CN109061556A - A kind of sparse iteration direction of arrival estimation method based on elastomeric network - Google Patents
A kind of sparse iteration direction of arrival estimation method based on elastomeric network Download PDFInfo
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- CN109061556A CN109061556A CN201811065197.1A CN201811065197A CN109061556A CN 109061556 A CN109061556 A CN 109061556A CN 201811065197 A CN201811065197 A CN 201811065197A CN 109061556 A CN109061556 A CN 109061556A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The present invention relates to signal processing technology field, the estimation method of especially a kind of sparse iteration direction of arrival based on elastomeric network.The specific steps of method include: the Nonparametric signal model that signal processing unit constructs array according to the quantity of signal to be estimated, and calculate initial covariance matrix and power matrix；The equation for establishing covariance fit standard goes out equation to be optimized in conjunction with elastomeric network model inference；The optimal covariance matrix for meeting precision is calculated by iterative algorithm, then calculates corresponding space spectrum density with Capon spectral method, and the DOA estimate value of signal to be estimated is obtained by spectrum peak search.The interference that signal variable is chosen can be evaded using method of the present invention, the case where being greater than number of sensors to the quantity of echo signal can guarantee enough precision, also there is very high resolution ratio to signal similar in direction of arrival, meets direction finding demand of each user under high complex electromagnetic environment.
Description
Technical field
The present invention relates to signal processing technology field, especially a kind of sparse iteration direction of arrival based on elastomeric network is estimated
Meter method.
Background technique
Currently, high interference degree, the electromagnetic environment of high complexity is to radar direction finding system with the fast development of information technology
Requirement in terms of precision and real-time processing speed is higher and higher, and traditional direction of arrival estimation method can no longer meet practical need
It asks, and these problems can just be efficiently solved by introducing sparse restructing algorithm.
The main method of sparse signal reconfiguring includes: 1, lpNorm optimization algorithm, such as base back tracking method；2, greedy algorithm, such as
Orthogonal matching pursuit method；3, management loading algorithm；4, compression sampling match tracing/subspace tracing algorithm etc..This hair
The sparse iteration DOA estimate algorithm based on covariance involved in bright belongs to the first lpThe improvement of norm optimization algorithm is calculated
Method, with the convex preferable global convergence for optimizing and having.
In the key technology research that sparse signal reconfiguring algorithm solves the problems, such as DOA estimate, estimation essence how is improved
The electromagnetic environment for spending, being suitble to high complexity is one of its emphasis, and utilizes elastomeric network model to penalty term in convex optimization method
Constraint be able to ascend resolution ratio between unlike signal and improve estimated accuracy.The introducing of elastomeric network can will avoid signal from becoming
Interference in amount selection, in the constraint to signal variable, generally using the form of two kinds of norms mixing, traditional elastomeric network
Used is l1Norm and l2Norm not only has l in this way1The sparsity of regularization has combined the change of selection signal correlation
The ability of amount.The present invention is based on the existing sparse iteration DOA estimate algorithms of elastomeric network model refinement, can solve well
Certainly close inter-signal interference problem and the inadequate problem of estimated accuracy, meet the direction finding demand of most users.
Summary of the invention
The present invention provides a kind of sparse iteration direction of arrival estimation method based on elastomeric network.This method is mainly for high-precision
Degree ground estimation arrival bearing, avoids the interference of clutter and close signal, meets the direction finding demand of most users.
To realize above-mentioned technical goal, the present invention provides a kind of sparse iteration DOA estimate side based on elastomeric network
Method, this method are used for the real-time detection of multi signal under high complexity electromagnetic environment, and the signal processing unit of the direction-finding system includes:
Sensor receiving module, to collect the electromagnetic signal of each arrival bearing；
Signal processing module, for the processing to acquisition signal data, the mainly calculating and iterative cycles of matrix.With full
Football association's variance fit standard is target, and guarantees the relative equilibrium of fidelity and degree of rarefication between each signal, realizes accurate direction finding
Function.
This method comprises the following steps:
(a) sensor receiving module collects the electromagnetic signal under current environment, and is translated into digital signal, i.e. vector
Form, and input signal processing module is handled；
(b) signal processing module is established according to collected signal vector and carrys out wave pattern accordingly, and calculates initial association side
Poor matrix；
(c) signal processing module establishes covariance fit standard using received signal vector, and calculates initial signal
Weight matrix and noise weight matrix derive corresponding convex optimization side by covariance fit standard in conjunction with elastomeric network model
Journey；
(d) whether the signal parameter of detection at this time meets Stopping criteria, and new antithesis is set if being unsatisfactory for criterion
Variable, and signal variable and noise variance are updated according to algorithm；
(e) step (d) is repeated, until meeting Stopping criteria in the case where setting accuracy error allows, then stopped
Iteration exports signal power matrix and covariance matrix at this time；
(f) corresponding space spectrum density is calculated according to Capon algorithm, by spectrum peak search, finds the corresponding sky of spectral peak
Between position the angle value to arrival bearing, complete direction finding.
