U.S. patent application number 11/497033 was filed with the patent office on 2009-02-12 for radar apparatus.
This patent application is currently assigned to DENSO Corporation. Invention is credited to Yasuyuki Miyake, Kazuma Natsume, Mai Sakamoto.
Application Number | 20090040097 11/497033 |
Document ID | / |
Family ID | 37670209 |
Filed Date | 2009-02-12 |
United States Patent
Application |
20090040097 |
Kind Code |
A1 |
Sakamoto; Mai ; et
al. |
February 12, 2009 |
RADAR APPARATUS
Abstract
A radar apparatus includes a receiving means that receives a
reflected signal, a beat signal generation means that generates
beat signals based on the reflected signal, a correlation matrix
generation means that calculates correlation matrices based on the
beat signals, a storing means that stores previous correlation
matrices, an addition means that calculates addition correlation
matrices by adding the correlation matrices to the previous
correlation matrices, a detection means that detects a frequency
component satisfying a predetermined condition by using the beat
signals, an extraction means that extracts an extraction matrix
corresponding to the detected frequency from the addition
correlation matrices, and a direction calculation means that
calculates a direction of the object with respect to the radar
apparatus based on the extraction matrix.
Inventors: |
Sakamoto; Mai; (Konan-city,
JP) ; Natsume; Kazuma; (Obu-city, JP) ;
Miyake; Yasuyuki; (Nisshin-city, JP) |
Correspondence
Address: |
HARNESS, DICKEY & PIERCE, P.L.C.
P.O. BOX 828
BLOOMFIELD HILLS
MI
48303
US
|
Assignee: |
DENSO Corporation
Kariya-city
JP
|
Family ID: |
37670209 |
Appl. No.: |
11/497033 |
Filed: |
July 31, 2006 |
Current U.S.
Class: |
342/118 ;
342/108 |
Current CPC
Class: |
G01S 13/42 20130101;
G01S 13/345 20130101; G01S 7/35 20130101; G01S 13/34 20130101; G01S
13/48 20130101; G01S 3/74 20130101 |
Class at
Publication: |
342/118 ;
342/108 |
International
Class: |
G01S 13/08 20060101
G01S013/08 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 2, 2005 |
JP |
2005-224636 |
Claims
1. A radar apparatus that uses a direction of arrival estimation
algorithm performed at a predetermined time interval to detect an
object, the radar apparatus comprising: transmitting means for
transmitting a wave signal to the object; receiving means that
receives for receiving the transmitted wave signal reflected from
the object; beat signal generation means for generating a plurality
of beat signals based on the received wave signal; correlation
matrix generation means for calculating a plurality of correlation
matrices based on the beat signals with respect to each frequency
component of the beat signals; addition means for calculating a
plurality of addition correlation matrices; strong means for
storing at least a portion of a plurality of previous correlation
matrices calculated by the correlation matrix generation means at a
previous time, or that stores at least a portion of a plurality of
previous addition correlation matrices calculated by the addition
correlation matrix generation means at the previous time, the
addition means calculating the addition correlation matrices by
adding the correlation matrices calculated by the correlation
matrix generation means to the previous correlation matrices or the
previous addition correlation matrices stored by the storing means;
detection means for detecting at least one frequency component of
the beat signals, the frequency component satisfying a
predetermined condition; extraction means for extracting an
extraction matrix from the addition correlation matrices, the
extraction matrix corresponding to a frequency component closest to
the frequency component detected by the detection means; and
direction calculation means for calculating a direction of the
object with respect to the radar apparatus based on the extraction
matrix, wherein the transmitting means or the receiving means
includes a plurality of elements arranged in an array, and each of
the beat signals generated by the beat signal generation means
corresponds to each of the elements.
2. The radar apparatus according to claim 1, wherein the receiving
means includes the elements arranged in the array.
3. The radar apparatus according to claim 1, wherein the
transmitting means includes the elements arranged in the array.
4. The radar apparatus according to claim 1, wherein the storing
means stores the previous correlation matrices, and the addition
means calculates the addition correlation matrices by adding the
correlation matrices to the previous correlation matrices.
5. The radar apparatus according to claim 1, wherein the storing
means stores the previous addition correlation matrices, and the
addition means calculates the addition correlation matrices by
adding the correlation matrices to the previous addition
correlation matrices.
6. The radar apparatus according to claim 4, wherein the addition
means further includes a multiplication means that multiplies the
correlation matrices by a first weighting factor and the previous
correlation matrices by a second weighting factor, and the addition
means calculates the addition correlation matrices by adding the
correlation matrices multiplied by the first weighting factor to
the previous correlation matrices multiplied by the second
weighting factor.
7. The radar apparatus according to claim 5, wherein the addition
means further includes multiplication means for multiplying the
correlation matrices by a first weighting factor and the previous
addition correlation matrices by a second weighting factor, and the
addition means calculates the addition correlation matrices by
adding the correlation matrices multiplied by the first weighting
factor to the previous addition correlation matrices multiplied by
the second weighting factor.
8. The radar apparatus according to claim 4, wherein the previous
correlation matrices includes a first portion and a second portion,
the storing means stores the first portion of the previous
correlation matrices, and the addition means further includes
estimation means for estimating the second portion of the previous
correlation matrices from the first portion to calculate the
addition correlation matrices.
9. The radar apparatus according to claim 5, wherein the previous
addition correlation matrices includes a first portion and a second
portion, the storing means stores the first portion of the previous
addition correlation matrices, and the addition means further
includes estimation means for estimating the second portion of the
previous addition correlation matrices from the first portion to
calculate the addition correlation matrices.
