CN107846241B - Beam forming method, storage medium and beam former under impulse noise environment - Google Patents
Beam forming method, storage medium and beam former under impulse noise environment Download PDFInfo
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Abstract
The invention discloses a beam forming method, a storage medium and a beam former in an impulse noise environment, wherein the method comprises the following steps: acquiring sampling data acquired from an antenna array preset by a base station, performing outlier detection on the sampling data and removing abnormal data; recovering missing data in the sampled data by using a matrix filling technology, and solving a weighting vector by using the recovered sampled data; and carrying out weighted summation on the sampling data according to the solved weighted vector to obtain the output of beam forming. The beam forming method provided by the invention can effectively inhibit the influence of impulse noise, can realize high-precision measurement on data signals under the interference of the impulse noise, improves the robustness of beam forming, and is favorable for realizing higher output signal-to-interference-and-noise ratio.
Description
Technical Field
The invention relates to the technical field of adaptive beam forming, in particular to a beam forming method, a storage medium and a beam forming device under an impulse noise environment.
Background
Adaptive beamforming techniques are widely used in many fields, such as phased array radar systems, active sonar, seismology, medical imaging, and the like. The theory of adaptive beamforming technology still faces many practical problems from knowledge to engineering applications. Most theoretical work mainly discusses the premise that the environmental noise is assumed to be in accordance with gaussian distribution, and in real application, the environmental noise is often impulse noise due to various factors.
In the prior art, in order to improve the robustness of beam forming in an impulse noise environment, a beam former based on a fractional low order statistics (flo) theory can be used to solve a weighting vector. Although the method can achieve certain robustness in an impulse noise environment, it needs to acquire prior information such as probability distribution of impulse noise, which is usually difficult to obtain in practical application. Or may be beamformed using a linear constrained (lcnv) beamformer based on minimizing normalized variance: firstly, sampling data is subjected to self-adaptive infinite norm normalization, then a data covariance matrix is calculated, and a weight vector is solved. The method can effectively improve the output performance of beam forming, but cannot realize sufficient robustness under strong pulse noise interference, and reduces the measurement accuracy of the array signal processing system.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The present invention is directed to a method, a storage medium, and a beamformer for beamforming under impulse noise environment, which are provided to solve the above-mentioned drawbacks of the prior art, and aims to solve the problem that sufficient robustness cannot be achieved under impulse noise environment in the prior art.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method of beamforming in an impulse noise environment, wherein the method comprises:
acquiring sampling data acquired from an antenna array preset by a base station, performing outlier detection on the sampling data and removing abnormal data;
recovering missing data in the sampled data after abnormal data are removed by using a matrix filling technology, and solving a weighting vector by using a worst performance optimization algorithm;
and carrying out weighted summation on the sampling data according to the solved weighted vector to obtain the output of beam forming.
The method for forming the beam in the impulse noise environment, wherein the acquiring of the sampling data collected from the antenna array preset in the base station, the outlier detection of the sampling data, and the elimination of the abnormal data specifically include:
an antenna array for acquiring signals is established in advance at a base station;
a beam former acquires signals acquired by each array element in the antenna array, and the acquired signals are used as sampling data;
performing outlier detection on all the sampled data by using a Hampel algorithm to determine abnormal data; the abnormal data represents data contaminated by impulse noise;
and eliminating the abnormal data.
In the beam forming method under the impulse noise environment, the impulse noise in each array element in the antenna array is independent and uncorrelated.
The beam forming method under the impulse noise environment, wherein the outlier detection is performed on all the sampled data by using a Hampel algorithm, and the determining of the abnormal data specifically includes:
firstly, solving a modulus value of sampling data acquired by an array element;
respectively calculating the median and the absolute median difference of the module values of the sampled data in the array elements according to a Hampel algorithm;
calling a function rho (-) to determine the position of data polluted by impulse noise;
the ρ (-) function is defined as:
wherein, Xm(k) Representing the k-th sampled data, mu, on the m-th array elementkThe average value is taken as the median value,
for the absolute median difference, Γ is the threshold parameter.
