CN115623589B - Arrival time estimation method, apparatus, device, storage medium, and program product - Google Patents

Arrival time estimation method, apparatus, device, storage medium, and program product Download PDF

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CN115623589B
CN115623589B CN202211231333.6A CN202211231333A CN115623589B CN 115623589 B CN115623589 B CN 115623589B CN 202211231333 A CN202211231333 A CN 202211231333A CN 115623589 B CN115623589 B CN 115623589B
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channel estimation
frequency domain
domain channel
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autocorrelation matrix
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CN115623589A (en
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曾鹏飞
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Shanghai Xingsi Semiconductor Co ltd
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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
    • 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
    • G01S5/0018—Transmission from mobile station to base station
    • G01S5/0027—Transmission from mobile station to base station of actual mobile position, i.e. position determined on mobile
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/04—Position of source determined by a plurality of spaced direction-finders
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • 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
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Mathematical Physics (AREA)
  • Auxiliary Devices For Music (AREA)

Abstract

The application provides a time-of-arrival estimation method, an apparatus, a device, a storage medium, and a program product. The method comprises the following steps: acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal; splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors; determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors; and obtaining the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm. According to the method and the device, the frequency domain channel estimation vector is split into the plurality of sub-frequency domain channel estimation vectors, the autocorrelation matrix is determined according to the plurality of sub-frequency domain channel estimation vectors, the dimension of the autocorrelation matrix is reduced, and then the calculation complexity of the MUSIC algorithm is reduced, so that the requirement on hardware performance is reduced.

Description

Arrival time estimation method, apparatus, device, storage medium, and program product
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for estimating arrival time.
Background
In a long term evolution (Long Term Evolution, LTE)/new air interface (NR) network, compared to the positioning accuracy requirement of about 10m for positioning reference signals (Positioning Reference Signal, PRS) in LTE, the requirement of NR for positioning is directly raised to about 0.5m indoors.
As the user's demand for positioning is increasing, positioning through NR is widely used. In the NR network, the multiple signal classification (Multiple Signal Classification, MUSIC) algorithm has the characteristic of high positioning precision, but the MUSIC algorithm has higher complexity and very high requirement on hardware performance.
Disclosure of Invention
An objective of the embodiments of the present application is to provide a method, an apparatus, a device, a storage medium and a program product for estimating arrival time, which are used for reducing the computational complexity of positioning and further reducing the requirement on hardware performance.
In a first aspect, an embodiment of the present application provides a method for estimating an arrival time, including: acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal; splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors; determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors; based on the MUSIC algorithm, the arrival time of the signal is obtained according to the autocorrelation matrix.
According to the method and the device, the frequency domain channel estimation vector is split into the plurality of sub-frequency domain channel estimation vectors, the autocorrelation matrix is determined according to the plurality of sub-frequency domain channel estimation vectors, the dimension of the autocorrelation matrix is reduced, the calculation complexity of the MUSIC algorithm is further reduced, and therefore the requirement on hardware performance is reduced.
In any embodiment, splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors comprises:
dividing the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors by adopting a mode of extracting channel estimation values from the frequency domain channel estimation vector at intervals to form the sub-frequency domain channel estimation vector;
or alternatively, the first and second heat exchangers may be,
splitting the frequency domain channel estimation vector by adopting a preset window and a preset step length to obtain a plurality of sub-frequency domain channel estimation vectors; the sub-frequency domain channel estimation vector is obtained by extracting channel estimation values from frequency domain channel estimation vectors corresponding to a preset window according to preset intervals, and the preset intervals are smaller than the length of the preset window.
According to the method and the device for obtaining the sub-frequency domain channel estimation vectors, the frequency domain channel estimation vectors are split in a space sampling mode or a preset window mode, so that the sub-frequency domain channel estimation vectors are obtained, and complexity in subsequent calculation is reduced.
In any embodiment, determining the autocorrelation matrix from the plurality of sub-frequency domain channel estimation vectors comprises:
constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector; an autocorrelation matrix is obtained from the plurality of intermediate autocorrelation matrices.
In the embodiment of the present application, since the dimension of the sub-frequency domain channel estimation vector is reduced in advance, the dimension of the autocorrelation matrix obtained according to the corresponding intermediate autocorrelation matrix is also relatively smaller, so that the subsequent calculation complexity is reduced.
In any embodiment, based on the MUSIC algorithm, obtaining the arrival time of the signal according to the autocorrelation matrix includes:
determining a scanning time interval according to the frequency domain channel estimation vector;
determining a MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval;
the arrival time of the signal is obtained based on MUSIC spatial spectrum.
