CN109274444A - A kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency - Google Patents
A kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency Download PDFInfo
- Publication number
- CN109274444A CN109274444A CN201811124363.0A CN201811124363A CN109274444A CN 109274444 A CN109274444 A CN 109274444A CN 201811124363 A CN201811124363 A CN 201811124363A CN 109274444 A CN109274444 A CN 109274444A
- Authority
- CN
- China
- Prior art keywords
- signal
- matrix
- frequency
- time
- dimensional
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
- H04W16/10—Dynamic resource partitioning
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The present invention provides a kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency, can obtain time, frequency and the angle information of primary user's signal.The described method includes: carrying out whitening pretreatment to primary user's signal that multiple cognitive users receive；The pretreated signal of whitening carries out spacial time-frequency distribution and converts to obtain four-matrix, wherein the information in four-matrix includes: time, frequency and two-dimensional space information；The time-frequency two-dimensional information of four-matrix is filtered, is extracted single from reference point；Reference point progress Joint diagonalization is originated to obtained list to obtain estimating for the steering vector of angle estimation；According to obtained steering vector, the angle of arrival of each primary user's signal is determined.The present invention relates to radio spectrum sensing technical fields.
Description
Technical field
The present invention relates to radio spectrum sensing technical fields, particularly relate to a kind of three-dimensional frequency spectrum perception side of Space-Time-frequency
Method.
Background technique
In recent years, as the global radio communication technology develops with rapid changepl. never-ending changes and improvementsly, the novel radio electric system phase of different function
After appearance.For wireless communication system using static spectral allocation strategy, frequency spectrum resource is also more and more in short supply at present.Cognition wireless
Electric energy reaches the variation of sensing external environment, improves the availability of frequency spectrum and power system capacity, solves the problems, such as spectrum shortage.It is common at present
Frequency spectrum sensing method be mainly divided into single cognitive user frequency spectrum perception and more cognitive user frequency spectrum perception technologies.
Existing frequency spectrum perception technology can only obtain the presence or absence of primary user's signal mostly, when can not obtain detailed
Between, frequency and angle information.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of three-dimensional frequency spectrum sensing methods of Space-Time-frequency, to solve existing skill
Frequency spectrum perception technology present in art cannot obtain the problem of detailed time, frequency and angle information.
In order to solve the above technical problems, the embodiment of the present invention provides a kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency, comprising:
Whitening pretreatment is carried out to primary user's signal that multiple cognitive users receive, wherein between each primary user's signal
It is irrelevant；
The pretreated signal of whitening carries out spacial time-frequency distribution and converts to obtain four-matrix, wherein in four-matrix
Information includes: time, frequency and two-dimensional space information；
The time-frequency two-dimensional information of four-matrix is filtered, is extracted single from reference point, wherein single source refers to each independence
Primary user's signal source；
Reference point progress Joint diagonalization is originated to obtained list to obtain estimating for the steering vector of angle estimation；
According to obtained steering vector, the angle of arrival of each primary user's signal is determined.
Further, the primary user's signal progress whitening pretreatment received to multiple cognitive users includes:
Primary user's signal that multiple cognitive users are received forms receipt signal matrix；
Whitening pretreatment is carried out to receipt signal matrix.
Further, primary user's signal composition receipt signal matrix that multiple cognitive users are received include:
N number of primary user emits signal from different directions, reaches M cognitive user end, wherein M > N；
The signal that M cognitive user is received forms receipt signal matrix Y=[Y1,Y2,...,YM]T, wherein subscript T
The transposition of representing matrix.
Further, described to include: to receipt signal matrix progress whitening pretreatment
Eigenvalues Decomposition is carried out to the covariance matrix of receipt signal matrix Y；
According to Eigenvalues Decomposition as a result, building whitening matrix W；
Whitening matrix W dot product receipt signal matrix Y, obtains whitened signal Z=WY, wherein Z=[Z1,Z2,...,ZN]T。
Further, it is described according to Eigenvalues Decomposition as a result, building whitening matrix W include:
Descending arrangement is carried out to obtained characteristic value, the characteristic value [λ after obtaining descending arrangement1,...,λM], and its it is corresponding
Feature vector [h1,...,hM]；
From M characteristic value after descending arrangement, take preceding 1~N number of characteristic value and its feature vector be used to construct albefaction square
Battle array W, wherein whitening matrix W is indicated are as follows:
Wherein, σ2For the average value of rear M-N lesser characteristic values after descending arrangement, the conjugation of subscript H representing matrix
Transposition.
