CN108366025A - A kind of signal synthesis method and system - Google Patents
A kind of signal synthesis method and system Download PDFInfo
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Abstract
The invention discloses a kind of signal synthesis method and systems.The method comprising the steps of：Foundation and the relevant object function of composite signal, and eigenmatrix to be solved corresponding with synthesis weight vector is determined according to object function；Symbolization polarization method and Coordinate Rotation Digital calculating method calculate eigenmatrix to be solved, with the eigenmatrix after being solved；Calculate the optimal synthesis weight vector corresponding to the eigenmatrix after solving；Coherent phase add operation is weighted to multiple signals according to optimal synthesis weight vector, to determine composite signal.The device includes：Determination unit, the first computing unit, the second computing unit and processing unit.Method and system provided by the invention is equal or unequal situation can use in the noise variance of each road signal, and while significantly simplifying calculation amount, the signal after synthesis is made to have good performance.
Description
Technical field
The present invention relates to sensor network technique field more particularly to a kind of signal synthesis method and systems.
Background technology
In recent years, sensor network have been widely used for environmental monitoring, health care, smart home, urban transportation with
And military security, but since sensor network nodes signal sensing capability is limited, the reception of some small-signals is handled
Scarce capacity, it is therefore desirable to the signal received by multiple nodes be synthesized, and then improve the signal-to-noise ratio for receiving signal.
The target of signal synthesis is exactly to keep the signal-to-noise ratio of composite signal maximum, in addition to by time delay and frequency etc. between multipath reception signal
Parameter differences, which compensate, to be made except signal alignment, it is also necessary to be weighted relevant addition according to optimal weights.Due to alignment
Signal section is relevant later is added, and noise is added at random, and the power ratio noise power of the useful part of composite signal is promoted
It is more, therefore the signal-to-noise ratio of composite signal is improved.
The article that K.M.Cheung et al. is delivered《Eigen Theory for Optical Signal Combining:A
Unified Approach》In propose Eigenvalues Decomposition algorithm (Signal- using composite signal signal-to-noise ratio as object function
To-Noise Ratio EIGEN, SNR EIGEN), wherein the usual way of estimation noise correlation matrix assumes that noise is high
This white noise, and correlation computations acquisition is carried out again by recording one section of pure noise.But in this way, answering for system on the one hand can be increased
On the other hand miscellaneous degree there is the risk that cannot accurately reflect noise characteristic in signal bandwidth more to be adopted so in practical application
Composite signal power is used to carry out weights estimation as object function.The article that C.H.Lee et al. is delivered《Large-Array
Signal Processing for Deep Space Application》In point out output power maximal criterion with synthesis believe
Number signal-to-noise ratio maximal criterion is of equal value.Spiking output power guidelines are exactly using the power of composite signal as target letter
Number, calculates optimal synthesis weights so that the output power of composite signal it is maximum (Combined Output Power EIGEN,
COP EIGEN).Using composite signal power as the Eigenvalues Decomposition algorithm of object function as it is assumed that the noise variance of each signal
It is equal, therefore the relevant influence of noise can be ignored, without estimating noise correlation matrix, but work as each road signal noise variance
When inconsistent, will be had using spiking output power guidelines calculating synthesis weights inclined.The article that B.Luo et al. is delivered
《On Eigen-Based Signal Combining Using the Autocorrelation Coefficient》In point out
The auto-correlation coefficient of composite signal and composite signal signal-to-noise ratio maximal criterion are (Autocorrelation of equal value
Coefficient EIGEN, AC EIGEN).
Three of the above algorithm SNR EIGEN, COP EIGEN are similar with the optimal synthesis weight computing process of AC EIGEN,
The optimal value for synthesizing weights is some corresponding feature vector of matrix maximum eigenvalue.But the main problem of these algorithms exists
It is larger in calculation amount.In particular, with the increase for the length L for receiving signal way N and correlation computations, signal Correlation Moment is calculated
The calculation amount of battle array feature vector corresponding with maximum eigenvalue is solved will be huge, it is therefore desirable to which innovatory algorithm is to calculation amount
Substantially reduced.
Invention content
It is an object of the present invention to which it is larger to solve computationally intensive in existing signal synthetic technology and synthesis loss
Problem provides a kind of signal synthesis method and system, and symbolization polarization method and Coordinate Rotation Digital calculating method, which calculate, receives letter
Number correlation matrix, optimal synthesis weight vector corresponding with correlation matrix is calculated using power method algorithm or CW iterative methods.It should
Method and system is equal or unequal situation can use in the noise variance of each road signal, and is significantly simplifying
While calculation amount, the signal after synthesis is made to have good performance.