The method for forming a beam in an impulse noise environment, wherein the removing the abnormal data specifically includes:
acquiring the position of data polluted by impulse noise in the sampling data;
the data at that location is set to zero.
The method for forming the beam under the impulse noise environment, wherein the recovering the missing data in the sampled data from which the abnormal data are removed by using the matrix filling technology, and the solving of the weighting vector by using the worst performance optimization algorithm specifically includes:
the beam former acquires the position of zero data in the sampled data;
performing data recovery on the position where the data after the abnormal data is removed is zero by using a matrix filling technology, and outputting the sampling data again;
and the beam former acquires the recovered sampling data again and solves the weighting vector by using a worst performance optimization algorithm.
The method for forming a beam in an impulse noise environment, where the recovering data from a position where data is zero by using a matrix filling technique specifically includes:
acquiring the number and the position of non-zero data in the sampling data;
modeling a data recovery problem as a matrix filling problem;
and solving the corresponding matrix filling problem by using a singular value threshold method and recovering complete sampling data.
The beam forming method under the impulse noise environment is characterized in that the weighting vector is obtained by solving through a worst performance optimization algorithm.
A computer readable storage medium having a computer program stored thereon, wherein the computer program is adapted to be loaded and executed by a processor to implement the method of beamforming in an impulse noise environment as defined in any of the above.
A beamformer, comprising: a processor, a storage device communicatively coupled to the processor, the storage device adapted to store a plurality of instructions; the processor is adapted to invoke instructions in the memory device to perform a method of beamforming in an impulse noise environment as described in any of the above.
The invention has the beneficial effects that: the beam forming method provided by the invention can effectively inhibit the influence of impulse noise, can realize high-precision measurement on data signals under the interference of the impulse noise, improves the robustness of beam forming, and is favorable for realizing higher output signal-to-interference-and-noise ratio.
Drawings
Fig. 1 is a flow chart of a beam forming method under impulse noise environment according to a first preferred embodiment of the present invention.
Fig. 2 is a diagram illustrating a first comparison effect between the beamforming method in an impulse noise environment of the present invention and the prior art.
Fig. 3 is a diagram illustrating a second comparison effect between the beamforming method in an impulse noise environment of the present invention and the prior art.
Fig. 4 is a functional block diagram of a preferred embodiment of the beamformer of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a flow chart of a beam forming method under impulse noise environment according to a first preferred embodiment of the present invention. The beam forming method under the impulse noise environment comprises the following steps:
step S100, acquiring sampling data acquired from an antenna array preset by a base station, performing outlier detection on the sampling data, and removing abnormal data.
Preferably, the step S100 specifically includes:
step S101, an antenna array for acquiring signals is established in a base station in advance;
step S102, a beam former acquires signals collected by each array element in the antenna array, and the collected signals are used as sampling data;
s103, performing outlier detection on all sampled data by using a Hampel algorithm to determine abnormal data; the abnormal data represents data contaminated by impulse noise;
and step S104, removing the abnormal data.
In specific implementation, the antenna array for acquiring data needs to be established in advance in the base station before acquiring the data signal. The antenna array comprises a plurality of array elements, and each array element collects signals. When the signals need to be acquired for beamforming, the beamformer may acquire the signals acquired by each array element in the antenna array, and use the acquired signals as sampling data. The beamformer of the present invention is used to acquire sampled data, solve weighting vectors and obtain beamformed outputs. Specifically, the sampling data received by the array element is set as x (k):
x(k)＝s0(k)a(θ0)+i(k)+n(k)
wherein s is0(k) A (θ) is a desired signal0) Denotes an incident angle of theta0I (k) represents an interference signal vector, and n (k) represents a noise vector. k denotes the kth sample.
The invention aims to improve the robustness of beam forming in an impulse noise environment. Assuming that the noise model is a Gaussian mixture model, then n (k) can be written as:
n(k)＝ng(k)+ni(k)
wherein n isgRepresenting Gaussian noise, niRepresenting impulse noise.
