CN106093871B - Smart antenna Mutual coupling system and method based on empirical mode decomposition - Google Patents
Smart antenna Mutual coupling system and method based on empirical mode decomposition Download PDFInfo
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Abstract
The invention discloses a kind of smart antenna Mutual coupling system and method based on empirical mode decomposition,System includes antenna array module,Antenna array module receives signal and is transmitted to empirical mode decomposition module,Empirical mode decomposition module is by signal decomposition at the local feature signal of different time scales,And the high-frequency signal in local feature signal is transmitted to high frequency direction estimation module,Obtain the corresponding array manifold spectral function of high-frequency signal,Low frequency signal in local feature signal is transmitted to low frequency direction estimation module,Obtain the corresponding array manifold spectral function of low frequency signal,Finally the corresponding array manifold spectral function of high-frequency signal and the corresponding array manifold spectral function of low frequency signal are sent to direction Fusion Module,The two is corresponded to by direction Fusion Module and is added up,By the peak value for searching for the array manifold spectral function after adding up,Determine the direction of arrival of signal.The present invention takes into account computation complexity and resolution ratio, and fast signal target can be accurately obtained under complex electromagnetic environment.
Description
Technical field
The present invention relates to smart antennas, estimate more particularly to a kind of smart antenna direction of arrival based on empirical mode decomposition
Meter systems and method.
Background technology
In recent years, smart antenna is improving system communication quality, alleviation frequency spectrum resource deficiency and is wirelessly communicating growing
Contradiction and reduce system cost and improve system administration level etc., all have the advantages that uniqueness.Smart antenna at present
Technology is applied not only to sonar, radar, military antijam communication, space orientation and filtering is completed, at modern digital signal
Reason technology forms antenna beam in base band, for the mobile communication with complicated radio propagation environment.It has been investigated that intelligent day
Line can generate spatial orientation wave beam, and antenna main lobe is made to be directed at desired signal arrival direction, and null is directed at interference signal arrival direction,
Achieve the purpose that efficient reception desired signal and inhibits interference signal.
Traditional smart antenna Wave arrival direction estimating method has delay-additive process (Bartlett), Multiple signal classification
(MUSIC) etc..Bartlett methods are not required to spectral factorization, calculate simply, but resolution ratio is relatively low.Due to growing use demand and
The complexity of electromagnetic environment, hyundai electronics measure equipment and need to detect with more accurately signal target than traditional detecting devices
Ability.Therefore the lower Bartlett methods of resolution ratio are restricted in practical applications.It is MUSIC methods high resolution, anti-interference
Effect is good, but computation complexity is higher, and live effect is poor, is not easy to capture fast signal target.Current main Research Thinking has three
Kind, the first is to find the small Wave arrival direction estimating method of new calculation amount, but the resolution ratio of such methods is often not as good as MUSIC
Algorithm；For second to improve existing Wave arrival direction estimating method, such methods be actually between calculation amount and precision into
Compromise is gone；The third according to operation flow rationally design hardware, such methods be to sacrifice hardware cost as cost, and imitate
Fruit is limited.Therefore, it is necessary to a kind of computation complexities and resolution ratio to take into account, and meet and accurately obtain fast signal mesh under complex electromagnetic environment
Target antenna Wave arrival direction estimating method.
Invention content
Goal of the invention：The object of the present invention is to provide one kind taking into account computation complexity and resolution ratio, in complex electromagnetic environment
The smart antenna Mutual coupling system and method based on empirical mode decomposition of fast signal target can be accurately obtained down.
Technical solution：To reach this purpose, the present invention uses following technical scheme：
Smart antenna Mutual coupling system of the present invention based on empirical mode decomposition, including antenna array mould
Block, antenna array module receive signal and are transmitted to empirical mode decomposition module, and empirical mode decomposition module is by signal decomposition at difference
The local feature signal of time scale, and the high-frequency signal in local feature signal is transmitted to high frequency direction estimation module, it obtains
The corresponding array manifold spectral function of high-frequency signal, low frequency direction estimation module is transmitted to by the low frequency signal in local feature signal,
The corresponding array manifold spectral function of low frequency signal is obtained, finally by the corresponding array manifold spectral function of high-frequency signal and low frequency signal
Corresponding array manifold spectral function is sent to direction Fusion Module, is corresponded to the two by direction Fusion Module and is added up, tired by searching for
The peak value of array manifold spectral function after adding, determines the direction of arrival of signal.