The combination elastomeric network of the step (c) establish convex optimization method specifically include derive covariance fit standard etc.
Valence formula, and the minimum value of the formula is solved, it is translated into optimization method.Then according to elastomeric network the characteristics of, setting two
The penalty of kind normal form guarantees that data fidelity and parameter degree of rarefication balance, the value range of two norm orders are equal
For the arbitrary number greater than 1, the best class value of effect in emulation experiment can be set as here according to demand, and be not limited to l1
Norm and l2Norm.Signal power matrix is separately handled with noise power matrix simultaneously, is subject to different weights and is constrained.
Whether the detection signal parameter of the step (d), which meets Stopping criteria, specifically includes estimated power square
Battle array substitutes into the model for receiving signal and calculates covariance matrix, calculate this covariance matrix with produced by a upper iteration before
Covariance matrix each element between mean square deviation, detect the error amount whether this error is less than setting, set up if being less than, it is full
Sufficient stop criterion.
The update signal variable and noise variance of the step (d) are specifically included according to algorithmic formula, from reception signal to
Amount calculates initial signal variable and noise variance.The optimal solution and covariance square of convex optimization method are acquired using gradient descent method
The relationship of battle array and signal power spectrum matrix, then by variables separation, the iteration for obtaining signal variable and noise variance updates public affairs
Formula.Noise Parameters have the index of not same order in the step (d), are solved using multi-parameter fitting method.
The Capon algorithm of the step (f), which specifically only refers to, calculates entire battle array using the covariance matrix of the signal estimated
The output of column obtains Capon Wave beam forming spectral curve, i.e. the space spectrum density of signal, finds angle value corresponding to each spectral peak
DOA estimate value is just obtained.
A kind of a kind of sparse iteration direction of arrival estimation method based on elastomeric network as described above, includes the following steps:
1. sensor receiving module collects the electromagnetic signal under current environment, and is translated into digital signal, mould is established according to signal
Type, it may be assumed that
Wherein,For the snap vector that t moment is observed,To be in direction of arrivalTarget letter
Number vector,It is for direction of arrivalSteering vector, M be snap sum；
Thus signal model calculates the covariance model of signal, it may be assumed that
Wherein,
2.
The characteristics of according to elastomeric network model, need to change the normal form of convex optimization method penalty term, and by signal variable and noise
Variable is separately handled, it may be assumed that
Weight matrix therein is defined as:
W=diag ([w1,…,wK])
Wσ=diag ([wK+1,…,wK+N])
3. with R (i)=AP (i) A*For Stopping criteria, determined, stops calculating entrance if meeting stop criterion
In next step, if being unsatisfactory for stop criterion, enter loop iteration process until meeting stop criterion, first update dual variable λ, make
For transition parameter, more new formula are as follows:
Then signal parameter p is updatedk(i), more new formula are as follows:
Final updating Noise Parameters σk(i), more new formula are as follows:
4. calculating space spectrum density, spatial spectrum equation according to Capon algorithm are as follows:
Wherein, functional image, that is, μ of space spectrum density and the functional image of θ.
Sparse iteration direction of arrival estimation method proposed by the present invention based on elastomeric network, it is distinctive to introduce elastomeric network
The form of norm is mixed to reinforce the constraint to signal variable, guarantees data fidelity and parameter degree of rarefication balance.It is this simultaneously
The case where method is greater than number of sensors to the quantity of echo signal can guarantee enough precision, to signal similar in direction of arrival
Also there is very high resolution ratio, meet the direction finding demand of most users.The method has comprehensively considered high complexity electromagnetism ring
The disturbed condition in border separately handles signal variable and noise variance, estimates its covariance matrix according to the sparsity feature of signal,
On the basis of guaranteeing to detect multiple echo signals simultaneously, high-precision DOA estimate is completed.
Detailed description of the invention
Fig. 1 is even linear array geometrical model in the method for the present invention；
Fig. 2 is Multivariate Linear array df system model in the method for the present invention；
Fig. 3 is the flow diagram of the method for the present invention.
Specific embodiment
The present invention is described in further detail with specific embodiment with reference to the accompanying drawing.
The present invention provides a kind of sparse iteration direction of arrival estimation method based on elastomeric network, this method is for high complicated
The real-time detection of multi signal under electromagnetic environment is spent, the signal processing unit of the direction-finding system includes:
Sensor receiving module, to collect the electromagnetic signal of each arrival bearing；
Signal processing module, for the processing to acquisition signal data, the mainly calculating and iterative cycles of matrix.With full
Football association's variance fit standard is target, and guarantees the relative equilibrium of fidelity and degree of rarefication between each signal, realizes accurate direction finding
Function.