Preferably, the impulse noise in each array element in the antenna matrix is assumed to be independent and uncorrelated in the present invention. Thus, the noise vector n on the m-th array elementm(k) Can be written as:
wherein b is a binary Bernoulli random variable, and the probability that b equals 1 is 1-p, the probability that b equals 0 is p, pIndicating the probability of impulse noise occurring.
Represents a variance of
Gaussian distribution with mean value of zero.
Represents a variance of
Gaussian distribution with mean value of zero. And is
And after the beam former acquires the sampling data, performing outlier detection on all the sampling data by using a Hampel algorithm to determine abnormal data. Specifically, the method comprises the following steps:
1. firstly, solving the modulus value of the data collected by each array element. For example, solving the modulus (amplitude) of the k-th sampled data on the m-th array element yields:
wherein Xm(k) Representing the k-th sampled data on the m-th array element.
2. Respectively calculating according to Hampel algorithm
Median value of (d)kAnd absolute median difference
The calculation method is as follows:
wherein med (-) represents the median of (-) NwWhich represents the length of the estimation window,
the definition is as follows:
3. and the beam former calls a preset function to process the obtained median and the absolute median difference and determine the data and the position polluted by the impulse noise. Specifically, a function ρ (-) is called to determine Xm(k) The location of the data contaminated by impulse noise. The data contaminated by impulse noise is abnormal data.
And (4) eliminating abnormal data, namely setting the data at the position to be zero. In particular, the determination of X is made using the ρ (·) functionm(k) The position of the data contaminated by impulse noise and the data at its position is zeroed out. The ρ (-) function is defined as follows:
where Γ is the threshold parameter.
Thus, for Xm(k) After treatment, the following are obtained:
and (3) performing the processing of the steps 1-3 on the sampling data of all the array elements: obtaining a processed data matrix:
to facilitate the following description, a coordinate set Θ is defined, the element representation in Θ
Coordinates of non-zero elements. Then there are:
wherein,
which represents the sampling of (-) to (c),
the definition is as follows:
the Hampel algorithm can be used for effectively determining the position of abnormal data polluted by impulse noise and eliminating the abnormal data, so that preparation is made for recovering data by using a matrix filling technology. The method for performing outlier detection on all sampled data by using the Hampel algorithm in this embodiment is not limited to the present invention, and other forms of detection methods still belong to the protection scope of the present invention.
And S200, recovering missing data in the sampled data by using a matrix filling technology, and solving a weighting vector by using the recovered sampled data.
Preferably, the step S200 specifically includes:
step S201, a beam former acquires the position of zero data in sampling data;
s202, performing data recovery on the position with zero data by using a matrix filling technology, and outputting sampling data again;
step S203, the beamformer re-acquires the recovered sample data, and performs weighted vector calculation on the recovered sample data.
In specific implementation, after the sampled data is processed by using the Hampel algorithm, the data polluted by the pulse noise can be effectively removed, but the optimal performance cannot be realized by directly using the processed sampled data to perform beam forming, and particularly when the occurrence probability of the pulse noise is high, the number of the zero points of the sampled data is too large, and the characteristics of the original array output cannot be reflected. Therefore, the missing data needs to be recovered by using a matrix filling technology, and better performance can be realized by using the recovered data to perform beam forming. And the beam former acquires the position of the zero data in the sampled data, and performs data recovery on the position of the zero data by using a matrix filling technology. Specifically, the data recovery problem is converted into a matrix filling problem, and then a singular value threshold method is used for solving, so that complete sampling data are recovered. The singular value threshold method is an effective matrix filling algorithm, has high recovery precision and is easy to realize. The corresponding matrix filling problem can be described as:
wherein | · | purple*The matrix kernel norm is expressed, i.e. equal to the sum of all singular values of the matrix. I | · | purple windFThe Frobenius norm of the matrix is represented. Xi is an error limit parameter and satisfies
||·||0L representing a matrix0Norm, i.e. the number of non-zero elements in the matrix. The optimization problem described above can be solved using singular value thresholding. Solving the above problem to obtain an optimal solution YoptLet the recovered data matrix be
Then there is
The matrix filling technique for recovering data disclosed in this embodiment is not limited to the present invention, and other forms of data recovery methods still fall within the scope of the present invention.