Smart antenna Wave arrival direction estimating method of the present invention based on empirical mode decomposition, includes the following steps：
S1：Antenna array module receives signal and is transmitted to empirical mode decomposition module；
S2：Empirical mode decomposition module by signal decomposition at the local feature signal of different time scales, then will be local
High-frequency signal in characteristic signal is transmitted to high frequency direction estimation module, and the low frequency signal in local feature signal is transmitted to low frequency side
To estimation module；
S3：It is corresponding that high frequency direction estimation module using the Multiple signal classification based on particle group optimizing obtains high-frequency signal
Array manifold spectral function；
S4：Low frequency direction estimation module is used obtains low frequency signal correspondence based on delay-additive process that Chebyshev constrains
Array manifold spectral function；
S5：The corresponding array manifold spectral function of high-frequency signal and the corresponding array manifold spectral function of low frequency signal are sent to side
To Fusion Module, the two is corresponded to the peak value of cumulative array manifold spectral function after adding up by search by direction Fusion Module,
Determine the direction of arrival of signal.
Further, the step S3 includes the following steps：
S3.1：The covariance matrix for the high-frequency signal that estimation empirical mode decomposition decomposition module goes outTo covariance matrix
Eigenvalues Decomposition is carried out, characteristic value and feature vector are obtained；
S3.2：Determine covariance matrixMinimal eigenvalue, obtain the corresponding feature vector of minimal eigenvalue, utilize spy
Sign vector construction noise feature vector matrix EN；
S3.3：The primary group that ethnic scale is L is randomly generated, the spatial spectrum letter of each particle is updated according to formula (1)
Number：
In formula (1), a (θi) it is the corresponding vector of i-th of particle in high-frequency signal, PMUSIC(θi) be i-th of particle battle array
Column space spectral function, i=1,2 ..., L；
S3.4：The error between the array manifold spectral function of each particle and object space spectral function is calculated, according to formula (2)
The fitness function fitness (x) for being minimised as population of step-up error maximum value：
Fitness (x)=min max (| PMUSIC(θi)-Pd(θi)|2) (2)
In formula (2), Pd(θi) be i-th of particle object space spectral function, when fitness function fitness (x) reaches most
Array manifold spectral function when small value is denoted as PG-MUSIC(θ)；
S3.5：For n-dimensional space, by the molecular population of L grain, the speed V of i-th of particleiFor：
Vi=(vi1,vi2,...,vin) (3)
In formula (3), vidIt is i-th of particle rapidity in the component of d dimension spaces, i=1,2 ..., L, d=1,2 ..., n；
The position X of i-th of particleiFor：
Xi=(xi1,xi2..., xin) (4)
In formula (4), xidFor i-th of particle position d dimension spaces component；
The individual extreme value P of i-th of particleiFor：
Pi=(pi1,pi2,...,pin) (5)
In formula (5), pidFor i-th of particle individual extreme value d dimension spaces component；
The global extremum P of all particlesqFor：
Pq=(pq1,pq2,...,pqn) (6)
In formula (6), pqdFor global extremum d dimension spaces component；
According to the position and speed of formula (7) and (8) more new particle：
In formula (7), (8),For i-th of particle rapidity of t moment d dimension spaces component,For the t+1 moment i-th
A particle rapidity d dimension spaces component,For i-th of particle position of t moment d dimension spaces component,For t+
1 i-th of moment particle position d dimension spaces component,It is i-th of particle individual extreme value of t moment in d dimension spaces
Component,For all particles of t moment global extremum in the component of d dimension spaces, c1、c2For Studying factors, r1、r2For (0,
1) random number in section, w are inertia weight；
S3.6：Check whether fitness function fitness (x) reaches minimum value：If reaching minimum value, terminate；It is no
Then, step S3.3 is gone to continue to execute.
Further, the value range of inertia weight w is 0.3~0.8 in the step S3.5.
