FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
1. Title of the Invention:
“GRAPH-BASED ARRAY SIGNAL DENOISING FOR
PERTURBED SYNTHETIC APERTURE RADAR”
2. APPLICANT (S) –
(a) Name : MITSUBISHI ELECTRIC CORPORATION
(b) Nationality : Japanese
(c)Address : 7-3, Marunouchi 2-chome, Chiyoda-ku, Tokyo
1008310, Japan.
The following specification particularly describes the invention and the manner
in which it is to be performed.
2
[Technical Field]
[0001]
The present invention is related to a radar system, and more particularly to a
moving radar system for generating a radar image of a region of interest from
noisy radar measurements.
[Background Art]
[0002]
Automotive radar market keeps growing in recent years and is expected to grow
dramatically in next few years. Compared to optical systems, automotive radar
has the advantage of all-weather operation. However, its angular resolution is
much lower than that of optical systems. In order to obtain high angular
resolution, a large aperture size is needed for the conventional radar. A
distributed synthetic aperture radar system forming a large virtual aperture is a
possible solution to solve such trade-off, although position errors and timing
errors of distributed sensor units degrade the sensing performance.
[0003]
Accordingly, there is a need to develop a method which processes noisy signals
collected by distributed sensor units mounted on automobiles based on a new
compensation technique of position errors and timing errors.
[Summary of Invention]
[0004]
The present disclosure relates to systems and methods for generating a radar
image from a region of interest using a synthetic aperture radar system arranged
on automobiles. Some embodiments of the present disclosure provide a method
which processes signals from distributed sensor units of a synthetic aperture
radar placed on automobiles with compensation technique of position errors
and timing errors.
3
[0005]
Some embodiments of the present disclosure understand that the performance
of the synthetic aperture radar degrades when its moving platform is perturbed
with unknown position errors or received signals are interfered by strong
random noise. Therefore, it is desirable to perform robust imaging with noisy
radar echoes even under large position perturbations. Some embodiments of the
present disclosure propose a graph-based denoising method combining with
robust perturbation estimation for processing noisy array signals received by a
perturbed moving radar platform. Simulation results demonstrate that our
method significantly improves SNR of array signals and imaging performance.
[0006]
The performance of synthetic aperture radar degrades when its moving
platform is perturbed with unknown position errors or the received signal is
interfered by strong noise. Therefore, it is desirable to perform robust imaging
with noisy radar echoes even under large position perturbations.
[0007]
At least one realization of the present disclosure understands that a distributed
radar system can be an advantageous solution to overcome technological
challenges, if position errors and timing errors of the distributed sensor units
are improved.
[0008]
According to some embodiments of the present disclosure, a graph-based
denoising method can be provided. The method includes combining with robust
perturbation estimation for processing noisy array signals received by a
perturbed moving radar platform. Simulation results demonstrate that our
method significantly improves SNR of array signals and imaging performance.
[0009]
4
Some embodiments of the present disclosure can provide a solution as to that
the angular resolution of a radar using a distributed radar system can be
improved by compensating the position errors and timing errors of the
distributed sensor units.
[0010]
In accordance with some embodiments of the present disclosure, a radar system
for generating a radar image from a region of interest (ROI) can be provided.
The radar system may include an interface configured to transmit radar pulses
to the ROI at different positions along a path of a moving radar platform and
receive radar echoes reflected from the ROI, wherein the at least one antenna
is arranged on the moving platform to emit radar pulses to the ROI using at
least one antenna; a memory configured to store computer-executable programs
including a range compressor, a graph modeling generator, a signal aligner and
a radar imaging generator and a focused image generator; a processor, in
connection with the memory, configured to: perform range compression on the
radar echoes by deconvolving the transmitted radar pulses with radar echoes to
obtain frequency-domain signals Y(i,k); generate a graph model represented by
sequential positions of the moving radar platform and a graph shift matrix A
computed using the frequency-domain signals Y(i,k); iteratively align and
denoise the frequency-domain signals Y(i,k) to obtained time shifts ti for
alignment and denoised data X(i,k) by solving a graph-based optimization
problem represented by the graph model, wherein the approximated time shifts
ti are corresponding to position perturbations of the moving radar platform; and
perform radar imaging based on the denoised data X(i,k) and the estimated time
shifts ti to generate focused radar images.
