CN109600152A - A kind of Adaptive beamformer method based on the transformation of subspace base - Google Patents
A kind of Adaptive beamformer method based on the transformation of subspace base Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
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- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
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Abstract
The present invention relates to a kind of Adaptive beamformer methods based on the transformation of subspace base, data covariance matrix progress feature decomposition will be received and obtain signal subspace, and under the premise of unknown signaling orientation, one group of reception signal array manifold matrix is constructed from the mathematical model of element position mismatch.Using subspace fitting technology, the present invention constructs the Combined estimator optimization problem of a sensor position uncertainties and aspect, and is solved using mature genetic algorithm.Using the array manifold matrix solved as one group of non-real intersection of signal subspace, the technology reengineering interference plus noise covariance matrix for recycling the base of subspace to convert finally obtains the weighing vector of Beam-former.Often there are sensor position uncertainties in the array solved in actual use, biggish sensor position uncertainties can greatly reduce the defect of existing Beam-former interference rejection capability.
Description
Technical field
The invention belongs to a kind of Beamforming Methods, are related to Adaptive beamformer field, and in particular to one kind is based on son
The Adaptive beamformer method of space base transformation, suitable for the airspace AF panel of linear array and airspace target detection etc.,
It is related to array signal processing field.
Background technique
Beam-forming technology in the past few decades in be rapidly developed, related ends have been widely used for sound
, the fields such as radar, wireless communication and Speech processing.Beam-forming technology includes the Wave beam forming side of fixed weight
Method (Fixed weighting beamforming method) and Adaptive beamformer method (adaptive
Beamforming method) etc., these technologies are common technological means in array signal processing.Due to fixed weight
Beamforming Method tend not to according to actual signal environment adjust Beam-former weight vector, anti-interference ability compared with
Difference, and Adaptive beamformer method can adjust weight effectively then to inhibit to interfere.
In the actual use environment, often there is steering vector mismatch caused by sensor position uncertainties, significantly reduce
The robustness of Beam-former, and the data received also tend to lead to traditional Capon Beam-former comprising desired signal
It is more sensitive to the mismatch of steering vector.In order to improve the robustness of adaptive beam former, recent domestic scholar is mentioned
Go out many robust ada- ptive beamformer devices, be summed up following a few classes:
1, " Robust Adaptive Beamforming, " the IEEE Transactions on of document 1 Acoustics
Speech and Signal Processing, vol.35, no.10, pp.1365-1376, Oct, 1987. " and document 2
“Robust Capon beamforming,”IEEE Signal Processing Letters,vol.10,no.6,pp.172-
Diagonal loading method disclosed in 175, Jun, 2003. " and uncertain collection constrained procedure can preferably improve robustness, but right
Angle loading capacity and uncertain collection size are difficult to determines according to actual conditions, it is difficult to be actually used.
2, " the Robust Adaptive Beamforming Based on Interference Covariance of document 3
Matrix Reconstruction and Steering Vector Estimation,IEEE Trans.Signal
Process., interference plus noise covariance matrix is reconstructed disclosed in vol.60, no.7, pp.3881-3885, Jul.2012. "
(interference-plus-noise covariance matrix, INCM) algorithm can be effectively removed covariance matrix
In desired signal components, reconstructed to reduce Beam-former to the sensibility of desired signal steering vector error, but simultaneously
INCM in there may be the steering vector errors of interference, especially sensor position uncertainties, to reduce interference rejection capability.
3, " the Robust Adaptive Beamforming with Sensor Position Errors Using of document 4
Weighted Subspace Fitting-Based Covariance Matrix Reconstruction,Sensors
(Basel), algorithm disclosed in vol.18, no.5, May 8.2018. " is by estimating and compensating sensor position uncertainties to mention
The robustness of the INCM of height reconstruct, but it requires the orientation of all signals substantially it is known that being difficult in actual use.
In conclusion the 1) class method directly using receiving data covariance matrix (Sample covariance
Matrix, SCM), in order to guarantee robustness, has to interference rejection capability and greatly sacrifice；2) class method can effectively ensure that
Robustness, but when there are sensor position uncertainties, interference rejection capability degradation；3) class method consider element position
Error, but it is unknown in aspect and when sensor position uncertainties are larger, and interference rejection capability is deteriorated.
Summary of the invention
Technical problems to be solved
In order to avoid the shortcomings of the prior art, the present invention proposes a kind of adaptive beam based on the transformation of subspace base
Forming method, under the premise of all senses are unknown, certainly the prior art when larger array element error, is done in facing linear array
Disturb the poor insufficient problem of rejection ability.
