CN112946564B - DOA estimation method and device based on DNN (digital optical network) beam space and computer storage medium - Google Patents
DOA estimation method and device based on DNN (digital optical network) beam space and computer storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a DOA estimation method, a DOA estimation device and a DOA estimation computer storage medium based on DNN (digital optical network) beam space; the method may include: acquiring a covariance matrix of an array receiving signal; generating received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of all angle intervals in the angle space; inputting received signal data in the beam space into a deep neural network DNN which has been trained using a training data set; and obtaining DOA estimated values from the signal data output by the DNN through linear amplitude interpolation.
Description
Technical Field
The embodiment of the invention relates to the technical field of signal processing, in particular to a method and a device for estimating the direction of arrival (DOA, direction Of Arrival) of a beam space based on a deep neural network (DNN, deep Neural Networks) and a computer storage medium.
Background
DOA estimation is an important research direction for array signal processing, and has been widely applied to various military and civil systems, including wireless communication, astronomical observation, radar and sonar. DOA estimation always progresses towards the trend of improving precision and super resolution, and the generalization capability of various unknown scenes such as array errors, low signal to noise ratio, limited snapshot and the like is enhanced. For the current conventional DOA estimation schemes, they are parameterized schemes, that is, they are forward mappings from signal direction to array output, assuming that the mapping is reversible. Based on the above assumption, the array outputs are matched by a pre-formed map, thereby achieving the direction estimation. The performance of such parameterization schemes depends largely on the consistency between the two mappings, i.e. the forward mapping from the signal direction to the array output and the reverse mapping from the array output to the signal direction during data acquisition.
However, since there may be various drawbacks in the array system, such as non-ideal sensor design, installation of the array, mutual interference between sensors, and influence by environmental factors, etc. Therefore, in actual signal estimation, the defects have a great influence on DOA estimation performance, and further, estimation accuracy is reduced.
Although more schemes exist at present to describe the influence of various defects by simplifying a model, and a corresponding automatic calibration process is proposed to improve the accuracy of DOA estimation; the above simplified models for array errors are triggered mathematically with various additional assumptions, such that these simplifications and assumptions deviate from reality to a different extent and do not accurately address array defects.
Disclosure of Invention
In view of this, embodiments of the present invention desire to provide a dna-based DOA estimation method, apparatus, and computer storage medium for beam space; the adaptive capacity of the deep neural network to the array errors is effectively utilized, the adaptive capacity to the array errors is further increased while the calculated amount is reduced by utilizing the advantages of the beam space, and therefore the DOA estimation accuracy is better improved.
The technical scheme of the embodiment of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a dna-based DOA estimation method for a beam space, including:
acquiring a covariance matrix of an array receiving signal;
generating received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of all angle intervals in the angle space;
inputting received signal data in the beam space into a deep neural network DNN which has been trained using a training data set;
and obtaining DOA estimated values from the signal data output by the DNN through linear amplitude interpolation.
In a second aspect, an embodiment of the present invention provides a dna-based beam space DOA estimation device, the device comprising: an acquisition part, a generation part, a deep neural network DNN and an interpolation part; wherein the acquisition section is configured to acquire a covariance matrix of the array reception signal;
the generating part is configured to generate received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of all angle intervals in the angle space;
the DNN configured to input received signal data within the beam space and output;
the interpolation part is configured to obtain DOA estimated values by linear amplitude interpolation of signal data output by the DNN.
In a third aspect, embodiments of the present invention provide a computing device comprising a receive array, a memory, and a processor; wherein,
the receiving array is used for receiving an incident far-field signal;
the memory is used for storing a computer program capable of running on the processor;
the processor is configured to perform the steps of the dna-based beam space DOA estimation method of the first aspect when running the computer program.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing a dnas-based beam space DOA estimation procedure, which when executed by at least one processor, implements the dnas-based beam space DOA estimation method steps of the first aspect.
The embodiment of the invention provides a DOA estimation method, a DOA estimation device and a computer storage medium based on DNN (digital optical network) beam space; the receiving signals are converted from the array element space to the wave beam space, so that the generalization capability of array errors is improved while the calculated amount is reduced and the signal-to-noise ratio resolution threshold is lowered; in addition, the DOA estimation is performed by adopting the deep neural network, so that the generalization capability of the technical scheme on array errors can be further improved; in addition, the technical scheme carries out spectrum estimation in a virtual beam space, so that even if array errors are not considered in the DNN training process, the method still has strong adaptability to the errors.
