CN113419228A - Sea surface small target detection method and device based on time-frequency ridge-Radon transformation - Google Patents

Sea surface small target detection method and device based on time-frequency ridge-Radon transformation Download PDF

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CN113419228A
CN113419228A CN202110615431.9A CN202110615431A CN113419228A CN 113419228 A CN113419228 A CN 113419228A CN 202110615431 A CN202110615431 A CN 202110615431A CN 113419228 A CN113419228 A CN 113419228A
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丁昊
伍僖杰
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刘宁波
董云龙
黄勇
王国庆
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School Of Aeronautical Combat Service Naval Aeronautical University Of People's Liberation Army
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Abstract

The invention relates to a sea surface small target detection method and device based on time-frequency ridge-Radon transformation, electronic equipment and a storage medium. The method comprises the following steps: detecting the sea surface by adopting a radar to obtain detection data; extracting a time-frequency image of a unit to be detected from the detection data; determining the peak characteristic and the bandwidth characteristic of a time-frequency ridge in the time-frequency image, and performing frame smoothing on the peak characteristic and the bandwidth characteristic along a frame time dimension; and fusing the peak characteristic and the frequency width characteristic after frame smoothing, and detecting and classifying the characteristic points. The method can better extract the characteristics from the time-frequency plane through time-frequency ridge-Radon transform (RRT) so as to distinguish a target unit and a clutter unit; in addition, the contrast of time-frequency feature distribution in the unit to be detected is further enhanced through frame smoothing; meanwhile, fusion detection is carried out on the dual-feature plane, so that the limitation of single-feature detection is avoided.

Description

Sea surface small target detection method and device based on time-frequency ridge-Radon transformation
Technical Field
The present invention relates to a radar detection method, and more particularly, to a method and an apparatus for detecting a small target on the sea surface based on time-frequency ridge-Radon transform, an electronic device, and a storage medium.
Background
Along with the complexity of radar detection environment and the diversification of target forms, the characteristics of low sea surface speed, small target speed and small scattering cross section area (RCS) are increasingly prominent, so that the detection of a radar on a weak and small target becomes an important and difficult problem.
Early radar target detection methods, which are based on threshold detection of a specific test statistic, such as doppler detection (MTI/MTD) and energy detection (CFAR), have limited performance for detecting weak targets in the background of sea clutter: when the radar works under the condition of a small ground wiping angle, a large number of sea spike signals are easily received, the echoes of the sea spike signals are similar to target echoes, the amplitude distribution presents strong non-Gaussian performance, so that a detector based on time domain energy accumulation can generate a large number of false alarms when detecting a weak target, and the performance is obviously reduced.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a sea surface small target detection method and device based on time-frequency ridge-Radon transformation, electronic equipment and a storage medium.
In a first aspect, the present invention provides a method for detecting a small sea surface target based on time-frequency ridge-Radon transform, comprising:
detecting the sea surface by adopting a radar to obtain detection data;
extracting a time-frequency image of a unit to be detected from the detection data;
determining the peak characteristic and the bandwidth characteristic of a time-frequency ridge in the time-frequency image, and performing frame smoothing on the peak characteristic and the bandwidth characteristic along a frame time dimension; and
and fusing the peak characteristic and the frequency width characteristic after frame smoothing, and detecting and classifying the characteristic points.
Further, the determining the peak feature and the frequency width feature of the time-frequency ridge in the time-frequency image includes:
and defining the position of the time-frequency ridge sequence in the time-frequency plane in the time-frequency image as 1, defining other positions as 0, and performing Radon transformation on the obtained time-frequency ridge image to obtain linear characteristics.
