910161390A CN109975842B CN 109975842 B CN109975842 B CN 109975842B CN 201910161390 A CN201910161390 A CN 201910161390A CN 109975842 B CN109975842 B CN 109975842B
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吴宗泽
黄婷婷
李建中
梁泽逍
张学文
张兴斌
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Guangdong University of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/246—Acquisition or tracking or demodulation of signals transmitted by the system involving long acquisition integration times, extended snapshots of signals or methods specifically directed towards weak signal acquisition

Abstract

The invention discloses a wavelet transform-based Beidou satellite signal high-precision blind capturing method, which comprises the following steps of: s1: acquiring satellite signals, and performing background noise reduction on the sampled signals by using a wavelet threshold noise reduction method to obtain noise-reduced sampled signals; s2: establishing a satellite signal model received by a satellite receiver, converting the satellite signal model into a single-input multi-output blind separation model, and taking a sampling signal subjected to noise reduction as an input signal of the blind separation model; s3: constructing a new space spectrum function by using a blind separation model, and searching the space spectrum function to obtain the frequency of the satellite signal; s4: and solving the initial phase of the signal according to the frequency of the satellite signal to finish the acquisition of the satellite. The method combines a blind separation model and a wavelet transformation theory, adopts a satellite signal blind separation method based on a subspace estimation algorithm to solve the Doppler effect frequency and phase of the Beidou satellite signals, and has the advantages of strong anti-noise capability, stable performance and high precision.

Description

Wavelet transform-based Beidou satellite signal high-precision blind capturing method
Technical Field
The invention relates to the field of satellite navigation, in particular to a Beidou satellite signal high-precision blind capturing method based on wavelet transformation.
Background
In the 21 st century, various fields such as national defense, civil aviation management and agriculture have been developed greatly, and satellite navigation plays a positive role therein. With the rapid increase of the demand of users for location services, the demand for rapidity and real-time performance of satellite navigation receiver positioning in the civil field and even the military field is continuously increasing. On one hand, the Beidou satellite navigation receiver for signal acquisition is a basic step of a baseband signal processing part and is a precondition of tracking and positioning, so the performance of the acquisition algorithm directly determines performance indexes such as the acquisition speed, the acquisition precision and the like of the receiver. On the other hand, because the satellite signal is in a strong noise environment, or is reflected by a mountain building or is shielded by a tree in the transmission process, the signal strength is much weaker than that of a normal signal at the moment, and the receiver cannot obtain a direct and effective satellite signal, the improvement of the existing capturing algorithm has very important practical significance in improving the capturing precision of the satellite signal.
Since the traditional serial and parallel acquisition algorithm is difficult to accurately acquire the Beidou signals in a noise environment, particularly in a weak signal environment, an incoherent accumulation acquisition method is usually adopted to improve the acquisition sensitivity, however, the method has the defect that the incoherent accumulation method has square loss, the square loss of the incoherent accumulation method is increased along with the increase of the accumulation times, so that the longer the accumulation time is, the faster the noise is increased, the lower the acquisition precision is, and the method cannot improve the acquisition success rate by infinitely increasing the accumulation time.
Disclosure of Invention
The invention provides a Beidou satellite signal high-precision blind acquisition method based on wavelet transformation, aiming at overcoming at least one defect in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a Beidou satellite signal high-precision blind acquisition method based on wavelet transformation comprises the following steps:
s1: acquiring satellite signals, and performing background noise reduction on the sampled signals by using a wavelet threshold noise reduction method to obtain noise-reduced sampled signals;
s2: establishing a satellite signal model received by a satellite receiver, converting the satellite signal model into a single-input multi-output blind separation model, and taking a sampling signal subjected to noise reduction as an input signal of the blind separation model;
s3: constructing a new space spectrum function by using a blind separation model, and searching the space spectrum function to obtain the frequency of the satellite signal;
s4: and solving the initial phase of the signal according to the frequency of the satellite signal to finish the acquisition of the satellite.
According to the scheme, wavelet transformation and blind signal separation are combined, noise reduction is performed on the Beidou satellite signals by adopting a wavelet threshold method to improve the signal-to-noise ratio, the influence of noise can be effectively reduced, so that the capturing performance of the Beidou satellite receiver is improved, then the denoised satellite signals are converted into a blind separation model, a space spectrum function is constructed according to the denoised signals, optimization search is performed, after the Doppler effect frequency of the satellite signals is obtained, the estimated value of a phase is obtained by using a subspace algorithm, so that accurate capturing of the satellite signals is achieved, and the capturing gain is improved.
