KR20130034095A - Apparatus and method estimating doa/toa of mobile signal - Google Patents

Apparatus and method estimating doa/toa of mobile signal Download PDF

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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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South Korea
Prior art keywords
arrival
arrival time
vectors
arrival angle
angle
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KR1020110097931A
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Korean (ko)
Inventor
정수엽
오덕길
강준혁
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한국전자통신연구원
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Publication of KR20130034095A publication Critical patent/KR20130034095A/en

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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
    • 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
    • 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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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The present invention relates to an apparatus and method for estimating a mobile station based on a base station. The present invention relates to an apparatus and method for estimating a base station. Estimate arrival time.

Figure P1020110097931

Description

Apparatus and Method estimating DOA / TOA of Mobile signal for estimating arrival angle / arrival time of mobile signal

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]

Figure pat00001

here,

Figure pat00002
Is the location coordinate of the mobile,
Figure pat00003
Is the arrival angle when the mobile signal arrives,
Figure pat00004
Is the arrival time when the mobile signal arrived,
Figure pat00005
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 correlation matrix calculator 110, an eigenvector analyzer 120, a spectrum calculator 130, and a detector 140.

The correlation matrix calculator 110 calculates a correlation matrix using the signal x received from the mobile.

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) "

Figure pat00006

 &Quot; (3) "

Figure pat00007

In Equations 2 and 3, m is

Figure pat00008
Is the antenna index where n is
Figure pat00009
Sampling index,
Figure pat00010
Is the arrival angle of the kth multipath,
Figure pat00011
Is the arrival time of the kth multipath,
Figure pat00012
Is the signal attenuation of the kth multipath,
Figure pat00013
Is the distance between the antennas,
Figure pat00014
Is the sampling rate,
Figure pat00015
Distributed
Figure pat00016
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 matrix calculation unit 110 may be expressed by Equation 4 below.

&Quot; (4) "

Figure pat00017

here,

Figure pat00018
Is the correlation matrix of the received signal x, E {} is the average equation,
Figure pat00019
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.

Figure pat00020
), And determine how many received signals (x) have been received through the multipath.

The spectrum calculator 130 may generate a noise eigenvector (

Figure pat00021
) And the multi-path number (K) to calculate the pseudo-spectrum. In this case, the water-spectrum may be calculated as in Equation 5 below.

[Equation 5]

Figure pat00022

here,

Figure pat00023
Is the spectral value,
Figure pat00024
Is the arrival angle when the mobile signal arrives,
Figure pat00025
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,
Figure pat00026
Is the eigenvector of the noise region,
Figure pat00027
Is random
Figure pat00028
,
Figure pat00029
A signal vector including
Figure pat00030
Is a hermitian operation.

The sensing unit 140 receives a parameter having a value corresponding to a portion having the largest value as a result of the spectrum calculation of the spectrum calculating unit 130.

Figure pat00031
) And arrival time (
Figure pat00032
Estimate).

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.

Figure pat00033
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 INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for estimating a mobile station based on a base station. The present invention relates to a method for estimating the arrival angle / arrival time of a mobile signal having high accuracy and low complexity by separating received signals and using a water-spectrum calculation using DFT. Can be.

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 separator 210, a correlation matrix calculator 220, an eigenvector analyzer 230, a spectrum calculator 240, an adder 250, and a detector. 260 and a mapping unit 270.

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 unit 210 divides the received signal x into arrival angle vectors and arrival time vectors by using distinctive estimation characteristics of the arrival angle and the arrival time. The separation unit 210 may divide the received signal x as shown in Equation 6 below.

&Quot; (6) "

Figure pat00034

here,

Figure pat00035
Are arrival angle vectors separated from the received signal x,
Figure pat00036
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 matrix calculation unit 220 generates N arrival angle vectors corresponding to the number of sampling (

Figure pat00037
) Are computed from the correlation matrix, and the N arrival angle correlation
Figure pat00038
) And M arrival time vectors (
Figure pat00039
) Are computed by correlating the
Figure pat00040
). The correlation matrix calculator 220 may calculate the correlation matrix by using Equation 4.

Eigen Vector Decomposition (EVD) 230 includes N arrival angle correlation matrices. ) And M arrival time correlation matrices (

Figure pat00042
), And the arrival angle correlation matrix (
Figure pat00043
) And arrival time correlation matrix (
Figure pat00044
In each of them, the arrival angle noise eigenvectors (
Figure pat00045
) And arrival time noise eigenvectors
Figure pat00046
) And determine how many K the received signal x has been received.

The spectrum calculation unit 240 is a arrival angle noise eigenvector (

Figure pat00047
) And arrival time noise eigenvectors
Figure pat00048
) And multipath number (K) to calculate pseudo-spectrum using Discrete Fourier transform (DFT).

The spectrum calculation unit 240 is a arrival angle noise eigenvector (

Figure pat00049
), The spectrum of arrival angle vectors
Figure pat00050
), And the arrival time noise eigenvectors (
Figure pat00051
), The spectrum of arrival times vectors
Figure pat00052
Calculate

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]

Figure pat00053

Where k and l are discrete spatial frequencies, and u and w are discrete distances.

Arrival angle noise eigenvectors

Figure pat00054
) And arrival time noise eigenvectors
Figure pat00055
Pseudo-spectrum calculation of the) can be expressed as shown in Equation 8 below.

[Equation 8]

Figure pat00056

here,

Figure pat00057
Is the arrival angle noise eigenvector,
Figure pat00058
Arrival time noise eigenvector,
Figure pat00059
Is the arrival angle when the mobile signal arrives,
Figure pat00060
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,
Figure pat00061
Is random
Figure pat00062
A signal vector including
Figure pat00063
Is random
Figure pat00064
A signal vector including
Figure pat00065
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.

Figure pat00066
Wow
Figure pat00067
Can be obtained as shown in Equation 9 below.

&Quot; (9) "

Figure pat00068

here,

Figure pat00069
Is the arrival angle when the mobile signal arrives,
Figure pat00070
Is the arrival time when the mobile signal arrived,
Figure pat00071
Is the discrete spatial frequency of the arrival angle,
Figure pat00072
Is the discrete spatial frequency of arrival time, L is the DFT length, d is the distance between antennas,
Figure pat00073
Is the wavelength,
Figure pat00074
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 spectrum calculator 240 uses the following equation (10) to obtain the spectrum of the arrival angle vectors (

Figure pat00075
) And the spectrum of arrival time vectors (
Figure pat00076
Calculate

 &Quot; (10) "

Figure pat00077

here,

Figure pat00078
Is the arrival angle noise eigenvector,
Figure pat00079
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,
Figure pat00080
Is the arrival angle when the mobile signal arrives,
Figure pat00081
Is the arrival time of the mobile signal, DFT () is a function representing the discrete Fourier transform,
Figure pat00082
Is the spectrum of the arrival angle vectors,
Figure pat00083
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 summation unit 250 is a spectrum of arrival angle vectors (

Figure pat00084
) And add the angle of arrival spectrum (
Figure pat00085
), And the spectrum of arrival time vectors (
Figure pat00086
) To sum up the arrival time spectrum (
Figure pat00087
).

The summation unit 250 uses the following equation (11) to arrive angle spectrum (

Figure pat00088
) And arrival time spectrum (
Figure pat00089
) Can be calculated.

&Quot; (11) "

Figure pat00090

here,

Figure pat00091
Is the spectrum of the arrival angle vectors,
Figure pat00092
Is the spectrum of the arrival time vectors, M is the number of antennas, N is the number of samplings,
Figure pat00093
Is an arrival angle spectrum obtained by summing the spectra of the N arrival angle vectors,
Figure pat00094
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 (

Figure pat00095
), And the dotted line indicates the arrival time spectrum (
Figure pat00096
).

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 detector 260 is a arrival angle spectrum (

Figure pat00097
Discrete spatial frequency of the arrival angle at which the value of
Figure pat00098
And arrival time spectrum (
Figure pat00099
Discrete spatial frequency of arrival time at which value of
Figure pat00100
Detect.

The mapping unit 270 is a discrete spatial frequency of the arrival angle (

Figure pat00101
) And the discrete spatial frequency of arrival time (
Figure pat00102
), The corresponding arrival angle (
Figure pat00103
) And arrival time (
Figure pat00104
).

The mapping unit 270 is a discrete spatial frequency of the arrival angle (Equation 9)

Figure pat00105
) And the discrete spatial frequency of arrival time (
Figure pat00106
) And the arrival angle (
Figure pat00107
) And arrival time (
Figure pat00108
)can confirm.

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 step 310, the estimating apparatus divides the received signal x into arrival angle vectors and arrival time vectors by using distinctive characteristics of arrival angle and arrival time.

In operation 312, the estimating apparatus includes N arrival angle vectors corresponding to the number of samplings.

Figure pat00109
) Are computed from the correlation matrix, and the N arrival angle correlation
Figure pat00110
) And M arrival time vectors (
Figure pat00111
) Are computed by correlating the
Figure pat00112
).

In step 314, the estimating apparatus obtains the correlation of the arrival angle correlation matrix through the eigenvector analysis (EVD).

Figure pat00113
) And arrival time correlation matrix (
Figure pat00114
In each of them, the arrival angle noise eigenvectors (
Figure pat00115
) And arrival time noise eigenvectors
Figure pat00116
) And determine how many K the received signal x has been received.

In operation 316, the estimating apparatus obtains the arrival angle noise eigenvector (

Figure pat00117
) And arrival time noise eigenvectors
Figure pat00118
) And multi-path number (K) to calculate the pseudo-spectrum using Discrete Fourier transform (DFT)
Figure pat00119
) And the spectrum of arrival time vectors (
Figure pat00120
Calculate).

In operation 318, the estimating apparatus obtains a spectrum of arrival angle vectors.

Figure pat00121
) And add the angle of arrival spectrum (
Figure pat00122
), And the spectrum of arrival time vectors (
Figure pat00123
) To sum up the arrival time spectrum (
Figure pat00124
).

In operation 320, the detector 260 detects an arrival angle spectrum (

Figure pat00125
Discrete spatial frequency of the arrival angle at which the value of
Figure pat00126
And arrival time spectrum (
Figure pat00127
Discrete spatial frequency of arrival time at which value of
Figure pat00128
Detect.

In operation 322, the mapping unit 270 may determine a discrete spatial frequency of the arrival angle.

Figure pat00129
) And the discrete spatial frequency of arrival time (
Figure pat00130
), The corresponding arrival angle (
Figure pat00131
) And arrival time (
Figure pat00132
).

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 separation unit for dividing the received signal into arrival angle vectors and arrival time vectors by using distinctive estimation characteristics of arrival angle and arrival time;
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 method of claim 1,
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.
The method of claim 1,
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 method of claim 3,
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]
Figure pat00133

here,
Figure pat00134
Is the arrival angle noise eigenvector,
Figure pat00135
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,
Figure pat00136
Is the arrival angle when the mobile signal arrives,
Figure pat00137
Is the arrival time of the mobile signal, DFT () is a function representing the discrete Fourier transform,
Figure pat00138
Is the spectrum of the arrival angle vectors,
Figure pat00139
Is the spectrum of arrival time vectors.
The method of claim 3,
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) "
Figure pat00140

here,
Figure pat00141
Is the arrival angle when the mobile signal arrives,
Figure pat00142
Is the arrival time when the mobile signal arrived,
Figure pat00143
Is the discrete spatial frequency of the arrival angle,
Figure pat00144
Is the discrete spatial frequency of arrival time, L is the DFT length, d is the distance between antennas,
Figure pat00145
Is the wavelength,
Figure pat00146
Is the sampling rate.
The method of claim 1,
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 method according to claim 6,
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) "
Figure pat00147

here,
Figure pat00148
Is the arrival angle noise eigenvector,
Figure pat00149
Arrival time noise eigenvector,
Figure pat00150
Is the arrival angle when the mobile signal arrives,
Figure pat00151
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,
Figure pat00152
Is random
Figure pat00153
A signal vector including
Figure pat00154
Is random
Figure pat00155
A signal vector including
Figure pat00156
Is a hermitian operation.
Dividing the received signal into arrival angle vectors and arrival time vectors by using distinctive estimation characteristics of arrival angle and arrival time;
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.
9. The method of claim 8,
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.
9. The method of claim 8,
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.
The method of claim 10,
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) "
Figure pat00157

here,
Figure pat00158
Is the arrival angle noise eigenvector,
Figure pat00159
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,
Figure pat00160
Is the arrival angle when the mobile signal arrives,
Figure pat00161
Is the arrival time of the mobile signal, DFT () is a function representing the discrete Fourier transform,
Figure pat00162
Is the spectrum of the arrival angle vectors,
Figure pat00163
Is the spectrum of arrival time vectors.
The method of claim 10,
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) "
Figure pat00164

here,
Figure pat00165
Is the arrival angle when the mobile signal arrives,
Figure pat00166
Is the arrival time when the mobile signal arrived,
Figure pat00167
Is the discrete spatial frequency of the arrival angle,
Figure pat00168
Is the discrete spatial frequency of arrival time, L is the DFT length, d is the distance between antennas,
Figure pat00169
Is the wavelength,
Figure pat00170
Is the sampling rate.
9. The method of claim 8,
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.
The method of claim 13,
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]
Figure pat00171

here,
Figure pat00172
Is the arrival angle noise eigenvector,
Figure pat00173
Arrival time noise eigenvector,
Figure pat00174
Is the arrival angle when the mobile signal arrives,
Figure pat00175
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,
Figure pat00176
Is random
Figure pat00177
A signal vector including
Figure pat00178
Is random
Figure pat00179
A signal vector including
Figure pat00180
Is a hermitian operation.
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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

Cited By (5)

* Cited by examiner, † Cited by third party
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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