Further, the step S4 includes the following steps：
S4.1：According to formula (9) cutting low frequency signal vector b (θ) dot product 20dB that empirical mode decomposition decomposition module goes out
Than avenging husband's coefficient wcheb, obtain bT：
bT=b (θ) × wcheb (9)
S4.2：Enable W=bT, substitute into formula (10) and obtain formula (11)：
Pout=WHRXXW (10)
In formula (10), PoutFor the gross output of antenna array, RXXFor the auto-correlation function of b (θ)；
Then, the corresponding array manifold spectral function P of low frequency signal is calculatedC-Bartlett(θ)：
PC-Bartlett(θ)=Pout=(b (θ) × wcheb)HRxx(b(θ).×wcheb) (11)。
Further, the step S5 includes the following steps：
S5.1：According to formula (12) by the corresponding array manifold spectral function of high-frequency signal and the corresponding array manifold of low frequency signal
Spectral function, which corresponds to, to add up, and obtains the array manifold spectral function P after the fusion of directionEMD-GMCB(θ)；
PEMD-GMCB(θ)=PG-MUSIC(θ)+PC-Bartlett(θ) (12)；
S5.2：Array manifold spectral function P after being merged by the direction of searchEMD-GMCBThe peak value of (θ) determines signal wave up to side
To.
Advantageous effect：
1) disadvantage that the present invention overcomes delay-additive process (Bartlett) resolution ratio is low, secondary lobe is more, secondary lobe amplitude
9dB is averagely reduced, the 62.9% of total amplitude is about reduced, reduces 37.5% compared with Bartlett method run times, to improve
System is to the restraint of interference signal and the accuracy of acquisition desired signal；
2) the present invention overcomes the high disadvantages of Multiple signal classification (MUSIC) computation complexity, reduce MUSIC methods 95.2%
Computation complexity, short compared with MUSIC method run times, average operating time about reduces 35.5%；
3) present invention realizes resolution ratio and the height of computation complexity is taken into account, and meets and is accurately obtained in complexity electromagnetic environment
Take the use demand of fast signal target.
Description of the drawings
Fig. 1 is the system block diagram of the present invention；
Fig. 2 is the concrete structure diagram of the present invention；
The oscillogram for the signal that Fig. 3 receives for antenna array module in the specific implementation mode of the present invention；
In the local feature signal that Fig. 4 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The oscillogram of first high-frequency signal imf1；
In the local feature signal that Fig. 5 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The oscillogram of second high-frequency signal imf2；
In the local feature signal that Fig. 6 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The oscillogram of third high-frequency signal imf3；
In the local feature signal that Fig. 7 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The oscillogram of 4th high-frequency signal imf4；
In the local feature signal that Fig. 8 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The oscillogram of first low frequency signal imf5；
In the local feature signal that Fig. 9 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The oscillogram of second low frequency signal imf6；
In the local feature signal that Figure 10 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
Third low frequency signal imf7 oscillogram；
In the local feature signal that Figure 11 obtains for empirical mode decomposition decomposition module in the specific implementation mode of the present invention
The 4th low frequency signal imf8 oscillogram；
The Multiple signal classification and tradition based on particle group optimizing that Figure 12 is used in the specific implementation mode for the present invention
The obtained comparison diagram of the array manifold spectral function of high-frequency signal of Multiple signal classification；
Delay-the additive process and biography that are constrained based on Chebyshev that Figure 13 is used in the specific implementation mode for the present invention
The comparison diagram of the array manifold spectral function for the low frequency signal that delay-additive process of system obtains；
Figure 14 be the specific implementation mode of the present invention obtain it is cumulative after array manifold spectral function and tradition is respectively adopted
Multiple signal classification and the obtained comparison diagram of array manifold spectral function of delay-additive process.
Specific implementation mode
Technical scheme of the present invention is further introduced With reference to embodiment.
The smart antenna Mutual coupling system based on empirical mode decomposition that the invention discloses a kind of, such as Fig. 1 and Fig. 2
Shown, including antenna array module 1, antenna array module 1 receives signal and is simultaneously transmitted to empirical mode decomposition module 2, empirical mode decomposition
Signal decomposition is transmitted to by module 2 at the local feature signal of different time scales, and by the high-frequency signal in local feature signal
High frequency direction estimation module 3 obtains the corresponding array manifold spectral function of high-frequency signal, by the low frequency signal in local feature signal
It is transmitted to low frequency direction estimation module 4, obtains the corresponding array manifold spectral function of low frequency signal, finally by the corresponding battle array of high-frequency signal
Column space spectral function and the corresponding array manifold spectral function of low frequency signal are sent to direction Fusion Module 5, will by direction Fusion Module 5
The two corresponds to the peak value of cumulative array manifold spectral function after adding up by search, determines the direction of arrival of signal.
