CN112799029B - High-frequency marine radar first-order echo extraction method based on watershed segmentation - Google Patents
High-frequency marine radar first-order echo extraction method based on watershed segmentation Download PDFInfo
- Publication number
- CN112799029B CN112799029B CN202011582741.7A CN202011582741A CN112799029B CN 112799029 B CN112799029 B CN 112799029B CN 202011582741 A CN202011582741 A CN 202011582741A CN 112799029 B CN112799029 B CN 112799029B
- Authority
- CN
- China
- Prior art keywords
- echo
- frequency
- order
- radar
- output
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000605 extraction Methods 0.000 title claims abstract description 21
- 230000011218 segmentation Effects 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000002592 echocardiography Methods 0.000 claims description 16
- 230000009466 transformation Effects 0.000 claims description 6
- 230000000877 morphologic effect Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000005530 etching Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000011426 transformation method Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 2
- 238000001228 spectrum Methods 0.000 abstract description 45
- 239000000284 extract Substances 0.000 abstract description 4
- 238000012544 monitoring process Methods 0.000 abstract description 3
- 230000002708 enhancing effect Effects 0.000 abstract 1
- 238000005457 optimization Methods 0.000 abstract 1
- 238000003672 processing method Methods 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 11
- 238000001514 detection method Methods 0.000 description 9
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000005404 monopole Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000002923 local minimum search Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
- G01S13/958—Theoretical aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a high-frequency ocean radar first-order echo extraction method based on watershed segmentation, and belongs to the technical field of high-frequency ground wave radar ocean environment monitoring. The method comprises the steps of regarding a distance-Doppler amplitude graph collected by a radar as a gray level image, enhancing a first-order ocean echo by adopting an image processing method, dividing two parts of images of positive and negative Doppler frequencies by utilizing a watershed dividing method based on mark control to extract an image area containing the first-order echo, finally further eliminating frequency points which do not meet the signal-to-noise ratio requirement by utilizing a preset signal-to-noise ratio threshold, and finally extracting a first-order ocean echo signal in a return spectrum. Compared with the prior art, the method does not extract the first-order echo from distance elements one by one, but extracts the first-order echo in the whole distance-Doppler spectrum at one time. The invention only needs to preset one signal-to-noise ratio parameter, thereby greatly simplifying the parameter adjustment and optimization process in the radar operation.
Description
Technical Field
The invention belongs to the technical field of high-frequency radar marine environment monitoring, and particularly relates to a method for extracting a marine echo signal in a high-frequency marine radar echo spectrum.
Background
High frequency ocean radars operating at 3 to 30MHz utilize electromagnetic wave energy to diffract and propagate along the earth's surface to achieve over-the-horizon detection of the ocean. Currently, high-frequency ocean radars are recognized as being capable of realizing large-range all-weather remote sensing of dynamic elements such as wind, waves, currents and the like on the ocean surface. The development of the high-frequency ocean radar ocean environment monitoring technology has important significance for the society and the economic construction of China and the realization of ocean strong countries.
The ocean echoes received by the high frequency radar include first-order bragg echoes generated by sea waves having a wavelength equal to half the wavelength of the electromagnetic waves emitted by the radar and higher-order echoes that enter the receiver after two or more reflections occur on the sea surface. Furthermore, the higher order echo spectrum always surrounds the first order echo spectrum. Since the acquisition of ocean surface flow field information is completely dependent on the first order echo signals. Therefore, accurate extraction of the first order echo signals is critical for flow field inversion. In the early development stage of the ocean radar, the current common first-order echo spectrum region detection method is a difference spectrum method. The basic idea of the difference spectrum method is to determine a first-order ocean echo spectrum region by utilizing a steeper falling edge between a first-order spectrum region and a high-order spectrum region. In practical use, the method finds that the first-order echo signals can not be well separated when the first-order echo signals of adjacent frequency points in the first-order echo spectrum region are jumped. Thereafter, another method based on local minimum search is widely used instead of the original difference spectrum method. The method needs to preset a plurality of parameters including a maximum flow velocity value in a radar detection area, a signal-to-noise ratio threshold value, the position of a frequency point set used for calculating noise intensity, the length of a Doppler spectrum smoothing window, a local minimum value searching initial position and the like. These parameters are determined by means of manual trial and error at the time of radar construction and are rarely changed afterwards. In fact, in the case where the overall flow velocity in the radar detection range is relatively stable or the sea state level in the radar detection range is relatively stable, the local minimum-based search method is a first-order peak region capable of detecting echo relatively accurately. However, the morphology of the radar return spectrum changes drastically with sea state. In the same radar detection area, the climate change is very large along with the change of seasons, so that the real sea state on the sea surface is greatly changed, and finally, the radar return spectrum is changed. At this time, the local minimum value-based search method needs to adjust preset parameters according to sea conditions to ensure the accuracy of the first-order echo extraction result. During radar operation, it is not practical to manually and constantly adjust the parameters of the difference spectrum. Therefore, a more robust first-order spectral region detection method is urgently needed to ensure the ocean current measurement performance of the high-frequency ocean radar under different sea conditions.