To achieve the goals above, on the one hand, the present invention provides a kind of signal synthesis methods.The method comprising the steps of：
Foundation and the relevant object function of composite signal, and determine according to the object function and synthesize weight vector pair
The eigenmatrix to be solved answered；
Symbolization polarization method and Coordinate Rotation Digital calculating method calculate the eigenmatrix to be solved, with
Eigenmatrix after being solved；
Calculate the optimal synthesis weight vector corresponding to the eigenmatrix after the solution；
Coherent phase add operation is weighted to multiple signals according to the optimal synthesis weight vector, with the determination conjunction
At signal.
Preferably, the foundation and the relevant object function of composite signal, and determine and synthesize according to the object function
The corresponding eigenmatrix to be solved of weight vector, including：
The object function of composite signal power is established, and the feature square to be solved is determined according to the object function
Battle array is reception signal correlation matrix；Or
The object function of composite signal auto-correlation coefficient is established, and is determined according to the object function described to be solved
Eigenmatrix is the product for two correlation matrixes for receiving signal.
Preferably, the eigenmatrix to be solved includes receiving signal cross-correlation function, is using the symbol pole
During change method calculates the eigenmatrix to be solved, including using following formula to the reception signal cross-correlation letter
Number is calculated：
Wherein, i and j indicates that road number, N indicate that total way, k indicate that sampling point number, L indicate total number of sample points, xi
(k) signal that k-th of sampled point receives from the i-th tunnel is indicated,Indicate that the estimation of polarized signal, τ indicate signal
Displacement,Indicate the estimation of polarized signal cross-correlation function,It indicates to receive estimating for signal cross-correlation function
Meter.
Preferably, in being calculated the eigenmatrix to be solved using the Coordinate Rotation Digital calculating method,
Including using following formula pairIt is calculated：
zl+1=zl-δlarctan(2-l) l=0,1,2 ..., n-1
Wherein,L indicates that iterations, n indicate total iterations.
Preferably, the optimal synthesis weight vector corresponding to the eigenmatrix after the calculating solution, including：
Using power method algorithm or CW iterative methods calculate the optimal synthesis weights corresponding to the eigenmatrix after the solution to
Amount.
On the other hand, the present invention provides a kind of signal synthesis systems.The system includes
Determination unit is determined and is closed for foundation and the relevant object function of composite signal, and according to the object function
At the corresponding eigenmatrix to be solved of weight vector；
First computing unit, for symbolization polarization method and Coordinate Rotation Digital calculating method to the spy to be solved
Sign matrix is calculated, with the eigenmatrix after being solved；
Second computing unit, for calculating the optimal synthesis weight vector corresponding to the eigenmatrix after the solution；
Processing unit is weighted coherent phase add operation, with true according to the optimal synthesis weight vector to multiple signals
The fixed composite signal.
Preferably, the determination unit is specifically used for：
The object function of composite signal power is established, and the feature square to be solved is determined according to the object function
Battle array is reception signal correlation matrix；Or
The object function of composite signal auto-correlation coefficient is established, and is determined according to the object function described to be solved
Eigenmatrix is the product for two correlation matrixes for receiving signal.
Preferably, the eigenmatrix to be solved that the determination unit determines includes receiving signal cross-correlation function,
In first computing unit, including following formula is used to calculate the reception signal cross-correlation function：
Wherein, i and j indicates that road number, N indicate that total way, k indicate that sampling point number, L indicate total number of sample points, xi
(k) signal that k-th of sampled point receives from the i-th tunnel is indicated,Indicate that the estimation of polarized signal, τ indicate signal
Displacement,Indicate the estimation of polarized signal cross-correlation function,It indicates to receive estimating for signal cross-correlation function
Meter.
Preferably, in first computing unit, including following formula pair is usedIt is calculated：
zl+1=zl-δlarctan(2-l) l=0,1,2 ..., n-1
Wherein,L indicates that iterations, n indicate total iterations.
Preferably, second computing unit is specifically used for：
Using power method algorithm or CW iterative methods calculate the optimal synthesis weights corresponding to the eigenmatrix after the solution to
Amount.