Drawings
Fig. 1 is a schematic flow chart of a dna-based DOA estimation method in beam space according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a level of DNN according to an embodiment of the present invention;
FIG. 3 is a graph showing the comparison result of angle estimation deviation under different SNR conditions according to an embodiment of the present invention;
FIG. 4 is a graph showing the comparison of the angle estimation value and the estimation error at an angle interval of 9.4 degrees according to the embodiment of the present invention;
FIG. 5 is a graph showing the comparison of the angle estimation values and the estimation errors at the angle intervals of 16.4 DEG and 60 DEG according to the embodiment of the present invention;
FIG. 6 is a graph of comparison results of angle estimation values in single-signal and three-signal scenarios according to an embodiment of the present invention;
FIG. 7 is a graph of the results of comparing the RMSE of direction of arrival estimates for two signals from the 31.5 and 41.5 directions in the presence of different array defects provided by an embodiment of the present invention;
fig. 8 is a schematic diagram of a DOA estimation device based on a beam space of DNN according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a DOA estimation device based on another beam space of DNN according to an embodiment of the present invention;
fig. 10 is a schematic diagram of hardware components of a computing device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
It should be noted that, in order to solve the problem of dependence on consistency of both input and output mappings in the conventional parameterization scheme, and it is desirable to improve the generalization capability of the array error while reducing the calculation amount and reducing the signal to noise ratio resolution threshold, referring to fig. 1, a dna estimation method based on the beam space provided by the embodiment of the present invention is shown, and the method may include:
s101: acquiring a covariance matrix of an array receiving signal;
s102: generating received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of all angle intervals in the angle space;
s103: inputting received signal data in the beam space into a deep neural network DNN which has been trained using a training data set;
s104: and obtaining DOA estimated values from the signal data output by the DNN through linear amplitude interpolation.
Through the technical scheme shown in fig. 1, the received signals are converted from the array element space to the beam space, so that the generalization capability of array errors is improved while the calculated amount is reduced and the signal-to-noise ratio resolution threshold is lowered; in addition, the DOA estimation is performed by adopting the deep neural network, so that the generalization capability of the technical scheme on array errors can be further improved; in addition, the technical scheme carries out spectrum estimation in a virtual beam space, so that even if array errors are not considered in the DNN training process, the method still has strong adaptability to the errors.
For the solution shown in fig. 1, in some possible implementations, the obtaining the covariance matrix of the array received signal includes:
based on that K independent far-field signals are incident into a uniform array with M array elements, setting the incident direction of the kth far-field signal as theta k The kth far-field signal received by the uniform array is s k (t)；
Sampling the received far-field signal through N sampling moments with the same interval to obtain a snapshot signal X= [ X (t) 1 ),...,x(t N )]；
Wherein,v (t) is zero-mean Gaussian white noise, and a (θ) represents an ideal steering vector;
calculating a covariance matrix of the array received signals according to:
R xx ＝E[x(t N )x H (t N )]＝ASA H +R N
wherein E [. Cndot.]And ( H Representing the desired operator and the conjugate transformation operator, respectively; a is an array steering matrix, and a= [ a (θ 1 ),a(θ 2 ),...,a(θ k )]The method comprises the steps of carrying out a first treatment on the surface of the S and R N Covariance matrix and noise matrix of ideal received signal respectively are defined as: s=e [ S (t) S H (t)]，R N ＝E[v(t)v H (t)]Since the noise follows a zero-mean gaussian distribution, R N ＝σ 2 I，σ 2 Is the noise power.