The invention also discloses a kind of smart antenna Wave arrival direction estimating method based on empirical mode decomposition, step is such as
Under：
S1：Antenna array module 1 receives signal and is transmitted to empirical mode decomposition module 2；Assuming that desired signal sk(t) from QkSide
To incidence, interference signal sj(t) from QjDirection is incident, and wherein interference signal amounts to k-1, then bay received signal x
(t) it is represented by：
In formula (1), n (t) is noise signal, Sk(t) and Sj(t) it is respectively：
Sk(t)=sk(t)·V(Qk)Sj(t)=sj(t)·V(Qj) (2)
In formula (2), V (θ) is the array element response in channel；
S2：Empirical mode decomposition module 2 is by signal decomposition at the local feature signal of different time scales, Ye Ji
In matlab, array signal X is decomposed by Multiple Time Scales frequency domain with sentence imf=emd (X) using EMD functions；Use sentence
plot(c,X)；Title (' rignal'), original signal figure can be obtained, with sentence subplot (4,1,2)；plot(c,imf
{1})；Title (' imf1'), can be obtained local feature signal imf1, can similarly obtain local feature signal imf2, imf3, imf4,
Imf5, imf6, imf7, imf8, wherein imf1, imf2, imf3, imf4 are high-frequency signal, as shown in Fig. 4-Fig. 7, imf5,
Imf6, imf7, imf8 are low frequency signal, as shown in Fig. 8-Figure 11；So as to realize the orthogonal processing of signal, can reduce certainly
The degree of scatter of the input vector autocorrelation matrix characteristic value of adaptive filter greatly increases the convergence step-length of algorithm, improves convergence
Speed；In addition, high-frequency signal imf1, imf2, imf3 and imf4 are also transmitted to high frequency direction by empirical mode decomposition module 2 estimates mould
Low frequency signal imf5, imf6, imf7, imf8 are transmitted to low frequency direction estimation module 4 by block 3；
S3：High frequency direction estimation module 3 using the Multiple signal classification based on particle group optimizing obtain high-frequency signal imf1,
The corresponding array manifold spectral function of imf2, imf3, imf4, wherein L grain is passed through based on the Multiple signal classification of particle group optimizing
Molecular particle group optimizing Multiple signal classification obtains, and includes the following steps：
S3.1：The covariance matrix for the high-frequency signal that estimation empirical mode decomposition module 2 decompositesTo covariance matrixEigenvalues Decomposition is carried out, characteristic value and feature vector are obtained；
S3.2：Determine covariance matrixMinimal eigenvalue, obtain the corresponding feature vector of minimal eigenvalue, utilize spy
Sign vector construction noise feature vector matrix EN；
S3.3：The primary group that ethnic scale is L is randomly generated, the spatial spectrum letter of each particle is updated according to formula (3)
Number：
In formula (3), a (θi) it is the corresponding vector of i-th of particle in high-frequency signal, PMUSIC(θi) be i-th of particle battle array
Column space spectral function, i=1,2 ..., L；
S3.4：The error between the array manifold spectral function of each particle and object space spectral function is calculated, according to formula (4)
The fitness function fitness (x) for being minimised as population of step-up error maximum value：
Fitness (x)=min max (| PMUSIC(θi)-Pd(θi)|2) (4)
In formula (4), Pd(θi) be i-th of particle object space spectral function, when fitness function fitness (x) reaches most
Array manifold spectral function when small value is denoted as PG-MUSIC(θ)；
S3.5：For n-dimensional space, by the molecular population of L grain, the speed V of i-th of particleiFor：
Vi=(vi1,vi2,...,vin) (5)
In formula (5), vidIt is i-th of particle rapidity in the component of d dimension spaces, i=1,2 ..., L, d=1,2 ..., n；
The position X of i-th of particleiFor：
Xi=(xi1,xi2..., xin) (6)
In formula (6), xidFor i-th of particle position d dimension spaces component；
The individual extreme value P of i-th of particleiFor：
Pi=(pi1,pi2,...,pin) (7)