A kind of signal synthesis method provided by the invention and system, symbolization polarization method and Coordinate Rotation Digital calculate
Method calculates the correlation matrix for receiving signal, and optimal synthesis corresponding with correlation matrix is calculated using power method algorithm or CW iterative methods
Weight vector.This method and system are equal or unequal situation can use in the noise variance of each road signal, and
While significantly simplifying calculation amount, the signal after synthesis is made to have good performance.
Description of the drawings
Fig. 1 is a kind of flow diagram of signal synthesis method provided in an embodiment of the present invention；
Fig. 2 is the flow diagram of another signal synthesis method provided in an embodiment of the present invention；
Synthesis loss diagram when Fig. 3 is noise variance provided in an embodiment of the present invention equal N=4；
Synthesis loss diagram when Fig. 4 is noise variance provided in an embodiment of the present invention unequal N=4；
Fig. 5 is a kind of structural schematic diagram of signal synthesis system provided in an embodiment of the present invention.
Specific implementation mode
Below by drawings and examples, technical scheme of the present invention is described in further detail.
Fig. 1 is a kind of flow diagram of signal synthesis method provided in an embodiment of the present invention.As shown in Figure 1, this method
Including step S110-S140：
Step S110, foundation and the relevant object function of composite signal, and determine according to this object function and synthesize weights
The corresponding eigenmatrix to be solved of vector.
Specifically, the object function of composite signal power is established, and determines feature square to be solved according to this object function
Battle array is reception signal correlation matrix.
Alternatively, establishing the object function of composite signal auto-correlation coefficient, and spy to be solved is determined according to this object function
Sign matrix is the product for two correlation matrixes for receiving signal.
In a possible embodiment, the object function of composite signal power, root are established using COP EIGEN algorithms
Object function determines that matrix to be solved is to receive signal correlation matrix accordinglyAnd detailed process is as follows：
First, the multiple signals that sensor receives are modeled as：
xi(k)=si(k)+ni(k) i=1,2 ..., N (1)
In formula (1), k is sampling point number, and subscript i indicates way number, xi(k) letter received by the i-th tunnel is indicated
Number, N is total way, si(k) source signal that the i-th tunnel receives, n are indicatedi(k) noise component(s) for indicating the i-th road signal, usually by ni
(k) it is modeled as zero mean Gaussian white noise.
The synthesis weight vector that multiple signals are synthesized is：
In formula (2), subscript T indicates transposition, can be expressed as composite signal according to formula (1) and (2)：
In formula (3), subscript H indicates conjugate transposition, and：
Receive signal correlation matrixDefinition be：
In formula (5), signal cross-correlation function R is receivedxixjThe definition of (τ) is：
In formula (6), L indicates the sum (length for being referred to as correlation computations) of sampling point number in each road signal.
From above-mentioned formula (4)-(6)：
According to formula (5) and formula (6) it is found that receiving signal correlation matrixIt, can be by its point for Hemitian matrixes
Xie Wei：
Wherein,Be by eigenvalue cluster at diagonal matrix, and eigenvalue λ1≥λ2≥…≥λN,It is by feature vector
The matrix of composition,Corresponding to λi, andSynthesize weight vectorIt can be expressed as feature vectorLinear combination,
I.e.：
Wherein,It is a coefficient vector.Therefore, the object function of the composite signal output power of foundation is：
By formula (15) it is found that the maximum value of composite signal output power is eigenvalue λ1, synthesis weight vector at this time
For maximum eigenvalue λ1Corresponding feature vectorAccordingly, the noise variance phase of each road signal is assumed using COP EIGEN algorithms
Deng, therefore need to only calculate reception signal correlation matrix
In another possible embodiment, this step includes：Composite signal auto-correlation is established using AC EIGEN algorithms
The object function of coefficient determines that matrix to be solved is two correlation matrixes for receiving signal according to this object function
WithProductAnd detailed process is as follows：
The auto-correlation coefficient of composite signal can be expressed as：
Wherein, τ is the displacement of signal, and value is integer, such as can take τ=1.
Formula (16) is askedPartial derivative, can obtain：
From the above equation, we can see that matrixThe corresponding feature vector of maximum eigenvalue be exactly to make composite signal
The maximum synthesis weight vector of auto-correlation coefficient.Accordingly, AC EIGEN algorithms only need to calculate two Correlation Moments for receiving signal
Battle arrayWith
Step S120, symbolization polarization method and Coordinate Rotation Digital calculating method count eigenmatrix to be solved
It calculates, with the eigenmatrix after being solved.