Preferably, the step S1 of collecting the satellite signal, and performing background noise reduction on the satellite signal by using a wavelet threshold noise reduction method to obtain a noise-reduced sampling signal includes the following steps:
s1.1: selecting a wavelet function and determining decomposition levels, and performing wavelet decomposition calculation on the satellite signal doped with background noise to divide the satellite signal into a plurality of levels;
s1.2: setting a threshold value and carrying out quantization processing on the high-frequency coefficient obtained by decomposing and calculating each layer according to the threshold value;
s1.3: and performing wavelet reconstruction on the satellite signal according to the detail coefficient obtained after wavelet decomposition and the quantized high-frequency coefficient, wherein the reconstructed signal is a sampling signal subjected to noise reduction.
Preferably, the step S2 of establishing a satellite signal model received by the satellite receiver and converting the satellite signal model into a single-input multiple-output blind separation model, and using the noise-reduced sampling signal as an input signal of the blind separation model includes the following steps:
s2.1: establishing a satellite signal model received by a satellite receiver:
Figure BDA0001984766950000021
wherein t is time, AnIs the amplitude of the nth satellite C/A code, and C (t) is the C/A code; d (t) is a navigation data code which is a string of binary codes containing navigation messages; f. ofIFIs intermediate frequency carrier frequency, and is added on the basis of Beidou satellite signalsOne intermediate frequency carrier frequency realizes spread spectrum, and reduces loss; f. ofnAnd thetan,0Is the Doppler frequency and phase of the nth satellite signal, n (t) is background noise which follows Gaussian distribution, the mean value of the noise is 0, and the variance is sigma2；
S2.2: converting a satellite signal model into a single-input multi-output blind separation model in a signal frequency sampling mode:
r＝A·x+n
in the formula:
Figure BDA0001984766950000022
Figure BDA0001984766950000031
Figure BDA0001984766950000032
Figure BDA0001984766950000033
in the formula, T represents transposition, H represents hermite transposition, r represents satellite signals acquired by the same sampling point and different channels, A is a coefficient matrix, and F isN＝fIF+fnM denotes the parameter A in the M-th sampling channel, column vector xNIs the satellite carrier signal strength, thetaN,0The method comprises the steps that the initial phase of a satellite carrier signal is obtained, P is the product of a C/A code and a navigation message code, the navigation message code is a data code obtained by a user receiver through carrier demodulation and pseudo code de-spread of the received satellite signal, then the data code is compiled into a navigation message according to the format of the navigation message, and the navigation message contains important information used for positioning, such as time, a satellite operation orbit, ionosphere delay and the like;
preferably, in step S3, a new spatial spectrum function is constructed by using a blind separation model, and the spatial spectrum function is searched for the frequency of the satellite signal, including the following steps:
s3.1: and (3) simultaneously solving autocorrelation matrixes according to two sides of a blind separation model calculation formula and combining the characteristic that the frequency of a sinusoidal signal correlation function is unchanged to obtain a matrix Q:
Q＝E(r·rH)＝A·E(x·xH)·AH+σ2I＝A·Rxx·AH+σ2I
wherein R isxx＝E(x·xH)，RxxIs the covariance matrix of the satellite signal, I is the identity matrix, E is the mathematical expectation, σ2Variance of background noise;
s3.2: and (3) carrying out eigenvalue decomposition on the matrix Q:
Figure BDA0001984766950000034
after matrix Q is mathematically decomposed, N large eigenvalues and M-N small eigenvalues exist, and matrix Q can be decomposed into two parts of signal information and noise information, UXIs a signal subspace, U, spanned by eigenvectors corresponding to large eigenvaluesNIs a noise subspace spanned by the feature vectors corresponding to the small feature values;
s3.3: since the signal subspace and the noise subspace are mutually orthogonal, a new spatial spectrum function is constructed by utilizing the orthogonality of the two:
Figure BDA0001984766950000041
wherein P is a constructed spatial spectrum function;
s3.4: and searching the spatial spectrum function, wherein the frequency corresponding to the extreme point is the frequency of the solved satellite signal.
Preferably, in step S4, the method for solving the initial phase of the signal according to the frequency of the satellite signal to complete the acquisition of the satellite includes the following steps:
s4.1: and performing a least square method on the noise-reduced sampling signal:
Figure BDA0001984766950000042
in the formula (I), the compound is shown in the specification,
Figure BDA0001984766950000043
an inverse pseudo matrix of A;
s4.2: solving the initial phase of the signal:
θ＝[θ1,0 θ2,0 … θN,0]T
Figure BDA0001984766950000044
the acquisition of the satellite is completed.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method combines a blind separation model and a wavelet transformation theory, adopts a satellite signal blind separation method based on a subspace estimation algorithm to solve the Doppler effect frequency and phase of the Beidou satellite signals, and compared with the traditional capture method, the algorithm has the advantages of strong anti-noise capability, relatively stable performance and high precision. The method for denoising based on the wavelet threshold is used for carrying out background denoising on the Beidou satellite signals, and the signal-to-noise ratio of the satellite signals can be improved by denoising through the wavelet threshold denoising method, so that the performance of the Beidou receiver is effectively improved, and the method is strong in noise resistance, stable in performance and relatively high in precision.