This method comprises the following steps:
(a) sensor receiving module collects the electromagnetic signal under current environment, and is translated into digital signal, i.e. vector
Form, and input signal processing module is handled；
(b) signal processing module is established according to collected signal vector and carrys out wave pattern accordingly, and calculates initial association side
Poor matrix；
(c) signal processing module establishes covariance fit standard using received signal vector, and calculates initial signal
Weight matrix and noise weight matrix derive corresponding convex optimization side by covariance fit standard in conjunction with elastomeric network model
Journey；
(d) whether the signal parameter of detection at this time meets Stopping criteria, and new antithesis is set if being unsatisfactory for criterion
Variable, and signal variable and noise variance are updated according to algorithm；
(e) step (d) is repeated, until meeting Stopping criteria in the case where setting accuracy error allows, then stopped
Iteration exports signal power matrix and covariance matrix at this time；
(f) corresponding space spectrum density is calculated according to Capon algorithm, by spectrum peak search, finds the corresponding sky of spectral peak
Between position obtain the angle value of arrival bearing, complete direction finding.
The combination elastomeric network of the step (c) establish convex optimization method specifically include derive covariance fit standard etc.
Valence formula, and the minimum value of the formula is solved, it is translated into optimization method.Then according to elastomeric network the characteristics of, setting two
The penalty of kind normal form guarantees that data fidelity and parameter degree of rarefication balance, the value range of two norm orders are equal
For the arbitrary number greater than 1, the best class value of effect in emulation experiment can be set as here according to demand, and be not limited to model
Several and norm.Signal power matrix is separately handled with noise power matrix simultaneously, is subject to different weights and is constrained.
Whether the detection signal parameter of the step (d), which meets Stopping criteria, specifically includes estimated power square
Battle array substitutes into the model for receiving signal and calculates covariance matrix, calculate this covariance matrix with produced by a upper iteration before
Covariance matrix each element between mean square deviation, detect the error amount whether this error is less than setting, set up if being less than, it is full
Sufficient stop criterion.
The update signal variable and noise variance of the step (d) are specifically included according to algorithmic formula, from reception signal to
Amount calculates initial signal variable and noise variance.The optimal solution and covariance square of convex optimization method are acquired using gradient descent method
The relationship of battle array and signal power spectrum matrix, then by variables separation, the iteration for obtaining signal variable and noise variance updates public affairs
Formula.
The Capon algorithm of the step (f), which specifically only refers to, calculates entire battle array using the covariance matrix of the signal estimated
The output of column obtains Capon Wave beam forming spectral curve, i.e. the space spectrum density of signal, finds angle value corresponding to each spectral peak
DOA estimate value is just obtained.
In conjunction with attached drawing, the present invention program is designed and makees further concrete analysis and description.
During the foundation of the step (b) carrys out wave pattern, definition is the uniform of K narrow band signal incidence N member
The expression formula of linear array, this model is as follows:
WhereinFor the snap vector that t moment is observed,To be in direction of arrivalEcho signal
Vector,It is for direction of arrivalSteering vector, M be snap sum.
Thus signal model calculates the covariance model of signal, it may be assumed that
Wherein,
In the step (c) during deriving convex optimization method by establishing covariance fit standard, according to elasticity
It the characteristics of network model, needs to change the normal form of convex optimization method penalty term, and signal variable and noise variance is separated
Processing, i.e.,
Weight matrix therein is defined as:
W=diag ([w1,…,wK])
Wσ=diag ([wK+1,…,wK+N])
Refer in the Stopping criteria of the step (d):
R (i)=AP (i) A*
The formula sets up the error met and refers to that every mean square deviation of both sides matrix is less than or equal to setting error.
If being unsatisfactory for stop criterion, enter loop iteration process, first updates dual variable λ, as transition parameter, more
New formula are as follows:
Then signal parameter p is updatedk(i), more new formula are as follows:
Final updating Noise Parameters σk(i), more new formula are as follows:
Here Noise Parameters have the index of not same order, so can not directly solve, are asked here using multi-parameter fitting method
Solution.
Space spectrum density, spatial spectrum equation are calculated according to Capon algorithm in the step (f) are as follows:
Functional image, that is, μ of space spectrum density and the functional image of θ.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, there can be various modifications and variations in the spirit and principles in the present invention, these equivalent variation or replacement etc.,
It is all included in the scope of protection of the present invention.