By formula (5) and (6) it is found that the floating-point multiplication calculation amount of correlation matrix is O (N in COP EIGEN algorithms2L),
And by formula (17) and (18) it is found that the floating-point multiplication calculation amount of correlation matrix is also O (N in AC EIGEN algorithms2L)。
Specifically, based on eigenmatrix to be solved, symbolization polarizes (Signum Polarization, abbreviation SP)
The algorithm docking collection of letters number carries out a polarization operations, and calculates (COordinate Rotation using Coordinate Rotation Digital
DIgital Computer, CORDIC) algorithm calculates SIN function, with the eigenmatrix after being solved, to solve phase
Close the calculation amount optimization problem of matrix.
In a possible embodiment, the reception in COP EIGEN algorithms is believed using SP algorithms and cordic algorithm
Number correlation matrixIt is calculated, with the eigenmatrix after being solved
In another possible embodiment, using SP algorithms and cordic algorithm to two phases in AC EIGEN algorithms
Close matrixWithIt is respectively calculated, then takes product with the eigenmatrix after being solved
In the following, to receiving signal correlation matrix to solving using SP algorithms and cordic algorithmIn each reception
Signal cross-correlation function RxixjThe process of (τ) is introduced.It similarly, can be right accordinglyIt is calculated.
First, a polarization operations are carried out using the docking collection of letters number of SP algorithms, detailed process is as follows：
1) polarized signal model is established：
Wherein,Indicate that the estimation of polarized signal, i indicate that road number, k indicate that sampling point number, L indicate sampled point
Sum, xi(k) signal that k-th of sampled point receives from the i-th tunnel is indicated.
2) according to polarized signal model, the estimation of polarized signal cross-correlation function is calculated：
Wherein,Indicate that the estimation of polarized signal cross-correlation function, i and j indicate that road number, N indicate total way, τ
Indicate the displacement of signal.
3) receiving the estimation of signal cross-correlation function can be expressed as：
By formula (20)-(23) and formula (23) it is found that by using SP algorithms, signal cross-correlation function is received
RxixjFloating-point multiplication in (τ) is polarized the estimation of signal cross-correlation functionIn plus the 1, operation (formula that subtracts 1
(22) τ can be taken by 1 or -1 in) and 1 SIN function operation substituted (as shown in formula (23)), calculation amount reduce.
Then, the SIN function in above-mentioned formula (23) is calculated using cordic algorithm, detailed process is as follows：
zl+1=zl-δlarctan(2-l) l=0,1,2 ..., n-1 (24)
Wherein,L indicates that iterations, n indicate total iterations.For public affairs
Arctan (2 in formula (24)-l), it can pre-save in a lookup table.
By formula (24)-(26) and formula (23) it is found that by using CORDIC algorithms, can incite somebody to actionIn
Sine operation is converted to shift operation and signed magnitude arithmetic(al), and each iteration only needs to carry out 3 signed magnitude arithmetic(al)s (respectively：Formula
(24) subtraction in a subtraction, formula (26) in and add operation) and 2 shift operations (in formula
(26) in, 2-lBecause of the operation that l plus 1 is generated, the electric signal for corresponding to 2 systems is shift operation) and a comparison operation is (such as
Shown in formula (25)), to further simplify calculation amount.
It should be noted that total iterations n can be as needed computational accuracy and determine.The meter of cordic algorithm
It is as shown in table 1 to calculate precision：
Table 1
As shown in Table 1, when iterations n is equal to 4, computational accuracy is poor, when iterations n is equal to 12, has non-
Often good computational accuracy, and when iterations n is equal to 8, it can be obtained between computational accuracy and calculation amount preferable flat
It weighs, preferably n is equal to 8 in the embodiment of the present invention.
Step S130 calculates the optimal synthesis weight vector corresponding to the eigenmatrix after solving.
Specifically, the optimal synthesis calculated corresponding to the eigenmatrix after solving using power method algorithm or CW iterative methods is weighed
Value vector.
In a possible embodiment, the spy after solving is calculated using power method (Power Method, abbreviation PM) algorithm
Levy the optimal synthesis weight vector corresponding to matrix.