Further, the obtaining of linear characteristics by performing Radon transform on the obtained time-frequency ridge image includes:
the Radon transform is performed by the following formula:
Figure BDA0003097838700000021
wherein R isRidges(p, theta) is the feature space after Radon transformation, DRidgesAnd (t, f) is the time-frequency ridge image, t represents a time axis coordinate, f represents a frequency axis coordinate, theta is an included angle between a perpendicular line from the original point to the radon transformation straight line and the positive direction of the X axis, rho is the distance from the original point to the radon transformation straight line, and delta is an impulse function.
Further, the determining the peak feature and the bandwidth feature of the time-frequency ridge in the time-frequency image further includes:
the dispersion of the time-frequency ridge on the frequency axis is determined by the following formula:
Figure BDA0003097838700000022
where COEFF is the bandwidth coefficient, P is the unit length, ρMAXAnd thetaMAXRespectively peak characteristic xiMAXIn the coordinates in the Radon space, rho is the distance from the origin to the Radon transformation straight line, and xi is a variable for establishing an inequality in the formula.
Further, the frame smoothing along the frame time dimension includes:
the frame smoothing is performed by the following formula:
Figure BDA0003097838700000023
wherein the content of the first and second substances,
Figure BDA0003097838700000024
is the extraction result of the single time-frequency characteristic,
Figure BDA0003097838700000025
is the frame time dimension, r is the distance dimension, M is the smoothing factor, AcIs the number of accumulated pulses processed per unit frame time,
Figure BDA0003097838700000031
represents rounding down, R is the number of distance units, and m is the operand.
Further, the fusing the peak feature and the frequency width feature and detecting and classifying the feature points includes:
constructing a dual feature plane (xi)MAX，ξBW) Wherein the horizontal axis xiMAXFor the peak feature, the longitudinal axis xiBWIs the bandwidth characteristic; and
based on said dual feature plane (ξ)MAX，ξBW) And detecting and classifying the feature points.
Further, before the extracting the time-frequency image of the unit to be detected from the detection data, the method further includes:
and carrying out blocking whitening filtering processing on the sea clutter.
In a second aspect, the present invention provides a sea surface small target detection device based on time-frequency ridge-Radon transform, including:
the detection unit is used for detecting the sea surface by adopting a radar to obtain detection data;
the time-frequency image extraction unit is used for extracting the time-frequency image of the unit to be detected from the detection data;
the characteristic determining and smoothing unit is used for determining the peak characteristic and the bandwidth characteristic of a time-frequency ridge in the time-frequency image and performing frame smoothing on the peak characteristic and the bandwidth characteristic along a frame time dimension; and
and the detection and classification unit is used for fusing the peak characteristic and the frequency width characteristic after frame smoothing and detecting and classifying the characteristic points.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for detecting a small sea surface object based on time-frequency ridge-Radon transform in any one of the first aspect when executing the computer program.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the time-frequency ridge-Radon transform-based sea surface small target detection method according to any one of the first aspect.
The method can better extract the characteristics from the time-frequency plane through time-frequency ridge-Radon transform (RRT) so as to distinguish a target unit and a clutter unit; in addition, the contrast of time-frequency feature distribution in the unit to be detected is further enhanced through frame smoothing; meanwhile, fusion detection is carried out on the dual-feature plane, so that the limitation of single-feature detection is avoided.
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Fig. 1 is a flowchart of a method for detecting a small sea surface target based on time-frequency ridge-Radon transform according to an embodiment of the present invention;
fig. 2 is a structural block diagram of a sea surface small target detection device based on time-frequency ridge-Radon transform according to an embodiment of the present invention;
fig. 3(a) to 3(d) respectively show the detection results of a detector based on the detection method of the embodiment of the present invention, a detector based on Hough transform, a fractal detector, and a frequency-domain CFAR detector; and
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for detecting a small sea surface target based on time-frequency ridge-Radon transform according to an embodiment of the present invention. Referring to fig. 1, the detection method includes:
step S101: detecting the sea surface by adopting a radar to obtain detection data;
step S103: extracting a time-frequency image of a unit to be detected from the detection data;
step S105: determining the peak characteristic and the frequency width characteristic of a time-frequency ridge in the time-frequency image, and performing frame smoothing on the peak characteristic and the frequency width characteristic along the time dimension of a frame; and
step S07: and fusing the peak characteristic and the frequency width characteristic after frame smoothing, and detecting and classifying the characteristic points.
