WO2020143606A1 - 信号到达角的估计方法及基站 - Google Patents

信号到达角的估计方法及基站 Download PDF

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Publication number
WO2020143606A1
WO2020143606A1 PCT/CN2020/070638 CN2020070638W WO2020143606A1 WO 2020143606 A1 WO2020143606 A1 WO 2020143606A1 CN 2020070638 W CN2020070638 W CN 2020070638W WO 2020143606 A1 WO2020143606 A1 WO 2020143606A1
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Prior art keywords
signal
angle
matrix
diagonal
arrival
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English (en)
French (fr)
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范绍帅
贾杨
李辉
田辉
周梦凡
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China Academy of Telecommunications Technology CATT
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China Academy of Telecommunications Technology CATT
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic or electromagnetic waves, or particle emission, not having a directional significance, are being received
    • G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
    • G01S3/14—Systems for determining direction or deviation from predetermined direction
    • G01S3/16—Systems for determining direction or deviation from predetermined direction using amplitude comparison of signals derived sequentially from receiving antennas or antenna systems having differently-oriented directivity characteristics or from an antenna system having periodically-varied orientation of directivity characteristic
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location

Definitions

  • the present disclosure relates to the field of communication technology, and in particular, to a method for estimating a signal angle of arrival and a base station.
  • the observation space of signal processing in geometry can be decomposed into orthogonal signal subspace and noise subspace.
  • the signal subspace consists of the feature vectors corresponding to the signals in the data covariance matrix received by the array
  • the noise subspace consists of the feature vectors corresponding to all the smallest eigenvalues (noise variance) in the covariance matrix.
  • the spatial spectral function is constructed according to its orthogonality, and the angle corresponding to its spectral peak is the signal arrival angle.
  • this algorithm Under the low signal-to-noise ratio environment, this algorithm has a significant difference between the signal and noise or the noise is stronger than the signal, which leads to a serious degradation in the estimation performance of the signal arrival angle; when the signal arrival angles of multiple coherent sources are similar, three Possible situations: (1) The strong signal in the spectrum function covers the peak of the weak signal, resulting in missed detection; (2) When the signal strength is equal, only one peak appears when the peaks of different signals are superimposed on each other, resulting in missed detection and Misdetection; (3) When the signal intensities are equal, the spectral peaks of different signals are superimposed on each other, and the spectral peaks move in the middle, and two spectral peaks still appear, leading to misdetection.
  • the traditional signal parameter estimation based on rotation invariant technology estimating signal parameter via variation in techniques (referred to as ESPRIT) method
  • ESPRIT estimating signal parameter via variation in techniques
  • the present disclosure provides a signal arrival angle estimation method and a base station to solve the problem of poor signal arrival angle estimation performance in a scenario where a low signal-to-noise ratio and multi-coherent source signals are close to each other.
  • An embodiment of the present disclosure provides a method for estimating the angle of arrival of a signal, including:
  • the estimated value of the signal arrival angle is determined.
  • the adjusting the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix includes:
  • adjusting the diagonal elements in the approximate autocorrelation matrix to obtain the adjusted autocorrelation matrix includes:
  • the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.
  • determining the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix includes:
  • the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.
  • adjusting the diagonal elements in the approximate autocorrelation matrix to obtain the adjusted autocorrelation matrix includes:
  • the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.
  • constructing the diagonal noise matrix according to the approximate autocorrelation matrix includes:
  • the step of constructing the noise diagonal matrix according to the P eigenvalues includes:
  • the average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.
  • adjusting the diagonal elements in the approximate autocorrelation matrix according to the noise diagonal matrix to obtain the adjusted autocorrelation matrix includes:
  • the difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.
  • the determining the estimated value of the signal arrival angle according to the adjusted autocorrelation matrix includes:
  • the estimated value of the angle of arrival of the D channel signal is determined.
  • constructing the signal subspace and noise subspace of the D signal according to the autocorrelation matrix includes:
  • a noise subspace is constructed according to P-D feature vectors corresponding to P-D eigenvalues other than the D eigenvalues among the P eigenvalues.
  • constructing the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues includes:
  • the acquisition signal adjustment factor includes:
  • a signal conditioning factor is determined.
  • determining the signal adjustment factor according to the D characteristic values includes:
  • the average value is determined as the signal adjustment factor.
  • the adjusting the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector includes:
  • the product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.
  • the i-th spectral function among the D spectral functions is:
  • P i is the spectral function corresponding to the i-th signal
  • a( ⁇ i , ) Is the steering vector of the i-th signal
  • ⁇ i is the azimuth of the i-th signal
  • E s i is the i-th path signal subspace signal
  • E n is the noise subspace
  • H denotes the conjugate transpose of a matrix
  • i is a positive integer, and i is less than or equal to D.
  • the determining the estimated value of the angle of arrival of the D signal according to the D spectral functions includes:
  • the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.
  • calculating the estimated value of the angle of arrival of the D signal according to the D spectral functions in order from the largest to the smallest of the D characteristic values includes:
  • the i-1 pair of angle values corresponding to the previous i-1 signal determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.
  • the pair of angle values corresponding to the maximum spectral peak of the D pair of angle values is determined as the angle of arrival of the i-th signal Estimates, including:
  • the pair of angle values corresponding to the maximum spectral peak among the k pairs of angle values is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.
  • D is preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.
  • Some embodiments of the present disclosure also provide a base station, including: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • a base station including: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor executes the computer program, the following steps are implemented :
  • the estimated value of the signal arrival angle is determined.
  • the processor implements the following steps when executing the computer program:
  • the processor implements the following steps when executing the computer program:
  • the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.
  • the processor implements the following steps when executing the computer program:
  • the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.
  • the processor implements the following steps when executing the computer program:
  • the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.
  • the processor implements the following steps when executing the computer program:
  • the processor implements the following steps when executing the computer program:
  • the average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.
  • the processor implements the following steps when executing the computer program:
  • the difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.
  • the processor implements the following steps when executing the computer program:
  • the estimated value of the angle of arrival of the D channel signal is determined.
  • the processor implements the following steps when executing the computer program:
  • a noise subspace is constructed.
  • the processor implements the following steps when executing the computer program:
  • the processor implements the following steps when executing the computer program:
  • a signal conditioning factor is determined.
  • the processor implements the following steps when executing the computer program:
  • the average value is determined as the signal adjustment factor.
  • the processor implements the following steps when executing the computer program:
  • the product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.
  • the i-th spectral function among the D spectral functions is:
  • P i is the spectral function corresponding to the i-th signal
  • a( ⁇ i , ) Is the steering vector of the i-th signal
  • ⁇ i is the azimuth of the i-th signal
  • E s i is the i-th path signal subspace signal
  • E n is the noise subspace
  • H denotes the conjugate transpose of a matrix
  • i is a positive integer, and i is less than or equal to D.
  • the processor implements the following steps when executing the computer program:
  • the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.
  • the processor implements the following steps when executing the computer program:
  • the i-1 pair of angle values corresponding to the previous i-1 signal determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.
  • the processor implements the following steps when executing the computer program:
  • the pair of angle values corresponding to the maximum spectral peak among the k pairs of angle values is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.
  • D is preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.
  • Some embodiments of the present disclosure also provide a base station, including:
  • An acquisition module for acquiring an approximate auto-correlation matrix of signals received by the surface antenna array
  • An adjustment module configured to adjust the approximate auto-correlation matrix according to noise transmission characteristics to obtain an adjusted auto-correlation matrix
  • the determining module is used to determine the estimated value of the angle of arrival of the signal according to the adjusted autocorrelation matrix.
  • Some embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the signal arrival angle estimation method described above.
  • the approximate auto-correlation matrix is adjusted, and then the estimated value of the signal angle of arrival is determined according to the adjusted auto-correlation matrix, so as to weaken the influence of noise on signal angle of arrival detection, Improve the accuracy of signal angle of arrival detection, and avoid missed detection and false detection.
  • the scheme can also be applied to the estimation of signal angle of arrival of various types of planar antenna arrays, which is beneficial to improve the adaptability of signal angle of arrival estimation.
  • FIG. 1 shows a flowchart of a method for estimating the angle of arrival of signals according to some embodiments of the present disclosure
  • FIG. 2 shows a schematic diagram of a uniform surface antenna array according to some embodiments of the present disclosure
  • FIG. 3 shows a schematic diagram of a 5G indoor positioning scenario in some embodiments of the present disclosure
  • FIG. 4 is a schematic diagram illustrating SRS reference signal SINR simulation results of some embodiments of the present disclosure
  • FIG. 5a shows one of the spectral peak schematic diagrams using the traditional MUISC algorithm in some embodiments of the present disclosure
  • FIG. 5b shows one of the spectrum peak schematic diagrams of the Topritz noise cancellation algorithm in some embodiments of the present disclosure
  • FIG. 6a shows a second schematic diagram of the spectral peak using the traditional MUISC algorithm in some embodiments of the present disclosure
  • FIG. 6b shows a second schematic diagram of the spectral peaks of the Topritz noise cancellation algorithm in some embodiments of the present disclosure
  • FIG. 7a shows one of the spectral peak schematic diagrams of the eigenvalue decomposition noise reduction algorithm in some embodiments of the present disclosure
  • FIG. 7b shows a second schematic diagram of the spectral peaks of the eigenvalue decomposition and noise reduction algorithm in some embodiments of the present disclosure
  • FIG. 7c shows a third schematic diagram of the spectral peaks of the eigenvalue decomposition noise reduction algorithm in some embodiments of the present disclosure
  • FIG. 8a shows the curve of the RMSE of the elevation angle with the number of antenna elements in some embodiments of the present disclosure
  • FIG. 8b shows a curve of the RMSE of the azimuth angle with the number of antenna elements in some embodiments of the present disclosure
  • FIG. 9 shows a block diagram of a base station according to some embodiments of the present disclosure.
  • FIG. 10 shows a structural block diagram of the base station of the present disclosure.
  • system and “network” are often used interchangeably in this document.
  • B corresponding to A means that B is associated with A, and B can be determined according to A.
  • determining B based on A does not mean determining B based on A alone, and B may also be determined based on A and/or other information.
  • the form of the access network is not limited, and may include a macro base station (Macro Base Station), a micro base station (Pico Base Station), a Node B (3G mobile base station), and an enhanced base station (eNB) , GNB (called 5G mobile base station), home enhanced base station (Femto eNB or Home eNode B or Home eNB or HeNB), relay station, access point, RRU (Remote Radio Unit), RRH (Remote Radio Unit) Head, RF remote head) and other access networks.
  • a macro base station Micro Base Station
  • Micro Base Station Micro Base Station
  • Pico Base Station a Node B
  • eNB enhanced base station
  • GNB called 5G mobile base station
  • home enhanced base station Femto eNB or Home eNode B or Home eNB or HeNB
  • relay station access point
  • RRU Remote Radio Unit
  • RRH Remote Radio Unit
  • the user terminal may be a mobile phone (or cell phone), or other devices capable of sending or receiving wireless signals, including user equipment, personal digital assistants (PDAs), wireless modems, wireless communication devices, handheld devices, laptop computers, cordless phones , Wireless Local Loop (WLL) station, CPE (Customer Equipment, Customer Terminal) capable of converting mobile signals into WiFi signals, or mobile smart hotspots, smart appliances, or other devices that can communicate with mobile communication networks spontaneously without human operation Equipment etc.