Disclosure of Invention
The invention aims to provide a high-frequency ocean radar first-order echo extraction method based on watershed segmentation, which is used for improving the extraction precision of first-order echoes in radar echoes, further improving the ocean current observation performance of the radar, and enabling high-frequency ocean radar first-order echo signals received under different sea conditions to be extracted more accurately.
The technical scheme of the invention is as follows: a first-order echo extraction method of a high-frequency ocean radar based on watershed segmentation, the first-order echo extraction method regards a distance-Doppler amplitude graph of ocean echo collected by the radar as a gray image, a watershed segmentation method based on mark control is adopted to divide a positive image and a negative image of Doppler frequency respectively so as to extract positive and negative first-order echoes, and a specific first-order echo extraction process comprises the following steps:
step 1: calculating the mean value and the maximum value of Doppler echo on each distance element in the radar echo;
step 2: subtracting the average value of the current distance element echo from the echo on each frequency point;
step 3: normalizing the data output in the step 2 by using the average value and the maximum value calculated in the step 1 to obtain a normalized gray level map;
step 4: calculating the fractional number of 90% on each distance element for the data output in the step 3;
step 5: dividing the echo on each frequency point by the fractional number on the current distance element output in the step 4;
step 6: setting the data which is output in the step 5 and is larger than the value 1 as the value 1;
step 7: performing morphological opening operation and closing operation on the data output in the step 6;
step 8: calculating a gradient amplitude graph of output data in the step 7, and normalizing;
step 9: calculating a superimposed image of the output image of the step 7 and the normalized gradient amplitude map output by the step 8;
step 10: calculating a local maximum value identification graph of the output image in the step 9 to obtain a binary image;
step 11: performing Euclidean distance conversion on the binary image output in the step 10;
step 12: dividing the distance output in the step 11 into images by using a watershed transformation method;
step 13: extracting a region containing positive and negative Bragg frequencies as a candidate region containing first-order ocean echoes;
step 14: the signal-to-noise ratio is utilized to constrain the frequency points in the candidate region to extract the first-order ocean echo.
The echo signal received by the radar is subjected to two Fourier transforms to obtain a range-Doppler power amplitude spectrum F (x, y), wherein x represents Doppler frequency and y represents range element. The maximum and average of Doppler echoes over the various range bins described in step 1 can be expressed as
Here P max (y) represents the maximum value of the Doppler echo amplitude over the distance element y; p (P) ave (y) represents the mean value of the Doppler echo amplitude over the distance element y.
The subtraction operation described in step 2 can be expressed as
F sa (x,y)＝F(x,y)-P ave (y)
The normalization operation described in step 3 is defined as follows:
the 90% quantile calculation method in the step 4 is as follows: taking F for Doppler spectrum over distance element y 90 (y) enable
p(F(x,y)＜F 90 (y))＝90％
Where p (·) represents the probability of getting.