In formula (7), pidFor i-th of particle individual extreme value d dimension spaces component；
The global extremum P of all particlesqFor：
Pq=(pq1,pq2,...,pqn) (8)
In formula (8), pqdFor global extremum d dimension spaces component；
According to the position and speed of formula (9) and (10) more new particle：
In formula (9), (10),For i-th of particle rapidity of t moment d dimension spaces component,For the t+1 moment i-th
A particle rapidity d dimension spaces component,For i-th of particle position of t moment d dimension spaces component,For t+
1 i-th of moment particle position d dimension spaces component,It is i-th of particle individual extreme value of t moment in d dimension spaces
Component,For all particles of t moment global extremum in the component of d dimension spaces, c1、c2For Studying factors, r1、r2For (0,
1) random number in section, w are inertia weight, and the value range of w is 0.3~0.8；
S3.6：Check whether fitness function fitness (x) reaches minimum value：If reaching minimum value, terminate；It is no
Then, step S3.3 is gone to continue to execute；
S4：Low frequency direction estimation module 4 obtains low frequency signal using based on delay-additive process that Chebyshev constrains
The corresponding array manifold spectral function of imf5, imf6, imf7, imf8, wherein delay-additive process based on Chebyshev's constraint is logical
Chebyshev coefficient constraint delay-additive process is crossed, is included the following steps：
S4.1：Low frequency signal vector b (θ) dot products 20dB's empirical mode decomposition module 2 decomposited according to formula (11)
Chebyshev coefficient wcheb, obtain bT：
bT=b (θ) × wcheb (11)
S4.2：Enable W=bT, substitute into formula (12) and obtain formula (13)：
Pout=WHRXXW (12)
In formula (12), PoutFor the gross output of antenna array, RXXFor the auto-correlation function of b (θ)；
Then, the corresponding array manifold spectral function P of low frequency signal is calculatedC-Bartlett(θ)：
PC-Bartlett(θ)=Pout=(b (θ) × wcheb)HRxx(b(θ).×wcheb) (13)。
S5：By the corresponding array manifold spectral function of high-frequency signal imf1, imf2, imf3, imf4 and low frequency signal imf5,
The corresponding array manifold spectral function of imf6, imf7, imf8 is sent to direction Fusion Module 5, is corresponded to the two by direction Fusion Module 5
It is cumulative, by searching for the peak value of the array manifold spectral function after adding up, determines the direction of arrival of signal, be as follows：
S5.1：According to formula (14) by the corresponding array manifold spectral function of high-frequency signal imf1, imf2, imf3, imf4 and low
The corresponding array manifold spectral functions of frequency signal imf5, imf6, imf7, imf8, which correspond to, to add up, and the array obtained after the fusion of direction is empty
Between spectral function PEMD-GMCB(θ)；
PEMD-GMCB(θ)=PG-MUSIC(θ)+PC-Bartlett(θ) (14)；
S5.2：Array manifold spectral function P after being merged by the direction of searchEMD-GMCBThe peak value of (θ) determines signal wave up to side
To.
In present embodiment, antenna array module 1 uses bay number for 20 line array, antenna array module 1
Receive respectively -10 ° of incident direction, the signal that 20 ° of two mean values are zero, as shown in Figure 3.
Multiple signal classification of the present embodiment to Multiple signal classification (MUSIC methods) and based on particle group optimizing
(G-MUSIC methods) compares, and both methods is respectively adopted and has obtained the array manifold spectral function of high-frequency signal, such as Figure 12
It is shown.Specific comparison procedure is as follows：
A) with Matlab emulation MUSIC methods experiments.If incident direction is respectively -10 °, 20 °, hits is K=500 fast
It claps, signal-to-noise ratio SNR=10dB, angle scanning range is [0 °, 180 °], and sweep spacing is 1 °.Using Multiple signal classification
(MUSIC) array manifold spectral function search is carried out, array manifold spectral function is as shown in figure 12.