Further, it is indicated by the four-matrix that spacial time-frequency distribution converts are as follows:
Wherein, DZZ(t, f) is two-dimensional covariance matrix, representation space information；Each elementIt is one two
The time-frequency matrix of dimension,Indicate i-th of signal ZiWith j-th of signal ZjMutual time-frequency distributions；T indicates the time；F is indicated
Frequency.
Further, the time-frequency two-dimensional information to four-matrix is filtered, and is extracted list from reference point and is included:
Pass through relational expressionExtract the auto-correlation point comprising all primary user's signals, wherein ε2
To extract threshold value, the mark of trace () representing matrix；
By cluster, the auto-correlation point of all primary user's signals extracted is divided into N class, certain one kind represents a certain signal
Auto-correlation point, obtain the K of N class list source signaliA auto-correlation pointWherein, i=1,2 ..., N, ki=1,2 ...,
Ki。
Further, described pair of obtained list is originated from reference point progress Joint diagonalization and obtains the guiding for angle estimation
Vector is estimated
To obtained N class auto-correlation pointThe two-dimensional space information of four-matrix is extracted, constitutes signal when corresponding
Correlation matrix on frequency point
To obtained correlation matrixJoint diagonalization processing is carried out, acquiring makesMost
Small N × N-dimensional unitary matrice U, wherein I is unit matrix, and off () is the quadratic sum of matrix off-diagonal element；
Steering vector estimation is carried out using the unitary matrice U that Joint diagonalization obtains:
Wherein, W#For the pseudoinverse of whitening matrix,For steering vector.
Further, the steering vector that the basis obtains determines that the angle of arrival of each primary user's signal includes:
According to obtained steering vector, the angle of arrival of each primary user's signal is determined using multiple signal classification.
Further, the steering vector that the basis obtains determines each primary user's signal using multiple signal classification
Angle of arrival includes:
It is rightSeek its covariance matrix:
Wherein, RAAFor the covariance matrix of M × M dimension；
By RAAFeature vector be divided into two parts by the size of characteristic value: UsAnd Un, wherein Us·Us H+Un·Un H=I, Us
=[U1,U2,...,UN] it is the unitary space that the corresponding feature vector of top n maximum eigenvalue is constituted, it opens into signal subspace；Un
=[UN+1,...,UM] it is the unitary matrice that the corresponding feature vector of rear M-N minimal eigenvalue is constituted, it opens into noise subspace；
Pass throughTo the angle of primary user's signal
Estimated, obtains the angle of arrival of each primary user's signal.
The advantageous effects of the above technical solutions of the present invention are as follows:
In above scheme, whitening pretreatment is carried out to primary user's signal that multiple cognitive users receive；Whitening is located in advance
Signal after reason carries out spacial time-frequency distribution and converts to obtain four-matrix, wherein the information in four-matrix includes: time, frequency
With two-dimensional space information；The time-frequency two-dimensional information of four-matrix is filtered, list is extracted and is originated from reference point, to obtain each
The accurate time-frequency distributions of primary user's signal；Reference point progress Joint diagonalization is originated to obtained list to obtain for angle estimation
Steering vector estimation；According to obtained steering vector, the angle of arrival of each primary user's signal is determined.In this way, based on empty time-frequency
Distribution transformation extracts the auto-correlation point of signal to obtain the accurate time frequency distribution map of signal, and so as to improve primary user's angle
Estimate accuracy and resolution ratio.
Detailed description of the invention
Fig. 1 is the flow diagram of the three-dimensional frequency spectrum sensing method of Space-Time provided in an embodiment of the present invention-frequency；
Fig. 2 is more primary users, cognitive user schematic diagram in cognitive radio networks provided in an embodiment of the present invention；
Fig. 3 is the time-frequency energy signal of the three-dimensional frequency spectrum sensing method of Space-Time-frequency of signal 1 provided in an embodiment of the present invention
Figure；
Fig. 4 is the time-frequency energy signal of the three-dimensional frequency spectrum sensing method of Space-Time-frequency of signal 2 provided in an embodiment of the present invention
Figure；
Fig. 5 is the angle estimation signal of the three-dimensional frequency spectrum sensing method of Space-Time-frequency of signal 1 provided in an embodiment of the present invention
Figure；
Fig. 6 is the angle estimation signal of the three-dimensional frequency spectrum sensing method of Space-Time-frequency of signal 2 provided in an embodiment of the present invention
Figure.