10. A radar apparatus that uses a direction of arrival estimation
algorithm performed at a predetermined time interval to detect an
object, the radar apparatus comprising: transmitting means for
transmitting a wave signal to an object; receiving means for
receiving the transmitted wave signal reflected from the object;
beat signal generation means for generating a plurality of beat
signals based on the received wave signal; correlation matrix
generation means for calculating a plurality of correlation
matrices based on the beat signals with respect to each frequency
component of the beat signals; storing means for storing at least a
portion of a plurality of previous correlation matrices that are
calculated by the correlation matrix generation means at a previous
time; detection means for detecting at least one frequency
component of the beat signals, the frequency component satisfying a
predetermined condition; extraction means for extracting an
extraction matrix from the correlation matrices, and that extracts
a previous extraction matrix from the previous correlation matrices
stored by the storing means, each of the extraction matrix and the
previous extraction matrix corresponding to the frequency component
detected by the detection means; addition means for calculating an
addition extraction matrix by adding the extraction matrix to the
previous extraction matrix; and direction calculation means for
calculating a direction of the object with respect to the radar
apparatus based on the addition extraction matrix, wherein the
transmitting means or the receiving means includes a plurality of
elements arranged in an array, and each of the beat signals
generated by the beat signal generation means corresponds to each
of the elements.
11. The radar apparatus according to claim 10, wherein the
receiving means includes the elements arranged in the array.
12. The radar apparatus according to claim 10, wherein the
transmitting means includes the elements arranged in the array.
13. The radar apparatus according to claim 10, wherein the addition
means further includes multiplication means for multiplying the
extraction matrix by a first weighting factor and the previous
extraction matrix by a second weighting factor, and the addition
means calculates the addition extraction matrix by adding the
extraction matrix multiplied by the first weighting factor to the
previous extraction matrix multiplied by the second weighting
factor.
14. The radar apparatus according to claim 10, wherein the previous
correlation matrices includes a first portion and a second portion,
the storing means stores the first portion of the previous
correlation matrices, and the extraction means further includes
estimation means for estimating the second portion of the previous
correlation matrices from the first portion to extract the previous
extraction matrix corresponding to the frequency component detected
by the detection means.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is based on and incorporates herein by
reference Japanese Patent Application No. 2005-224636 filed on Aug.
2, 2005.
FIELD OF THE INVENTION
[0002] The present invention relates to a radar apparatus.
BACKGROUND OF THE INVENTION
[0003] There are various types of radar apparatus for detecting a
distance and a direction of an object with respect to the radar
apparatus. A frequency modulated continuous wave (FMCW) radar
apparatus is a radar apparatus that continuously transmits a
frequency-modulated radar signal to the object and detects the
distance or a relative velocity of the object based on the
transmitted radar signal reflected from the object.
[0004] In one method for detecting the direction of the object, a
transmitting means for transmitting a radar signal mechanically
turns and scans the transmitted radar signal reflected from the
object. In another method that uses a digital beam forming (DBF)
algorithm, the transmitting means is fixed and an antenna having
multiple elements arranged in an array receives the transmitted
radar signal. The received radar signal is digitally processed and
the direction of the object is detected based on the digital
signal. Specifically, in the DBF algorithm, an angular spectrum is
generated based on the received radar signal on each of the
elements and a peak of the angular spectrum is detected. The
direction of the object is estimated based on the peak of the
angular spectrum.
[0005] In a beamformer algorithm as the DBF algorithm, the angular
spectrum is generated such that amplitudes of the received signal
at a given time are connected as shown in FIGS. 10A and 10B. A
multiple signal classification (MUSIC) algorithm is known as a
high-resolution direction of arrival (DOA) estimation
algorithm.
[0006] In the DOA estimation algorithm, correlation matrices are
calculated, eigenvalue expansions are performed on each of the
correlation matrices, the angular spectrum is calculated from
eigenvectors of the correlation matrices, and the direction of the
object is calculated based on the angular spectrum.
[0007] A FMCW radar apparatus disclosed in U.S. Pat. No. 6,121,917
corresponding to JP-A-H11-133142 detects the direction of the
object by using the beamformer algorithm. In the FMCW radar
apparatus, a fast Fourier transform (FFT) is applied to the
received wave signal to obtain a peak frequency of a distance power
spectrum. Then, the beamformer algorithm is applied to only the
peak frequency component of the received signal so that the amount
of calculation required to detect the direction of the object is
reduced.
[0008] However, when the beamformer algorithm is used in the radar
apparatus, resolution of the radar apparatus depends on the number
of elements arranged in the array. Therefore, the radar apparatus
using the beamformer algorithm needs to be increased in size to
obtain high resolution.
[0009] The high-resolution DOA estimation algorithm such as the
MUSIC algorithm achieves the high resolution without an increase in
the number of the elements. In the DOA estimation algorithm, the
resolution may be increased by reducing noise with summation of the
received signal with respect to time. The summation is performed
such that a present angular spectrum calculated in a present
process and a previous angular spectrum calculated in a previous
process are summed up.
