CN113985404B - High-resolution runway foreign object detection system and phase drift correction method thereof - Google Patents

High-resolution runway foreign object detection system and phase drift correction method thereof Download PDF

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CN113985404B
CN113985404B CN202111637336.5A CN202111637336A CN113985404B CN 113985404 B CN113985404 B CN 113985404B CN 202111637336 A CN202111637336 A CN 202111637336A CN 113985404 B CN113985404 B CN 113985404B
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CN113985404A (en
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王玉明
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Hunan Normal University
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    • G—PHYSICS
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    • 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
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    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
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    • 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
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Abstract

The invention discloses a high-resolution runway foreign object detection system and a phase drift correction method thereof, wherein the method comprises the following steps: observing the runway through an AS-SAR system, and generating a high-resolution image to carry out SPD modeling; processing the complex image acquired by the AS-SAR system to form a complex image sequence with a fixed length, and carrying out classification processing after feature extraction; estimating SPD of the obtained SPCP based on the SPD model to obtain an SPD estimation value; the phase of the current complex image is subtracted from the estimated SPD value to realize SPD correction of the image, and the AS-SAR complex image with high resolution after phase correction is output; the detection system is an AS-SAR system in the phase drift correction method; the invention obtains a high-resolution AS-SAR complex image after phase correction; the phase drift can be effectively inhibited, the stable target phase can be obtained, and a foundation is laid for subsequent SNR enhancement and target identification.

Description

High-resolution runway foreign object detection system and phase drift correction method thereof
Technical Field
The invention relates to the technical field of airport safety protection, in particular to a high-resolution runway foreign object detection system and a phase drift correction method thereof.
Background
Foreign Object Debris (FOD) includes foreign objects that may damage airplanes and equipment or threaten the safety of ground passengers in active areas such as airport personnel and airport runways. In recent years, with the rapid development of civil aviation industry in the world, it has been found that from time to time, FOD can puncture tires and damage airplanes, not only causing greater economic loss to airlines, but also presenting greater risks to flight safety and personnel safety. Therefore, research on FOD detection devices is urgent and receives increasing attention. At present, FOD detection equipment mainly comprises photoelectric detection equipment and radar detection equipment. The photoelectric detection equipment is low in installation and maintenance cost, the detection performance is continuously improved along with the development of an image processing technology, and the recognition rate is gradually improved. However, such improvements are limited by low light conditions such as nighttime, and are also susceptible to rain, snow and other extreme weather conditions. The radar detection equipment has the characteristics of high detection rate, high positioning accuracy, high reliability, full-day working, strong environmental adaptability and the like, and is the key point of the current research. However, the current radar detection equipment adopts a real-aperture working mode, and has the problems of low direction finding precision, difficult identification, high false alarm rate and the like.
Synthetic Aperture Radar (SAR) has high-resolution earth observation capability, can realize high-precision detection of a small ground target, and has great potential in the aspect of FOD target detection. An Arc-scanning synthetic aperture radar (AS-SAR) is a special SAR with an Arc aperture and has 360-degree omnibearing observation capability. The application of the FOD detection method in airport runways has natural advantages: the system antenna has small volume, light weight and low integral use cost; secondly, the system realizes extremely high imaging resolution through an algorithm, and is favorable for detecting small targets; thirdly, the system has lower requirements on the rotation precision of the platform and can keep higher target direction finding and distance measuring precision; and fourthly, the target information is richer, the target identification is facilitated, and the false alarm is reduced. At present, the university of the south of Hunan professor, the information technology Limited company of the gigahertz of the south of Hunan, and the like begin to utilize AS-SAR to detect FOD targets, and the basic flow of the detection is signal acquisition, imaging, coherent accumulation, detection, identification, and the like.