Drawings
Fig. 1 is a schematic flow chart of a wavelet transform-based Beidou satellite signal high-precision blind capturing method.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
A Beidou satellite signal high-precision blind acquisition method based on wavelet transformation, as shown in figure 1, comprises the following steps:
s1: acquiring satellite signals, and performing background noise reduction on the sampled signals by using a wavelet threshold noise reduction method to obtain noise-reduced sampled signals;
s1 includes the steps of:
s1.1: selecting a wavelet function and determining decomposition levels, and performing wavelet decomposition calculation on the satellite signal doped with background noise to divide the satellite signal into a plurality of levels;
s1.2: setting a threshold value and carrying out quantization processing on the high-frequency coefficient obtained by decomposing and calculating each layer according to the threshold value;
s1.3: and performing wavelet reconstruction on the satellite signal according to the detail coefficient obtained after wavelet decomposition and the quantized high-frequency coefficient, wherein the reconstructed signal is a sampling signal subjected to noise reduction.
S2: establishing a satellite signal model received by a satellite receiver, converting the satellite signal model into a single-input multi-output blind separation model, and taking a sampling signal subjected to noise reduction as an input signal of the blind separation model;
s2 includes the steps of:
s2.1: establishing a satellite signal model received by a satellite receiver:
Figure BDA0001984766950000051
wherein t is time, AnAmplitude of the nth satellite C/A code, C (t) C/A code, D (t) navigation data code, fIFAt an intermediate frequency carrier frequency, fnAnd thetan,0The Doppler frequency and phase of the nth satellite signal, and n (t) is background noise;
s2.2: converting a satellite signal model into a single-input multi-output blind separation model in a signal frequency sampling mode:
r＝A·x+n
in the formula:
Figure BDA0001984766950000052
Figure BDA0001984766950000061
Figure BDA0001984766950000062
Figure BDA0001984766950000063
in the formula, T represents transposition, H represents hermite transposition, r represents satellite signals acquired by the same sampling point and different channels, A is a coefficient matrix, and F isN＝fIF+fnM denotes the parameter A in the M-th sampling channel, column vector xNIs the satellite carrier signal strength, thetaN,0For the initial phase of the satellite carrier signal, P is the product of the C/A code and the navigation message code.
S3: constructing a new space spectrum function by using a blind separation model, and searching the space spectrum function to obtain the frequency of the satellite signal;
s3 includes the steps of:
s3.1: and (3) simultaneously solving autocorrelation matrixes according to two sides of a blind separation model calculation formula and combining the characteristic that the frequency of a sinusoidal signal correlation function is unchanged to obtain a matrix Q:
Q＝E(r·rH)＝A·E(x·xH)·AH+σ2I＝A·Rxx·AH+σ2I
wherein R isxx＝E(x·xH)，RxxIs the covariance matrix of the satellite signal, I is the identity matrix, E is the mathematical expectation, σ2Variance of background noise;
s3.2: and (3) carrying out eigenvalue decomposition on the matrix Q:
Figure BDA0001984766950000064
after the matrix Q is mathematically decomposed, there will be N large eigenvalues, M-N small eigenvalues, UXIs a signal subspace, U, spanned by eigenvectors corresponding to large eigenvaluesNIs a noise subspace spanned by the feature vectors corresponding to the small feature values;
s3.3: constructing a new spatial spectrum function:
Figure BDA0001984766950000065
wherein P is a constructed spatial spectrum function;
s3.4: and searching the spatial spectrum function, wherein the frequency corresponding to the extreme point is the frequency of the solved satellite signal.