[0010] However, when the object moves, a frequency corresponding to
the distance changes between in the previous process and in the
present process. Therefore, the DOA estimation algorithm needs to
be applied to all the frequency components of the received signal
on each process to calculate the angular spectrum, and the
calculated angular spectrum needs to be stored in a memory. The
calculation of the angular spectrum requires an eigenvalue
expansion that requires a lot of calculation. Therefore, when the
angular spectrum is calculated on each frequency component, the
amount of calculation is significantly increased. In the DOA
estimation algorithm, the high resolution results in a significant
increase in the amount of calculation.
SUMMARY OF THE INVENTION
[0011] In view of the above-described problem, it is an object of
the present invention to provide a radar apparatus that achieves a
high resolution without a significant increase in the amount of
calculation.
[0012] A radar apparatus uses a DOA estimation algorithm performed
at a predetermined time interval to detect an object. The radar
apparatus includes a transmitting means that transmits a wave
signal to the object, a receiving means that receives the
transmitted wave signal reflected from the object, a beat signal
generation means that generates a plurality of beat signals based
on the received wave signal, a correlation matrix generation means
that calculates a plurality of correlation matrices based on the
beat signals with respect to each frequency component of the beat
signals, an addition means that calculates a plurality of addition
correlation matrices, a storing means that stores at least a
portion of a plurality of previous correlation matrices calculated
by the correlation matrix generation means at a previous time, a
detection means that detects at least one frequency component of
the beat signals, the frequency component satisfying a
predetermined condition, an extraction means that extracts an
extraction matrix from the addition correlation matrices, the
extraction matrix corresponding to a frequency component closest to
the frequency component detected by the detection means, and a
direction calculation means that calculates a direction of the
object with respect to the radar apparatus based on the extraction
matrix. The receiving means includes a plurality of elements
arranged in an array and each of the beat signals generated by the
beat signal generation means corresponds to each of the elements.
The addition means calculates the addition correlation matrices by
adding the correlation matrices calculated by the correlation
matrix generation means to the previous correlation matrices stored
by the storing means.
[0013] In a conventional DOA estimation algorithm, correlation
matrices are generated from beat signals. Then, angular spectrums
are calculated from each eigenvector of each of the correlation
matrices. The storing means stores the angular spectrums as
previous information and the angular spectrums are used in a next
process to reduce noise.
[0014] In the DOA estimation algorithm used in the radar apparatus,
a position of the object (i.e., a frequency component indicating a
presence of the object) is estimated from the beat signals and then
an angular spectrum corresponding to the position is calculated.
Correlation matrices calculated in a present process and previous
correlation matrices calculated in a previous process are summed up
to reduce noise. In the radar apparatus, therefore, the storing
means stores the correlation matrices as the previous information,
not the angular spectrums.
[0015] For example, in a MUSIC algorithm, the amount of calculation
required to calculate the correlation matrices is about
one-thirteenth the amount of calculation required to calculate
MUSIC spectrums. Because the radar apparatus uses the correlation
matrices instead of the angular spectrums to reduce the noise, a
significant increase in the amount of calculation can be prevented.
Thus, the radar apparatus achieves a high resolution without the
significant increase in the amount of calculation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and other objectives, features and advantages of
the present invention will become more apparent from the following
detailed description made with reference to the accompanying
drawings. In the drawings:
[0017] FIG. 1 is a block diagram showing a radar apparatus
according to a first embodiment of the present invention;
[0018] FIG. 2 is an arrangement of elements of a receiving antenna
of the radar apparatus of FIG. 1;
[0019] FIGS. 3A and 3B are diagrams showing a principal of beat
signals generated in the radar apparatus of FIG. 1;
[0020] FIG. 4A is a diagram showing a reflected wave signal
received by the elements of the receiving antenna of the radar
apparatus of FIG. 1, FIG. 4B is FFT beat signals generated by
applying a FFT to beat signals of FIG. 3B, and FIG. 4C is a diagram
showing a sum beat signal into which the FFT beat signals of FIG.
4B are summed;
[0021] FIG. 5 is a flow chart illustrating a process performed by a
microcomputer of the radar apparatus of FIG. 1;
[0022] FIG. 6 is a table showing the amount of calculation
performed by the microcomputer of the radar apparatus of FIG.
1;
[0023] FIG. 7 is a flow chart illustrating a process performed by a
microcomputer of a radar apparatus according to a second embodiment
of the present invention;
[0024] FIG. 8 is a flow chart illustrating a process performed by a
microcomputer of a radar apparatus according to a third embodiment
of the present invention;
[0025] FIG. 9 is a flow chart illustrating a process performed by a
microcomputer of a radar apparatus according to a fourth embodiment
of the present invention; and
[0026] FIG. 10A is a diagram showing an angular spectrum generated
when reflected wave signals arrive at elements from a front
direction in a beam former method, and FIG. 10B is a diagram
showing the angular spectrum generated when the reflected wave
signals arrive at the elements from an oblique direction in the
beam former method.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
First Embodiment
[0027] A radar apparatus 100 according to a first embodiment of the
present invention will now be described with FIGS. 1 to 5. The
radar apparatus 100 includes a transmitting antenna 11, a receiving
antenna 12 having N elements E1-EN arranged in an array, where N is
a positive integer, a high-frequency switch 13, a mixer 14, an
oscillator 15, a digital-to-analog (D/A) converter 16, an
analog-to-digital (A/D) converter 17, a microcomputer 18, a switch
controller 19, and a timer 20.
[0028] The microcomputer 18 outputs a digital transmit signal to
the D/A converter 16. The D/A converter 16 converts the digital
transmit signal into an analog transmit signal and outputs the
analog transmit signal to the oscillator 15. The oscillator 15
outputs the analog transmit signal to the transmitting antenna 11
at a predetermined frequency. The transmitting antenna 11 converts
the analog transmit signal into a radar signal and transmits the
radar signal to an object.