On the basis of meeting the requirement of Federal Communications Commission (FCC), in order to reduce atmospheric attenuation, effectively utilize an atmospheric window and better detect an FOD target with the size of about 1cm, the AS-SAR operating frequency is 94 GHz, and the operating wavelength is only 3.2 mm. Atmospheric phase perturbations may be caused by changes in the electromagnetic signal propagation path due to atmospheric scattering. And when the target is far away from the radar, phase disturbance caused by atmospheric changes such as temperature and humidity is more obvious. In general, the shorter the transmission signal wavelength, the more significant the atmospheric phase perturbation will be. Meanwhile, phase drift also exists in system hardware under different temperature conditions. The phase stability of the target is the basis for improving the signal-to-noise ratio by coherent accumulation and is an important characteristic of the background of target identification. Therefore, the phase drift affects the results of coherent accumulation, identification and the like in the system detection process. The present invention addresses this problem with phase drift correction. For ease of analysis, all of these phase drifts are collectively referred to as System Phase Drift (SPD).
Disclosure of Invention
The invention aims to provide a high-resolution runway foreign object detection system and a phase drift correction method thereof, and solves the problems that the existing runway foreign object detection system is easily influenced by the outside, so that the direction finding precision is low, the identification is difficult, the false alarm rate is high and the like.
The invention is realized in such a way that the phase drift correction method of the high-resolution runway foreign object detection system firstly models the SPD, then screens Stable Phase Control Points (SPCP), and then estimates the SPD through the SPCP, thereby realizing SPD correction.
The further technical scheme of the invention is as follows: modeling and estimating SPD:
setting monitoring target values in two consecutive images to
Figure 167940DEST_PATH_IMAGE001
Then SPD Δ φ may be expressed as:
Figure 903815DEST_PATH_IMAGE002
(10)
wherein
Figure 571557DEST_PATH_IMAGE003
Represents the calculated phase angle and represents the complex conjugate.
Considering that the SPD is perturbed by the system itself and the atmospheric phase, Δ φ may be expressed as:
Figure 506015DEST_PATH_IMAGE004
(11)
wherein
Figure 670017DEST_PATH_IMAGE005
Indicating the phase induced by the system itself,
Figure 689926DEST_PATH_IMAGE006
which represents the phase induced by the atmosphere,
Figure 364621DEST_PATH_IMAGE007
representing the noise phase. In general terms, the amount of the solvent to be used,
Figure 356848DEST_PATH_IMAGE008
is constant. It is known that the temperature and humidity of the atmosphere vary with time, resulting in non-uniformity of electromagnetic properties, causing the propagation speed and direction of electromagnetic waves to vary continuously as they pass through the atmosphere. When the atmosphere changes independently along with the distance direction and the azimuth direction, a binary linear function model of the SPD can be established:
Figure 989954DEST_PATH_IMAGE009
(12)
wherein
Figure 700421DEST_PATH_IMAGE010
Is the wavelength of the carrier frequency and,
Figure 709966DEST_PATH_IMAGE011
is the ground range from the target to the radar,
Figure 55234DEST_PATH_IMAGE012
is the angle between the target and the initial scan direction of the radar.
Figure 124821DEST_PATH_IMAGE013
Are weighting coefficients.