In the embodiment of the present invention, the step S101 includes acquiring the detection data by radar, and since the acquisition of the detection data by radar may be performed by using a technique that is conventional in the art, a detailed description thereof will be omitted.
In the embodiment of the present invention, specifically, step S103 (extracting the time-frequency image of the unit to be detected) is as follows:
and analyzing the unit to be tested by using a Cohen-type time-frequency distribution method. In an example, a smooth Pseudo Wigner-Ville (SPWVD) Distribution is used as a time-frequency analysis tool for detection. The SPWVD of the cell signals to be detected { x (N) ═ 1,2, …, N } can be expressed as:
Figure BDA0003097838700000051
wherein t represents time axis coordinate and is equal to the length of the echo unit sequence x (n), f represents frequency axis coordinate, h (M), g (K) represent time smoothing window with length of 2M +1 and frequency smoothing window with length of 2K +1 respectively, M, K represents corresponding half window length, M and K are operation variables, and delta fdIs the frequency resolution.
In order to better distinguish the target echo from the pure clutter, the blocking whitening filtering is carried out on the received echo signal in the embodiment of the invention so as to achieve the purpose of suppressing the sea clutter. The method comprises the following specific steps:
firstly, the sequence x (n) of the unit to be measured and the sequence x of the reference unit arer(n) non-overlapping vector u truncated to length Li、up，iThe following formula:
Figure BDA0003097838700000052
wherein, N is the length of the unit sequence x (N) to be detected. Here, in order to ensure the performance of the detector, the reference unit number P and the truncation length L need to satisfy the relationship: p is more than or equal to 2L. The clutter covariance matrix of the cell to be detected can be estimated using the Normalized Sample Covariance Matrix (NSCM) of the reference cell as:
Figure BDA0003097838700000053
wherein u isp,iAn ith truncated vector representing the p-th reference unit. By using
Figure BDA0003097838700000054
Whitening inhibition is carried out on the echo section of the corresponding unit to be measured, and a processed echo vector of the unit to be measured can be obtained, wherein the whitening inhibition is shown as follows:
Figure BDA0003097838700000061
wherein the content of the first and second substances,
Figure BDA0003097838700000062
representing a covariance matrix
Figure BDA0003097838700000063
Cholesky decomposition of (c). Similarly, block whitening clutter suppression is performed on the time series over all range units. The SPWVD of the block whitened signal to be detected can be expressed as:
Figure BDA0003097838700000064
wherein the meaning of each symbol is the same as that represented by the same symbol described above, and the description thereof is omitted here. In the embodiment of the present invention, specifically, step S105 (time-frequency ridge-Radon transform extraction features) is as follows:
defining the time-frequency ridge sequence Ridges (n) as DSPWVD(t, f) a set of maxima along the time dimension, which can then be expressed as:
Ridges(n)＝max(DSPWVD(t,f)|t＝t(n))，n＝1,2，…，N
in the embodiment of the invention, the basic idea of time-frequency ridge-Radon transform (RRT) is as follows: will be the time-frequency plane DSPWVDAnd (3) defining the position of the middle time-frequency ridge sequence Ridges (n) as 1, defining other positions as 0, and performing Radon transformation on the obtained time-frequency ridge image to obtain the linear characteristic of the time-frequency ridge sequence Ridges (n). The time-frequency ridge-Radon transform (RRT) is defined as follows:
Figure BDA0003097838700000065
wherein R isRidges(rho, theta) is the feature space after Radon transformation, DRidges(t, f) is a time-frequency ridge image, t represents a time axis coordinate, f represents a frequency axis coordinate, and theta is a perpendicular line from an original point to a radon transformation straight line and an X axisAnd the included angle in the positive direction is rho, the distance from the original point to the radon transformation straight line is rho, and delta is an impulse function.