The division operation described in step 5 can be expressed as:
here F b (x, y) is the output result of this step. Since 10% of the data on any distance element is greater than F 90 (y), thus F b 10% of the data in (x, y) are greater than the value 1, and step 6 is to set the amplitude of this part of the data directly to the value 1. Through the processes of steps 1 to 6, the amplitude of the first-order ocean echo received by the radar will not decrease with the increase of the distance, and the first-order ocean echo at different distances appears to have similar gray values in the image. The gray value which does not change with distance is beneficial to the detection of the first-order ocean echo.
The opening operation in the step 7 refers to the operation of etching before reconstructing the image, and the closing operation refers to the operation of expanding before reconstructing the image. The on operation is used to eliminate speckle in the image and smooth the edges of the first order echo region. The closing operation can fill tiny holes in the first-order echo spectrum region image, and image fracture caused by echo amplitude fluctuation is connected.
The gradient amplitude map calculation method in the step 8 is as follows:
let the image output in step 7 be G (x, y), x and y representing the doppler frequency and range bin, respectively. With the Sobel operator
Here S x And S is y Is a Sobel operator defined as:
and is also provided withRepresenting a convolution operation. Then gradient magnitude image G grad The calculation method of (x, y) is that
Normalized gradient amplitudeThe calculation method comprises the following steps: each pixel divided by the maximum of the whole gradient amplitude can be expressed as a mathematical formula
Superimposed image G as described in step 9 sp The calculation method of (x, y) is that
The method for calculating the local maximum value identification chart in the step 10 is as follows: and judging whether eight surrounding pixel points are larger than the pixel value of the current pixel point or not for each pixel point, if so, setting the pixel value of the current pixel point to be 0, and otherwise, setting the pixel value of the current pixel point to be 1.
The Euclidean distance transformation calculation method in the step 11 is as follows: for each pixel, the Euclidean distance between the nearest non-zero pixel and the current pixel is calculated.
The method for calculating the positive and negative Bragg frequencies in the step 13 is as follows:
wherein f b Is positive oneOrder Bragg frequency, -f b Is a negative first order Bragg frequency; f (f) c Is the radar operating frequency in MHz.
The step 14 of constraining the frequency points in the candidate region by using the signal-to-noise ratio refers to removing the frequency points in the candidate region that do not meet the signal-to-noise ratio requirement, and reserving the frequency points that meet the signal-to-noise ratio threshold.
Steps 1 to 5 must be performed on the positive and negative doppler frequency images respectively, while steps 6 to 14 have no effect on the result if the positive and negative frequency images are processed separately.
The beneficial effects of the invention are as follows:
the invention provides a high-frequency ocean radar first-order echo extraction method based on watershed segmentation, which inputs a distance-Doppler two-dimensional spectrum image and outputs the boundary of a first-order ocean echo in the whole two-dimensional spectrum, and the traditional method processes Doppler echo by distance elements, so that the method fully utilizes the correlation of echo spectrum morphology between adjacent distance elements and can reduce the influence of echo spectrum abnormality on the first-order spectrum extraction on local distance elements; in addition, the existing method is strongly dependent on fluctuation of echo spectrum in frequency space, a plurality of parameters are needed to quantify the fluctuation to extract the first-order echo, and different radar stations are needed to be adjusted and optimized according to actual working environments.
Drawings
Fig. 1: the two-dimensional distance-Doppler power magnitude spectrum, and a black rectangular frame is a marked approximate first-order echo region;
fig. 2: the mean and maximum of the Doppler echoes over each range bin;
fig. 3: f output in step 2 sa (x,y)；
Fig. 4: normalized range-doppler spectrum;
fig. 5: 90% of the quantiles on each distance element;
fig. 6: f (F) b (x,y)；
Fig. 7: the output image of the step 6;
fig. 8: an image after performing morphological opening and closing operations;
fig. 9: a gradient magnitude map;
fig. 10: an image output in the step 9;
fig. 11: the local maximum identifies a binary image;
fig. 12: a distance transformation result;
fig. 13: segmentation results based on watershed;
fig. 14: regional screening results;
fig. 15: the location of the candidate region in the original spectrum;
fig. 16: extracting a first-order echo when the signal-to-noise ratio is 5 dB;
fig. 17: the extraction result of the whole frequency spectrum;
fig. 18: a flow chart.