In detail, for the above implementation, it is assumed that K independent far-field signals are incident on a uniform array (ULA, uniform Linear Array) having M array elements, and the incident directions of the far-field signals are respectively θ 1 ,...,θ K The actual waveform of the kth far-field signal is s k (t), which may also be referred to as an ideal received signal. The actual received signal is sampled at N sampling moments t at the same interval 1 ,...,t N Sampling, wherein the obtained matrix is a plurality of fast obtained by samplingBeat signal x= [ X (t) 1 ),...,x(t N )]. Wherein,
v (t) is zero-mean Gaussian white noise; a (θ) represents a defect-free steering vector; and R is xx (θ)＝E[x(t N )x H (t N )]＝ASA H +R N ；E[·]And ( H Representing the desired operator and the conjugate transformation operator, respectively; a may be referred to as an array steering matrix and is defined as a= [ a (θ) 1 ),a(θ 2 ),...,a(θ K )]S and R N Covariance matrix and noise matrix for ideal received signal are defined as: s=e [ S (t) S H (t)]、R N ＝E[v(t)v H (t)]. Note that, since noise follows a zero-mean gaussian distribution, the noise matrix can be expressed as: r is R N ＝σ 2 I。
For the solution shown in fig. 1, in some possible implementations, the generating, according to the covariance matrix of the array received signals and the beam pointing covariance matrix of each angle interval in the angle space, the received signal data in the beam space includes:
dividing an angle space into a plurality of angle intervals, and determining the beam direction corresponding to each angle interval;
determining a covariance matrix corresponding to each angle interval according to the beam direction corresponding to each angle interval;
converting the covariance matrix corresponding to each angle interval into a column vector corresponding to each angle interval;
converting the covariance matrix of the array received signal into a column vector of the array received signal;
according to the column vector r (gamma) corresponding to the nth angle interval n ) And the column vector r (theta) of the array receiving signal is subjected to beam conversion according to the following formula, so that the beam space value of the array receiving signal in the nth angle interval is obtained:
g n ＝r H (γ n )r(θ)
wherein, gamma n A beam pointing value representing the nth angle interval,
and forming the signal data of the array receiving signals in the beam space according to the beam space values of all angle intervals:
g＝[g 1 ,g 2 ,...,g n ] T
where T represents the transpose operator.
In detail, the above implementation manner is that, first, the angular space is divided into n parts, n angle intervals are obtained, and the angle in the center of each angle interval is taken as the beam direction corresponding to the angle interval, for example, the first angular space is [ β 1 ,β 2 ]The beam direction of the first interval can be taken as gamma 1 ＝(β 1 +β 2 ) 2; if each angular interval is implemented in the same way, n beam pointing values (gamma 1 ,γ 2 ,...,γ n ). Then, the covariance matrix of each beam pointing value can be calculated, and the covariance matrix R (gamma) corresponding to each angle interval can be calculated i ). After R (gamma) n ) Then, it is converted into a column vector r (γ i )＝rec{R(γ i ) And, wherein rec {.cndot } represents converting the matrix to a column vector operator. Similarly, the covariance matrix R formed by the received signals xx After conversion into the column vector r (θ), beam conversion may be performed according to the following equation:
g i ＝r H (γ i )r(θ)
wherein g n For the beam space value corresponding to the i-th angle interval, i=1.
Finally, the beam space values of all angle intervals are integrated together to form the received signal data g= [ g ] of the beam space 1 ,g 2 ,...,g n ] T G is the received signal data of the beam space; that means that the conversion of the received signal from the element space to the beam space is completed.
For the solution shown in fig. 1, in some possible implementations, the method further includes completing a process of training the deep neural network DNN with a training data set, and in some examples, the training process may include:
firstly, generating a training set and corresponding labels; in training, a two-signal training set is typically employed to generalize to single-signal as well as three-signal scenarios. In some examples, some more specific angular intervals Δ= { Δ may be selected 1 ,Δ 2 ,...,Δ J And takes the input vector r (θ, Δ) j ) Representing directions θ and θ+Δ from j Wherein θ ranges from [ θ ] 1 ,θ I -Δ j ) Where j=1,..j represents the angular separation of the two signals and I represents the number of grids of the spatial range divided by unit angle. Based on the above, the training data set for DNN may be written as: Γ= [ Γ ] 1 ,...,Γ J ]WhereinRepresenting the angle value corresponding to each grid; the corresponding tag is ψ= [ ψ ] 1 ,...,Ψ J ]Wherein-> Indicating from direction +.>And->Corresponding to the tag vector of the two signals of (a).