S4: solving the initial phase of the signal according to the frequency of the satellite signal to complete the acquisition of the satellite;
s4 includes the steps of:
s4.1: and performing a least square method on the noise-reduced sampling signal:
Figure BDA0001984766950000071
in the formula (I), the compound is shown in the specification,
Figure BDA0001984766950000072
an inverse pseudo matrix of A;
s4.2: solving the initial phase of the signal:
θ＝[θ1,0 θ2,0 … θN,0]T
Figure BDA0001984766950000073
the acquisition of the satellite is completed.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (4)

1. A Beidou satellite signal high-precision blind acquisition method based on wavelet transformation is characterized by comprising the following steps:
s1: acquiring satellite signals, and performing background noise reduction on the sampled signals by using a wavelet threshold noise reduction method to obtain noise-reduced sampled signals;
s2: establishing a satellite signal model received by a satellite receiver, converting the satellite signal model into a single-input multi-output blind separation model, and taking a sampling signal subjected to noise reduction as an input signal of the blind separation model;
s3: constructing a new space spectrum function by using a blind separation model, and searching the space spectrum function to obtain the frequency of the satellite signal; the method comprises the following steps:
s3.1: and (3) simultaneously solving autocorrelation matrixes according to two sides of a blind separation model calculation formula and combining the characteristic that the frequency of a sinusoidal signal correlation function is unchanged to obtain a matrix Q:
Q＝E(r·rH)＝A·E(x·xH)·AH+σ2I＝A·Rxx·AH+σ2I
wherein R isxx＝E(x·xH)，RxxIs a covariance matrix of the satellite signals, I is an identity matrix, E is a mathematical expectation,σ2variance of background noise;
s3.2: and (3) carrying out eigenvalue decomposition on the matrix Q:
Figure FDA0002764647420000011
after the matrix Q is mathematically decomposed, there will be N large eigenvalues, M-N small eigenvalues, UXIs a signal subspace, U, spanned by eigenvectors corresponding to large eigenvaluesNIs a noise subspace spanned by the feature vectors corresponding to the small feature values;
s3.3: constructing a new spatial spectrum function:
Figure FDA0002764647420000012
wherein P is a constructed spatial spectrum function;
s3.4: searching the space spectrum function, wherein the frequency corresponding to the extreme point is the frequency of the solved satellite signal;
s4: and solving the initial phase of the signal according to the frequency of the satellite signal to finish the acquisition of the satellite.
2. The wavelet transform-based Beidou satellite signal high-precision blind capturing method according to claim 1, wherein the satellite signal is collected in step S1, background noise reduction is performed on the satellite signal by using a wavelet threshold noise reduction method, and a noise-reduced sampling signal is obtained, comprising the following steps:
s1.1: selecting a wavelet function and determining decomposition levels, and performing wavelet decomposition calculation on the satellite signal doped with background noise to divide the satellite signal into a plurality of levels;
s1.2: setting a threshold value and carrying out quantization processing on the high-frequency coefficient obtained by decomposing and calculating each layer according to the threshold value;
s1.3: and performing wavelet reconstruction on the satellite signal according to the detail coefficient obtained after wavelet decomposition and the quantized high-frequency coefficient, wherein the reconstructed signal is a sampling signal subjected to noise reduction.
3. The wavelet transform-based Beidou satellite signal high-precision blind acquisition method of claim 2, wherein in the step S2, a satellite signal model received by a satellite receiver is established and converted into a single-input multiple-output blind separation model, and the noise-reduced sampling signal is used as an input signal of the blind separation model, comprising the following steps:
s2.1: establishing a satellite signal model received by a satellite receiver:
Figure FDA0002764647420000021
wherein t is time, AnAmplitude of the nth satellite C/A code, C (t) C/A code, D (t) navigation data code, fIFAt an intermediate frequency carrier frequency, fnAnd thetan,0The Doppler frequency and phase of the nth satellite signal, and n (t) is background noise;
s2.2: converting a satellite signal model into a single-input multi-output blind separation model in a signal frequency sampling mode:
r＝A·x+n
in the formula:
Figure FDA0002764647420000022
Figure FDA0002764647420000023
Figure FDA0002764647420000024
Figure FDA0002764647420000025
in the formula, T represents transposition, H represents hermite transposition, r represents satellite signals acquired by the same sampling point and different channels, A is a coefficient matrix, and F isN＝fIF+fnM denotes the parameter A in the M-th sampling channel, column vector xNIs the satellite carrier signal strength, thetaN,0For the initial phase of the satellite carrier signal, P is the product of the C/A code and the navigation message code.
4. The wavelet transform-based Beidou satellite signal high-precision blind acquisition method according to claim 1, wherein in the step S4, according to the frequency of the satellite signal, the initial phase of the signal is solved to complete the acquisition of the satellite, and the method comprises the following steps:
s4.1: and performing a least square method on the noise-reduced sampling signal:
x＝A+·r
in the formula, A+An inverse pseudo matrix of A;
s4.2: solving the initial phase of the signal:
θ＝[θ1,0 θ2,0 … θN,0]T
Figure FDA0002764647420000031
the acquisition of the satellite is completed.
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