As can be seen from equation (12), as long as three stable strong scattering points with high signal-to-noise ratio (SNR) are found, an equation set can be established by the phase shift thereof, and a weighting coefficient can be obtained by solving the equation set. The stable high signal-to-noise ratio strong scattering point is referred to herein as SPCP. In fact, it is possible to use,we can obtain a number of SPCPs much greater than 3. Therefore, the weighting coefficients will be estimated using the least squares method. Let
Figure 57005DEST_PATH_IMAGE014
The equation can be expressed as a matrix as follows
Figure 870240DEST_PATH_IMAGE015
(13)
Wherein
Figure 571480DEST_PATH_IMAGE016
Is a noise matrix. And is
Figure 811969DEST_PATH_IMAGE017
(14)
For multiple SPCP points, the equation can be expressed in a matrix
Figure 231449DEST_PATH_IMAGE018
(15)
Using a least squares algorithm, one can obtain
Figure 379533DEST_PATH_IMAGE019
(16)
Then
Figure 902656DEST_PATH_IMAGE020
Can be estimated by
Figure 314046DEST_PATH_IMAGE021
(17)
Due to the time continuity of the SPD caused by temperature and humidity changes, the estimated SPD may be filtered by a kalman filter. Establishing a state transition matrix, such as the formula:
Figure 17560DEST_PATH_IMAGE022
(18)
wherein
Figure 172598DEST_PATH_IMAGE023
Is a gaussian noise that is a function of the noise,
Figure 848430DEST_PATH_IMAGE024
is a state transition matrix. The observation equation is determined as:
Figure 430721DEST_PATH_IMAGE025
(19)
according to actual measurement, corresponding to
Figure 824793DEST_PATH_IMAGE026
Of the variance matrix
Figure 517942DEST_PATH_IMAGE027
Is 4, correspond to
Figure 48281DEST_PATH_IMAGE028
Of the variance matrix
Figure 34429DEST_PATH_IMAGE029
Is 9. And
Figure 915798DEST_PATH_IMAGE030
. Time domain filtering of SPCP may then be implemented.
The further technical scheme of the invention is as follows: the SPCP screening method comprises the following steps: the selection of SPCP is very important in the SPD computation process. For amplitude images, we define three functions of screening SPCP: local image contrast
Figure 412638DEST_PATH_IMAGE031
Amplitude dispersion
Figure 63062DEST_PATH_IMAGE032
And correlation coefficient
Figure 987156DEST_PATH_IMAGE033
. The screening process flow of SPCP is shown in fig. 4.
Make the sequence image as
Figure 886979DEST_PATH_IMAGE034
Wherein
Figure 921931DEST_PATH_IMAGE035
Is the number of images.
Figure 692441DEST_PATH_IMAGE036
Representing pixel points
Figure 787436DEST_PATH_IMAGE037
Image of (2)
Figure 876352DEST_PATH_IMAGE038
An amplitude value. Then, the user can use the device to perform the operation,
Figure 714995DEST_PATH_IMAGE039
the definitions are as follows.
For the
Figure 340012DEST_PATH_IMAGE040
First of all, obtain
Figure 605908DEST_PATH_IMAGE041
Each pixel in
Figure 683585DEST_PATH_IMAGE042
Neighborhood image slice of
Figure 60340DEST_PATH_IMAGE043
Then calculating the average value
Figure 539863DEST_PATH_IMAGE044
And standard deviation of
Figure 976660DEST_PATH_IMAGE045
Then calculate
Figure 40169DEST_PATH_IMAGE046
(20)
Figure 220615DEST_PATH_IMAGE047
The larger the SNR, the more stable for the corresponding pixel.
For the
Figure 289065DEST_PATH_IMAGE048
We calculate the average value of the amplitude of each pixel in the radar sequence image
Figure 162343DEST_PATH_IMAGE049
And standard deviation of
Figure 949033DEST_PATH_IMAGE050
Then, it is calculated using the following formula:
Figure 198749DEST_PATH_IMAGE051
(21)
Figure 387285DEST_PATH_IMAGE052
the larger the amplitude information, the more stable it is. Due to the fact that
Figure 165885DEST_PATH_IMAGE053
The method only uses amplitude information for measurement, and has the advantages of small calculated amount, convenient extraction and the like.
For the
Figure 203986DEST_PATH_IMAGE054
The correlation coefficient of each pixel in the sequence image needs to be according to the following equationFormula calculation
Figure 726234DEST_PATH_IMAGE055
(22)
Wherein
Figure 769277DEST_PATH_IMAGE056
Sliding the neighborhood window size. Then calculate each
Figure 718778DEST_PATH_IMAGE057
Average of (2)
Figure 745640DEST_PATH_IMAGE058
And calculate
Figure 806000DEST_PATH_IMAGE059
Figure 969128DEST_PATH_IMAGE060
(23)
Figure 355110DEST_PATH_IMAGE061
The larger the correlation, the stronger the correlation.