Initializing a neural network which comprises a plurality of hidden layers and adopts a nonlinear activation function;
training the neural network with an RMSProp optimizer through the training dataset and updating parameters of the neural network by back propagation during training toMinimizing reconstruction errors of the spatial spectrum; wherein the reconstruction error is expressed as:y is the ideal output of the neural network, < >>Representing an actual output of the neural network; the loss function used to characterize the minimized reconstruction error is l, which is the square of the spectral reconstruction error 2 Norms: />
For the above examples, in detail, in DNN, embodiments of the present invention preferably employ multiple hidden layers and add nonlinear activation functions to enhance expressivity, thereby achieving accurate direction of arrival estimation. In order to preserve the polarity of the input at each layer of the classifier, the nonlinear activation function used is the hyperbolic tangent function tanh (σ) = [ tanh (σ) 1 ),tanh(σ 2 ),...,tanh(σ -1 )] T Wherein, the method comprises the steps of, wherein,σ -1 as the last element of vector sigma, sigma i Representing the i-th element in the vector sigma. In the process of training DNN, relevant parameters in DNN are updated through back propagation, so that the reconstruction error of the minimized spatial spectrum is realized, and the variable iteration update is as follows: />Alpha can be any weight matrix and bias vector, mu is learning rate, alpha old And alpha new Representing the variable values before and after the current update, respectively. In the embodiment of the present invention, the reconstruction error may be expressed as: />Where y is the ideal output data corresponding to the input, and +.>Representing the actual output data. The loss function of DNN may be set to l, which is the square of the spectral reconstruction error 2 Norms, i.e.)>
For the above examples and detailed description, in the implementation, the training process of DNN is preferably accomplished in accordance with the above description using an RMSProp Optimizer (Optimizer) that is self-contained in the Tensorflow.
Based on the above description, in some examples, the inputting of the received signal data within the beam space into the deep neural network DNN that has been trained with a training data set includes:
separating the real part from the imaginary part of the signal data g of the array received signal in the beam space;
and taking the real column vector obtained after separation as input data of the DNN which is trained by using the training data set.
Based on the above, in some examples, the obtaining the DOA estimate from the signal data output by the DNN by linear amplitude interpolation includes:
based on the fact that the signal data spectrum output by the DNN only has non-zero positive values on grids adjacent to the actual signal direction, the DOA of each far-field signal is estimated through linear amplitude interpolation between two adjacent grids.
Specifically, the linear amplitude difference value can be obtained by firstly extracting a peak value from a frequency spectrum, recording an index of a non-zero positive value, and counting the number of non-zero positive value areas in an output frequency spectrum; secondly, angle estimation is carried out, total energy of the current non-zero region (namely, the sum of output values of the current region) is calculated, the output frequency spectrum value corresponds to the angle of the angle space one by one, the angle value obtained by the current non-zero region estimation is calculated (namely, the products of the frequency spectrum value and the angle value of the corresponding angle space in the non-zero region are summed, and the quotient is calculated between the products and the total energy of the current region); and finally, sorting according to the total energy of each non-zero region, and selecting the first corresponding angle values with the maximum total energy corresponding to the number of signals as the estimated angle values.
For the above example, it is noted that the reconstructed spectrum has non-zero positive values only on the grid adjacent to the true signal direction, and that the direction of each far-field signal can be estimated by linear amplitude interpolation between two adjacent grids.
For the description of the DOA estimation method based on the beam space of the deep neural network DNN provided by the embodiment of the invention, the embodiment of the invention verifies the effectiveness and feasibility of the DOA estimation method through specific experiments. In this experiment, a Uniform Linear Array (ULA) of 10 array elements was used to estimate the far-field signal direction incident from the spatial range [ -60 °,60 °), i.e. m=10, θ (0) ＝-60°，θ (I) =60°. The spacing between adjacent array elements of the ULA is half the wavelength of the signal. The spatial spectrum consists of 1 ° grids, so there are a total of i=120 grids, i.eCovariance vector r in the training dataset of DNN, and vector in the test dataset were obtained from k=400 shots. Based on the angular space divided into 20 parts, i.e., n=20, the area range of each angle interval is 6 °, and each angle interval takes the value of the center angle as the beam direction, i.e., γ 1 ＝-57.5°,γ 2 ＝-51.5°,...,γ n ＝56.5°。
For the training set of DNN networks, the [ -60 °,60 °) space is also sampled at 1 ° intervals to obtainAnd calculates covariance vectors and associated labels. The signal to noise ratio of the snapshot was 10db. Batch size 32, learning rate μ 1 =0.001, the iteration number is set to 300, and the dataset is shuffled every iteration. The size of the input layer is 40, the size of the hidden layer is 4, respectively [80,160,320,240 ]]The size of the output layer corresponds to 120 number of angle intervals. Each layer of the DNN network is a full-connection layerFCL, fully Connected Layer) is shown in fig. 2, and the sizes of the layers of the DNN network are shown in table 1.