Technical solution
A kind of Adaptive beamformer method based on the transformation of subspace base, it is characterised in that steps are as follows:
Step 1: the sample covariance matrix that M member linear hydrophone array is receivedCarry out feature decomposition:
Wherein: λm(m=1 ..., M) is the characteristic value arranged according to descending order, vmIt is corresponding feature vector；
All larger list of feature values are shown as diagonal matrix DS=diag { [λ1,…,λQ+1]T, all larger characteristic values are corresponding
Feature vector be expressed as subspace matrices VS=[v1,…,vQ+1]；
The larger characteristic value is basisEstimated disturbance signal number is Q, in addition 1 signal number
It is so that the minimum positive integer that above formula is set up for Q+1, Q+1；The Q interference signal number arranged from large to small adds 1 signal
Number Q+1 forms larger characteristic value；
Step 2: calculating the weighting matrix B of subspace fitting
The matrixWhereinFor noise power, it is estimated asMinimal eigenvalue λM, I is
One (Q+1) × unit matrix of (Q+1)；
Step 3: construction receives the orthogonal intersection cast shadow matrix of signal array manifold matrix
The matrix AS=[a (θ0),a(θ1),…,a(θQ)],For ASGeneralized inverse matrix；
The steering vector is, in formulaFor the hypothesis element position vector of linear array, q=0,1 ..., Q；Take first battle array
Member is reference array element, error 0, de=[0, e2,…,eM]TFor sensor position uncertainties vector, θqThe corresponding true bearing of signal；
Step 4: defining solution space vector s=[0, e2,…,eM,θ0,…θQ]T, and construct following optimization problem
The optimization problem is solved using genetic algorithm, and obtains the estimated value of solution space vector:
It willIn corresponding value bring step 3 matrix A intoS=[a (θ0),a(θ1),…,a(θQ)] in, obtain all signal guides
The set matrix of vector
Step 5:Total Q+1 is estimated in obtained angle, and specified beams need the direction being directed toward, and by its
?In it is corresponding that column adjust toFirst row, it is final to guaranteeThe first guiding for being classified as beam position direction
Vector；
Step 6: define basic transformation matrices:
The interference covariance matrix of reconstructed reception data are as follows:
The DS=diag { [0,11×Q] it is a diagonal matrix, INCM is calculated by following formula:
Step 7: estimating the steering vector of desired signal are as follows:
It is describedFor matrixThe corresponding feature vector of maximum eigenvalue；
Step 8: beamformer weightings vector:
Beneficial effect
A kind of Adaptive beamformer method based on the transformation of subspace base proposed by the present invention, solves in actual use
Often there are sensor position uncertainties in array, biggish sensor position uncertainties can greatly reduce existing Beam-former AF panel
The defect of ability.The present invention will receive data covariance matrix progress feature decomposition and obtain signal subspace, and in unknown signaling
Under the premise of orientation, one group of reception signal array manifold matrix is constructed from the mathematical model of element position mismatch.Utilize son
Spatial fit technology, the present invention constructs the Combined estimator optimization problem of a sensor position uncertainties and aspect, and utilizes
Mature genetic algorithm is solved.Using the array manifold matrix solved as one group of non-real intersection of signal subspace, then benefit
The technology reengineering interference plus noise covariance matrix converted with the base of subspace, finally obtains the weighing vector of Beam-former.
The present invention passes through the true bearing of Combined estimator sensor position uncertainties and all signals, so that the guiding that estimation obtains
Vector setIt is with signal subspace fitting best,In can be seen as signal subspace one group of steering vector it is non-
Orthogonal basis.Pass through againThis Non-orthogonal basis set and VSThe base transformation of this group of orthogonal basis, adds interference covariance square in accurate signal
In battle array, removal signal component directly obtains interference covariance matrix and INCM, using leading when avoiding conventional method reconstruct INCM
To vector sum spatial spectrum airspace integrate or it is cumulative obtain INCM when bring error.This method can be in unknown all signal sides
To all unknown and there are when sensor position uncertainties, by beam position desired signal direction, and accurately inhibit other directions
Interference maximizes output Signal to Interference plus Noise Ratio.
Detailed description of the invention
Fig. 1: there are when sensor position uncertainties, output believes the dry SINR that makes an uproar than the variation with Signal to Noise Ratio (SNR)；
Fig. 2: Signal to Interference plus Noise Ratio SINR is exported with the variation of the sensor position uncertainties upper limit；
Fig. 3: there are when sensor position uncertainties, the wave beam response diagram of Beam-former.
Specific embodiment
Now in conjunction with embodiment, attached drawing, the invention will be further described:
The present invention is constructed using subspace fitting technology from the mathematical model of line array element position mismatch and solves one
The Combined estimator problem of a sensor position uncertainties and aspect, the final technology reengineering using the transformation of signal subspace base interfere
Plus noise covariance matrix obtains the weighing vector of Beam-former.