[0011]
Further, yet another embodiment of the present disclosure can provide a radar
image processing device for generating a radar image from a region of interest
5
(ROI). The radar image processing device may include a network interface
controller (NIC) configured to receive transmitted radar pulses and radar
echoes reflected from the ROI in response to the transmitted radar pulses at
different perturbed positions along a path of a moving radar platform; a
memory configured to store computer-executable programs including a range
compressor, a graph modeling generator, a signal aligner and a radar imaging
generator and a focused image generator; a processor, in connection with the
memory, configured to perform range compression on the radar echoes by
deconvolving the transmitted radar pulses with radar echoes to obtain
frequency-domain signals Y(i,k); generate a graph model represented by
sequential positions of the moving radar platform and a graph shift matrix A
computed using the frequency-domain signals Y(i,k); iteratively align and
denoise the frequency-domain signals Y(i,k) to obtain time shifts ti and
denoised data X(i,k) by solving a graph-based optimization problem
represented by the graph model, wherein the approximated time shifts ti are
corresponding to position perturbations of the moving radar platform; and
perform radar imaging based on the denoised data X(i,k) and the estimated time
shifts ti to generate focused radar images.
[0012]
The accompanying drawings, which are included to provide a further
understanding of the invention, illustrate embodiments of the invention and
together with the description serve to explain the principle of the invention.
[Brief Description of Drawings]
[0013]
[Fig. 1A]
Figure 1A shows a schematic illustrating a concept of a synthetic aperture of
antennas.
[Fig. 1B]
6
Figure 1B shows a simulation setup for radar data collection, according to
embodiments of the present disclosure.
[Fig. 1C]
Figure 1C shows actual perturbed radar positions and estimated positions,
according to embodiments of the present disclosure.
[Fig. 2]
Figure 2 shows a procedure for performing radar imaging, according to
embodiments of the present disclosure.
[Fig. 3A]
Figures 3A shows noisy and misaligned time-domain radar signal, denoised
and misaligned time-domain radar signal, and denoised aligned time-domain
radar signal respectively, according to embodiments of the present disclosure.
[Fig. 3B]
Figures 3B shows noisy and misaligned time-domain radar signal, denoised and
misaligned time-domain radar signal, and denoised aligned time-domain radar
signal respectively, according to embodiments of the present disclosure.
[Fig. 3C]
Figures 3C shows noisy and misaligned time-domain radar signal, denoised and
misaligned time-domain radar signal, and denoised aligned time-domain radar
signal respectively, according to embodiments of the present disclosure.
[Fig. 4A]
Figures 4A shows region of interest, radar imaging result of proposed method,
radar imaging result of coherence analysis respectively, according to
embodiments of the present disclosure.
[Fig. 4B]
Figures 4B shows region of interest, radar imaging result of proposed method,
radar imaging result of coherence analysis respectively, according to
embodiments of the present disclosure.
7
[Fig. 4C]
Figures 4C shows region of interest, radar imaging result of proposed method,
radar imaging result of coherence analysis respectively, according to
embodiments of the present disclosure.
[Fig. 5]
Figure 5 is a schematic diagram illustrating a radar system 100 for generating
a radar image from a region of interest (ROI), according to embodiments of the
present disclosure.
[Description of Embodiments]
[0014]
Various embodiments of the present invention are described hereafter with
reference to the figures. It would be noted that the figures are not drawn to
scale elements of similar structures or functions are represented by like
reference numerals throughout the figures. It should be also noted that the
figures are only intended to facilitate the description of specific embodiments
of the invention. They are not intended as an exhaustive description of the
invention or as a limitation on the scope of the invention. In addition, an aspect
described in conjunction with a particular embodiment of the invention is not
necessarily limited to that embodiment and can be practiced in any other
embodiments of the invention.
[0015]
Figure 1A is a schematic illustrating a concept of a synthetic aperture of
antennas. The figure illustrates an example case where the synthethic aperture
of antennas are configured to detect objects at side scenes while a car is moving
forward on a road. However, this configuration is not limited to the side scenes.