In PM algorithms, it is assumed that initial weights areAnd it is iterated by the following method：
Wherein,Indicate the eigenmatrix after solving, such asOrM indicates iterations,Table
Show the synthesis weight vector of the m times iteration, with the increase of iterations, finallyMaximum eigenvalue pair will be converged on
The feature vector answeredAnd convergence rate is by λ2/λ1It determines, and becauseIn conjunction with formula (11) and (27), repeatedly
It can be expressed as process：
Formula (29) expression is normalized after iteration each time.Known λ1It is the largest characteristic value, (λi/λ1) ＜ 1,
With the increase (λ of mi/λ1)mConverge on 0.
In another possible embodiment, asked using the calculating of power method (Collatz-Wielandt, abbreviation CW) iterative method
Optimal synthesis weight vector corresponding to eigenmatrix after solution.
In CW iterative methods, ifFor arbitrary N × N ranks irreducible nonnegative matrices, definition withCollatz-
Wielandt functions are：
Wherein,OrIt can conductIt enables
Wherein,For unit matrix, CW algorithms assume that initial weights areAnd it is iterated by the following method：
Formula (33) expression is normalized after iteration each time.λ1Characteristic value is the largest,It is corresponding spy
Sign vector, that is, synthesize weight vector.
Step S140 is weighted coherent phase add operation according to optimal synthesis weight vector to multiple signals, is closed with determining
At signal.
From the foregoing, it will be observed that in a kind of signal synthesis method provided by the invention, symbolization polarization method and coordinate rotation number
Word calculating method calculates the correlation matrix for receiving signal, corresponding with correlation matrix most using power method algorithm or the calculating of CW iterative methods
Excellent synthesis weight vector.The method is equal or unequal situation can use in the noise variance of each road signal, and
While significantly simplifying calculation amount, the signal after synthesis is made to have good performance.
Fig. 2 is the flow diagram of another signal synthesis method provided in an embodiment of the present invention.As shown in Fig. 2, the party
Method includes step S210-S270：
Step S210, signal composition algorithm start.
Step S220 carries out polarization operations using the polarization model in formula (20) to input signal, this step needs to use
To comparison operation.
Step S230, according to correlation matrixOr(for example, the noise variance when each road signal is equal
When, select the correlation matrix in COP EIGEN algorithmsAlternatively, when the noise variance of each road signal is unequal, select
Correlation matrix in AC EIGEN algorithms), each polarized signal cross-correlation is calculated successively using formula (22)
The estimation of functionThis step only needs to use plus the 1, operation that subtracts 1.
Step S240 obtains receiving the estimation of signal cross-correlation function using cordic algorithmThis step needs
Use displacement and signed magnitude arithmetic(al).
Step S250, checks whether the calculating of correlation matrix part is completed, if it is not complete, then returning to step
S230 calculates the estimation of next polarized signal cross-correlation functionIf completed, S260 is thened follow the steps.
Step S260 is completed the feature vector for solving correlation matrix using PM algorithms or CW algorithms, obtains synthesis weights
Vector.
Step S270, signal composition algorithm are completed.
From the foregoing, it will be observed that in a kind of signal synthesis method provided by the invention, symbolization polarization method and coordinate rotation number
Word calculating method calculates the correlation matrix for receiving signal, corresponding with correlation matrix most using power method algorithm or the calculating of CW iterative methods
Excellent synthesis weight vector.The method is equal or unequal situation can use in the noise variance of each road signal, and
While significantly simplifying calculation amount, the signal after synthesis is made to have good performance.
In addition, the present invention and its calculation amount of related algorithm are analyzed, calculation amount such as table 2 needed for unlike signal composition algorithm
It is shown.
Table 2
As seen from the above table, SNR EIGEN, COP EIGEN and each correlation matrix of AC EIGEN calculating need N2L times floating
The calculating solution of point multiplication operation, feature vector part takes around N3Secondary operation.As signal way N and correlation computations are long
The increase of L is spent, the increase of calculation amount will be huge.
PM algorithms and CW algorithms only calculate the feature vector corresponding to maximum eigenvalue, therefore reduce feature vector and ask
The operation of part is solved, but this method does not solve the calculation amount optimization problem of correlation matrix part.
MF (Matrix-free) algorithms are identical as COP EIGEN algorithms, when the noise variance of each road signal is unequal,
The synthesis weight vector calculated using the algorithm will have inclined.