Figure 400426DEST_PATH_IMAGE062
The neighborhood of the involved pixels has a large number of computations, but is functionally stable and immune to noise.
Each of the extracted features is formed into a vector,
Figure 763012DEST_PATH_IMAGE063
. Considering that the value of each component of the feature vector is not uniform, it has different effects on the linear classifier, and thus it is necessary to normalize it.
Here, the feature vector is normalized according to the mahalanobis distance principle. Since the covariance matrix is a real symmetric matrix, the unitary matrix can be diagonalized, so
Figure 46226DEST_PATH_IMAGE064
(24)
Wherein
Figure 337530DEST_PATH_IMAGE065
Is a unitary matrix of the matrix,
Figure 73405DEST_PATH_IMAGE066
is the average of the sample feature vectors,
Figure 475567DEST_PATH_IMAGE067
is a transposition of the two-dimensional image,
Figure 613288DEST_PATH_IMAGE068
a covariance eigenvalue matrix. The normalized feature vector
Figure 341072DEST_PATH_IMAGE069
Comprises the following steps:
Figure 564243DEST_PATH_IMAGE070
(25)
in the classification process, a linear classification decision criterion is established:
Figure 268632DEST_PATH_IMAGE071
(26)
wherein
Figure 260858DEST_PATH_IMAGE072
Are weight coefficients, which can be trained using known calibration target feature vectors. The classification process is broadly applicable when the radar performance is stable and consistent, and the SNR is large and meets the monitoring conditions.
Finally, the SPCP within a certain distance range from the runway is selected through distance constraint, and the accuracy of SPD estimation is further improved.
The further technical scheme of the invention is as follows: the correction method comprises the following steps: fig. 1 shows a complete flow of a phase drift correction method for a high-resolution runway foreign object detection system. It consists of four stages:
SPD modeling: observing the runway through an AS-SAR system, generating a high-resolution image, and modeling the SPD;
screening of SPCP: this stage can be done in real time or offline. To prevent FOD targets from being screened as SPCP, it requires data of empty scenes. It processes the sequence magnitude image by feature extraction and classification and sends the obtained SPCP to the second stage.
Estimation of SPD: in the actual processing, the SPCP provided in the first stage is used and based on the SPD model, the SPD between two complex images is estimated in real time by using an LS algorithm. And finally, performing a Kalman filter according to the multi-frame SPD (greater than 2) to acquire the SPD for correction.
SPD correction: the prospective correction is achieved by subtracting the SPD estimate, and a phase corrected sequence complex image is generated and output.
A high-resolution runway foreign object detection system is an AS-SAR system in the phase drift correction method.
The invention has the beneficial effects that: the method adopts the AS-SAR system to detect the foreign matters on the runway, has high-resolution ground observation capability, and can realize high-precision detection of the ground surface micro target; meanwhile, through the steps of SPD modeling, SPCP screening, SPD estimation and SPD correction recorded in the invention, the phase drift of the system caused by the system and the external environment can be effectively corrected, and the AS-SAR complex image with high resolution after phase correction is obtained; meanwhile, the invention can effectively inhibit phase drift and stabilize the target phase; the invention can realize SPD correction of the target in the AS-SAR system image, obtain stable target phase and lay a foundation for subsequent SNR enhancement and target identification.