For instance, the synthetic apperture of antennas can be arranged to detect
scenes at angles toward the advancing side of the car. In some case, the
synthetic apperture of antennas disposed on the care may be referred to as a
8
moving radar platform.
[0016]
Figure 2 shows a radar imaging method 200 for performing radar imaging,
according to embodiments of the present disclosure. In this case, the method
200 can be data process steps 131, 132, 133, 134 and 135, which are performed
by a processor (or processors) 120 in Figure 5. The data process steps 131, 132,
133, 134 and 135 may be computer-executable programs 131, 132, 133, 134
and 135 stored in a storage 130 in Figure 5, and the programs 131, 132, 133,
134 and 135 are performed by the processor 120 for the data process steps.
[0017]
The data process 131 can be performed by periodically transmitting a series of
radar pulses p(t) to a region of interest (ROI) and receiving radar echoes z(ri,
t) reflected from the ROI using a moving radar platform. In this case, each pulse
and its corresponding echo together include information of a perturbed radar
platform position.
[0018]
Next, the data process 132 may perform range compression on the radar echoes
z(ri,t) by deconvolving the transmitted pulse p(t) and achieve time-domain
noisy signal y(ri,t), or frequency-domain noisy signal Yi,k=Y(i,k). For the ith
radar position, Yi = [Y(i,1) , Y(i,2) , … Y(i,K)] is the frequency-domain
measurement and is the time-domain measurement.
[0019]
The data process 133 can build a graph model of noisy signal Yi by estimating
the graph shift , or a weighted adjacent matrix A, using equation:
.
[0020]
9
The data process 134 can iteratively denoise signal Y(i,k) using the graph-based
method and align signals by determining time shifts ti due to position
perturbations.
[0021]
For denoising, the data process 134 imposes smoothness and sparseness on
signal based on a graph model, refer equations (5-7). For time shifts ti, the data
process forms a time shift matrix, then solve ti using equations (8-10).
[0022]
Further, the data process 135 can perform a radar imaging with denoised data
X(i,k) and estimated time shifts ti to generate focused radar images.
[0023]
Synthetic aperture radar uses a moving platform to form a large virtual aperture
and consequently realizes high imaging resolution. However, in practice the
performance of synthetic aperture radar degrades due to position perturbations
of the moving radar platform and interferences to the radar echoes received by
the platform. When the position perturbation level and the noise level are
relatively low, one may analyze data coherence of received signals to correct
phase errors caused by the perturbations or impose sparsity on the final radar
image to realize auto-focused imaging. With the increase of perturbation and
noise level, it becomes more and more challenging due to the nonconvexity of
data coherence analysis. Imaging methods may be either time-consuming due
to greedy search for unknown position errors, or perform poorly due to out-offocus.
[0024]
Graph signal processing (GSP) has been an active research topic in image and
signal processing areas for years. GSP basically exploits the underlying specific
data structure defined by the graphs to enhance signal or image quality.
Recently, GSP has been applied to synthetic aperture radar to improve the
10
imaging performance by modeling the final radar image as a graph where nodes
are pixels of the radar image and edges are correlations between pixels. As a
result, the radar image quality is enhanced with reduced noise. However, this
image-based GSP cannot fundamentally solve the out-of-focus problem caused
by radar position errors. Although the processed image is clean with fewer
noise, its blurring imaging quality is still not good enough for further detection
purpose. Therefore, it is desirable to perform robust imaging with noisy radar
echoes even under large position perturbations.
[0025]
At least one object of the present disclosure is to aim to improve the imaging
performance of perturbed synthetic aperture radars using noisy radar echoes.
To that end, we propose a graph-based array signal denoising method
combining with a robust perturbation estimation. We treat the synthetic
aperture radar system as graph, each transmitting and receiving position as a
node of the graph, and the corresponding radar signal as the time-series
associated with each node. To denoise the array signals, we formulate a graphbased objective function, which regularizes both the smoothness in the graph
domain and the sparse gradients in the time domain. The main difference
between our proposed method and the previous GSP-based method is that we
build graph model in the radar signal domain instead of the image domain, such
that we can jointly denoise signal and estimate position perturbations,
providing focused imaging results. Preliminary experimental results show that
the dual promotion significantly improves the denoising performance
combining with a robust decomposition method to estimate the position
perturbation.