TABLE 1
| Layer name | Size and dimensions of |
| Input layer | 40 |
| First layer hidden layer | 80 |
| Second hidden layer | 160 |
| Third hidden layer | 320 |
| Fourth hidden layer | 240 |
| Output layer | 120 |
Furthermore, all weights and deviations of the DNN network are randomly initialized according to a uniform distribution between-0.1 and 0.1.
Based on the experimental conditions and DNN network training set forth above, the DOa estimation method based on the Beam space of the deep neural network DNN according to the embodiment of the present invention (may be simply referred to as Beam space) is compared with the conventional DOa estimation method based on the array element space (may be simply referred to as Array element space), and the experimental results are shown in fig. 3 to 7.
Fig. 3 compares the angular deviation of the Beam space method and Array element space method for two signals in the 31 ° and 41 ° directions under different signal-to-noise conditions. The abscissa represents the signal-to-noise ratio of the signal, varying from 0db to 10db, and the ordinate represents the root mean square error of the DOA estimate. As can be seen from fig. 3, with the increase of the signal-to-noise ratio, the estimation accuracy of both methods is significantly improved. In contrast, the dnan-based beam space DOA estimation method according to the embodiments of the present invention is superior to the array element space-based DOA estimation method. Therefore, in different signal-to-noise ratio environments, the DOA estimation method based on the DNN beam space has strong adaptability.
Fig. 4 compares the angle estimation and estimation error for the Beam space method and Array element space method at an angle interval of 9.4 °. The angular separation between the two signals does not occur in the training set and the direction of the second signal deviates from the preset training direction and the output spectral grid. Fig. 4 (a) and (b) show the estimated direction and the estimated error of the two signals when the first signal direction increases from-60 ° to 50 ° in steps of 1 ° in the virtual array beam space. In fig. 4 (c) and (d), the DOA estimation direction and estimation error based on the array element space in the same scene are plotted. The two methods are compared under the same training scene and test scene, and the signal to noise ratio is 10db. As can be seen from fig. 4, the estimation accuracy of both methods is high, the direction of arrival estimates match their true values well, and most estimation errors are less than 0.5 °. It can be seen that the performance of the two methods in this scenario is almost equal.
Fig. 5 expands the angular separation of the two signals by 16.4 ° and 60 °, respectively. The angular direction of the first signal varies between-60 deg. and 43 deg. when the angular interval is 16.4, and the angular direction of the second signal varies between-60 deg. and 0 deg. when the interval is 60 deg.. Fig. 5 (a) and (c) show the estimated directions of the Beam space method at two angle intervals, respectively, and (b) and (d) show the estimated directions of the Array element space method at two angle intervals, respectively. It can be seen that the two methods have almost the same distribution of estimated directions at the same angular interval, and their training and testing remain in the 10db environment. Thus, both methods exhibit good generalization ability for unknown scenarios.
Fig. 6 shows the behavior of the Beam space method and Array element space method when the test data contains different numbers of signals. Both methods have been trained with array outputs in a dual signal scenario. The test scene is still a 10db scene, the angular interval between adjacent signals in the three-signal scene is 20 degrees, the first signal is changed between-60 degrees and 40 degrees, and the first signal in the single-signal scene is changed between-60 degrees and 60 degrees. Fig. 6 (a) and (c) show the results of angle estimation of the Beam space method in a single signal scene and a three signal scene, and (b) and (d) show the estimation results of the Array element space method. From experimental results, in single-signal and double-signal scenes, the two methods show good generalization capability for unknown scenes; in a three-signal scenario, however, the Array element space method suffers from a certain degree of degradation in accuracy of the corresponding direction of arrival estimate, even vanishing on the estimated spectrum, when some of the incoming signal is at the edge of the filter sub-region. The DOA estimation method based on the DNN beam space still shows satisfactory performance.
Fig. 7 compares the Beam space method and Array element space method without doping errors during training, while taking into account errors for four different situations during testing. It is assumed that two signals with signal to noise ratios of 10db impinge on the array from directions of 31.5 ° and 41.5 °, both of which deviate from the training and output spectral grid. When there is no error, both methods obtain very high accuracy direction of arrival estimates. However, as array defects become more apparent, the direction of arrival estimation error increases almost linearly for both approaches. From the experimental results, it can be seen that when the error intensity is small, the estimated deviation of the two methods is relatively close. With the gradual increase of the error intensity, the deviation rise amplitude estimated by the DOA estimation method based on the DNN beam space is obviously smaller than that estimated by the DOA estimation method based on the array element space. This result is also expected, because the dna-based beam space DOA estimation method according to the embodiments of the present invention performs spatial spectrum estimation in the virtual array beam space, and has a strong adaptability to errors. Even though array errors are not considered during training, the training device has strong adaptability to errors.
From the above experiments and the description and illustration of the experimental results, it can be seen that: the DOA estimation method based on the DNN beam space has effectiveness and feasibility.
Based on the same inventive concept as the foregoing technical solution, referring to fig. 8, which shows a dna-based beam space DOA estimation device 80 provided by an embodiment of the present invention, the device 80 may include: an acquisition section 801, a generation section 802, a deep neural network DNN 803, and an interpolation section 804; wherein,
the acquisition section 801 is configured to acquire a covariance matrix of the array reception signal;
the generating section 802 is configured to generate received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of each angle section in an angle space;
the DNN 803 configured to input received signal data within the beam space and output;
the interpolation section 804 is configured to obtain the DOA estimation value by linear amplitude interpolation of the signal data output from the DNN 803.
In the above aspect, the acquiring section 801 is configured to:
based on that K independent far-field signals are incident into a uniform array with M array elements, setting the incident direction of the kth far-field signal as theta k The kth far-field signal received by the uniform array is s k (t)；
Sampling the received far-field signal through N sampling moments with the same interval to obtain a snapshot signal X= [ X (t) 1 ),...,x(t N )]；
Wherein,v (t) is zero-mean Gaussian white noise, and a (θ) represents an ideal steering vector;
calculating a covariance matrix of the array received signals according to:
R xx ＝E[x(t N )x H (t N )]＝ASA H +R N
wherein E [. Cndot.]And ( H Representing the desired operator and the conjugate transformation operator, respectively; a is an array steering matrix, and a= [ a (θ 1 ),a(θ 2 ),...,a(θ k )]The method comprises the steps of carrying out a first treatment on the surface of the S and R N Covariance matrix and noise matrix of ideal received signal respectively are defined as: s=e [ S (t) S H (t)]，R N ＝E[v(t)v H (t)]Since the noise follows a zero-mean gaussian distribution, R N ＝σ 2 I，σ 2 Is the noise power.
In the above aspect, the generating section 802 is configured to:
dividing an angle space into a plurality of angle intervals, and determining the beam direction corresponding to each angle interval;
determining a covariance matrix corresponding to each angle interval according to the beam direction corresponding to each angle interval;
converting the covariance matrix corresponding to each angle interval into a column vector corresponding to each angle interval;
converting the covariance matrix of the array received signal into a column vector of the array received signal;
according to the column vector r (gamma) corresponding to the nth angle interval n ) And the column vector r (theta) of the array receiving signal is subjected to beam conversion according to the following formula, so that the beam space value of the array receiving signal in the nth angle interval is obtained:
g n ＝r H (γ n )r(θ)
wherein, gamma n A beam pointing value representing the nth angle interval,
and forming the signal data of the array receiving signals in the beam space according to the beam space values of all angle intervals:
g＝[g 1 ,g 2 ,...,g n ] T
where T represents the transpose operator.
In the above aspect, the generating section 802 is further configured to:
separating the real part from the imaginary part of the signal data g of the array received signal in the beam space;
and taking the real column vector obtained after separation as input data of the DNN 803 which is trained by using the training data set.
In the above-described aspect, as shown in fig. 9, the apparatus 80 further includes a training section 805 configured to:
initializing a neural network which comprises a plurality of hidden layers and adopts a nonlinear activation function;
training the neural network by using an RMSProp optimizer through the training data set, and updating parameters of the neural network through back propagation in the training process so as to minimize reconstruction errors of a spatial spectrum; wherein the reconstruction error is expressed as:y is the ideal output of the neural network, < >>Representing an actual output of the neural network; the loss function used to characterize the minimized reconstruction error is l, which is the square of the spectral reconstruction error 2 Norms: />
In the above scheme, the nonlinear activation function is a hyperbolic tangent function:
tanh(σ)＝[tanh(σ 1 ),tanh(σ 2 ),...,tanh(σ -1 )] T wherein, the method comprises the steps of, wherein,σ -1 as the last element of vector sigma, sigma i Representing the i-th element in the vector sigma.