Wherein the system cost is fully considered when the SPD modelsThe body and the external environment are influenced by the phase, and the body and the external environment are uniformly corrected; meanwhile, due to the fact that the time continuity of the SPD is caused by temperature and humidity changes, the SPD is estimated to be filtered through a Kalman filter, and the accuracy is improved; during SPCP screening, the contrast characteristic of a local image based on SNR is adopted during SPCP screening, and three functions of screening SPCP are defined: local image contrast
Figure 159544DEST_PATH_IMAGE073
Amplitude dispersion
Figure 604432DEST_PATH_IMAGE074
And correlation coefficient
Figure 348397DEST_PATH_IMAGE075
The three characteristics are used jointly, so that the selected SPCP point is more stable; in order to reduce the influence of the features on the linear classifier during SPCP screening, the feature vector is normalized by using a covariance matrix, so that the contribution degrees of the three extracted features are consistent; aiming at the shape characteristics of the runway area, the distance from the screened SPCP point to the runway is limited to be smaller than a specified value through distance constraint, and the SPD of the runway area can be estimated more accurately; and (3) by utilizing the continuity of time change such as temperature, humidity and the like, time domain Kalman filtering is adopted for the estimated SPD, so that the abnormal value of the SPD is filtered, and the continuity of the estimated SPD value is improved.
Drawings
FIG. 1 is a flow chart of a phase drift correction method for a high resolution runway foreign object detection system provided by the present invention;
FIG. 2 is a diagram of the AS-SAR imaging geometry model provided by the present invention;
FIG. 3 is a diagram of the result of AS-SAR imaging of a runway in an uncorrected state;
FIG. 4 is a flow chart of SPCP screening provided by the present invention;
FIG. 5 is an AS-SAR imaging of a cylindrical target provided by the present invention;
FIG. 6 is a graph of the phase change of the cylindrical target 1 in FIG. 5 according to the present invention;
FIG. 7 is a comparison graph of SNR after coherent accumulation of cylindrical objects before and after SPD correction in FIG. 5 according to the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
It should be noted that the structures, ratios, sizes, and the like shown in the drawings attached to the present specification are only used for matching the disclosure of the present specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions of the present invention, so that the present invention has no technical essence, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
The first embodiment is as follows:
a high resolution runway foreign object detection system: the detection system is an AS-SAR system: the AS-SAR is that the phase center of an antenna forms a circular arc aperture by using the rotation of a rotating arm, and then a high resolution image is generated by using the synthetic aperture principle. The detection system structure base is used for installing, storing and protecting other parts and is also used for fixing the radar and enabling the radar to work smoothly; the power supply device consists of an AC-DC converter, a battery and the like and provides power for the operation of the whole system; the turntable adopts the combination of a high-speed servo motor and a speed reducing mechanism, realizes the low-speed and high-torque input of the actuating mechanism through mechanical transmission, avoids the interval of low-speed vibration of the motor, accurately controls the motion process, and ensures the accuracy of the rotating position and the smoothness of the trigger pulse frequency; conductive slip rings are a rotating electrical interface that utilize sliding contact and electromagnetic coupling of conductive parts to address the problem of transferring power and data signals from a stationary structure to a rotating structure during unlimited continuous rotation; the rotating arm is an aluminum alloy square tube arranged on the rotary table, is hollow and is used for installing a radar sensor subsystem component; and finally, the antenna seat is arranged at the front part of the rotating arm and consists of an aluminum alloy substrate, a pitch support and a glass fiber reinforced plastic antenna housing, and the pitch posture is adjustable.
In FOD detection, it is necessary to detect small size targets and to separate multiple small targets in very close proximity. Therefore, on the basis of ensuring that the AS-SAR has a high image resolution, it should also ensure that the system itself has a high distance resolution. Here, the system bandwidth B is 1 GHz. The range resolution of the system can then be calculated as:
Figure 460710DEST_PATH_IMAGE076
(1)
when the system turntable speed is 360 deg./min, the step size of each pulse is 0.02 deg., so the system's azimuth resolution is as follows:
Figure 530297DEST_PATH_IMAGE077
(2)
the system uses a frequency-modulated continuous wave (FMCW) signal, the radar signal being as follows:
Figure 728060DEST_PATH_IMAGE078
(3)
wherein
Figure 774251DEST_PATH_IMAGE079
Is the carrier frequency of the carrier wave,
Figure 944333DEST_PATH_IMAGE080
is the duration of the scan and is,
Figure 184821DEST_PATH_IMAGE081
is a repetition period of the time period of the first,
Figure 604301DEST_PATH_IMAGE082
is used for adjusting the frequency of the frequency,
Figure 955648DEST_PATH_IMAGE083
is the time within the modulation period. The microwave is reflected by the target, and the received radar signal is:
Figure 573711DEST_PATH_IMAGE084
(4)
where Δ t is the echo delay,
Figure 985101DEST_PATH_IMAGE085
. R is the distance between the target and the radar and a is the amplitude attenuation coefficient.