Array data collection
[0026]
We consider a 2D radar imaging problem for simplicity in which a mono-static
11
moving radar platform is utilized to detect localized targets situated in a ROI.
We use
p(t)
and
P()
to denote the transmitted time-domain source pulse and
its frequency spectrum respectively, where
(1)
[0027]
Without loss of generality we assume there are up to
M
localized targets, each
corresponds to a phase center located in the ROI. Let
m
l
be the location of the
th
m
target. Ideally, the mono-static radar performs as a uniform linear array,
with the
th i
radar position located at
i
r
, for
i =1,2,...N
. Due to position
perturbations, the actual measurements are taken at 𝒓
~
𝑖 = 𝒓𝑖 + 𝜀𝑖
, where
i

stands for the unknown position perturbation of the
th i
radar position. The
overall signal received by the perturbed array is then a superposition of
scattered waves from all targets in the ROI. We consider measurements at
discrete frequency
k
, where
k =1,2,...,K
. After range compression, we achieve
the radar measurement in the frequency domain, an
NK
data matrix
=[ ] Y Yi,k
with
(2)
where
( , ) S k m  l
is a complex-valued function of frequency
k
and it accounts
for scattering strength of the
th
m
target located at
m
l
; accounts for the
overall magnitude attenuation caused by the antenna beam-pattern and the
propagation between
i
r
and
m
l
; is the phase change term of the
received signal relative to the source pulse; and

is the overall noise.
Correspondingly,
Yi = [Y(i,1) , Y(i,2) , … Y(i,K)] is the frequency-domain
measurement and is the time-domain measurement,
both associated with the
th i
radar position.
[0028]
12
Note that in applications of radar target detection, radar measurements have
distinct properties: slow transition in the frequency domain and sparse gradients
in the time domain. The physical mechanism is as follows. Since both the
scattering strength of targets and the antenna beam-pattern change gradually in
the spatial domain, the scattered electromagnetic field of ROI will also be
smooth in the spatial domain. When there are several isolated targets located in
the ROI, each target will generate a response or signature to radar excitation.
Therefore, the time-domain gradient of radar measurement at each position will
be sparse, and the sparsity level is related to the total number of targets.
Graph-based denoising
[0029]
To reduce the influence of noise and position perturbations, we treat the
synthetic aperture radar as a graph
G = (V,A)
, where
={ ,..., ,..., ,..., } 1 i j N V v v v v
is
the set of nodes, represented by sequential positions of the moving radar
platform, and is the graph shift, or a weighted adjacency matrix that
represents the pairwise proximity between nodes, radar signal is then
the noisy time-series associated with the
th i
node of the graph. We can estimate
the graph shift through the radar measurements after range compression as
i j R
j
H
i j
H
i
j
H
i
i j
, for| − |
| |
, = r r
Y Y Y Y
Y Y
A
(3)
where
H
indicates the Hermitian transpose, and
R
is the maximum distance of
connected neighborhood nodes in the graph. The intuition is that when radar
measurements are taken in nearby positions, the measurements should have
strong pairwise correlations in the frequency domain.
[0030]
Let
X
and
x(t)
be the denoised frequency-domain signal and time-domain
signal, respectively. To denoise radar measurements, we consider a graphbased optimization problem
13
(4)
where
,
are hyperparameters, stands for the element-wised product,
, which compensates the time shift misalignment caused by
position perturbations,
t i  x
represents the gradient of the time series
i
x
associated with the
th i
node, and
A
is a normalized graph shift matrix whose
entries are computed as . The intuition is to ensure each row
of
A
sum up to
1.