The autocorrelation matrix in the embodiment of the application is subjected to dimension reduction, so that the workload of the MUSIC algorithm is reduced.
In any embodiment, determining the scan time interval from the frequency domain channel estimation vector includes:
acquiring a power time delay spectrum corresponding to a frequency domain channel estimation vector;
and determining a scanning time interval corresponding to the spectrum peak from the power time delay spectrum.
According to the method and the device for determining the scanning time interval through the power time delay spectrum, the scanning range can be reduced, the occurrence of false peaks can be avoided, and the calculation complexity of the MUSIC algorithm is further reduced.
In any embodiment, the determining, from the power delay spectrum, a scan time interval corresponding to a peak of the spectrum includes: and determining a time point and a preset time length corresponding to each spectrum peak in the power time delay spectrum, and taking a time interval with the preset time length containing the time point as a scanning time interval corresponding to the spectrum peak.
The embodiment of the application comprises the time interval with the preset time length of the time point as the scanning time interval corresponding to the spectrum peak, so that the scanning time of the MUSCI algorithm is reduced, the possibility of occurrence of a false peak is reduced, and the true peak is determined through the fuzzy position.
In any embodiment, the taking the time interval including the preset time length of the time point as the scanning time interval corresponding to the spectrum peak includes: taking a time interval with a preset time length taking the time point as a midpoint as a scanning time interval corresponding to the spectrum peak.
According to the embodiment of the application, the time interval with the time point as the midpoint and the preset time length is used as the scanning time interval corresponding to the spectrum peak, so that the scanning time of the MUSCI algorithm is reduced, the possibility of occurrence of a false peak is reduced, and the true peak is determined through the fuzzy position.
In any embodiment, the preset time length is determined according to the dimension of the autocorrelation matrix and the subcarrier interval corresponding to the frequency domain signal.
According to the method and the device, the preset time length is determined through the dimension of the autocorrelation matrix and the subcarrier interval corresponding to the frequency domain signal, so that the scanning time interval determined according to the preset time length comprises the peak value of the whole spectrum peak.
In any embodiment, the determining the autocorrelation matrix based on the plurality of sub-frequency domain channel estimation vectors includes: constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector; an autocorrelation matrix is obtained from the plurality of intermediate autocorrelation matrices.
The autocorrelation matrix in the embodiment of the application is a matrix subjected to dimension reduction, and the calculated amount of the MUSIC algorithm is greatly reduced by using the autocorrelation matrix.
In any embodiment, the determining the MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval includes: performing characteristic decomposition on the autocorrelation matrix to obtain a noise subspace; and determining the MUSIC spatial spectrum according to the noise subspace and the scanning time interval.
In the embodiment of the application, since the autocorrelation matrix is a matrix after dimension reduction, the calculation amount for performing feature decomposition and determining the MUSIC time spectrum is greatly reduced.
In any embodiment, the power delay spectrum corresponding to the frequency domain channel estimation vector includes: performing inverse discrete Fourier transform on the frequency domain channel estimation vector to obtain a time domain channel impulse response; and obtaining a power delay spectrum according to the time domain channel impulse response.
According to the embodiment of the application, the scanning time interval is determined by utilizing the power time delay spectrum, and the scanning is performed in the scanning time interval, so that the scanning time is reduced, and the probability of occurrence of false peaks is further reduced.
In a second aspect, an embodiment of the present application provides an arrival time estimation apparatus, including: the acquisition module is used for acquiring the frequency domain channel estimation vector and a power time delay spectrum corresponding to the frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal; the vector splitting module is used for splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors; the matrix determining module is used for determining an autocorrelation matrix according to the plurality of sub-frequency domain channel estimation vectors; and the arrival time estimation module is used for obtaining the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm.
In a third aspect, an embodiment of the present application provides an electronic device, including: the device comprises a processor, a memory and a bus, wherein the processor and the memory are communicated with each other through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the method of the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium comprising: the non-transitory computer readable storage medium stores computer instructions that cause a computer to perform the method of the first aspect.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for estimating arrival time according to an embodiment of the present application;
fig. 2 is a schematic diagram of a power delay spectrum according to an embodiment of the present application;
Fig. 3 is a schematic diagram of another arrival time estimation flow provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an arrival time estimation apparatus according to an embodiment of the present application;
fig. 5 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present application.
Detailed Description
Currently, according to the basic requirements of NR, for a system with 50M bandwidth, combN, symbol num=n, the number of REs is 270×12=3240; when estimating the arrival time by using the MUSIC algorithm, if the autocorrelation matrix is calculated according to the number of REs, the dimension of the autocorrelation matrix is 3240×3240, and in the MUSIC algorithm, the autocorrelation matrix is subjected to feature decomposition, so that the calculation amount is very large, and the requirement on hardware performance is high in the face of the large calculation amount.
In order to solve the above technical problems, embodiments of the present application provide a method for estimating arrival time, and related concepts related to the embodiments of the present application are described:
observed time difference of arrival (Observed Time Difference of Arrival, OTDOA): OTDOA is a technique for locating based on the time difference of signal propagation between 3 base stations and a mobile terminal. OTDOA calculates the distance difference between a mobile terminal and any two base stations by measuring the difference in propagation time of wireless signals (TDOA, time Difference Of Arrival) from the UE to the base stations.
Non-blind channel estimation requires channel estimation using pilot sequences known to both the base station and the receiver and using different time-frequency domain interpolation techniques to estimate the channel response on sub-carriers between pilots or between symbols. Non-blind channel estimation mainly includes Least Squares (LS) channel estimation, minimum Mean Square Error (MMSE) channel estimation, DFT-based channel estimation, decision feedback-based channel estimation, and the like.
LS channel estimation: a channel estimation method according to a least squares criterion.
MUSIC algorithm: the basic principle is that the covariance matrix of the output data of the antenna array of the terminal is subjected to characteristic decomposition to obtain a signal subspace corresponding to the signal component and a noise subspace orthogonal to the signal component, and then the orthogonality of the two subspaces is utilized to realize the estimation of the arrival time of the signal.
Embodiments of the technical solutions of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present application, and thus are only examples, and are not intended to limit the scope of protection of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions.
In the description of the embodiments of the present application, the technical terms "first," "second," etc. are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, which means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In the description of the embodiments of the present application, the term "plurality" refers to two or more (including two), and similarly, "plural sets" refers to two or more (including two), and "plural sheets" refers to two or more (including two).
In the description of the embodiments of the present application, the orientation or positional relationship indicated by the technical terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationship shown in the drawings, and are merely for convenience of describing the embodiments of the present application and for simplifying the description, rather than indicating or implying that the apparatus or element referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the embodiments of the present application.
In the description of the embodiments of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; or may be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to the specific circumstances.
A terminal in the embodiments of the present application may refer to various forms of UE, access terminal, subscriber unit, subscriber station, mobile station, MS (english: mobile station), remote station, remote terminal, mobile device, user terminal, terminal device (english: terminal equipment), wireless communication device, user agent, or user apparatus. The terminal device may also be a cellular phone, a cordless phone, a SIP (english: session initiation protocol, chinese: session initiation protocol) phone, a WLL (english: wireless local loop, chinese: wireless local loop) station, a PDA (english: personal digital assistant, chinese: personal digital processing), a handheld device with wireless communication functionality, a computing device or other processing device connected to a wireless modem, a car-mounted device, a wearable device, a terminal device in a future 5G network or a terminal device in a future evolved PLMN (english: public land mobile network, chinese: public land mobile network), etc., which the embodiments of the present application are not limited.
Fig. 1 is a schematic flow chart of a method for estimating arrival time according to an embodiment of the present application, as shown in fig. 1, where the method includes:
Step 101: acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal.
In this step, after receiving the signal in the time domain, the terminal converts the signal into a frequency domain signal and performs channel estimation on the frequency domain signal to obtain a frequency domain channel estimation vector.
Step 102: the frequency domain channel estimation vector is split into a plurality of sub-frequency domain channel estimation vectors.
After obtaining the frequency domain channel estimation vector, the terminal splits the frequency domain channel estimation vector, i.e. splits the high-dimensional frequency domain channel estimation vector into a plurality of low-dimensional sub-frequency domain channel estimation vectors. It should be noted that when splitting the frequency domain channel estimation vector, there are various splitting methods, for example: the frequency domain channel estimation vector may be split in a manner of extracting one of every preset number, or may be split in a manner of sliding a window, which is not particularly limited in the embodiment of the present application. In addition, the larger the dimension of the sub-frequency domain channel estimation vector obtained after splitting is, the larger the subsequent calculated amount is, and the corresponding calculation accuracy is also more accurate; the smaller the dimension of the sub-frequency domain channel estimation vector obtained after splitting is, the smaller the subsequent calculated amount is, and the corresponding calculation accuracy is lower. Therefore, the dimension of the sub-frequency domain channel estimation vector can be determined according to the actual situation.
Step 103: an autocorrelation matrix is determined based on a plurality of the sub-frequency domain channel estimation vectors.
After obtaining a plurality of sub-frequency domain channel estimation vectors, the terminal constructs a corresponding matrix according to each sub-frequency domain channel estimation vector, and obtains an autocorrelation matrix according to the constructed matrix.
Step 104: based on the MUSIC algorithm, the arrival time of the signal is obtained according to the autocorrelation matrix.
After obtaining the autocorrelation matrix, the terminal performs feature decomposition on the autocorrelation matrix according to the MUSIC algorithm to obtain a noise subspace, and determines the arrival time of the signal based on the noise subspace. It is understood that the MUSIC algorithm is an existing algorithm, and will not be described here. The arrival time of the signal refers to the arrival time of the time domain signal received by the terminal.
According to the method and the device, the frequency domain channel estimation vector is split into the plurality of sub-frequency domain channel estimation vectors, the autocorrelation matrix is determined according to the plurality of sub-frequency domain channel estimation vectors, the dimension of the autocorrelation matrix is reduced, the calculation complexity of the MUSIC algorithm is further reduced, and therefore the requirement on hardware performance is reduced.
On the basis of the foregoing embodiment, the splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors includes:
Splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors by adopting a mode of extracting channel estimation values from the frequency domain channel estimation vector at intervals to form the sub-frequency domain channel estimation vector;
or alternatively, the first and second heat exchangers may be,
splitting the frequency domain channel estimation vector by adopting a preset window and a preset step length to obtain a plurality of sub-frequency domain channel estimation vectors; the sub-frequency domain channel estimation vector is obtained by extracting channel estimation values from the frequency domain channel estimation vector corresponding to a preset window according to a preset interval, and the preset interval is smaller than the length of the preset window.
The embodiment of the application provides three methods for splitting frequency domain channel estimation vectors, and the method is described for each splitting method:
first kind: interval taking. The specific splitting mode is to take one number from the frequency domain channel estimation vectors in a preset number per interval, and the taken number forms a sub-frequency domain channel estimation vector. After the point separation is completed, the frequency domain channel estimation vector can be split into a plurality of sub-frequency domain channel estimation vectors. For easy understanding, the frequency domain channel estimation vector is used in the embodiment of the present application: [1 2 3 4 5 6 7 8 9], the preset number of intervals is 3, and the sub-frequency domain channel estimation vectors obtained by splitting are respectively: [1 4 7],[2 5 8],[3 6 9]. It should be noted that, in practical application, the dimension of the frequency domain channel estimation vector is 3240, the preset number of intervals may be 82, or other numbers capable of dividing 3240 completely, the larger the preset number of intervals is, the smaller the dimension of the obtained sub-frequency domain channel estimation vector is, and further, the smaller the calculation amount of the MUSIC algorithm is, and the lower the accuracy of the arrival time obtained by the corresponding subsequent estimation is. Conversely, if the preset number of intervals is smaller, the dimension of the obtained sub-frequency domain channel estimation vector is larger, the calculated amount of the MUSIC algorithm is larger, and the accuracy of estimating the obtained arrival time is higher. Therefore, the accuracy of the hardware performance and the arrival time can be balanced in determining the preset number of intervals.
Second kind: splitting according to a preset window and a preset step length, wherein the preset interval is 1. The preset window is used for representing the quantity of pre-frame channel estimation values in the frequency domain channel estimation vector; the preset interval is the number of intervals at which the final channel estimate is extracted from the channel estimates selected in the block. The preset step size is the length of each sliding of the window. For example: the preset window is 3, the preset step length is 3, the preset interval is 1, and the frequency domain channel estimation vector is: [1 2 3 4 5 6 7 8 9. ], then the sub-frequency domain channel estimation vectors obtained after splitting are respectively: [1 2 3],[4 5 6],[7 8 9],....
Thirdly, splitting according to a preset window and a preset step length, wherein the preset interval is N, and N is an integer greater than 1. It can be appreciated that the definition of the preset window, the preset step size and the preset interval refers to the second splitting method, which is not described herein. For example: the preset window is 7, the preset step length is 1, the preset interval is 3, and the frequency domain channel estimation vector is: [1 2 3 4 5 6 7 8 9 10. ], then after framing according to a preset window, the following can be obtained: [1 2 3 4 5 6 7],[2 3 4 5 6 7 8],[3 4 5 6 7 8 9],.... And extracting again according to the preset interval 3 for [1 2 3 4 5 6 7] to obtain a sub-frequency domain channel estimation vector [1 4 ] of the channel. Similarly, the vectors selected by other frames may be extracted according to the preset interval 3, and the obtained sub-frequency domain channel estimation vectors are respectively: [2 5 8],[3 6 9],[4 7 10],....
According to the method and the device for obtaining the sub-frequency domain channel estimation vectors, the frequency domain channel estimation vectors are split in a space sampling mode or a preset window mode, so that the sub-frequency domain channel estimation vectors are obtained, and complexity in subsequent calculation is reduced.
