WO2023134430A1 - AoA估计方法、装置、基站、存储介质和计算机程序产品 - Google Patents

AoA估计方法、装置、基站、存储介质和计算机程序产品 Download PDF

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WO2023134430A1
WO2023134430A1 PCT/CN2022/141822 CN2022141822W WO2023134430A1 WO 2023134430 A1 WO2023134430 A1 WO 2023134430A1 CN 2022141822 W CN2022141822 W CN 2022141822W WO 2023134430 A1 WO2023134430 A1 WO 2023134430A1
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aoa
estimated value
initial
value
estimated
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PCT/CN2022/141822
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English (en)
French (fr)
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郑旺
齐望东
黄永明
刘升恒
潘孟冠
贾兴华
李晓东
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网络通信与安全紫金山实验室
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Publication of WO2023134430A1 publication Critical patent/WO2023134430A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • the present application relates to the technical field of 5G, and in particular to an AoA estimation method, device, base station, storage medium and computer program product.
  • 5G base stations can measure parameters such as Angle of Arrival (AoA) and Angle of Departure (AoD) based on positioning signals sent by terminals to determine
  • AoA Angle of Arrival
  • AoD Angle of Departure
  • the antenna spacing of the antenna array mainly used in the current 5G base station is less than or equal to half a wavelength.
  • the present application provides an AoA estimation method, the AoA estimation method is applied to a base station, the base station includes at least two sub-arrays, and the method includes:
  • a target AoA interval is determined according to the candidate AoA estimated value, and a target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is smaller than a second threshold.
  • the acquiring an initial AoA estimated value of at least one subarray in the preset AoA interval, and determining at least one other AoA estimated value of the subarray according to the initial AoA estimated value includes:
  • the preset first AoA interval acquire a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array;
  • At least one first further AoA estimate is determined based on a linear relationship between the AoA estimates and said first initial AoA estimate.
  • the determining candidate AoA estimated values according to the initial AoA estimated value and the other AoA estimated values includes:
  • the candidate AoA estimate is determined based on the first initial AoA estimate, the at least one first other AoA estimate and the correction function.
  • the determining the candidate AoA estimated value according to the first initial AoA estimated value, the at least one first other AoA estimated value and the correction function includes:
  • the estimated AoA value corresponding to the largest value of the correction function is determined as the candidate AoA estimated value.
  • the acquiring an initial AoA estimated value of at least one subarray in the preset AoA interval, and determining at least one other AoA estimated value of the subarray according to the initial AoA estimated value includes:
  • a first initial AoA estimated value is obtained according to the positioning signal of the terminal received by the first sub-array; and based on the linear relationship between the AoA estimated values and the first initial AoA estimated value, determining at least one first other AoA estimated value to obtain the first AoA estimated value set; the first AoA estimated value set includes the first initial AoA estimated value and the first other AoA estimated value;
  • the preset second AoA interval In the preset second AoA interval, acquiring a second initial AoA estimated value according to the positioning signal of the terminal received by the second subarray; and based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, Determine at least one second other estimated AoA value to obtain a second set of estimated AoA values; the second set of estimated AoA values includes the second initial estimated AoA value and the second other estimated AoA value.
  • the determining candidate AoA estimated values according to the initial AoA estimated value and the other AoA estimated values includes:
  • An average value of the first AoA estimate and the second AoA estimate is determined as the candidate AoA estimate.
  • the determining the target AoA interval according to the candidate AoA estimated value, and determining the target AoA estimated value based on the target AoA interval includes:
  • the estimated value of the target AoA is determined by using a preset search method in the target AoA interval according to the positioning signals of the terminals received by all the sub-arrays.
  • the antenna spacing of the target sub-array in each of the sub-arrays is less than or equal to the first threshold, and the antenna spacing of other sub-arrays except the target sub-array is greater than the first threshold, or, the antenna spacing of each of the sub-arrays The distance between the antennas is greater than the first threshold; the target sub-array is any sub-array in each of the sub-arrays.
  • the present application also provides an AoA estimating device, the AoA estimating device is applied to a base station, the base station includes at least two sub-arrays, and the device includes:
  • a processing module configured to obtain an initial AoA estimated value of at least one subarray in a preset AoA interval, and determine at least one other AoA estimated value of the subarray according to the initial AoA estimated value;
  • a first determination module configured to determine a candidate AoA estimate based on the initial AoA estimate and the other AoA estimates
  • a second determining module configured to determine a target AoA interval according to the candidate AoA estimated value, and determine a target AoA estimated value based on the target AoA interval; the length of the target AoA interval is less than a second threshold.
  • the present application also provides a base station, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: the AoA estimation method Applied to a base station, where the base station includes at least two subarrays, the method includes:
  • a target AoA interval is determined according to the candidate AoA estimated value, and a target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is smaller than a second threshold.
  • the present application also provides a computer-readable storage medium.
  • the computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented: the AoA estimation method is applied to a base station, and the base station includes at least two subarrays, and the method includes :
  • a target AoA interval is determined according to the candidate AoA estimated value, and a target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is smaller than a second threshold.
  • the present application also provides a computer program product.
  • the computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented: the AoA estimation method is applied to a base station, and the base station includes at least two subarrays, and the method includes:
  • a target AoA interval is determined according to the candidate AoA estimated value, and a target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is smaller than a second threshold.
  • the AoA estimation method is applied to the base station, the base station includes at least two sub-arrays, and the base station obtains an initial AoA estimation value of at least one sub-array in the preset AoA interval, which can be At least one other estimated AoA value of the subarray is determined according to the first initial AoA estimated value, so that candidate AoA estimated values can be determined according to the initial AoA estimated value and other AoA estimated values, and the target AoA interval can be determined according to the candidate AoA estimated value, and then the target AoA interval can be determined based on the target The AoA interval determines the target AoA estimated value.
  • the calculation process is simple and the calculation amount is small; at the same time, from the initial AoA estimate Determine a candidate AoA estimate with low accuracy from other AoA estimates, set the surrounding cells of the candidate AoA estimate as the target AoA interval, and re-search the target AoA estimate in the target AoA interval to improve the target AoA estimate The precision of the value.
  • FIG. 1 is an application environment diagram of an AoA estimation method in an embodiment
  • FIG. 2 is a schematic flowchart of a method for determining a target AoA estimated value in an embodiment
  • Fig. 3 is a schematic structural diagram of an antenna array in an embodiment
  • FIG. 4 is a schematic flowchart of a method for determining multiple AoA estimated values in an embodiment
  • FIG. 5 is a schematic flowchart of a method for determining candidate AoA estimated values in an embodiment
  • FIG. 6 is a schematic flowchart of a method for determining candidate AoA estimated values through a correction function in an embodiment
  • Fig. 7 is a schematic diagram of the MUSIC spectrum function in an embodiment
  • FIG. 8 is a schematic flowchart of a method for determining an initial AoA estimated value in an embodiment
  • FIG. 9 is a schematic flowchart of an AoA estimation method in an embodiment
  • FIG. 10 is a first structural block diagram of an AoA estimation device in an embodiment
  • Fig. 11 is a second structural block diagram of an AoA estimating device in an embodiment
  • Fig. 12 is a third structural block diagram of an AoA estimating device in an embodiment
  • Fig. 13 is a fourth structural block diagram of an AoA estimation device in an embodiment
  • Fig. 14 is a fifth structural block diagram of an AoA estimation device in an embodiment
  • Fig. 15 is a sixth structural block diagram of an AoA estimation device in an embodiment
  • Fig. 16 is an internal structure diagram of a base station in an embodiment.
  • the existing wireless positioning base station to estimate the angle of arrival of the terminal mainly uses a uniform antenna array whose antenna spacing is less than or equal to half the wavelength, and the Cramereau lower bound of the direction finding error is inversely proportional to the antenna aperture.
  • Increasing the aperture of the antenna array can effectively Improve direction finding accuracy and resolution.
  • increasing the spacing between antenna units will weaken the coupling effect between antenna units.
  • the coupling effect is not only unfavorable to positioning, but also unfavorable to communication, especially in the case of multiple users, which will increase the bit error rate.
  • the AoA estimation method provided in the embodiment of the present application can be applied to the application environment shown in FIG. 1 .
  • the terminal 102 communicates with the base station 104 .
  • the base station 104 calculates the estimated AoA value of the terminal 102 according to the positioning signal, so as to obtain the specific position of the terminal 102.
  • the terminal 102 can be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc.
  • Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, and the like.
  • the base station can be a macro base station, a micro base station, a remote radio station, a repeater or an indoor distribution system, etc.
  • FIG. 2 a schematic flowchart of a method for determining an estimated target AoA value is provided.
  • the method is applied to the base station in FIG. 1 as an example for illustration.
  • the AoA estimation method is applied to a base station, and the base station includes at least Two subarrays, including the following steps:
  • the antenna spacing of the target sub-array in each sub-array is less than or equal to the first threshold, and the antenna spacing of other sub-arrays except the target sub-array is greater than the first threshold, or, the antenna spacing of each sub-array is greater than the first threshold ;
  • the target sub-array is any sub-array in each sub-array.
  • FIG. 3 is a schematic structural diagram of a large-spacing antenna array.
  • the large-spacing antenna array used by the base station includes two large-spacing antenna sub-arrays, the total number of antennas is T, and the two large-spacing antenna sub-arrays are respectively the first sub-array and
  • the second sub-array for example, the first sub-array can be sub-array one among Fig. 3, and the second sub-array can be sub-array two among Fig. 3, perhaps, the first sub-array can be sub-array two among Fig. 3 , the second sub-array may be sub-array 1 in FIG. 3 .
  • the number of antennas in sub-array 1 is T 1 , and its antenna spacing is Md; the number of antennas in sub-array 2 is T 2 , and its antenna spacing is Nd, where M and N are positive integers, d is the unit spacing, and ⁇ is the center frequency the corresponding wavelength.
  • the antenna spacing between the first sub-array and the second sub-array is less than or equal to the first threshold, that is, there is only a direction vector of one angle, and an estimated value of AoA can be obtained.
  • M and N are mutually prime.
  • M is 1 and N is a positive integer greater than 1
  • the antenna spacing of the first sub-array is equal to the first threshold, and there is only a direction vector of one angle, and the first sub-array corresponds to an estimated AoA value.
  • the antenna spacing of the second antenna is greater than the first threshold, there are direction vectors of multiple angles, and the second subarray corresponds to at least two AoA estimated values; when M and N are both positive integers greater than 1 and M and N are mutually prime, The antenna spacings of the first sub-array and the second sub-array are both greater than the first threshold, that is, there are direction vectors with different angles, and multiple AoA estimation values can be obtained. Since the distance between the antennas in the first sub-array and the second sub-array is greater than the first threshold, both the first sub-array and the second sub-array correspond to at least two AoA estimation values.
  • the base station can input the positioning signal received by the second subarray into the preset neural network model, and output the second subarray in the preset AoA interval through the training of the neural network model.
  • the base station may also perform calculations based on the positioning signals received by the second sub-array and related algorithms to obtain the initial AoA estimated value of the second sub-array in the preset AoA interval.
  • the relevant algorithm can be an algorithm for obtaining a closed-form solution or an algorithm for spectral peak search
  • the algorithm for obtaining a closed-form solution can be an estimation algorithm based on rotation invariant technology (Estimating Signal Parameters via Rotational Invariance Techniques, ESPRIT),
  • the root-finding algorithm Multiple Signal Classification, MUSIC
  • the algorithm of spectral peak search can be multiple signal classification (Multiple Signal Classification, MUSIC) algorithm, (Capon Algorithm, Capon) algorithm, etc.