Emulation 1: the underwater velocity of sound is 1500m/s, considers 10 yuan of linear arrays, it is assumed that array element spacing be 0.5m, it is expected that
Signal hypothesis is located at 15 °, and signal-to-noise ratio change from -10dB to 20dB, and interference hypothesis is dry to make an uproar than making an uproar for 20dB positioned at -25 ° and 35 °
Sound is Gauss uniform white noise.Desired signal and interference signal are the irrelevant signal in narrowband that centre frequency is 1500Hz.Battle array
First location error is obeyed the zero-mean gaussian that variance is 0.01m and is distributed, and signal angle incidence angle error obeys the uniform of [- 2 °, 2 °]
Distribution, received data packet contain 30 snaps.
It in order to illustrate the statistic property of this method, emulates 200 times altogether, finally obtains and export newly dry make an uproar than with input signal-to-noise ratio
The statistical result of variation.For emulating every time, the following operating procedure of repetition this method is to obtain weighing vector:
Step 1: the sample covariance matrix that 10 yuan of linear hydrophone arrays are receivedFeature decomposition is carried out, and
It is expressed as form:
Wherein λm(m=1 ..., 10) is the characteristic value arranged according to descending order, vmIt is corresponding feature vector.
So thatThe minimum positive integer of establishment is 2, therefore the interference signal number estimated is 2.
All larger list of feature values are shown as diagonal matrix DS=diag { [λ1,…,λ3]T, all larger characteristic values are corresponding
Feature vector is expressed as subspace matrices VS=[v1,…,v3]。
Step 2: the weighting matrix B of subspace fitting is calculated, is obtained by following formula:
The matrixWhereinFor noise power, can usually be estimated asMinimal characteristic
Value λM, I is one 3 × 3 unit matrix.
Step 3: construction receives the orthogonal intersection cast shadow matrix of signal array manifold matrix, is obtained by following formula:
The matrix AS=[a (θ0),a(θ1),a(θ2)],For ASGeneralized inverse matrix.
Steering vector described in above formula isIn formulaFor the vacation of linear array
If element position vector；Taking first array element is reference array element, error 0, de=[0, e2,…,eM]TFor sensor position uncertainties
Vector, θ0The corresponding true bearing of signal.Other steering vectors can by with a (θ0) similarly make acquisition.
Step 4: defining solution space vector s=[0, e2,…,e10,θ0,…θ2]T, and construct following optimization problem:
Genetic algorithm can be used to solve for the optimization problem, and obtain the estimated value of solution space vector:
It willIn corresponding value bring into step 3, obtain the set matrix of all signal guide vectors
Step 5:Estimate in obtained angle for totally 3, specified beams need the direction being directed toward, and by itsIn it is corresponding that column adjust toFirst row, it is final to guaranteeFirst be classified as beam position direction guiding arrow
Amount；
Step 6: define following basic transformation matrices:
The interference covariance matrix of reconstructed reception data are as follows:
The DS=diag { [0,1,1]TIt is a diagonal matrix, INCM is calculated by following formula:
Step 7: estimating the steering vector of desired signal are as follows:
It is describedFor matrixThe corresponding feature vector of maximum eigenvalue.
Step 8: beamformer weightings vector is finally calculated by following formula:
It is average that Fig. 1 gives 200 Monte Carlo Experiments, and output believes the dry SINR that makes an uproar than the variation diagram with Signal to Noise Ratio (SNR).
As can be seen that the Beam-former of document 2 shows preferably under low signal-to-noise ratio, but very poor under high s/n ratio；The wave beam shape of document 3
It grows up to be a useful person due to the presence of sensor position uncertainties, performance is even lower than the Beam-former of document 2；Document 4 is due to aspect
Error leads to performance and optimal theoretical upper limit difference 3dB；And Beam-former of the invention, the closest theoretical upper limit of performance,
Possess it is high performance under the premise of, it is steady to keep.
Emulation 2: input signal-to-noise ratio is fixed as 10dB, allows sensor position uncertainties in (- ep,ep) in be uniformly distributed, wherein epFor
The coboundary of sensor position uncertainties.Remaining simulated conditions is similar with emulation 1.Output Signal to Interference plus Noise Ratio SINR is probed into element position
The situation of change of the error upper limit.
Fig. 2 gives output Signal to Interference plus Noise Ratio SINR with the variation diagram of the sensor position uncertainties upper limit, and λ is the wave of signal in figure
It is long.As can be seen that presence of the Beam-former of document 2 due to sensor position uncertainties, performance are poor always；With element position
The Beam-former performance of the increase of error, document 3 and document 4 sharply declines；And Beam-former output letter of the invention is dry
It makes an uproar more insensitive than for sensor position uncertainties, is better than other methods.