If calculated just using the method for Chebyshev polynomials (Chebyshev Polynomials, abbreviation CP) fitting
String function：
Wherein, p is Chebyshev polynomials exponent number, θpFor corresponding multinomial coefficient.With three rank multinomial of Chebyshev
For, the estimation for receiving signal cross-correlation function is approximately equal to：
The computational accuracy of CP algorithms is as shown in table 3：
Table 3
As shown in Table 3, when polynomial order p is equal to 1, computational accuracy is very poor, when polynomial order p is equal to 5, tool
There is extraordinary computational accuracy, and when polynomial order p is equal to 3, it can be obtained between computational accuracy and calculation amount preferably
Balance, p is chosen in the embodiment of the present invention and is equal to 3.
For the algorithm of SP+CP, by formula (37) it is found that solving each estimation for receiving signal cross-correlation function
When, the CP for p equal to 3 includes 4 floating-point multiplications.By taking 32 hardware systems as an example, each floating-point multiplication include 32 times plus,
Subtraction and 32 shift operations, 4 floating-point multiplications need altogether 4*32=128 times plus, subtraction and 4*32=128 time move
Bit arithmetic.
And solve each estimation for receiving signal cross-correlation function using algorithm SP+CORDIC proposed by the invention
When, the CORDIC for n equal to 8 include only 24 times plus, subtraction, 16 shift operations and 8 comparison operations, therefore can be with
It is further simplified the calculation amount of correlation matrix part.
And compared with MF algorithms, SP+CORDIC+PM algorithms or SP+CORDIC+ CW algorithms can be according to noise variances
Different situations are flexibly applied to COP EIGEN or AC EIGEN, and synthesis weight vector is avoided to have inclined problem.
A kind of fast signal synthetic method proposed by the present invention is further described below by one embodiment.
For assessment algorithm performance, definition synthesis loss ζ：
Wherein theoretical maximum composite signal signal-to-noise ratioIt is equal to：
In embodimentAnd PsIt indicates signal power, is 1 in embodiment.
Practical composite signal signal-to-noise ratio in formula (38)
Wherein, signal power Ps, reception gain αi, noise varianceIt is known simulation parameter, passes through algorithms of different
Calculate synthesis weight wi, and then obtainThe present invention respectively with COP EIGEN, AC EIGEN, MF and SP+CP scheduling algorithms
Compare.S (k) uses 80KHz sinusoidal signals, sampling rate 1.4MHz, ni(k) it is white Gaussian noise, carries out 500 independent surveys
Examination.
In embodiment 1, signal way N=4, synthesis when noise variance phase is lost as shown in figure 3, wherein
In Fig. 3 in the length L=1024, Fig. 3 of the correlation computations of (a) correlation computations of (b) length L=2048.It can be seen that this hair
The synthesis loss of the algorithm SP+CORDIC+PM of bright proposition and the synthesis loss of COP EIGEN, MF and SP+CP+ PM are basic
It is identical, and the synthesis of AC EIGEN loss is maximum.Simultaneously with the increase of L, the synthesis loss of each algorithm is reduced.
In embodiment 2, signal way N=4, in the case that noise variance it is unequal synthesis loss as shown in figure 4, its
In middle Fig. 4 in the length L=1024, Fig. 4 of the correlation computations of (a) (c) correlation computations length L=2048.It can be seen that AC
EIGEN has minimum synthesis loss, while the synthesis damage of algorithm SP+CORDIC+PM proposed by the present invention and SP+CP+PM
The synthesis much smaller than COP EIGEN and MF is lost to lose.
In conjunction with above example as a result, no matter waiting noise variances or not waiting noise variances, this
The fast signal synthetic method of invention can be flexibly applied to the signals composition algorithms such as COP EIGEN and AC EIGEN, and obtain
To good signal synthesis performance.This method can significantly simplify calculation amount simultaneously, be easy to hardware realization.
It is corresponding with the above signal synthesis method, a kind of signal synthesis system is also provided in the embodiment of the present invention, is such as schemed
Shown in 5, system 500 includes：
Determination unit 510 is determined and is closed for foundation and the relevant object function of composite signal, and according to object function
At the corresponding eigenmatrix to be solved of weight vector；
First computing unit 520, for symbolization polarization method and Coordinate Rotation Digital calculating method to feature to be solved
Matrix is calculated, with the eigenmatrix after being solved；
Second computing unit 530, for calculating the optimal synthesis weight vector corresponding to the eigenmatrix after solving；
Processing unit 540 is weighted coherent phase add operation, with true according to optimal synthesis weight vector to multiple signals
Determine composite signal.