On the basis of the foregoing embodiment, the determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors includes:
constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector;
and obtaining the autocorrelation matrix according to a plurality of intermediate autocorrelation matrices.
In a specific implementation process, after obtaining a plurality of sub-frequency domain channel estimation vectors, the terminal constructs a corresponding intermediate autocorrelation matrix for each sub-frequency domain channel estimation vector. A final autocorrelation matrix is then obtained using the plurality of intermediate autocorrelation matrices. Specifically, the autocorrelation matrix can be obtained by adding up corresponding elements of a plurality of intermediate autocorrelation matrices, or averaging the added up elements.
In the embodiment of the present application, since the dimension of the sub-frequency domain channel estimation vector is reduced in advance, the dimension of the autocorrelation matrix obtained according to the corresponding intermediate autocorrelation matrix is also relatively smaller, so that the subsequent calculation complexity is reduced.
On the basis of the foregoing embodiment, the MUSIC-based algorithm obtains the arrival time of the signal according to the autocorrelation matrix, including:
determining a scanning time interval according to the frequency domain channel estimation vector;
determining a MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval;
and obtaining the arrival time of the signal based on the MUSIC spatial spectrum.
In a specific implementation, the terminal determines the scan time interval according to the frequency domain channel estimation vector, and may generate a curve for characterizing a relationship among time, power and energy based on the frequency domain channel estimation vector, for example: may be a power delay profile. A scan time interval is determined based on the curve.
After obtaining the autocorrelation matrix and the scanning time interval, the terminal constructs a time scanning function according to the scanning time interval, and constructs a MUSIC spatial spectrum according to the time scanning function and the autocorrelation matrix, and it can be understood that the MUSIC spatial spectrum is a curve corresponding to a time point.
After the terminal obtains the MUSIC spatial spectrum corresponding to each time point, the position of the spectral peak can be determined from the MUSIC spatial spectrum, and the arrival time of the signal can be determined according to the position of the spectral peak.
On the basis of the above embodiment, determining a scanning time interval according to the frequency domain channel estimation vector includes:
Acquiring a power time delay spectrum corresponding to a frequency domain channel estimation vector; the power delay spectrum is obtained by calculating a frequency domain channel estimation vector by the terminal; and determining a scanning time interval corresponding to the spectrum peak from the power time delay spectrum.
Fig. 2 is a schematic diagram of a power delay spectrum provided in the embodiment of the present application, where peak positions on a power delay spectrum PDP may be considered as peak arrival times, so that the power delay spectrum may give a rough peak position, that is, a blurred peak position, and the blurred peak position may be used as a positioning reference for determining a scanning time interval. And intercepting a time interval containing a time point corresponding to the peak position from the PDP spectrum as a scanning time interval. Since a plurality of peaks may be included in the PDP spectrum, a plurality of scan time intervals may be acquired from the PDP spectrum. Compared with the whole time range as the scanning time region, the scanning is performed in the obtained scanning time interval, so that the scanning range can be reduced, and false peaks can be avoided.
According to the method and the device, the frequency domain channel estimation vector is split into the plurality of sub-frequency domain channel estimation vectors, the autocorrelation matrix is determined according to the plurality of sub-frequency domain channel estimation vectors, and the scanning time interval corresponding to the spectrum peak is determined according to the power time delay spectrum.
On the basis of the foregoing embodiment, the determining, from the power delay spectrum, a scan time interval corresponding to a peak includes:
and determining a time point and a preset time length corresponding to each spectrum peak in the power time delay spectrum, and taking a time interval with the preset time length containing the time point as a scanning time interval corresponding to the spectrum peak.
In a specific implementation, the preset time length is used to characterize the size of the scan time interval determined from the PDP spectrum. In determining the preset time length, it may be determined according to the MUSIC period size, that is, the preset time length is a value smaller than the MUSIC period, but the preset time length should be able to include a spectrum peak of the entire PDP spectrum.
After the preset time length is determined, taking a time interval with the preset time length containing the time point corresponding to the spectrum peak as a scanning time interval of the spectrum peak.
Based on the above embodiment, the MUSIC period may be determined according to the dimension of the autocorrelation matrix and the subcarrier interval corresponding to the frequency domain signal, specifically as shown in formula (1):
Figure BDA0003880583870000131
wherein T is a MUSIC period, m is the dimension of the autocorrelation matrix, and Δf is the subcarrier spacing.
The larger the MUSIC period is, the larger the scannable range is, and the preset time length may be a time period smaller than T, and after the preset time length is determined, a time interval corresponding to the preset time length including a time point corresponding to a peak value in the PDP spectrum is taken as a scanning time interval. It is understood that each scan time interval may include a spectral peak of at least one PDP spectrum.
In another embodiment, when determining the scan time interval, the time point corresponding to the peak may be taken as the midpoint of the scan time interval, so that the spectral peak is in the middle of the scan time interval.
The embodiment of the application comprises the time interval with the preset time length of the time point as the scanning time interval corresponding to the spectrum peak, so that the scanning time of the MUSCI algorithm is reduced, the possibility of occurrence of a false peak is reduced, and the true peak is determined through the fuzzy position.
On the basis of the above embodiment, determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors includes:
constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector;
corresponding elements in the plurality of intermediate autocorrelation matrices are added to obtain an autocorrelation matrix.
In a specific implementation process, a corresponding intermediate autocorrelation matrix can be constructed for each sub-frequency domain channel estimation vector, and it can be understood that a construction method of the intermediate autocorrelation matrix can be obtained according to a method for calculating an autocorrelation matrix in the prior art, which is not described herein in detail.
Since the dimensions of the channel estimation vectors of each sub-frequency domain are equal, the dimensions of the corresponding intermediate autocorrelation matrices are also equal. After obtaining intermediate autocorrelation matrixes corresponding to the channel estimation vectors of each sub-frequency domain respectively, the terminal adds the numerical values of the corresponding element positions in the intermediate autocorrelation matrixes to obtain the autocorrelation matrixes, wherein the autocorrelation matrixes are shown in a formula (2):
Figure BDA0003880583870000132
Wherein Rmm is an autocorrelation matrix; hls is the frequency domain channel estimation vector; hls ([ i: n2: n 1)]Is the ith intermediate autocorrelation matrix; n2 is at the beginning of the disassemblyThe preset number of intervals when the frequency domain channel estimation vector is divided; n1 is the dimension of the frequency domain channel estimation vector. The dimension of the autocorrelation matrix is
Figure BDA0003880583870000141
It can be understood that for the above method of taking the number of points, the element in the intermediate autocorrelation matrix is +.>
Figure BDA0003880583870000142
In another embodiment, when the autocorrelation matrix is obtained, the values of the positions of the corresponding elements in the intermediate autocorrelation matrix may also be obtained by adding up and then obtaining an average calculation.
The autocorrelation matrix in the embodiment of the application is a matrix subjected to dimension reduction, and the calculated amount of the MUSIC algorithm is greatly reduced by using the autocorrelation matrix.
On the basis of the foregoing embodiment, the determining a MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval includes:
performing feature decomposition on the autocorrelation matrix to obtain a noise subspace;
and determining the MUSIC spatial spectrum according to the noise subspace and the scanning time interval.
In a specific implementation process, after obtaining an autocorrelation matrix, a terminal performs feature decomposition on the autocorrelation matrix to obtain a signal subspace v= [ V ] 1 ,V 2 ,...,V D ]And noise subspace U n ＝[V D+1 ,V D+2 ,...,V m ]Where m is the dimension of the autocorrelation matrix. It can be appreciated that the method for performing feature decomposition on the autocorrelation matrix can be referred to in the prior art, and will not be described herein.
Let a = [ a (t 1), a (t 2), a..a (tn), ], where t1 to tn are the time ranges that need to be scanned, a (ti) is the scanning time function, i = 1,2, a..n. And, tn e { [ a1: b1], [ a2: b2], [ ak: bk ] }, where [ aj: bj ] represents a time interval corresponding to the j-th spectral peak, j=1, 2. Therefore, only the time interval corresponding to the position of the blurred peak may be scanned. It will be appreciated that the formula for the calculation of a (tn) is given in equation (4) below.
The MUSIC spatial spectrum function is shown in formula (3):
Figure BDA0003880583870000143
wherein P (t) is a MUSIC spatial spectrum function; a (t) is a time scanning function; u (U) n Is a noise subspace.
And (3) searching a spectrum peak according to the formula (3), and finding out a scanning time point corresponding to the maximum value of P (t), namely the arrival time. It will be appreciated that since the sweep time function is mutually orthogonal to the noise subspace, when the sweep time point coincides with the peak arrival time, a H (t)U n U n H a (t) will approach 0, with the corresponding P (t) being the maximum. Thus, the position of the spatial spectrum peak is the arrival time of the peak.
It will be appreciated that the time-sweep function is as shown in equation (4):
Figure BDA0003880583870000151
wherein Δf is the subcarrier spacing; m is the dimension of the autocorrelation matrix; t is the scanning time point.
In the embodiment of the application, since the autocorrelation matrix is a matrix after dimension reduction, the calculation amount for performing feature decomposition and determining the MUSIC time spectrum is greatly reduced.
On the basis of the above embodiment, the power delay profile may be obtained by the following method:
performing inverse discrete Fourier transform on the frequency domain channel estimation vector to obtain a time domain channel impulse response;
and obtaining a power delay spectrum according to the time domain channel impulse response.
In a specific implementation process, after receiving a frequency domain signal, a terminal performs LS channel estimation on the frequency domain signal to obtain a frequency domain channel estimation vector. Wherein, the frequency domain channel estimation vector can be obtained by the calculation of the formula (5):
Hls＝y*conj(RS) (5)
wherein Hls is a frequency domain channel estimation vector; y is a frequency domain signal received by the terminal; RS is a pilot signal corresponding to the frequency domain signal.
After obtaining Hls, inverse discrete fourier transform is performed on Hls, resulting in a time-domain channel impulse response Hcir, i.e., hcir=ifft (Hls).
The power delay profile PDP can be calculated according to equation (6):
PDP＝abs(Hcir)∧2 (6)
Fig. 3 is a schematic diagram of another arrival time estimation flow provided in an embodiment of the present application, as shown in fig. 3, and related descriptions of the method flow are as follows:
step 1: after receiving the frequency domain signal, the terminal carries out LS channel estimation on the frequency domain signal to obtain an HLS vector; (it will be appreciated that the pilot signal is known).
Step 2: performing inverse discrete Fourier transform on the channel matrix to obtain a time domain channel impulse response Hcir;
step 3: obtaining a power delay spectrum PDP according to Hcir;
step 4: acquiring a curve with a certain range of the peak position left and right in the PDP curve as a PDP fuzzy position;
step 5: performing dimension reduction processing on the channel matrix, and determining an autocorrelation matrix according to the dimension-reduced channel matrix;
step 6: performing eigenvalue decomposition on the autocorrelation matrix to obtain a signal subspace and a noise subspace;
step 7: determining a time scanning interval according to the PDP fuzzy position;
step 8: determining a spatial spectrum according to the time scanning interval, the signal subspace and the noise subspace;
step 9: arrival times are obtained from the spatial spectrum.
Fig. 4 is a schematic structural diagram of an apparatus for estimating arrival time according to an embodiment of the present application, where the apparatus may be a module, a program segment, or a code on an electronic device. It should be understood that the apparatus corresponds to the embodiment of the method of fig. 1 described above, and is capable of performing the steps involved in the embodiment of the method of fig. 1, and specific functions of the apparatus may be referred to in the foregoing description, and detailed descriptions thereof are omitted herein as appropriate to avoid redundancy. The device comprises: an acquisition module 401, a vector splitting module 402, a matrix determination module 403, and a time of arrival estimation module 404, wherein:
The acquisition module 401 is configured to acquire a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal;
the vector splitting module 402 is configured to split the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors;
the matrix determining module 403 is configured to determine an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors;
the arrival time estimation module 404 is configured to obtain the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm.
Based on the above embodiments, the vector splitting module 402 is specifically configured to:
splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors by adopting a mode of extracting channel estimation values from the frequency domain channel estimation vector at intervals to form the sub-frequency domain channel estimation vector;
or alternatively, the first and second heat exchangers may be,
splitting the frequency domain channel estimation vector by adopting a preset window and a preset step length to obtain a plurality of sub-frequency domain channel estimation vectors; the sub-frequency domain channel estimation vector is obtained by extracting channel estimation values from the frequency domain channel estimation vector corresponding to the preset window according to a preset interval, and the preset interval is smaller than the length of the preset window.
Based on the above embodiment, the matrix determining module 403 is specifically configured to:
constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector;
and obtaining the autocorrelation matrix according to a plurality of intermediate autocorrelation matrices.
Based on the above embodiments, the arrival time estimation module 404 is specifically configured to:
determining a scanning time interval according to the frequency domain channel estimation vector;
determining a MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval;
and obtaining the arrival time of the signal based on the MUSIC spatial spectrum.
Based on the above embodiments, the arrival time estimation module 404 is specifically configured to:
acquiring a power time delay spectrum corresponding to the frequency domain channel estimation vector;
and determining a scanning time interval corresponding to the spectrum peak from the power time delay spectrum.
Based on the above embodiments, the arrival time estimation module 404 is specifically configured to:
and determining a time point and a preset time length corresponding to each spectrum peak in the power time delay spectrum, and taking a time interval of the preset time length containing the time point as a scanning time interval corresponding to the spectrum peak.
Based on the above embodiments, the arrival time estimation module 404 is specifically configured to:
And taking the time interval of the preset time length with the time point as a midpoint as a scanning time interval corresponding to the spectrum peak.
On the basis of the above embodiment, the preset time length is determined according to the dimension of the autocorrelation matrix and the subcarrier interval corresponding to the frequency domain signal.
Based on the above embodiment, the matrix determining module 403 is specifically configured to:
constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector;
and obtaining the autocorrelation matrix according to corresponding elements in the plurality of intermediate autocorrelation matrices.
Based on the above embodiments, the arrival time estimation module 404 is specifically configured to:
performing characteristic decomposition on the autocorrelation matrix to obtain a signal subspace and a noise subspace;
and determining the MUSIC spatial spectrum according to the signal subspace, the noise subspace and the scanning time interval.
On the basis of the above embodiment, the obtaining module 401 is specifically configured to:
performing inverse discrete Fourier transform on the frequency domain channel estimation vector to obtain a time domain channel impulse response;
and obtaining the power delay spectrum according to the time domain channel impulse response.
Fig. 5 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present application, as shown in fig. 5, where the electronic device includes: a processor (processor) 501, a memory (memory) 502, and a bus 503; wherein, the liquid crystal display device comprises a liquid crystal display device,
The processor 501 and the memory 502 complete communication with each other via the bus 503;
the processor 501 is configured to invoke the program instructions in the memory 502 to perform the methods provided in the above method embodiments, for example, including: acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal; splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors; determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors; and obtaining the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm.
The processor 501 may be an integrated circuit chip having signal processing capabilities. The processor 501 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Which may implement or perform the various methods, steps, and logical blocks disclosed in embodiments of the present application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Memory 502 may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), and the like.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the above-described method embodiments, for example comprising:
acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal; splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors; determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors; and obtaining the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm.
The present embodiment provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above-described method embodiments, for example, including: acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal; splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors; determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors; and obtaining the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims (7)

1. A method of estimating time of arrival, comprising:
acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal;
splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors;
determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors;
acquiring the arrival time of the signal according to the autocorrelation matrix based on a MUSIC algorithm;
the determining an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors includes:
Constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector;
obtaining the autocorrelation matrix according to a plurality of intermediate autocorrelation matrices;
based on the MUSIC algorithm, obtaining the arrival time of the signal according to the autocorrelation matrix, including:
acquiring a power time delay spectrum corresponding to the frequency domain channel estimation vector;
determining a scanning time interval corresponding to a spectrum peak from the power time delay spectrum;
determining a MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval;
and obtaining the arrival time of the signal based on the MUSIC spatial spectrum.
2. The method of claim 1, wherein said splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors comprises:
splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors by adopting a mode of extracting channel estimation values from the frequency domain channel estimation vector at intervals to form the sub-frequency domain channel estimation vector;
or alternatively, the first and second heat exchangers may be,
splitting the frequency domain channel estimation vector by adopting a preset window and a preset step length to obtain a plurality of sub-frequency domain channel estimation vectors; the sub-frequency domain channel estimation vector is obtained by extracting channel estimation values from the frequency domain channel estimation vector corresponding to the preset window according to a preset interval, and the preset interval is smaller than the length of the preset window.
3. The method of claim 1, wherein determining a scan time interval corresponding to a spectral peak from the power delay profile comprises:
and determining a time point and a preset time length corresponding to each spectrum peak in the power time delay spectrum, and taking a time interval of the preset time length containing the time point as a scanning time interval corresponding to the spectrum peak.
4. A time of arrival estimation apparatus, comprising:
the acquisition module is used for acquiring a frequency domain channel estimation vector; the frequency domain channel estimation vector is obtained by carrying out channel estimation on the frequency domain signal;
the vector splitting module is used for splitting the frequency domain channel estimation vector into a plurality of sub-frequency domain channel estimation vectors;
a matrix determining module, configured to determine an autocorrelation matrix according to a plurality of the sub-frequency domain channel estimation vectors;
the arrival time estimation module is used for obtaining the arrival time of the signal according to the autocorrelation matrix based on the MUSIC algorithm;
the matrix determining module is specifically configured to:
constructing an intermediate autocorrelation matrix corresponding to each sub-frequency domain channel estimation vector;
obtaining the autocorrelation matrix according to a plurality of intermediate autocorrelation matrices;
The arrival time estimation module is specifically configured to:
acquiring a power time delay spectrum corresponding to the frequency domain channel estimation vector;
determining a scanning time interval corresponding to a spectrum peak from the power time delay spectrum;
determining a MUSIC spatial spectrum according to the autocorrelation matrix and the scanning time interval;
and obtaining the arrival time of the signal based on the MUSIC spatial spectrum.
5. An electronic device, comprising: a processor, a memory, and a bus, wherein,
the processor and the memory complete communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-3.
6. A non-transitory computer readable storage medium storing computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-3.
7. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the method of any of claims 1-3.
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