Wherein, the time-frequency ridge image DRidges(t, f) can be expressed as:
Figure BDA0003097838700000066
wherein, the location (Ridges (n)) represents the (t, f) coordinate position of the time-frequency ridge sequence Ridges (n) in the time-frequency plane. Because the time-frequency ridge containing the target unit has good aggregation and is approximate to a horizontal line, the purpose of reducing the calculation amount can be achieved by limiting the angle theta of Radon in the actual detection.
According to the obvious difference between the pure clutter unit and the target-containing unit in Radon space, the peak value is taken as one of the characteristics of the detector, namely:
ξMAX＝max{RRidges(ρ,θ)}
wherein ξMAXRepresents the peak feature, RRidges(ρ, θ) represents the feature space after a time-frequency ridge-Radon transform (RRT). In general, ξ of the target unitMAXThe characteristic value is large; conversely, xi of pure clutter unitMAXThe eigenvalues are small.
The dispersion of the time-frequency ridge on the frequency axis f can be characterized by the bandwidth xiBWTo be embodied. Definition xiBWTo satisfy the value of the frame smoothing definition formula (to be described later):
Figure BDA0003097838700000071
where COEFF is the bandwidth coefficient, generally 0.8 is taken, P is the unit length, ρMAXAnd thetaMAXRespectively peak characteristic xiMAXIn the coordinates in the Radon space, ρ is the distance from the origin to the Radon transformation straight line, and ξ is a variable for which the inequality in this formula holds. With peak characteristic xiMAXIn contrast, xi of the target unitBWSmall characteristic value, pure clutter unit xiBWThe eigenvalue is large.
Next, smoothing is performed on the peak and bandwidth feature distribution space, which is as follows:
let the extraction result of single time-frequency feature be
Figure BDA0003097838700000072
Wherein
Figure BDA0003097838700000073
Represents the frame time dimension, referred to as the burst accumulation time (CPI), and r represents the distance dimension. In the embodiment of the invention, a frame smoothing method is adopted to carry out smoothing processing on the peak value and bandwidth characteristic distribution space, and the contrast between the target unit and the clutter unit is enhanced. Single feature extraction results
Figure BDA0003097838700000074
The frame smoothing of (a) may be defined as:
Figure BDA0003097838700000075
wherein M is a smoothing factor, and the value range is generally (10-30). A. thecIs the number of accumulated pulses processed per unit frame time,
Figure BDA0003097838700000076
represents rounding down, R is the number of distance units, and m is the operand.
In the embodiment of the present invention, specifically, step S107 is as follows:
first, a dual feature plane (xi) is constructedMAX，ξBW) Wherein the horizontal axis is the peak characteristic xiMAXWith the vertical axis being the bandwidth characteristic xiBW. Typically, the peak feature ξ comprising the target unitMAXLarger, bandwidth feature xiBWSmaller, should distribute in the right lower of the characteristic plane; in contrast, peak feature xi of sea clutter unitMAXSmaller, bandwidth feature xiBWLarger, should be distributed to the upper left of the feature plane.
Then, detecting and classifying the feature points by adopting a convex hull algorithm, which is concretely as follows:
(1) initialization: setting the feature training set of the sea clutter data as H, the number of the feature training set as W, and calculating the false alarm number as L ═ W · pFWherein p isFIs the set false alarm rate. Let the iteration condition value l be 0.