Further, the beamformer re-acquires the recovered sampled data and performs a weight vector calculation on the recovered sampled data. Because errors exist in the matrix filling process, if the recovered data matrix is directly used for calculating the weighting vector, sufficient robustness cannot be realized, and therefore the method needs to use a worst performance optimization algorithm to solve the weighting vector. The weighted vector calculated by the worst performance optimization algorithm is used for beam forming, so that the influence of estimation errors introduced in the matrix filling process on the beam forming performance can be effectively reduced.
To facilitate the following description, the sampled data received by the array elements may be rewritten as:
X＝X0+Ng+Ni＝Xg+Ni
wherein x is0Representing a noise-free sampled data matrix, NgRepresenting a Gaussian noise matrix, NiRepresenting an impulse noise matrix, XgA matrix of sampled data representing no impulse noise pollution.
Specifically, the error is defined as follows:
where Δ is an error matrix, satisfying:
wherein C is0Is a constant, M represents the array element number of the array, and K represents the sampling times.
Solving the weight vector of the beam forming through the worst performance optimization algorithm can be carried out in the following way:
the above problem is non-convex and cannot be solved directly by using convex optimization technology, so the above problem is transformed into:
wherein (·)HRepresenting a conjugate transpose. The method is a second-order cone programming problem, and can solve and obtain an optimal weight vector w by utilizing a CVX tool box in MATLABopt. Thereby deriving an optimal weight vector for beamforming.
And step S300, carrying out weighted summation on the sampling data according to the calculated weighted vector to obtain the output of beam forming.
In specific implementation, the invention performs weighted summation on received sampling data x (k) according to solved weighted vectors, and obtains output y (k) of beam forming:
y(k)＝wHx(k)。
based on the above embodiment, the invention also provides a simulation effect of the specific application of the beam forming method in the impulse noise environment, and compared with the prior art. As shown in fig. 2 and fig. 3, fig. 2 is a first comparative effect diagram of the beam forming method under impulse noise environment of the present invention and the prior art. Fig. 3 is a diagram illustrating a second comparison effect between the beamforming method in an impulse noise environment of the present invention and the prior art. The specific simulation parameters in this embodiment are as follows: the antenna array is a uniform linear array, the number of array elements M is 10, the incidence angle of the expected signal is 0 degree, and the incidence angles of the two interference signals are respectively-20 degrees and 40 degrees. Assuming Gaussian noise power
Power of impulse noise
The probability of impulse noise occurrence is 0.1. The interference to noise ratio is 20 dB. The sampling number K is 500. All results were obtained from 200 monte carlo experiments.
As can be seen from fig. 2, the beam forming method proposed by the present invention can achieve a higher output signal-to-interference-and-noise ratio under the impulse noise environment under different input signal-to-noise ratios compared with the lcnv beam former and the EM-Capon beam former in the prior art. If the input signal-to-noise ratio is set to 0dB, the probability of impulse noise occurrence changes from 0 to 0.5, and other parameters are the same as those in fig. 2, it can be seen from fig. 3 that, under the environment of impulse noise of different degrees, the beam forming method provided by the present invention can achieve a higher output signal-to-interference-and-noise ratio than the prior art.
Based on the above embodiment, the invention also discloses a beam former. As shown in fig. 4, includes: a processor (processor)10, a storage device (memory)20 connected to the processor; the processor 10 is configured to call program instructions in the storage device 20 to execute the method provided in the foregoing embodiments, for example, to execute:
s100, acquiring sampling data acquired from an antenna array preset by a base station, performing outlier detection on the sampling data, and removing abnormal data;
s200, recovering missing data in the sampled data by using a matrix filling technology, and solving a weighting vector by using the recovered sampled data;
and step S300, carrying out weighted summation on the sampling data according to the solved weighted vector to obtain the output of beam forming.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the storage device, and the computer program enables a computer to execute the methods provided in the foregoing embodiments.