Preferably, determination unit 510 is specifically used for：
The object function of composite signal power is established, and determines that eigenmatrix to be solved is to receive according to object function
Signal correlation matrix；Or
The object function of composite signal auto-correlation coefficient is established, and determines eigenmatrix to be solved according to object function
For the product of two correlation matrixes of reception signal.
Preferably, the eigenmatrix to be solved that determination unit 510 determines includes receiving signal cross-correlation function,
In first computing unit 520, including collection of letters cross-correlation function is docked using following formula and is calculated：
Wherein, i and j indicates that road number, N indicate that total way, k indicate that sampling point number, L indicate total number of sample points, xi
(k) signal that k-th of sampled point receives from the i-th tunnel is indicated,Indicate that the estimation of polarized signal, τ indicate signal
Displacement,Indicate the estimation of polarized signal cross-correlation function,It indicates to receive estimating for signal cross-correlation function
Meter.
Preferably, in the first computing unit 520, including following formula pair is usedIt is calculated：
zl+1=zl-δlarctan(2-l) l=0,1,2 ..., n-1
Wherein,L indicates that iterations, n indicate total iterations.
Preferably, the second computing unit 530 is specifically used for：
Optimal synthesis weight vector corresponding to eigenmatrix after solving is calculated using power method algorithm or CW iterative methods.
From the foregoing, it will be observed that in a kind of signal synthesis system provided by the invention, 520 symbolization pole of the first computing unit
Change method and Coordinate Rotation Digital calculating method calculate the correlation matrix for receiving signal, the second computing unit 530 using power method algorithm or
CW iterative methods calculate optimal synthesis weight vector corresponding with correlation matrix.The method is equal in the noise variance of each road signal
Or unequal situation can use, and while significantly simplifying calculation amount, the signal after synthesis is made to have well
Performance.
Above specific implementation mode has carried out further in detail the purpose of the present invention, technical solution and advantageous effect
Illustrate, it should be understood that these are only the specific implementation mode of the present invention, the protection being not intended to limit the present invention
Range, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in this
Within the protection domain of invention.
Claims (10)
1. a kind of signal synthesis method, which is characterized in that including：
Establish with the relevant object function of composite signal, and determine according to the object function and synthesize that weight vector is corresponding waits for
The eigenmatrix of solution；
Symbolization polarization method and Coordinate Rotation Digital calculating method calculate the eigenmatrix to be solved, to be asked
Eigenmatrix after solution；
Calculate the optimal synthesis weight vector corresponding to the eigenmatrix after the solution；
Coherent phase add operation is weighted to multiple signals according to the optimal synthesis weight vector, is believed with the determination synthesis
Number.
2. according to the method described in claim 1, it is characterized in that, it is described foundation with the relevant object function of composite signal, and
Eigenmatrix to be solved corresponding with synthesis weight vector is determined according to the object function, including：
The object function of composite signal power is established, and determines that the eigenmatrix to be solved is to connect according to the object function
Receive signal correlation matrix；Or
The object function of composite signal auto-correlation coefficient is established, and the feature square to be solved is determined according to the object function
Battle array is the product for two correlation matrixes for receiving signal.
3. method according to claim 1 or 2, which is characterized in that the eigenmatrix to be solved includes receiving letter
Number cross-correlation function, in being calculated the eigenmatrix to be solved using the symbol polarization method, including use with
Lower formula calculates the reception signal cross-correlation function：
Wherein, i and j indicates that road number, N indicate that total way, k indicate that sampling point number, L indicate total number of sample points, xi(k) it indicates
The signal that k-th of sampled point receives from the i-th tunnel,Indicate that the estimation of polarized signal, τ indicate the displacement of signal,Indicate the estimation of polarized signal cross-correlation function,Indicate the estimation of reception signal cross-correlation function.
4. according to the method described in claim 3, it is characterized in that, being waited for described using the Coordinate Rotation Digital calculating method
During the eigenmatrix of solution is calculated, including use following formula pairIt is calculated：
zl+1=zl-δlarctan(2-l) l=0,1,2 ..., n-1
Wherein,L indicates that iterations, n indicate total iterations.
5. according to the method described in claim 1, it is characterized in that, corresponding to the eigenmatrix calculated after the solution
Optimal synthesis weight vector, including：
The optimal synthesis weight vector corresponding to the eigenmatrix after the solution is calculated using power method algorithm or CW iterative methods.