Specific embodiment
To keep the technical problem to be solved in the present invention, technical solution and advantage clearer, below in conjunction with attached drawing and tool
Body embodiment is described in detail.
The present invention aiming at the problem that existing frequency spectrum perception technology cannot obtain detailed time, frequency and angle information,
A kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency is provided.
As shown in Figure 1, the three-dimensional frequency spectrum sensing method of Space-Time provided in an embodiment of the present invention-frequency, comprising:
S101 carries out whitening pretreatment to primary user's signal that multiple cognitive users receive, wherein each primary user's signal
Between it is irrelevant；
S102, the pretreated signal of whitening carry out spacial time-frequency distribution (Spatial Time-frequency
Distribution, STFD) transformation obtain four-matrix, wherein the information in four-matrix includes: time, frequency and bidimensional
Spatial information；
S103 is filtered the time-frequency two-dimensional information of four-matrix, extracts single from reference point, wherein single source refers to often
A independent primary user's signal source；
S104 is originated from reference point progress Joint diagonalization to obtained list and obtains estimating for the steering vector of angle estimation
Meter；
S105 determines the angle of arrival of each primary user's signal according to obtained steering vector.
The three-dimensional frequency spectrum sensing method of Space-Time described in the embodiment of the present invention-frequency, receives multiple cognitive users primary
Family signal carries out whitening pretreatment；The pretreated signal of whitening carries out spacial time-frequency distribution and converts to obtain four-matrix, wherein
Information in four-matrix includes: time, frequency and two-dimensional space information；The time-frequency two-dimensional information of four-matrix was carried out
Filter extracts list and is originated from reference point, to obtain the accurate time-frequency distributions of each primary user's signal；Reference point is originated to obtained list
Joint diagonalization is carried out to obtain estimating for the steering vector of angle estimation；According to obtained steering vector, determine each primary
The angle of arrival of family signal.In this way, based on spacial time-frequency distribution convert, extract the auto-correlation point of signal obtain signal it is accurate when
Frequency division Butut, and so as to improve the estimation accuracy and resolution ratio of primary user's angle.
It is further, described to recognize multiple in the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency
Know that primary user's signal that user receives carries out whitening pretreatment and includes:
Primary user's signal that multiple cognitive users are received forms receipt signal matrix；
Whitening pretreatment is carried out to receipt signal matrix.
It is further, described to recognize multiple in the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency
Know that primary user's signal composition receipt signal matrix that user receives include:
N number of primary user emits signal from different directions, reaches M cognitive user end, wherein M > N；
The signal that M cognitive user is received forms receipt signal matrix Y=[Y1,Y2,...,YM]T, wherein subscript T
The transposition of representing matrix.
In the present embodiment, it is assumed that N number of primary user emits signal from different directions, and reaches M (M > N) a cognitive user
End, angle of arrival is respectively θ1,θ2,...,θN.Each primary user's signal source is mutually indepedent, irrelevant between each primary user's signal,
And it is irrelevant between primary user's signal and noise.The signal received is sent to decision center by each cognitive user, by certainly
Plan is centrally formed receipt signal matrix Y=[Y1,Y2,...,YM]T。
In the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency, further, the docking is collected mail
Number matrix carries out whitening pretreatment
Eigenvalues Decomposition is carried out to the covariance matrix of receipt signal matrix Y；
According to Eigenvalues Decomposition as a result, building whitening matrix W；
Whitening matrix W dot product receipt signal matrix Y, obtains whitened signal Z=WY, wherein Z=[Z1,Z2,...,ZN]T。
In the present embodiment, it is described according to Eigenvalues Decomposition as a result, building whitening matrix W specific steps may include:
Eigenvalues Decomposition is carried out to the covariance matrix of receipt signal matrix Y；
Descending arrangement is carried out to obtained characteristic value, the characteristic value [λ after obtaining descending arrangement1,...,λM] and its it is corresponding
Feature vector [h1,...,hM], from M characteristic value after descending arrangement, take preceding 1~N number of characteristic value and its feature vector be used for
Constructing whitening matrix W, that is to say, that the formula of whitening matrix W only used 1~N number of characteristic value and its feature vector.