[0031]
Note that the cost function in (4) includes three terms. The first term represents
signal fidelity term with the appropriate time shift
i
t
to compensate the phase
misalignment of the
th i
position perturbation. The second term is the
2
 -norm
graph total variation of denoised signal
X
, which is widely used in the graph
signal processing. The graph total variation
compares the difference between the radar measurements associated with each
node and the weighted average of its neighbors. Minimizing this term promotes
the graph smoothness; that is, neighbouring nodes should share similar radar
measurements in the frequency domain. The third term is the
1
 -norm total
variation of the time-domain signal
i
x
. It promotes the sparse gradients in the
time domain. Overall, we use dual regularization terms to capture the physical
properties of radar measurements for target detection.
[0032]
To solve the optimization problem (4), we alternately update the denoised
signal
X
and the time shift
i
t
due to position perturbations.
[0033]
To optimize
X
, we fix the time shift
i
t
for
i =1,...,N
. According to the signal
14
processing theory, we can rewrite
t i  x
as
where
−1 F
is the inverse Fourier transform. We solve
X
through softthresholding the closed-form solution of the two quadratic terms. The denoised
signal at the
th i
node is
(5)
where the soft-thresholding operator
S
is defined as
(6)
and
. (7)
[0034]
To optimize the time shift
i
t
for
i =1,...,N
, we fix the estimated signals
X
as
newly updated , i.e., . The time shift
i
t
can be estimated by
(8)
which can be implemented by the inverse Fourier transform. Note that
Y
is
noisy and the estimation of
i
t
is not convex. Therefore,
i
t
using (8) maybe not
accurate. In order to improve the accuracy of the time shift
i
t
, we use cross
validation
(9)
to form a time shift matrix
=[ ]
i, j Φ t
, where
i j
t
,
represents the time shift between
radar signal measured at
th i
and
th j
positions due to position perturbations. Let
T
N
=[t ,t ,...,t ]  1 2
and
T T L() =1 −1
. Ideally, we have
i j i j
t = t −t
,
, i.e.,
Φ = L() ,
where
L()
is a low-rank matrix of rank not great than two. However, due to
noisy measurement, the time shift matrix acquired by (9) is not a low rank
matrix. Inspired by the robust principal component analysis, we achieve

by
15
decomposing the time shift matrix
Φ
into a low-rank matrix and a sparse
matrix as follows
(10)
where

is a hyperparameter, and
S
represents a sparse matrix which absorbs
spike errors in the time shift matrix. Similar to (10), the above equation (10)
can be solved by a least-squares solution followed by a soft-thresholding
process. Once

is achieved by solving (10), the time shift
i
t
is straightforward
according to
T
N
=[t ,t ,...,t ]  1 2
. The position perturbation at the
th i
radar position
is then estimated by .
2
| |= t ci
i

Example of simulation results
[0035]
The simulation setup is depicted in Fig. 1B, where we use black dots to indicate
ideal moving radar positions, and x-marks to indicate perturbed radar positions.
Further Figure 1C shows comparison between actual perturbed radar positions
and estimated positions.
[0036]
We use a differential Gaussian pulse to illuminate the region of interest (ROI),
as indicated by the dashed rectangle, to detect targets represented by four black
dots in the ROI. The received signals are simulated using (2) with added white
Gaussian noise. Fig. 3A shows the simulated noisy signal whose peak-signalto-noise ratio (PSNR) is
10
dB.
[0037]
In our graph-based denoising method, we choose
/20 1
=10PSNR +
 , where PSNR is
our estimated data peak signal-to-noise ratio in dB,
 = 0.15max | x(t)|
, and
9
= 0.05 10−
 
. We present the denoised graph signal using our proposed method
in Fig. 3B, from which we see that radar echoes from targets are much clearer
16
than the noisy one. A further quantitative analysis shows that the PSNR is
improved from
10
dB to
20.2
dB . With time compensation, the denoised signal
are well aligned as shown in Fig. 3C. The corresponding position perturbations
are also estimated, and compared with that estimated by coherence analysis, as
shown in Fig. 1C. We see that the estimated positions using our proposed
method are very well matched with the actual perturbed positions. However,
the position estimates based on coherence analysis exhibit large errors. This is
because data coherence-based perturbation estimation is not stable due to the
noisy data. With the denoised radar signal, we perform radar imaging, with the
results shown in Fig. 4B. For comparison, the imaging result based on
coherence analysis is shown in Fig. 4C. We see a significant improvement of
our method over coherence based method. We have examined our method on
other scenarios of different target positions and different perturbed radar
positions, all with consistent outperformed results.