Detailed Description
In order to facilitate the understanding and practice of the invention, a more particular description of the invention will be rendered by reference to specific examples that are illustrated in the appended drawings and are therefore not to be considered limiting of the invention.
The invention directly processes the two-dimensional range-Doppler power magnitude spectrum acquired by the high-frequency radar. Therefore, the embodiment specifically explains the technical scheme of the invention by taking the processing of the echo spectrum received by the monopole antenna in the high-frequency marine radar as an example. Figure 1 is a typical two-dimensional range-doppler power magnitude spectrum F (x, y) received by a monopole antenna, x being the doppler frequency and y being the range bin. The black rectangular box in the figure is a rough first order marine echo region. The invention aims to automatically and accurately extract first-order ocean echo signals in a rectangular frame by a computer. In addition, the first-order ocean echoes are backward scattered echoes generated by two rows of waves with the same wavelength but opposite propagation directions, wherein the waves close to the radar are in the propagation direction in the positive frequency part, and the waves far from the radar are in the propagation direction in the negative frequency part. The two rows of waves generally have different energies, and thus the resulting echo intensities have a certain difference. The invention divides the frequency spectrum into two images of positive and negative Doppler frequencies to respectively divide the images so as to respectively extract positive and negative first-order echoes. Since both parts of the spectrum perform the same operation, here illustrated by way of example as a negative frequency part of the spectrum (i.e. the x.ltoreq.0 part of F (x, y)), the reference to F (x, y) below will no longer refer to the whole spectrum, but to the F (x, y) of the x.ltoreq.0 part, i.e. the reference to F (x, y) is equivalent to F (x, y) and x.ltoreq.0. The specific steps are as follows:
step 1: calculating the mean value and the maximum value of Doppler echo on each distance element in the radar echo;
the method for calculating the maximum value and the average value of the Doppler echo on each distance element in the step 1 is as follows
Here P max (y) represents the maximum value of the Doppler echo amplitude over the distance element y; p (P) ave (y) represents the mean value of the Doppler echo amplitude over the distance element y. Figure 2 shows the maxima and averages of the spectrum corresponding to the negative frequency portion of the range-doppler spectrum of figure 1.
Step 2: subtracting the average value of the current distance element echo from the echo on each frequency point;
the subtraction operation described in step 2 can be expressed as
F sa (x,y)＝F(x,y)-P ave (y)
FIG. 3 shows the F corresponding to the negative frequency portion spectrum of FIG. 1 sa (x,y)。
Step 3: normalizing the data output in the step 2 by using the average value and the maximum value calculated in the step 1 to obtain a normalized gray level map;
the normalization operation described in step 3 is defined as follows:
figure 4 shows a normalized range-doppler two-dimensional image.
Step 4: calculating the fractional number of 90% on each distance element for the data output in the step 3;
the 90% quantile calculation method in the step 4 is as follows: taking F for Doppler spectrum over distance element y 90 (y) enable
p(F(x,y)＜F 90 (y))＝90％
Where p (·) represents the probability of getting. FIG. 5 shows F calculated by taking FIG. 4 as an example 90 (y)。
Step 5: dividing the echo on each frequency point by the fractional number on the current distance element output in the step 4;
the division operation described in step 5 can be expressed as:
here F b (x, y) is the output result of this step. FIG. 6 shows F of the current data output b (x,y)。
Step 6: setting the data which is output in the step 5 and is larger than the value 1 as the value 1;
figure 7 shows the output range-doppler two-dimensional image after step 6 is performed. As can be seen from the figure, the amplitude of the first-order marine echo received by the radar will not decrease with increasing distance after the processing of steps 1 to 6, and it appears in the image that the first-order marine echoes at different distances have similar gray values. The gray value which does not change with distance is beneficial to the detection of the first-order ocean echo.
Step 7: performing morphological opening operation and closing operation on the data output in the step 6;
the opening operation in the step 7 refers to the operation of etching before reconstructing the image, and the closing operation refers to the operation of expanding before reconstructing the image. The on operation is used to eliminate speckle in the image and smooth the edges of the first order echo region. The closing operation can fill tiny holes in the first-order echo spectrum region image, and image fracture caused by echo amplitude fluctuation is connected. Fig. 8 shows the image after morphological opening and closing operations are performed.