B) with Matlab emulation G-MUSIC methods experiments.If population L is 20, particle maximum rate is that vm max1 are 40,
Vm max1 are 190, and maximum inertial factor is 0.8, and minimum inertial factor is 0.1, and Studying factors c1, c2 are 2, and maximum is evolved
Algebraically is 300.If incident direction is respectively -10 °, 20 °, hits is set as K=500 snap, and signal-to-noise ratio is set as SNR=
10dB, angle scanning range are [0 °, 180 °], and sweep spacing is 1 °.Using particle group optimizing Multiple signal classification (G-MUSIC)
Array manifold spectral function search is carried out, array manifold spectral function is as shown in figure 12.
C) comparative analysis MUSIC methods and G-MUSIC methods.In figure 12 it can be seen that passing through search array space spectral function
Peak value, MUSIC methods and G-MUSIC methods can determine that signal direction of arrival is respectively -10 °, 20 °.The peak value point of two methods
Sharp and all without secondary lobe, algorithm resolution ratio is all higher, and G-MUSIC methods are wider compared with MUSIC method main lobes.Multiple signal classification
(MUSIC), the number that entire space search needs fitness to calculate is 360 × 90=32400 times, and based on the more of population
Modulation recognition method (G-MUSIC), the number that entire space search needs fitness to calculate are 80 × 20=1600 times.Therefore G-
The computation complexity of MUSIC methods is far smaller than the computation complexity of MUSIC methods, and only there are about MUSIC calculation amounts for G-MUSIC methods
5%, computation complexity has obtained largely improving.
Delay-phase that present embodiment is constrained to delay-additive process (Bartlett methods) and based on Chebyshev
Addition (C-Bartlett methods) compares, and both methods is respectively adopted and has obtained the array manifold spectrum letter of low frequency signal
Number, as shown in figure 13.Specific comparison procedure is as follows：
A) with Matlab emulation Bartlett methods experiments.If incident direction is respectively -10 °, 20 °, hits K=500
A snap, signal-to-noise ratio SNR=10dB.Angle scanning range is [0 °, 180 °], and sweep spacing is 1 °.Using delay-addition
Method (Bartlett) carries out array manifold spectral function search, and array manifold spectral function is as shown in figure 13.
B) with Matlab emulation C-Bartlett methods experiments.Two information source incident directions are respectively -10 °, and 20 °, hits is set
For K=500 snap, signal-to-noise ratio is set as SNR=10dB.Angle scanning range is [0 °, 180 °], and sweep spacing is 1 °.If cutting
Than avenging husband's coefficient wchebFor -20dB, delay-additive process (C-Bartlett) is constrained using Chebyshev and carries out array manifold spectrum
Selecting Function System, array manifold spectral function are as shown in figure 13.
C) comparative analysis Bartlett methods and C-Bartlett methods.As seen from Figure 13, with the angle of entry in Bartlett methods
The change output power of degree corresponds to -10 ° of the incident direction of two signals there are two peak value respectively, 20 °, but peak value is not sharp enough
Sharp and secondary lobe is more, it is seen that Bartlett algorithms resolution ratio is not very high.But the C-Bartlett after Chebyshev constrains
Method realizes the direction estimation of incoming signal, and secondary lobe number is reduced, and secondary lobe amplitude averagely reduces 9dB, about reduces total amplitude
62.9%, main lobe is also slightly more sharp than common Bartlett methods, and resolution ratio is improved.
In addition, present embodiment also by the method for the present invention (EMD-GMCB methods) and traditional MUSIC methods and
Bartlett methods compare, as a result as shown in figure 14.Specific comparison procedure is as follows：
A) calculation amount compares
Remember that MUSIC methods, Bartlett method computation complexities are respectively O1(M)、O2(M), wherein O1(M) ＞ ＞ O2(M).Note
G-MUSIC methods, C-Bartlett method computation complexities are respectively O '1(M)、O'2(M), data shows O '1(M) ＞ O '2(M).Before
Text shows that G-MUSIC methods only there are about the 5% of MUSIC calculation amounts, are denoted as O '1(M) ≈ 5%O1(M).Again very according to imf1, imf2,
Imf3, imf4, imf5, imf6, imf7, imf8 energy are substantially distributed, it is known that and higher-frequency part signal accounts for about the 95% of gross energy,
Account for about the 5% of gross energy compared with low frequency signal.Can then obtain computation complexity of the EMD-GMCB methods in an iteration be about 95% ×
O′1(M)+5% × O'2(M) 95% × O ' of ≈1(M) 95% × 5%O of ≈1(M) ≈ 4.8%O1(M), it is denoted as O3(M).Therefore it can obtain
O3(M) ＜ ＜ O1(M), the computation complexity O of EMD-GMCB methods3(M) well below the complexity O of MUSIC methods1(M), it reduces
The computation complexity of MUSIC methods 95.2%, it is seen that EMD-GMCB methods proposed by the present invention are a kind of lower sides of computation complexity
Method.