  • PDAs personal digital assistants
  • WLL Wireless Local Loop
  • CPE Customer Equipment, Customer Terminal
  • the embodiments of the present disclosure provide a signal arrival angle estimation method, which solves the problem of poor signal arrival angle estimation performance in a scenario where a low signal-to-noise ratio and multi-coherent source signals are close to each other.
  • the method for estimating the angle of arrival of the signal in some embodiments of the present disclosure is executed by the base station side, and the terminal side reports the angle of arrival estimation request message.
  • an embodiment of the present disclosure provides a method for estimating the angle of arrival of a signal, which specifically includes the following steps:
  • Step 11 Obtain the approximate auto-correlation matrix of the signal received by the surface antenna array.
  • planar antenna array includes but is not limited to: uniform array, non-uniform array, rectangular array, circular array, etc.
  • the approximate auto-correlation matrix is the mean value of the auto-correlation matrix of K received signals at different times; K is a positive integer.
  • the autocorrelation matrix of the received signal at time t may be determined in the following manner: the received signal of the antenna array at time t is determined based on the flow matrix of the antenna array; the antenna array is determined according to the received signal of the antenna array at time t The autocorrelation matrix of the received signal.
  • the mean value of the auto-correlation matrix of K received signals at different times is solved, and the approximate auto-correlation matrix of the received signal of the antenna array is determined. It should be noted that the larger the value of K, the better the approximation effect.
  • the value of K can be determined according to actual needs, and is not specifically limited here.
  • the uniform planar antenna array 20 has N rows and M columns of uniformly isotropic antenna elements 201 (M and N are positive integers), and D channels
  • M and N are positive integers
  • D channels When the signal reaches the antenna array (D is a positive integer, and D is less than or equal to P, P is associated with M and N, and P is the product of M and N), the flow matrix of the antenna array is composed as follows:
  • A represents the flow matrix of the antenna array, a( ⁇ i , ) Is the steering vector of the i-th signal;
  • T represents the transpose of the matrix
  • the steering vector of the i-th signal is the organic splicing of the steering vector of the i-th signal in the N-row of the antenna array.
  • the received signal at the antenna array end at time t is:
  • x(t) is the received signal at the antenna array at time t
  • s(t) is the complex amplitude vector of the D signal at time t
  • n(t) is the noise signal from the antenna array
  • A is the flow pattern of the antenna array matrix
  • the approximate autocorrelation matrix is:
  • R x is the approximate auto-correlation matrix of the received signal at the antenna array end
  • x i (t) is the received signal at the i-th t time
  • K is the number of snapshots.
  • Step 12 Adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.
  • the noise transmission characteristics include noise transmission parameters, such as: signal-to-noise ratio.
  • the approximate autocorrelation matrix is adjusted to the Toplitz matrix
  • the approximate autocorrelation matrix is adjusted by the approximate variance of noise to weaken
  • Step 13 Determine the estimated value of the angle of arrival of the signal according to the adjusted autocorrelation matrix.
  • the approximate autocorrelation matrix is adjusted by adjusting the noise transmission characteristics, and then the estimated value of the signal angle of arrival is determined according to the adjusted autocorrelation matrix, so as to weaken the influence of noise on signal angle of arrival detection and improve the signal Arrival angle detection accuracy, and avoid missing detection and wrong detection; in addition, the scheme can also be applied to the estimation of signal arrival angles of various types of area antenna arrays, which is beneficial to improve the adaptability of signal arrival angle estimation.
  • the above step 12 specifically includes: adjusting the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.
  • Adjusting the diagonal elements in the approximate autocorrelation matrix includes but is not limited to the following ways:
  • Method 1 Determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;
  • the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.
  • determining the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix may include:
  • i is the row index of the elements in the approximate autocorrelation matrix
  • j is the column index of the elements in the approximate autocorrelation matrix
  • l - (MN-1),-(MN-2),...,-1,0,1,...,(MN-2),(MN-1).
  • the average value is determined as the adjustment target value of the diagonal elements on the respective diagonal lines; wherein the diagonal line is the main diagonal line or the sub-diagonal line parallel to the main diagonal line.
  • the approximate autocorrelation matrix is adjusted to the Toplitz matrix, and the adjusted autocorrelation matrix is obtained as:
  • the approximate autocorrelation matrix of the received and received signals is adjusted to the Toplitz matrix to weaken the noise on the signal
  • the influence of angle of arrival detection is beneficial to improve the accuracy of signal angle of arrival detection.
  • Method 2 Construct a diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal noise matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;
  • the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.
  • the approximate variance of noise can be determined according to the approximate autocorrelation matrix; the diagonal diagonal matrix of noise can be constructed according to the approximate variance of the noise.
  • constructing the diagonal noise matrix may include:
  • constructing the noise diagonal matrix may include:
  • P eigenvalues obtained by performing eigenvalue decomposition on the approximate autocorrelation matrix, and arranging the P eigenvalues in descending order as: ⁇ 1 , ⁇ 2 , ... ⁇ P ;
  • the first D first eigenvalues that are largest among the P eigenvalues in a large to small order, and the second eigenvalues other than the D first eigenvalues in the P eigenvalues are the smallest of the P eigenvalues.
  • the P feature values are: 15,13,10,10,7,6,5,5,5,4,2,1; the value of D is 5, then the largest top D feature values are: 15,13,10,10,7; the smallest PD feature value is: 6,5,5,5,4,2,1.
  • some embodiments of the present disclosure only need to be able to distinguish the largest D feature values from the P feature values and the smallest P-D feature values, and the sorting step is not necessarily required.
  • the sorting step is not necessarily required.
  • the P feature values may not be arranged in order, but the P The smallest PD feature value among the feature values.
  • the average value of the smallest P-D feature values among the P feature values is:
  • I the average value of the smallest PD feature values among the P feature values; ⁇ D+1 , ⁇ D+2 ,..., ⁇ P are the smallest PD feature values among the P feature values.
  • the average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.
  • D may be preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.
  • adjusting the diagonal elements in the approximate autocorrelation matrix according to the noise diagonal matrix, and the adjusted autocorrelation matrix may include:
  • the difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.
  • the adjusted autocorrelation matrix is obtained. among them, R x is the approximate auto-correlation matrix, and R n is the diagonal noise matrix.
  • the noise diagonal matrix constructed by the average value adjusts the approximate auto-correlation matrix of the received signal to eliminate the influence of part of the noise on the eigenvalue decomposition (such as the effect of noise on the eigenvalue decomposition when the incoming direction is closer), Furthermore, the influence of noise on signal angle of arrival detection is weakened, which is beneficial to improve the accuracy of signal angle of arrival detection.
  • the above step 13 specifically includes: constructing the signal subspace and noise subspace of the D-channel signal according to the autocorrelation matrix; where D is a positive integer, D can be preset by the base station side, or determined from signaling , Or set by the terminal side and fed back to the base station side;
  • the estimated value of the angle of arrival of the D channel signal is determined.
  • constructing the signal subspace and noise subspace of the D-channel signal may include:
  • a noise subspace is constructed according to P-D feature vectors corresponding to P-D eigenvalues other than the D eigenvalues among the P eigenvalues.
  • eigenvalue decomposition is performed on the adjusted autocorrelation matrix to obtain P eigenvalues, feature vectors corresponding to the P eigenvalues, respectively, and a feature matrix composed of the feature vectors; the P eigenvalues Sorting according to the order from large to small, the feature matrix is adjusted according to the correspondence between the feature vector and the feature value, and finally the P feature values after sorting, the feature vectors corresponding to the P feature values, and the adjusted Feature matrix.
  • the largest first D eigenvalues of the P eigenvalues correspond to the signal, and the smallest last P-D feature values correspond to noise.
  • constructing the signal subspace of the D channel signal may include:
  • a signal adjustment factor may be determined according to the D characteristic values to characterize the energy sum of the signal.
  • the average value of the D characteristic values may be calculated; the average value is determined as the signal adjustment factor.
  • the product of the adjustment factor and the i-th feature vector may be determined as the adjusted i-th feature vector, that is, by increasing the amplitude of the i-th feature vector, Adjustment of the i-th feature vector.
  • the signal subspace of the i-th signal is:
  • ⁇ 1 , ⁇ 2 , ⁇ 3 ..., ⁇ D are the eigenvectors corresponding to the D eigenvalues respectively
  • ⁇ i is the eigenvector corresponding to the eigenvalue ⁇ i , which is also the i-th column in the feature matrix .
  • the noise subspace is:
  • ⁇ D+1 , ⁇ D+2 , ..., ⁇ P are eigenvectors corresponding to the PD eigenvalues, respectively.
  • the i-th spectral function among the D spectral functions is:
  • P i is the spectral function corresponding to the i-th signal
  • a( ⁇ i , ) Is the steering vector of the i-th signal
  • ⁇ i is the azimuth of the i-th signal
  • E s i is the i-th path signal subspace signal
  • E n is the noise subspace
  • H denotes the conjugate transpose of a matrix
  • i is a positive integer, and i is less than or equal to D.
  • adjusting the i-th feature vector by an adjustment factor is equivalent to using the energy of all signals and adjusting the i-th signal to make it a strong signal, avoiding that the spectral peak of the i-th signal may be affected by a strong signal
  • the problem of spectral peak coverage can also avoid the leakage and false detection problems caused by the signal strength of the i-th signal and other signals may be equivalent, so as to solve the problem of spectral peak interference of neighbor signals, which is beneficial to increase the angle of arrival of the signal
  • determining the estimated value of the angle of arrival of the D channel signal according to the D spectral functions may include: sequentially calculating the D channels according to the D spectral functions according to the order of the D characteristic values from large to small Estimated angle of arrival of the signal.
  • the eigenvalue corresponds to the eigenvector
  • the signal subspace is associated with the eigenvector
  • the D spectral functions correspond one-to-one to the signal subspace of the D signal
  • the step of sequentially calculating the estimated value of the angle of arrival of the D signal according to the D spectral functions in descending order of the D feature values includes:
  • the i-1 pair of angle values corresponding to the previous i-1 signal determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.
  • it may be to determine k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values; and map a pair corresponding to the largest spectral peak value among the k pairs of angle values
  • the angle value is determined to be the estimated value of the angle of arrival of the i-th signal; where k is a positive integer, and k is less than or equal to D.
  • the verification process of the i-th pair of angle values is: store the detected first i-1 pair of angle values in the array Angle, and perform spectral peak on the i-th spectral function search for. Take out the D pairs of angle values corresponding to the first D spectral peaks, and remove the detected i-1 pairs of angles in the array Angle from the D pairs of angle values, and the remaining D-i+1 (that is, k) pairs of angles Among the values, the pair of angle values corresponding to the maximum spectral peak value is the i-th pair of angle values (estimated value of the i-th signal arrival angle). In this way, after D times of searching for the same spectral peak, the azimuth and elevation angles of the D pairs corresponding to the D signals can be successfully detected, and have good accuracy.
  • Figure 3 shows a 5G indoor positioning scenario.
  • the distance between base stations is 20m, and the height is 3m, which is consistent with the indoor environment.
  • the user terminal may have a direct path with multiple base stations at the same time.
  • BS represents a base station (Base Station).
  • the uplink channel sounding signal (SRS) is reported by the user terminal to the base station periodically and is independent of the data sent. It occupies independent frequency domain resources. It was originally used to estimate the frequency domain information of the uplink channel. Do frequency selective scheduling; used to estimate uplink channels, do downlink beamforming, etc. Due to the periodicity, configurability and independent existence of the SRS signal, it does not need to send data, and it can also exist independently, which is beneficial as a real-time positioning reference signal.