Step 8: calculating a gradient amplitude graph of output data in the step 7, and normalizing;
let the image output in step 7 be G (x, y), x and y representing the doppler frequency and range bin, respectively. With the Sobel operator
Here, the
And is also provided withRepresenting a convolution operation. Then gradient magnitude image G grad The calculation method of (x, y) is that
Normalized gradient amplitudeThe calculation method comprises the following steps: each pixel divided by the maximum of the whole gradient amplitude can be expressed as a mathematical formula
Fig. 9 shows the normalized gradient magnitude.
Step 9: calculating a superimposed image of the output image of the step 7 and the normalized gradient amplitude map output by the step 8;
superimposed image G as described in step 9 sp The calculation method of (x, y) is that
Fig. 10 shows the image output in step 9.
Step 10: calculating a local maximum value identification graph of the output image in the step 9 to obtain a binary image;
the method for calculating the local maximum value identification chart in the step 10 is as follows: and judging whether eight surrounding pixel points are larger than the pixel value of the current pixel point or not for each pixel point, if so, setting the pixel value of the current pixel point to be 0, and otherwise, setting the pixel value of the current pixel point to be 1. Fig. 11 shows a local maximum identification binary map calculated from the current example data.
Step 11: performing Euclidean distance conversion on the binary image output in the step 10;
the Euclidean distance transformation calculation method in the step 11 is as follows: for each pixel, the Euclidean distance between the nearest non-zero pixel and the current pixel is calculated. Fig. 12 shows the result of the distance transformation.
Step 12: dividing the distance output in the step 11 into images by using a watershed transformation method;
fig. 13 shows the segmentation result based on watershed transformation, the region at frequency-0.4 Hz being the region containing the first order ocean echoes.
Step 13: the region containing the positive and negative Bragg frequencies is extracted as a candidate region containing the first order ocean echo.
The method for calculating the positive and negative Bragg frequencies in the step 13 is as follows:
wherein f b Is a positive first order Bragg frequency, -f b Is a negative first order Bragg frequency; f (f) c Is the radar operating frequency in MHz.
The region surrounded by two white lines in fig. 14 is the selected candidate region. Fig. 15 shows the locations of candidate regions in the original spectrum.
Step 14: the signal-to-noise ratio is utilized to constrain the frequency points in the candidate region to extract the first-order ocean echo.
The step 14 of constraining the frequency points in the candidate region by using the signal-to-noise ratio refers to removing the frequency points in the candidate region that do not meet the signal-to-noise ratio requirement, and reserving the frequency points that meet the signal-to-noise ratio threshold. The region surrounded by the black lines in fig. 16 is the frequency point which is extracted from the candidate region and satisfies the signal-to-noise ratio when the signal-to-noise ratio is 5dB, namely the extracted first-order ocean echo signal.
The above steps are performed again for the positive frequency portion spectrum to extract the first order ocean echo at the positive frequency portion. Fig. 17 shows the first-order echo extraction result obtained after performing the above steps for the original spectrum of fig. 1, and the frequency points included inside the black line are the finally extracted first-order echoes.
Fig. 18 finally summarizes the overall flow of the first order echo extraction method described in this specification.
It should be understood that the foregoing description of the steps and embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that substitutions and modifications can be made by one of ordinary skill in the art without departing from the scope of the invention as defined by the appended claims.