B) resolution ratio compares
Under the same terms, Bartlett, MUSIC, EMD-GMCB method space spectrogram are as shown in figure 14.As it can be seen that EMD-GMCB
Method overcomes the higher disadvantage of Bartlett method secondary lobes, secondary lobe amplitude averagely to reduce 9dB, about reduce the 62.9% of total amplitude, to
Improve accuracy of the system to the restraint of interference signal and acquisition desired signal.It is analyzed according to calculation amount, EMD-GMCB
Method computation complexity is O3(M) ≈ 4.8%O1(M), i.e. O3(M) ＜ ＜ O1(M), the calculating for reducing MUSIC methods 95.2% is complicated
Degree.It can be seen that EMD-GMCB methods proposed by the present invention realize resolution ratio and the height of computation complexity is taken into account.
C) run time compares
This experiment is corresponding in Legend computer G460AP types operation Bartlett methods, MUSIC methods, EMD-GMCB methods
Matlab programs, three kinds of method run times are as shown in table 1.As seen from Table 1, at identical array element number M, EMD-GMCB methods
Run time is most short, and 37.5% is improved compared with Bartlett method run times, and 35.5% is improved compared with MUSIC method run times.
Run time can Response calculation complexity can also react convergence, it is seen that EMD-GMCB methods proposed by the present invention are a kind of convergences
The higher method of property.
1 Bartlett algorithms of table, MUSIC methods, EMD-GMCB method run times
Claims (5)
1. the smart antenna Mutual coupling system based on empirical mode decomposition, it is characterised in that：Including antenna array module
(1), antenna array module (1) receives signal and is transmitted to empirical mode decomposition module (2), and empirical mode decomposition module (2) is by signal
The local feature signal of different time scales is resolved into, and the high-frequency signal in local feature signal is transmitted to high frequency direction estimation
Module (3) obtains the corresponding array manifold spectral function of high-frequency signal, the low frequency signal in local feature signal is transmitted to low frequency side
To estimation module (4), the corresponding array manifold spectral function of low frequency signal is obtained, finally composes the corresponding array manifold of high-frequency signal
Function and the corresponding array manifold spectral function of low frequency signal are sent to direction Fusion Module (5), by direction Fusion Module (5) by the two
It is corresponding cumulative, by searching for the peak value of the array manifold spectral function after adding up, determine the direction of arrival of signal.
2. the smart antenna Wave arrival direction estimating method based on empirical mode decomposition, it is characterised in that：Include the following steps：
S1：Antenna array module (1) receives signal and is transmitted to empirical mode decomposition module (2)；
S2：Signal decomposition at the local feature signal of different time scales, it is special then to be incited somebody to action part by empirical mode decomposition module (2)
High-frequency signal in reference number is transmitted to high frequency direction estimation module (3), and the low frequency signal in local feature signal is transmitted to low frequency
Direction estimation module (4)；
S3：It is corresponding that high frequency direction estimation module (3) uses the Multiple signal classification based on particle group optimizing to obtain high-frequency signal
Array manifold spectral function；
The step S3 includes the following steps：
S3.1：The covariance matrix for the high-frequency signal that estimation empirical mode decomposition module (2) decompositesTo covariance matrix
Eigenvalues Decomposition is carried out, characteristic value and feature vector are obtained；
S3.2：Determine covariance matrixMinimal eigenvalue, obtain the corresponding feature vector of minimal eigenvalue, using feature to
Amount construction noise feature vector matrix EN；
S3.3：The primary group that ethnic scale is L is randomly generated, the space spectral function of each particle is updated according to formula (1)：
In formula (1), a (θi) it is the corresponding vector of i-th of particle in high-frequency signal, PMUSIC(θi) be i-th of particle array manifold
Spectral function, i=1,2 ..., L；