  • SRS uplink channel sounding signal
  • the SRS resource is configured by the SRS resource information part (SRS-Resource) IE, which mainly includes:
  • SRS antenna ports are between 1000 and 2000, and the number of ports can be selected from 1, 2, and 4.
  • l 0 refers to the start position of the time domain, by It is concluded that l offset ⁇ 0, 1, ..., 5 ⁇ , Represents the number of symbols in a single time slot, Indicates that a single time slot is guaranteed to be completed Symbol transmission.
  • k 0 refers to the beginning of the frequency domain.
  • K TC log 2 (K TC )
  • K TC transmission comb number (transmission comb number)
  • ⁇ i the cyclic shift of the antenna port pi.
  • the SRS sequence is mapped to the physical layer:
  • m SRS, b can be obtained by looking at Table 1 for different cell reference signal (CRS) configuration modes, Represents the number of subcarriers in a Radio Bearer (Radio Bearer, RB for short), otherwise represents other.
  • CRS cell reference signal
  • n shift is the amount of frequency shift configured by the upper layer.
  • the SRS allocation is adjusted to the common resource block grid by a multiple of four.
  • n RRC is the quantity of the upper layer configuration, and N b can be obtained by referring to Table 1.
  • the multiplexing factor of the SRS signal can be roughly expressed as K TC *N b , and K TC takes a value of 2 or 4.
  • K TC takes a value of 2 or 4.
  • N b (N1, N2, N3) from Table 1 below. The value ranges from 1 to 17 depending on the CRS configuration method.
  • Table 1 SRS CRS configuration method.
  • SINR Signal to Interference plus Noise Ratio
  • the user's position can be modeled by the homogeneous Poisson point process ⁇ u with density ⁇ u , and the location of the base station in the network is also modeled by the homogeneous Poisson point process ⁇ with density ⁇ independent of ⁇ u .
  • the SINR of the user i reference signal received by the base station may be defined as:
  • h is the small-scale fading between the user and the base station, h ⁇ exp(1).
  • the typical user's probability of successful reception can be expressed as:
  • the probability that there is one less user in the radius R area is The probability density function of the distance from the user to the base station can be expressed as:
  • Table 2 SRS reference signal SINR simulation parameter table.
  • Fig. 5a is a schematic diagram of the spectral peak of the traditional MUISC algorithm using the above parameters, and there is only a sharp peak point in Fig. 5a.
  • FIG. 5b is a schematic diagram of the spectral peaks of the Topritz noise reduction algorithm in some embodiments of the present disclosure using the above parameters. It can be seen that some embodiments of the present disclosure can clearly distinguish three peak points.
  • the horizontal axis represents the signal arrival angle (unit: °)
  • the vertical axis represents the signal-to-noise ratio (unit: dB).
  • FIG. 6a it is a schematic diagram of a modified spectral peak based on FIG. 5a.
  • FIG. 6a is a schematic diagram of a spectral peak using the above parameters for a traditional MUISC algorithm. Schematic diagram of the spectral peaks of the algorithm. In Figs.
  • the horizontal axis represents the signal arrival angle (unit: °), and the vertical axis represents the signal-to-noise ratio (unit: dB).
  • the signal-to-noise ratio is -10dB
  • the number of snapshots is 1024
  • the peak scan step is 0.05°
  • the antenna elements of the antenna array The number is 20.
  • three signal arrival angles can be obtained, respectively 30.95°, 35.65°, and 33.25°.
  • the corresponding peaks are shown in Figures 7a and 7b.
  • the horizontal axis represents the signal arrival angle (unit: °)
  • the vertical axis represents the signal-to-noise ratio (unit: dB).
  • the difference from the true value is 0.95°, 0.25°, 0.35°. It can be seen that the eigenvalue decomposition reduces the influence of noise, and adopts the spectral function construction method and the spectral peak scanning strategy in some embodiments of the present disclosure to provide a more accurate angle estimation result when the signal arrival angle is closer.
  • RMSE root-mean-square error
  • Elevation angle ⁇ [51 55 59 63 67; 23 35 47 59 71], azimuth Set two sets of three-dimensional angle values, which are the scenarios where the incoming angles differ by about 10° and 4°.
  • the RMSE results are shown in Table 3.
  • Simulation environment The signal-to-noise ratio is fixed at -5dB, 200 drops, and the number of snapshots is 1024.
  • the MSEIC algorithm's RMSE is about 0.004° for the elevation angle, and the RMSE for the azimuth is about 7e-4°.
  • the TOPLZ noise reduction MUSIC algorithm has an RMSE of elevation angle of about 0.001°, and an RMSE of azimuth angle of about 4e-4°, which can provide more accurate accuracy.
  • the Topsight's noise reduction MUSIC algorithm has an elevation RMSE of about 20° and an azimuth RMSE of about 15°.
  • the eigenvalue decomposition weakens the noise.
  • the RMSE of the elevation angle of the MUSIC algorithm is around 0.9°, and the RMSE of the azimuth angle is around 0.6°.
  • the signal arrival angle estimation methods in some embodiments of the present disclosure can better cope with the environment with a low signal-to-noise ratio and the multi-channel signal arrival angles are similar.
  • the accuracy of the azimuth angle is generally higher than the estimation accuracy of the elevation angle.
  • a stereo antenna array can be used to improve the estimation accuracy of the elevation angle.
  • this method By estimating the RMSE from the angle of different antenna array elements, it can be seen that this method has a lower limit on the number of antenna array elements. That is, in order to ensure the accuracy of the signal angle of arrival estimation, an antenna array of a certain size needs to be provided. It can be known from the simulation results that when the number of antenna elements reaches the threshold, the angle estimation accuracy is no longer sensitive to the antenna size. Therefore, as long as the antenna size reaches the threshold value in production, the angle estimation can obtain a better profit.
  • the base station 900 of some embodiments of the present disclosure includes:
  • the obtaining module 910 is used to obtain an approximate auto-correlation matrix of signals received by the surface antenna array
  • the adjustment module 920 is configured to adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain an adjusted auto-correlation matrix
  • the determining module 930 is configured to determine the estimated value of the angle of arrival of the signal according to the adjusted auto-correlation matrix.
  • the adjustment module 920 includes:
  • the adjustment sub-module is used to adjust the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.
  • the adjustment sub-module includes:
  • a determining unit configured to determine the adjustment target value of diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix
  • the first adjustment unit is configured to adjust the approximate auto-correlation matrix to a Toeplitz matrix according to the adjustment target value to obtain an adjusted auto-correlation matrix.
  • the determining unit includes:
  • the first calculation subunit is used to calculate the average value of the diagonal elements on each diagonal line in the approximate autocorrelation matrix respectively;
  • a first determining subunit configured to determine the average value as the adjustment target value of the diagonal elements on the respective diagonal lines
  • the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.
  • the adjustment sub-module includes:
  • a construction unit configured to construct a diagonal diagonal noise matrix based on the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;
  • the second adjusting unit is configured to adjust the diagonal elements in the approximate auto-correlation matrix according to the noise diagonal matrix to obtain an adjusted auto-correlation matrix.
  • the building unit includes:
  • a decomposition subunit configured to perform eigenvalue decomposition on the approximate autocorrelation matrix to obtain P eigenvalues
  • the first construction subunit is configured to construct the diagonal diagonal matrix of noise according to the P eigenvalues; where P is a positive integer.
  • the first constructing subunit is specifically used to: calculate the average value of the smallest PD characteristic values among the P characteristic values; determine the average value as the value of the diagonal element in the diagonal matrix, to Construct the noise diagonal matrix; where D is a positive integer and D is less than K.
  • the second adjustment unit includes:
  • the second calculation subunit is used to calculate the difference between the approximate auto-correlation matrix and the noise diagonal matrix to obtain an adjusted auto-correlation matrix.
  • the determining module 930 includes:
  • a first construction submodule configured to construct a signal subspace and a noise subspace of the D signals according to the autocorrelation matrix; where D is a positive integer;
  • a second construction submodule configured to construct D spectral functions according to the signal subspace and the noise subspace of the D channel signals
  • the determination submodule is used for determining the estimated value of the angle of arrival of the D channel signal according to the D spectral functions.
  • the first building sub-module includes:
  • a first decomposition unit configured to perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;
  • a first construction unit configured to construct a signal subspace of the D-channel signal according to the largest D of the P feature values and the D feature vectors corresponding to the D feature values;
  • the second construction unit is configured to construct a noise subspace according to P-D feature vectors corresponding to P-D feature values of the P feature values except the D feature values.
  • the first building unit includes:
  • Acquisition subunit for acquiring signal conditioning factors
  • An adjustment subunit configured to adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector
  • a second constructing subunit configured to construct a signal subspace of the i-th signal according to the adjusted i-th feature vector and the feature vectors other than the i-th feature vector among the D feature vectors ; Where i is a positive integer and i is less than or equal to D.
  • the obtaining subunit is specifically used to determine the signal adjustment factor according to the D characteristic values.
  • the obtaining subunit is further specifically used for: calculating an average value of the D characteristic values; and determining the average value as the signal adjustment factor.
  • the adjustment subunit is specifically used to determine the product of the adjustment factor and the i-th feature vector as the adjusted i-th feature vector.
  • the i-th spectral function among the D spectral functions is:
  • P i is the spectral function corresponding to the i-th signal
  • ⁇ ( ⁇ i , ) Is the steering vector of the i-th signal
  • ⁇ i is the azimuth of the i-th signal
  • E s i is the i-th path signal subspace signal
  • E n is the noise subspace
  • H denotes the conjugate transpose of a matrix
  • i is a positive integer, and i is less than or equal to D.
  • the determining sub-module includes:
  • the calculation unit is configured to sequentially calculate the estimated value of the angle of arrival of the D-channel signal according to the D spectral functions in order from the largest to the smallest.
  • the calculation unit includes:
  • the third calculation subunit is used to calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;
  • a second determining subunit configured to determine a pair of angle values corresponding to the maximum spectral peak value of the D pair angle values as the i-th signal according to the i-1 pair angle values corresponding to the previous i-1 way signals
  • the estimated value of the angle of arrival of; i is a positive integer, and i is less than or equal to D.
  • the second determining subunit is specifically configured to: determine k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values; correspond to the maximum spectral peak value of the k pairs of angle values The pair of angle values of is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.
  • D is preset in the base station side or determined through signaling or set in the terminal side and fed back to the base station side.
  • the embodiment of the base station of the present disclosure corresponds to the embodiment of the method for estimating the angle of arrival of the signal described above. All the implementation means in the embodiment of the method described above are applicable to the embodiment of the base station, and the same technical effect can also be achieved.
  • the base station 900 adjusts the approximate auto-correlation matrix by adjusting the noise transmission characteristics, and then determines the estimated value of the signal arrival angle according to the adjusted auto-correlation matrix, so as to weaken the noise
  • the impact of detection improves the accuracy of signal arrival angle detection and avoids missed detection and false detection; in addition, the scheme can also be applied to the estimation of signal arrival angles of various types of area antenna arrays, which is beneficial to improve the appropriateness of signal arrival angle estimation Matching.
  • some embodiments of the present disclosure further provide a base station, which includes: a processor 1000; a memory 1020 connected to the processor 1000 through a bus interface 1030, And a transceiver 1010 connected to the processor 1000 through a bus interface 1030; the memory 1020 is used to store programs and data used by the processor 1000 when performing operations; and send data information or guides through the transceiver 1010 Frequency, it also receives the uplink control channel through the transceiver 1010; when the processor 1000 calls and executes the programs and data stored in the memory 1020, the following functions are realized.
  • the processor 1000 is used to read the program in the memory 1020.
  • the processor 1000 executes the computer program, the following steps are realized: obtaining an approximate autocorrelation matrix of the received signal of the surface antenna array; according to the noise transmission characteristics, the approximate self The correlation matrix is adjusted to obtain an adjusted auto-correlation matrix; according to the adjusted auto-correlation matrix, the estimated value of the signal arrival angle is determined.
  • the processor 1000 executes the computer program, the following steps are implemented: adjusting the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.
  • the processor 1000 executes the computer program, the following steps are implemented: according to the element values in the approximate autocorrelation matrix, determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix; according to the adjustment target value , Adjust the approximate auto-correlation matrix to a Toplitz matrix to obtain an adjusted auto-correlation matrix.
  • the processor 1000 executes the computer program, the following steps are implemented: separately calculating the average value of the diagonal elements on each diagonal line in the approximate autocorrelation matrix; determining the average value as the respective diagonal line The adjustment target value of the diagonal elements on the above; wherein the diagonal line is the main diagonal line or the sub-diagonal line parallel to the main diagonal line.
  • the processor 1000 executes the computer program, the following steps are implemented: constructing a diagonal diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix and the number of rows and columns of the approximate autocorrelation matrix Equality; adjust the diagonal elements in the approximate auto-correlation matrix according to the noise diagonal matrix to obtain the adjusted auto-correlation matrix.
  • the processor 1000 executes the computer program, the following steps are implemented: performing eigenvalue decomposition on the approximate autocorrelation matrix to obtain P eigenvalues; constructing the noise diagonal matrix according to the P eigenvalues; wherein , P is a positive integer.
  • the processor 1000 executes the computer program, the following steps are realized: calculating the average value of the smallest PD characteristic values among the P characteristic values; determining the average value as the value of diagonal elements in the diagonal matrix To construct the diagonal matrix of noise; where D is a positive integer and D is less than K.
  • the processor 1000 executes the computer program, the following steps are implemented: calculating the difference between the approximate auto-correlation matrix and the diagonal noise matrix to obtain an adjusted auto-correlation matrix.
  • the processor 1000 executes the computer program, the following steps are implemented: constructing the signal subspace and noise subspace of the D-channel signal according to the autocorrelation matrix; where D is a positive integer; and the signal of the D-channel signal Constructing D spectral functions in the subspace and the noise subspace; according to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.
  • the processor 1000 executes the computer program, the following steps are implemented: performing eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; wherein, P It is a positive integer; according to the largest D first eigenvalues of the P eigenvalues, and the D eigenvectors corresponding to the D eigenvalues, construct a signal subspace of the D channel signal; according to the P eigenvalues In the PD feature vectors corresponding to the PD feature values except the D feature values, a noise subspace is constructed.
  • the processor 1000 executes the computer program, the following steps are realized: acquiring a signal adjustment factor; adjusting the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector; according to the adjustment The i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector to construct the signal subspace of the i-th signal; where i is a positive integer and i is less than or equal to D.
  • the processor 1000 executes the computer program, the following steps are implemented: calculating an average value of the D feature values; and determining the average value as the signal adjustment factor.
  • the processor 1000 executes the computer program, the following steps are implemented: the product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.
  • the i-th spectral function among the D spectral functions is:
  • P i is the spectral function corresponding to the i-th signal
  • ⁇ ( ⁇ i , ) Is the steering vector of the i-th signal
  • ⁇ i is the azimuth of the i-th signal
  • E s i is the i-th path signal subspace signal
  • E n is the noise subspace
  • H denotes the conjugate transpose of a matrix
  • i is a positive integer, and i is less than or equal to D.
  • the processor 1000 executes the computer program, the following steps are implemented: in accordance with the order of the D feature values from largest to smallest, the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.
  • the processor 1000 executes the computer program, the following steps are realized: calculating the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, the angle value includes the azimuth angle value and the elevation angle value; according to the previous i- I-1 pair of angle values corresponding to one signal, the pair of angle values corresponding to the maximum spectral peak among the D pair of angle values is determined as the estimated value of the angle of arrival of the i-th signal; i is a positive integer, And i is less than or equal to D.
  • the processor 1000 executes the computer program, the following steps are realized: determining k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values; and determining the maximum spectrum among the k pairs of angle values
  • the pair of angle values corresponding to the peak value is determined to be the estimated value of the angle of arrival of the i-th signal; where k is a positive integer, and k is less than or equal to D.
  • D is preset in the base station side or determined by signaling or set in the terminal side and fed back to the base station side.
  • the base station in some embodiments of the present disclosure adjusts the approximate auto-correlation matrix by adjusting the noise transmission characteristics, and then determines the estimated value of the signal angle of arrival according to the adjusted auto-correlation matrix, so as to weaken the noise for signal angle of arrival detection Impact, improve the accuracy of signal arrival angle detection, and avoid missed detection and false detection; in addition, the scheme can also be applied to the estimation of the signal arrival angle of many types of surface antenna arrays, which is beneficial to improve the adaptation of the signal arrival angle estimation Sex.
  • the transceiver 1010 is used to receive and transmit data under the control of the processor 1000.
  • the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by the processor 1000 and various circuits of the memory represented by the memory 1020 are linked together.
  • the bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and therefore, they will not be further described in this article.
  • the bus interface provides an interface.
  • the transceiver 1010 may be a plurality of elements, including a transmitter and a transceiver, and provides a unit for communicating with various other devices on a transmission medium.
  • the processor 1000 is responsible for managing the bus architecture and general processing, and the memory 1020 may store data used by the processor 1000 when performing operations.
  • Some embodiments of the present disclosure also provide a computer-readable storage medium that stores a computer program on the computer-readable storage medium.
  • the computer program is executed by a processor, each process of the above-described signal angle of arrival estimation method embodiments is implemented, and can To achieve the same technical effect, in order to avoid repetition, it will not be repeated here.
  • the computer-readable storage medium such as read-only memory (Read-Only Memory, ROM for short), random access memory (Random Access Memory, RAM for short), magnetic disk or optical disk, etc.
  • each component or each step can be decomposed and/or recombined.
  • These decompositions and/or recombinations should be regarded as equivalent solutions of the present disclosure.
  • the steps for performing the above-mentioned series of processing can naturally be executed in chronological order in the order described, but it does not necessarily need to be executed in chronological order, and some steps can be executed in parallel or independently of each other.
  • the purpose of the present disclosure can also be achieved by running a program or a group of programs on any computing device.
  • the computing device may be a well-known general-purpose device. Therefore, the object of the present disclosure can also be achieved only by providing a program product containing program code for implementing the method or device. That is, such a program product also constitutes the present disclosure, and a storage medium storing such a program product also constitutes the present disclosure. Obviously, the storage medium may be any known storage medium or any storage medium developed in the future.
  • each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent solutions of the present disclosure.
  • the steps for performing the above-mentioned series of processing may naturally be performed in chronological order in the order described, but it is not necessary to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

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Abstract

一种信号到达角的估计方法及基站，其中该方法包括：获取面天线阵列接收信号的近似自相关矩阵(11)；根据噪声传输特性，对近似自相关矩阵进行调整，得到调整后的自相关矩阵(12)；根据调整后的自相关矩阵，确定信号到达角的估计值(13)。

Description

信号到达角的估计方法及基站
相关申请的交叉引用
本申请主张在2019年1月11日在中国提交的中国专利申请号No.201910028442.X的优先权，其全部内容通过引用包含于此。
技术领域
本公开涉及通信技术领域，尤其涉及一种信号到达角的估计方法及基站。
背景技术
基于矩阵特征空间分解的多重信号分类(Multiple Signal Classification，简称MUSIC)方法，在几何上信号处理的观测空间可以分解为彼此正交的信号子空间和噪声子空间。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成。根据其正交性构建空间谱函数，其谱峰对应的角度即是信号到达角。这种算法在低信噪比环境下，由于信号和噪声差别不大或者噪声强于信号，导致信号到达角的估计性能严重下降；当多个相干信源的信号到达角相近时，会出现三种可能情况：(1)谱函数中强信号将弱信号的谱峰覆盖，导致漏检；(2)信号强度相当时，不同信号的谱峰互相叠加后只出现一个谱峰，导致漏检并错检；(3)信号强度相当时,不同信号的谱峰互相叠加后谱峰中移，仍然出现两个谱峰，导致错检。此外，传统的基于旋转不变技术的信号参数估计(estimating signal parameter via rotational invariance techniques，简称ESPRIT)方法受限于天线阵列的物理结构，也不利于信号到达角的估计性能。
发明内容
本公开提供一种信号到达角的估计方法及基站，以解决低信噪比和多相干源信号相近到达场景下，信号到达角度估计性能差的问题。
本公开的实施例提供一种信号到达角的估计方法，包括：
获取面天线阵列接收信号的近似自相关矩阵；
根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
根据所述调整后的自相关矩阵，确定信号到达角的估计值。
其中，所述根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵，包括：
根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
其中，所述对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵，包括：
根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；
根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵。
其中，所述根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值，包括：
分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；
将所述平均值确定为各自对角线上的对角线元素的调整目标值；
其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
其中，所述对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵，包括：
根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；
根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
其中，所述根据所述近似自相关矩阵，构建噪声对角矩阵，包括：
对所述近似自相关矩阵进行特征值分解，得到P个特征值；
根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数。
其中，所述根据所述P个特征值，构建所述噪声对角矩阵的步骤，包括：
计算所述P个特征值中最小的P-D个特征值的平均值；
将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
其中，所述根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵，包括：
计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
其中，所述根据调整后的自相关矩阵，确定信号到达角的估计值，包括：
根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数；
根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；
根据所述D个谱函数，确定所述D路信号到达角的估计值。
其中，所述根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间，包括：
对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；
根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；
根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
其中，所述根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间，包括：
获取信号调节因子；
根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量；
根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
其中，所述获取信号调节因子，包括：
根据所述D个特征值，确定信号调节因子。
其中，所述根据所述D个特征值，确定信号调节因子，包括：
计算所述D个特征值的平均值；
将所述平均值确定为所述信号调节因子。
其中，所述根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量，包括：
将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量。
其中，所述D个谱函数中的第i个谱函数为：
Figure PCTCN2020070638-appb-000001
其中，P i为第i路信号对应的谱函数，a(θ i,
Figure PCTCN2020070638-appb-000002
)为第i路信号的导向向量，θ i为第i路信号的方位角，
Figure PCTCN2020070638-appb-000003
为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
其中，所述根据所述D个谱函数，确定D路信号到达角的估计值，包括：
按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
其中，所述按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值，包括：
计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；
根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
其中，所述根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值，包括：
确定所述D对角度值中除所述i-1对角度值之外的k对角度值；
将所述k对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
其中，D由基站侧预先设定，或者从信令中确定，或者由终端侧设定并反馈至基站侧。
本公开一些实施例还提供了一种基站，包括：收发机、存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现以下步骤：
获取面天线阵列接收信号的近似自相关矩阵；
根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
根据所述调整后的自相关矩阵，确定信号到达角的估计值。
其中，所述处理器执行所述计算机程序时实现以下步骤：
根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
其中，所述处理器执行所述计算机程序时实现以下步骤：
根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；
根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵。
其中，所述处理器执行所述计算机程序时实现以下步骤：
分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；
将所述平均值确定为各自对角线上的对角线元素的调整目标值；
其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
其中，所述处理器执行所述计算机程序时实现以下步骤：
根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；
根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
其中，所述处理器执行所述计算机程序时实现以下步骤：
对所述近似自相关矩阵进行特征值分解，得到P个特征值；
根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数。
其中，所述处理器执行所述计算机程序时实现以下步骤：
计算所述P个特征值中最小的P-D个特征值的平均值；
将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
其中，所述处理器执行所述计算机程序时实现以下步骤：
计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
其中，所述处理器执行所述计算机程序时实现以下步骤：
根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数；
根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；
根据所述D个谱函数，确定所述D路信号到达角的估计值。
其中，所述处理器执行所述计算机程序时实现以下步骤：
对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；
根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；
根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
其中，所述处理器执行所述计算机程序时实现以下步骤：
获取信号调节因子；
根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量；
根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
其中，所述处理器执行所述计算机程序时实现以下步骤：
根据所述D个特征值，确定信号调节因子。
其中，所述处理器执行所述计算机程序时实现以下步骤：
计算所述D个特征值的平均值；
将所述平均值确定为所述信号调节因子。
其中，所述处理器执行所述计算机程序时实现以下步骤：
将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量。
其中，所述D个谱函数中的第i个谱函数为：
Figure PCTCN2020070638-appb-000004
其中，P i为第i路信号对应的谱函数，a(θ i,
Figure PCTCN2020070638-appb-000005
)为第i路信号的导向向量，θ i为第i路信号的方位角，
Figure PCTCN2020070638-appb-000006
为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
其中，所述处理器执行所述计算机程序时实现以下步骤：
按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
其中，所述处理器执行所述计算机程序时实现以下步骤：
计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；
根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
其中，所述处理器执行所述计算机程序时实现以下步骤：
确定所述D对角度值中除所述i-1对角度值之外的k对角度值；
将所述k对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
其中，D由基站侧预先设定，或者从信令中确定，或者由终端侧设定并反馈至基站侧。
本公开一些实施例还提供了一种基站，包括：
获取模块，用于获取面天线阵列接收信号的近似自相关矩阵；
调整模块，用于根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
确定模块，用于根据所述调整后的自相关矩阵，确定信号到达角的估计值。
本公开一些实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如上所述的信号到达角的估计方法的步骤。
本公开的上述技术方案的有益效果是：
本公开一些实施例，通过对噪声传输特性，对所述近似自相关矩阵进行调整，进而根据调整后的自相关矩阵，确定信号到达角的估计值，以削弱噪声对于信号到达角检测的影响，提高信号到达角检测精度，并且避免漏检测和错检测；此外，该方案还可以应用于多种类型的面天线阵列的信号到达角的估计，进而利于提高信号到达角估计的适配性。
附图说明
图1表示本公开一些实施例的信号到达角的估计方法的流程图；
图2表示本公开一些实施例的均匀面天线阵列的示意图；
图3表示本公开一些实施例中5G室内定位场景的示意图；
图4表示本公开一些实施例SRS参考信号SINR仿真结果的示意图；
图5a表示本公开一些实施例中采用传统MUISC算法的谱峰示意图之一；
图5b表示本公开一些实施例中拓普利兹消除噪声算法的谱峰示意图之一；
图6a表示本公开一些实施例中采用传统MUISC算法的谱峰示意图之二；
图6b表示本公开一些实施例中拓普利兹消除噪声算法的谱峰示意图之二；
图7a表示本公开一些实施例中特征值分解削弱噪声算法的谱峰示意图之一；
图7b表示本公开一些实施例中特征值分解削弱噪声算法的谱峰示意图之二；
图7c表示本公开一些实施例中特征值分解削弱噪声算法的谱峰示意图之三；
图8a表示本公开一些实施例中仰角的RMSE随天线元素数量变化的曲线；
图8b表示本公开一些实施例中方位角的RMSE随天线元素数量变化的曲线；
图9表示本公开一些实施例的基站的框图；
图10表示本公开的基站的结构框图。
具体实施方式
为使本公开要解决的技术问题、技术方案和优点更加清楚，下面将结合附图及具体实施例进行详细描述。在下面的描述中，提供诸如具体的配置和组件的特定细节仅仅是为了帮助全面理解本公开的实施例。因此，本领域技术人员应该清楚，可以对这里描述的实施例进行各种改变和修改而不脱离本公开的范围和精神。另外，为了清楚和简洁，省略了对已知功能和构造的描述。
应理解，说明书通篇中提到的“一个实施例”或“一实施例”意味着与实施例有关的特定特征、结构或特性包括在本公开的至少一个实施例中。因此，在整个说明书各处出现的“在一个实施例中”或“在一实施例中”未必一定指相同的实施例。此外，这些特定的特征、结构或特性可以任意适合的方式结合在一个或多个实施例中。
在本公开的各种实施例中，应理解，下述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本公开一些实施例的实施过程构成任何限定。
另外，本文中术语“系统”和“网络”在本文中常可互换使用。
在本申请所提供的实施例中，应理解，“与A相应的B”表示B与A相关联，根据A可以确定B。但还应理解，根据A确定B并不意味着仅仅根据A确定B，还可以根据A和/或其它信息确定B。
本公开一些实施例中，接入网的形式不限，可以是包括宏基站(Macro Base Station)、微基站(Pico Base Station)、Node B(3G移动基站的称呼)、增强型基站(eNB)、gNB(5G移动基站的称呼),家庭增强型基站(Femto eNB或Home eNode B或Home eNB或HeNB)、中继站、接入点、RRU(Remote Radio Unit，远端射频模块)、RRH(Remote Radio Head，射频拉远头)等的接入网。用户终端可以是移动电话(或手机)，或者其他能够发送或接收无线信号的设备，包括用户设备、个人数字助理(PDA)、无线调制解调器、无线通信装置、手持装置、膝上型计算机、无绳电话、无线本地回路(WLL)站、能够将移动信号转换为WiFi信号的CPE(Customer Premise Equipment，客户终端)或移动智能热点、智能家电、或其他不通过人的操作就能自发与移动通信网络通信的设备等。
具体地，本公开的实施例提供了一种信号到达角的估计方法，解决了低信噪比和多相干源信号相近到达场景下，信号到达角度估计性能差的问题。
本公开一些实施例中的信号到达角的估计方法由基站侧执行，由终端侧上报到达角估计请求消息。
如图1所示，本公开的实施例提供了一种信号到达角的估计方法，具体包括以下步骤：
步骤11：获取面天线阵列接收信号的近似自相关矩阵。
其中，该面天线阵列包括但不限于：均匀阵列、非均匀阵列、长方形阵列、圆形阵列等。
其中，近似自相关矩阵是K个不同时刻的接收信号的自相关矩阵的均值；K为正整数。
具体的，在t时刻的接收信号的自相关矩阵可以采用如下方式确定：基于天线阵列的流型矩阵确定天线阵列在t时刻的接收信号；根据天线阵列在t时刻的接收信号，确定该天线阵列的接收信号的自相关矩阵。
进而基于大数据定理，求解K个不同时刻的接收信号的自相关矩阵的均值，确定为天线阵列接收信号的近似自相关矩阵。需要说明的是，K的取值越大近似效果越好，K的取值可以根据实际需求确定，这里不做具体限定。
为了方便说明，以下以均匀面阵列为例：
如图2，给出了一种均匀的面天线阵列的示例，该均匀的面天线阵列20有N行M列的均匀各向同性的天线元素201(M、N为正整数)，有D路信号到达该天线阵列(D为正整数，且D小于或者等于P，P与M、N相关联，P为M、N的乘积)则天线阵列的流型矩阵构成如下：
Figure PCTCN2020070638-appb-000007
其中，A表示天线阵列的流型矩阵，a(θ i,
Figure PCTCN2020070638-appb-000008
)为第i路信号的导向向量；
Figure PCTCN2020070638-appb-000009
其中，a k(θ i,
Figure PCTCN2020070638-appb-000010
)为第i路信号在第k行的导向向量，T表示矩阵的转置；第i路信号的导向向量是第i路信号在天线阵列N行的导向向量的有机拼接。
Figure PCTCN2020070638-appb-000011
其中，θ i为第i路信号的方位角，
Figure PCTCN2020070638-appb-000012
为第i路信号的仰角；d r为天线阵列元素的行间距，d c为天线阵列元素的列间距；i为正整数且i小于或者等于D；k为正整数且k小于或者等于N。
在t时刻天线阵列端的接收信号为：
x(t)＝A×s(t)+n(t)
其中，x(t)为t时刻天线阵列端的接收信号，s(t)为D路信号在t时刻的复幅值向量，n(t)为天线阵列的噪声信号，A为天线阵列的流型矩阵。
根据公式
Figure PCTCN2020070638-appb-000013
求解天线阵列端的接收信号x(t)在t时刻的自相关。其中，
Figure PCTCN2020070638-appb-000014
为天线阵列端的接收信号x(t)在t时刻的自相关，E表示期望，H表示共轭转置。
进一步，通过求解K个不同时刻接收信号的自相关矩阵的均值，近似求解天线阵列端接收信号的近似自相关矩阵，该近似自相关矩阵为：
Figure PCTCN2020070638-appb-000015
其中，R x为天线阵列端接收信号的近似自相关矩阵，x i(t)为第i个t时刻的接收信号，K为快拍数。
步骤12：根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵。
其中，噪声传输特性包括噪声传输参数，如：信噪比。
例如：在来波方向差别较大时，将所述近似自相关矩阵调整为拓普利兹矩阵；在来波方向差别较小时，通过噪声的近似方差对所述近似自相关矩阵进行调整，以削弱噪声对于信号到达角检测的影响，提高信号到达角检测精度。
步骤13：根据所述调整后的自相关矩阵，确定信号到达角的估计值。
上述方案中，通过对噪声传输特性，对所述近似自相关矩阵进行调整，进而根据调整后的自相关矩阵，确定信号到达角的估计值，以削弱噪声对于信号到达角检测的影响，提高信号到达角检测精度，并且避免漏检测和错检测；此外，该方案还可以应用于多种类型的面天线阵列的信号到达角的估计，进而利于提高信号到达角估计的适配性。
其中，上述步骤12具体包括：根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
对所述近似自相关矩阵中的对角线元素进行调整包括但不限于以下方式：
方式一：根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；
根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵。
作为一种具体的实现方式，根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值可以包括：
分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；
其中，每条对角线上的对角线元素的平均值为：
Figure PCTCN2020070638-appb-000016
其中，
Figure PCTCN2020070638-appb-000017
为第l条对角线上的对角线元素的平均值，i为所述近似自相关矩阵中元素的行角标，j为所述近似自相关矩阵中元素的列角标，l＝-(MN-1),-(MN-2),…,-1,0,1,…,(MN-2),(MN-1)。
将所述平均值确定为各自对角线上的对角线元素的调整目标值；其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
进而根据所述调整目标值(每条对角线上的对角线元素的平均值)，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵为：
Figure PCTCN2020070638-appb-000018
该实施例中，当噪声统计特征不理想，或者噪声较大时(如：来波方向较远时)，通过将接接收信号的近似自相关矩阵调整为拓普利兹矩阵，以削弱噪声对信号到达角检测的影响，从而有利于提高信号到达角检测的精度。
方式二：根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；
根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
例如：可以根据所述近似自相关矩阵，确定噪声的近似方差；根据该噪声的近似方差，构建噪声对角矩阵。
作为一种具体的实现方式，根据所述近似自相关矩阵，构建噪声对角矩阵可以包括：
对所述近似自相关矩阵进行特征值分解，得到P个特征值；
根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数，P为M*N。
进一步地，根据所述P个特征值，构建所述噪声对角矩阵可以包括：
计算所述P个特征值中最小的P-D个特征值的平均值(即噪声的近似方差)；
具体的，对所述近似自相关矩阵进行特征值分解得到的P个特征值，将该P个特征值按照从大到小的顺序排列为：λ 1，λ 2，…λ P；取该从大到小排 列的P个特征值中最大的前D个第一特征值，该P个特征值中除该D个第一特征值之外的第二特征值即为P个特征值中最小的P-D个特征值。
例如：该P个特征值为：15,13,10,10,7,6,5,5,5,4,2,1；D的取值为5，则最大的前D个特征值为：15,13,10,10,7；最小的P-D个特征值为：6,5,5,5,4,2,1。
需要说明的是，本公开一些实施例在能够区分P个特征值中最大的前D个特征值，以及最小的P-D个特征值即可，并非一定需要执行排序的步骤。例如：在该P个特征值的大小顺序随机(可能不是按照从大到小或者从小到大的顺序)的情况下，可以不对该P个特征值的大小顺序进行排列，而直接筛选出该P个特征值中最小的P-D个特征值。
所述P个特征值中最小的P-D个特征值的平均值为：
Figure PCTCN2020070638-appb-000019
其中，
Figure PCTCN2020070638-appb-000020
为所述P个特征值中最小的P-D个特征值的平均值；λ D+1,λ D+2,…,λ P为所述P个特征值中最小的P-D个特征值。
将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
其中，D可以由基站侧预先设定，或者从信令中确定，或者由终端侧设定并反馈至基站侧。
作为一种具体的实现方式，根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵可以包括：
计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
具体的，可以通过公式：
Figure PCTCN2020070638-appb-000021
得到调整后的自相关矩阵。其中，
Figure PCTCN2020070638-appb-000022
为调整后的自相关矩阵，R x为所述近似自相关矩阵，R n为噪声对角矩阵。
该实施例中，通过计算所述P个特征值中最小的P-D个特征值的平均值，也即计算噪声特征值的平均值，用以表征噪声水平，即近似为噪声的方差，并通过该平均值构建的噪声对角矩阵，对接收信号的近似自相关矩阵进行调 整，以消除部分噪声对特征值分解的影响(如：在来波方向较近时，噪声对特征值分解的影响)，进而削弱噪声对信号到达角检测的影响，从而有利于提高信号到达角检测的精度。
其中，上述步骤13具体包括：根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数，D可以由基站侧预先设定，或者从信令中确定，或者由终端侧设定并反馈至基站侧；
根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；
根据所述D个谱函数，确定所述D路信号到达角的估计值。
进一步地，根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间可以包括：
对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；
根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；
根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
具体的，对所述调整后的自相关矩阵进行特征值分解，得到P个特征值、所述P个特征值分别对应的特征向量以及所述特征向量构成的特征矩阵；将该P个特征值按照从大到小的顺序进行排序，特征矩阵根据特征向量与特征值之间的对应关系进行调整，最终得到排序后的P个特征值、P个特征值分别对应的特征向量，以及调整后的特征矩阵。其中，P个特征值中最大的前D个特征值对应信号，最小的后P-D各特征值对应噪声。
更进一步地，根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间可以包括：
获取信号调节因子；
具体的，可以根据所述D个特征值，确定信号调节因子，用以表征信号的能量和。作为一种具体的实现方式，可以是计算所述D个特征值的平均值；将所述平均值确定为所述信号调节因子。
其中，信号调节因子为：α＝λ 1 2+λ 2 2+…+λ D 2，其中α为信号调节因 子；λ 1，λ 2，…，λ D为P个特征值中最大的前D个特征值。
根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量；
作为一种具体的实现方式，可以是将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量，即增大第i个特征向量的幅值，实现对该第i个特征向量的调节。
根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
第i路信号的信号子空间为：
E s i＝[ν 1 ν 2 ν 3 … α*ν i … ν D]
其中，ν 1，ν 2，ν 3…，ν D是与所述D个特征值分别对应的特征向量，ν i是与特征值λ i对应的特征向量，也即时特征矩阵中的第i列。
噪声子空间为：
E n＝[ν D+1 ν D+2 ν 3 … ν P]
其中，ν D+1，ν D+2，…，ν P是与所述P-D个特征值分别对应的特征向量。
所述D个谱函数中的第i个谱函数为：
Figure PCTCN2020070638-appb-000023
其中，P i为第i路信号对应的谱函数，a(θ i,
Figure PCTCN2020070638-appb-000024
)为第i路信号的导向向量，θ i为第i路信号的方位角，
Figure PCTCN2020070638-appb-000025
为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
该实施例中，通过调节因子对第i个特征向量进行调节，相当于用所有信号的能量和去调节第i路信号，使其成为强信号，避免第i路信号的谱峰可能被强信号的谱峰覆盖的问题，还可以避免第i路信号的信号与其他信号的信号强度可能相当时，导致的漏、错检问题，从而解决邻居信号的谱峰干扰问题，有利于提升信号到达角相差较小的情况下信号到达角的估计精度， 并且具有较好的稳定性，有效减少了漏检和错检率。
进一步地，根据所述D个谱函数，确定D路信号到达角的估计值可以包括：按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
其中，特征值与特征向量对应，信号子空间与特征向量关联，D个谱函数与D路信号的信号子空间一一对应，则D个谱函数的计算次序即为该D个特征值从大到小的顺序。
更进一步地，所述按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值，包括：
计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；
根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
作为一种具体的实现方式，可以是确定所述D对角度值中除所述i-1对角度值之外的k对角度值；将所述k对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
例如：第i对角度值(第i路信号到达角的估计值)的检验过程为：将已检出的前i-1对角度值存于数组Angle中，对第i个谱函数进行谱峰搜索。取出前D个谱峰值对应的D对角度值，并从该D对角度值中剔除掉数组Angle中已检出的i-1对角度，剩余的D-i+1(即为k)对角度值中与最大谱峰值对应的一对角度值即是第i对角度值(第i路信号到达角的估计值)。这样，经过D次相同的谱峰搜索过程，D路信号分别对应的D对方位角和仰角可以被成功检出，并且具有较好的精度。
以下结合具体应用场景对本公开一些实施例的信号到达角的估计方法进行说明：
如图3，给出了一种5G室内定位场景。在120×50m的室内空间内，基站间距20m，高3m与室内环境高度一致进行部署。在此场景下，由于基站的 密集部署，用户终端在同一时刻可能与多个基站之间存在直射径。图3中，BS表示基站(Base Station)。
上行链路的信道探测信号(Sounding reference signal，简称SRS)，由用户终端周期性的上报给基站，且与发送的数据无关，占用独立频域资源，本是用于估计上行信道频域信息，做频率选择性调度；用于估计上行信道，做下行波束赋形等。由于SRS信号周期性、可配置性以及独立存在性即不需要发送数据，也可以独立存在，有利于作为实时定位参考信号。
5G中的SRS信号的序列生成和物理资源映射过程如下：
SRS资源由SRS资源信息部分(SRS—Resource IE)配置完成，主要包含：
Figure PCTCN2020070638-appb-000026
指天线端口数目
Figure PCTCN2020070638-appb-000027
p i∈{1000,1001,...}。SRS天线端口在1000～2000间，端口数目可以有1，2，4三种选择。
Figure PCTCN2020070638-appb-000028
指连续的OFDM符号数。
l 0,指时域的开始位置，由
Figure PCTCN2020070638-appb-000029
得出，其中l offset∈{0，1，...，5}，
Figure PCTCN2020070638-appb-000030
代表单个时隙里的符号数，
Figure PCTCN2020070638-appb-000031
表示单个时隙保证完成
Figure PCTCN2020070638-appb-000032
符号的传输。
k 0，指频域的开始位置。
SRS序列生成：
Figure PCTCN2020070638-appb-000033
Figure PCTCN2020070638-appb-000034
Figure PCTCN2020070638-appb-000035
Figure PCTCN2020070638-appb-000036
是SRS序列长度，
Figure PCTCN2020070638-appb-000037
是随机序列生成器，δ＝log 2(K TC)，K TC是传输梳数(transmission comb number)，取值2或4，α i是天线端口pi的循环移位。
SRS序列向物理层映射：
即将SRS序列中的(k′,l′)符号按照某种规则映射到资源块的
Figure PCTCN2020070638-appb-000038
进行传输。
Figure PCTCN2020070638-appb-000039
Figure PCTCN2020070638-appb-000040
m SRS,b可以通过查看表一不同的小区参考信号(Cell Reference Signal，简称CRS)配置模式得到，
Figure PCTCN2020070638-appb-000041
表示一个无线电报务员(Radio Bearer，简称RB)内子载波数目，otherwise表示其他。
Figure PCTCN2020070638-appb-000042
Figure PCTCN2020070638-appb-000043
Figure PCTCN2020070638-appb-000044
Figure PCTCN2020070638-appb-000045
是上层配置的传输梳补偿(transmission comb offse)，n shift是上层配置的频率移位量，以四的倍数调整SRS分配与公共资源块网格对齐。
Figure PCTCN2020070638-appb-000046
n RRC是上层配置的量，N b可以通过查下表一得到。
基于以上公式，SRS信号的复用因子大致可表示为K TC*N b,K TC取值为2或4。由下表一查看N b(N1，N2，N3)取值，根据CRS配置方式不同，取值从1到17。
表一：SRS的CRS配置方式。
Figure PCTCN2020070638-appb-000047
Figure PCTCN2020070638-appb-000048
Figure PCTCN2020070638-appb-000049
本公开一些实施例中使用用户密度、SRS复用因子、干扰模型结合点对点协议(Point-to-Point Protocol，简称PPP)推演5G室内(Indoor)场景下SRS作为上行定位参考信号的典型信号与干扰加噪声比(Signal to Interference plus Noise Ratio，简称SINR)，以便进行下一步的仿真，具体过程如下：
用户位置可以用密度为λ u的齐次泊松点过程Φ u进行建模，网络中基站的位置同样采用密度为λ的独立于Φ u的齐次泊松点过程Φ进行建模。
基站接收到用户i参考信号的SINR可定义为：
Figure PCTCN2020070638-appb-000050
其中，h为用户与基站间的小尺度衰落，h～exp(1)。
对于一定的SINR门限值τ，典型用户的成功接收概率可表示为：
Figure PCTCN2020070638-appb-000051
半径R区域内是少存在一个用户的概率为
Figure PCTCN2020070638-appb-000052
用户到基站距离的概率密度函数可以表示为：
Figure PCTCN2020070638-appb-000053
将参考信号SINR改写为
Figure PCTCN2020070638-appb-000054
其中Q＝I+σ 2。因此，
Figure PCTCN2020070638-appb-000055
其中，干扰项I的拉普拉斯变换表示为：
Figure PCTCN2020070638-appb-000056
其中，
Figure PCTCN2020070638-appb-000057
综上可以得到：
Figure PCTCN2020070638-appb-000058
所以，
Figure PCTCN2020070638-appb-000059
表二：SRS参考信号SINR仿真参数表。
仿真参数 取值
λ u 1/16
P 23dBm
α 4
距基站最小距离 5m
噪声 -174dBm/Hz
σ 2 10^-13.4
如图4，给出了一种SRS参考信号SINR仿真结果的示例，其中，y轴表示成功接收的概率，x轴表示正确接收门限设定值τ(单位为dB)，由图4可知，当接收门限设定为-10dB时，将近90％的传输可以被正确接收，即表示该场景下有90％用户SRS信号的SINR在-10dB及以上。
以下为本公开一些实施例的信号达到角估计方法仿真分析：
拓普利兹削弱噪声：
仿真中有三路信号分别以30°，34°，38°到达天线阵列，信噪比-10dB，快拍数为1024，谱峰扫描步长0.05°，天线阵列的天线元素数目为20。图5a是采用以上参数进行传统MUISC算法的谱峰示意图，图5a中只有锋利的一个峰值点。图5b是采用以上参数进行本公开一些实施例中的拓普利兹消除噪声算法的谱峰示意图，可见本公开一些实施例能够清晰地分辨出三个峰值 点。图5a和图5b中横轴表示信号到达角(单位为°)，纵轴表示信噪比(单位为dB)。
仿真中有三路信号分别以30°，33°，36°到达天线阵列，信噪比-10dB，快拍数为1024，谱峰扫描步长0.05°，天线阵列的天线元素数目为20。如图6a，是基于图5a进行修改后的谱峰示意图，图6a是采用以上参数进行传统MUISC算法的谱峰示意图，图6b是采用以上参数进行本公开一些实施例中的拓普利兹消除噪声算法的谱峰示意图，图6a和图6b中横轴表示信号到达角(单位为°)，纵轴表示信噪比(单位为dB)。通过对比修改前后的仿真结果发现，该操作只是增加了谱峰的锐利程度并不能提升角度估计的精度和分辨率,拓普利兹消除噪声算法在信号到达角相差较小时存在局限性，在信号到达角相差较大时具有提升角度估计的精度和分辨率的显著效果。
特征值分解削弱噪声：
在同样的仿真环境下(即三路信号分别以30°，33°，36°到达天线阵列，信噪比-10dB，快拍数为1024，谱峰扫描步长0.05°，天线阵列的天线元素数目为20)，采用特征值分解削弱噪声方法以及到达角谱峰扫描策略可以得出三个信号到达角度，分别为30.95°，35.65°，33.25°,各自对应的谱峰如图7a、图7b、图7c所示，其中横轴表示信号到达角(单位为°)，纵轴表示信噪比(单位为dB)。与真实值分别相差0.95°，0.25°，0.35°。可见，通过特征值分解削弱噪声影响，并采用本公开一些实施例中的谱函数构建法和谱峰扫描策略可以在信号到达角度较近时提供较为准确的角度估计结果。
不同信号到达情形下的各方案均方根误差(root-mean-square error，简称RMSE)仿真：
仿真环境：
仰角θ＝[51 55 59 63 67；23 35 47 59 71]，方位角
Figure PCTCN2020070638-appb-000060
Figure PCTCN2020070638-appb-000061
设定两组三维角度值，分别是来波角度相差大约10°和4°的情景，RMSE结果如表三。
表三：
Figure PCTCN2020070638-appb-000062
Figure PCTCN2020070638-appb-000063
仿真环境：信噪比固定为-5dB，200次drop，快拍数1024。
如图8a，是仰角的RMSE随天线元素数量变化的曲线，如图8b，是方位角的RMSE随天线元素数量变化的曲线。
当信号到达角度较远，各路信号信噪比随机产生于-10～0dB时，特征值分解削弱噪声MUSIC算法仰角的RMSE在0.004°左右，方位角的RMSE在7e-4°左右。而拓普利兹削弱噪声MUSIC算法仰角的RMSE在0.001°左右，方位角的RMSE值在4e-4°左右，可以提供更加准确的精度。
当信号到达角度较近，各路信号信噪比随机产生于-10～0dB时，拓普利兹削弱噪声MUSIC算法仰角的RMSE在20°左右，方位角的RMSE值在15°左右。而特征值分解削弱噪声MUSIC算法仰角的RMSE在0.9°左右，方位角的RMSE在0.6°左右。
由以上的仿真结果可知，本公开一些实施例中的信号到达角估计方法，能够更好地应对低信噪比环境和多路信号到达角度相近的情景。其中方位角的精度普遍高于仰角的估计精度，可采用立体天线阵列提高仰角的估计精度。
由仿真不同天线阵列元素个数的角度估计RMSE，可以看出该方法存在天 线阵列元素个数的下限，即为了保证信号到达角的估计精度，需要提供一定尺寸的天线阵列。通过仿真结果可知，当天线元素个数到达门限后，角度估计精度对于天线尺寸不再敏感。因此，在生产实际中只要保证天线尺寸达到门限值，角度估计就可以获得较好的收益。
以上实施例就本公开的信号到达角的估计方法做出介绍，下面本实施例将结合附图对其对应的基站做进一步说明。
具体地，如图9所示，本公开一些实施例的基站900，包括：
获取模块910，用于获取面天线阵列接收信号的近似自相关矩阵；
调整模块920，用于根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
确定模块930，用于根据所述调整后的自相关矩阵，确定信号到达角的估计值。
其中，所述调整模块920包括：
调整子模块，用于根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
其中，所述调整子模块包括：
确定单元，用于根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；
第一调整单元，用于根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵。
其中，所述确定单元包括：
第一计算子单元，用于分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；
第一确定子单元，用于将所述平均值确定为各自对角线上的对角线元素的调整目标值；
其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
其中，所述调整子模块包括：
构建单元，用于根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；
第二调整单元，用于根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
其中，所述构建单元包括：
分解子单元，用于对所述近似自相关矩阵进行特征值分解，得到P个特征值；
第一构建子单元，用于根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数。
其中，所述第一构建子单元具体用于：计算所述P个特征值中最小的P-D个特征值的平均值；将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
其中，所述第二调整单元包括：
第二计算子单元，用于计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
其中，所述确定模块930包括：
第一构建子模块，用于根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数；
第二构建子模块，用于根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；
确定子模块，用于根据所述D个谱函数，确定所述D路信号到达角的估计值。
其中，所述第一构建子模块包括：
第一分解单元，用于对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；
第一构建单元，用于根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；
第二构建单元，用于根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
其中，所述第一构建单元包括：
获取子单元，用于获取信号调节因子；
调节子单元，用于根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量；
第二构建子单元，用于根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
其中，所述获取子单元具体用于：根据所述D个特征值，确定信号调节因子。
其中，所述获取子单元还具体用于：计算所述D个特征值的平均值；将所述平均值确定为所述信号调节因子。
其中，所述调节子单元具体用于：将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量。
其中，所述D个谱函数中的第i个谱函数为：
Figure PCTCN2020070638-appb-000064
其中，P i为第i路信号对应的谱函数，α(θ i,
Figure PCTCN2020070638-appb-000065
)为第i路信号的导向向量，θ i为第i路信号的方位角，
Figure PCTCN2020070638-appb-000066
为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
其中，所述确定子模块包括：
计算单元，用于按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
其中，所述计算单元包括：
第三计算子单元，用于计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；
第二确定子单元，用于根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
其中，所述第二确定子单元具体用于：确定所述D对角度值中除所述i-1对角度值之外的k对角度值；将所述k对角度值中最大谱峰值对应的一对角 度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
其中，D为基站侧预先设定或者通过信令确定或者通过终端侧设定并反馈至基站侧。
本公开的基站实施例是与上述信号到达角的估计方法的实施例对应的，上述方法实施例中的所有实现手段均适用于该基站的实施例中，也能达到相同的技术效果。
本公开一些实施例中的基站900，通过对噪声传输特性，对所述近似自相关矩阵进行调整，进而根据调整后的自相关矩阵，确定信号到达角的估计值，以削弱噪声对于信号到达角检测的影响，提高信号到达角检测精度，并且避免漏检测和错检测；此外，该方案还可以应用于多种类型的面天线阵列的信号到达角的估计，进而利于提高信号到达角估计的适配性。
为了更好的实现上述目的，如图10所示，本公开一些实施例还提供了一种基站，该基站包括：处理器1000；通过总线接口1030与所述处理器1000相连接的存储器1020，以及通过总线接口1030与处理器1000相连接的收发机1010；所述存储器1020用于存储所述处理器1000在执行操作时所使用的程序和数据；通过所述收发机1010发送数据信息或者导频，还通过所述收发机1010接收上行控制信道；当处理器1000调用并执行所述存储器1020中所存储的程序和数据时，实现如下的功能。
处理器1000用于读取存储器1020中的程序，所述处理器1000执行所述计算机程序时实现以下步骤：获取面天线阵列接收信号的近似自相关矩阵；根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；根据所述调整后的自相关矩阵，确定信号到达角的估计值。
所述处理器1000执行所述计算机程序时实现以下步骤：根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
所述处理器1000执行所述计算机程序时实现以下步骤：根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调 整后的自相关矩阵。
所述处理器1000执行所述计算机程序时实现以下步骤：分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；将所述平均值确定为各自对角线上的对角线元素的调整目标值；其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
所述处理器1000执行所述计算机程序时实现以下步骤：根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
所述处理器1000执行所述计算机程序时实现以下步骤：对所述近似自相关矩阵进行特征值分解，得到P个特征值；根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数。
所述处理器1000执行所述计算机程序时实现以下步骤：计算所述P个特征值中最小的P-D个特征值的平均值；将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
所述处理器1000执行所述计算机程序时实现以下步骤：计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
所述处理器1000执行所述计算机程序时实现以下步骤：根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数；根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；根据所述D个谱函数，确定所述D路信号到达角的估计值。
所述处理器1000执行所述计算机程序时实现以下步骤：对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
所述处理器1000执行所述计算机程序时实现以下步骤：获取信号调节因子；根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i 个特征向量；根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
所述处理器1000执行所述计算机程序时实现以下步骤：根据所述D个特征值，确定信号调节因子。
所述处理器1000执行所述计算机程序时实现以下步骤：计算所述D个特征值的平均值；将所述平均值确定为所述信号调节因子。
所述处理器1000执行所述计算机程序时实现以下步骤：将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量。
其中，所述D个谱函数中的第i个谱函数为：
Figure PCTCN2020070638-appb-000067
其中，P i为第i路信号对应的谱函数，α(θ i,
Figure PCTCN2020070638-appb-000068
)为第i路信号的导向向量，θ i为第i路信号的方位角，
Figure PCTCN2020070638-appb-000069
为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
所述处理器1000执行所述计算机程序时实现以下步骤：按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
所述处理器1000执行所述计算机程序时实现以下步骤：计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
所述处理器1000执行所述计算机程序时实现以下步骤：确定所述D对角度值中除所述i-1对角度值之外的k对角度值；将所述k对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
其中，D为基站侧预先设定或者通过信令确定或者通过终端侧设定并反 馈至基站侧。
本公开一些实施例中的基站，通过对噪声传输特性，对所述近似自相关矩阵进行调整，进而根据调整后的自相关矩阵，确定信号到达角的估计值，以削弱噪声对于信号到达角检测的影响，提高信号到达角检测精度，并且避免漏检测和错检测；此外，该方案还可以应用于多种类型的面天线阵列的信号到达角的估计，进而利于提高信号到达角估计的适配性。
收发机1010，用于在处理器1000的控制下接收和发送数据。
其中，在图10中，总线架构可以包括任意数量的互联的总线和桥，具体由处理器1000代表的一个或多个处理器和存储器1020代表的存储器的各种电路链接在一起。总线架构还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路链接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口提供接口。收发机1010可以是多个元件，即包括发送机和收发机，提供用于在传输介质上与各种其他装置通信的单元。处理器1000负责管理总线架构和通常的处理，存储器1020可以存储处理器1000在执行操作时所使用的数据。
本公开一些实施例还提供一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，该计算机程序被处理器执行时实现上述信号到达角的估计方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。其中，所述的计算机可读存储介质，如只读存储器(Read-Only Memory，简称ROM)、随机存取存储器(Random Access Memory，简称RAM)、磁碟或者光盘等。
本领域技术人员可以理解，实现上述实施例的全部或者部分步骤可以通过硬件来完成，也可以通过计算机程序来指示相关的硬件来完成，所述计算机程序包括执行上述方法的部分或者全部步骤的指令；且该计算机程序可以存储于一可读存储介质中，存储介质可以是任何形式的存储介质。
此外，需要指出的是，在本公开的装置和方法中，显然，各部件或各步骤是可以分解和/或重新组合的。这些分解和/或重新组合应视为本公开的等效方案。并且，执行上述系列处理的步骤可以自然地按照说明的顺序按时间顺序执行，但是并不需要一定按照时间顺序执行，某些步骤可以并行或彼此 独立地执行。对本领域的普通技术人员而言，能够理解本公开的方法和装置的全部或者任何步骤或者部件，可以在任何计算装置(包括处理器、存储介质等)或者计算装置的网络中，以硬件、固件、软件或者它们的组合加以实现，这是本领域普通技术人员在阅读了本公开的说明的情况下运用他们的基本编程技能就能实现的。
因此，本公开的目的还可以通过在任何计算装置上运行一个程序或者一组程序来实现。所述计算装置可以是公知的通用装置。因此，本公开的目的也可以仅仅通过提供包含实现所述方法或者装置的程序代码的程序产品来实现。也就是说，这样的程序产品也构成本公开，并且存储有这样的程序产品的存储介质也构成本公开。显然，所述存储介质可以是任何公知的存储介质或者将来所开发出来的任何存储介质。还需要指出的是，在本公开的装置和方法中，显然，各部件或各步骤是可以分解和/或重新组合的。这些分解和/或重新组合应视为本公开的等效方案。并且，执行上述系列处理的步骤可以自然地按照说明的顺序按时间顺序执行，但是并不需要一定按照时间顺序执行。某些步骤可以并行或彼此独立地执行。
以上所述是本公开的一些实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本公开所述原理的前提下，还可以作出若干改进和润饰，这些改进和润饰也应视为本公开的保护范围。

Claims (40)

  1. 一种信号到达角的估计方法，包括：
    获取面天线阵列接收信号的近似自相关矩阵；
    根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
    根据所述调整后的自相关矩阵，确定信号到达角的估计值。
  2. 根据权利要求1所述的信号到达角的估计方法，其中，所述根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵，包括：
    根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
  3. 根据权利要求2所述的信号到达角的估计方法，其中，所述对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵，包括：
    根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；
    根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵。
  4. 根据权利要求3所述的信号到达角的估计方法，其中，所述根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值，包括：
    分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；
    将所述平均值确定为各自对角线上的对角线元素的调整目标值；
    其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
  5. 根据权利要求2所述的信号到达角的估计方法，其中，所述对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵，包括：
    根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；
    根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调 整，得到调整后的自相关矩阵。
  6. 根据权利要求5所述的信号到达角的估计方法，其中，所述根据所述近似自相关矩阵，构建噪声对角矩阵，包括：
    对所述近似自相关矩阵进行特征值分解，得到P个特征值；
    根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数。
  7. 根据权利要求6所述的信号到达角的估计方法，其中，所述根据所述P个特征值，构建所述噪声对角矩阵，包括：
    计算所述P个特征值中最小的P-D个特征值的平均值；
    将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
  8. 根据权利要求5所述的信号到达角的估计方法，其中，所述根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵，包括：
    计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
  9. 根据权利要求1所述的信号到达角的估计方法，其中，所述根据调整后的自相关矩阵，确定信号到达角的估计值，包括：
    根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数；
    根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；
    根据所述D个谱函数，确定所述D路信号到达角的估计值。
  10. 根据权利要求9所述的信号到达角的估计方法，其中，所述根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间，包括：
    对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；
    根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；
    根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
  11. 根据权利要求10所述的信号到达角的估计方法，其中，所述根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间，包括：
    获取信号调节因子；
    根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量；
    根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
  12. 根据权利要求11所述的信号到达角的估计方法，其中，所述获取信号调节因子，包括：
    根据所述D个特征值，确定信号调节因子。
  13. 根据权利要求12所述的信号到达角的估计方法，其中，所述根据所述D个特征值，确定信号调节因子，包括：
    计算所述D个特征值的平均值；
    将所述平均值确定为所述信号调节因子。
  14. 根据权利要求11所述的信号到达角的估计方法，其中，所述根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量，包括：
    将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量。
  15. 根据权利要求9所述的信号到达角的估计方法，其中，所述D个谱函数中的第i个谱函数为：
    Figure PCTCN2020070638-appb-100001
    其中，P i为第i路信号对应的谱函数，
    Figure PCTCN2020070638-appb-100002
    为第i路信号的导向向量，θ i为第i路信号的方位角，
    Figure PCTCN2020070638-appb-100003
    为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
  16. 根据权利要求9所述的信号到达角的估计方法，其中，所述根据所述D个谱函数，确定D路信号到达角的估计值，包括：
    按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
  17. 根据权利要求16所述的信号到达角的估计方法，其中，所述按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值，包括：
    计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；
    根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
  18. 根据权利要求17所述的信号到达角的估计方法，其中，所述根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值，包括：
    确定所述D对角度值中除所述i-1对角度值之外的k对角度值；
    将所述k对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
  19. 根据权利要求7、9至18中任一项所述的信号到达角的估计方法，其中，D由基站侧预先设定，或者从信令中确定，或者由终端侧设定并反馈至基站侧。
  20. 一种基站，包括：收发机、存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现以下步骤：
    获取面天线阵列接收信号的近似自相关矩阵；
    根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
    根据所述调整后的自相关矩阵，确定信号到达角的估计值。
  21. 根据权利要求20所述的基站，其中，所述处理器执行所述计算机程 序时实现以下步骤：
    根据噪声传输特性对应的预设调整方式，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
  22. 根据权利要求21所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    根据所述近似自相关矩阵中的元素值，确定所述近似自相关矩阵中对角线元素的调整目标值；
    根据所述调整目标值，将所述近似自相关矩阵调整为拓普利兹矩阵，得到调整后的自相关矩阵。
  23. 根据权利要求22所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    分别计算所述近似自相关矩阵中每条对角线上的对角线元素的平均值；
    将所述平均值确定为各自对角线上的对角线元素的调整目标值；
    其中，所述对角线为主对角线或与所述主对角线平行的副对角线。
  24. 根据权利要求21所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    根据所述近似自相关矩阵，构建噪声对角矩阵；其中，所述噪声对角矩阵的行列数与所述近似自相关矩阵的行列数相等；
    根据所述噪声对角矩阵，对所述近似自相关矩阵中的对角线元素进行调整，得到调整后的自相关矩阵。
  25. 根据权利要求24所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    对所述近似自相关矩阵进行特征值分解，得到P个特征值；
    根据所述P个特征值，构建所述噪声对角矩阵；其中，P为正整数。
  26. 根据权利要求25所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    计算所述P个特征值中最小的P-D个特征值的平均值；
    将所述平均值确定为对角矩阵中对角线元素的值，以构建所述噪声对角矩阵；其中，D为正整数，且D小于K。
  27. 根据权利要求24所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    计算所述近似自相关矩阵与所述噪声对角矩阵之差，得到调整后的自相关矩阵。
  28. 根据权利要求20所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    根据所述自相关矩阵，构建D路信号的信号子空间以及噪声子空间；其中，D为正整数；
    根据所述D路信号的信号子空间和所述噪声子空间，构建D个谱函数；
    根据所述D个谱函数，确定所述D路信号到达角的估计值。
  29. 根据权利要求28所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    对所述调整后的自相关矩阵进行特征值分解，得到P个特征值和所述P个特征值分别对应的特征向量；其中，P为正整数；
    根据所述P个特征值中最大的前D个特征值，以及所述D个特征值对应的D个特征向量，构建D路信号的信号子空间；
    根据所述P个特征值中除所述D个特征值外的P-D个特征值对应的P-D个特征向量，构建噪声子空间。
  30. 根据权利要求29所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    获取信号调节因子；
    根据所述信号调节因子对第i个特征向量进行调节，得到调节后的第i个特征向量；
    根据所述调节后的第i个特征向量和所述D个特征向量中除所述第i个特征向量之外的特征向量，构建第i路信号的信号子空间；其中，i为正整数，且i小于或者等于D。
  31. 根据权利要求30所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    根据所述D个特征值，确定信号调节因子。
  32. 根据权利要求31所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    计算所述D个特征值的平均值；
    将所述平均值确定为所述信号调节因子。
  33. 根据权利要求30所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    将所述调节因子与所述第i个特征向量的乘积，确定为调节后的第i个特征向量。
  34. 根据权利要求28所述的基站，其中，所述D个谱函数中的第i个谱函数为：
    Figure PCTCN2020070638-appb-100004
    其中，P i为第i路信号对应的谱函数，
    Figure PCTCN2020070638-appb-100005
    为第i路信号的导向向量，θ i为第i路信号的方位角，
    Figure PCTCN2020070638-appb-100006
    为第i路信号的仰角，E s i为第i路信号的信号子空间，E n为噪声子空间，H表示矩阵的共轭转置；i为正整数，且i小于或者等于D。
  35. 根据权利要求28所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    按照所述D个特征值从大到小的顺序，依次根据所述D个谱函数计算所述D路信号到达角的估计值。
  36. 根据权利要求35所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    计算第i个谱函数中前D个谱峰值对应的D对角度值，所述角度值包括方位角值和仰角值；
    根据前i-1路信号对应的i-1对角度值，将所述D对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号的到达角的估计值；i为正整数，且i小于或者等于D。
  37. 根据权利要求36所述的基站，其中，所述处理器执行所述计算机程序时实现以下步骤：
    确定所述D对角度值中除所述i-1对角度值之外的k对角度值；
    将所述k对角度值中最大谱峰值对应的一对角度值，确定为所述第i路信号到达角的估计值；其中，k为正整数，且k小于或者等于D。
  38. 根据权利要求26、28至37中任一项所述的基站，其中，D由基站侧预先设定，或者从信令中确定，或者由终端侧设定并反馈至基站侧。
  39. 一种基站，包括：
    获取模块，用于获取面天线阵列接收信号的近似自相关矩阵；
    调整模块，用于根据噪声传输特性，对所述近似自相关矩阵进行调整，得到调整后的自相关矩阵；
    确定模块，用于根据所述调整后的自相关矩阵，确定信号到达角的估计值。
  40. 一种计算机可读存储介质，其上存储有计算机程序，其中，该计算机程序被处理器执行时实现如权利要求1至19中任一项所述的信号到达角的估计方法的步骤。
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