6. a kind of signal synthesis system, which is characterized in that including：
Determination unit determines for foundation and the relevant object function of composite signal, and according to the object function and synthesizes power
The corresponding eigenmatrix to be solved of value vector；
First computing unit, for symbolization polarization method and Coordinate Rotation Digital calculating method to the eigenmatrix to be solved
It is calculated, with the eigenmatrix after being solved；
Second computing unit, for calculating the optimal synthesis weight vector corresponding to the eigenmatrix after the solution；
Processing unit is weighted coherent phase add operation, to determine according to the optimal synthesis weight vector to multiple signals
State composite signal.
7. system according to claim 6, which is characterized in that the determination unit is specifically used for：
The object function of composite signal power is established, and determines that the eigenmatrix to be solved is to connect according to the object function
Receive signal correlation matrix；Or
The object function of composite signal auto-correlation coefficient is established, and the feature square to be solved is determined according to the object function
Battle array is the product for two correlation matrixes for receiving signal.
8. the system described according to claim 6 or 7, which is characterized in that the feature square to be solved that the determination unit determines
Battle array includes receiving signal cross-correlation function, is believed the reception in first computing unit, including using following formula
Number cross-correlation function is calculated：
Wherein, i and j indicates that road number, N indicate that total way, k indicate that sampling point number, L indicate total number of sample points, xi(k) it indicates
The signal that k-th of sampled point receives from the i-th tunnel,Indicate that the estimation of polarized signal, τ indicate the displacement of signal,Indicate the estimation of polarized signal cross-correlation function,Indicate the estimation of reception signal cross-correlation function.
9. system according to claim 8, which is characterized in that in first computing unit, including use following public affairs
Formula pairIt is calculated：
zl+1=zl-δlarctan(2-l) l=0,1,2 ..., n-1
Wherein,L indicates that iterations, n indicate total iterations.
10. system according to claim 6, which is characterized in that second computing unit is specifically used for：
The optimal synthesis weight vector corresponding to the eigenmatrix after the solution is calculated using power method algorithm or CW iterative methods.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6714585B1 (en) * | 1999-06-25 | 2004-03-30 | Ericsson Inc. | Rake combining methods and apparatus using weighting factors derived from knowledge of spreading spectrum signal characteristics |
| CN101411055A (en) * | 2006-03-27 | 2009-04-15 | Nxp股份有限公司 | Radio frequency PWM & PPM modulator |
| EP2778718A2 (en) * | 2013-03-14 | 2014-09-17 | PGS Geophysical AS | Systems and methods for frequency-domain filtering and space-time domain discrimination of seismic data |
| CN106656882A (en) * | 2016-11-29 | 2017-05-10 | 中国科学院声学研究所 | Signal synthesizing method and system |
| CN106973023A (en) * | 2017-02-14 | 2017-07-21 | 中国科学院声学研究所 | A kind of signal synthesis method and system |
-
2018
- 2018-01-22 CN CN201810060997.8A patent/CN108366025B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6714585B1 (en) * | 1999-06-25 | 2004-03-30 | Ericsson Inc. | Rake combining methods and apparatus using weighting factors derived from knowledge of spreading spectrum signal characteristics |
| CN101411055A (en) * | 2006-03-27 | 2009-04-15 | Nxp股份有限公司 | Radio frequency PWM & PPM modulator |
| EP2778718A2 (en) * | 2013-03-14 | 2014-09-17 | PGS Geophysical AS | Systems and methods for frequency-domain filtering and space-time domain discrimination of seismic data |
| CN106656882A (en) * | 2016-11-29 | 2017-05-10 | 中国科学院声学研究所 | Signal synthesizing method and system |
| CN106973023A (en) * | 2017-02-14 | 2017-07-21 | 中国科学院声学研究所 | A kind of signal synthesis method and system |
Non-Patent Citations (2)
| Title |
|---|
| B. LUO 等: "On eigen-based signal combining using the autocorrelation coefficient", 《IET COMMUNICATIONS》 * |
| CHARLES H. LEE 等: "Fast eigen-based signal combining algorithms for large antenna arrays", 《2003 IEEE AEROSPACE CONFERENCE PROCEEDINGS》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109344783A (en) * | 2018-10-11 | 2019-02-15 | 中国科学院声学研究所 | A kind of determination method and system of composite signal |
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