In summary, the beam forming method, the storage medium, and the beam former in the impulse noise environment provided by the present invention include: acquiring sampling data acquired from an antenna array preset by a base station, performing outlier detection on the sampling data and removing abnormal data; recovering missing data in the sampled data by using a matrix filling technology, and solving a weighting vector by using the recovered sampled data; and carrying out weighted summation on the sampling data according to the solved weighted vector to obtain the output of beam forming. The beam forming method provided by the invention can effectively inhibit the influence of impulse noise, can realize high-precision measurement on data signals under the interference of the impulse noise, improves the robustness of beam forming, and is favorable for realizing higher output signal-to-interference-and-noise ratio.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (9)
1. A method of beamforming in an impulse noise environment, the method comprising:
acquiring sampling data acquired from an antenna array preset by a base station, performing outlier detection on the sampling data and removing abnormal data;
recovering missing data in the sampled data after abnormal data are removed by using a matrix filling technology, and solving a weighting vector by using a worst performance optimization algorithm;
and carrying out weighted summation on the sampling data according to the solved weighted vector to obtain the output of beam forming.
2. The method according to claim 1, wherein the acquiring of the sampling data collected from an antenna array preset in the base station, the performing of outlier detection on the sampling data, and the removing of the abnormal data specifically include:
an antenna array for acquiring signals is established in advance at a base station;
a beam former acquires signals acquired by each array element in the antenna array, and the acquired signals are used as sampling data;
performing outlier detection on all the sampled data by using a Hampel algorithm to determine abnormal data; the abnormal data represents data contaminated by impulse noise;
and eliminating the abnormal data.
3. The method of claim 2, wherein the impulse noise of each array element in the antenna array is independent and uncorrelated.
4. The method of claim 2, wherein the determining abnormal data by performing outlier detection on all sampled data using a Hampel algorithm specifically comprises:
firstly, solving a modulus value of sampling data acquired by an array element;
respectively calculating the median and the absolute median difference of the module values of the sampled data according to a Hampel algorithm;
calling a function rho (-) to determine the position of data polluted by impulse noise;
the ρ (-) function is defined as:
wherein, Xm(k) Representing the k-th sampled data, mu, on the m-th array elementkThe average value is taken as the median value,
for the absolute median difference, Γ is the threshold parameter.
5. The method according to claim 2, wherein the removing the abnormal data specifically comprises:
acquiring the position of data polluted by impulse noise in the sampling data;
the data at that location is set to zero.
6. The method according to claim 1, wherein the recovering missing data in the sampled data from which the abnormal data are removed by using a matrix filling technique, and the solving of the weight vector by using a worst-case performance optimization algorithm specifically includes:
the beam former acquires the position of zero data in the sampling data after the abnormal data are eliminated;
performing data recovery on the position with zero data by using a matrix filling technology, and outputting sampling data again;
and the beam former acquires the recovered sampling data again and solves the weighting vector by using a worst performance optimization algorithm.
7. The method according to claim 6, wherein the performing data recovery on the zero-data position by using the matrix filling technique specifically comprises:
acquiring the number and the position of non-zero data in the sampling data;
modeling a data recovery problem as a matrix filling problem;
and solving the corresponding matrix filling problem by using a singular value threshold method and recovering complete sampling data.
8. A computer readable storage medium, having a computer program stored thereon, wherein the computer program is adapted to be loaded and executed by a processor to perform the method of beamforming in an impulse noise environment according to any of the claims 1-7.
9. A beamformer, comprising: a processor, a storage device communicatively coupled to the processor, the storage device adapted to store a plurality of instructions; the processor is adapted to invoke instructions in the memory device to perform a method of implementing beamforming in an impulse noise environment according to any of the preceding claims 1-7.
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