S3.4：The error between the array manifold spectral function of each particle and object space spectral function is calculated, is arranged according to formula (2)
The fitness function fitness (x) for being minimised as population of max value of error：
Fitness (x)=minmax (| PMUSIC(θi)-Pd(θi)|2) (2)
In formula (2), Pd(θi) be i-th of particle object space spectral function, when fitness function fitness (x) reaches minimum value
When array manifold spectral function be denoted as PG-MUSIC(θ)；
S3.5：For n-dimensional space, by the molecular population of L grain, the speed V of i-th of particleiFor：
Vi=(vi1,vi2,...,vin) (3)
In formula (3), vidIt is i-th of particle rapidity in the component of d dimension spaces, i=1,2 ..., L, d=1,2 ..., n；
The position X of i-th of particleiFor：
Xi=(xi1,xi2..., xin) (4)
In formula (4), xidFor i-th of particle position d dimension spaces component；
The individual extreme value P of i-th of particleiFor：
Pi=(pi1,pi2,...,pin) (5)
In formula (5), pidFor i-th of particle individual extreme value d dimension spaces component；
The global extremum P of all particlesqFor：
Pq=(pq1,pq2,...,pqn) (6)
In formula (6), pqdFor global extremum d dimension spaces component；
According to the position and speed of formula (7) and (8) more new particle：
In formula (7), (8),For i-th of particle rapidity of t moment d dimension spaces component,For t+1 moment i-th
Sub- speed d dimension spaces component,For i-th of particle position of t moment d dimension spaces component,For t+1 when
Carve i-th of particle position d dimension spaces component,For i-th of particle individual extreme value of t moment d dimension spaces point
Amount,For all particles of t moment global extremum in the component of d dimension spaces, c1、c2For Studying factors, r1、r2For (0,1)
Random number in section, w are inertia weight；
S3.6：Check whether fitness function fitness (x) reaches minimum value：If reaching minimum value, terminate；Otherwise, turn
It is continued to execute to step S3.3；
S4：Low frequency direction estimation module (4) obtains low frequency signal correspondence using based on delay-additive process that Chebyshev constrains
Array manifold spectral function；
S5：The corresponding array manifold spectral function of high-frequency signal and the corresponding array manifold spectral function of low frequency signal are sent to direction and melted
Block (5) is molded, the two is corresponded to by direction Fusion Module (5) and is added up, passes through the peak for searching for the array manifold spectral function after adding up
Value, determines the direction of arrival of signal.
3. the smart antenna Wave arrival direction estimating method according to claim 2 based on empirical mode decomposition, feature exist
In：The value range of inertia weight w is 0.3~0.8 in the step S3.5.
4. the smart antenna Wave arrival direction estimating method according to claim 2 based on empirical mode decomposition, feature exist
In：The step S4 includes the following steps：
S4.1：Low frequency signal vector b (θ) dot products 20dB that empirical mode decomposition module (2) decomposites is cut into ratio according to formula (9)
Avenge husband's coefficient wcheb, obtain bT：
bT=b (θ) × wcheb (9)
S4.2：Enable W=bT, substitute into formula (10) and obtain formula (11)：
Pout=WHRXXW (10)
In formula (10), PoutFor the gross output of antenna array, RXXFor the auto-correlation function of b (θ)；
Then, the corresponding array manifold spectral function P of low frequency signal is calculatedC-Bartlett(θ)：
PC-Bartlett(θ)=Pout=(b (θ) × wcheb)HRxx(b(θ).×wcheb) (11)。
5. the smart antenna Wave arrival direction estimating method according to claim 2 based on empirical mode decomposition, feature exist
In：The step S5 includes the following steps：
S5.1：The corresponding array manifold spectral function of high-frequency signal and the corresponding array manifold of low frequency signal are composed into letter according to formula (12)
Number is corresponding cumulative, obtains the array manifold spectral function P after the fusion of directionEMD-GMCB(θ)；
PEMD-GMCB(θ)=PG-MUSIC(θ)+PC-Bartlett(θ) (12)；
S5.2：Array manifold spectral function P after being merged by the direction of searchEMD-GMCBThe peak value of (θ) determines signal direction of arrival.
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