In the above aspect, the interpolation section 804 is configured to:
based on the fact that the signal data spectrum output by the DNN 803 has non-zero positive values only on the grids adjacent to the actual signal direction, the DOA of each far-field signal is estimated by linear amplitude interpolation between two adjacent grids.
It will be appreciated that in this embodiment, a "part" may be a part of a circuit, a part of a processor, a part of a program or software, etc., and of course may be a unit, or a module may be non-modular.
In addition, each component in the present embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional modules.
The integrated units, if implemented in the form of software functional modules, may be stored in a computer-readable storage medium, if not sold or used as separate products, and based on such understanding, the technical solution of the present embodiment may be embodied essentially or partly in the form of a software product, which is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or processor to perform all or part of the steps of the method described in the present embodiment. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Accordingly, the present embodiment provides a computer storage medium storing a dnan-based beam space DOA estimation program, which when executed by at least one processor, implements the dnan-based beam space DOA estimation method steps in the above technical solutions.
According to the aforementioned dnan-based beam space DOA estimation device 80 and computer storage medium, referring to fig. 10, a specific hardware structure of a computing apparatus 100 capable of implementing the aforementioned dnan-based beam space DOA estimation device 80 is shown in an embodiment of the present invention, the computing apparatus 100 may be a wireless device, a mobile or cellular phone (including a so-called smart phone), a Personal Digital Assistant (PDA), a video game console (including a video display, a mobile video game device, a mobile video conference unit), a laptop computer, a desktop computer, a television set-top box, a tablet computing device, an electronic book reader, a fixed or mobile media player, etc. The computing device 100 includes: a receive array 1001, a memory 1002 and a processor 1003; the various components are coupled together by a bus system 1004. It is to be appreciated that the bus system 1004 serves to facilitate connective communication between these components. The bus system 1004 includes a power bus, a control bus, and a status signal bus in addition to the data bus. The various buses are labeled in fig. 10 as bus system 1004 for clarity of illustration. Wherein,
the receiving array 1001 is configured to receive an incident far-field signal;
the memory 1002 for storing a computer program capable of running on the processor 1003;
the processor 1003 is configured to execute the step of the dna-based beam space DOA estimation method in the foregoing technical solution when running the computer program.
It is to be appreciated that the memory 1002 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). The memory 1002 of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
While processor 1003 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry of hardware in the processor 1003 or instructions in the form of software. The processor 1003 may be a general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 1002, and the processor 1003 reads the information in the memory 1002, and in combination with the hardware, performs the steps of the above method.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (Application Specific Integrated Circuits, ASIC), digital signal processors (Digital Signal Processing, DSP), digital signal processing devices (DSP devices, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field-Programmable Gate Array, FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
It will be appreciated that the exemplary solution of the dnan-based beam space DOA estimation device 80 and the computing device 100 described above belongs to the same concept as the solution of the dnan-based beam space DOA estimation method described above, and therefore, for details not described in detail in the solution of the dnan-based beam space DOA estimation device 80 and the solution of the computing device 100, reference may be made to the description of the solution of the dnan-based beam space DOA estimation method described above. The embodiments of the present invention will not be described in detail.
It should be noted that: the technical schemes described in the embodiments of the present invention may be arbitrarily combined without any collision.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. The DOA estimation method based on the beam space of the deep neural network DNN is characterized by comprising the following steps of:
acquiring a covariance matrix of an array receiving signal;
generating received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of all angle intervals in the angle space;
inputting received signal data in the beam space into a deep neural network DNN which has been trained using a training data set;
obtaining DOA estimated values by linear amplitude interpolation of signal data output by the DNN;
the method for obtaining the DOA estimated value by linear amplitude interpolation of the signal data output by the DNN comprises the following steps:
estimating DOA of each far-field signal by linear amplitude interpolation between two adjacent grids based on that the signal data spectrum output by DNN only has non-zero positive values on grids adjacent to the actual signal direction;
the generating the received signal data in the beam space according to the covariance matrix of the array received signals and the beam pointing covariance matrix of each angle interval in the angle space comprises the following steps:
dividing an angle space into a plurality of angle intervals, and determining the beam direction corresponding to each angle interval;
determining a covariance matrix corresponding to each angle interval according to the beam direction corresponding to each angle interval;
converting the covariance matrix corresponding to each angle interval into a column vector corresponding to each angle interval;
converting the covariance matrix of the array received signal into a column vector of the array received signal;
according to the firstnColumn vectors corresponding to each angle intervalAnd column vector of the array reception signal +.>Beam switching is performed according to the following formula, and the array receiving signals are obtained in the first placenBeam space values for individual angle intervals:
wherein,represent the firstnThe beam pointing values for the individual angle intervals,
and forming the signal data of the array receiving signals in the beam space according to the beam space values of all angle intervals:
wherein,Trepresenting a transpose operator;
the inputting of the received signal data in the beam space into the deep neural network DNN that has been trained with a training data set, comprises:
signal data of the array reception signals in a beam spaceIs separated from the real part and the imaginary part of (a);
and taking the real column vector obtained after separation as input data of the DNN which is trained by using the training data set.
2. The method of claim 1, wherein the obtaining the covariance matrix of the array received signal comprises:
based onKIndividual far field signals are incident onMA uniform array of array elements, setting the firstkThe incidence direction of the far-field signals isThe uniform array receives the firstkThe number of far-field signals is +.>；
By passing throughNSampling the far-field signals received at the same sampling time intervals to obtain snapshot signals of the array received signals；
Wherein,，/>gaussian white noise with zero mean value +.>Representing an ideal steering vector;
calculating a covariance matrix of the array received signals according to:
wherein,and->Representing the desired operator and the conjugate transformation operator, respectively; />Is an array guide matrix, and；/>and->Covariance matrix and noise matrix of ideal received signal respectively are defined as: />，/>Since the noise follows a zero-mean gaussian distribution +.>，/>Is the noise power.
3. The method according to claim 1, wherein the method further comprises:
initializing a neural network which comprises a plurality of hidden layers and adopts a nonlinear activation function;
training the neural network by using an RMSProp optimizer through the training data set, and updating parameters of the neural network through back propagation in the training process so as to minimize reconstruction errors of a spatial spectrum; wherein the reconstruction error is expressed as:y is the ideal output of the neural network, < >>Representing an actual output of the neural network; the loss function used to characterize the minimization of the reconstruction error is +.>Norms: />。
4. A method according to claim 3, wherein the nonlinear activation function is a hyperbolic tangent functionWherein->，/>For vector->Is the last element of->Representation vector->The first of (3)iThe elements.
5. A dnas-based beam space DOA estimation device, the device comprising: an acquisition part, a generation part, a deep neural network DNN and an interpolation part; wherein,
the acquisition part is configured to acquire a covariance matrix of the array receiving signals;
the generating part is configured to generate received signal data in a beam space according to a covariance matrix of the array received signals and beam pointing covariance matrices of all angle intervals in the angle space;
the DNN configured to input received signal data within the beam space and output;
the interpolation part is configured to obtain DOA estimated values by linear amplitude interpolation of signal data output by the DNN;
wherein the interpolation section is configured to; estimating DOA of each far-field signal by linear amplitude interpolation between two adjacent grids based on that the signal data spectrum output by DNN only has non-zero positive values on grids adjacent to the actual signal direction;
the generation section configured to:
dividing an angle space into a plurality of angle intervals, and determining the beam direction corresponding to each angle interval;
determining a covariance matrix corresponding to each angle interval according to the beam direction corresponding to each angle interval;
converting the covariance matrix corresponding to each angle interval into a column vector corresponding to each angle interval;
converting the covariance matrix of the array received signal into a column vector of the array received signal;
according to the firstnColumn vectors corresponding to each angle intervalAnd column vector of the array reception signal +.>Beam switching is performed according to the following formula, and the array receiving signals are obtained in the first placenBeam space values for individual angle intervals:
wherein,represent the firstnThe beam pointing values for the individual angle intervals,
and forming the signal data of the array receiving signals in the beam space according to the beam space values of all angle intervals:
wherein,Trepresenting a transpose operator;
the generation portion is further configured to:
signal data of the array reception signals in a beam spaceIs separated from the real part and the imaginary part of (a);
and taking the real column vector obtained after separation as input data of the DNN which is trained by using the training data set.
6. A computing device, the computing device comprising a receive array, a memory, and a processor; wherein,
the receiving array is used for receiving an incident far-field signal;
the memory is used for storing a computer program capable of running on the processor;
the processor for performing the steps of the dna-based beam space DOA estimation method as claimed in any of the claims 1 to 4 when running the computer program.
7. A computer storage medium storing a dnas-based beam space DOA estimation procedure, which when executed by at least one processor implements the dnas-based beam space DOA estimation method steps of any of claims 1 to 4.
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