The present invention is described in detail by embodiment below:
Lake, which is had a try, to be tested: array number 9, and array element spacing is the linear array of 30cm.Sample frequency is 16kHz, three sound source hairs
The narrow band signal of 2490~2510Hz is penetrated respectively from 11 °, -33.1 °, -9.4 ° of three directions issue, wherein from 11 ° of signal
For desired signal.To meet covariance matrix inversion operation, using the data of 0.5s when estimate covariance matrix, it is calculated and adopts
Sample covariance matrix, since the element position of array itself is more accurate, we assume element position are as follows:
The element position that table 1 is assumed
Using the element position of hypothesis, one group of solution vector of estimation is obtained by calculationAnd then available steering vector
CollectionIt converts to obtain INCM by signal subspace base, finally calculates weighing vector w.It is calculated using actual element position
From -90 ° to 90 °, be divided into 1 ° of steering vector a (θ), and calculate actual beam response B (θ) of Beam-former=| | wHa
(θ)||2。
Fig. 3 gives wave beam response diagram of the invention in the examination experiment of lake, it can be seen that the present invention is at -33.1 ° and -9.4 °
Two interference radiating way form apparent groove, show extremely strong to the inhibiting effect of interference.
Claims (1)
1. a kind of Adaptive beamformer method based on the transformation of subspace base, it is characterised in that steps are as follows:
Step 1: the sample covariance matrix that M member linear hydrophone array is receivedCarry out feature decomposition:
Wherein: λm(m=1 ..., M) is the characteristic value arranged according to descending order, vmIt is corresponding feature vector；
All larger list of feature values are shown as diagonal matrix DS=diag { [λ1,…,λQ+1]T, the corresponding spy of all larger characteristic values
Sign vector is expressed as subspace matrices VS=[v1,…,vQ+1]；
The larger characteristic value is basisEstimated disturbance signal number is Q, in addition 1 signal number is Q+1,
Q+1 is so that the minimum positive integer that above formula is set up；The Q interference signal number arranged from large to small adds 1 signal number Q+1 group
At larger characteristic value；
Step 2: calculating the weighting matrix B of subspace fitting
The matrixWhereinFor noise power, it is estimated asMinimal eigenvalue λM, I is one
(Q+1) × (Q+1) unit matrix；
Step 3: construction receives the orthogonal intersection cast shadow matrix of signal array manifold matrix
The matrix AS=[a (θ0),a(θ1),…,a(θQ)],For ASGeneralized inverse matrix；
The steering vector is, in formulaFor the hypothesis element position vector of linear array, q=0,1 ..., Q；First array element is taken to be
Reference array element, error 0, de=[0, e2,…,eM]TFor sensor position uncertainties vector, θqThe corresponding true bearing of signal；
Step 4: defining solution space vector s=[0, e2,…,eM,θ0,θQ]T, and construct following optimization problem
The optimization problem is solved using genetic algorithm, and obtains the estimated value of solution space vector:
It willIn corresponding value bring step 3 matrix A intoS=[a (θ0),a(θ1),…,a(θQ)] in, obtain all signal guide vectors
Set matrix
Step 5:Total Q+1 is estimated in obtained angle, and specified beams need the direction being directed toward, and by itsIn
It is corresponding that column adjust toFirst row, it is final to guaranteeThe first steering vector for being classified as beam position direction；
Step 6: define basic transformation matrices:
The interference covariance matrix of reconstructed reception data are as follows:
The DS=diag { [0,11×Q] it is a diagonal matrix, INCM is calculated by following formula:
Step 7: estimating the steering vector of desired signal are as follows:
It is describedFor matrixThe corresponding feature vector of maximum eigenvalue；
Step 8: beamformer weightings vector:
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| CN111540371A (en) * | 2020-04-22 | 2020-08-14 | 深圳市友杰智新科技有限公司 | Method and device for beamforming microphone array and computer equipment |
| CN111585632A (en) * | 2020-04-29 | 2020-08-25 | 中国电子科技集团公司第五十四研究所 | Broadband self-adaptive beam forming method based on interference suppression model optimization |
| CN111610489A (en) * | 2020-05-27 | 2020-09-01 | 西北工业大学 | Random array super-directivity beam optimization method based on order adjustment |
| CN113422629A (en) * | 2021-06-17 | 2021-09-21 | 长安大学 | Covariance matrix reconstruction self-adaptive beam forming method and system |
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| CN111610489A (en) * | 2020-05-27 | 2020-09-01 | 西北工业大学 | Random array super-directivity beam optimization method based on order adjustment |
| CN113422629A (en) * | 2021-06-17 | 2021-09-21 | 长安大学 | Covariance matrix reconstruction self-adaptive beam forming method and system |
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