Claims (8)
1. a kind of sparse iteration direction of arrival estimation method based on elastomeric network, characterized by the following steps:
(a) sensor receiving module collects the electromagnetic signal under current environment, and is translated into digital signal, i.e. vector shape
Formula, and input signal processing module is handled；
(b) signal processing module is established according to collected signal vector and carrys out wave pattern accordingly, and calculates initial covariance square
Battle array；
(c) signal processing module establishes covariance fit standard using received signal vector, and calculates initial signal weight
Matrix and noise weight matrix derive corresponding convex optimization method by covariance fit standard in conjunction with elastomeric network model；
(d) whether detection signal parameter at this time meets Stopping criteria, if being unsatisfactory for criterion, setting it is new to mutation
Amount, and signal variable and noise variance are updated according to algorithm；
(e) step (d) is repeated, until meeting Stopping criteria in the case where setting accuracy error allows, then stops iteration,
The signal power matrix and covariance matrix of output at this time；
(f) corresponding space spectrum density is calculated according to Capon algorithm, by spectrum peak search, finds the corresponding space bit of spectral peak
It sets to obtain the angle value of arrival bearing, completes direction finding.
2. a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, it is characterised in that:
The step (c) includes the equivalence formula for deriving covariance fit standard, and solves the minimum value of the formula, is translated into excellent
Change equation, then according to elastomeric network the characteristics of, set the penalty of two kinds of normal forms, guarantee data fidelity and parameter
Degree of rarefication balance, the value range of two norm orders is the arbitrary number greater than 1, is set as imitating in emulation experiment according to demand
The best class value of fruit, and it is not limited to norm and norm, signal power matrix is separately handled with noise power matrix, is subject to
Different weights are constrained.
3. a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, it is characterised in that:
Whether detection signal parameter meets Stopping criteria in the step (d), receives including substituting into estimated power matrix
Covariance matrix is calculated in the model of signal, calculates this covariance matrix and covariance square caused by a upper iteration before
Mean square deviation between battle array each element, detects the error amount whether this error is less than setting, sets up if being less than, and it is quasi- to meet termination
Then.
4. a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, it is characterised in that: described
Signal variable is updated in step (d) and noise variance includes that initial signal is calculated by received signal vector according to algorithmic formula
Variable and noise variance acquire the optimal solution and covariance matrix and signal power spectral moment of convex optimization method using gradient descent method
The relationship of battle array obtains the iteration more new formula of signal variable and noise variance then by variables separation.
5. a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, it is characterised in that: described
Capon algorithm refers to that the covariance matrix using the signal estimated calculates the output of entire array in step (f), obtains Capon
Wave beam forming spectral curve, i.e. the space spectrum density of signal, find angle value corresponding to each spectral peak and have just obtained DOA estimate
Value.
6. a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, it is characterised in that: in institute
State step (c) by establishing during covariance fit standard derives convex optimization method, according to the spy of elastomeric network model
Point needs to change the normal form of convex optimization method penalty term, and signal variable is separately handled with noise variance, i.e.,
Weight matrix therein is defined as:
W=diag ([w1,…,wK])
Wσ=diag ([wK+1,…,wK+N])
7. a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, it is characterised in that:
Noise Parameters have the index of not same order in the step c, are solved using multi-parameter fitting method.
8. -7 any a kind of sparse iteration direction of arrival estimation method based on elastomeric network according to claim 1, special
Sign is, comprising the following steps:
1. sensor receiving module collects the electromagnetic signal under current environment, and is translated into digital signal, built according to signal
Formwork erection type:
Wherein,For the snap vector that t moment is observed,To be in direction of arrivalEcho signal to
Amount,It is for direction of arrivalSteering vector, M be snap sum；
Thus signal model calculates the covariance model of signal, it may be assumed that
Wherein,
2. the characteristics of according to elastomeric network model, need to change the normal form of convex optimization method penalty term, and by signal variable
It is separately handled with noise variance, it may be assumed that
Weight matrix therein is defined as:
W=diag ([w1,…,wK])
Wσ=diag ([wK+1,…,wK+N])
3. with R (i)=AP (i) A*For Stopping criteria, determined, if meeting stop criterion, stops calculating into next
Step, if being unsatisfactory for stop criterion, more new data enters loop iteration process until meeting stop criterion, first updates dual variable
λ, as transition parameter, more new formula are as follows:
Then signal parameter p is updatedk(i), more new formula are as follows:
Final updating Noise Parameters σk(i), more new formula are as follows:
4. calculating space spectrum density, spatial spectrum equation according to Capon algorithm are as follows:
Wherein, functional image, that is, μ of space spectrum density and the functional image of θ.
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| US11906651B2 (en) | 2021-02-25 | 2024-02-20 | Nxp B.V. | Radar-based detection using sparse array processing |
| US11927664B2 (en) | 2021-02-25 | 2024-03-12 | Nxp B.V. | Radar-based detection using angle of arrival estimation based on sparse array processing |
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| US11927664B2 (en) | 2021-02-25 | 2024-03-12 | Nxp B.V. | Radar-based detection using angle of arrival estimation based on sparse array processing |
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