In the present embodiment, whitening matrix W is indicated are as follows:
Wherein, σ2For the average value of rear M-N lesser characteristic values after descending arrangement, the conjugation of subscript H representing matrix
Transposition.
In the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency, further, frequency division when passing through sky
The four-matrix that cloth converts indicates are as follows:
Wherein, DZZ(t, f) is two-dimensional covariance matrix, representation space information；Each elementIt is one two
The time-frequency matrix of dimension,Indicate i-th of signal ZiWith j-th of signal ZjMutual time-frequency distributions；T indicates the time；F is indicated
Frequency.
In the present embodiment, the pretreated signal of whitening carries out spacial time-frequency distribution and converts to obtain four-matrix DZZ(t, f),
Concrete form are as follows:
In the present embodiment, DZZ(t, f) is two-dimensional covariance matrix, wherein each elementIt is one two again
The time-frequency matrix of dimension is eventually exhibited as a four-dimensional form.
In the present embodiment, according to obtained four-matrix DZZ(t, f) extracts from reference point, and to the auto-correlation of extraction point
Eigenvalues Decomposition is carried out, realizes Signal parameter estimation using subspace method.
It is further, described to four-dimensional square in the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency
The time-frequency two-dimensional information of battle array is filtered, and is extracted list from reference point and is included:
Pass through relational expressionExtract the auto-correlation point comprising all primary user's signals, wherein ε2
To extract threshold value, the mark of trace () representing matrix；
By cluster, the auto-correlation point of all primary user's signals extracted is divided into N class, certain one kind represents a certain signal
Auto-correlation point, obtain the K of N class list source signaliA auto-correlation pointWherein, i=1,2 ..., N, ki=1,2 ...,
Ki。
In the present embodiment, ε2It is the close but positive real number less than 1, it is related with noise, come from phase it is possible thereby to extract
Guan Dian.
In the present embodiment, KiValue be every one kind in the N class extracted by all of above extraction step time frequency point
Number, be not a determining value, method each run can all generate different results.
In the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency, further, described pair is obtained
List is obtained from reference point progress Joint diagonalization
To obtained N class auto-correlation pointThe two-dimensional space information of four-matrix is extracted, constitutes signal when corresponding
Correlation matrix on frequency point
To obtained correlation matrixJoint diagonalization processing is carried out, acquiring makes
The smallest N × N-dimensional unitary matrice U, wherein I is unit matrix, and off () is the quadratic sum of matrix off-diagonal element；
Steering vector estimation is carried out using the unitary matrice U that Joint diagonalization obtains:
Wherein, W#For the pseudoinverse of whitening matrix,For steering vector.
In the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency, further, the basis is obtained
Steering vector, determine that the angle of arrival of each primary user's signal includes:
According to obtained steering vector, using multiple signal classification (Multiple Signal Classification,
MUSIC the angle of arrival of each primary user's signal) is determined.
In the specific embodiment of the three-dimensional frequency spectrum sensing method of aforementioned Space-Time-frequency, further, the basis is obtained
Steering vector, the angle of arrival for determining each primary user's signal using multiple signal classification includes:
It is rightSeek its covariance matrix:
Wherein, RAAFor the covariance matrix of M × M dimension；
By RAAFeature vector be divided into two parts by the size of characteristic value: UsAnd Un, wherein Us·Us H+Un·Un H=I, Us
=[U1,U2,...,UN] it is the unitary space that the corresponding feature vector of top n maximum eigenvalue is constituted, it opens into signal subspace；Un
=[UN+1,...,UM] it is the unitary matrice that the corresponding feature vector of rear M-N minimal eigenvalue is constituted, it opens into noise subspace；
Pass throughTo the angle of primary user's signal
Estimated, obtains the angle of arrival of each primary user's signal.
