WO2023010763A1 - 定位参数确定方法、装置、设备和存储介质 - Google Patents

定位参数确定方法、装置、设备和存储介质 Download PDF

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WO2023010763A1
WO2023010763A1 PCT/CN2021/139679 CN2021139679W WO2023010763A1 WO 2023010763 A1 WO2023010763 A1 WO 2023010763A1 CN 2021139679 W CN2021139679 W CN 2021139679W WO 2023010763 A1 WO2023010763 A1 WO 2023010763A1
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flow pattern
positioning
matrix
tof
pattern matrix
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PCT/CN2021/139679
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English (en)
French (fr)
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潘孟冠
尤肖虎
齐望东
黄永明
刘升恒
贾兴华
王绍磊
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网络通信与安全紫金山实验室
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Publication of WO2023010763A1 publication Critical patent/WO2023010763A1/zh

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    • 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
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009—Transmission of position information to remote stations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/024—Guidance services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings

Definitions

  • the present application relates to the technical field of communications, and in particular to a positioning parameter determination method, device, device and storage medium.
  • Satellite navigation and positioning technology has the advantages of wide-area coverage and good universality, but because of low signal power and weak penetration, it is mainly used for terminal positioning in open outdoor environments, and cannot provide navigation and positioning in sheltered and indoor environments Serve.
  • the related technology is to use the infrastructure of the wireless communication system or deploy a dedicated wireless positioning base station to locate the terminal equipment.
  • Positioning parameters Propagation delay and angle of arrival. Based on the positioning parameters, the positioning information of the terminal device can be determined.
  • the present application provides a positioning parameter determination method, device, computer equipment and storage medium.
  • the present application provides a method for determining positioning parameters, the method including:
  • the multi-channel positioning signal sent by the terminal to be positioned determine the time-of-flight ToF spectrum data of the positioning signal
  • the ideal airspace flow pattern matrix is corrected to obtain the corrected airspace flow pattern matrix; each element in the airspace flow pattern matrix indicates that each element in the antenna array is in the preset corresponding angle range The response to the positioning signal; the antenna array deviation function characterizes the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal;
  • the positioning parameters of the direct path of the positioning signal are determined; the direct path is the shortest path from the terminal to be positioned to the antenna array.
  • the present application provides a device for determining positioning parameters, which includes:
  • the first determination module is used to determine the time-of-flight ToF spectrum data of the positioning signal according to the multi-channel positioning signal sent by the terminal to be positioned;
  • the correction module is used to correct the ideal airspace flow pattern matrix according to the preset antenna array deviation function to obtain the corrected airspace flow pattern matrix; each element in the airspace flow pattern matrix represents the The response of the corresponding angle range to the positioning signal; the antenna array deviation function characterizes the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal;
  • the second determining module is configured to determine the positioning parameters of the direct path of the positioning signal according to the ToF spectrum data and the corrected airspace flow pattern matrix; the direct path is the shortest path from the terminal to be positioned to the antenna array.
  • the present application provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the embodiments of the first aspect above are implemented.
  • the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in any one of the above-mentioned first aspect embodiments are implemented.
  • Fig. 1 is an application environment diagram of a positioning parameter determination method in an embodiment
  • FIG. 2 is a schematic flowchart of a method for determining positioning parameters in an embodiment
  • FIG. 3 is a schematic flowchart of a method for determining positioning parameters in another embodiment
  • FIG. 4 is a schematic flowchart of a method for determining positioning parameters in another embodiment
  • FIG. 5 is a schematic flowchart of a method for determining positioning parameters in another embodiment
  • FIG. 6 is a schematic flowchart of a method for determining positioning parameters in another embodiment
  • FIG. 7 is a schematic flowchart of a method for determining positioning parameters in another embodiment
  • FIG. 8 is a schematic flowchart of a method for determining positioning parameters in another embodiment
  • FIG. 9 is a schematic flowchart of a method for determining positioning parameters in another embodiment.
  • Fig. 10 is a phase deviation function measurement value and function estimation result in an embodiment
  • Fig. 11 is the measured value and function estimation result of amplitude pattern function in one embodiment
  • Figure 12 is a diagram of the relative positions of the RRU and the terminal in one embodiment
  • Fig. 13 is a CDF curve of SRS symbol AoA estimation error in one embodiment
  • Fig. 14 is a structural block diagram of a device for determining positioning parameters in an embodiment
  • Fig. 15 is a structural block diagram of a positioning parameter determining device in another embodiment
  • Figure 16 is a diagram of the internal structure of a computer device in one embodiment.
  • Satellite navigation and positioning technology has the advantages of wide-area coverage and good universality, but because of low signal power and weak penetration, it is mainly used for terminal positioning in open outdoor environments, and cannot provide navigation and positioning in sheltered and indoor environments Serve.
  • a main technical approach is to use the infrastructure of the wireless communication system or deploy a dedicated wireless positioning base station to locate the terminal equipment.
  • Typical positioning technologies include: cellular network positioning, wireless local area network (Wireless Local Area Network, WLAN) positioning, Bluetooth positioning, ultra-wideband (Ultra-Wide Band, UWB) positioning, etc.
  • broadband transmission signals and array antennas are widely used in cellular mobile networks, WLAN, UWB and other systems, which can simultaneously measure propagation delay (Time of Flight, ToF) and angle of arrival (Angle of Arrival, AoA).
  • ToF Time of Flight
  • AoA angle of arrival
  • the array pattern of each array element in the array is different.
  • the amplitude and phase response of the incident signal at different angles are different, and It is especially significant when the angle of arrival is large, and the existing research has rarely mentioned the estimation performance of the positioning base station at a large angle of arrival; in addition, in the complex path environment, the real-time positioning requirements for fast moving targets require the positioning algorithm to be able to estimate precision and real-time.
  • the present application provides a method for determining positioning parameters, which can take into account the accuracy and real-time performance of measuring propagation delay and angle of arrival, and can improve the accuracy of positioning parameters.
  • the positioning parameter determination method provided in this application can be applied to the application environment shown in FIG. 1 .
  • the terminal to be positioned 102 communicates with the base station 104 through the network.
  • the terminal to be positioned can send a positioning signal to the base station, and the base station receives the positioning signal through the antenna array, and calculates and corrects the positioning signal in real time to determine the positioning parameters of the positioning signal.
  • the terminal to be positioned 102 may include mobile terminal devices such as vehicles and airplanes, and the base station 104 may include: a macro base station, a distributed base station, etc., which are not limited here.
  • a positioning parameter determination method is provided.
  • the method is applied to the base station in FIG. 1 as an example for illustration, including the following steps:
  • the terminals to be positioned may include mobile terminal devices such as vehicles and airplanes.
  • the positioning signal is a signal including a positioning sequence, which may be a 5G signal, a 4G signal, etc., and is not limited here.
  • Time of flight ToF is the propagation delay, that is, the flight time of the signal from the transmitter to the receiver, so it is also called ToF (Time of Flight).
  • the base station can receive the positioning signal through the antenna array, and after receiving the positioning signal, perform ToF super-resolution spectrum estimation on the positioning signal, and then obtain the time-of-flight ToF spectrum data .
  • the antenna array may be a line array or a circular array, which is not limited here.
  • the antenna array may include multiple array elements, and each array element may correspond to a receiving channel.
  • the ToF super-resolution spectrum estimation is performed on each receiving channel, and the fading coefficient on the time delay area of interest can be estimated by scanning.
  • This process is also called delay spectrum estimation or ToF spectrum estimation.
  • the scanning delay corresponding to the peak point of the ToF spectrum represents the signal component of the multiple paths of the positioning signal reflected by obstacles and directly reaching the antenna array, and the corresponding ToF value corresponding to the peak point of the ToF spectrum is the ToF of the corresponding path.
  • the ToF spectrum data can be solved by single-point least squares algorithm, amplitude and phase estimation (Amplitude and Phase EStimation, APES) algorithm, iterative adaptive approach (IAA), sparse reconstruction algorithm, etc., to determine the positioning signal
  • the time-of-flight ToF spectral data can be solved by single-point least squares algorithm, amplitude and phase estimation (Amplitude and Phase EStimation, APES) algorithm, iterative adaptive approach (IAA), sparse reconstruction algorithm, etc.
  • each element in the airspace flow pattern matrix indicates that each element in the antenna array is in the preset The response of the angular range to the positioning signal; the antenna array bias function characterizes the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal.
  • the preset corresponding angle range is the angle range of each part after the coverage area [ ⁇ min , ⁇ max ] of the antenna array is divided into Q parts.
  • Antenna array coverage [ ⁇ min , ⁇ max ] can be divided by equal arrival angle interval ⁇ .
  • the antenna deviation coefficient function may be a function including antenna phase deviation and amplitude deviation obtained through off-line measurement in advance, which is the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal.
  • the ideal airspace flow pattern matrix is the airspace flow pattern matrix in the perfect situation, but in the actual use of the antenna array, due to factors such as antenna tooling and mutual coupling between array elements, the antenna array is not in a perfect situation. Therefore, The antenna array has a phase deviation related to the angle of arrival. If the ideal flow pattern matrix is used for direction finding processing, there will be a large direction finding deviation due to the mismatch between the ideal and actual flow patterns. Through the preset antenna array deviation function , to modify the ideal airspace flow pattern matrix, and obtain a result that is more consistent with the actual situation.
  • the direct path is the shortest path from the terminal to be positioned to the antenna array.
  • the digital beam forming (Digital Beam Forming, DBF) method, the multiple signal classification (Multiple SIgnal Classification, MUSIC) algorithm, etc. can be used to perform AoA spectrum Estimation, get the ToF-AoA two-dimensional spectrum estimation result, carry out spectral peak extraction on the obtained AoA-ToF two-dimensional spectrum, obtain the fading coefficient, AoA and ToF estimated value corresponding to the largest K1 spectral peaks, respectively denoted as : According to the fading coefficient corresponding to K 1 spectral peak component and ToF value That is, the fading coefficient corresponding to each path and ToF value In , the direct path (Line-of-Sight, LOS) component is extracted, and the ToF estimation and AoA estimation results of the LOS component are output.
  • DBF Digital Beam Forming
  • MUSIC Multiple SIgnal Classification
  • the signal will reach the antenna array after reflection and refraction, so there are multiple paths for the signal to reach the antenna array from the transmitting end, and the direct path is the shortest path from the terminal to be positioned to the antenna array. It can also be understood as the path where the positioning signal directly reaches the antenna array without refraction and reflection.
  • the time-of-flight ToF spectrum data of the positioning signal is determined according to the multi-channel positioning signal sent by the terminal to be positioned; according to the preset antenna array deviation function, the ideal airspace flow pattern matrix is corrected to obtain The corrected airspace flow pattern matrix; according to the ToF spectrum data and the corrected airspace flow pattern matrix, the positioning parameters of the direct path of the positioning signal are determined.
  • the ideal airspace flow pattern matrix can be corrected by using the preset antenna array deviation function including phase and amplitude to reduce the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal, to be determined The direct path of the shortest path from the bit terminal to the antenna array, thereby improving the accuracy of measuring the positioning parameters of the positioning signal.
  • this solution avoids the problem of complicated calculation caused by simultaneous measurement of ToF and AoA in the prior art.
  • the above-mentioned embodiment has explained the method for determining the positioning parameters, mainly based on the antenna array bias function to correct the airspace flow pattern matrix reflecting the response of the antenna array to receive the positioning signal.
  • the construction process of the antenna array bias function includes:
  • the simulated real signal is the signal emitted by the signal generator in the dark room to simulate the real signal.
  • the antenna array is placed on a turntable in the darkroom, which can be rotated from -60° to 60°, and each rotation of 5° is set as a sampling angle, and each sampling angle constitutes a set of discrete arrival angles
  • each sampling angle arrival obtain the amplitude measurement value of the amplitude pattern of the simulated real signal arriving at each element of the antenna array, and form all the obtained amplitude measurement values into a set of amplitude measurement values, and the simulated real signal arriving at the antenna array phase measurement values of the phase deviations of each array element, and the acquired phase measurement values of the phase deviations form a set of phase measurement values. set in discrete angles of arrival
  • the functions ⁇ n ( ⁇ ) and ⁇ n ( ⁇ ) can be estimated by using polynomial fitting, support vector machine or neural network, etc. .
  • the process is described.
  • the fitting result of the remembered function is Among them, g ⁇ R J ⁇ 1 is the polynomial weight, J is the polynomial fitting order, in the form of where g l is the lth element of the weight vector g.
  • the objective function of polynomial fitting is as follows: Solve the objective function, after obtaining the weight g, make That is, and then the phase deviation function can be obtained to use the polynomial fitting method for the function Taking estimation as an example, the process will be described.
  • the fitting result of the remembered function is Among them, g ⁇ R J ⁇ 1 is the polynomial weight, J is the polynomial fitting order, in the form of where g l is the lth element of the weight vector g.
  • the objective function of polynomial fitting is as follows: Solve the objective function, after obtaining the weight g, make That is, and then the phase deviation function can be obtained
  • the ideal airspace flow pattern matrix includes a rough ideal airspace flow pattern Matrix and fine ideal airspace flow pattern matrix; each element in the rough ideal airspace flow pattern matrix represents the response of each element in the antenna array to the positioning signal in the first preset corresponding angle range; each element in the fine ideal airspace flow pattern matrix The element represents the response of each array element in the antenna array to the positioning signal in the second preset corresponding angle range; the first preset corresponding angle range is larger than the second preset corresponding angle range.
  • the ideal airspace flow pattern matrix includes a rough ideal airspace flow pattern matrix and a fine ideal airspace flow pattern matrix; each element in the rough ideal airspace flow pattern matrix represents the corresponding angular range of each element in the antenna array in the first preset The response to the positioning signal; each element in the fine ideal airspace flow pattern matrix represents the response of each array element in the antenna array to the positioning signal in the second preset corresponding angle range; the first preset corresponding angle range is greater than the second preset The corresponding angle range is set.
  • the ideal array steering vector a ⁇ ( ⁇ ) is determined by the array structure, and at the arrival angle set
  • a ⁇ [a ⁇ ( ⁇ 1 ), a ⁇ ( ⁇ 2 ),..., a ⁇ ( ⁇ Q )].
  • a ⁇ [a ⁇ ( ⁇ 1 ), a ⁇ ( ⁇ 2 ),..., a ⁇ ( ⁇ Q )].
  • the ideal airspace flow pattern matrix includes a rough ideal airspace flow pattern matrix and a fine ideal airspace flow pattern matrix; each element in the rough ideal airspace flow pattern matrix represents that each element in the antenna array is in the first preset The response of the corresponding angle range to the positioning signal; each element in the thin ideal airspace flow pattern matrix represents the response of each array element in the antenna array to the positioning signal in the second preset corresponding angle range. Since the corresponding angular range of the first preset is larger than that of the second preset, the ideal airspace flow pattern matrix can be divided and corrected with grid points of different thicknesses to ensure higher accuracy of the obtained results.
  • the positioning parameters include AoA and ToF; according to the ToF spectrum data and the corrected airspace flow pattern matrix, the positioning parameters of the direct path of the positioning signal are determined, including:
  • the antenna array deviation function is determined; the antenna deviation coefficient function can be expressed as Among them, the antenna phase deviation function and magnitude pattern function is a function obtained by offline measurement in advance. According to the formula Then the rough ideal airspace flow pattern matrix A ⁇ can be corrected, and the corrected rough airspace flow pattern matrix A ⁇ ′ can be obtained.
  • the time delay range of interest [ ⁇ min , ⁇ max ] can be divided into P parts at equal intervals.
  • P>>K the number of paths
  • ⁇ n, p , p 1,...
  • P is the fading coefficient on each scanning grid point, when hour, ⁇ n,k represents the response of the nth receiving element to the incident signal of the kth path, is the fading coefficient of the kth path.
  • ⁇ n,p 0.
  • a ⁇ [a ⁇ ( ⁇ 1 ),. .., a ⁇ ( ⁇ P )] is the delay matching matrix on the set of scanning grid points.
  • h n is the nth column element in the channel frequency domain response matrix CFR formed after channel estimation of the received positioning signal, indicating the CFR of the nth receiving channel.
  • R n,p represents the interference covariance matrix at the nth receiving channel and the pth scanning grid point, where the interference is composed of signal components other than the current grid point ⁇ p .
  • 1 represents the l 1 norm of the P-dimensional vector X, defined as
  • the spectrum estimation method may use a digital beam forming (Digital Beam Forming, DBF) method, a multiple signal classification (MUltiple SIgnal Classification, MUSIC) algorithm, and the like.
  • the spectrum estimation result is: remember is the estimation result of ToF-AoA two-dimensional spectrum, the element in the qth row and the pth column Indicates the estimated value of the channel fading coefficient at ToF ⁇ p , AoA ⁇ q .
  • the fine ideal airspace flow pattern matrix is determined, including:
  • the corrected rough airspace flow regime matrix A ⁇ ′ and ToF spectrum data use the formula Determine the two-dimensional positioning parameter spectrum data of N receiving channels. Spectrum peak extraction is performed according to the two-dimensional positioning parameter spectrum data, and the ToF, reference AoA, and attenuation coefficient of each path of the positioning signal are obtained.
  • K 2 components whose energy exceeds the preset spectral peak intensity threshold ⁇ among the K 1 spectral peak components can be extracted through the preset spectral peak intensity threshold ⁇ , and then from the K 2 components Among them, the corresponding component with the smallest ToF is extracted as the LOS component, that is, the component corresponding to the direct path. That is, among the spectral peak components whose attenuation coefficients of each path exceed the preset spectral peak intensity threshold, according to the ToF of each path that has been solved, the component with the smallest ToF can be directly determined as the ToF corresponding to the direct path.
  • the direct path component to extract.
  • the fine search arrival angle set can be brought into the preset ideal flow pattern matrix to determine the corresponding ideal flow pattern matrix A ⁇ , fine , which is:
  • the modified flow pattern matrix is A ⁇ , fine ′, then the element of row n and column q of A ⁇ , fine ′ is:
  • the corrected fine airspace flow pattern matrix and the ToF corresponding to the direct path can be substituted into the beam scanning peak value
  • the angle function corresponding to the criterion or the angle function corresponding to the subspace orthogonality criterion is used for AoA fine estimation.
  • b LOS , A ⁇ , fine ′ substituting b LOS , A ⁇ , fine ′ into the angle function of the beam scanning peak criterion is: Among them, determine ⁇ , which is the AoA of the direct approach path; In addition, b LOS , A ⁇ , fine ′ can also be substituted into the angle function of the subspace orthogonal criterion as: Among them, the final value of ⁇ is determined, that is, the AoA of the direct path; among them,
  • the rough ideal airspace flow pattern matrix is corrected according to the antenna array deviation function to obtain the corrected rough airspace flow pattern matrix; according to the corrected rough airspace flow pattern matrix and ToF spectrum data, the fine ideal Airspace flow pattern matrix and the ToF corresponding to the direct path; according to the antenna array deviation function, the fine ideal airspace flow pattern matrix is corrected to obtain the corrected fine airspace flow pattern matrix; using the preset angle function, according to the corrected fine airspace flow pattern matrix
  • the flow pattern matrix and the ToF corresponding to the direct path determine the AoA of the direct path.
  • the antenna error related to the angle of arrival can be effectively compensated, and through the multi-level cascade signal processing method of first coarse-grained search and then fine search by delay spectrum estimation and direct arrival angle,
  • the calculation complexity of searching ToF and AoA simultaneously in two dimensions in the prior art is reduced, and the real-time performance of positioning is improved.
  • the phase error related to the angle of arrival can be accurately compensated, and the accuracy of direction finding and positioning is improved, especially when the wireless signal has a large angle of arrival, the accuracy of direction finding can be significantly improved.
  • the above-mentioned embodiment explains how to apply the ideal airspace flow pattern matrix in two coarse and fine forms to determine the positioning parameters of the direct path of the positioning signal.
  • it is first necessary to analyze Carry out relevant processing, then determine the time-of-flight ToF spectrum data of the positioning signal, and illustrate it with an embodiment now, as shown in Figure 6, determine the time-of-flight ToF of the positioning signal according to the multi-channel positioning signal sent by the terminal to be positioned Spectral data, including:
  • S602. Perform Fourier transform on the multi-channel positioning signal sent by the positioning terminal to obtain a multi-channel frequency domain signal.
  • the base station receives the positioning signal of the known sequence sent by the terminal to be positioned through the antenna array. Since the positioning signal is a time-domain signal, it can first perform Fast Fourier Transform (FFT) on the received signals of each channel to obtain multiple Received signal in channel frequency domain.
  • C M ⁇ 1 there is x n ⁇ C M ⁇ 1 , where Indicates a complex number space, and C M ⁇ 1 indicates an M*1-dimensional complex number space, that is, an M-dimensional complex vector space.
  • vectors all refer to column vectors.
  • the M subbands are uniformly distributed, and the distribution interval is ⁇ f; and the receiving antenna array is assumed to be an equidistant linear array (Uniform Linear Array, ULA), and the array element spacing is d.
  • the transmitted signal propagates to the receiving array via K paths, the propagation time delay (ToF), angle of arrival (AoA) and fading coefficient of the kth path are respectively in, Defined as the angle between the incident direction of the signal and the normal direction of the ULA.
  • the time delay of signal transmission can represent the distance of the signal transmission, and the time delay and the distance can be transformed into each other through the speed of light c. Therefore, the received signal matrix X of multiple channels can also be expressed as:
  • a ⁇ ( ⁇ ) ⁇ T ⁇ C M ⁇ 1 is the delay domain matching vector function, its input is the propagation delay ⁇ , and the output is the delay domain matching vector.
  • T ⁇ C M ⁇ 1 means that the scope of the function is T, and the value range is the M-dimensional delay domain matching vector, and T is the set of all possible path delays ⁇ , namely in, represents the space of real numbers.
  • the mth element of the delay domain matching vector represents the phase offset caused by the signal propagation delay in the mth subband, therefore, there are: where j represents the imaginary unit and is defined as:
  • the distribution interval is ⁇ f.
  • a ⁇ ′( ⁇ ) ⁇ C N ⁇ 1 represents the actual receiving array steering vector function, whose input is the signal arrival angle ⁇ , and the output is the array steering vector corresponding to the arrival angle.
  • ⁇ C N ⁇ 1 means that its scope is ⁇ , its value range is an N-dimensional vector, and ⁇ is the space composed of all possible arrival angles of incident signals, namely further, in is an ideal array steering vector, when the receiving array is ULA, its nth element is: Represents the angle-dependent phase deviation caused by factors such as antenna tooling, mutual coupling between array elements, and the disturbance term caused by the amplitude pattern of the antenna array elements, that is, the antenna deviation coefficient function, which represents the real array response and the ideal array response The deviation between , whose nth element is:
  • ⁇ n ( ⁇ ) ⁇ R represents the amplitude pattern function on the nth array element, its input is the signal arrival angle ⁇ , and the output is the amplitude pattern of the nth array
  • ⁇ n ( ⁇ ) ⁇ [- ⁇ , + ⁇ ] represents the angle-dependent phase deviation function on the nth array element, its input is the signal arrival angle ⁇ , and the output is the nth array element at the corresponding arrival angle phase deviation.
  • the specific forms of ⁇ n ( ⁇ ) and ⁇ n ( ⁇ ) depend on the array antenna used, and their values on certain angle-of-arrival grids can be obtained through darkroom measurements or numerical calculations using electromagnetic simulation software. Among them, the phase deviation function plays a decisive role in AoA estimation, and in general, when ⁇ is small, ⁇ n ( ⁇ ) is close to 0, and when ⁇ is large, ⁇ n ( ⁇ ) will fluctuate greatly .
  • the operator ⁇ denotes the Hadamard product.
  • ⁇ C M ⁇ N is the broadband response of analog devices such as base station receiving channel front-end amplifiers, filters, mixers, etc., and its mth row and nth column elements are the nth receiving channel at the Responses for m subbands.
  • W ⁇ C M ⁇ N is a noise matrix, and its mth row and nth column elements represent the nth receiving channel and the noise component on the mth subband.
  • the base station may use the least squares method to perform channel estimation according to the received signal matrix X in the frequency domain to obtain a Channel Frequency Response (Channel Frequency Response, CFR) matrix, denoted as H 0 .
  • CFR Channel Frequency Response
  • H 0 S -1 X; where H 0 ⁇ C M ⁇ N , the nth column is the channel frequency domain response matrix of the nth receiving channel; S -1 is: the positioning signal
  • the positioning sequence data matrix S the inverse matrix of diag([S[1], S[2], . . . S[M]] T ).
  • the channel frequency domain response matrix can also be expressed as:
  • h n can be expressed as:
  • ⁇ n,k represents the response of the nth receiving element to the incident signal of the kth path, which is a vector The nth element of .
  • w n ⁇ C M ⁇ 1 represents the noise vector of this channel, which is the nth column of the matrix W′.
  • the scan method is used to estimate the ToF spectrum of the fading coefficient on the time delay area of interest, and the scan time delay corresponding to the peak point of the ToF spectrum represents the ToF of the strong path.
  • the time delay range [ ⁇ min , ⁇ max ] is divided into P parts at equal intervals.
  • P>>K the number of paths
  • a variety of parameter estimation methods can be used to solve this spectrum estimation problem, such as single-point least squares algorithm, amplitude and phase estimation (Amplitude and Phase EStimation, APES) algorithm, iterative adaptive method (Iterative Adaptive Approach, IAA), Sparse reconstruction algorithm, etc.
  • APES amplitude and Phase EStimation
  • IAA iterative adaptive method
  • IAA iterative Adaptive Approach
  • Sparse reconstruction algorithm etc.
  • the ToF spectrum data is obtained, including:
  • the actual amplitude-phase response of each receiving channel at each frequency point is different, which causes the ⁇ term in formula (1).
  • the ⁇ matrix can be measured before the positioning experiment or with the help of a dedicated calibration channel during the positioning experiment, assuming that the measured channel amplitude-phase response matrix is due to the matrix
  • the function of is used for channel correction, so it is usually called channel correction coefficient, or simply called channel coefficient.
  • the channel frequency domain response matrix CFR matrix obtained after channel amplitude and phase deviation correction is H
  • its mth row and nth column elements are: Among them, H 0
  • h n 1, . . . , N be the nth column element of the matrix H, which represents the CFR of the nth receiving channel.
  • h n can be expressed as:
  • ⁇ n,k represents the response of the nth receiving element to the incident signal of the kth path, which is a vector The nth element of .
  • w n ⁇ C M ⁇ 1 represents the noise vector of this channel, which is the nth column of the matrix W′.
  • the scan method is used to estimate the ToF spectrum of the fading coefficient on the time delay area of interest, and the scan time delay corresponding to the peak point of the ToF spectrum represents the ToF of the strong path.
  • time delay range [ ⁇ min , ⁇ max ] is divided into P parts at equal intervals.
  • P>>K the number of paths
  • a variety of parameter estimation methods can be used to solve this spectrum estimation problem, such as single-point least squares algorithm, amplitude and phase estimation (Amplitude and Phase EStimation, APES) algorithm, iterative adaptive method (Iterative Adaptive Approach, IAA), Sparse reconstruction algorithm, etc.
  • APES amplitude and Phase EStimation
  • IAA iterative adaptive method
  • IAA iterative Adaptive Approach
  • Sparse reconstruction algorithm etc.
  • multi-channel frequency domain signals are obtained by performing Fourier transform on the multi-channel positioning signals sent by the terminal to be positioned; channel estimation is performed on the multi-channel frequency domain signals to obtain the channel frequency domain response matrix; based on the channel frequency Domain response matrix, to obtain ToF spectrum data.
  • the received positioning signal can be transformed to facilitate subsequent data analysis.
  • the accuracy of the subsequently determined ToF spectrum data and AoA is further improved.
  • the channel correction coefficient is obtained, including:
  • the positioning sequence of each subband is occupied by the positioning signal; the positioning sequence matrix is constructed by using the positioning sequence of each subband; each element in the positioning sequence matrix is used as the main diagonal element to obtain the diagonal matrix to It is used for channel estimation and measures the channel amplitude-phase response matrix as the channel correction coefficient.
  • the channel correction coefficient for correcting the channel frequency domain matrix can be determined, and then the channel frequency domain matrix can be corrected.
  • the positioning parameter determination method includes:
  • the measured channel amplitude response matrix is used as the channel correction coefficient to correct the channel frequency domain response matrix to obtain a corrected channel frequency domain response matrix.
  • the ideal airspace flow pattern matrix includes a rough ideal airspace flow pattern matrix and a fine ideal airspace flow pattern matrix; each element in the rough ideal airspace flow pattern matrix represents that each array element in the antenna array is in the first preset corresponding angle range The response of the positioning signal; each element in the fine ideal airspace flow pattern matrix represents the response of each array element in the antenna array to the positioning signal in the second preset corresponding angle range; the corresponding angle range of the first preset is greater than the second preset corresponding angle range.
  • the 5G sounding reference signal (Sounding Reference Signal, SRS) is used as the positioning signal, and the SRS is a broadband OFDM signal.
  • SRS Sounding Reference Signal
  • the SRS is a broadband OFDM signal.
  • the interval between subcarriers is 60kHz.
  • two 5G RRUs were used as receiving devices, each RRU was equipped with 4 array elements ULA, the array element spacing was 5.8cm, and the array was placed horizontally. Before the start of the positioning experiment, it is necessary to complete the estimation of the antenna phase deviation function and amplitude pattern function in the offline stage.
  • the function curve is shown as the solid line in Fig. 11 . It can be seen from Fig. 10 and Fig. 11 that the phase deviation function and amplitude pattern function obtained by polynomial fitting can better approximate the corresponding measurement quantities in the microwave anechoic chamber.
  • FIG. 12 shows the relative position diagram of the RRU and the terminal in an experiment.
  • the dotted line in the figure indicates the normal direction of the two RRU antenna arrays, and the five-pointed star is the location of the terminal in this experiment. It can be seen that the terminal is close to the normal direction of RRU-1 at this time, and the real arrival angle is -1.5°; and the terminal signal reaches RRU-2 at a relatively large angle, and the real arrival angle is -55.8°.
  • the AoA estimation error cumulative distribution probability (Cumulative Distribution Function, CDF) curve of 1500 SRS symbols is shown in the hollow circle in Figure 13, for comparison, Figure 13
  • CDF curve obtained by using the ideal flow pattern matrix is drawn, which is represented by a solid point.
  • the antenna error including the phase deviation of each array element and the difference in the antenna pattern, has an obvious angular correlation.
  • the angle of arrival is small, the error is small, and the resulting AoA estimation error is small;
  • the angle of arrival is large, the error is large, and the resulting AoA estimation error is also large.
  • the antenna error related to the angle of arrival is effectively compensated, and the improvement is particularly obvious when the angle of arrival of the signal is large.
  • the time-of-flight ToF spectrum data of the positioning signal is determined according to the multi-channel positioning signal sent by the terminal to be positioned; according to the preset antenna array deviation function, the ideal airspace flow pattern matrix is corrected to obtain the corrected The final airspace flow pattern matrix; according to the ToF spectrum data and the corrected airspace flow pattern matrix, determine the positioning parameters of the direct path of the positioning signal.
  • the ideal airspace flow pattern matrix can be corrected by using the preset antenna array deviation function including phase and amplitude to reduce the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal, to be determined The direct path of the shortest path from the bit terminal to the antenna array, thereby improving the accuracy of measuring the positioning parameters of the positioning signal.
  • this solution avoids the problem of complicated calculation caused by simultaneous measurement of ToF and AoA in the prior art.
  • a device for determining positioning parameters including:
  • the first determining module 141 is configured to determine the time-of-flight ToF spectrum data of the positioning signal according to the multi-channel positioning signal sent by the terminal to be positioned;
  • the correction module 142 is used to correct the ideal airspace flow pattern matrix according to the preset antenna array deviation function to obtain the corrected airspace flow pattern matrix; each element in the airspace flow pattern matrix represents the position of each array element in the antenna array.
  • the response of the preset corresponding angle range to the positioning signal; the antenna array deviation function characterizes the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal;
  • the second determination module 143 is configured to determine the positioning parameters of the direct path of the positioning signal according to the ToF spectrum data and the corrected airflow pattern matrix; the direct path is the shortest path from the terminal to be positioned to the antenna array.
  • the first determination module determines the time-of-flight ToF spectrum data of the positioning signal according to the multi-channel positioning signal sent by the terminal to be positioned; the correction module calculates the ideal airspace flow pattern matrix according to the preset antenna array deviation function Perform correction to obtain a corrected airspace flow pattern matrix; the second determination module determines the positioning parameters of the direct path of the positioning signal according to the ToF spectrum data and the corrected airspace flow pattern matrix.
  • the ideal airspace flow pattern matrix can be corrected by using the preset antenna array deviation function including phase and amplitude to reduce the deviation between the response of the real antenna array to the signal and the response of the ideal antenna array to the signal, to be determined
  • the direct path of the shortest path from the bit terminal to the antenna array thereby improving the accuracy of measuring the positioning parameters of the positioning signal.
  • this solution avoids the problem of complicated calculation caused by simultaneous measurement of ToF and AoA in the prior art.
  • the device for determining positioning parameters further includes:
  • the simulation parameter collection module 144 is used to obtain the amplitude measurement value set of the amplitude pattern of the amplitude pattern of the simulated real signal arriving at each array element of the antenna array, and the phase measurement value set of the phase deviation of the simulated real signal arriving at each array element of the antenna array;
  • Constructing a deviation function module 145 configured to construct an amplitude pattern function according to a set of amplitude measurement values; and construct a phase deviation function according to a set of phase measurement values; determine the antenna according to the amplitude pattern function and the phase deviation function Array bias function.
  • the positioning parameters include AoA and ToF;
  • the second determining module 143 includes:
  • the first correction unit 1431 is configured to correct the rough ideal airspace flow pattern matrix according to the antenna array bias function to obtain the corrected rough airspace flow pattern matrix;
  • the first determination unit 1432 is used to determine the fine ideal airspace flow pattern matrix and the ToF corresponding to the direct path according to the corrected coarse airspace flow pattern matrix and ToF spectrum data;
  • the second correction unit 1433 is configured to correct the fine ideal airspace flow pattern matrix according to the antenna array deviation function to obtain the corrected fine airspace flow pattern matrix;
  • the second determining unit 1434 is configured to use a preset angle function to determine the AoA of the direct path according to the corrected fine airspace flow pattern matrix and the ToF corresponding to the direct path.
  • the first determination unit 1432 is specifically configured to determine the ToF, reference AoA, and attenuation coefficient of each path of the positioning signal according to the corrected coarse airflow pattern matrix and the ToF spectrum data; according to the attenuation coefficient of each path and each ToF of the path, determine the direct path from each path; divide the reference AoA corresponding to the direct path to obtain the second preset corresponding angle range; determine the fine ideal airspace flow pattern matrix according to the second preset corresponding angle range. According to the ToF of each path and the attenuation coefficient of each path, the ToF corresponding to the direct path is determined.
  • the first determining unit 1432 is specifically configured to determine two-dimensional positioning parameter spectrum data according to the corrected rough airspace flow pattern matrix and ToF spectrum data; Peak extraction to obtain the ToF of each path of the positioning signal, the reference AoA of each path, and the attenuation coefficient.
  • the first determining module 141 includes:
  • the time-frequency transform unit 1411 is configured to perform Fourier transform on the multi-channel positioning signal sent by the terminal to be positioned to obtain a multi-channel frequency domain signal;
  • a channel estimation unit 1412 configured to perform channel estimation on multi-channel frequency domain signals to obtain a channel frequency domain response matrix
  • the acquiring unit 1413 is configured to acquire ToF spectrum data based on the channel frequency domain response matrix.
  • the obtaining unit 1413 is specifically configured to obtain channel correction coefficients, correct the channel frequency domain response matrix according to the channel correction coefficients, and obtain the corrected channel frequency domain response matrix; according to the corrected channel frequency domain response matrix , to obtain ToF spectral data.
  • the channel estimation unit 1412 is specifically configured to obtain the positioning sequence of each subband occupied by the positioning signal; use the positioning sequence of each subband to construct a positioning sequence matrix; use each element in the positioning sequence matrix as the main diagonal Line elements obtain a diagonal matrix for channel estimation.
  • the obtaining unit 1413 is specifically configured to measure the channel amplitude-phase response matrix as the channel correction coefficient.
  • Each module in the above positioning parameter determination 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 computer device is provided.
  • the computer device may be a terminal, and its internal structure may be as shown in FIG. 16 .
  • the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Wherein, the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device 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 computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, an operator network, NFC (Near Field Communication) or other technologies.
  • a positioning parameter determination method is realized.
  • the display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen
  • the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball or a touch pad provided on the casing of the computer device , and can also be an external keyboard, touchpad or mouse.
  • 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 to 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 computer device including a memory and a processor, where a computer program is stored in the memory, and the processor implements the steps in the foregoing method embodiments when executing the computer program.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.
  • Non-volatile memory may include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory or optical memory, etc.
  • Volatile memory can include Random Access Memory (RAM) or external cache memory.
  • RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

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Abstract

本申请涉及一种定位参数确定方法、装置、计算机设备和存储介质。该方法包括：根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对定位信号的响应；天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差；根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数；直达径为待定位终端到天线阵列的最短路径。

Description

定位参数确定方法、装置、设备和存储介质
相关申请的交叉引用
本申请要求2021年08月03日递交的、标题为“定位参数确定方法、装置、设备和存储介质”、申请号为2021108839132的中国申请的优先权，其公开内容通过引用全部结合在本申请中。
技术领域
本申请涉及通信技术领域，特别是涉及一种定位参数确定方法、装置、设备和存储介质。
背景技术
随着工业互联网、物联网和车联网的快速发展，高精度定位成为智能机器人、无人车等移动终端不可或缺的关键支撑服务。卫星导航定位技术具有广域覆盖、普适性好的优点，但是因为信号功率低、穿透力弱，主要用于室外开阔环境下的终端定位，无法在受遮蔽的环境和室内环境提供导航定位服务。
为了解决上述问题，相关技术是利用无线通信系统的基础设施或者部署专用的无线定位基站对终端设备进行定位，例如，可以利用空时超分辨算法同时测量终端设备发出的定位信号，以确定相应的定位参数：传播时延和到达角度，基于该定位参数即可确定终端设备的定位信息。
发明内容
本申请提供一种定位参数确定方法、装置、计算机设备和存储介质。
第一方面，本申请提供一种定位参数确定方法，该方法包括：
根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；
根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对定位信号的响应；天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差；
根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数；直达径为待定位终端到天线阵列的最短路径。
第二方面，本申请提供一种定位参数确定装置，该装置包括：
第一确定模块，用于根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；
修正模块，用于根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对定位信号的响应；天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差；
第二确定模块，用于根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数；直达径为待定位终端到天线阵列的最短路径。
第三方面，本申请提供一种计算机设备，包括存储器和处理器，存储器存储有计算机程序，处理器执行计算机程序时实现上述第一方面中任一项实施例中方法的步骤。
第四方面，本申请提供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上述第一方面中任一项实施例中方法的步骤。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征、目的和优点将从说明书、附图以及权利要求书变得明显。
附图说明
图1为一个实施例中定位参数确定方法的应用环境图；
图2为一个实施例定位参数确定方法的流程示意图；
图3为另一个实施例中定位参数确定方法的流程示意图；
图4为另一个实施例中定位参数确定方法的流程示意图；
图5为另一个实施例中定位参数确定方法的流程示意图；
图6为另一个实施例中定位参数确定方法的流程示意图；
图7为另一个实施例中定位参数确定方法的流程示意图；
图8为另一个实施例中定位参数确定方法的流程示意图；
图9为另一个实施例中定位参数确定方法的流程示意图；
图10为一个实施例中相位偏差函数测量值与函数估计结果；
图11为一个实施例中幅度方向图函数测量值与函数估计结果；
图12为一个实施例中RRU和终端的相对位置图；
图13为一个实施例中SRS符号AoA估计误差CDF曲线；
图14为一个实施例中定位参数确定装置的结构框图；
图15为另一个实施例中定位参数确定装置的结构框图；
图16为一个实施例中计算机设备的内部结构图。
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。
随着工业互联网、物联网和车联网的快速发展，高精度定位成为智能机器人、无人车等移动终端不可或缺的关键支撑服务。卫星导航定位技术具有广域覆盖、普适性好的优点，但是因为信号功率低、穿透力弱，主要用于室外开阔环境下的终端定位，无法在受遮蔽的环境和室内环境提供导航定位服务。
为了解决上述问题，一种主要的技术途径是利用无线通信系统的基础设施或者部署专用的无线定位基站对终端设备进行定位。典型的定位技术包括：蜂窝网定位、无线局域网(Wireless Local Area Network,WLAN)定位、蓝牙定位、超宽带(Ultra-Wide Band,UWB)定位等。其中，蜂窝移动网络、WLAN、UWB等系统中都普遍采用了宽带发射信号以及阵列天线，能够同时测量传播时延(Time of Flight，ToF)和到达角度(Angle of Arrival，AoA)。而在实际系统中，因受加工工艺、工装、阵元间互耦的影响，阵列中各阵元的阵中方向图存在差异，此时不同角度入射信号的幅度和相位响应都存在区别，且在大到达角度时尤为显著，而现有研究对定位基站在大到达角度的估计性能较少有提及；此外，复杂路径环境中，对快速运动目标的实时定位需求则要求定位算法能够兼顾估计精度和实时性。
基于此本申请提供一种定位参数确定方法，能够兼顾测量传播时延和到达角度的精度和实时性，能够提高定位参数的精度。本申请提供的定位参数确定方法，可以应用于如图1所示的应用环境中。其中，待定位终端102通过网络与基站104通过网络进行通信。待定位终端可以向基站发送定位信号，基站通过天线阵列接收该定位信号，并对定位信号进行计算并实时修正，以确定定位信号的定位参数。其中，待定位终端102可以包括车辆、飞机等移动终端设备，基站104可以包括：宏基站、分布式基站等，在此不加以限制。
在一个实施例中，如图2所示，提供了一种定位参数确定方法，以该方法应用于图1中的基站为例进行说明，包括以下步骤：
S202，根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据。
其中，待定位终端可以包括车辆、飞机等移动终端设备。定位信号为包括定位序列的信号，可以为5G信号、4G信号等等，在此不加以限制。飞行时间ToF为传播时延，也就是信号从发射端传播到接收端的飞行时间，因此也称为ToF(Time of Flight)。
具体地，在待定位终端发送定位信号至基站时，基站可以通过天线阵列接收该定位信号，并在接收到该定位信号，对该定位信号进行ToF超分辨谱估计，然后得到飞行时间ToF谱数据。其中，天线阵列可以是线阵列、也可以是圆形阵列，在此不加以限制。并且，天线阵列中可以包括多个阵元，每个阵元可以对应一个接收通道。
进一步地，对每个接收通道进行ToF超分辨谱估计，可以采用扫描的方式，对感兴趣的时延区域上的衰落系数进行估计，这个过程也称为时延谱估计或ToF谱估计。其中，ToF谱峰值点对应的扫描时延代表了定位信号经过障碍物反射以及直接到达天线阵列的多条路径的信号分量，相应的ToF谱峰值点对应的ToF值为相应路径的ToF。
可以通过单点最小二乘算法、幅度相位估计(Amplitude and Phase EStimation,APES)算法、迭代自适应方法(Iterative Adaptive Approach,IAA)、稀疏重构算法等对该ToF谱数据进行求解，确定定位信号的飞行时间ToF谱数据。
S204，根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对定位信号的响应；天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差。
其中，预设的相应角度范围为将天线阵覆盖范围[θ min，θ max]划分为Q份后，每一份的角度范围。可以使用等到达角度间隔δθ对天线阵覆盖范围[θ min，θ max]划分。其中，天线偏差系数函数可以是包括天线相位偏差和幅度偏差预先通过离线测量得到的函数，为真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差。
具体地，由于理想的空域流型矩阵是在完美情况下的空域流型矩阵，但是天线阵列在实际使用中，由于天线工装、阵元间互耦等因素，天线阵列并不是完美情况，因此，天线阵列存在与到达角度相关 的相位偏差，若使用理想流型矩阵进行测向处理，则由于理想和实际流型的不匹配，会出现较大的测向偏差，通过预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到与实际更相符的结果。
S206，根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数；直达径为待定位终端到天线阵列的最短路径。
具体地，可以对各个时延栅格点上第N个接收通道的ToF谱数据，采用数字波束形成(Digital Beam Forming,DBF)法，多重信号分类(MUltiple SIgnal Classification,MUSIC)算法等进行AoA谱估计，得到ToF-AoA二维谱谱估计结果，对获得的AoA-ToF二维谱进行谱峰提取，得到最大的K 1个谱峰对应的衰落系数、AoA以及ToF的估计值，分别记为：
Figure PCTCN2021139679-appb-000001
根据K 1个谱峰分量对应的衰落系数
Figure PCTCN2021139679-appb-000002
以及ToF值
Figure PCTCN2021139679-appb-000003
即从各个路径对应的衰落系数
Figure PCTCN2021139679-appb-000004
以及ToF值
Figure PCTCN2021139679-appb-000005
中，提取直达径(Line-of-Sight,LOS)分量，并输出LOS分量的ToF估计和AoA估计结果。也可以在获取到该AoA结果后，在进行精细时延栅格点的划分，并对空域流型矩阵进行修正，然后对修正后的空域流型矩阵细搜索，确定精细的AoA结果，在此不加以限制。其中，由于在实际环境中存在一些障碍物，会导致信号进行反射折射后，到达天线阵列，所以信号从发射端到达天线阵列存在多条路径，直达径为待定位终端到天线阵列的最短路径，也可以理解为定位信号不经折射反射直接到达天线阵列的路径。
上述定位参数确定方法中，通过根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数。能够利用预设的包含相位和幅度的天线阵列偏差函数对理想的空域流型矩阵进行修正，减小真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差，确定待定位终端到天线阵列的最短路径的直达径，进而提高测量定位信号定位参数的精度。并且，本方案避免了现有技术中同时测量ToF、AoA造成的计算复杂的问题。
上述实施例对定位参数确定方法进行了说明，主要是依据天线阵列偏差函数对反映天线阵列接收定位信号的响应的空域流型矩阵进行修正，现以一个实施例对如何构建天线阵列偏差函数进行说明，在一个实施例中，如图3所示，天线阵列偏差函数的构建过程包括：
S302，获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值集合，和模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值集合。
其中，模拟的真实信号为在暗室中的信号发生器模拟真实信号发射的信号。
具体地，将天线阵列放置于暗室中的转台上，可以将其旋转-60°至60°，设定每旋转5°为一个采样角度，各个采样角度构成离散到达角度集合
Figure PCTCN2021139679-appb-000006
在每个采样角度到达上，获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值，并将所有获取的幅度测量值构成幅度测量值集合，以及模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值，并将所述获取的相位偏差的相位测量值构成相位测量值集合。在离散到达角度集合
Figure PCTCN2021139679-appb-000007
上，天线各阵元n的幅度方向图和相位偏差的测量集合
Figure PCTCN2021139679-appb-000008
S304，根据幅度测量值集合，构建幅度方向图函数；以及根据相位测量值集合，构建相位偏差函数。
具体地，根据天线各阵元n的幅度方向图和相位偏差的测量集合，可以使用多项式拟合、支撑向量机或神经网络等方式，对函数ρ n(θ)和φ n(θ)进行估计。以使用多项式拟合方法对函数φ n(θ)进行估计为例，对该过程进行说明。记得到的函数拟合结果为
Figure PCTCN2021139679-appb-000009
其中，g∈R J×1为多项式权重，J为多项式拟合阶数，
Figure PCTCN2021139679-appb-000010
的形式为
Figure PCTCN2021139679-appb-000011
其中g l权重向量g的第l个元素。则多项式拟合的目标函数如下所示：
Figure PCTCN2021139679-appb-000012
对该目标函数进行求解，得到权值g后，使
Figure PCTCN2021139679-appb-000013
即可，进而即可得到相位偏差 函数
Figure PCTCN2021139679-appb-000014
以使用多项式拟合方法对函数
Figure PCTCN2021139679-appb-000015
进行估计为例，对该过程进行说明。记得到的函数拟合结果为
Figure PCTCN2021139679-appb-000016
其中，g∈R J×1为多项式权重，J为多项式拟合阶数，
Figure PCTCN2021139679-appb-000017
的形式为
Figure PCTCN2021139679-appb-000018
其中g l权重向量g的第l个元素。则多项式拟合的目标函数如下所示：
Figure PCTCN2021139679-appb-000019
对该目标函数进行求解，得到权值g后，使
Figure PCTCN2021139679-appb-000020
即可，进而即可得到相位偏差函数
Figure PCTCN2021139679-appb-000021
在本实施例中，通过获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值集合，和模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值集合；根据幅度测量值集合，构建幅度方向图函数；以及根据相位测量值集合，构建相位偏差函数，能够对反映天线阵列接收定位信号的响应的空域流型矩阵进行修正，进而提高测量定位信号的定位参数的精度。
上述实施例对如何构建天线偏离函数进行了说明，现以一个实施例对利用天线偏离函数修正的空域流型矩阵进行说明，在一个实施例中，理想的空域流型矩阵包括粗理想空域流型矩阵和细理想空域流型矩阵；粗理想空域流型矩阵中每个元素表示天线阵列中各阵元在第一预设的相应角度范围对定位信号的响应；细理想空域流型矩阵中每个元素表示天线阵列中各阵元在第二预设的相应角度范围对定位信号的响应；第一预设的相应角度范围大于第二预设的相应角度范围。
其中，第一预设的相应角度范围为使用等到达角度间隔δθ将天线阵覆盖范围[θ min，θ max]划分为Q份，对应的到达角度集合为
Figure PCTCN2021139679-appb-000022
可以采用均匀栅格δθ 1对根据对粗理想空域流型矩阵修正后的矩阵进行计算后得到的粗结果AoA区间
Figure PCTCN2021139679-appb-000023
进行划分，得到Q 1个精搜栅格集合，记为
Figure PCTCN2021139679-appb-000024
其中Δθ可由粗搜栅格的大小确定，例如可选为Δθ＝δθ，δθ 1可选择为Δθ的1/10左右。
具体地，理想的空域流型矩阵包括粗理想空域流型矩阵和细理想空域流型矩阵；粗理想空域流型矩阵中每个元素表示天线阵列中各阵元在第一预设的相应角度范围对定位信号的响应；细理想空域流型矩阵中每个元素表示天线阵列中各阵元在第二预设的相应角度范围对定位信号的响应；第一预设的相应角度范围大于第二预设的相应角度范围。
在到达角度θ处，理想的阵列导向矢量a θ(θ)由阵列结构所决定，在到达角度集
Figure PCTCN2021139679-appb-000025
上的阵列导向矢量的集合就构成了该阵列的理想流型矩阵，记为A θ，有：A θ＝[a θ(θ 1），a θ(θ 2)，...，a θ(θ Q)]。确定精搜到达角度集对应的理想流型矩阵A θ，fine，有：
Figure PCTCN2021139679-appb-000026
在本实施例中，理想的空域流型矩阵包括粗理想空域流型矩阵和细理想空域流型矩阵；粗理想空域流型矩阵中每个元素表示天线阵列中各阵元在第一预设的相应角度范围对定位信号的响应；细理想空域流型矩阵中每个元素表示天线阵列中各阵元在第二预设的相应角度范围对定位信号的响应。由于第一预设的相应角度范围大于第二预设的相应角度范围，因此可以对理想的空域流型矩阵进行不同粗细栅格点的划分并修正，以确保得到的结果精度更高。
上述实施例中对理想的空域流型矩阵的两种粗细理想形式进行了介绍，现以一个实施例对如何应用这两种粗细形式的理想的空域流型矩阵确定定位信号的直达径的定位参数进行说明，在一个实施例中，如图4所示，定位参数包括AoA和ToF；根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数，包括：
S402，根据天线阵列偏差函数，对粗理想空域流型矩阵进行修正，得到修正后的粗空域流型矩阵。
具体地，根据幅度方向图函数和相位偏差函数，确定天线阵列偏差函数；天线偏差系数函数可以表示为
Figure PCTCN2021139679-appb-000027
其中，天线相位偏差函数
Figure PCTCN2021139679-appb-000028
和 幅度方向图函数
Figure PCTCN2021139679-appb-000029
为预先通过离线测量得到的函数。根据公式
Figure PCTCN2021139679-appb-000030
即可对粗理想空域流型矩阵A θ进行修正，得到修正后的粗空域流型矩阵A θ′。
S404，根据修正后的粗空域流型矩阵和ToF谱数据，确定细理想空域流型矩阵和直达径对应的ToF。
其中，飞行时间ToF谱数据可以包括由各个扫描栅格点对应时延τ p，p＝1，...，P组成的向量β n＝[β n，1，β n，2，...，β n，P] T，其中，P为每个扫描栅格点上的衰落系数，n为天线阵列中第n个接收阵元，即第n个接收通道。
可选地，可以通过设将感兴趣的时延范围[τ min，τ max]等间隔划分为P份，一般情况下P＞＞K(路径数量)，这P个扫描栅格点对应的时延分别为τ p，p＝1，...，P。记β n，p，p＝1，...，P为每个扫描栅格点上的衰落系数，当
Figure PCTCN2021139679-appb-000031
时，
Figure PCTCN2021139679-appb-000032
α n，k表示第n个接收阵元对第k条路径入射信号的响应，
Figure PCTCN2021139679-appb-000033
为第k条路径的衰落系数。在其它P-K个栅格点上，β n，p＝0。记β n＝[β n，1，β n，2，...，β n，P] T为扫描栅格点集合上的衰落系数向量，且A τ＝[a τ(τ 1)，...，a τ(τ P)]为扫描栅格点集合上的时延匹配矩阵。则有：h n≈A τβ n+w n，n＝1，...，N。h n为接收的定位信号进行信道估计后形成的信道频域响应矩阵CFR中的第n列元素，表示第n个接收通道的CFR。
可选地，对于第n个通道的ToF谱估计，使用IAA进行求解的目标函数为：
Figure PCTCN2021139679-appb-000034
得到各个接收通道的每个扫描栅格点上的衰落系数，并构成β n＝[β n，1，β n，2，...，β n，P] T，即ToF谱数据；其中，
Figure PCTCN2021139679-appb-000035
表示向量x的加权l 2范数。R n,p表示第n个接收通道，第p个扫描栅格点处的干扰协方差矩阵，其中的干扰由当前栅格点τ p以外的信号分量构成。
使用基于l 1范数的稀疏重构算法进行谱求解的目标函数为：
Figure PCTCN2021139679-appb-000036
得到各个接收通道的每个扫描栅格点上的衰落系数，并构成β n＝[β n，1，β n，2，...，β n，P] T，即ToF谱数据；其中，||X|| 1表示P维向量X的l 1范数，定义为
Figure PCTCN2021139679-appb-000037
具体地，可以设第p个时延单元，N个接收通道的ToF谱数据向量为b p∈C N×1，有：
Figure PCTCN2021139679-appb-000038
对ToF谱数据向量b p∈C N×1，p＝1，...，P依次进行AoA谱估计，记第p个ToF单元上的AoA谱估计结果为
Figure PCTCN2021139679-appb-000039
谱估计方法可采用数字波束形成(Digital Beam Forming,DBF)法，多重信号分类(MUltiple SIgnal Classification,MUSIC)算法等。以DBF算法为例，谱估计结果为：
Figure PCTCN2021139679-appb-000040
记
Figure PCTCN2021139679-appb-000041
为ToF-AoA二维谱谱估计结果，其第q行第p列元素
Figure PCTCN2021139679-appb-000042
表示对ToF τ p，AoA θ q处的信道衰落系数的估计值。
进一步地，如图5所示，根据修正后的粗空域流型矩阵和ToF谱数据，确定细理想空域流型矩阵，包括：
S502，根据修正后的粗空域流型矩阵和ToF谱数据，确定定位信号各路径的ToF、各路径的参考AoA、各路径的衰减系数。
具体地，根据修正后的粗空域流型矩阵A θ′和ToF谱数据
Figure PCTCN2021139679-appb-000043
利用公式
Figure PCTCN2021139679-appb-000044
确定N个接收通道二维定位参数谱数据。根据二维定位参数谱数据进行谱峰提取，得到定位信号各路径的ToF、参考AoA、各路径的衰减系数。具体包括：遍历二维谱中的每一个点，判断该点相对于邻接的8个点的二维谱谱强度，若该点强度大于所有8个点的强度，则判决其为一个谱峰值点，找出二维谱的所有谱峰值点之后，按照谱峰强度进行排序，提取出其中最大的K 1个谱峰，根据这K 1个谱峰，分别按照谱峰的横纵坐标确定对应的AoA
Figure PCTCN2021139679-appb-000045
和ToF
Figure PCTCN2021139679-appb-000046
根据谱峰强度确定衰落系数
Figure PCTCN2021139679-appb-000047
S504，根据各路径衰减系数和各路径的ToF，从各路径中确定直达径。
具体地，可以通过预设的谱峰强度门限值Ξ，提取出K 1个谱峰分量中，能量超过预设的谱峰强度门限值Ξ的K 2个分量，再从K 2个分量中，提取出对应的ToF最小的分量作为LOS分量即，直达径对应的分量。即在各路径的衰减系数中超过预设的谱峰强度门限值的谱峰分量中，根据已经求解的各路径的ToF，可以直接将ToF最小的分量确定为直达径对应的ToF。
可选地，还可以基于直达径相比于反射径传播时间更短、直达径比反射径能量更强或多帧间直达径分量的ToF和AoA估计方差更小等基本准则，对直达径分量进行提取。
S506，对直达径对应的参考AoA进行划分，得到第二预设的相应角度范围。
具体地，采用均匀栅格δθ对AoA区间
Figure PCTCN2021139679-appb-000048
进行划分，得到Q 1个精搜栅格集合，记为
Figure PCTCN2021139679-appb-000049
其中Δθ可由粗搜栅格的大小确定，例如可选为Δθ＝δθ，δθ 1可选择为Δθ的1/10左右。
S508，根据第二预设的相应角度范围确定细理想空域流型矩阵。
具体地，可以将精搜到达角度集合带入预设的理想流型矩阵中，确定对应的理想流型矩阵A θ，fine，有：
Figure PCTCN2021139679-appb-000050
S406，根据天线阵列偏差函数，对细理想空域流型矩阵进行修正，得到修正后的细空域流型矩阵。
具体地，根据天线相位偏差函数和幅度方向图函数的估计值
Figure PCTCN2021139679-appb-000051
计算在精搜栅格点上天线偏差系数函数的函数值，即天线偏差函数：
Figure PCTCN2021139679-appb-000052
则修正后的流型矩阵为A θ，fine′，则A θ，fine′第n行第q列元素为：
Figure PCTCN2021139679-appb-000053
S408，采用预设的角度函数，根据修正后的细空域流型矩阵和直达径对应的ToF，确定直达径的AoA。
具体地，记b LOS为直达径分量所在ToF单元上的N个接收通道的ToF谱数据，可基于波束扫描峰值准则，将修正后的细空域流型矩阵和直达径对应的ToF代入波束扫描峰值准则对应的角度函数或子空间正交等准则对应的角度函数进行AoA精估计，例如，将b LOS、A θ，fine′代入波束扫描峰值 准则的角度函数为：
Figure PCTCN2021139679-appb-000054
中，确定θ，即直达径的AoA；。另外，也可将b LOS、A θ，fine′代入子空间正交准则的角度函数为：
Figure PCTCN2021139679-appb-000055
其中，确定最终的θ值，即直达径的AoA；其中，||X|| F表示矩阵X的Frobenius范数，定义为：
Figure PCTCN2021139679-appb-000056
为根据向量b LOS，得到的噪声子空间的估计结果。
在本实施例中，通过根据天线阵列偏差函数，对粗理想空域流型矩阵进行修正，得到修正后的粗空域流型矩阵；根据修正后的粗空域流型矩阵和ToF谱数据，确定细理想空域流型矩阵和直达径对应的ToF；根据天线阵列偏差函数，对细理想空域流型矩阵进行修正，得到修正后的细空域流型矩阵；采用预设的角度函数，根据修正后的细空域流型矩阵和直达径对应的ToF，确定直达径的AoA。能够通过对理想流型矩阵的修正，对该到达角度相关的天线误差进行了有效补偿，并且通过时延谱估计与直达径到达角度先粗粒度搜索后精细搜索的多级级联信号处理方式，降低现有技术中利用二维同时搜索ToF、AoA时的计算复杂度，提高定位实时性。并且，能够精确补偿与到达角度相关的相位误差，提高了测向和定位精度，尤其是在无线信号大波达角度时，能显著改进测向的精度。
上述实施例对如何应用这两种粗细形式的理想的空域流型矩阵确定定位信号的直达径的定位参数进行说明，在进行确定定位信号的直达径的定位参数之前，首先需要对接收的定位信号进行相关处理，然后确定定位信号的飞行时间ToF谱数据，现以一个实施例对其进行说明，如图6所示，根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据，包括：
S602，对待定位终端发送的多通道的定位信号进行傅里叶变换，得到多通道频域信号。
具体地，基站通过天线阵接收待定位终端发送的已知序列的定位信号，由于定位信号为时域信号，可以先对各通道接收信号进行快速傅里叶变换(Fast Fourier Transform,FFT)得到多通道频域接收信号。天线阵列中可以包括N个阵元，每个阵元对应一个接收通道。若宽带定位信号占用的子带数量为M，则从接收通道n接收的频域定位信号可表示为向量X n＝[X 1，n，X 2，n，...，X M，n] T，其中X m，n表示第n个接收通道、第m个子带接收的频域定位信号。有x n∈C M×1，其中
Figure PCTCN2021139679-appb-000057
表示复数空间，C M×1表示M*1维复数空间，也就是M维复向量空间。本申请中，向量均指列向量。
基站所有通道的接收数据矩阵可表示为X＝[x 1，x 2，...，x N]∈C M×N，即多通道频域信号。其中，在M个子带上发送的定位信号序列为S[m]，m＝1，2，...，M，发送信号中心载频为f c，对应波长为λ＝c/f c，其中c为真空中光速。不失一般性，假设M个子带呈均匀分布，分布间隔为Δf；且假设接收天线阵为等距线阵(Uniform Linear Array,ULA)，且阵元间距为d。此外，假设发射信号经由K条路径传播至接收阵列，第k条路径的传播时延(ToF)、到达角度(AoA)以及衰落系数分别为
Figure PCTCN2021139679-appb-000058
其中，
Figure PCTCN2021139679-appb-000059
定义为信号入射方向与ULA法线方向的夹角。其中，信号传输的时延可代表该信号传输的距离，时延与距离之间通过光速c可以相互转化。因此，多个通道的接收信号矩阵X还可以表示为：
Figure PCTCN2021139679-appb-000060
其中，式(1)中，S＝diag([S[1]，S[2]，...，S[M]] T)为定位信号的定位序列数据矩阵，diag(·)运算符表示以向量的每个元素作为主对角线元素，获得对角矩阵。
其中，式(1)中，a τ(·)∈T→C M×1为时延域匹配矢量函数，其输入为传播时延τ，输出为时延域匹配向量。具体地，T→C M×1表示该函数的作用域为T，值域为M维时延域匹配向量，T为所有可能的路径时延τ的集合，即
Figure PCTCN2021139679-appb-000061
其中，
Figure PCTCN2021139679-appb-000062
表示实数空间。时延域匹配向量的第m个元素代表信号传播时延在第m个子带造成的相位偏移，因此，有：
Figure PCTCN2021139679-appb-000063
其中j代表虚数单位，定义为：
Figure PCTCN2021139679-appb-000064
分布间隔为Δf。
其中，式(1)中，a θ′(·)∈Θ→C N×1表示实际接收阵列导向矢量函数，其输入为信号到达角度θ，输出为对应到达角度的阵列导向矢量。具体地，Θ→C N×1表示其作用域为Θ，值域为N维向量，Θ为所有可能的入射信号到达角度所组成的空间，即
Figure PCTCN2021139679-appb-000065
进一步，
Figure PCTCN2021139679-appb-000066
其中
Figure PCTCN2021139679-appb-000067
为理想的阵列导向矢量，当接收阵为ULA时，其第n个元素为：
Figure PCTCN2021139679-appb-000068
代表由天线工装、阵元间互耦等因素共同造成的角度依赖相位偏差以及天线阵元的幅度方向图所带来的扰动项，即天线偏差系数函数，代表真实的阵列响应和理想的阵列响应之间的偏差，其第n个元素为：
Figure PCTCN2021139679-appb-000069
式中：ρ n(·)∈Θ→R表示第n个阵元上的幅度方向图函数，其输入为信号到达角度θ，输出为第n个阵元的幅度方向图。φ n(·)∈Θ→[-π，+π]表示第n个阵元上的角度依赖相位偏差函数，其输入为信号到达角度θ，输出为第n个阵元在相应到达角度上的相位偏差。ρ n(·)和φ n(·)的具体形式取决于所使用的阵列天线，其在某些到达角度栅格上的值可通过暗室测量或电磁仿真软件数值计算得到。其中，相位偏差函数对于AoA估计而言有着决定性的作用，且一般情况下，当θ较小时，φ n(θ)接近0，当θ较大时，φ n(θ)会出现较大的起伏。运算符⊙表示Hadamard积。
其中，式(1)中，γ∈C M×N为基站接收通道前端放大器、滤波器、混频器等模拟器件的宽带响应，其第m行第n列元素为第n个接收通道在第m个子带的响应。W∈C M×N为噪声矩阵，其第m行第n列元素表示第n个接收通道，第m个子带上的噪声分量。
S604，对多通道频域信号进行信道估计，得到信道频域响应矩阵。
具体地，基站根据频域接收信号矩阵X，可以采用最小二乘法进行信道估计，得到信道频域响应(Channel Frequency Response,CFR)矩阵，记为H 0。例如，假设接收端已知发送的定位信号在频域上的具体形式，且已经获取定位信号的定位序列，并根据定位序列确定，且使用经典的最小二乘(Least Square,LS)算法进行信道估计，则可得到：H 0＝S -1X；式中，H 0∈C M×N，其第n列为第n个接收通道的信道频域响应矩阵；S -1为：定位信号的定位序列数据矩阵S＝diag([S[1]，S[2]，...S[M]] T)的逆矩阵。其中，信道频域响应矩阵还可以表示为：
Figure PCTCN2021139679-appb-000070
S606，基于信道频域响应矩阵，获取ToF谱数据。
具体地，根据信道频域响应矩阵H 0，对每个接收通道进行ToF超分辨谱估计。设h n，n＝1，...，N为矩阵H 0的第n列元素，表示第n个接收通道的CFR。则h n可表示为：
Figure PCTCN2021139679-appb-000071
式中，α n，k表示第n个接收阵元对第k条路径入射信号的响应，其为向量
Figure PCTCN2021139679-appb-000072
的第n个元素。式中：w n∈C M×1表示该通道的噪声向量，为矩阵W′的第n列。采用扫描的方式，对感兴趣的时延区域上的衰落系数进行ToF谱估计，ToF谱峰值点对应的扫描时延代表了强路径的ToF。设将时延范围[τ min，τ max]等间隔划分为P份，一般情况 下P＞＞K(路径数量)，这P个扫描栅格点对应的时延分别为τ p，p＝1，...，P。记β n，p，p＝1，...，P为每个扫描栅格点上的衰落系数，当
Figure PCTCN2021139679-appb-000073
时，
Figure PCTCN2021139679-appb-000074
在其它P-K个栅格点上，β n，p＝0。记β n＝[β n，1，β n，2，...，β n，P] T为扫描栅格点集合上的衰落系数向量，即ToF谱数据，且A τ＝[a τ(τ 1)，...，a τ(τP)]为扫描栅格点集合上的时延匹配矩阵。则有：h n≈A τβ n+w n，n＝1，...，N。
可以采用有多种参数估计方法可以对此谱估计问题进行求解，例如单点最小二乘算法、幅度相位估计(Amplitude and Phase EStimation,APES)算法、迭代自适应方法(Iterative Adaptive Approach,IAA)、稀疏重构算法等。例如，对于第n个通道的ToF谱估计，将h n、A τ＝[a τ(τ 1)，...，a τ(τ P)]以及未知β n，p，p＝1，...，P代入目标函数：
Figure PCTCN2021139679-appb-000075
中，利用IAA求解得到β n，p，p＝1，...，P。其中，
Figure PCTCN2021139679-appb-000076
表示向量X的加权l 2范数。R n,p表示第n个接收通道，第p个扫描栅格点处的干扰协方差矩阵，其中的干扰由当前栅格点τ p以外的信号分量构成。还可以将h n、A τ＝[a τ(τ 1)，...，a τ(τ P)]以及未知β n，p，p＝1，...，P代入使用基于l 1范数的稀疏重构算法的目标函数为：
Figure PCTCN2021139679-appb-000077
进行谱求解得到β n，p，p＝1，...，P；其中，||X|| 1表示P维向量X的l 1范数，定义为
Figure PCTCN2021139679-appb-000078
进一步地，如图7所示，基于信道频域响应矩阵，获取ToF谱数据，包括：
S702，获取通道校正系数，根据通道校正系数对信道频域响应矩阵进行校正，得到校正后的信道频域响应矩阵。
具体地，实际各接收通道在各频点的幅相响应不同，引起了式(1)中的γ项。一般情况下，γ矩阵可在定位实验前测量得到或在定位实验中借助专用校正通道测量得到，假设测量得到的通道幅相响应矩阵为
Figure PCTCN2021139679-appb-000079
由于矩阵
Figure PCTCN2021139679-appb-000080
的作用是用来进行通道校正，因此通常也称其为通道校正系数，或简称为通道系数。记经通道幅相偏差校正后得到的信道频域响应矩阵CFR矩阵为H，则其第m行第n列元素为：
Figure PCTCN2021139679-appb-000081
其中，H 0| (m，n)表示校正前信道频域响应矩阵H 0第m行第n列元素，
Figure PCTCN2021139679-appb-000082
表示通道校正系数矩阵
Figure PCTCN2021139679-appb-000083
第m行第n列元素。假设通道幅相响应的测量误差可忽略，则有：
Figure PCTCN2021139679-appb-000084
式中：W′∈C M×N表示经通道幅相偏差校正后，CFR矩阵中的噪声分量。
S704，根据校正后的信道频域响应矩阵，获取ToF谱数据。
具体地，设h n，n＝1，...，N为矩阵H的第n列元素，表示第n个接收通道的CFR。则h n可表示为：
Figure PCTCN2021139679-appb-000085
式中，α n，k表示第n个接收阵元对第k条路径入射信号的响应，其为向量
Figure PCTCN2021139679-appb-000086
的第n个元素。式中：w n∈C M×1表示该通道的噪声向量，为矩阵W′的第n列。采用扫描的方式，对感兴趣的时延区域上的衰落系数进行ToF谱估计，ToF谱峰值点对应的扫描时延代表了强路径的ToF。设将时延范围[τ min，τ max]等间隔划分为P份，一般情况下P＞＞K(路径数量)，这P个扫描栅格点对应的时延分别为τ p，p＝1，...，P。记β n，p，p＝1，...，P为每个扫描栅格点上的衰落系数，当
Figure PCTCN2021139679-appb-000087
时，
Figure PCTCN2021139679-appb-000088
在其 它P-K个栅格点上，β n，p＝0。记β n＝[β n，1，β n，2，...，β n，P] T为扫描栅格点集合上的衰落系数向量，即ToF谱数据，且A τ＝[a τ(τ 1)，...，a τ(τ P)]为扫描栅格点集合上的时延匹配矩阵。则有：h n≈A τβ n+w n，n＝1，...，N。
可以采用有多种参数估计方法可以对此谱估计问题进行求解，例如单点最小二乘算法、幅度相位估计(Amplitude and Phase EStimation,APES)算法、迭代自适应方法(Iterative Adaptive Approach,IAA)、稀疏重构算法等。例如，对于第n个通道的ToF谱估计，将h n、A τ＝[a τ(τ 1)，...，a τ(τ P)]以及未知β n，p，p＝1，...，P代入目标函数：
Figure PCTCN2021139679-appb-000089
中，利用IAA求解得到β n，p，p＝1，...，P。其中，
Figure PCTCN2021139679-appb-000090
表示向量X的加权l 2范数。R n,p表示第n个接收通道，第p个扫描栅格点处的干扰协方差矩阵，其中的干扰由当前栅格点τ p以外的信号分量构成。还可以将h n、A τ＝[a τ(τ 1)，...，a τ(τ P)]以及未知β n，p，p＝1，...，P代入使用基于l 1范数的稀疏重构算法的目标函数为：
Figure PCTCN2021139679-appb-000091
进行谱求解得到β n，p，p＝1，...，P；其中，||X|| 1表示P维向量X的l 1范数，定义为
Figure PCTCN2021139679-appb-000092
在本实施例中，通过对待定位终端发送的多通道的定位信号进行傅里叶变换，得到多通道频域信号；对多通道频域信号进行信道估计，得到信道频域响应矩阵；基于信道频域响应矩阵，获取ToF谱数据。能够对接收到的定位信号进行变换，便于后续的数据分析。并且通过首先对根据定位信号的得到的信道频域响应矩阵进行修正，进一步的使后续确定的ToF谱数据、AoA的精度更高。
上述是实施例对定位信号处理并如何确定ToF谱数据进行了说明，在对定位信号进行处理时，对由定位信号构成的信道频域响应矩阵进行修正，现以一个实施例对进行修正时的修正系数进行说明，在一个实施例中，如图8所示，获取通道校正系数，包括：
S802，获取定位信号占用各个子带的定位序列。
具体地，由于待定位终端发送的定位信号具有抑制的定位序列，因此，在M个子带上发送的定位信号序列为S[m]，m＝1，2，...，M。
S804，利用各子带的定位序列构建定位序列矩阵。
具体地，利用M个子带上发送的定位信号序列为S[m]，m＝1，2，...，M，构建定位序列矩阵S＝diag([S[1]，S[2]，...，S[M]] T)。
S806，以定位序列矩阵中的每个元素作为主对角线元素获得对角矩阵以用于信道估计，并测量通道幅相响应矩阵作为通道校正系数。
具体地，以定位序列矩阵中的S＝diag([S[1]，S[2]，...，S[M]] T)，其中，diag(·)运算符表示以向量的每个元素作为主对角线元素，即获得对角矩阵。
在本实施例中，通过获取定位信号占用各个子带的定位序列；利用各子带的定位序列构建定位序列矩阵；以定位序列矩阵中的每个元素作为主对角线元素获得对角矩阵以用于信道估计，并测量通道幅相响应矩阵作为通道校正系数。能够确定对信道频域矩阵进行校正的通道校正系数，进而对信道频域矩阵进行校正。
为了便于本领域技术人员的理解，现以一个实施例进一步对定位参数确定方法进行说明，在一个实施例中，如图9所示，定位参数确定方法包括：
S902，对待定位终端发送的多通道的定位信号进行傅里叶变换，得到多通道频域信号。
S904，获取定位信号占用各个子带的定位序列。
S906，利用各子带的定位序列构建定位序列矩阵。
S908，以定位序列矩阵中的每个元素作为主对角线元素，获得对角矩阵以用于信道估计。
S910，对多通道频域信号进行信道估计，得到信道频域响应矩阵。
S912，测量通道幅度响应矩阵作为通道校正系数对信道频域响应矩阵进行校正，得到校正后的信道频域响应矩阵。
S914，根据校正后的信道频域响应矩阵，获取ToF谱数据。
S916，获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值集合，和模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值集合。
S918，根据幅度测量值集合，构建幅度方向图函数；以及根据相位测量值集合，构建相位偏差函数。
S920，根据天线阵列偏差函数，对粗理想空域流型矩阵进行修正，得到修正后的粗空域流型矩阵。其中，理想的空域流型矩阵包括粗理想空域流型矩阵和细理想空域流型矩阵；粗理想空域流型矩阵中每个元素表示天线阵列中各阵元在第一预设的相应角度范围对定位信号的响应；细理想空域流型矩阵中每个元素表示天线阵列中各阵元在第二预设的相应角度范围对定位信号的响应；第一预设的相应角度范围大于第二预设的相应角度范围。
S922，根据修正后的粗空域流型矩阵和ToF谱数据，确定定位信号各路径的ToF、各路径的参考AoA、衰减系数。
S924，根据各路径衰减系数和各路径的ToF，从各路径中确定直达径。
S926，对直达径对应的参考AoA进行划分，得到第二预设的相应角度范围。
S928，根据第二预设的相应角度范围确定细理想空域流型矩阵。
S930，根据天线阵列偏差函数，对细理想空域流型矩阵进行修正，得到修正后的细空域流型矩阵。
S932，采用预设的角度函数，根据修正后的细空域流型矩阵和直达径对应的ToF，确定直达径的AoA。
具体地，以某基于FR1频段5G系统的室内定位实验为例，来说明算法有效性。采用5G探测参考信号(Sounding Reference Signal,SRS)作为定位信号，SRS为宽带OFDM信号，实验中配置其占据1632个子载波，子载波间间隔60kHz。实验过程中使用了两个5G RRU作为接收设备，每个RRU配备4阵元ULA，阵元间距为5.8cm，阵列水平放置。在定位实验开始前，需要完成离线阶段的天线相位偏差函数和幅度方向图函数估计。如图10、11中空心圆圈所示，为在微波暗室以5°为间隔，在天线阵所覆盖的-60°至60°扇区内测量得到的天线阵各阵元的相位偏差以及幅度方向图的采样。采用多项式拟合方法对相位偏差函数φ n(θ)和幅度方向图函数ρ n(θ)，n＝1，...，4进行估计，估计相位偏差函数时，采用的多项式阶数为6，4个天线阵元分别估计得到的相位偏差函数曲线如图10中实线所示；估计幅度方向图函数时，采用的多项式阶数为4，4个天线阵元分别估计得到的幅度方向图函数曲线如图11中实线所示。从图10、图11中能够看到，采用多项式拟合得到的相位偏差函数和幅度方向图函数能够较好地逼近微波暗室中相应的测量量。
固定两个RRU位置，放置终端在不同的相对位置，每次终端静止不动，采集1500个连续的SRS符号进行定位参数估计，此处列出其中一个RRU相对于终端到达角度较大，且另一个RRU相对于终端到达角度较小时的结果，以说明本申请所提方法对大到达角度(AoA)信号相位偏差的适应性。如图12所示为某次实验的RRU和终端的相对位置图，图中虚线表示两个RRU天线阵列的法线方向，五角星为这次实验终端所在的位置。可以看到，此时终端接近RRU-1的法向，真实到达角度为-1.5°；且终端信号到达RRU-2的角度较大，真实到达角度为-55.8°。
使用本申请所提方法对两个RRU的SRS数据进行处理，1500个SRS符号的AoA估计误差累积分布概率(Cumulative Distribution Function,CDF)曲线如图13中空心圆圈所示，为进行对比，图13中同样绘制了使用理想流型矩阵进行处理，得到的CDF曲线，用实心点表示。根据图13可知，天线的误差，包括各阵元的相位偏差以及天线方向图的差异，存在明显的角度相关性，在小到达角度时，误差较小，引起的AoA估计误差较小；在大到达角度时，误差较大，引起的AoA估计误差也较大。通过流型矩阵的修正，对该到达角度相关的天线误差进行了有效补偿，在信号到达角度较大时改善尤为明显。
在本实施例中，通过根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数。能够利用预设的包含 相位和幅度的天线阵列偏差函数对理想的空域流型矩阵进行修正，减小真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差，确定待定位终端到天线阵列的最短路径的直达径，进而提高测量定位信号定位参数的精度。并且，本方案避免了现有技术中同时测量ToF、AoA造成的计算复杂的问题。
应该理解的是，虽然图2-9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，图2-9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。
在一个实施例中，如图14所示，提供了一种定位参数确定装置，包括：
第一确定模块141，用于根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；
修正模块142，用于根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对定位信号的响应；天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差；
第二确定模块143，用于根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数；直达径为待定位终端到天线阵列的最短路径。
在本实施例中，第一确定模块根据待定位终端发送的多通道的定位信号，确定定位信号的飞行时间ToF谱数据；修正模块根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；第二确定模块根据ToF谱数据和修正后的空域流型矩阵，确定定位信号的直达径的定位参数。能够利用预设的包含相位和幅度的天线阵列偏差函数对理想的空域流型矩阵进行修正，减小真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差，确定待定位终端到天线阵列的最短路径的直达径，进而提高测量定位信号定位参数的精度。并且，本方案避免了现有技术中同时测量ToF、AoA造成的计算复杂的问题。
在一个实施例中，如图15所示，定位参数确定装置还包括：
模拟参数集合模块144，用于获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值集合，和模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值集合；
构建偏差函数模块145，用于根据幅度测量值集合，构建幅度方向图函数；以及根据相位测量值集合，构建相位偏差函数；根据所述幅度方向图函数和所述相位偏差函数，确定所述天线阵列偏差函数。。
在一个实施例中，参照图15所示，定位参数包括AoA和ToF；第二确定模块143，包括：
第一修正单元1431，用于根据天线阵列偏差函数，对粗理想空域流型矩阵进行修正，得到修正后的粗空域流型矩阵；
第一确定单元1432，用于根据修正后的粗空域流型矩阵和ToF谱数据，确定细理想空域流型矩阵和直达径对应的ToF；
第二修正单元1433，用于根据天线阵列偏差函数，对细理想空域流型矩阵进行修正，得到修正后的细空域流型矩阵；
第二确定单元1434，用于采用预设的角度函数，根据修正后的细空域流型矩阵和直达径对应的ToF，确定直达径的AoA。
在一个实施例中，第一确定单元1432具体用于，根据修正后的粗空域流型矩阵和ToF谱数据，确定定位信号各路径的ToF、参考AoA、衰减系数；根据各路径衰减系数和各路径的ToF，从各路径中确定直达径；对直达径对应的参考AoA进行划分，得到第二预设的相应角度范围；根据第二预设的相应角度范围确定细理想空域流型矩阵。根据各路径的ToF以及各路径的衰减系数，确定直达径对应的ToF。
在一个实施例中，参照图15所示，第一确定单元1432具体用于根据修正后的粗空域流型矩阵和ToF谱数据确定二维定位参数谱数据；根据二维定位参数谱数据进行谱峰提取，得到定位信号各路径的ToF、各路径的参考AoA、衰减系数。
在一个实施例中，参照图15所示，第一确定模块141包括：
时频变换单元1411，用于对待定位终端发送的多通道的定位信号进行傅里叶变换，得到多通道频域信号；
信道估计单元1412，用于对多通道频域信号进行信道估计，得到信道频域响应矩阵；
获取单元1413，用于基于信道频域响应矩阵，获取ToF谱数据。
在一个实施例中，获取单元1413，具体用于获取通道校正系数，根据通道校正系数对信道频域响应矩阵进行校正，得到校正后的信道频域响应矩阵；根据校正后的信道频域响应矩阵，获取ToF谱数据。
在一个实施例中，信道估计单元1412，具体用于获取定位信号占用各个子带的定位序列；利用各子带的定位序列构建定位序列矩阵；以定位序列矩阵中的每个元素作为主对角线元素获得对角矩阵以用于信道估计。获取单元1413具体用于测量通道幅相响应矩阵作为通道校正系数。
关于定位参数确定装置的具体限定可以参见上文中对于定位参数确定方法的限定，在此不再赘述。在上述定位参数确定方法的实施例阐述的技术特征及其有益效果均适用于定位参数确定装置的实施例中，具体内容可参见本申请定位参数确定方法实施例中的叙述。
上述定位参数确定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，提供了一种计算机设备，该计算机设备可以是终端，其内部结构图可以如图16所示。该计算机设备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，该计算机设备的处理器用于提供计算和控制能力。该计算机设备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。该计算机设备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、运营商网络、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参数确定方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
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，提供了一种计算机设备，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上述各方法实施例中的步骤。
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上述各方法实施例中的步骤。
上述计算机设备和计算机可读存储介质实施例中所实现的步骤与前述定位参数确定方法的步骤对应，在上述定位参数确定方法的实施例阐述的技术特征及其有益效果均适用于计算机设备和计算机可读存储介质的实施例中，具体限定可以参见上文中对于定位参数确定方法的限定，在此不再赘述。
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，所述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、存储、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、闪存或光存储器等。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。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。
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。
以上所述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，本申请专利的保护范围应以所附权利要求为准。

Claims (21)

  1. 一种定位参数确定方法，包括：
    根据待定位终端发送的多通道的定位信号，确定所述定位信号的飞行时间ToF谱数据；
    根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；所述空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对所述定位信号的响应；所述天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差；
    根据所述ToF谱数据和所述修正后的空域流型矩阵，确定所述定位信号的直达径的定位参数；所述直达径为所述待定位终端到所述天线阵列的最短路径。
  2. 根据权利要求1所述的方法，其中，所述天线阵列偏差函数的构建过程包括：
    获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值集合，和所述模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值集合；
    根据所述幅度测量值集合，构建所述幅度方向图函数；以及根据所述相位测量值集合，构建所述相位偏差函数；
    根据所述幅度方向图函数和所述相位偏差函数，确定所述天线阵列偏差函数。
  3. 根据权利要求1所述的方法，其中，所述理想的空域流型矩阵包括粗理想空域流型矩阵和细理想空域流型矩阵；所述粗理想空域流型矩阵中每个元素表示天线阵列中各阵元在第一预设的相应角度范围对所述定位信号的响应；所述细理想空域流型矩阵中每个元素表示天线阵列中各阵元在第二预设的相应角度范围对所述定位信号的响应；所述第一预设的相应角度范围大于所述第二预设的相应角度范围。
  4. 根据权利要求3所述的方法，其中，所述定位参数包括到达角度AoA和ToF；所述根据所述ToF谱数据和所述修正后的空域流型矩阵，确定所述定位信号的直达径的定位参数，包括：
    根据所述天线阵列偏差函数，对所述粗理想空域流型矩阵进行修正，得到修正后的粗空域流型矩阵；
    根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述细理想空域流型矩阵和所述直达径对应的ToF；
    根据所述天线阵列偏差函数，对所述细理想空域流型矩阵进行修正，得到修正后的细空域流型矩阵；
    采用预设的角度函数，根据所述修正后的细空域流型矩阵和所述直达径对应的ToF，确定所述直达径的AoA。
  5. 根据权利要求4所述的方法，其中，所述根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述细理想空域流型矩阵，包括：
    根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述定位信号各路径的ToF、各路径的参考AoA、各路径的衰减系数；
    根据各所述路径的衰减系数和各所述路径的ToF，从各所述路径中确定直达径；
    对所述直达径对应的参考AoA进行划分，得到所述第二预设的相应角度范围；
    根据所述第二预设的相应角度范围确定所述细理想空域流型矩阵。
  6. 根据权利要求5所述的方法，其中，所述根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述直达径对应的ToF，包括；
    根据各所述路径的ToF以及各所述路径的衰减系数，确定所述直达径对应的ToF。
  7. 根据权利要求5所述的方法，其中，所述根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述定位信号各路径的ToF、各路径的参考AoA、各路径的衰减系数，包括：
    根据所述修正后的粗空域流型矩阵和所述ToF谱数据确定二维定位参数谱数据；
    根据所述二维定位参数谱数据进行谱峰提取，得到所述定位信号各所述路径的ToF、各所述路径的参考AoA、各所述路径的衰减系数。
  8. 根据权利要求1所述的方法，其中，所述根据待定位终端发送的多通道的定位信号，确定所述定位信号的飞行时间ToF谱数据，包括：
    对所述待定位终端发送的多通道的定位信号进行傅里叶变换，得到多通道频域信号；
    对所述多通道频域信号进行信道估计，得到信道频域响应矩阵；
    基于所述信道频域响应矩阵，获取所述ToF谱数据。
  9. 根据权利要求8所述的方法，其中，所述基于所述信道频域响应矩阵，获取所述ToF谱数据， 包括：
    获取通道校正系数，根据所述通道校正系数对所述信道频域响应矩阵进行校正，得到校正后的信道频域响应矩阵；
    根据所述校正后的信道频域响应矩阵，获取所述ToF谱数据。
  10. 根据权利要求9所述的方法，其中，对所述多通道频域信号进行信道估计，包括：
    获取所述定位信号占用各个子带的定位序列；
    利用各所述子带的定位序列构建定位序列矩阵；
    以所述定位序列矩阵中的每个元素作为主对角线元素获得对角矩阵以用于进行信道估计；并且
    获取通道校正系数包括：测量通道幅相响应矩阵作为所述通道校正系数。
  11. 一种定位参数确定装置，包括：
    第一确定模块，用于根据待定位终端发送的多通道的定位信号，确定所述定位信号的飞行时间ToF谱数据；
    修正模块，用于根据预设的天线阵列偏差函数，对理想的空域流型矩阵进行修正，得到修正后的空域流型矩阵；所述空域流型矩阵中每个元素表示天线阵列中各阵元在预设的相应角度范围对所述定位信号的响应；所述天线阵列偏差函数表征真实的天线阵列对信号的响应和理想的天线阵列对信号的响应之间的偏差；
    第二确定模块，用于根据所述ToF谱数据和所述修正后的空域流型矩阵，确定所述定位信号的直达径的定位参数；所述直达径为所述待定位终端到所述天线阵列的最短路径。
  12. 根据权利要求11所述的装置，还包括：
    模拟参数集合模块，用于获取模拟的真实信号到达天线阵列各阵元的幅度方向图的幅度测量值集合，和所述模拟的真实信号到达天线阵列各阵元的相位偏差的相位测量值集合；
    构建偏差函数模块，根据所述幅度测量值集合，构建所述幅度方向图函数；以及根据所述相位测量值集合，构建所述相位偏差函数；根据所述幅度方向图函数和所述相位偏差函数，确定所述天线阵列偏差函数。
  13. 根据权利要求11所述的装置，其中，所述理想的空域流型矩阵包括粗理想空域流型矩阵和细理想空域流型矩阵；所述粗理想空域流型矩阵中每个元素表示天线阵列中各阵元在第一预设的相应角度范围对所述定位信号的响应；所述细理想空域流型矩阵中每个元素表示天线阵列中各阵元在第二预设的相应角度范围对所述定位信号的响应；所述第一预设的相应角度范围大于所述第二预设的相应角度范围。
  14. 根据权利要求13所述的装置，其中，定位参数包括到达角度AoA和飞行时间ToF；
    所述第二确定模块包括：
    第一修正单元，用于根据所述天线阵列偏差函数，对所述粗理想空域流型矩阵进行修正，得到修正后的粗空域流型矩阵；
    第一确定单元，用于根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述细理想空域流型矩阵和所述直达径对应的ToF；
    第二修正单元，用于根据所述天线阵列偏差函数，对所述细理想空域流型矩阵进行修正，得到修正后的细空域流型矩阵；
    第二确定单元，用于采用预设的角度函数，根据所述修正后的细空域流型矩阵和所述直达径对应的ToF，确定所述直达径的AoA。
  15. 根据权利要求14所述的装置，其中，所述第一确定单元还用于：
    根据所述修正后的粗空域流型矩阵和所述ToF谱数据，确定所述定位信号各路径的ToF、各路径的参考AoA、各路径的衰减系数；
    根据各所述路径的衰减系数和各所述路径的ToF，从各所述路径中确定直达径；
    对所述直达径对应的参考AoA进行划分，得到所述第二预设的相应角度范围；
    根据所述第二预设的相应角度范围确定所述细理想空域流型矩阵；
    根据各所述路径的ToF以及各所述路径的衰减系数，确定所述直达径对应的ToF。
  16. 根据权利要求15所述的装置，其中，所述第一确定单元还用于：
    根据所述修正后的粗空域流型矩阵和所述ToF谱数据确定二维定位参数谱数据；
    根据所述二维定位参数谱数据进行谱峰提取，得到所述定位信号各所述路径的ToF、各所述路径的参考AoA、各所述路径的衰减系数。
  17. 根据权利要求11所述的装置，其中，所述第一确定模块包括：
    时频变换单元，用于对所述待定位终端发送的多通道的定位信号进行傅里叶变换，得到多通道频域信号；
    信道估计单元，用于对所述多通道频域信号进行信道估计，得到信道频域响应矩阵；
    获取单元，用于基于所述信道频域响应矩阵，获取所述ToF谱数据。
  18. 根据权利要求17所述的装置，其中，所述获取单元还用于：
    获取通道校正系数，根据所述通道校正系数对所述信道频域响应矩阵进行校正，得到校正后的信道频域响应矩阵；
    根据所述校正后的信道频域响应矩阵，获取所述ToF谱数据。
  19. 根据权利要求18所述的装置，其中，所述信道估计单元还用于：
    获取所述定位信号占用各个子带的定位序列；
    利用各所述子带的定位序列构建定位序列矩阵；
    以所述定位序列矩阵中的每个元素作为主对角线元素获得对角矩阵以用于进行信道估计；并且
    所述获取单元还用于：测量通道幅相响应矩阵作为所述通道校正系数。
  20. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其中，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。
  21. 一种计算机可读存储介质，其上存储有计算机程序，其中，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。
PCT/CN2021/139679 2021-08-03 2021-12-20 定位参数确定方法、装置、设备和存储介质 WO2023010763A1 (zh)

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