  • This embodiment does not limit the manner of acquiring the initial AoA estimated value of the subarray in the preset AoA interval.
  • the base station may also obtain the initial AoA estimated value of the first subarray in the preset AoA interval, or may simultaneously obtain the initial AoA estimated value of the first subarray and the initial AoA estimated value of the second subarray in the preset AoA interval.
  • other estimated AoA values of the second sub-array can be obtained according to the distribution law of the initial AoA estimated value of the second sub-array and all historical AoA estimated values, or, according to the second The relationship between the initial AoA estimate for the sub-array and the individual AoA estimates to obtain other AoA estimates for the second sub-array.
  • This embodiment does not limit the method for obtaining other estimated AoA values.
  • other estimated AoA values of the first sub-array can be obtained according to the above method.
  • the base station may calculate an average of the initial AoA estimated value and other AoA estimated values, and use the average as a candidate AoA estimated value.
  • the base station may calculate a weighted average of the initial AoA estimated value and other AoA estimated values, and use the weighted average as a candidate AoA estimated value.
  • the base station may also use the historical candidate AoA estimate as a reference, select the AoA estimate closest to the historical candidate AoA estimate among the initial AoA estimate and other AoA estimate, and use this AoA estimate as the candidate AoA estimated value.
  • the initial AoA estimated value and other AoA estimated values corresponding to the first sub-array can be obtained through the positioning signal received by the first sub-array
  • the correction function is constructed by the positioning signal received by the second sub-array
  • the candidate AoA estimated value is determined according to the initial AoA estimated value and other AoA estimated values corresponding to the first sub-array, and the correction function corresponding to the second sub-array, or, can be obtained by
  • the positioning signal received by the first sub-array obtains the initial AoA estimated value and other AoA estimated values corresponding to the first sub-array
  • the initial AoA estimated value and other AoA values corresponding to the second sub-array are obtained through the positioning signal received by the second sub-array
  • the estimated value is to compare the estimated AoA value corresponding to the first sub-array with the estimated AoA value corresponding to the second sub-array
  • the second threshold may be a shorter interval between a length of 2/N and a length of 2/M.
  • the cell intervals around the candidate AoA estimated value are selected as the target AoA interval, and the length of the cell interval is set to be less than 2/N.
  • the estimated candidate AoA value is 9.8, 8.8 to 10.8 may be selected as the target AoA interval.
  • the positioning signal can be input into the preset neural network model, and the estimated value of the target AoA within the target AoA interval can be output through the training of the neural network model, or the base station can also calculate according to the positioning signal of the terminal and related algorithms to obtain The target AoA estimate within the target AoA interval.
  • the AoA estimation method is applied to the base station of large-spacing antennas, an initial AoA estimation value of at least one sub-array is obtained in the preset AoA interval, and at least one of the sub-arrays can be determined according to the first initial AoA estimation value.
  • Other estimated AoA values so that the candidate AoA estimated value can be determined according to the initial AoA estimated value and other AoA estimated values, the target AoA interval can be determined according to the candidate AoA estimated value, and the target AoA estimated value can be determined based on the target AoA interval.
  • the initial AoA estimated value of at least one sub-array in the AoA interval, and other AoA estimated values can be obtained according to the relationship between the initial AoA estimated value and the AoA estimated value.
  • the process of obtaining all AoA estimated values There is no need to traverse the entire AoA interval, which shortens the workload and does not need to calculate each AoA estimated value.
  • the calculation process is simple and the calculation amount is small; at the same time, a candidate with low accuracy is determined from the initial AoA estimated value and other AoA estimated values.
  • the surrounding cells of the candidate AoA estimate are set as the target AoA interval, and the target AoA estimate is searched again in the target AoA interval, which improves the accuracy of the target AoA estimate.
  • Fig. 4 is a schematic flowchart of a method for determining multiple AoA estimated values provided by an embodiment of the present application.
  • This embodiment relates to an optional implementation manner based on obtaining an initial AoA estimated value of at least one subarray in a preset AoA interval, and determining at least one other AoA estimated value of the subarray according to the first initial AoA estimated value .
  • the above S201 may include the following steps:
  • the first sub-array may be sub-array 1 in FIG. 3 or sub-array 2.
  • the process of obtaining the estimated AoA value according to the positioning signal can be expressed as:
  • x 1 (t) represents the positioning signal of the terminal received by the first subarray
  • s(t) represents the positioning signal sent by the terminal
  • n(t) represents the noise subspace
  • a 1 represents the set of direction vectors of the angle of arrival .
  • a 1 [a( ⁇ 1 ),a( ⁇ 2 ),...a( ⁇ l )] (2)
  • a() represents the direction vector of the arrival angle
  • ⁇ l represents the lth arrival angle, that is, the AoA estimated value corresponding to the first sub-array.
  • the preset first AoA interval can refer to any interval with a length of 2/N. If the direction vector corresponding to the preset first AoA interval is a( ⁇ 1 ), the formula of the direction vector can be expressed as:
  • ⁇ represents the estimated value of the angle of arrival
  • d t is the position coordinate of the antenna in the first sub-array
  • ⁇ is the wavelength corresponding to the sub-carrier.
  • the estimated value of the angle of arrival ⁇ 1 is calculated through the above formula (1) to formula (3), that is, ⁇ 1 is the first initial AoA estimate.
  • S402. Determine at least one first other estimated AoA value based on the linear relationship between the estimated AoA values and the first initial estimated AoA value.
  • linear relationship between estimated AoA values means that the difference between the sine values of each estimated AoA value is an integer multiple of 2/N. This linear relationship can be expressed as:
  • other estimated AoA values of the first subarray can be obtained according to the linear relationship between the estimated AoA values. For example, when the value of N is 2, the corresponding first sub-array has two AoA estimated values. When the initial AoA estimated value of the first sub-array is 30°, the sine value of the initial AoA estimated value is 1/2, and the other The difference between the AoA estimated value and the initial AoA estimated value is 1, then the sine value of the other AoA estimated value is -1/2, and the other AoA estimated value is -30°, that is, the first other of the first sub-array can be determined The AoA estimate is -30°.
  • the base station in the preset first AoA interval, can quickly obtain the first initial AoA estimation value according to the positioning signal of the terminal received by the first subarray, based on the linear relationship between the AoA estimation values and The first initial AoA estimate value determines at least one first other AoA estimate value. Due to the characteristics of the large spacing antenna, it is only necessary to perform a spectral peak search in a sine value interval with a length of 2/N to obtain an AoA estimate value. According to the obtained The linear relationship between the estimated AoA value and the estimated AoA value can quickly obtain all estimated AoA values, which can greatly reduce the amount of calculation compared to obtaining all estimated AoA values by traversing the entire sine value interval.
  • FIG. 5 is a schematic flowchart of a method for determining candidate AoA estimated values provided by an embodiment of the present application. This embodiment relates to an optional implementation manner of determining a candidate AoA estimated value from an initial AoA estimated value and other AoA estimated values according to a correction function.
  • the above S202 may include the following steps:
  • the second subarray may be set as subarray 2 in FIG. 3 , and optionally, the correction function may be a digital beamforming (Digital Beam-Forming, DBF) algorithm, a Capon algorithm, or a MUSIC algorithm.
  • DBF Digital Beam-Forming
  • Capon a Capon algorithm
  • MUSIC MUSIC algorithm
  • the constructed MUSIC spectral function can be expressed as:
  • U n is the noise subspace obtained by using the positioning signal of the terminal received by the second subarray as x 2 (t).
  • the base station may bring the first initial AoA estimated value and other AoA estimated values into the correction function, and determine whether the correction function value corresponding to the first initial AoA estimated value and other AoA estimated values is greater than a preset threshold, greater than the preset threshold
  • the AoA estimated values corresponding to the correction function of the correction function are averaged, and the average is used as a candidate AoA estimated value, or the AoA estimated values corresponding to the correction function greater than the preset threshold are weighted average, and the weighted average is used as a candidate AoA estimate value.
  • FIG. 6 is a schematic flowchart of a method for determining candidate AoA estimated values through a correction function provided by the embodiment of the present application.
  • the above S502 may include the following steps:
  • the first initial AoA estimated value and the sine values of other AoA estimated values in the first sub-array obtained in step S402 are calculated, and the obtained first initial AoA estimated value and the sine values of other AoA estimated values are input into the constructed correction
  • the value of the correction function can be obtained by calculating the correction function. For example, when the first initial estimated AoA value is 9.8 and the other estimated AoA values are -30, the correction function value corresponding to the first initial estimated AoA value is -0.15dB, and the correction function value corresponding to the other estimated AoA values is -32dB.
  • the base station may sort the obtained correction function values, which may be arranged in descending order, and the value of the first correction function is the largest value of the correction function, or may be arranged in descending order.
  • the value of the last correction function is the largest value of the correction function
  • the estimated AoA value corresponding to the value of the largest correction function is used as a candidate AoA estimate.
  • the values of the correction function are -0.15 and -32
  • the estimated AoA value corresponding to -0.15 is 9.8, and 9.8 is used as a candidate AoA estimated value.
  • the candidate AoA estimation value and the noise subspace belong to an orthogonal relationship, and the denominator value in the corresponding MUSIC spectral function is close to 0, and the function value of the corresponding MUSIC spectral function is larger, so the correction function
  • the AoA estimated value corresponding to the output maximum value is used as a candidate AoA estimated value.
  • FIG. 7 shows a schematic diagram of the MUSIC spectral function, where the abscissa in the figure represents the estimated value of AoA, and the ordinate represents the amplitude.
  • the antenna spacing is d, it corresponds to 1 AoA estimated value
  • the antenna spacing is 2d, it corresponds to 2 AoA estimated values
  • the antenna spacing is 3d, it corresponds to 3 AoA estimated values.
  • the sub-array whose antenna spacing is 2d in Fig. 7 is set as the first sub-array
  • the sub-array whose antenna spacing is d is set as the second sub-array, or, the sub-array whose antenna spacing is 3d in Fig.
  • the sub-array with the antenna spacing of d is set as the second sub-array
  • the two AoA estimated values corresponding to the first sub-array are input into the correction function constructed by the second sub-array, and the AoA estimated value is around 9.8
  • the correction function has the largest value, and 9.8 is taken as the candidate AoA estimate.
  • the base station constructs a correction function according to the positioning signal of the terminal received by the second subarray, so that the first initial AoA estimated value and each first other AoA estimated value can be respectively substituted into the correction function to obtain the value of the correction function , and then the estimated AoA value corresponding to the largest value of the correction function can be determined as a candidate AoA estimated value, the AoA estimated value corresponding to the first sub-array is determined according to the positioning signal received by the first sub-array, and the AoA estimated value corresponding to the first sub-array is determined according to the received positioning signal of the second sub-array.
  • the correction function is determined by the positioning signal, and the estimated AoA value corresponding to the first sub-array is brought into the correction function, and the candidate AoA estimate value can be quickly determined according to the properties of the correction function.
  • FIG. 8 is a schematic flowchart of a method for determining an initial AoA estimated value provided by an embodiment of the present application.
  • This embodiment relates to an optional implementation of obtaining an initial AoA estimated value of at least one subarray in a preset AoA interval, and determining at least one other AoA estimated value of a subarray according to the first initial AoA estimated value Way.
  • the above S202 may include the following steps:
  • the preset first AoA interval obtain a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array; and based on the linear relationship between the AoA estimated values and the first initial AoA estimated value, Determine at least one first other AoA estimated value to obtain a first AoA estimated value set; the first AoA estimated value set includes the first initial AoA estimated value and the first other AoA estimated value.
  • the estimated value of the angle of arrival ⁇ 1 is calculated through formulas (1) to (3), that is, ⁇ 1 is the first initial AoA estimated value.
  • ⁇ 1 is the first initial AoA estimated value.
  • other estimated AoA values of the first subarray can be obtained according to the linear relationship between the estimated AoA values.
  • the preset second AoA interval obtain a second initial AoA estimated value according to the positioning signal of the terminal received by the second subarray; and based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, Determine at least one second other estimated AoA value to obtain a second set of estimated AoA values; the second set of estimated AoA values includes the second initial estimated AoA value and the second other estimated AoA value.
  • the process of obtaining the estimated AoA value according to the positioning signal can be expressed as:
  • x 2 (t) represents the positioning signal of the terminal received by the second subarray
  • s(t) represents the positioning signal sent by the terminal
  • n(t) represents the noise subspace
  • a 2 represents the set of direction vectors of the angle of arrival .
  • a 2 [a( ⁇ 1 ),a( ⁇ 2 ),...a( ⁇ l )] (8)
  • a() represents the direction vector of the arrival angle
  • ⁇ l represents the lth arrival angle, that is, the AoA estimated value corresponding to the second sub-array.
  • the preset second AoA interval can refer to any interval with a length of 2/N. If the direction vector corresponding to the preset second AoA interval is a( ⁇ 2 ), the formula of the direction vector can be expressed as:
  • ⁇ 2 represents the estimated value of the arrival angle
  • d t is the position coordinate of the antenna in the second subarray
  • ⁇ is the wavelength corresponding to the subcarrier.
  • the estimated value of the angle of arrival ⁇ 2 is calculated through formula (7) to formula (9), that is, ⁇ 2 is the second initial AoA estimated value.
  • ⁇ 2 is the second initial AoA estimated value.
  • This embodiment relates to an optional implementation manner of determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values.
  • the above S202 may include the following steps:
  • the base station may make a difference between the first AoA estimated value in the first AoA estimated value set and each AoA estimated value in the second AoA estimated value set to obtain the first group of difference results, and according to the first AoA estimated value
  • the order of each AoA estimated value in the value set, the second AoA estimated value in the first AoA estimated value set and each AoA estimated value in the second AoA estimated value set are differenced to obtain the second set of difference results , and so on, until the last AoA estimated value in the first AoA estimated value set is subtracted from each AoA estimated value in the second AoA estimated value set to obtain the last set of difference results.
  • the first set of AoA estimated values is 9.8 and 15, and the second set of AoA estimated values is 9.9, 5, and 2, and the obtained difference results are 0.1, 4.8, 7.8, 5.1, 10, and 13, respectively.
  • the base station can sort the difference results, and when the difference results are arranged in order from large to small, select the last difference as the smallest difference; when When the difference results are arranged in ascending order, the first difference is selected as the smallest difference. According to the minimum difference in absolute value, the first estimated AoA value and the second estimated AoA value corresponding to the minimum difference can be obtained.
  • the difference results are 0.1, 4.8, 7.8, 5.1, 10, and 13, arrange the difference results in descending order, and the interpolation results after the arrangement are 13, 10, 7.8, 5.1, 4.8 and 0.1, the smallest difference in absolute value is 0.1, the first estimated AoA value corresponding to the smallest difference in absolute value is 9.8, and the second estimated AoA value corresponding to the smallest difference in absolute value is 9.9.
  • the first AoA estimate corresponding to the minimum difference is 9.8
  • the second AoA estimate corresponding to the minimum difference is 9.9
  • the average of the first AoA estimate and the second AoA estimate is 9.85
  • 9.85 is used as the candidate AoA estimated value.
  • the base station can obtain the first initial AoA estimation value according to the positioning signal of the terminal received by the first sub-array in the preset first AoA interval, based on the linear relationship between the AoA estimation values and the first
  • the initial AoA estimated value is to determine at least one first other AoA estimated value to obtain a first AoA estimated value set.
  • the second initial AoA estimated value can be obtained according to the positioning signal of the terminal received by the second sub-array.
  • the AoA estimated value based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, at least one second other AoA estimated value is determined to obtain a second AoA estimated value set, and the base station acquires the first AoA estimated value set.
  • the difference between each AoA estimated value and each AoA estimated value in the second AoA estimated value set, so that the first AoA estimated value and the first AoA estimated value in the corresponding first AoA estimated value set corresponding to the difference with the smallest absolute value can be obtained
  • the second AoA estimated value in the second AoA estimated value set, and the average value of the first AoA estimated value and the second AoA estimated value may be determined as a candidate AoA estimated value, wherein the first AoA estimated value set includes the first The initial AoA estimated value and the first other AoA estimated value, the second AoA estimated value set includes the second initial AoA estimated value and the second other AoA estimated value, by dividing the antenna array into the first sub-array and the second sub-array, According to the positioning signals received by the first sub-array and the second sub-array respectively, the combination of the first AoA estimated value corresponding to the first sub-array and the second AoA estimated value set corresponding to the second sub
  • the first AoA estimated value corresponding to the first sub-array and the estimated AoA value corresponding to the second sub-array can be quickly obtained.
  • the second AoA estimated value corresponding to the matrix can quickly determine the candidate AoA estimated value.
  • This embodiment relates to an optional implementation manner of determining the target AoA interval according to the candidate AoA estimated value, and determining the target AoA estimated value based on the target AoA interval.
  • the above S203 may include: according to the positioning signals of terminals received by all sub-arrays, using a preset search method to determine the target AoA estimated value in the target AoA interval.
  • the target AoA interval refers to a small interval around the candidate AoA estimated value.
  • the target AoA interval may be 8.8° to 10.8°. Since the spectral peak search algorithm can set the search range, the spectral peak search algorithm can be used for local search in the target AoA interval.
  • the process of obtaining the target AoA estimation value according to the positioning signals can be expressed as:
  • x(t) represents the positioning signal received by all subarrays from the terminal
  • s(t) represents the positioning signal sent by the terminal
  • n(t) represents the noise subspace
  • A represents the set of direction vectors of the angle of arrival.
  • a 1 ( ⁇ ) and a 2 ( ⁇ ) are the direction vectors of the first sub-array and the second sub-array respectively, therefore, assuming that there is a fuzzy angle value ⁇ ', then:
  • the base station uses the preset search method to determine the target AoA estimated value in the target AoA interval according to the terminal positioning signals received by all sub-arrays.
  • the accuracy of the candidate AoA estimated value is not enough. Searching in a small surrounding area makes the target AoA estimate more accurate, and at the same time, compared with the method of determining the target AoA estimate only through the positioning signals of terminals received by all sub-arrays, the calculation amount is less, and there is no need to traverse The entire AoA interval.
  • the AoA estimation method is described in detail below, and the method includes:
  • S911 according to the positioning signals of terminals received by all sub-arrays, use a preset search method to determine an estimated target AoA value in the target AoA interval.
  • the base station can obtain the first initial AoA estimation value according to the positioning signal of the terminal received by the first subarray in the preset first AoA interval, based on the linear relationship between the AoA estimation values and the first initial AoA estimated value, determine at least one first other AoA estimated value, obtain the first AoA estimated value set, construct a correction function according to the positioning signal of the terminal received by the second sub-array, and combine the first initial AoA estimated value and each first other
  • the AoA estimated values are respectively substituted into the correction function to obtain the value of the correction function, and the AoA estimated value corresponding to the maximum value of the correction function is determined as the candidate AoA estimated value.
  • the preset search method determines the target AoA estimated value; in addition, on the basis of obtaining the first AoA estimated value set, in the preset second AoA interval, according to the positioning signal of the terminal received by the second sub-array to obtain the second Two initial AoA estimated values, based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, at least one second other AoA estimated value is determined to obtain a second set of AoA estimated values, and the first set of AoA estimated values is obtained.
  • the average value of the first AoA estimated value and the second AoA estimated value is determined as the candidate AoA estimated value, and according to the positioning signals of terminals received by all sub-arrays, in the target AoA interval
  • the preset search method is used to determine the target AoA estimated value.
  • the AoA estimated value corresponding to sub-array 1 can be determined according to the positioning signal received by sub-array 1
  • the AoA estimated value corresponding to sub-array 2 can be determined according to the positioning signal received by sub-array 2.
  • the received positioning signal constructs a correction function, and the estimated AoA value corresponding to sub-array 1 is input into the correction function corresponding to sub-array 2, and a candidate AoA estimated value with insufficient accuracy can be obtained, or, the positioning signal received by sub-array 1 can be Determine the estimated AoA value corresponding to sub-array 1, determine the estimated AoA value corresponding to sub-array 2 according to the positioning signal received by sub-array 2, and compare the estimated AoA value corresponding to the first sub-array with the estimated AoA value corresponding to the second sub-array By comparison, the two closest AoA estimates are obtained, and the average value of the two AoA estimates is used as a candidate AoA estimate with insufficient precision, and the cells around the candidate AoA estimate are selected, and the positioning signal received by the antenna array is in the candidate AoA estimate.
  • the search is performed in the small area around the AoA estimated value, so that the accuracy of the obtained target AoA estimated value is higher.
  • steps in the flow charts involved in the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flow charts involved in the above embodiments may include multiple steps or stages, and these steps or stages are not necessarily executed at the same time, but may be executed at different times, The execution order of these steps or stages is not necessarily performed sequentially, but may be executed in turn or alternately with other steps or at least a part of steps or stages in other steps.
  • an embodiment of the present application further provides an AoA estimation device for implementing the above-mentioned AoA estimation method.
  • the solution to the problem provided by the device is similar to the implementation described in the above method, so for the specific limitations in one or more embodiments of the AoA estimation device provided below, please refer to the definition of the AoA estimation method above, I won't repeat them here.
  • a first structural block diagram of an AoA estimation device including: a processing module 11, a first determining module 12 and a second determining module 13, wherein:
  • a processing module 11 configured to obtain an initial AoA estimated value of at least one subarray in a preset AoA interval, and determine at least one other AoA estimated value of the subarray according to the initial AoA estimated value;
  • a first determining module 12 configured to determine candidate AoA estimated values according to the initial AoA estimated value and the other AoA estimated values;
  • the second determining module 13 is configured to determine a target AoA interval according to the candidate AoA estimated value, and determine a target AoA estimated value based on the target AoA interval; the length of the target AoA interval is smaller than a second threshold.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • FIG. 11 a second structural block diagram of an AoA estimating device is provided.
  • the above-mentioned processing module 11 includes: a first obtaining unit 111 and a first determining unit 112, wherein:
  • the first obtaining unit 111 is configured to obtain a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array in the preset first AoA interval;
  • the first determining unit 112 is configured to determine at least one first other estimated AoA value based on a linear relationship between estimated AoA values and the first initial estimated AoA value.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • a third structural block diagram of an AoA estimation device is provided, and the above-mentioned first determination module 12 includes: a construction unit 121 and a second determination unit 122, wherein:
  • a construction unit 121 configured to construct a correction function according to the positioning signal of the terminal received by the second sub-array
  • the second determining unit 122 is configured to determine the candidate AoA estimated value according to the first initial AoA estimated value, the at least one first other AoA estimated value and the correction function.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • the above-mentioned second determination unit 122 is specifically configured to substitute the first initial AoA estimated value and each of the first other AoA estimated values into the correction function to obtain the value of the correction function;
  • the estimated AoA value corresponding to the correction function value is determined as the candidate AoA estimated value.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • a fourth structural block diagram of an AoA estimating device is provided, and the above-mentioned processing module 11 includes: a first obtaining unit 111 and a second obtaining unit 113, wherein:
  • the first obtaining unit 111 is configured to obtain a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array in the preset first AoA interval; and based on the linear relationship between the AoA estimated values and the obtained The first initial AoA estimated value, determine at least one first other AoA estimated value, and obtain the first AoA estimated value set; the first AoA estimated value set includes the first initial AoA estimated value and the first AoA estimated value - other AoA estimates;
  • the second obtaining unit 113 is configured to obtain a second initial AoA estimated value according to the positioning signal of the terminal received by the second sub-array in the preset second AoA interval; and based on the linear relationship between the AoA estimated values and the obtained The second initial AoA estimated value, determine at least one second other AoA estimated value, and obtain a second AoA estimated value set; the second AoA estimated value set includes the second initial AoA estimated value and the second other estimated value AoA estimate.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • a fifth structural block diagram of an AoA estimation device is provided, and the above-mentioned first determination module 12 includes: a third acquisition unit 123, a fourth acquisition unit 124, and a third determination unit 125, in:
  • a third acquiring unit 123 configured to acquire a difference between each AoA estimated value in the first AoA estimated value set and each AoA estimated value in the second AoA estimated value set;
  • the fourth acquiring unit 124 is configured to acquire the first AoA estimated value in the first AoA estimated value set and the second AoA estimated value in the second AoA estimated value set corresponding to the difference with the smallest absolute value ;
  • the third determining unit 125 is configured to determine an average value of the first AoA estimated value and the second AoA estimated value as the candidate AoA estimated value.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • a sixth structural block diagram of an AoA estimation device is provided, and the above-mentioned second determination module 13 includes: a fourth determination unit 131, wherein:
  • the fourth determination unit 131 is configured to determine the estimated target AoA value in the target AoA interval by using a preset search method according to the positioning signals of terminals received by all sub-arrays.
  • the AoA estimating apparatus provided in this embodiment can execute the foregoing method embodiment, and its implementation principle and technical effect are similar, and details are not repeated here.
  • Each module in the above-mentioned AoA estimating device can be fully or partially realized by software, hardware and a combination thereof.
  • the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.
  • a base station is provided, and its internal structure diagram may be as shown in FIG. 16 .
  • the base station includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus.
  • the processor of the base station is used to provide calculation and control capabilities.
  • the memory of the base station includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system and computer programs.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the communication interface of the base station is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be realized through WIFI, mobile cellular network, NFC (Near Field Communication) or other technologies.
  • WIFI wireless cellular network
  • NFC Near Field Communication
  • FIG. 16 is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer equipment on which the solution of this application is applied.
  • the specific computer equipment can be More or fewer components than shown in the figures may be included, or some components may be combined, or have a different arrangement of components.
  • a base station including a memory and a processor, a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: the AoA estimation method is applied to the base station, and the base station includes at least two sub-arrays, Methods include:
  • the target AoA interval is determined according to the candidate AoA estimated value, and the target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is less than a second threshold.
  • the processor executes the computer program, the following steps are further implemented: obtaining an initial AoA estimated value of at least one sub-array in the preset AoA interval, and determining at least one other of the sub-array according to the first initial AoA estimated value AoA estimates, including:
  • the preset first AoA interval acquire a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array;
  • At least one first other AoA estimate is determined.
  • determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values including:
  • Candidate AoA estimates are determined based on the first initial AoA estimate, at least one first other AoA estimate, and a correction function.
  • the AoA estimated value corresponding to the largest correction function value is determined as a candidate AoA estimated value.
  • the processor executes the computer program, the following steps are further implemented: obtaining an initial AoA estimated value of at least one sub-array in the preset AoA interval, and determining at least one other of the sub-array according to the first initial AoA estimated value AoA estimates, including:
  • the first initial AoA estimated value is obtained according to the positioning signal of the terminal received by the first sub-array; and based on the linear relationship between the AoA estimated values and the first initial AoA estimated value, determine at least A first other AoA estimated value to obtain a first AoA estimated value set; the first AoA estimated value set includes a first initial AoA estimated value and a first other AoA estimated value;
  • the second initial AoA estimated value is obtained according to the positioning signal of the terminal received by the second sub-array; and based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, determine at least A second other estimated AoA value to obtain a second set of estimated AoA values; the second set of estimated AoA values includes the second initial estimated AoA value and the second other estimated AoA value.
  • determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values including:
  • An average of the first AoA estimate and the second AoA estimate is determined as a candidate AoA estimate.
  • the processor executes the computer program, the following steps are further implemented: determining the target AoA interval according to the candidate AoA estimated value, and determining the target AoA estimated value based on the target AoA interval, including:
  • a preset search method is used to determine the target AoA estimated value in the target AoA interval.
  • the antenna spacing of the target sub-array in each of the sub-arrays is less than or equal to the first threshold, and the antenna spacing of other sub-arrays except the target sub-array is greater than the first threshold.
  • the threshold, or, the antenna spacing of each of the sub-arrays is greater than the first threshold; the target sub-array is any sub-array in each of the sub-arrays.
  • a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the following steps are implemented: the AoA estimation method is applied to a base station, and the base station includes at least two sub-arrays.
  • the method includes :
  • the target AoA interval is determined according to the candidate AoA estimated value, and the target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is less than a second threshold.
  • the following steps are further implemented: obtaining an initial AoA estimated value of at least one sub-array in the preset AoA interval, and determining at least one of the sub-arrays according to the first initial AoA estimated value
  • Other AoA estimates including:
  • the preset first AoA interval acquire a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array;
  • At least one first other AoA estimate is determined.
  • determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values including:
  • Candidate AoA estimates are determined based on the first initial AoA estimate, at least one first other AoA estimate, and a correction function.
  • determining a candidate AoA estimate according to the first initial AoA estimate, at least one first other AoA estimate and a correction function including:
  • the AoA estimated value corresponding to the largest correction function value is determined as a candidate AoA estimated value.
  • the following steps are further implemented: obtaining an initial AoA estimated value of at least one sub-array in the preset AoA interval, and determining at least one of the sub-arrays according to the first initial AoA estimated value
  • Other AoA estimates including:
  • the first initial AoA estimated value is obtained according to the positioning signal of the terminal received by the first sub-array; and based on the linear relationship between the AoA estimated values and the first initial AoA estimated value, determine at least A first other AoA estimated value to obtain a first AoA estimated value set; the first AoA estimated value set includes a first initial AoA estimated value and a first other AoA estimated value;
  • the second initial AoA estimated value is obtained according to the positioning signal of the terminal received by the second sub-array; and based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, determine at least A second other estimated AoA value to obtain a second set of estimated AoA values; the second set of estimated AoA values includes the second initial estimated AoA value and the second other estimated AoA value.
  • determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values including:
  • An average of the first AoA estimate and the second AoA estimate is determined as a candidate AoA estimate.
  • the following steps are further implemented: determining the target AoA interval according to the candidate AoA estimated value, and determining the target AoA estimated value based on the target AoA interval, including:
  • a preset search method is used to determine the target AoA estimated value in the target AoA interval.
  • the antenna spacing of the target sub-array in each of the sub-arrays is less than or equal to the first threshold, and the antenna spacing of other sub-arrays except the target sub-array is greater than the first threshold.
  • a threshold, or, the antenna spacing of each of the sub-arrays is greater than the first threshold; the target sub-array is any sub-array in each of the sub-arrays.
  • a computer program product including a computer program, which implements the following steps when the computer program is executed by a processor: the AoA estimation method is applied to a base station, and the base station includes at least two subarrays, and the method includes:
  • the target AoA interval is determined according to the candidate AoA estimated value, and the target AoA estimated value is determined based on the target AoA interval; the length of the target AoA interval is less than a second threshold.
  • the following steps are further implemented: obtaining an initial AoA estimated value of at least one sub-array in the preset AoA interval, and determining at least one of the sub-arrays according to the first initial AoA estimated value
  • Other AoA estimates including:
  • the preset first AoA interval acquire a first initial AoA estimated value according to the positioning signal of the terminal received by the first sub-array;
  • At least one first other AoA estimate is determined.
  • determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values including:
  • Candidate AoA estimates are determined based on the first initial AoA estimate, at least one first other AoA estimate, and a correction function.
  • determining a candidate AoA estimate according to the first initial AoA estimate, at least one first other AoA estimate and a correction function including:
  • the AoA estimated value corresponding to the largest correction function value is determined as a candidate AoA estimated value.
  • the following steps are further implemented: obtaining an initial AoA estimated value of at least one sub-array in the preset AoA interval, and determining at least one of the sub-arrays according to the first initial AoA estimated value
  • Other AoA estimates including:
  • the first initial AoA estimated value is obtained according to the positioning signal of the terminal received by the first sub-array; and based on the linear relationship between the AoA estimated values and the first initial AoA estimated value, determine at least A first other AoA estimated value to obtain a first AoA estimated value set; the first AoA estimated value set includes a first initial AoA estimated value and a first other AoA estimated value;
  • the second initial AoA estimated value is obtained according to the positioning signal of the terminal received by the second sub-array; and based on the linear relationship between the AoA estimated values and the second initial AoA estimated value, determine at least A second other estimated AoA value to obtain a second set of estimated AoA values; the second set of estimated AoA values includes the second initial estimated AoA value and the second other estimated AoA value.
  • determining candidate AoA estimated values according to the initial AoA estimated value and other AoA estimated values including:
  • An average of the first AoA estimate and the second AoA estimate is determined as a candidate AoA estimate.
  • the following steps are further implemented: determining the target AoA interval according to the candidate AoA estimated value, and determining the target AoA estimated value based on the target AoA interval, including:
  • a preset search method is used to determine the target AoA estimated value in the target AoA interval.
  • the antenna spacing of the target sub-array in each of the sub-arrays is less than or equal to the first threshold, and the antenna spacing of other sub-arrays except the target sub-array is greater than the first threshold.
  • a threshold, or, the antenna spacing of each of the sub-arrays is greater than the first threshold; the target sub-array is any sub-array in each of the sub-arrays.
  • user information including but not limited to user equipment information, user personal information, etc.
  • data including but not limited to data used for analysis, stored data, displayed data, etc.
  • any reference to storage, database or other media used in the various embodiments provided in the present application may include at least one of non-volatile and volatile storage.
  • Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive variable memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory, MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (Phase Change Memory, PCM), graphene memory, etc.
  • the volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, etc.
  • RAM Random Access Memory
  • RAM Random Access Memory
  • RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
  • the databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database.
  • the non-relational database may include a blockchain-based distributed database, etc., but is not limited thereto.
  • the processors involved in the various embodiments provided by this application can be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., and are not limited to this.

Abstract

本申请涉及一种AoA估计方法、装置、基站、存储介质和计算机程序产品。AoA估计方法应用于基站，基站包括至少两个子阵，该方法包括：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值，目标AoA区间的长度小于第二阈值。采用本方法能够提高AoA的识别精度。

Description

AoA估计方法、装置、基站、存储介质和计算机程序产品
相关申请:
本申请要求2022年01月14日申请的，申请号为2022100409291，名称为“AoA估计方法、装置、基站、存储介质和计算机程序产品”的中国专利申请的优先权，在此将其全文引入作为参考。
技术领域
本申请涉及5G技术领域，特别是涉及一种AoA估计方法、装置、基站、存储介质和计算机程序产品。
背景技术
随着工业互联网、车联网的高速发展，高精度定位成为不可或缺的关键支撑服务。在停车场、隧道等GPS信号很弱甚至不可用的情况下，5G基站可以根据终端发送的定位信号测量到达角(Angle of Arrival，AoA)、离开角(Angle of Departure，AoD)等参数以确定终端的位置，在当前5G基站主要使用的天线阵列的天线间距为小于等于半波长，通过增加天线间距可以提高测向精度和分辨率，但会得到多个模糊AoA估计值。
现有技术中通过多个基站接收多个定位信号，将多个定位信号得到的多组模糊AoA估计值比较，消除由于增加天线间距导致的模糊解，得到真实的AoA估计值。但现有技术的方法识别精度不高。
发明内容
基于此，有必要针对上述技术问题，提供一种能够提高AoA的识别精度的AoA估计方法、装置、基站、存储介质和计算机程序产品。
第一方面，本申请提供了一种AoA估计方法，所述AoA估计方法应用于基站，所述基站包括至少两个子阵，所述方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
在其中一个实施例中，所述在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值。
在其中一个实施例中，所述根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值，包括：
根据所述第二子阵接收到的终端的定位信号构建校正函数；
根据所述第一初始AoA估计值、所述至少一个第一其他AoA估计值和所述校正函数，确定所述候选AoA估计值。
在其中一个实施例中，所述根据所述第一初始AoA估计值、所述至少一个第一其他AoA估计值和所述校正函数，确定所述候选AoA估计值，包括：
将所述第一初始AoA估计值和各所述第一其他AoA估计值分别代入所述校正函数，得到所述校正函数的值；
将最大的所述校正函数的值对应的AoA估计值确定为所述候选AoA估计值。
在其中一个实施例中，所述在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值， 得到所述第一AoA估计值集合；所述第一AoA估计值集合中包括所述第一初始AoA估计值和所述第一其他AoA估计值；
在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；所述第二AoA估计值集合中包括所述第二初始AoA估计值和所述第二其他AoA估计值。
在其中一个实施例中，所述根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值，包括：
获取所述第一AoA估计值集合中的各AoA估计值与所述第二AoA估计值集合中的各AoA估计值之间的差值；
获取绝对值最小的所述差值对应的所述第一AoA估计值集合中的第一AoA估计值和所述第二AoA估计值集合中的第二AoA估计值；
将所述第一AoA估计值和所述第二AoA估计值的平均值确定为所述候选AoA估计值。
在其中一个实施例中，所述根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值，包括：
根据所有子阵接收到的终端的定位信号，在所述目标AoA区间内采用预设的搜索方法确定所述目标AoA估计值。
在其中一个实施例中，各所述子阵中目标子阵的天线间距小于等于第一阈值，除目标子阵外的其他子阵的天线间距大于第一阈值，或，各所述子阵的天线间距都大于第一阈值；所述目标子阵为各所述子阵中任一子阵。
第二方面，本申请还提供了一种AoA估计装置，所述AoA估计装置应用于基站，所述基站包括至少两个子阵，所述装置包括：
处理模块，用于在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
第一确定模块，用于根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
第二确定模块，用于根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
第三方面，本申请还提供了一种基站，所述计算机设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现以下步骤：所述AoA估计方法应用于基站，所述基站包括至少两个子阵，所述方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述第一初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
第四方面，本申请还提供了一种计算机可读存储介质。所述计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现以下步骤：所述AoA估计方法应用于基站，所述基站包括至少两个子阵，所述方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述第一初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
第五方面，本申请还提供了一种计算机程序产品。所述计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实现以下步骤：所述AoA估计方法应用于基站，所述基站包括至少两个子阵， 所述方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述第一初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
上述AoA估计方法、装置、基站、存储介质和计算机程序产品，AoA估计方法应用于基站，基站包括至少两个子阵，基站在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，可以根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，从而可以根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，根据候选AoA估计值确定目标AoA区间，进而可以基于目标AoA区间确定目标AoA估计值，通过在预设的AoA区间中获取至少一个子阵的初始AoA估计值，根据该初始AoA估计值和AoA估计值之间的关系可以得到其他AoA估计值，相比于传统的方法，在获得全部AoA估计值的过程中无需遍历整个AoA区间，缩短了工作量，无需对每个AoA估计值进行计算，计算过程简单，计算量较小；同时从初始AoA估计值和其他AoA估计值中确定一个精度不高的候选AoA估计值，将候选AoA估计值的周围小区间设定为目标AoA区间，在目标AoA区间中重新搜索目标AoA估计值，提高了目标AoA估计值的精确度。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为一个实施例中AoA估计方法的应用环境图；
图2为一个实施例中确定目标AoA估计值方法的流程示意图；
图3为一个实施例中天线阵列的结构示意图；
图4为一个实施例中确定多个AoA估计值方法的流程示意图；
图5为一个实施例中确定候选AoA估计值方法的流程示意图；
图6为一个实施例中通过校正函数确定候选AoA估计值方法的流程示意图；
图7为一个实施例中MUSIC谱函数的示意图；
图8为一个实施例中确定初始AoA估计值方法的流程示意图；
图9为一个实施例中AoA估计方法的流程示意图；
图10为一个实施例中AoA估计装置的第一结构框图；
图11为一个实施例中AoA估计装置的第二结构框图；
图12为一个实施例中AoA估计装置的第三结构框图；
图13为一个实施例中AoA估计装置的第四结构框图；
图14为一个实施例中AoA估计装置的第五结构框图；
图15为一个实施例中AoA估计装置的第六结构框图；
图16为一个实施例中基站的内部结构图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
首先，在具体介绍本申请实施例的技术方案之前，先对本申请实施例基于的技术背景进行介绍。
现有的利用无线定位基站对终端到达角估计，主要利用的天线间距小于等于半波长的均匀天线阵列，而测向误差的克拉美罗下界与天线孔径成反比，增大天线阵列的孔径可以有效提高测向精度与分辨率。但是增大天线单元之间的间距会减弱天线单元之间的耦合效应，耦合效应不但对定位不利，对通信也不利，特别是在多用户情况下会增加误码率。
有学者提出了一些复杂的大间距天线阵列结构，如互质阵、嵌套阵等。但是这类天线阵列不仅结构 复杂，而且需要特殊设计的构造虚拟阵列的算法。并且，在实际应用中，天线之间存在的硬件损伤以及相干信号会导致无法构造准确的虚拟阵天线阵列，从而导致该类方法失效。
实际工程中，一般需要多个基站联合对终端进行定位，由于基站中部署了大间距天线阵列，只能获得对应同一目标的多个角度估计值，然后利用部署在不同位置的基站获得多组角度估计值，消除模糊解，获得对应该目标的真实角度估计值。多个基站联合会造成系统的复杂度和计算量的增加，而且使得硬件成本较高，特别是在多个场景中，识别精度不高。
因此，针对以上问题，下面结合本公开实施例所应用的场景，对本公开实施例涉及的技术方案进行介绍。
本申请实施例提供的AoA估计方法，可以应用于如图1所示的应用环境中。其中，终端102与基站104进行通信。终端102发送定位信号给基站104后，基站104根据该定位信号计算终端102的AoA估计值，从而可以得出终端102的具体位置。其中，终端102可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑、物联网设备和便携式可穿戴设备，物联网设备可为智能音箱、智能电视、智能空调、智能车载设备等。便携式可穿戴设备可为智能手表、智能手环、头戴设备等。基站可以是宏基站、微基站、射频拉远、直放站或室内分布系统等。
在一个实施例中，如图2所示，提供了一个确定目标AoA估计值方法的流程示意图，以该方法应用于图1中的基站为例进行说明，AoA估计方法应用于基站，基站包括至少两个子阵，包括以下步骤：
S201，在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值。
可选的，各子阵中目标子阵的天线间距小于等于第一阈值，除目标子阵外的其他子阵的天线间距大于第一阈值，或，各子阵的天线间距都大于第一阈值；目标子阵为各子阵中任一子阵。
示例性的，图3为大间距天线阵列的结构示意图，基站使用的大间距天线阵列包括两个大间距天线子阵，天线总数为T，两个大间距天线子阵分别为第一子阵和第二子阵，例如，第一子阵可以是图3中的子阵一，第二子阵可以是图3中的子阵二，或者，第一子阵可以是图3中的子阵二，第二子阵可以是图3中的子阵一。其中，子阵一的天线数为T 1，其天线间距为Md，子阵二的天线数为T 2，其天线间距为Nd，M、N为正整数，d为单位间距，λ为中心频率对应的波长。
当
Figure PCTCN2022141822-appb-000001
M、N为1时，第一子阵和第二子阵的天线间距都小于等于第一阈值，即仅存在一个角度的方向矢量，能得到一个AoA估计值。在本实施例中，当
Figure PCTCN2022141822-appb-000002
M、N互质，当M为1，N为大于1的正整数时，第一子阵的天线间距等于第一阈值，只存在一个角度的方向矢量，第一子阵对应一个AoA估计值，第二天线的天线间距大于第一阈值，存在多个角度的方向矢量，第二子阵对应至少两个AoA估计值；当M、N都为大于1的正整数且M、N互质时，第一子阵和第二子阵的天线间距都大于第一阈值，即存在不同角度的方向矢量，可以得到多个AoA估计值。由于第一子阵和第二子阵中天线之间的距离大于第一阈值，第一子阵和第二子阵都对应至少两个AoA估计值。
以第二子阵为例，可选的，基站可以将第二子阵接收到的定位信号输入至预设的神经网络模型中，通过神经网络模型的训练，输出预设AoA区间中第二子阵的初始AoA估计值，或者，基站也可以根据第二子阵接收到的定位信号和相关算法进行计算，得到预设AoA区间中的第二子阵的初始AoA估计值。其中，相关算法可以为获得闭式解的算法或谱峰搜索类的算法，获得闭式解的算法可以是基于旋转不变技术的信号参数估计算法(Estimating Signal Parameters via Rotational Invariance Techniques，ESPRIT)、多重信号分类的求根算法(Multiple Signal Classification，MUSIC)等，谱峰搜索类的算法可以是多重信号分类(Multiple Signal Classification，MUSIC)算法、(Capon Algorithm，Capon)算法等。本实施例对获取预设AoA区间中子阵的初始AoA估计值的方式不做限定。基站也可以获取预设AoA区间中第一子阵的初始AoA估计值，也可以同时获取预设AoA区间中第一子阵的初始AoA估计值和第二子 阵的初始AoA估计值。
进一步的，以第二子阵为例，可以根据第二子阵的初始AoA估计值和历史的所有AoA估计值的分布规律，得到第二子阵的其他AoA估计值，或者，可以根据第二子阵的初始AoA估计值和各个AoA估计值之间的关系，得到第二子阵的其他AoA估计值。本实施例对于通过何种方式获得其他AoA估计值的方法不做限定。同样，可以根据上述方法获得第一子阵的其他AoA估计值。
S202，根据初始AoA估计值和其他AoA估计值确定候选AoA估计值。
可选的，基站可以对初始AoA估计值与其他AoA估计值求平均值，将该平均值作为候选AoA估计值。可选的，基站可以对初始AoA估计值与其他AoA估计值求加权平均值，将该加权平均值作为候选AoA估计值。可选的，基站也可以将历史的候选AoA估计值作为参考，选择初始AoA估计值和其他AoA估计值中与历史的候选AoA估计值最接近的AoA估计值，将该AoA估计值作为候选AoA估计值。可选的，基站包括的两个子阵分别为第一子阵和第二子阵时，可以通过第一子阵接收到的定位信号得到第一子阵对应的初始AoA估计值和其他AoA估计值，通过第二子阵接收到的定位信号构建校正函数，根据第一子阵对应的初始AoA估计值和其他AoA估计值、第二子阵对应的校正函数确定候选AoA估计值，或者，可以通过第一子阵接收到的定位信号得到第一子阵对应的初始AoA估计值和其他AoA估计值，通过第二子阵接收到的定位信号得到第二子阵对应的初始AoA估计值和其他AoA估计值，将第一子阵对应的AoA估计值和第二子阵对应的AoA估计值进行对比，将最接近的两个AoA估计值的平均值确定为候选AoA估计值。本实施例对于根据初始AoA估计值和其他AoA估计值确定候选AoA估计值的方式不做限定。
S203，根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值；目标AoA区间的长度小于第二阈值。
可选地，第二阈值可以是长度为2/N和长度为2/M中长度较小的一个区间。
具体的，根据步骤S202得到候选AoA估计值后，选择候选AoA估计值周围的小区间作为目标AoA区间，设定小区间的长度小于2/N。例如，候选AoA估计值为9.8时，可以选择8.8至10.8作为目标AoA区间。可以将定位信号输入至预设的神经网络模型中，通过神经网络模型的训练，输出在目标AoA区间内的目标AoA估计值，或者，基站也可以根据终端的定位信号和相关算法进行计算，得到目标AoA区间内的目标AoA估计值。
上述AoA估计方法中，AoA估计方法应用于大间距天线的基站中，在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，可以根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，从而可以根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，根据候选AoA估计值确定目标AoA区间，进而可以基于目标AoA区间确定目标AoA估计值，通过在预设的AoA区间中获取至少一个子阵的初始AoA估计值，根据该初始AoA估计值和AoA估计值之间的关系可以得到其他AoA估计值，相比于传统的方法，在获得全部AoA估计值的过程中无需遍历整个AoA区间，缩短了工作量，无需对每个AoA估计值进行计算，计算过程简单，计算量较小；同时从初始AoA估计值和其他AoA估计值中确定一个精度不高的候选AoA估计值，将候选AoA估计值的周围小区间设定为目标AoA区间，在目标AoA区间中重新搜索目标AoA估计值，提高了目标AoA估计值的精确度。
图4为本申请实施例提供的确定多个AoA估计值方法的流程示意图。本实施例涉及的是根据在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值一种可选的实现方式。在上述实施例的基础上，如图4所示，上述S201可以包括如下步骤：
S401，在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值。
具体的，第一子阵可以是图3中的子阵一，也可以是子阵二，第一子阵接收到终端发送的定位信号后，根据定位信号获取AoA估计值的过程可以表示为：
x 1(t)＝As(t)+n(t)         (1)
式中，x 1(t)表示第一子阵接收到终端的定位信号，s(t)表示终端发送的定位信号，n(t)表示噪声子空间，A 1表示到达角的方向矢量的集合。
A 1＝[a(θ 1),a(θ 2),...a(θ l)]       (2)
式中，a()表示到达角的方向矢量，θ l表示第l个到达角，即第一子阵对应的AoA估计值。
预设的第一AoA区间可以是指长度为2/N的任意区间，若预设的第一AoA区间对应的方向矢量为a(θ 1)，该方向矢量的公式可以表示为：
Figure PCTCN2022141822-appb-000003
式中，θ表示到达角的估计值，d t为第一子阵中天线的位置坐标，λ为子载波对应的波长。需要说明的是，此处选取第一子阵最左边的天线作为参考天线，天线的位置坐标可以表示为d t＝(t-1)Nd。
进一步的，可以理解的是，根据基站接收到的定位信号、终端发送的定位信号和噪声子空间的值通过上述公式(1)到公式(3)计算得到到达角的估计值θ 1，即θ 1为第一初始AoA估计值。
S402，基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值。
具体的，AoA估计值之间的线性关系是指各个AoA估计值的正弦值之间的差值为2/N的整数倍。该线性关系可以表示为：
Figure PCTCN2022141822-appb-000004
当得到第一子阵的第一初始AoA估计值后，根据AoA估计值之间的线性关系可以求得第一子阵的其他AoA估计值。例如，N取值为2时，对应的第一子阵为两个AoA估计值，当第一子阵的初始AoA估计值为30°，初始AoA估计值的正弦值为1/2，另外一个AoA估计值与初始AoA估计值的差值为1，则另外一个AoA估计值的正弦值为-1/2，另外一个AoA估计值为-30°，即可以确定第一子阵的第一其他AoA估计值为-30°。
上述AoA估计方法中，在预设的第一AoA区间中，基站可以根据第一子阵接收到的终端的定位信号快速的获取第一初始AoA估计值，基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，由于大间距天线的特性，只需要在一个长度为2/N的正弦值区间进行谱峰搜索得到一个AoA估计值，根据得到的AoA估计值和AoA估计值之间的线性关系可以快速的得到全部的AoA估计值，相比于遍历整个正弦值区间得到全部AoA估计值来说，可以大大减少计算量。
图5为本申请实施例提供的确定候选AoA估计值方法的流程示意图。本实施例涉及的是根据校正函数从初始AoA估计值和其他AoA估计值中确定候选AoA估计值一种可选的实现方式。在上述实施例的基础上，如图5所示，上述S202可以包括如下步骤：
S501，根据第二子阵接收到的终端的定位信号构建校正函数。
设定第二子阵可以为图3中的子阵二，可选的，校正函数可以是数字波束形成(Digital Beam-Forming，DBF)算法、Capon类算法或MUSIC算法等。例如，第二子阵接收到的终端的定位信号为x 2(t)，构建的DBF校正函数可以表示为：
f DBF(μ)＝a(μ) Hx 2(t)            (5)
式中，μ＝sinθ表示到达角的正弦值。
构建的MUSIC谱函数可以表示为：
Figure PCTCN2022141822-appb-000005
式中，U n为利用第二子阵接收到的终端的定位信号为x 2(t)获得的噪声子空间。
S502，根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值。
具体的，基站可以将第一初始AoA估计值和其他AoA估计值带入校正函数中，判断第一初始AoA估计值和其他AoA估计值对应的校正函数值是否大于预设阈值，大于预设阈值的校正函数对应的AoA估计值取平均值，将该平均值作为候选AoA估计值，或者，大于预设阈值的校正函数对应的AoA估计值取加权平均值，将该加权平均值作为候选AoA估计值。
可选的，本实施例涉及的是根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值的一种可选的实现方式。在上述实施例的基础上，图6为本申请实施例提供的通过校正函数确定候选AoA估计值方法的流程示意图，如图6所示，上述S502可以包括如下步骤：
S601，将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值。
具体的，计算步骤S402得到的第一子阵中的第一初始AoA估计值和其他AoA估计值的正弦值，将得到的第一初始AoA估计值和其他AoA估计值的正弦值输入构建的校正函数中，通过校正函数的计算，可以得到校正函数的值。例如，第一初始AoA估计值为9.8，其他AoA估计值为-30时，第一初始AoA估计值对应的校正函数值为-0.15dB，其他AoA估计值对应的校正函数值为-32dB。
S602，将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值。
具体的，基站可以将得到的校正函数的值进行排序，可以按照从大到校的顺序排列，排在第一位的校正函数的值为最大的校正函数的值，或者，也可以按照从小到大的顺序排列，排在最后一位的校正函数的值为最大的校正函数的值，将该最大的校正函数的值对应的AoA估计值作为候选AoA估计值。例如，校正函数的值为-0.15和-32，-0.15对应的AoA估计值为9.8，将9.8作为候选AoA估计值。
进一步的，可以理解的是，候选AoA估计值和噪声子空间属于正交关系，对应MUSIC谱函数中的分母值接近于0，对应的MUSIC谱函数的函数值也就越大，因此将校正函数输出的最大值对应的AoA估计值作为候选AoA估计值。
示例性的，以MUSIC谱函数为例，图7表示MUSIC谱函数的示意图，图中横坐标表示AoA估计值，纵坐标表示振幅。天线间距为d时，对应1个AoA估计值，天线间距为2d时，对应2个AoA估计值，天线间距为3d时，对应3个AoA估计值。将图7中天线间距为2d的子阵设定为第一子阵，天线间距为d的子阵设定为第二子阵，或者，将图7中天线间距为3d的子阵设定为第一子阵，天线间距为d的子阵设定为第二子阵，将第一子阵对应两个AoA估计值输入至第二子阵构建的校正函数中，AoA估计值在9.8左右的校正函数值最大，将9.8作为候选AoA估计值。
上述AoA估计方法中，基站根据第二子阵接收到的终端的定位信号构建校正函数，从而可以将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值，进而可以将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值，根据第一子阵接收到的定位信号确定第一子阵对应的AoA估计值，根据第二子阵接收到的定位信号确定校正函数，将第一子阵对应的AoA估计值带入校正函数中，根据校正函数的性质可以快速的确定候选AoA估计值。
图8为本申请实施例提供的确定初始AoA估计值方法的流程示意图。本实施例涉及的是根据在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值的一种可选的实现方式。在上述实施例的基础上，如图8所示，上述S202可以包括如下步骤：
S801，在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合；第一AoA估计值集合中包括第一初始AoA估计值和第一其他AoA估计值。
具体的，根据基站接收到的定位信号、终端发送的定位信号和噪声子空间的值通过公式(1)到公式(3)计算得到到达角的估计值θ 1，即θ 1为第一初始AoA估计值。当得到第一子阵的第一初始AoA估计值后，根据AoA估计值之间的线性关系可以求得第一子阵的其他AoA估计值。
S802，在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；第二AoA估计值集合中包括第二初始AoA估计值和第二其他AoA估计值。
具体的，第二子阵接收到终端发送的定位信号后，根据定位信号获取AoA估计值的过程可以表示为：
x 2(t)＝A 2s(t)+n(t)          (7)
式中，x 2(t)表示第二子阵接收到终端的定位信号，s(t)表示终端发送的定位信号，n(t)表示噪声子空间，A 2表示到达角的方向矢量的集合。
A 2＝[a(θ 1),a(θ 2),...a(θ l)]          (8)
式中，a()表示到达角的方向矢量，θ l表示第l个到达角，即第二子阵对应的AoA估计值。
预设的第二AoA区间可以是指长度为2/N的任意区间，若预设的第二AoA区间对应的方向矢量为a(θ 2)，该方向矢量的公式可以表示为：
Figure PCTCN2022141822-appb-000006
式中，θ 2表示到达角的估计值，d t为第二子阵中天线的位置坐标，λ为子载波对应的波长。需要说明的是，此处选取第二子阵最左边的天线作为参考天线，天线的位置坐标可以表示为d t＝(t-1)Nd。
具体的，根据基站接收到的定位信号、终端发送的定位信号和噪声子空间的值通过公式(7)到公式(9)计算得到到达角的估计值θ 2，即θ 2为第二初始AoA估计值。当得到第二子阵的第二初始AoA估计值后，根据AoA估计值之间的线性关系可以求得第二子阵的其他AoA估计值。
本实施例涉及的是根据初始AoA估计值和其他AoA估计值确定候选AoA估计值的一种可选的实现方式。在上述实施例的基础上，如图8所示，上述S202可以包括如下步骤：
S803，获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值。
具体的，基站可以将第一AoA估计值集合中的第一个AoA估计值与第二AoA估计值集合中的各AoA估计值进行作差，得到第一组差值结果，按照第一AoA估计值集合中的各AoA估计值的顺序，将第一AoA估计值集合中的第二个AoA估计值与第二AoA估计值集合中的各AoA估计值进行做差，得到第二组差值结果，依此类推，直到第一AoA估计值集合中的最后一个AoA估计值与第二AoA估 计值集合中的各AoA估计值进行作差，得到最后一组差值结果。例如，第一AoA估计值集合为9.8和15，第二AoA估计值集合为9.9、5和2，得到的差值结果分别为0.1、4.8、7.8、5.1、10和13。
S804，获取绝对值最小的差值对应的对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值。
具体的，通过步骤S901得到的差值结果，基站可以对差值结果进行排序，当差值结果是从大到小的顺序排列时，选择排在最后一个的差值作为最小的差值；当差值结果是按照从小到大的顺序排列时，选择排在第一个的差值作为最小的差值。根据绝对值最小差值可以获取最小差值对应的第一AoA估计值和第二AoA估计值。例如，若差值结果分别为0.1、4.8、7.8、5.1、10和13时，对差值结果按照从大到小的顺序排列，排列后的插值结果分别为13、10、7.8、5.1、4.8和0.1，绝对值最小的差值为0.1，绝对值最小差值对应的第一AoA估计值为9.8，绝对值最小差值对应的第二AoA估计值为9.9。
S805，将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值。
具体的，得到绝对值最小的差值对应的第一AoA估计值和第二AoA估计值后，计算第一AoA估计值和第二AoA估计值的平均值，将得到的平均值作为候选AoA估计值。例如，最小差值对应的第一AoA估计值为9.8，最小差值对应的第二AoA估计值为9.9，第一AoA估计值和第二AoA估计值的平均值为9.85，将9.85作为候选AoA估计值。
上述AoA估计方法中，基站在预设的第一AoA区间中，可以根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值，基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合，在预设的第二AoA区间中，可以根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值，基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合，基站通过获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值，从而可以获取绝对值最小的差值对应的对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值，进而可以将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值，其中，第一AoA估计值集合中包括第一初始AoA估计值和第一其他AoA估计值，第二AoA估计值集合中包括第二初始AoA估计值和第二其他AoA估计值，通过将天线阵列分为第一子阵和第二子阵，再分别根据第一子阵和第二子阵接收到的定位信号可以快速的获取第一子阵对应的第一AoA估计值结合和第二子阵对应的第二AoA估计值集合，相比于通过天线阵列接收到的定位信号获取天线阵列对应的AoA估计值结合，计算量较小，计算过程较简单；同时由于第一子阵对应的第一AoA估计值和第二子阵对应的第二AoA估计值是接近的，通过将第一子阵对应的AoA估计值与第二子阵对应的AoA估计值比较，能够快速的得出第一子阵对应的第一AoA估计值和第二子阵对应的第二AoA估计值，从而可以快速的确定候选AoA估计值。
本实施例涉及的是根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值的一种可选的实现方式。在上述实施例的基础上，上述S203可以包括：根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值。
其中，目标AoA区间是指候选AoA估计值周围的一个小区间。例如，候选AoA估计值为9.8°时，目标AoA区间可以为8.8°到10.8°。由于谱峰搜索算法可以设定搜索范围，在目标AoA区间内可以采用谱峰搜索算法进行局部搜索。
具体的，所有子阵接收到的终端的定位信号后，根据定位信号获取目标AoA估计值的过程可以表示为：
x(t)＝As(t)+n(t)         (10)
式中，x(t)表示所有子阵接收到终端的定位信号，s(t)表示终端发送的定位信号，n(t)表示噪声子空间，A表示到达角的方向矢量的集合。
a t(θ)＝[a 1(θ) T,a 2(θ) T] T        (11)
式中，a 1(θ)和a 2(θ)分别为第一子阵和第二子阵的方向矢量，因此，假设存在模糊角度值θ'，则：
a t(θ)＝a t(θ')
＝[a 1(θ) T,a 2(θ) T] T
＝[a 1(θ') T,a 2(θ') T] T           (12)
上式中，可以得到a 1(θ)＝a 1(θ')，a 2(θ)＝a 2(θ')，除了目标AoA估计值外，两个子阵获得的两组模糊AoA不存在额外相等关系，即a 1(θ)＝a 1(θ')，a 2(θ)＝a 2(θ')不可能同时成立。因此，通过所有子阵收到的终端的定位信号，在目标AoA区间内只有一个目标AoA估计值，该目标AoA估计值的精度比候选AoA估计值的精度更高。
上述AoA估计方法中，基站根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值，候选AoA估计值的精度不够，通过在候选AoA估计值周围的小范围进行搜索，使得到的目标AoA估计值精度更高，同时相比于仅通过所有子阵接收到的终端的定位信号确定目标AoA估计值的方法，计算量更少，且无需遍历整个AoA区间。
在另一个实施例中，如图9所示，为了便于本领域技术人员的理解，以下对AoA估计方法进行详细介绍，该方法包括：
S901，在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
S902，基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合；
S903，根据第二子阵接收到的终端的定位信号构建校正函数；
S904，将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值；
S905，将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值；
S906，在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；
S907，基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；
S908，获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值；
S909，获取绝对值最小的差值对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值；
S910，将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值；
S911，根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值。
需要说明的是，针对上述S901-S911中的描述可以参见上述实施例中相关的描述，且其效果类似，本实施例在此不再赘述。
上述AoA估计方法，基站在预设的第一AoA区间中，可以根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值，基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合，根据第二子阵接收到的终端的定位信号构建校正函数，将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值，将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值，根据所有子阵接收到的终端的定位信号， 在目标AoA区间内采用预设的搜索方法确定目标AoA估计值；另外，在获得第一AoA估计值集合的基础上，在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值，基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合，获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值，获取绝对值最小的差值对应的对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值，将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值，根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值，通过将天线阵列分为两个天线子阵列，可以根据子阵列1接收到的定位信号确定子阵列1对应的AoA估计值，根据子阵列2接收到的定位信号构建校正函数，将子阵列1对应的AoA估计值输入至子阵列2对应的校正函数中，能够得到精度不够的候选AoA估计值，或者，可以根据子阵列1接收到的定位信号确定子阵列1对应的AoA估计值，根据子阵列2接收到的定位信号确定子阵列2对应的AoA估计值，将第一子阵对应的AoA估计值与第二子阵对应的AoA估计值进行对比，得到最接近的两个AoA估计值，将两个AoA估计值取平均值作为精度不够的候选AoA估计值，取候选AoA估计值周围的小区间，根据天线阵列接收到的定位信号在候选AoA估计值周围的小区间内进行搜索，使得到的目标AoA估计值的精度更高。
应该理解的是，虽然如上的各实施例所涉及的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，如上的各实施例所涉及的流程图中的至少一部分步骤可以包括多个步骤或者多个阶段，这些步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。
基于同样的发明构思，本申请实施例还提供了一种用于实现上述所涉及的AoA估计方法的AoA估计装置。该装置所提供的解决问题的实现方案与上述方法中所记载的实现方案相似，故下面所提供的一个或多个AoA估计装置实施例中的具体限定可以参见上文中对于AoA估计方法的限定，在此不再赘述。
在一个实施例中，如10所示，提供了一个AoA估计装置的第一结构框图，包括：处理模块11、第一确定模块12和第二确定模块13，其中：
处理模块11，用于在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
第一确定模块12，用于根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
第二确定模块13，用于根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
在一个实施例中，如图11所示，提供了一个AoA估计装置的第二结构框图，上述处理模块11包括：第一获取单元111和第一确定单元112，其中：
第一获取单元111，用于在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
第一确定单元112，用于基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
在一个实施例中，如图12所示，提供了一个AoA估计装置的第三结构框图，上述第一确定模块12包括：构建单元121和第二确定单元122，其中：
构建单元121，用于根据所述第二子阵接收到的终端的定位信号构建校正函数；
第二确定单元122，用于根据所述第一初始AoA估计值、所述至少一个第一其他AoA估计值和所 述校正函数，确定所述候选AoA估计值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
可选的，上述第二确定单元122具体用于将所述第一初始AoA估计值和各所述第一其他AoA估计值分别代入所述校正函数，得到所述校正函数的值；将最大的所述校正函数的值对应的AoA估计值确定为所述候选AoA估计值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
在一个实施例中，如图13所示，提供了一个AoA估计装置的第四结构框图，上述处理模块11包括：第一获取单元111和第二获取单元113，其中：
第一获取单元111，用于在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到所述第一AoA估计值集合；所述第一AoA估计值集合中包括所述第一初始AoA估计值和所述第一其他AoA估计值；
第二获取单元113，用于在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；所述第二AoA估计值集合中包括所述第二初始AoA估计值和所述第二其他AoA估计值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
在一个实施例中，如图14所示，提供了一个AoA估计装置的第五结构框图，上述第一确定模块12包括：第三获取单元123、第四获取单元124和第三确定单元125，其中：
第三获取单元123，用于获取所述第一AoA估计值集合中的各AoA估计值与所述第二AoA估计值集合中的各AoA估计值之间的差值；
第四获取单元124，用于获取绝对值最小的所述差值对应的所述第一AoA估计值集合中的第一AoA估计值和所述第二AoA估计值集合中的第二AoA估计值；
第三确定单元125，用于将所述第一AoA估计值和所述第二AoA估计值的平均值确定为所述候选AoA估计值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
在一个实施例中，如图15所示，提供了一个AoA估计装置的第六结构框图，上述第二确定模块13包括：第四确定单元131，其中：
第四确定单元131，用于根据所有子阵接收到的终端的定位信号，在所述目标AoA区间内采用预设的搜索方法确定所述目标AoA估计值。
本实施例提供的AoA估计装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
上述AoA估计装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
在一个实施例中，提供了一种基站，其内部结构图可以如图16所示。该基站包括通过系统总线连接的处理器、存储器、通信接口、显示屏和输入装置。其中，该基站的处理器用于提供计算和控制能力。该基站的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该基站的通信接口用于与外部的终端进行有线或无线方式的通信，无线方式可通过WIFI、移动蜂窝网络、NFC(近场通信)或其他技术实现。该计算机程序被处理器执行时以实现一种定位方法。
本领域技术人员可以理解，图16中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种基站，包括存储器和处理器，存储器中存储有计算机程序，该处理器执行计算机程序时实现以下步骤：AoA估计方法应用于基站，基站包括至少两个子阵，方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值；
根据初始AoA估计值和其他AoA估计值确定候选AoA估计值；
根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值；目标AoA区间的长度小于第二阈值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，包括：
根据第二子阵接收到的终端的定位信号构建校正函数；
根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值，包括：
将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值；
将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合；第一AoA估计值集合中包括第一初始AoA估计值和第一其他AoA估计值；
在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；第二AoA估计值集合中包括第二初始AoA估计值和第二其他AoA估计值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，包括：
获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值；
获取绝对值最小的差值对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值；
将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值，包括：
根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：各所述子阵中目标子阵的天线间距小于等于第一阈值，除目标子阵外的其他子阵的天线间距大于第一阈值，或，各所述子阵的天线间距都大 于第一阈值；所述目标子阵为各所述子阵中任一子阵。
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现以下步骤：AoA估计方法应用于基站，基站包括至少两个子阵，方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值；
根据初始AoA估计值和其他AoA估计值确定候选AoA估计值；
根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值；目标AoA区间的长度小于第二阈值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，包括：
根据第二子阵接收到的终端的定位信号构建校正函数；
根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值，包括：
将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值；
将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合；第一AoA估计值集合中包括第一初始AoA估计值和第一其他AoA估计值；
在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；第二AoA估计值集合中包括第二初始AoA估计值和第二其他AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，包括：
获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值；
获取绝对值最小的差值对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值；
将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值，包括：
根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：各所述子阵中目标子阵的天线间距小于等于第一阈值，除目标子阵外的其他子阵的天线间距大于第一阈值，或，各所述子阵的天线间距都大于第一阈值；所述目标子阵为各所述子阵中任一子阵。
在一个实施例中，提供了一种计算机程序产品，包括计算机程序，该计算机程序被处理器执行时实 现以下步骤：AoA估计方法应用于基站，基站包括至少两个子阵，方法包括：
在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值；
根据初始AoA估计值和其他AoA估计值确定候选AoA估计值；
根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值；目标AoA区间的长度小于第二阈值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，包括：
根据第二子阵接收到的终端的定位信号构建校正函数；
根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据第一初始AoA估计值、至少一个第一其他AoA估计值和校正函数，确定候选AoA估计值，包括：
将第一初始AoA估计值和各第一其他AoA估计值分别代入校正函数，得到校正函数的值；
将最大的校正函数的值对应的AoA估计值确定为候选AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据第一初始AoA估计值确定子阵的至少一个其他AoA估计值，包括：
在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到第一AoA估计值集合；第一AoA估计值集合中包括第一初始AoA估计值和第一其他AoA估计值；
在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；第二AoA估计值集合中包括第二初始AoA估计值和第二其他AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据初始AoA估计值和其他AoA估计值确定候选AoA估计值，包括：
获取第一AoA估计值集合中的各AoA估计值与第二AoA估计值集合中的各AoA估计值之间的差值；
获取绝对值最小的差值对应的第一AoA估计值集合中的第一AoA估计值和第二AoA估计值集合中的第二AoA估计值；
将第一AoA估计值和第二AoA估计值的平均值确定为候选AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：根据候选AoA估计值确定目标AoA区间，并基于目标AoA区间确定目标AoA估计值，包括：
根据所有子阵接收到的终端的定位信号，在目标AoA区间内采用预设的搜索方法确定目标AoA估计值。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：各所述子阵中目标子阵的天线间距小于等于第一阈值，除目标子阵外的其他子阵的天线间距大于第一阈值，或，各所述子阵的天线间距都大于第一阈值；所述目标子阵为各所述子阵中任一子阵。
需要说明的是，本申请所涉及的用户信息(包括但不限于用户设备信息、用户个人信息等)和数据(包括但不限于用于分析的数据、存储的数据、展示的数据等)，均为经用户授权或者经过各方充分授权的信息和数据。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、数据库或其它介质的任何引用，均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-Only Memory，ROM)、磁带、软盘、闪存、光存储器、高密度嵌入式非易失性存储器、阻变存储器(ReRAM)、磁变存储器(Magnetoresistive Random Access Memory，MRAM)、铁电存储器(Ferroelectric Random Access Memory，FRAM)、相变存储器(Phase Change Memory，PCM)、石墨烯存储器等。易失性存储器可包括随机存取存储器(Random Access Memory，RAM)或外部高速缓冲存储器等。作为说明而非局限，RAM可以是多种形式，比如静态随机存取存储器(Static Random Access Memory，SRAM)或动态随机存取存储器(Dynamic Random Access Memory，DRAM)等。本申请所提供的各实施例中所涉及的数据库可包括关系型数据库和非关系型数据库中至少一种。非关系型数据库可包括基于区块链的分布式数据库等，不限于此。本申请所提供的各实施例中所涉及的处理器可为通用处理器、中央处理器、图形处理器、数字信号处理器、可编程逻辑器、基于量子计算的数据处理逻辑器等，不限于此。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对本申请专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种AoA估计方法，其特征在于，所述AoA估计方法应用于基站，所述基站包括至少两个子阵，所述方法包括：
    在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
    根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
    根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
  2. 根据权利要求1所述的方法，其特征在于，所述在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值，包括：
    在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
    基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值。
  3. 根据权利要求2所述的方法，其特征在于，所述根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值，包括：
    根据所述第二子阵接收到的终端的定位信号构建校正函数；
    根据所述第一初始AoA估计值、所述至少一个第一其他AoA估计值和所述校正函数，确定所述候选AoA估计值。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述第一初始AoA估计值、所述至少一个第一其他AoA估计值和所述校正函数，确定所述候选AoA估计值，包括：
    将所述第一初始AoA估计值和各所述第一其他AoA估计值分别代入所述校正函数，得到所述校正函数的值；
    将最大的所述校正函数的值对应的AoA估计值确定为所述候选AoA估计值。
  5. 根据权利要求3所述的方法，其特征在于，所述校正函数为数字波束形成算法、Capon类算法或MUSIC算法。
  6. 根据权利要求1所述的方法，其特征在于，所述在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值，包括：
    在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到所述第一AoA估计值集合；所述第一AoA估计值集合中包括所述第一初始AoA估计值和所述第一其他AoA估计值；
    在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；所述第二AoA估计值集合中包括所述第二初始AoA估计值和所述第二其他AoA估计值。
  7. 根据权利要求6所述的方法，其特征在于，所述根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值，包括：
    获取所述第一AoA估计值集合中的各AoA估计值与所述第二AoA估计值集合中的各AoA估计值之间的差值；
    获取绝对值最小的所述差值对应的所述第一AoA估计值集合中的第一AoA估计值和所述第二AoA估计值集合中的第二AoA估计值；
    将所述第一AoA估计值和所述第二AoA估计值的平均值确定为所述候选AoA估计值。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，所述根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值，包括：
    根据所有子阵接收到的终端的定位信号，在所述目标AoA区间内采用预设的搜索方法确定所述目 标AoA估计值。
  9. 根据权利要求8所述的方法，其特征在于，所述预设的搜索方法为谱峰搜索算法。
  10. 根据权利要求1-7任一项所述的方法，其特征在于，各所述子阵中目标子阵的天线间距小于等于第一阈值，除目标子阵外的其他子阵的天线间距大于第一阈值，或，各所述子阵的天线间距都大于第一阈值；所述目标子阵为各所述子阵中任一子阵。
  11. 一种AoA估计装置，其特征在于，所述AoA估计装置应用于基站，所述基站包括至少两个子阵，所述装置包括：
    处理模块，用于在预设AoA区间中获取至少一个子阵的一个初始AoA估计值，以及根据所述初始AoA估计值确定所述子阵的至少一个其他AoA估计值；
    第一确定模块，用于根据所述初始AoA估计值和所述其他AoA估计值确定候选AoA估计值；
    第二确定模块，用于根据所述候选AoA估计值确定目标AoA区间，并基于所述目标AoA区间确定目标AoA估计值；所述目标AoA区间的长度小于第二阈值。
  12. 根据权利要求11所述的装置，其特征在于，上述处理模块包括：
    第一获取单元，用于在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；
    第一确定单元，用于基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值。
  13. 根据权利要求11所述的装置，其特征在于，所述第一确定模块包括：
    构建单元，用于根据所述第二子阵接收到的终端的定位信号构建校正函数；
    第二确定单元，用于根据所述第一初始AoA估计值、所述至少一个第一其他AoA估计值和所述校正函数，确定所述候选AoA估计值。
  14. 根据权利要求13所述的装置，其特征在于，所述第二确定单元，用于将所述第一初始AoA估计值和各所述第一其他AoA估计值分别代入所述校正函数，得到所述校正函数的值；将最大的所述校正函数的值对应的AoA估计值确定为所述候选AoA估计值。
  15. 根据权利要求11所述的装置，其特征在于，上述处理模块包括：
    第一获取单元，用于在预设的第一AoA区间中，根据第一子阵接收到的终端的定位信号获取第一初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第一初始AoA估计值，确定至少一个第一其他AoA估计值，得到所述第一AoA估计值集合；所述第一AoA估计值集合中包括所述第一初始AoA估计值和所述第一其他AoA估计值；
    第二获取单元，用于在预设的第二AoA区间中，根据第二子阵接收到的终端的定位信号获取第二初始AoA估计值；以及基于AoA估计值之间的线性关系和所述第二初始AoA估计值，确定至少一个第二其他AoA估计值，得到第二AoA估计值集合；所述第二AoA估计值集合中包括所述第二初始AoA估计值和所述第二其他AoA估计值。
  16. 根据权利要求11所述的装置，其特征在于，上述第一确定模块包括：
    第三获取单元，用于获取所述第一AoA估计值集合中的各AoA估计值与所述第二AoA估计值集合中的各AoA估计值之间的差值；
    第四获取单元，用于获取绝对值最小的所述差值对应的所述第一AoA估计值集合中的第一AoA估计值和所述第二AoA估计值集合中的第二AoA估计值；
    第三确定单元，用于将所述第一AoA估计值和所述第二AoA估计值的平均值确定为所述候选AoA估计值。
  17. 根据权利要求11所述的装置，其特征在于，上述第二确定模块包括：
    第四确定单元，用于根据所有子阵接收到的终端的定位信号，在所述目标AoA区间内采用预设的搜索方法确定所述目标AoA估计值。
  18. 一种基站，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至10中任一项所述的方法的步骤。
  19. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至10中任一项所述的方法的步骤。
  20. 一种计算机程序产品，包括计算机程序，其特征在于，该计算机程序被处理器执行时实现权利要求1至10中任一项所述的方法的步骤。
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110199263A1 (en) * 2010-02-18 2011-08-18 Samsung Electronics Co., Ltd. Method and apparatus for estimating angle of arrival
CN109613504A (zh) * 2018-12-07 2019-04-12 中国人民解放军空军工程大学 一种稀疏线性阵列的快速角度估计方法
CN111988740A (zh) * 2020-08-14 2020-11-24 锐捷网络股份有限公司 一种AoA估计方法、装置、设备及介质
CN113438738A (zh) * 2021-08-27 2021-09-24 网络通信与安全紫金山实验室 定位参数估计方法、装置、计算机设备和存储介质
US20210302528A1 (en) * 2020-03-31 2021-09-30 WiTagg, Inc. System and method for estimating the angle of arrival using antenna arrays
CN114339991A (zh) * 2022-01-14 2022-04-12 网络通信与安全紫金山实验室 AoA估计方法、装置、基站、存储介质和计算机程序产品

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109738854B (zh) * 2018-12-14 2020-07-10 北京邮电大学 一种天线阵列来波方向的到达角估计方法
CN111142061B (zh) * 2019-12-24 2022-06-14 泰凌微电子(上海)股份有限公司 一种信号到达角估计方法、装置及计算机可读存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110199263A1 (en) * 2010-02-18 2011-08-18 Samsung Electronics Co., Ltd. Method and apparatus for estimating angle of arrival
CN109613504A (zh) * 2018-12-07 2019-04-12 中国人民解放军空军工程大学 一种稀疏线性阵列的快速角度估计方法
US20210302528A1 (en) * 2020-03-31 2021-09-30 WiTagg, Inc. System and method for estimating the angle of arrival using antenna arrays
CN111988740A (zh) * 2020-08-14 2020-11-24 锐捷网络股份有限公司 一种AoA估计方法、装置、设备及介质
CN113438738A (zh) * 2021-08-27 2021-09-24 网络通信与安全紫金山实验室 定位参数估计方法、装置、计算机设备和存储介质
CN114339991A (zh) * 2022-01-14 2022-04-12 网络通信与安全紫金山实验室 AoA估计方法、装置、基站、存储介质和计算机程序产品

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
SUN FENGGANG; LAN PENG: "Two-Dimensional DOA Estimation by Using Two Parallel Coprime-Nested Arrays", 2021 CIE INTERNATIONAL CONFERENCE ON RADAR (RADAR), IEEE, 15 December 2021 (2021-12-15), pages 2016 - 2019, XP034287071, DOI: 10.1109/Radar53847.2021.10028196 *

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