The three-dimensional frequency spectrum sensing method of Space-Time described in embodiment-frequency for a better understanding of the present invention, it is specific with one
Embodiment is described, as shown in Fig. 2, N=2 is set, M=6:
Step 1: N number of primary user emit from different directions signal (assuming that: primary user 1 emit signal 1, primary user 2 send out
Signal 2 is penetrated), and M cognitive user end is reached, angle of arrival is respectively -2 ° and 2 °, and the instantaneous frequency of two signals is respectively
1e9 and 2e9, signal-to-noise ratio are -5dB.
In the present embodiment, by 2 " statistically " independent signals, observe 6 include white noise mixed signals.
In the present embodiment, N number of primary user emits signal from different directions, and reaches a cognitive user end M (M > N), arrives
It is respectively θ up to angle1,θ2,...,θN.It is irrelevant between each primary user's signal, and mutual not phase between primary user's signal and noise
It closes.The signal received is sent to decision center by each cognitive user, forms receipt signal matrix Y=[Y by decision center1,
Y2,...,YM]T。
Step 2: carrying out whitening pretreatment to receipt signal matrix, the specific steps are as follows:
A21 carries out Eigenvalues Decomposition to the covariance matrix for receiving signal, if [λ1,...,λM] it is the feature that descending arranges
Value, [h1,...,hM] it is corresponding feature vector, σ2For the average value of rear M-N lesser characteristic values of the covariance matrix.
Define whitening matrix W are as follows:
Wherein, subscript H is the conjugate transposition of matrix.
A22, then signal after albefaction are as follows:
Z=WY
Wherein, Z=[Z1,Z2,...,ZN]T。
It converts to obtain four-matrix D Step 3: the pretreated signal of whitening carries out spacial time-frequency distributionZZ(t, f), specifically
Form are as follows:
Wherein, wherein DZZ(t, f) is two-dimensional covariance matrix, representation space information；Each elementIt is
One two-dimensional time-frequency matrix,Indicate i-th of signal ZiWith j-th of signal ZjMutual time-frequency distributions；When t is indicated
Between；F indicates frequency.
Step 4: the time-frequency two-dimensional information to four-matrix is filtered, extracts list and be originated from reference point, obtain each primary
The accurate time-frequency distributions of family signal, the specific steps are as follows:
A41, extracting the auto-correlation point process comprising all primary user's signals can indicate to extract the time frequency point for meeting following formula:
Wherein, the mark of trace () representing matrix, ε2It is the close but positive real number less than 1, it is related with noise, by
This can extract auto-correlation point.
The auto-correlation point of all primary user's signals is divided into N class by clustering method by A42, certain one kind represents a certain signal
Auto-correlation point, obtain the K of N class list source signaliA auto-correlation pointWherein i=1,2 ..., N, ki=1,2 ...,
Ki。
Step 5: the four-matrix Jing Guo filtration treatment, which is carried out Joint diagonalization, obtains the guiding arrow for being used for angle estimation
Amount estimation, the specific steps are as follows:
A51, to obtained N class auto-correlation pointExtract four-matrix DZZTwo-dimensional space information, constitute corresponding
Correlation matrix in time frequency point
A52, to what is obtainedJoint diagonalization processing is carried out, that is, acquiring makes the smallest N of following formula × N-dimensional tenth of the twelve Earthly Branches square
Battle array U:
Wherein, I is unit matrix, and off () is the quadratic sum of matrix off-diagonal element.
A53 carries out steering vector estimation using the unitary matrice U that Joint diagonalization obtains:
Wherein, W#For the pseudoinverse of whitening matrix,For steering vector.
Step 6: gained steering vector to be estimated to the angle estimation for being acquired signal using MUSIC method, specific steps are such as
Under:
A 61 is rightSeek its covariance matrix:
Wherein, RAAFor the covariance matrix of M × M dimension；
A 62, by RAAFeature vector be divided into two parts by the size of characteristic value: UsAnd Un, wherein Us·Us H+Un·Un H
=I, Us=[U1,U2,...,UN] it is the unitary space that the corresponding feature vector of top n maximum eigenvalue is constituted, it opens into signal subspace sky
Between；Un=[UN+1,...,UM] it is the unitary matrice that the corresponding feature vector of rear M-N minimal eigenvalue is constituted, it opens empty at noise
Between；
A63 is estimated using angle of the following formula to primary user's signal:
In the embodiment of the present invention, Fig. 3 and Fig. 4 are described and are collected signal from phase to the time-frequency two dimensions of information of four-matrix
The filtration treatments such as Guan Dian, cluster extract list and are originated from reference point, obtain the accurate time-frequency estimation of two primary user's signals.Fig. 3 and figure
Abscissa in 4 is temporal operator (s), and ordinate is the instantaneous frequency of signal, should be the result is that being 2 in signal number, cognition is used
Family end is 6, and snapshot number is 1000, time interval 1s, and the frequency of two signals is respectively that the conditions such as 1e9HZ and 2e9HZ repeat
What experiment obtained.As Fig. 3 and Fig. 4 it is known that the time-frequency figure that the embodiment of the present invention obtains not only reduces present in system
The problems such as noise, and obtained better performance.This illustrates that method proposed by the present invention is comprehensive and effective.Together
When, the method for as a result also indicating that the three-dimensional frequency spectrum perception research of Space-Time provided in an embodiment of the present invention-frequency has excellent performance.
Fig. 5 and Fig. 6, which is described, passes through the side such as Joint diagonalization, MUSIC using single STFD matrix for being originated from reference point composition
Method obtains the angle of each signal.Abscissa in Fig. 5 and Fig. 6 is azimuth, and ordinate is the peak value of signal, should the result is that
Signal number is 2, and cognitive user end is 6, and snapshot number is 1000, time interval 1s, the angles of two signals be respectively -2 ° and
The conditions such as 2 ° repeat what experiment obtained.By Fig. 5 and Fig. 6 it is known that the obtained angle figure of the present invention not only peak value is more sharp,
And resolution ratio is greatly improved.This illustrates that method proposed by the present invention is comprehensive and effective.Meanwhile as a result also table
The method of the three-dimensional frequency spectrum perception research of bright Space-Time provided in an embodiment of the present invention-frequency has excellent performance.
To sum up, greater concentration of auto-correlation point can be obtained during frequency spectrum perception using the method for the design, reduce
The influence of noise, while the resolution ratio of signal is improved, mainly there is following advantage:
1) using STFD matrix can include simultaneously space, the time, three dimensions of frequency spectrum information；
2) filtration treatments such as signal autocorrelation point, cluster, Neng Gouti are collected to the time-frequency two dimensions of information of four-matrix
The auto-correlation point for taking out each signal, reduces the operand of angle calculation；Can effectively it press down in white Gaussian noise simultaneously
The interference of cross term processed and noise have higher time-frequency convergence property；
3) the steady of algorithm is enhanced come processing space time-frequency matrix using Joint diagonalization and classics MUSIC method
Property, direction of arrival (Direction Of Arrival, the DOA) estimated capacity of algorithm under Low SNR is improved, together
When improve the resolution ratio and accuracy of angle.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, without departing from the principles of the present invention, several improvements and modifications can also be made, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (10)
1. a kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency characterized by comprising
Whitening pretreatment is carried out to primary user's signal that multiple cognitive users receive, wherein between each primary user's signal mutually not
It is related；
The pretreated signal of whitening carries out spacial time-frequency distribution and converts to obtain four-matrix, wherein the information in four-matrix
It include: time, frequency and two-dimensional space information；
The time-frequency two-dimensional information of four-matrix is filtered, is extracted single from reference point, wherein single source refers to each independent master
Subscriber signal source；
Reference point progress Joint diagonalization is originated to obtained list to obtain estimating for the steering vector of angle estimation；
According to obtained steering vector, the angle of arrival of each primary user's signal is determined.
2. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 1-frequency, which is characterized in that described to use multiple cognitions
Primary user's signal that family receives carries out whitening pretreatment
Primary user's signal that multiple cognitive users are received forms receipt signal matrix；
Whitening pretreatment is carried out to receipt signal matrix.
3. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 2-frequency, which is characterized in that described to use multiple cognitions
Primary user's signal that family receives forms receipt signal matrix
N number of primary user emits signal from different directions, reaches M cognitive user end, wherein M > N；
The signal that M cognitive user is received forms receipt signal matrix Y=[Y1,Y2,...,YM]T, wherein subscript T is indicated
The transposition of matrix.
4. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 3-frequency, which is characterized in that described pair of reception signal square
Battle array carries out whitening pretreatment
Eigenvalues Decomposition is carried out to the covariance matrix of receipt signal matrix Y；
According to Eigenvalues Decomposition as a result, building whitening matrix W；
Whitening matrix W dot product receipt signal matrix Y, obtains whitened signal Z=WY, wherein Z=[Z1,Z2,...,ZN]T。
5. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 4-frequency, which is characterized in that described according to characteristic value point
Solution is as a result, building whitening matrix W includes:
Descending arrangement is carried out to obtained characteristic value, the characteristic value [λ after obtaining descending arrangement1,...,λM] and its corresponding spy
Levy vector [h1,...,hM]；
From M characteristic value after descending arrangement, take preceding 1~N number of characteristic value and its feature vector be used to construct whitening matrix W,
Wherein, whitening matrix W is indicated are as follows:
Wherein, σ2For the average value of rear M-N lesser characteristic values after descending arrangement, the conjugate transposition of subscript H representing matrix.
6. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 5-frequency, which is characterized in that become by spacial time-frequency distribution
The four-matrix got in return indicates are as follows:
Wherein, DZZ(t, f) is two-dimensional covariance matrix, representation space information；Each elementIt is one two-dimensional
Time-frequency matrix,Indicate i-th of signal ZiWith j-th of signal ZjMutual time-frequency distributions；T indicates the time；F indicates frequency
Rate.
7. Space-Time according to claim 6-the three-dimensional frequency spectrum sensing method of frequency, which is characterized in that it is described to four-matrix
Time-frequency two-dimensional information is filtered, and is extracted list from reference point and is included:
Pass through relational expressionExtract the auto-correlation point comprising all primary user's signals, wherein ε2To extract
Threshold value, the mark of trace () representing matrix；
By cluster, the auto-correlation point of all primary user's signals extracted is divided into N class, certain one kind represents oneself of a certain signal
Reference point obtains Ki auto-correlation point of N class list source signalWherein, i=1,2 ..., N, ki=1,2 ..., Ki。
8. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 7-frequency, which is characterized in that described pair of obtained single source
Auto-correlation point progress Joint diagonalization obtains
To obtained N class auto-correlation pointThe two-dimensional space information of four-matrix is extracted, constitutes signal in corresponding time frequency point
On correlation matrix
To obtained correlation matrixJoint diagonalization processing is carried out, acquiring makes
The smallest N × N-dimensional unitary matrice U, wherein I is unit matrix, and off () is the quadratic sum of matrix off-diagonal element；
Steering vector estimation is carried out using the unitary matrice U that Joint diagonalization obtains:
Wherein, W#For the pseudoinverse of whitening matrix,For steering vector.
9. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 8-frequency, which is characterized in that the basis obtained leads
To vector, determine that the angle of arrival of each primary user's signal includes:
According to obtained steering vector, the angle of arrival of each primary user's signal is determined using multiple signal classification.
10. the three-dimensional frequency spectrum sensing method of Space-Time according to claim 9-frequency, which is characterized in that the basis obtained
Steering vector, the angle of arrival for determining each primary user's signal using multiple signal classification include:
It is rightSeek its covariance matrix:
Wherein, RAAFor the covariance matrix of M × M dimension；
By RAAFeature vector be divided into two parts by the size of characteristic value: UsAnd Un, wherein Us·Us H+Un·Un H=I, Us=
[U1,U2,...,UN] it is the unitary space that the corresponding feature vector of top n maximum eigenvalue is constituted, it opens into signal subspace；Un=
[UN+1,...,UM] it is the unitary matrice that the corresponding feature vector of rear M-N minimal eigenvalue is constituted, it opens into noise subspace；
Pass throughThe angle of primary user's signal is carried out
Estimation, obtains the angle of arrival of each primary user's signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811124363.0A CN109274444B (en) | 2018-09-26 | 2018-09-26 | Space-time-frequency three-dimensional spectrum sensing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811124363.0A CN109274444B (en) | 2018-09-26 | 2018-09-26 | Space-time-frequency three-dimensional spectrum sensing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109274444A true CN109274444A (en) | 2019-01-25 |
| CN109274444B CN109274444B (en) | 2020-03-10 |
Family
ID=65198232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811124363.0A Active CN109274444B (en) | 2018-09-26 | 2018-09-26 | Space-time-frequency three-dimensional spectrum sensing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109274444B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109697758A (en) * | 2019-02-18 | 2019-04-30 | 中电科仪器仪表有限公司 | A kind of multi-view angle three-dimensional curved surface spectrogram display methods |
| CN111262617A (en) * | 2020-01-16 | 2020-06-09 | 南京邮电大学 | Spectrum sensing method based on cooperation of multiple low-orbit satellites |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101592721A (en) * | 2009-06-19 | 2009-12-02 | 中国人民解放军理工大学 | Method for estimating angle of arrival of coherent signal based on eigenvalue reconstruction |
| CN106291453A (en) * | 2016-09-18 | 2017-01-04 | 电子科技大学 | A kind of two-dimentional ripple based on beam space conversion reaches angle assay method |
-
2018
- 2018-09-26 CN CN201811124363.0A patent/CN109274444B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101592721A (en) * | 2009-06-19 | 2009-12-02 | 中国人民解放军理工大学 | Method for estimating angle of arrival of coherent signal based on eigenvalue reconstruction |
| CN106291453A (en) * | 2016-09-18 | 2017-01-04 | 电子科技大学 | A kind of two-dimentional ripple based on beam space conversion reaches angle assay method |
Non-Patent Citations (1)
| Title |
|---|
| 应武: "《时、空、频三维信号处理技术在雷达侦察中的应用》", 《硕士论文》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109697758A (en) * | 2019-02-18 | 2019-04-30 | 中电科仪器仪表有限公司 | A kind of multi-view angle three-dimensional curved surface spectrogram display methods |
| CN111262617A (en) * | 2020-01-16 | 2020-06-09 | 南京邮电大学 | Spectrum sensing method based on cooperation of multiple low-orbit satellites |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109274444B (en) | 2020-03-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107347210B (en) | A kind of precision target localization method based on channel state information | |
| Yousefi et al. | A survey on behavior recognition using WiFi channel state information | |
| CN108924736B (en) | PCA-Kalman-based passive indoor personnel state detection method | |
| CN103971124B (en) | Multi-class motor imagery brain electrical signal classification method based on phase synchronization | |
| CN106019214B (en) | Wide-band coherent signal source DOA estimation method | |
| CN106899968A (en) | A kind of active noncontact identity identifying method based on WiFi channel condition informations | |
| CN110197209A (en) | A kind of Emitter Recognition based on multi-feature fusion | |
| CN105472733B (en) | It is a kind of applied in indoor positioning based on AP selective positioning method | |
| CN104023395B (en) | The scattering signal source locating method changed in extensive MIMO based on beam space | |
| CN109274444A (en) | A kind of three-dimensional frequency spectrum sensing method of Space-Time-frequency | |
| CN108459706A (en) | Wi-Fi gesture identification methods based on relative movement orbit tracking | |
| CN110062379B (en) | Identity authentication method based on channel state information under human behavior scene | |
| CN109497990A (en) | A kind of electrocardiosignal personal identification method and system based on canonical correlation analysis | |
| CN109375154A (en) | Coherent signal method for parameter estimation based on uniform circular array under a kind of impulsive noise environment | |
| CN109375152A (en) | The DOA and polarization combined estimation method of L gusts of electromagnetic vector nesting lower low complex degrees | |
| CN106226729B (en) | Relatively prime array direction of arrival angle method of estimation based on fourth-order cumulant | |
| CN105979485A (en) | Personnel detection method in indoor environment based on channel state information (CSI) | |
| Chen et al. | Cross-domain wifi sensing with channel state information: A survey | |
| Sabek et al. | MonoStream: A minimal-hardware high accuracy device-free WLAN localization system | |
| Zhuang et al. | A multi-antenna spectrum sensing scheme based on main information extraction and genetic algorithm clustering | |
| Kong et al. | Multiauth: Enable multi-user authentication with single commodity wifi device | |
| CN106230544A (en) | The monitoring identification of a kind of automobile remote-control interference signal and localization method | |
| He et al. | Imar: Multi-user continuous action recognition with wifi signals | |
| Yu et al. | SoDar: Multitarget gesture recognition based on SIMO Doppler radar | |
| CN105184264B (en) | Radio-frequency fingerprint feature extracting method based on Wigner diagonal slice spectrum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |