KR20130034095A - Apparatus and method estimating doa/toa of mobile signal - Google Patents
Apparatus and method estimating doa/toa of mobile signal Download PDFInfo
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- KR20130034095A KR20130034095A KR1020110097931A KR20110097931A KR20130034095A KR 20130034095 A KR20130034095 A KR 20130034095A KR 1020110097931 A KR1020110097931 A KR 1020110097931A KR 20110097931 A KR20110097931 A KR 20110097931A KR 20130034095 A KR20130034095 A KR 20130034095A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
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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
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- Position Fixing By Use Of Radio Waves (AREA)
Abstract
Description
The present invention relates to an apparatus and method for estimating the position of a mobile station based on a base station. The present invention relates to an apparatus and a method for estimating the position of a mobile device by estimating an arrival angle and an arrival time of a mobile signal.
For base station-based mobile location estimation, using the direction of arrival (DOA) and the time of arrival (TOA) from the mobile to the base station, The position can be calculated.
[Equation 1]
here,
Is the location coordinate of the mobile, Is the arrival angle when the mobile signal arrives, Is the arrival time when the mobile signal arrived, Is the speed of light.At this time, the base station simultaneously estimates the arrival angle and the arrival time information from the received signal, 2-D MULTIple SIgnal Classification (MUSIC), 2-D Matrix Enhancement Matrix Pencil (MEMP), and 2-D Estimation of Signal Parameters via Algorithms such as Rotational Invariance Technique are used. However, none of these algorithms satisfies both accuracy and complexity, which makes it difficult to implement.
1 shows an apparatus for estimating mobile location using a conventional 2-D MUSIC.
Referring to FIG. 1, an apparatus for estimating a mobile position includes a
The
The received signal x received from the mobile is a signal coming through K multipaths and may be expressed as Equation 2 below. If Equation 2 is expressed in a vector form, it may be expressed as Equation 3 below.
&Quot; (2) "
&Quot; (3) "
In Equations 2 and 3, m is
Is the antenna index where n is Sampling index, Is the arrival angle of the kth multipath, Is the arrival time of the kth multipath, Is the signal attenuation of the kth multipath, Is the distance between the antennas, Is the sampling rate, Distributed AWGN (Additive White Gaussian Noise), S is a matrix representation of the signal component, a is a vector representation of the signal attenuation component, w is a vector representation of the AWGN.The correlation matrix calculation of the correlation
&Quot; (4) "
here,
Is the correlation matrix of the received signal x, E {} is the average equation, Is a hermitian operation.The eigenvector decomposition unit (EVD) 120 receives the correlation matrix of the received signal x and receives an eigenvector corresponding to the noise region in the correlation matrix of the received signal x.
), And determine how many received signals (x) have been received through the multipath.The
[Equation 5]
here,
Is the spectral value, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, M is the number of antennas, N is the number of sampling, K is the number of multipaths via the received signal x, Is the eigenvector of the noise region, Is random , A signal vector including Is a hermitian operation.The
The apparatus for estimating the mobile position of FIG. 1 has high accuracy by processing two MN length signal vectors at once and estimating two parameters simultaneously.
The high dimensional EVD calculation of the correlation matrix and two-dimensional retrieval in the DOA-TOA region have the disadvantage of very high computational complexity.Embodiments of the present invention provide an apparatus and method for estimating the arrival angle / arrival time of a mobile signal.
Embodiments of the present invention provide an apparatus and method for separating received signals to reduce complexity and estimating arrival angle / arrival time of a low complexity mobile signal using a water-spectrum calculation using DFT.
An apparatus for estimating the arrival angle / arrival time of a mobile signal according to an embodiment of the present invention includes a separation unit for dividing a received signal into arrival angle vectors and arrival time vectors by using distinctive characteristics of arrival angle and arrival time. And a correlation matrix calculation unit and an eigenvector analysis (EVD) for calculating arrival correlation vectors and arrival time correlation matrices by calculating a correlation matrix of each of the arrival angle vectors and the arrival time vectors. Analyze the arrival angle noise eigenvectors and the arrival time noise eigenvectors, which are the eigenvectors corresponding to the noise region, in each of the arrival angle correlation matrices and the arrival time correlation matrices, and multi-path through the received signal. It includes an eigenvector analysis unit for identifying the number.
In this case, the apparatus for estimating the arrival angle / arrival time of the mobile signal may perform a Discrete Fourier transform (DFT) using the arrival angle noise eigenvectors, the arrival time noise eigenvectors, and the multipath number. A spectrum calculator for calculating spectrums of arrival angle vectors and spectra of arrival time vectors by calculating a pseudo-spectrum, and calculating the arrival angle spectrum by summing the spectra of the arrival angle vectors; A summation unit that calculates an arrival time spectrum by summing the spectra of the time vectors, a discrete spatial frequency of the arrival angle at which the value of the arrival angle spectrum is maximum, and an arrival at which the value of the arrival time spectrum is maximum A detector for detecting a discrete spatial frequency of time and the discrete spatial frequency of the arrival angle And further comprising a map for calculating the arrival angle and the arrival time corresponding to the spatial frequency by using a discrete time.
According to an embodiment of the present invention, a method of estimating an arrival angle / arrival time of a mobile signal includes: dividing the arrival angle vectors into arrival vectors and correlating each of the arrival angle vectors with the arrival time vectors. Calculating a correlation matrix and arrival time correlation matrices by calculating a matrix; and in each of the arrival angle correlation matrices and the arrival time correlation matrices through eigen vector analysis (EVD). Analyzing the arrival angle noise eigenvectors and the arrival time noise eigenvectors corresponding to the eigenvector and the eigenvector analysis to identify the number of multipaths received by the received signal.
In this case, the method of estimating the arrival angle / arrival time of the mobile signal may include a Discrete Fourier transform (DFT) using the arrival angle noise eigenvectors, the arrival time noise eigenvectors, and the multipath number. Computing the pseudo-spectrum used to calculate the spectra of the arrival angle vectors and the spectra of the arrival time vectors, summing the spectra of the arrival angle vectors to calculate the arrival angle spectrum, the arrival time vector Calculating a time-of-arrival spectrum by summing up the spectrums of each other, and a discrete spatial frequency of the time of arrival at which the value of the time-of-arrival spectrum is maximum and a discrete time of arrival at which the value of the time of arrival spectrum is maximum. Detecting a spatial frequency and a discrete ball of discrete spatial frequency of said arrival angle and said arrival time Calculating the arrival angle and the arrival time corresponding to the use frequencies further comprises.
BACKGROUND OF THE
1 shows an apparatus for estimating mobile location using a conventional 2-D MUSIC.
2 illustrates an apparatus for estimating mobile location according to an embodiment of the present invention.
3 is a flowchart illustrating estimating an arrival angle and an arrival time in an apparatus for estimating a mobile location according to an exemplary embodiment of the present invention.
4 is a graph showing an example when the spectra of arrival time vectors are accumulated and accumulated.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
2 illustrates an apparatus for estimating mobile location according to an embodiment of the present invention.
Referring to FIG. 2, the apparatus for estimating a mobile position includes a
As shown in Equation 3, the MN length signal vector that is the received signal x has a very high computational complexity depending on the number of antennas M and the number N of sampling times. The method for solving this complexity problem is to reduce the size of the received signal (x).
The separating
&Quot; (6) "
here,
Are arrival angle vectors separated from the received signal x, Are the arrival time vectors separated from the received signal x, M is the number of antennas and N is the number of samplings.The correlation
Eigen Vector Decomposition (EVD) 230 includes N arrival angle correlation matrices. ) And M arrival time correlation matrices (
), And the arrival angle correlation matrix ( ) And arrival time correlation matrix ( In each of them, the arrival angle noise eigenvectors ( ) And arrival time noise eigenvectors ) And determine how many K the received signal x has been received.The
The
Looking at the L point DFT expression, Equation 7 below.
In Mathematical 7, x [u] is reconstructed into L complex X [k] sequences, where L complex x [u] permutations are another permutation as inputs of the DFT operation.
[Equation 7]
Where k and l are discrete spatial frequencies, and u and w are discrete distances.
Arrival angle noise eigenvectors
) And arrival time noise eigenvectors Pseudo-spectrum calculation of the) can be expressed as shown in Equation 8 below.[Equation 8]
here,
Is the arrival angle noise eigenvector, Arrival time noise eigenvector, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, M is the number of antennas, N is the number of samplings, K is the number of multipaths received by the received signal, Is random A signal vector including Is random A signal vector including Is a hermitian operation.It can be seen that the exponential component of Equation 7 and the signal vector structure of Equation 8 are similar. Using this feature, it is possible to replace the signal vector part of the existing pseudo-spectrum with the DFT equation.
Wow Can be obtained as shown in Equation 9 below.&Quot; (9) "
here,
Is the arrival angle when the mobile signal arrives, Is the arrival time when the mobile signal arrived, Is the discrete spatial frequency of the arrival angle, Is the discrete spatial frequency of arrival time, L is the DFT length, d is the distance between antennas, Is the wavelength, Is the sampling rate.As shown in Equation 9, the k and l index values must be found to estimate the arrival angle and arrival time.
Accordingly, the
&Quot; (10) "
here,
Is the arrival angle noise eigenvector, Arrival time noise eigenvectors, M is the number of antennas, N is the number of samplings, K is the number of multipaths received by the received signal, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, DFT () is a function representing the discrete Fourier transform, Is the spectrum of the arrival angle vectors, Is the spectrum of arrival time vectors.Equation 10 reduces the complexity by DFT noise eigenvectors instead of multiplying the signal vector and noise eigenvector, which occupy high amounts of computation in the conventional pseudo-spectrum calculation.
The
The
&Quot; (11) "
here,
Is the spectrum of the arrival angle vectors, Is the spectrum of the arrival time vectors, M is the number of antennas, N is the number of samplings, Is an arrival angle spectrum obtained by summing the spectra of the N arrival angle vectors, Is a time-of-arrival spectrum that sums up the spectra of M time-of-arrival vectors.4 is a graph showing an example when the spectra of arrival time vectors are accumulated and accumulated.
Referring to FIG. 4, the solid line represents the spectrum of M arrival time vectors (
), And the dotted line indicates the arrival time spectrum ( ).When the circled portions of the solid lines accumulate, the dashed curve rises noticeably. This improves accuracy because the l-index needed to calculate arrival time can be better found in noisy environments. The sharp part is the cumulative spectrum value corresponding to the k and l indices we are looking for.
The
The
The
Hereinafter, a method of estimating arrival angle / arrival time of a mobile signal according to the present invention configured as described above will be described with reference to the accompanying drawings.
3 is a flowchart illustrating estimating an arrival angle and an arrival time in an apparatus for estimating a mobile location according to an exemplary embodiment of the present invention.
Referring to FIG. 3, in
In
In
In
In
In
In
The methods according to embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software.
As described above, the present invention has been described by way of limited embodiments and drawings, but the present invention is not limited to the above embodiments, and those skilled in the art to which the present invention pertains various modifications and variations from such descriptions. This is possible.
Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the equivalents of the claims, as well as the claims.
Claims (14)
A correlation matrix calculator for calculating a correlation matrix of each of the arrival angle vectors and the arrival time vectors and calculating arrival angle correlation matrices and arrival time correlation matrices; And
Eigen Vector Decomposition (EVD) analyzes the arrival angle noise eigenvectors and the arrival time noise eigenvectors, which are the eigenvectors corresponding to the noise region in each of the arrival angle correlation matrices and the arrival time correlation matrices. And an eigenvector analyzer for checking the number of multipaths received by the received signal.
A device for estimating the arrival angle / arrival time of a mobile signal.
The analysis unit,
Dividing the received signal into the arrival angle vectors and the arrival time vectors corresponding to a sampling number
A device for estimating the arrival angle / arrival time of a mobile signal.
A pseudo-spectrum using a Discrete Fourier transform (DFT) is calculated using the arrival angle noise eigenvectors, the arrival time noise eigenvectors, and the multipath number to obtain a pseudo-spectrum of the arrival angle vectors. A spectrum calculator for calculating spectra of the spectra and arrival time vectors;
A summing unit for calculating an arrival angle spectrum by summing the spectra of the arrival angle vectors, and calculating an arrival time spectrum by summing the spectra of the arrival time vectors;
A detector for detecting a discrete spatial frequency of the arrival angle at which the value of the arrival angle spectrum is maximum and a discrete spatial frequency of the arrival time at which the value of the arrival time spectrum is maximum; And
And a mapping unit configured to calculate a corresponding arrival angle and arrival time using the discrete spatial frequency of the arrival angle and the discrete spatial frequency of the arrival time.
A device for estimating the arrival angle / arrival time of a mobile signal.
The spectrum calculation unit,
Using Equation 12 below to calculate the spectra of the arrival angle vectors and the spectra of the arrival time vectors,
A device for estimating the arrival angle / arrival time of a mobile signal.
[Equation 12]
here, Is the arrival angle noise eigenvector, Arrival time noise eigenvectors, M is the number of antennas, N is the number of samplings, K is the number of multipaths received by the received signal, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, DFT () is a function representing the discrete Fourier transform, Is the spectrum of the arrival angle vectors, Is the spectrum of arrival time vectors.
The mapping unit,
Equation 13 is calculated by substituting the discrete spatial frequency of the arrival angle and the discrete spatial frequency of the arrival time.
A device for estimating the arrival angle / arrival time of a mobile signal.
&Quot; (13) "
here, Is the arrival angle when the mobile signal arrives, Is the arrival time when the mobile signal arrived, Is the discrete spatial frequency of the arrival angle, Is the discrete spatial frequency of arrival time, L is the DFT length, d is the distance between antennas, Is the wavelength, Is the sampling rate.
A spectrum for calculating a pseudo-spectrum using the arrival angle noise eigenvectors, the arrival time noise eigenvectors, and the multipath number to calculate spectra of arrival angle vectors and spectrums of arrival time vectors. A calculator;
A summing unit for calculating an arrival angle spectrum by summing the spectra of the arrival angle vectors, and calculating an arrival time spectrum by summing the spectra of the arrival time vectors; And
And a sensing unit configured to detect an arrival angle at which the value of the arrival angle spectrum is maximum and an arrival time at which the value of the arrival time spectrum is maximum.
A device for estimating the arrival angle / arrival time of a mobile signal.
The spectrum calculation unit,
Using Equation 14 below, the spectra of the arrival angle vectors and the spectra of the arrival time vectors are calculated.
A device for estimating the arrival angle / arrival time of a mobile signal.
&Quot; (14) "
here, Is the arrival angle noise eigenvector, Arrival time noise eigenvector, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, M is the number of antennas, N is the number of samplings, K is the number of multipaths received by the received signal, Is random A signal vector including Is random A signal vector including Is a hermitian operation.
Calculating arrival angle correlation matrices and arrival time correlation matrices by calculating a correlation matrix of each of the arrival angle vectors and the arrival time vectors;
Analyzing the arrival angle noise eigenvectors and the arrival time noise eigenvectors, which are eigenvectors corresponding to the noise region, in each of the arrival angle correlation matrices and the arrival time correlation matrices through eigenvector analysis (EVD) step; And
Determining the number of multipaths received by the received signal through the eigenvector analysis;
A method of estimating the arrival angle / arrival time of a mobile signal.
The step of dividing the arrival angle vectors and the arrival time vectors,
Dividing the received signal into the arrival angle vectors and the arrival time vectors corresponding to a sampling number
A method of estimating the arrival angle / arrival time of a mobile signal.
A pseudo-spectrum using a Discrete Fourier transform (DFT) is calculated using the arrival angle noise eigenvectors, the arrival time noise eigenvectors, and the multipath number to obtain a pseudo-spectrum of the arrival angle vectors. Calculating spectra of the spectra and arrival time vectors;
Calculating arrival angle spectrum by summing the spectra of the arrival angle vectors, and calculating arrival time spectrum by summing the spectra of the arrival time vectors;
Detecting a discrete spatial frequency of the arrival angle at which the value of the arrival angle spectrum is maximum and a discrete spatial frequency of the arrival time at which the value of the arrival time spectrum is maximum; And
Calculating a corresponding arrival angle and arrival time using the discrete spatial frequency of the arrival angle and the discrete spatial frequency of the arrival time.
A method of estimating the arrival angle / arrival time of a mobile signal.
Computing the spectra of the arrival angle vectors and the spectra of the arrival time vectors,
Calculated using Equation 15 below
A method of estimating the arrival angle / arrival time of a mobile signal.
&Quot; (15) "
here, Is the arrival angle noise eigenvector, Arrival time noise eigenvectors, M is the number of antennas, N is the number of samplings, K is the number of multipaths received by the received signal, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, DFT () is a function representing the discrete Fourier transform, Is the spectrum of the arrival angle vectors, Is the spectrum of arrival time vectors.
Computing the arrival angle and the arrival time,
It is calculated by substituting the discrete spatial frequency of the arrival angle and the discrete spatial frequency of the arrival time in Equation 16 below.
A method of estimating the arrival angle / arrival time of a mobile signal.
&Quot; (16) "
here, Is the arrival angle when the mobile signal arrives, Is the arrival time when the mobile signal arrived, Is the discrete spatial frequency of the arrival angle, Is the discrete spatial frequency of arrival time, L is the DFT length, d is the distance between antennas, Is the wavelength, Is the sampling rate.
Computing a pseudo-spectrum using the arrival angle noise eigenvectors, the arrival time noise eigenvectors, and the multipath number, and calculating the spectrums of the arrival angle vectors and the spectrums of the arrival time vectors. ;
Calculating arrival angle spectrum by summing the spectra of the arrival angle vectors, and calculating arrival time spectrum by summing the spectra of the arrival time vectors; And
Detecting an arrival angle at which the value of the arrival angle spectrum is maximum and an arrival time at which the value of the arrival time spectrum is maximum;
A method of estimating the arrival angle / arrival time of a mobile signal.
Computing the spectra of the arrival angle vectors and the spectra of the arrival time vectors,
Calculated using Equation 17 below
A method of estimating the arrival angle / arrival time of a mobile signal.
[Equation 17]
here, Is the arrival angle noise eigenvector, Arrival time noise eigenvector, Is the arrival angle when the mobile signal arrives, Is the arrival time of the mobile signal, M is the number of antennas, N is the number of samplings, K is the number of multipaths received by the received signal, Is random A signal vector including Is random A signal vector including Is a hermitian operation.
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
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WO2019095912A1 (en) * | 2017-11-16 | 2019-05-23 | 华南理工大学 | Underwater direction of arrival estimation method and device based on uniform linear array with adjustable angle |
CN110391820A (en) * | 2019-06-11 | 2019-10-29 | 东南大学 | A kind of Novel Communication method of reseptance for evading co-channel interference based on DFT |
CN111580048A (en) * | 2020-05-09 | 2020-08-25 | 中国科学院声学研究所 | Broadband sound source depth estimation method using single-vector hydrophone |
WO2024067730A1 (en) * | 2022-09-29 | 2024-04-04 | 中兴通讯股份有限公司 | Multi-target position classification wireless sensing method, device, and medium |
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2019095912A1 (en) * | 2017-11-16 | 2019-05-23 | 华南理工大学 | Underwater direction of arrival estimation method and device based on uniform linear array with adjustable angle |
CN110391820A (en) * | 2019-06-11 | 2019-10-29 | 东南大学 | A kind of Novel Communication method of reseptance for evading co-channel interference based on DFT |
CN111580048A (en) * | 2020-05-09 | 2020-08-25 | 中国科学院声学研究所 | Broadband sound source depth estimation method using single-vector hydrophone |
CN111580048B (en) * | 2020-05-09 | 2020-12-29 | 中国科学院声学研究所 | Broadband sound source depth estimation method using single-vector hydrophone |
WO2024067730A1 (en) * | 2022-09-29 | 2024-04-04 | 中兴通讯股份有限公司 | Multi-target position classification wireless sensing method, device, and medium |
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