Claims (6)
1. A high-frequency marine radar first-order echo extraction method based on watershed segmentation is characterized by comprising the following steps of: comprises the following steps:
step 1: calculating the mean value and the maximum value of Doppler echo on each distance element in the radar echo;
step 2: subtracting the average value of the current distance element echo from the echo on each frequency point;
step 3: normalizing the data output in the step 2 by using the average value and the maximum value calculated in the step 1 to obtain a normalized gray level map;
step 4: calculating the fractional number of 90% on each distance element for the data output in the step 3;
step 5: dividing the echo on each frequency point by the fractional number on the current distance element output in the step 4;
step 6: setting the data which is output in the step 5 and is larger than the value 1 as the value 1;
step 7: performing morphological opening operation and closing operation on the data output in the step 6;
step 8: calculating a gradient amplitude graph of output data in the step 7, and normalizing;
step 9: calculating a superimposed image of the output image of the step 7 and the normalized gradient amplitude map output by the step 8;
step 10: calculating a local maximum value identification graph of the output image in the step 9 to obtain a binary image;
step 11: performing Euclidean distance conversion on the binary image output in the step 10;
step 12: dividing the distance output in the step 11 into images by using a watershed transformation method;
step 13: extracting a region containing positive and negative Bragg frequencies as a candidate region containing first-order ocean echoes;
step 14: the signal-to-noise ratio is utilized to constrain the frequency points in the candidate region to extract the first-order ocean echo.
2. The watershed segmentation-based high-frequency marine radar first-order echo extraction method according to claim 1, wherein the method is characterized by comprising the following steps of: the normalization in the step 3 is to divide the pixel value of the image output in the step 2 by the difference between the maximum value and the average value obtained in the step 1.
3. The watershed segmentation-based high-frequency marine radar first-order echo extraction method according to claim 1, wherein the method is characterized by comprising the following steps of: the opening operation in step 7 refers to the pre-etching and then reconstructing operation of the image, and the closing operation refers to the pre-expanding and then reconstructing operation of the image.
4. The watershed segmentation-based high-frequency marine radar first-order echo extraction method according to claim 1, wherein the method is characterized by comprising the following steps of: the euclidean distance transformation in step 11 refers to calculating the euclidean distance from each pixel to the nearest non-zero pixel.
5. The watershed segmentation-based high-frequency marine radar first-order echo extraction method according to claim 1, wherein the method is characterized by comprising the following steps of: the method for calculating the positive and negative Bragg frequencies in the step 13 is as follows:
wherein f b Is a positive first order Bragg frequency, -f b Is a negative first order Bragg frequency; f (f) c Is the radar operating frequency in MHz.
6. The watershed segmentation-based high-frequency marine radar first-order echo extraction method according to claim 1, wherein the method is characterized by comprising the following steps of: steps 1 to 5 must be performed on the positive and negative doppler frequency images respectively, while steps 6 to 14 have no effect on the result if the positive and negative frequency images are processed separately.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011582741.7A CN112799029B (en) | 2020-12-28 | 2020-12-28 | High-frequency marine radar first-order echo extraction method based on watershed segmentation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011582741.7A CN112799029B (en) | 2020-12-28 | 2020-12-28 | High-frequency marine radar first-order echo extraction method based on watershed segmentation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112799029A CN112799029A (en) | 2021-05-14 |
| CN112799029B true CN112799029B (en) | 2023-10-31 |
Family
ID=75805196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011582741.7A Active CN112799029B (en) | 2020-12-28 | 2020-12-28 | High-frequency marine radar first-order echo extraction method based on watershed segmentation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112799029B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5361072A (en) * | 1992-02-28 | 1994-11-01 | Codar Ocean Sensors, Ltd. | Gated FMCW DF radar and signal processing for range/doppler/angle determination |
| CN102538768A (en) * | 2012-01-10 | 2012-07-04 | 武汉大学 | Method for measuring water depth of shallow sea based on double-frequency high-frequency ground wave radar |
| CN106959442A (en) * | 2016-12-21 | 2017-07-18 | 国家海洋局第海洋研究所 | Ground wave radar first-order sea echo composes extracting method under strong interference environment based on many domain informations |
| CN110954886A (en) * | 2019-11-26 | 2020-04-03 | 南昌大学 | High-frequency ground wave radar first-order echo spectrum region detection method taking second-order spectrum intensity as reference |
| CN111751797A (en) * | 2020-06-10 | 2020-10-09 | 南昌大学 | High-frequency ground wave radar first-order and second-order echo spectrum boundary determining method based on azimuth angle |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10042051B2 (en) * | 2016-04-27 | 2018-08-07 | Codar Ocean Sensors, Ltd. | Coastal HF radar system for tsunami warning |
-
2020
- 2020-12-28 CN CN202011582741.7A patent/CN112799029B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5361072A (en) * | 1992-02-28 | 1994-11-01 | Codar Ocean Sensors, Ltd. | Gated FMCW DF radar and signal processing for range/doppler/angle determination |
| CN102538768A (en) * | 2012-01-10 | 2012-07-04 | 武汉大学 | Method for measuring water depth of shallow sea based on double-frequency high-frequency ground wave radar |
| CN106959442A (en) * | 2016-12-21 | 2017-07-18 | 国家海洋局第海洋研究所 | Ground wave radar first-order sea echo composes extracting method under strong interference environment based on many domain informations |
| CN110954886A (en) * | 2019-11-26 | 2020-04-03 | 南昌大学 | High-frequency ground wave radar first-order echo spectrum region detection method taking second-order spectrum intensity as reference |
| CN111751797A (en) * | 2020-06-10 | 2020-10-09 | 南昌大学 | High-frequency ground wave radar first-order and second-order echo spectrum boundary determining method based on azimuth angle |
Non-Patent Citations (3)
| Title |
|---|
| Effect of Current on the First-Order Spectral Power of High-Frequency Radar;Yuming Zeng et al.;《 IEEE Transactions on Geoscience and Remote Sensing》;第第57卷卷(第第8期期);全文 * |
| 船载高频地波雷达的一阶海洋回波分析;文必洋;周企豪;韩金柱;金丽洁;;华中科技大学学报(自然科学版)(第10期);全文 * |
| 高频雷达海面回波信号的数字模拟与分析;雷前召;王菊霞;;西安工业大学学报(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112799029A (en) | 2021-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102968798B (en) | SAR (Synthetic Aperture Radar) image sea-land segmentation method based on wavelet transform and OTSU threshold | |
| Li et al. | Automatic detection of ship targets based on wavelet transform for HF surface wavelet radar | |
| CN102324021B (en) | Infrared dim-small target detection method based on shear wave conversion | |
| CN106709426B (en) | Ship target detection method based on infrared remote sensing image | |
| CN107808383B (en) | Rapid detection method for SAR image target under strong sea clutter | |
| WO2019193702A1 (en) | Image processing device, image processing method, and recording medium having image processing program stored thereon | |
| CN104599273B (en) | Sea and sky background infrared small target detection method based on multi-scale wavelet crossing operation | |
| CN110287898A (en) | A kind of optical satellite remote sensing image cloud detection method of optic | |
| CN111751797B (en) | Method for determining first-order and second-order echo spectrum boundary of high-frequency ground wave radar based on azimuth angle | |
| CN103984945A (en) | Optical remote sensing image ship detection method | |
| CN101556693A (en) | Division method for extracted watershed SAR image with threshold method and marking | |
| CN108333634A (en) | A kind of Ground Penetrating Radar buried target localization method based on frequency spectrum remnants conspicuousness detection methods | |
| CN112799029B (en) | High-frequency marine radar first-order echo extraction method based on watershed segmentation | |
| CN110517245B (en) | Mechanical equipment fault diagnosis system | |
| CN109872358B (en) | Marine oil film identification method for shipborne radar image based on active contour model | |
| CN107967696B (en) | Radar remote sensing detection method for water surface ship and electronic equipment | |
| CN116047504A (en) | Method for improving deconvolution to inhibit ground penetrating radar multiple | |
| CN114283322A (en) | SAR image ship detection method | |
| KR101813357B1 (en) | Clutter elimination Method and Apparatus for applying variable data map and variable statistic characteristics information for identifying underwater fixed target and clutter | |
| Sun et al. | Ship detection method based on frequency enhanced MSER for high resolution SAR image | |
| Zhai et al. | Segmentation-based ship detection in harbor for SAR images | |
| CN114236490A (en) | X-band navigation radar oil spill detection system based on water surface echo model | |
| Wu et al. | Sea-clutter region extraction based on image segmentation methods for over-the-horizon radar | |
| Baorong et al. | Research of the infrared ship target recognition technology based on the complex background | |
| He et al. | Marine multi-target detection based on improved wavelet transform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |