CN107045126A - A kind of synthetic aperture radar movement overseas Ship Target Detection method - Google Patents
A kind of synthetic aperture radar movement overseas Ship Target Detection method Download PDFInfo
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- CN107045126A CN107045126A CN201710129385.5A CN201710129385A CN107045126A CN 107045126 A CN107045126 A CN 107045126A CN 201710129385 A CN201710129385 A CN 201710129385A CN 107045126 A CN107045126 A CN 107045126A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9029—SAR image post-processing techniques specially adapted for moving target detection within a single SAR image or within multiple SAR images taken at the same time
-
- 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
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9004—SAR image acquisition techniques
- G01S13/9005—SAR image acquisition techniques with optical processing of the SAR signals
-
- 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
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9027—Pattern recognition for feature extraction
Abstract
The present invention provides a kind of method detected using SAR to movement overseas Ship Target.Technical scheme is：To using image to be detected after DPCA processing, the clutter that is inhibited using the SAR front view pictures obtained and rearview picture；If the sea clutter in image to be detected obeys K distributions, the parameter for estimating the K distributions is converted using Mellin；The parameter estimation result being distributed according to K, calculates CFAR detection threshold values；The differentiation of moving ship targets pixel is carried out using CFAR detection threshold values, the testing result of moving ship targets is finally given.The present invention has preferable clutter recognition effect, can significantly improve image signal to noise ratio, can effectively improve the detection performance of movement overseas Ship Target.
Description
Technical field
The invention belongs to SAR (synthetic aperture radar, synthetic aperture radar) technical field, it is related to one kind
The method detected using SAR to the Ship Target of movement overseas.
Background technology
SAR moving object detections are a key areas of SAR applications.Military information monitoring, illegal immigrant supervision and
The fields such as a wide range of sea traffic supervision have a wide range of applications.By carrying two secondary or how secondary SAR antennas on flying platform
Moving target is detected, existing SAR moving object detections are that method mainly there are DPCA (displaced phase
Center antenna, offset antenna phase center) method.Referring to document：Chapin E and Chen C W.Airborne
along-track interferometry for GMTI.Aerospace and Electronics Systems
Magazines,IEEE,2009,24(5):13-18.
It is miscellaneous that DPCA methods utilize the two width complex patterns obtained in very short time interval to be realized by the method for clutter cancellation
Ripple suppresses, and then realizes moving object detection.But existing DPCA technologies mainly apply to GMTI (ground moving
Target indication, Ground moving target detection), for movement overseas Ship Target detection it is not tangible enter
Exhibition, this is primarily due to the lack of theory of DPCA sea clutter modeled segments so that DPCA technologies can not directly apply to marine fortune
In dynamic Ship Target Detection problem.
The content of the invention
The present invention provides a kind of method detected using SAR to movement overseas Ship Target.This method has preferable
Clutter recognition effect, image signal to noise ratio can be significantly improved, the detection performance of movement overseas Ship Target can be effectively improved.
The technical scheme is that：
To using to be detected after DPCA processing, the clutter that is inhibited using the SAR front view pictures obtained and rearview picture
Image；If the sea clutter in image to be detected obeys K distributions, the parameter for estimating the K distributions is converted using Mellin；According to K
The parameter estimation result of distribution, calculates CFAR detection threshold values；Sentencing for moving ship targets pixel is carried out using CFAR detection threshold values
Not, the testing result of moving ship targets is finally given.
The beneficial effects of the invention are as follows：
1. the present invention is handled by DPCA and the information in two images is synthesized in a width image to be detected, can be abundant
Signal to noise ratio is improved using image information.It is to obey K to demonstrate sea clutter in image after being handled through DPCA by theory deduction
Distribution, CFAR detection threshold values are determined using K distributions, higher verification and measurement ratio and lower false alarm rate can be obtained.
2. extra parameter or condition need not be set using moving ship targets detection method proposed by the present invention, succinctly
It is easy.
Brief description of the drawings
Fig. 1 and Fig. 2 is experimental data of the present invention；
Fig. 3 is flow chart of the present invention；
Fig. 4, Fig. 5 and Fig. 6 are experimental result picture of the invention.
Embodiment
Fig. 1 and Fig. 2 be experimental data of the present invention, Fig. 1 be the front view obtained using SAR as complex data, Fig. 2 be utilization
The rearview that SAR is obtained is as complex data, and Fig. 1 and Fig. 2 abscissa represents orientation, ordinate represent distance to.This two
Width image is all obtained by NASA/JPL AIRSAR carried SAR systematic collections, and two images size is 6500 × 1001,
Include two moving ship targets in each image, it can be seen that a target is located on the left of image.Another mesh
Mark is in the image lower right corner.
Fig. 3 is flow chart of the present invention, and specific implementation step is as follows：
The first step, DPCA processing, i.e., treating after being handled using formula one are used to front view picture and rearview picture
Detection image Z：
Z=| X1-X2| (formula one)
Wherein X1Represent front view as complex data, X2Rearview is represented as complex data, | | it is modulo operation.
Second step, if the sea clutter in image to be detected obeys K distributions, using needed for Mellin conversion estimation CFAR detections
The parameter of the distributions of K described in background window.The selection of dimension of CFAR detection sliding windows will depending on the actual conditions according to Ship Target,
General principle is that target window width can not be less than the width of minimum Ship Target, protects the width of window and can not be less than maximum naval vessel
The width of target, the size of background window wants the sufficiently large accuracy to ensure sea clutter model parameter estimation.
The probability density function f of K distributionsK(x；α, λ, it is n) as follows：
Above-mentioned K distribution probabilities density function fK(x；α, λ, n) include three unknown numbers, i.e. form parameter α, the chi of K distributions
Spend parameter lambda, the equivalent number n of K distributions.Because when subsequently carrying out ShipTargets detection, using CFAR method, passing through number
Learn the pixel being derived from CFAR detection sliding windows in background window and obey K distributions, therefore detected in this step for CFAR
Pixel in sliding window in background window can obtain the estimation for above three parameter using Mellin conversion, specific meter
Calculation method is as follows：
WhereinxiWhat is represented is the gray value of ith pixel point in background window, and what N was represented is background
The number of pixel in window, what Ψ () was represented is Psi functions, and what Ψ () was represented is Polygamma functions, Kα-n(·)
What is represented is Equations of The Second Kind modified Bessel function.
3rd step, if the coordinate of current pixel to be detected is (k, l), the current back of the body of method estimation according to second step
K distributed constants { α in scape windowk,l,λk,l,nk,l, so as to calculate the corresponding CFAR detection threshold values th of current detection sliding windowk,l,
Its specific formula for calculation is as follows：
Wherein PfaThe false alarm rate of expression, is manually set generally according to being actually needed.
Compare current detection pixel gray value tk,lWith threshold value thk,lSize, judge current pixel whether be motion warship
Ship object pixel, its specific decision rule is as follows：
4th step, in image to be detected each pixel repeat the 3rd step, judge each pixel whether be
Moving ship targets pixel, so as to complete the detection for moving ship targets.
Fig. 4, Fig. 5 and Fig. 6 are experimental result picture of the invention.Fig. 4, Fig. 5 and Fig. 6 abscissa represent orientation, indulge
Coordinate represent distance to.What Fig. 4 was represented is the image to being obtained after Fig. 1 and Fig. 2 progress DPCA processing, and Fig. 5 is the present invention
Method is for the testing result figure of moving ship targets, and Fig. 6 is existing ATI (along-track interferometry, edge
Mark interfere) method for moving ship targets testing result figure.CFAR detects being chosen for for sliding window in experiment：Target window chi
Very little is 100*100, and protection window size is 150*150, and background window size is 200*200, and the false alarm rate of setting is Pfa=10-3.From
As can be seen that passing through the image after DPCA is handled in Fig. 4, the contrast of its Ship Target and background is remarkably reinforced, this explanation
DPCA processing can effectively improve image signal to noise ratio, it is adaptable to the moving ship targets detection under low signal to noise ratio environment.It is white in Fig. 5
Color rectangle frame institute enclosing region is moving ship targets domain of the existence, and white pixel point therein is the naval vessel mesh detected
Pixel is marked, from figure 5 it can be seen that false-alarm is less in entire image, two Ship Targets all have detected by more complete
Out, Detection results are preferable.And the ATI detection algorithms corresponding to Fig. 6, not only false-alarm is more but also does not almost detect naval vessel
Target, Detection results are poor.
The innovative point of the present invention is that the sea clutter obedience K set in image to be detected is distributed, therefore the CFAR used
Background window in detection also obeys K distributions.Its theoretical proof is as follows：
According to product model, the front view picture or rearview obtained using SAR is as Ai(k) shape that can be expressed as
Formula：
Ai(k)=Xi(k)Yi(k) i=1,2 (formula six)
Wherein Xi(k) texture variable, Y are representedi(k) Gauss coherent spot variable, i=1 correspondence front view pictures, i=2 pairs are represented
Rearview picture is answered, k represents that k-th of son in multiple look processing is regarded.
DPCA processing is carried out to front view picture and rearview picture, square operation is then carried out, the DPCA intensity that regard is obtained more and unites
H is measured, is shown below：
Wherein m represents that the total of multiple look processing regards number.
In the case of radar resolution is sufficiently high, it is generally recognized that RCS (the radar cross section, thunder of sea clutter
Up to cross-sectional area) in fluctuation of each son depending between of single passage it is extremely small, this also implies that Xi(k)=XiI=
1,2, if being further contemplated that two passages are duplicate, then X can be obtained1(k)=X2(k)=X, so as to incite somebody to action
Formula seven is reduced to：
For sea clutter, texture variable X belongs to Γ1/2(μ v) is distributed, now can preferably reflect texture variable X
Feature.Γ1/2(μ, the expression formula of probability density function v) is as follows：
Wherein v represents scale parameter, and μ represents form parameter.
Make W=X2, Γ is obeyed by X1/2(μ, v) is distributed, and can obtain W and obey χ2Distribution, i.e. W~χ2(μ, v), so as to obtain W's
The expression formula of probability density function is：
H is normalized, can be obtained：
E () represents to take desired operation in formula.
OrderThen formula 11 can be rewritten as：
According to formula 12, it can obtainProbability density function be：
For further abbreviation formula 13, it is necessary to be solved using equation below to the integral term in formula 13：
According to formula 14, formula 13 can further be written as form:
Variable replacement is made to formula 15Obtain the sea clutter in image to be detected and obey following distribution：
OrderFormula 16 can be turned to：
Formula 17 is exactly the probability density function expression formula of K distributions, it is hereby achieved that extra large miscellaneous in image to be detected
Ripple is to obey K distributions.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (1)
1. a kind of synthetic aperture radar movement overseas Ship Target Detection method, it is characterised in that comprise the steps：
To using image to be detected after DPCA processing, the clutter that is inhibited using the SAR front view pictures obtained and rearview picture；
If the sea clutter in image to be detected obeys K distributions, the parameter for estimating the K distributions is converted using Mellin；It is distributed according to K
Parameter estimation result, calculates CFAR detection threshold values；The differentiation of moving ship targets pixel is carried out using CFAR detection threshold values, finally
Obtain the testing result of moving ship targets；
Wherein, SAR refers to synthetic aperture radar；DPCA refers to offset antenna phase center.
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| CN109031293A (en) * | 2018-07-17 | 2018-12-18 | 中国人民解放军国防科技大学 | Offshore ship target detection method based on ODPCA |
| CN109359595A (en) * | 2018-10-17 | 2019-02-19 | 四川航天系统工程研究所 | A kind of novel SAR image statistical model and its method for parameter estimation |
| CN110389366A (en) * | 2019-08-05 | 2019-10-29 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of naval target method for estimating based on multi-source SAR satellite |
| CN111896958A (en) * | 2020-08-11 | 2020-11-06 | 西安电子科技大学 | Ship target foresight three-dimensional imaging method based on correlation algorithm |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109031293A (en) * | 2018-07-17 | 2018-12-18 | 中国人民解放军国防科技大学 | Offshore ship target detection method based on ODPCA |
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| CN110389366A (en) * | 2019-08-05 | 2019-10-29 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of naval target method for estimating based on multi-source SAR satellite |
| CN110389366B (en) * | 2019-08-05 | 2021-03-30 | 中国人民解放军军事科学院国防科技创新研究院 | Maritime target motion estimation method based on multi-source SAR satellite |
| CN111896958A (en) * | 2020-08-11 | 2020-11-06 | 西安电子科技大学 | Ship target foresight three-dimensional imaging method based on correlation algorithm |
| CN111896958B (en) * | 2020-08-11 | 2023-04-21 | 西安电子科技大学 | Ship target forward-looking three-dimensional imaging method based on correlation algorithm |
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