[0029] The receiving antenna 12 receives the transmitted radar
signal reflected from the object. The received radar signal is
converted into an analog receive signal on each of the elements
E1-EN. The high-frequency switch 13 in turn sends the analog
receive signal to the mixer 14. In the mixer 14, the analog receive
signal is mixed with the analog transmit signal to generate a beat
signal on each of the elements E1-EN. The beat signal is input to
the A/D converter 17 and converted into a digital receive signal.
The digital signal is input to the microcomputer 18.
[0030] The microcomputer 18 controls the high-frequency switch 13
through the switch controller 19 and controls a sampling rate of
the A/D converter 17 though the timer 20. The microcomputer 18 has
a memory 21.
[0031] The microcomputer 18 performs a distance calculation process
for calculating the distance between the radar apparatus 100 and
the object and a direction calculation process for calculating the
direction between the radar apparatus 100 and the object.
[0032] Referring to FIGS. 2 and 3, the distance calculation process
is described.
[0033] As shown in FIG. 2, the elements E1-EN of the receiving
antenna 12 are spaced from each other by a predetermined spacing
S.
[0034] There arises a time delay and a frequency shift between the
transmitted radar signal, which is frequently modulated by the
oscillator 15, and the received radar signal. The time delay
corresponds to the distance between the radar apparatus 100 and the
object, and the frequency shift corresponds to a relative velocity
between the radar apparatus 100 and the object. A phase shift of
the received radar signal with respect to the transmitted radar
signal increases with the distance between the radar apparatus 100
and the object. The distance and relative velocity between the
radar apparatus 100 and the object can be detected based on the
phase shift. Therefore, a beat signal is calculated as a frequency
difference between the transmitted radar signal and the received
radar signal.
[0035] As shown in FIGS. 3A and 3B, the beat signal has a first
beat frequency Bu in an increase area where the frequency of the
transmitted radar signal increases, and has a second beat frequency
Bd in a decrease area where the frequency of the transmitted radar
signal decreases. Thus, the beat signal includes a first beat
signal having the first beat frequency Bu and a second beat signal
having the second beat frequency Bd.
[0036] When the receiving antenna 12 has the elements E1-EN, the
beat signal is generated on each of the elements E1-EN. In the
whole receiving antenna 12, therefore, 2N beat signals are
generated. Specifically, N first beat signals having the first beat
frequency Bu and N second beat signals having the second beat
frequency Bd are generated.
[0037] The distance and the relative velocity between the radar
apparatus 100 and the object are given by the following
equations:
D={CT/(4.DELTA.F)}(BuH+BdH) (1)
V={C/(4F0)}(BuH-BdH) (2)
[0038] In the above equations (1) (2), D represents the distance, V
represents the relative velocity, C represents the speed of light,
.DELTA.F represents a frequency range of the transmitted radar
signal, and F0 represents the center of the .DELTA.F. BuH is the
first beat signal generated based on the received radar signal that
is received by the element EH, where H is a positive integer less
than or equal to N (i.e., 1.ltoreq.H.ltoreq.N). BdH is the second
beat signal generated based on the received radar signal that is
received by the element EH.
[0039] Referring to FIGS. 4A-4C, the direction calculation process
is described. The microcomputer 18 performs the direction
calculation process at a predetermined time interval Ts.
[0040] First, a fast Fourier transform (FFT) is applied to each of
the N beat signals Bu1-BuN to generate N FFT beat signals
Bfu1-BfuN. Likewise, the fast Fourier transform (FFT) is applied to
the N beat signals Bd1-BdN to generate N FFT beat signals
Bfd1-BfdN. Although only the FFT beat signals Bfu1-BfuN are
illustrated in FIG. 4B, the FFT beat signals Bfd1-BfdN are
processed in the same way as the FFT beat signals Bfu1-BfuN.
[0041] Next, a correlation matrix group RG of correlation matrices
are generated by using each of the FFT beat signals. For example,
in the case of FIG. 4B, the correlation matrix group RG includes M
correlation matrices R(F1)-R(FM), where M is a positive integer
greater than 1. A correlation matrix R(FI) corresponds to a
frequency FI, where I is a positive integer less than or equal to M
(i.e., 1.ltoreq.I.ltoreq.M). When the receiving antenna 12 has the
elements E1-EN, each of the correlation matrices R(F1)-R(FM) is an
N.times.N matrix.
[0042] As described later, an addition correlation matrix group UG
having addition correlation matrices U(F1)-U(FM) is generated such
that the correlation matrix group RG generated in a present process
is added to a previous correlation matrix group RoG that is the
correlation matrix group RG generated in a previous process (i.e.,
Ts earlier) and stored in the memory 21. The addition correlation
matrix group UG has less noise than the correlation matrix group
RG. The use of the previous correlation matrix group RoG as
previous information reduces the noise.
[0043] A MUSIC algorithm used in the radar apparatus 100 is
described below. The MUSIC algorithm allows the radar apparatus 100
to achieve the high resolution without an increase in the amount of
calculation.
[0044] The FFT beat signals Bfu1-BfuN shown in FIG. 4B are summed
up to generate a sum beat signal Bfu0 shown in FIG. 4C. As can be
seen from FIG. 4C, the sum beat signal Bfu0 has less noise than
each of the FFT beat signals Bfu1-BfuN. Likewise, the FFT beat
signals Bfd1-BfdN are summed up to generate a sum beat signal Bfd0
having less noise than each of the FFT beat signals Bfd1-BfdN.
[0045] When the received radar signal contains a reflected wave
from the object, each of the sum beat signals Bfu0, Bfd0 has peak
strength. For example, in FIG. 4C, the sum beat signal Bfu0 has the
peak strength at a frequency FP, where P is a positive integer less
than or equal to M. The peak frequency FP is detected and an
extraction matrix C(FP), which is an addition correlation matrix
U(FP) corresponding to the peak frequency FP, is extracted from the
addition correlation matrices U(F1)-U(FM) of the addition
correlation matrix group UG.
[0046] Referring to FIG. 5, a process 500 including the distance
calculating process and the direction calculating process is
described. Although the beat signals Bu1-BuN are only discussed
below, the beat signals Bd1-BdN are processed in the same way as
the beat signals Bu1-BuN. The microcomputer 18 performs the process
500 as an interrupt process at the predetermined time interval
Ts.
[0047] The process 500 starts with step S501, where the
microcomputer 18 obtains the beat signals Bu1-BdN.
[0048] Then, the process 500 proceeds to step S502, where the FFT
is applied to each of the beat signals Bu1-BdN to generate the FFT
beat signals Bfu1-BfuN.
[0049] Then, the process 500 proceeds to step S503, where the FFT
beat signals Bfu1-BfuN are summed into the sum beat signal
Bfu0.
[0050] Then, the process 500 proceeds to step S504, where the peak
frequency FP of the sum beat signal Bfu0 is detected.
[0051] Then, the process 500 proceeds to step S505, where the
correlation matrix group RG having the correlation matrices
R(F1)-R(FM) is calculated from the FFT beat signals Bu1-BdN
generated in step S502.
[0052] Then, the process 500 proceeds to step S506, where each of
the correlation matrices R(F1)-R(FM) of the correlation matrix
group RG is multiplied by a weighting factor (1-K) and each of
correlation matrices Ro(F1)-Ro(FM) of the previous correlation
matrix group RoG is multiplied by a weighting factor K, where K is
a fixed value between 0.0 and 1.0. As described above, the previous
correlation matrix group RoG is the correlation matrix group RG
that is generated in a previous loop (i.e., Ts earlier) of the
process 500. Then, the correlation matrix group RG multiplied by
the weighting factor (1-K) and the previous correlation matrix
group RoG multiplied by the weighting factor K are added together
to produce the addition correlation matrix group UG having addition
correlation matrices U(F1)-U(FM). Therefore, the addition
correlation matrix U(FI), i.e., each of the addition correlation
matrices U(F1)-(FM) of the addition correlation matrix group UG is
given by:
U(FI)=R(FI)(1-K)+Ro(FI)K (3)
[0053] Then, the process 500 proceeds to step S507, where the
extraction matrix C(FP) is extracted from the addition correlation
matrix group UG. The extraction matrix C(FP) is the addition
correlation matrices U(FP) corresponding to the peak frequency FP
detected in step S504,
[0054] Then, the process 500 proceeds to step S508, where an
eigenvalue expansion of the extraction matrix C(FP) is
performed.
[0055] Then, the process 500 proceeds to step S509, where a MUSIC
spectrum is calculated based on an eigenvector of the extraction
matrix C(FP).
[0056] Then, the process 500 proceeds to step S510, where the
direction of the object in the increase area is calculated based on
the MUSIC spectrum. Because the beat signals Bd1-BdN are processed
in the same way as the beat signals Bu1-BuN, the direction of the
object in the decrease area is also calculated.
[0057] Then, the process 500 proceeds to step S511, where the
correlation matrix group RG generated in step S505 is stored in the
memory 21 as the previous correlation matrix group RoG that is used
in a next loop of the process 500.
[0058] Then, the process 500 proceeds to step S512, where pair
matching of the object is performed based on the strength of the
sum beat signal Bfu0, the direction of the object in the increase
area, the strength of the sum beat signal Bfd0, and the direction
of the object in the decrease area. Thus, the distance and the
relative velocity between the radar apparatus 100 and the object
are detected.
[0059] After step S512 is finished, the process 500 returns to step
S501.
[0060] The correlation matrix group RG generated in a present loop
of the process 500 is added to the previous correlation matrix
group RoG that is generated in the previous loop of the process 500
and stored in the memory 21. In such an approach, the addition
correlation matrix group UG can have less noise than the
correlation matrix group RG.
[0061] The peak frequency FP, which indicates the presence of the
object, is detected and the extraction matrix C(FP) corresponding
to the peak frequency FP is extracted from the additional
correlation matrix group UG. The MUSIC spectrum is generated by
using the extraction matrix C(FP). Specifically, the eigenvalue
expansion is performed on only the extraction matrix C(FP) to
generate the MUSIC spectrum. Therefore, the amount of calculation
executed by the microcomputer 18 is very small, as compared to when
the eigenvalue expansion is performed on each of the addition
correlation matrices U(F1)-U(FM).
[0062] The previous information is stored in the memory 21 in the
form of the correlation matrix for the following reason.
[0063] FIG. 6 is a table showing the amount of calculation for
converting the FFT beat signals Buf0-BufN into each form per
frequency (i.e., one of the frequencies F1-FM). In the table,
R.+-.R represents an addition/subtraction of real numbers and
R.times.R represents a multiplication of real numbers.
[0064] As show in the table, 500 addition/subtractions and 500
multiplications are required to calculate one of the correlation
matrices R(F1)-R(FM) from the FFT beat signals Bfu1-BfuN. Likewise,
3500 addition/subtractions and 3500 multiplications are required to
calculate the eigenvectors of one of the correlation matrices
R(F1)-R(FM) from the FFT beat signals Bfu1-BfuN. In other words,
3000 addition/subtractions and 3000 multiplications are required to
calculate the eigenvectors from one of the correlation matrices
R(F1)-R(FM).
[0065] In the case of FIG. 4, 500.times.M addition/subtractions and
500.times.M multiplications are performed to calculate the
correlation matrices R(F1)-R(FM) from the FFT beat signals
Bfu1-BfuN. For example, when the number M is 10, 50000
addition/subtractions and 50000 multiplications are performed to
calculate the 10 correlation matrices R(F1)-R(F10) from the FFT
beat signals Bfu1-BfuN.
[0066] If the memory 21 stores the eigenvectors of each of the
correlation matrices R(F1)-R(FM), 3000.times.M
addition/subtractions and 3000.times.M multiplications are further
performed to calculate the eigenvectors. However, the eigenvector
of the correlation matrix R(FP) corresponding to the peak frequency
FP is only used to generate the extraction matrix C(FP). Therefore,
when the peak frequency in the previous loop is equal to that in
the present loop, 3000.times.(M-1) addition/subtractions and
3000.times.(M-1) multiplications are wasted. Likewise, when the
peak frequency in the previous loop is not equal to that in the
present loop, 3000.times.(M-2) addition/subtractions and
3000.times.(M-2) multiplications are wasted.
[0067] In view of the amount of calculation, therefore, it is
appropriate that the previous information should be stored in the
memory 21 in the form of the correlation matrices R(F1)-R(FM).
[0068] Thus, the radar apparatus 100 achieves the high resolution
without the increase in the amount of calculation.
[0069] Although the case where one object is detected is discussed
in the first embodiment, the radar apparatus 100 can detect two or
more objects.
[0070] For example, when the number of the objects is two and the
distance between one object and the radar apparatus 100 is not
equal to that between the other object and the radar apparatus 100,
the sum beat signal Bfu0 has two peak strengths, i.e., two peak
frequencies. In this case, two extraction matrices C(FP), one of
which corresponds to one peak frequency and the other of which
corresponds to the other peak frequency, are extracted from the
additional correlation matrix group UG. The MUSIC spectrums are
calculated based on each of the two extraction matrices C(FP) so
that each direction of the two objects can be detected.
[0071] In contrast, when the distance between one object and the
radar apparatus 100 is equal to that between the other object and
the radar apparatus 100, the sum beat signal Bfu0 has only one peak
strength. In this case, one extraction matrix C(FP) corresponding
to the peak frequency is extracted from the additional correlation
matrix group UG. The MUSIC spectrum is calculated based on the
extraction matrix C(FP). Because the MUSIC spectrum contains
signals indicating each direction of the two objects, i.e., the
MUSIC spectrum has two peaks, each direction of the two objects can
be detected.
Second Embodiment
[0072] Referring to FIGS. 5 and 7, a second embodiment of the
present invention is described. In the second embodiment, the
microcomputer 18 performs a process 700 shown in FIG. 7 instead of
the process 500 shown in FIG. 5. As shown in FIG. 7, the process
700 includes steps S705-S711 instead of steps S505-S511 of the
process 500. Although the beat signals Bu1-BuN are only discussed
below, the beat signals Bd1-BdN are processed in the same way as
the beat signals Bu1-BuN.
[0073] After steps S501-S504 are finished, the process 700 proceeds
to step S705, where the correlation matrix group RG having the
correlation matrices R(F1)-R(FM) is calculated from the FFT beat
signals Bfu1-BfuN generated in step S502.
[0074] Then, the process 700 proceeds to step S706, where each of
the correlation matrices R(F1)-R(FM) of the correlation matrix
group RG is multiplied by the weighting factor (1-K) and each of
previous addition correlation matrices Uo(F1)-Uo(FM) of a previous
correlation matrix group UoG is multiplied by a weighting factor K.
The previous correlation matrix group RoG is the addition
correlation matrix group UG generated in a previous loop (i.e., Ts
earlier) of the process 700. Then, the correlation matrix group RG
multiplied by the weighting factor (1-K) and the previous addition
correlation matrix group UoG multiplied by the weighting factor K
are added together to produce the addition correlation matrix group
UG having the addition correlation matrices U(F1)-U(FM). Therefore,
the addition correlation matrix U(FI), i.e., each of the addition
correlation matrices U(F1)-(FM) of the addition correlation matrix
group UG is given by:
U(FI)=R(FI)(1-K)+Uo(FI)K (4)
[0075] Then, the process 700 proceeds to step S707, where the
extraction matrix C(FP) is extracted from the addition correlation
matrix group UG. The extraction matrix C(FP) is the addition
correlation matrices U(FP) corresponding to the peak frequency FP
detected in step S504.
[0076] Then, the process 700 proceeds to step S708, where the
eigenvalue expansion of the extraction matrix C(FP) is
performed.
[0077] Then, the process 700 proceeds to step S709, where the MUSIC
spectrum is calculated based on the eigenvector of the extraction
matrix C(FP).
[0078] Then, the process 700 proceeds to step S710, where the
direction of the object in the increase area is calculated based on
the MUSIC spectrum. Because the beat signals Bd1-BdN are processed
in the same way as the beat signals Bu1-BuN, the direction of the
object in the decrease area is also calculated.
[0079] Then, the process 700 proceeds to step S711, where the
addition correlation matrix group UG generated in step S706 is
stored in the memory 21 as the previous addition correlation matrix
group UoG that is used in a next loop of the process 700.
[0080] Then, the process 700 proceeds to step S512.
[0081] In the process 700, thus, the previous additional
correlation matrix UoG generated in the previous loop is used to
generate the addition correlation matrix group UG. In such an
approach, the addition correlation matrix UG can be generated based
on two or more previous correlation matrix groups so that the
addition correlation matrix group UG of the second embodiment can
have less noise than that of the first embodiment.
Third Embodiment
[0082] Referring to FIGS. 5 and 8, a third embodiment of the
present invention is described. In the third embodiment, the
microcomputer 18 performs a process 800 shown in FIG. 8 instead of
the process 500 shown in FIG. 5. As shown in FIG. 8, the process
800 includes steps S805-S811 instead of steps S505-S511 of the
process 500. Although the beat signals Bu1-BuN are only discussed
below, the beat signals Bd1-BdN are processed in the same way as
the beat signals Bu1-BuN.
[0083] After steps S501-S504 are finished, the process 800 proceeds
to step S805, where the correlation matrix group RG having the
correlation matrices R(F1)-R(FM) is calculated from the FFT beat
signals Bfu1-BfuN generated in step S502.
[0084] Then, the process 800 proceeds to step S806, where a
correlation matrix R(FP) corresponding to the peak frequency FP
detected in step S504 is extracted from the correlation matrix
group RG. Further, a previous correlation matrix Ro(FP)
corresponding to the peak frequency FP is extracted from a previous
correlation matrix group RoG having correlation matrices
Ro(F1)-Ro(FM). The previous correlation matrix group RoG is the
correlation matrix group RG that is generated in a previous loop
(i.e., Ts earlier) of the process 800 and stored in the memory
21.
[0085] Then, the process 800 proceeds to step S807, where the
correlation matrix R(FP) is multiplied by the weighting factor
(1-K) and the previous correlation matrix Ro(FP) is multiplied by
the weighting factor K. Then, the correlation matrix R(FP)
multiplied by the weighting factor (1-K) and the previous
correlation matrix Ro(FP) multiplied by the weighting factor K are
added together to produce the addition correlation matrix U(FP).
Therefore, the addition correlation matrix U(FP) is given by:
U(FP)=R(FP)(1-K)+Ro(FP)K (5)
[0086] Then, the process 800 proceeds to step S808, where the
eigenvalue expansion of the addition correlation matrix U(FP) is
performed.
[0087] Then, the process 800 proceeds to step S809, where the MUSIC
spectrum is calculated based on the eigenvector of the addition
correlation matrix U(FP).
[0088] Then, the process 800 proceeds to step S810, where the
direction of the object in the increase area is calculated based on
the MUSIC spectrum. Because the beat signals Bd1-BdN are processed
in the same way as the beat signals Bu1-BuN, the direction of the
object in the decrease area is also calculated.
[0089] Then, the process 800 proceeds to step S811, where the
correlation matrix group RG generated in step S805 is stored in the
memory 21 as the previous correlation matrix group RoG that is used
in a next loop of the process 800.
[0090] Then, the process 800 proceeds to step S512.
[0091] In the process 500 according to the first embodiment, the
addition correlation matrix group UG having the addition
correlation matrices U(F1)-U(FM) is generated such that the
correlation matrix group RG is added to the previous correlation
matrix group RoG. When the number of the objects is one, the sum
beat signal Bfu0 has only one peak frequency. Therefore, although
each of the addition correlation matrices U(F1)-U(FM) is
calculated, the addition correlation matrix U(FP) corresponding to
the peak frequency FP is only used. In other words, the calculation
of the addition correlation matrices U(F1)-U(FM) except for the
addition correlation matrix U(FP) may result in waste. In contrast,
in the process 800 according to the third embodiment, the addition
correlation matrix U(FP) is generated such that the correlation
matrix R(FP) is added to the previous correlation matrix Ro(FP).
Thus, the wasted calculation can be avoided.
[0092] In the process 500, the memory 21 needs to store the
previous correlation matrix group RoG and the addition correlation
matrix group UG at the same time. In contrast, in the process 800,
the memory 21 needs to store the previous correlation matrix group
RoG and the addition correlation matrix U(FP) at the same time.
Therefore, the memory 21 can have a small amount of storage
capacity in the process 800, as compared to in the process 500.
Fourth Embodiment
[0093] A fourth embodiment of the present invention is described.
In the third embodiment, the memory 21 stores each of the M
correlation matrices R(F1)-R(FM) for the next loop. In contrast, in
the fourth embodiment, the memory 21 stores M/2 correlation
matrices R(F1), R(F3), R(F5) .cndot. .cndot. .cndot. . Thus, the M
correlation matrices R(F1)-R(FM) are thinned out to the M/2
correlation matrices R(F1), R(F3), R(F5) .cndot. .cndot. .cndot. .
In other words, the M correlation matrices R(F1)-R(FM) are
alternately stored in the memory 21 such that the memory 21 stores
the M/2 correlation matrices R(F1), R(F3), R(F5) .cndot. .cndot.
.cndot. .
[0094] For example, when the correlation matrices R(F1)-R(F3) are
generated in the Sth loop, where S is a positive integer, the
memory 21 stores the correlation matrices R(F1), R(F3) as the
previous correlation matrices Ro(F1), Ro(F3). In other words, the
correlation matrix R(F2) is not stored in the memory 21 in the Sth
loop. In this case, if the peak frequency FP is F2 in the (S+1)th
loop, the previous correlation matrix Ro(F2) is generated such that
a weighted average of the previous correlation matrix Ro(F1) is
added to a weighted average of the previous correlation matrix
Ro(F3).
[0095] In the fourth embodiment, the microcomputer 18 performs a
process 900 shown in FIG. 9 instead of the process 500 shown in
FIG. 5. As shown in FIG. 9, the process 900 includes steps
S905-S912 instead of steps S505-S511 of the process 500. Although
the beat signals Bu1-BuN are only discussed below, the beat signals
Bd1-BdN are processed in the same way as the beat signals
Bu1-BuN.
[0096] After steps S501-S504 are finished, the process 900 proceeds
to step S905, where the correlation matrix group RG having the
correlation matrices R(F1)-R(FM) is calculated from the FFT beat
signals Bfu1-BfuN generated in step S502.
[0097] Then, the process 900 proceeds to step S906, where it is
determined whether the previous correlation matrix Ro(FP)
corresponding to the peak frequency FP is stored in the memory
21.
[0098] If the previous correlation matrix Ro(FP) is stored in the
memory 21, the process 900 proceeds to step S908 directly.
[0099] If the previous correlation matrix Ro(FP) is not stored in
the memory 21, the process 900 proceeds to step S908 through step
S907, where the previous correlation matrix Ro(FP) is generated
such that a weighted average of a previous correlation matrix
Ro(FP-1) is added to a weighted average of a previous correlation
matrix Ro(FP+1).
[0100] At step S908, the correlation matrix R(FP) corresponding to
the peak frequency FP is extracted from the correlation matrix
group RG. The correlation matrix R(FP) is multiplied by the
weighting factor (1-K) and the previous correlation matrix Ro(FP)
is multiplied by the weighting factor K. Then, the correlation
matrix R(FP) multiplied by the weighting factor (1-K) and the
previous correlation matrix Ro(FP) multiplied by the weighting
factor K are added together to produce the addition correlation
matrix U(FP). Therefore, the addition correlation matrix U(FP) is
given by:
U(FP)=R(FP)(1-K)+Ro(FP)K (6)
[0101] Then, the process 900 proceeds to step S909, where the
eigenvalue expansion of the addition correlation matrix U(FP) is
performed.
[0102] Then, the process 900 proceeds to step S910, where the MUSIC
spectrum is calculated based on the eigenvector of the addition
correlation matrix U(FP).
[0103] Then, the process 900 proceeds to step S911, where the
direction of the object in the increase area is calculated based on
the MUSIC spectrum. Because the beat signals Bd1-BdN are processed
in the same way as the beat signals Bu1-BuN, the direction of the
object in the decrease area is also calculated.
[0104] Then, the process 900 proceeds to step S912, where the
correlation matrix group RG is thinned out and stored in the memory
21 as the previous correlation matrix group RoG that is used in a
next loop of the process 900.
[0105] Then, the process 900 proceeds to step S512.
[0106] As described above, in the process 900, the correlation
matrix group RG is thinned out and stored in the memory 21 as the
previous correlation matrix group RoG. Thus, the memory 21 can have
a small amount of storage capacity in the process 900, as compared
to in the process 800 according to the third embodiment. Even when
the previous correlation matrix Ro(FP) is not stored in the memory
21, the previous correlation matrix Ro(FP) is estimated from
previous correlation matrices Ro(FP-1), Ro(FP+1).
[0107] (Modifications)
[0108] The embodiments described above may be modified in various
ways. For example, the radar apparatus 100 may use the received
radar signal received by some of the elements E1-EN of the
receiving antenna 12, not each of the elements E1-EN. In such an
approach, the amount of calculation can be reduced.
[0109] The correlation matrix group RG or the addition correlation
matrix group UG may be stored in the memory 21 after being
compressed by a data compression algorithm. Thus, the memory 21 may
store the previous information in a form of data containing
elements of the correlation matrix group RG or the addition
correlation matrix group UG.
[0110] The weighting factor K may be a variable. For example, when
the received radar signal has considerable instantaneous noise, the
weighing factor K may be increased. In such an approach, an
influence of the noise can be reduced.
[0111] The algorithm used in the present invention can be applied
to various types of the DOA estimation algorithms such as
unitary-MUSIC algorithm, ESPRIT algorithm, unitary-ESPRIT
algorithm, Capon algorithm, and Beam Former algorithm. In
particular, when the unitary-MUSIC or the unitary-ESPRIT is used,
only the real part of the matrix is stored in the memory 21.
Therefore, the amount of calculation can be significantly reduced
and the memory 21 can have a very small amount of storage capacity.
The algorithm used in the present invention also can be applied to
a spatial smoothing algorithm.
[0112] The previous information (i.e., the previous correlation
matrix group RoG, or the previous addition correlation matrix group
UG) may be generated in two or more previous loop. For example, the
previous information may be generated in two previous loop (i.e.,
generated 2Ts earlier).
[0113] The transmitting antenna 11 instead of the receiving antenna
12 may have the elements arranged in the array to generate the beat
signals.
* * * * *