(2) Computing a convex hull CH (H) of the current data point, wherein the vertex of CH (H) is { v } [, (H) }1,v2,…,vr}; then, the number of feature points falling into the convex hull CH (H) is counted and set as nall。
(3) Set a loop variable q from 1 to r, compute the convex hull CH (H- { v)qH), i.e. removing vertex v from set HqThen calculate the new convex hull CH (H- { v)qIs set to n)q。
(4) Set of comparisons { nall-n1,…,nall-nrRemoving the vertex v corresponding to the maximum value in the seti。
(5) Let H- { vi}＝H,l+1＝l。
(6) And if L is less than L, returning to the step (2) to continue the removal of the next vertex. Otherwise, the removal process is terminated, and the final decision region Ω ═ ch (h) is output.
And after a new feature point is generated, if the new feature point falls in the region omega, the unit to be detected is a clutter unit, otherwise, the unit to be detected is judged to contain a target unit. It should be noted that when the sea clutter characteristics change significantly due to changes in sea conditions or radar view angles, the sea clutter data needs to be reacquired to determine a new convex hull decision region.
Fig. 2 is a structural block diagram of a sea surface small target detection device based on time-frequency ridge-Radon transform according to an embodiment of the present invention. Referring to fig. 2, the apparatus 200 includes:
a detection unit 201, configured to detect the sea surface by using a radar to obtain detection data;
the time-frequency image extraction unit 203 is used for extracting the time-frequency image of the unit to be detected from the detection data;
a feature determining and smoothing unit 205, configured to determine a peak feature and a bandwidth feature of a time-frequency ridge in the time-frequency image, and perform frame smoothing on the peak feature and the bandwidth feature along a frame time dimension; and
and a detection and classification unit 207, configured to fuse the peak feature and the bandwidth feature after frame smoothing, and detect and classify the feature points.
As is clear from the above, the respective units 201 to 207 of the apparatus 200 can respectively perform the respective steps in the identification method described with reference to the above-mentioned embodiments, and the details thereof will not be described here.
In order to illustrate the advantages of the method for detecting the small sea surface target based on the time-frequency ridge-Radon transformation, which is provided by the embodiment of the invention, the method adopts the measured data for analysis and verification.
FIGS. 3(a) to 3(d) respectively show the detection results of the detector based on the detection method of the embodiment of the present invention, the detector based on Hough transform, the fractal detector, and the frequency-domain CFAR detector, and the false alarm rates are all 10-3。
As can be seen from fig. 3(a) to 3(d), the detection method of the embodiment of the present invention has significant advantages in the actual false alarm rate control and the detection probability improvement.
According to the embodiment of the invention, the characteristics are better extracted from the time-frequency plane through time-frequency ridge-Radon transform (RRT) so as to distinguish a target unit from a clutter unit; in addition, the contrast of time-frequency feature distribution in the unit to be detected is further enhanced through frame smoothing; meanwhile, fusion detection is carried out on the dual-feature plane, so that the limitation of single-feature detection is avoided.
In another aspect, the present invention provides an electronic device. As shown in fig. 4, the electronic device 400 includes a processor 401, a memory 402, a communication interface 403, and a communication bus 404.
The processor 401, the memory 402 and the communication interface 403 complete mutual communication through the communication bus 404;
the processor 401 is configured to call a computer program in the memory 402, and when the processor 401 executes the computer program, the steps of the method for detecting a small object on the sea surface based on time-frequency ridge-Radon transform provided by the embodiment of the present invention as described above are implemented.
Further, the computer program in the memory may be implemented in the form of a software functional unit and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several computer programs to make a computer device (which may be a personal computer, a server, or a network device) execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for detecting a small target on a sea surface based on time-frequency ridge-Radon transform provided in the embodiments of the present invention as described above.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above examples are only for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A sea surface small target detection method based on time-frequency ridge-Radon transformation is characterized by comprising the following steps:
detecting the sea surface by adopting a radar to obtain detection data;
extracting a time-frequency image of a unit to be detected from the detection data;
determining the peak characteristic and the bandwidth characteristic of a time-frequency ridge in the time-frequency image, and performing frame smoothing on the peak characteristic and the bandwidth characteristic along a frame time dimension; and
and fusing the peak characteristic and the frequency width characteristic after frame smoothing, and detecting and classifying the characteristic points.
2. The method for detecting the sea surface small target based on the time-frequency ridge-Radon transformation as claimed in claim 1, wherein the determining the peak feature and the bandwidth feature of the time-frequency ridge in the time-frequency image comprises:
and defining the position of the time-frequency ridge sequence in the time-frequency plane in the time-frequency image as 1, defining other positions as 0, and performing Radon transformation on the obtained time-frequency ridge image to obtain linear characteristics.
3. The method for detecting the small sea surface target based on the time-frequency ridge-Radon transformation as claimed in claim 2, wherein the Radon transformation is performed on the obtained time-frequency ridge image to obtain the linear characteristic, and the method comprises the following steps:
the Radon transform is performed by the following formula:
Figure FDA0003097838690000011
wherein R isRidges(p, theta) is subjected to the Radon transformCharacteristic space of the rear, DRidgesAnd (t, f) is the time-frequency ridge image, t represents a time axis coordinate, f represents a frequency axis coordinate, theta is an included angle between a perpendicular line from the original point to the radon transformation straight line and the positive direction of the X axis, rho is the distance from the original point to the radon transformation straight line, and delta is an impulse function.
4. The method according to claim 3, wherein the determining the peak feature and the bandwidth feature of the time-frequency ridge in the time-frequency image further comprises:
the dispersion of the time-frequency ridge on the frequency axis is determined by the following formula:
Figure FDA0003097838690000021
where COEFF is the bandwidth coefficient, P is the unit length, ρMAXAnd thetaMAXRespectively peak characteristic xiMAXIn the coordinates in the Radon space, rho is the distance from the origin to the Radon transformation straight line, and xi is a variable for establishing an inequality in the formula.
5. The method of claim 1, wherein the frame smoothing of the peak feature and the bandwidth feature along a frame time dimension comprises:
the frame smoothing is performed by the following formula:
Figure FDA0003097838690000022
wherein the content of the first and second substances,
Figure FDA0003097838690000023
is the extraction result of the single time-frequency characteristic,
Figure FDA0003097838690000024
is the frame time dimension, r is the distance dimension, M is the smoothing factor, AcIs the number of accumulated pulses processed in unit frame time, | · | represents rounding-down, R is the number of distance units, and m is an operation variable.
6. The time-frequency ridge-Radon transform-based sea surface small target detection method according to claim 4, wherein the fusion of the peak feature and the frequency width feature after frame smoothing, and the detection and classification of the feature points comprise:
constructing a dual feature plane, wherein the horizontal axis is the peak feature and the vertical axis is the bandwidth feature; and
and detecting and classifying the feature points based on the dual feature planes.
7. The method according to claim 1, wherein before extracting the time-frequency image of the unit to be detected from the probe data, the method further comprises:
and carrying out blocking whitening filtering processing on the sea clutter.
8. A sea surface small target detection device based on time-frequency ridge-Radon transformation is characterized by comprising:
the detection unit is used for detecting the sea surface by adopting a radar to obtain detection data;
the time-frequency image extraction unit is used for extracting the time-frequency image of the unit to be detected from the detection data;
the characteristic determining and smoothing unit is used for determining the peak characteristic and the bandwidth characteristic of a time-frequency ridge in the time-frequency image and performing frame smoothing on the peak characteristic and the bandwidth characteristic along a frame time dimension; and
and the detection and classification unit is used for fusing the peak characteristic and the frequency width characteristic after frame smoothing and detecting and classifying the characteristic points.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for detecting small sea-surface objects based on time-frequency ridge-Radon transform as claimed in any one of claims 1 to 7 when executing the computer program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the time-frequency ridge-Radon transform-based sea surface small object detection method according to any one of claims 1 to 7.
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Application publication date: 20210921