[0038]
Some embodiments of the present disclosusre can provide a method to perform
a graph-based algorithm to denoise array signals collected by a perturbed
synthetic aperture radar. The method performs joint radar signal denoising and
radar perturbation estimation by using a dual-regularization-based
optimization. Simulation results demonstrated indcates that the method
significantly improves the imaging performance for noisy radar measurements
of PSNR lower than 10dB.
[0039]
Figure 5 is a schematic diagram illustrating a radar system 500 for generating
a radar image from a region of interest, according to embodiments of the
present disclosure. The radar system 500 can be installed/arranged on vehicles.
The vehicles can be automobiles such as trucks, motor cycles or the like. The
radar system 500 may be referred to as a moving radar platform when the radar
17
system is arranged on automobiles.
[0040]
The radar system 500 may include a network interface controller (interface)
150 configured to receive radar measurements (radar measurement data) 195B
from a radar measurement device (not shown) via a network 190. The radar
measurements 195B are signals indicating objects at a region of interest (ROI),
including echoes reflected from the ROI. In this case, each pulse and each echo
include information of a perturbed radar platform position.
[0041]
Further, the radar system 500 may include a memory 140 to store computerexecutable programs used in the radar imaging method 200 in a storage 130.
The computer-executable programs/algorithms may be a radar signal
processing program 131, a graph model construction program 133, a range
compression program 132, a graph-based denoising program 134, an image
construction program 135, and a processor 120 (or more than one processor)
configured to the computer-executable programs in connection with the
memory 140 that accesses the storage 130 to load the computer-executable
programs. Further, the processor 120 is configured to receive the radar
measurements (data) of scene 195 from the radar measurement device via a
network 190 and perform the radar imaging method 200 discussed above. The
radar system 500 can further include a human machine interface (HMI) 110, a
transmitter/receiver interface 160 and output interface 170. The HMI 110 can
be connected to a keyboard, a pointing device/medium 112 or the like to receive
instruction commands from an operator to start or stop the radar imaging
process. The transmitter/receiver interface 160 can be connected to an antenna
unit 180 that includes a transmitter 161, a receiver 162 and an antenna 163. The
radar system 500 can transmit reconstructed image(s) 175 generated by the
processer 120 to a display device (not shown) via the output interface 170. In
18
some embodiments, the NIC may be configured to include the HMI 110, the
transmitter/receiver interface 160 and the output 170 as an integrated interface.
[0042]
According to another embodiment of the present disclosure, a radar image
processing device 100 can be provided for generating a radar image from a
region of interest (ROI). The radar image processing device 100 can be
constructed by including a network interface controller (NIC) 150 configured
to receive transmitted radar pulses and radar echoes reflected from the ROI in
response to the transmitted radar pulses at different positions along a path of a
moving radar platform, a memory 140 configured to store computer-executable
programs (the radar imaging method 200) including a range compressor 132, a
graph modeling generator 133, a signal aligner and a radar imaging generator
and a focused image generator, a processor 120, in connection with the memory
140. The processor 120 is configured to perform range compression on the
radar echoes by deconvolving the transmitted radar pulses and perform a radar
measurement to obtain frequency-domain signals Y(i,k), generate a graph
model represented by sequential positions of the moving radar platform and a
graph shift matrix A computed using the frequency-domain signals Y(i,k),
iteratively align and denoise the frequency-domain signals Y(i,k) to obtained
denoised data X(i,k) and time shifts ti by solving a graph-based optimization
problem represented by the graph model, wherein the approximated time shifts
ti compensate phase errors caused by position perturbations of the moving radar
platform, and perform radar imaging based on the denoised data X(i,k) and the
estimated time shifts ti to generate focused radar images. The radar image
processing device 100 can reconstruct radar images by receiving radar
measurements (data) of scene 195B via the NIC 150 so that the radar image
processing device 100 reconstruct radar imaged from the receiving radar
measurements (data) of scene 195B.
19
[0043]
Further, the radar system 500 may include at least one antenna that is arranged
toward an advancing side of the moving radar platform, and the at least one
antenna may transmit the generated focus radar images to a display device via
the interface.
[0044]
In some cases, the graph-based optimization problem imposes smoothness of
X(i,k) in the frequency domain and sparsity of x(t) in the time domain. Further,
the time shifts may be cross-validated by decomposing a time shift matrix into
a sparse matrix and a low-rank matrix. Also, the time shifts are configured to
compensate phase errors caused by position perturbations of the moving radar
platform.
[0045]
In some cases, the radar image processing device 100 can be a standalone
device that can compute and output radar reconstructed image by receiving the
radar measurements which include the information required to reconstruct
radar images the processer 120 can receive the radar measurements via the
NIC150 in connection with the network 190.
[0046]
The radar measurements 195B include pulse signals indicating objects in the
ROI and echoes reflected from the ROI. In this case, each of the pulse signals
and each of the echoes include information of perturbed positions of the radar
system.
[0047]
In some embodiments, a method for denoising radar measurements of scenes
can be provided. The method may include steps of generating a graph model
represented by sequential positions of a moving radar platform and a graph shift
matrix A computed using frequency-domain signals Y(i,k), and iteratively
20
denoising and aligning the frequency-domain signals Y(i,k) to obtain denoised
data X(i,k) and time shifts ti by solving a graph-based optimization problem
represented by the graph model, wherein the time shifts are configured to
compensate phase misalignments of positions of the moving radar platform. In
some cases, the method may be a computer-executable program that causes a
process to perform the steps of the method, and the computer-executable
program can be stored into at least one memory or at storage device.
[0048]
The above-described embodiments of the present invention can be
implemented in any of numerous ways. For example, the embodiments may
be implemented using hardware, software or a combination thereof. When
implemented in software, the software code can be executed on any suitable
processor or collection of processors, whether provided in a single computer or
distributed among multiple computers. Such processors may be implemented
as integrated circuits, with one or more processors in an integrated circuit
component. Though, a processor may be implemented using circuitry in any
suitable format.
[0049]
Also, the embodiments of the invention may be embodied as a method, of
which an example has been provided. The acts performed as part of the method
may be ordered in any suitable way. Accordingly, embodiments may be
constructed in which acts are performed in an order different than illustrated,
which may include performing some acts simultaneously, even though shown
as sequential acts in illustrative embodiments.
[0050]
Use of ordinal terms such as “first,” “second,” in the claims to modify a claim
element does not by itself connote any priority, precedence, or order of one
claim element over another or the temporal order in which acts of a method are
21
performed, but are used merely as labels to distinguish one claim element
having a certain name from another element having a same name (but for use
of the ordinal term) to distinguish the claim elements.
[0051]
Although the invention has been described by way of examples of preferred
embodiments, it is to be understood that various other adaptations and
modifications can be made within the spirit and scope of the invention.
[0052]
Therefore, it is the object of the appended claims to cover all such variations
and modifications as come within the true spirit and scope of the invention.
22
[CLAIMS]
[Claim 1]
A radar system for generating a radar image from a region of interest
(ROI) comprising:
an interface configured to transmit radar pulses to the ROI at different
positions along a path of a moving radar platform and receive radar echoes
reflected from the ROI, wherein the at least one antenna is arranged on the
moving platform to emit radar pulses to the ROI using at least one antenna;
a memory configured to store computer-executable programs including
a range compressor, a graph modeling generator, a signal aligner and a radar
imaging generator and a focused image generator;
a processor, in connection with the memory, configured to:
perform range compression on the radar echoes by deconvolving the
transmitted radar pulses and perform a radar measurement to obtain frequencydomain signals Y(i,k);
generate a graph model represented by sequential positions of the
moving radar platform and a graph shift matrix A computed using the
frequency-domain signals Y(i,k);
iteratively denoise and align the frequency-domain signals Y(i,k) to
obtain denoised data X(i,k) and time shifts ti by solving a graph-based
optimization problem represented by the graph model; and
perform radar imaging based on the denoised data X(i,k) and the time
shifts ti to generate focused radar images.
[Claim 2]
The radar system of claim 1, wherein the radar echoes include position
perturbations with respect to the moving radar platform.
[Claim 3]
The radar system of claim 1, wherein the processer is configured to
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receive the radar measurements, wherein the radar measurements include pulse
signals indicating objects in the ROI and echoes reflected from the ROI.
[Claim 4]
The radar system of claim 3, wherein each of the pulse signals and each
of the echoes include information of perturbed positions of the radar system.
[Claim 5]
The radar system of claim 1, wherein the at least one antenna is arranged
toward an advancing side of the moving radar platform.
[Claim 6]
The radar system of claim 1, wherein the at least one antenna transmits
the generated focus radar images to a display device via the interface.
[Claim 7]
The radar system of claim 1, wherein the graph-based optimization
problem imposes smoothness of X(i,k) in the frequency domain and sparsity of
x(t) in the time domain.
[Claim 8]
The radar system of claim 1, wherein the time shifts are cross-validated
by decomposing a time shift matrix into a sparse matrix and a low-rank matrix.
[Claim 9]
The radar system of claim 8, wherein the sparse matrix is a function of
time shifts.
[Claim 10]
The radar system of claim 1, wherein the time shifts are configured to
compensate phase errors caused by position perturbations of the moving radar
platform.
[Claim 11]
A radar image processing device for generating a radar image from a
region of interest (ROI) comprising:
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a network interface controller (NIC) configured to receive transmitted
radar pulses and radar echoes reflected from the ROI in response to the
transmitted radar pulses at different positions along a path of a moving radar
platform;
a memory configured to store computer-executable programs including
a range compressor, a graph modeling generator, a signal aligner and a radar
imaging generator and a focused image generator;
a processor, in connection with the memory, configured to:
perform range compression on the radar echoes by deconvolving the
transmitted radar pulses and perform a radar measurement to obtain frequencydomain signals Y(i,k);
generate a graph model represented by sequential positions of the
moving radar platform and a graph shift matrix A computed using the
frequency-domain signals Y(i,k);
iteratively denoise and align the frequency-domain signals Y(i,k) to
obtain denoised data X(i,k) and time shifts ti by solving a graph-based
optimization problem represented by the graph model, wherein the time shifts
are configured to compensate phase misalignments of positions of the moving
radar platform; and
perform radar imaging based on the denoised data X(i,k) and the
estimated time shifts ti to generate focused radar images.
[Claim 12]
The radar image processing device of claim 11, wherein the moving
radar platform emits radar pulses to the ROI using at least one antenna.
[Claim 13]
The radar image processing device of claim 12, wherein the radar
imaging device is arranged toward an advancing side of the moving radar
platform.
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[Claim 14]
The radar image processing device of claim 11, wherein the radar
imaging device transmits the generated focus radar images to a display device
via the NIC.
[Claim 15]
The radar image processing device of claim 11, wherein the radar echoes
include position perturbations with respect to the moving radar platform.
[Claim 16]
The radar image processing device of claim 11, wherein a time-domain
signal y(ri,t) is used instead of frequency-domain signals Y(i,k).
[Claim 17]
The radar image processing device of claim 11, wherein the graph-based
optimization problem imposes smoothness of X(i,k) in the frequency domain
and sparsity of x(t) in the time domain,
[Claim 18]
The radar image processing device of claim 11, wherein the time shifts
are cross-validated by decomposing a time shift matrix into a sparse matrix and
a low-rank matrix.
[Claim 19]
The radar image processing device of claim 11, wherein the time shifts
are configured to compensate phase errors caused by position perturbations of
the moving radar platform.
[Claim 20]
A method for denoising radar measurements of scenes, comprising steps
of:
generating a graph model represented by sequential positions of a
moving radar platform and a graph shift matrix A computed using frequencydomain signals Y(i,k); and
26
iteratively denoising and aligning the frequency-domain signals Y(i,k)
to obtain denoised data X(i,k) and time shifts ti by solving a graph-based
optimization problem represented by the graph model, wherein the time shifts
are configured to compensate phase misalignments of positions of the moving
radar platform.