The system receiver mixes the received signal with a reference signal to obtain a demodulated Intermediate Frequency (IF) signal:
Figure 891877DEST_PATH_IMAGE086
(5)
wherein
Figure 279871DEST_PATH_IMAGE087
。
The AS-SAR antenna is arranged on a mechanical rotating arm with a fixed length, and the relative motion with a target can be realized through the rotating arm, so that Doppler information is obtained. The geometric model of AS-SAR imaging is shown in FIG. 2, where G is the surface plane; o is the center of the axis of rotation, FOD target TfIs positioned on the G plane; h is the radar height; ap is the phase center of the radar antenna; l is the length of the rotating arm (distance from Ap to the axis of rotation);
Figure 221282DEST_PATH_IMAGE088
is the angular velocity, fillet, of the turntable
Figure 537994DEST_PATH_IMAGE089
Corresponding to time
Figure 197645DEST_PATH_IMAGE090
A radar antenna Ap of (a), wherein
Figure 890795DEST_PATH_IMAGE091
Is the time corresponding to the closest distance between the antenna Ap and the target;
Figure 421133DEST_PATH_IMAGE092
is the angle between the OTf line and the plane of rotation of the radar swivel arm. Assume that the distance from the center of the rotation axis O to the target Tf is R, and at time
Figure 174326DEST_PATH_IMAGE093
The distance from the antenna Ap to the target is
Figure 790115DEST_PATH_IMAGE094
。
Then it is corresponding
Figure 785490DEST_PATH_IMAGE095
Figure 435914DEST_PATH_IMAGE096
(6)
It can be seen that there are three exponential terms, corresponding to range Phase, Residual Video Phase (RVP), and azimuth doppler Phase. Therefore, the process of AS-SAR imaging can be divided into three steps: firstly, distance dimension pulse compression is carried out, matched filtering is carried out in a fast time, deskew processing is realized, and the echo signal-to-noise ratio of the radar is improved; the RVP compensation operation is to align the deskewed echoes in distance; and thirdly, azimuth dimension compression can be realized through a frequency domain algorithm. Suppose the horizontal beamwidth of the antenna is
Figure 94429DEST_PATH_IMAGE097
Frequency band and rotational speed of the turntable
Figure 463093DEST_PATH_IMAGE098
And carrier frequency wavelength
Figure 498045DEST_PATH_IMAGE099
In connection with this, the two-dimensional frequency domain data can be calculated by the following equation:
Figure 65293DEST_PATH_IMAGE100
Figure 894709DEST_PATH_IMAGE101
Figure 953932DEST_PATH_IMAGE102
(7)
a phase-matched filter can be designed:
Figure 556689DEST_PATH_IMAGE103
(8)
then the matched filter output is
Figure 916126DEST_PATH_IMAGE104
(9)
Wherein
Figure 916443DEST_PATH_IMAGE105
The amplitude coefficient. To pair
Figure 994121DEST_PATH_IMAGE106
Two-dimensional inverse Fourier transform is carried out to obtain a time domain SAR image with polar coordinates
Figure 636455DEST_PATH_IMAGE107
. Fig. 3 shows a graph of uncorrected imaging results of the AS-SAR on a runway.
Example two:
a phase drift correction method of a high-resolution runway foreign object detection system comprises the steps of firstly modeling SPDs, then screening Stable Phase Control Points (SPCPs), and then estimating the SPDs through the SPCPs to further realize SPD correction.
Modeling and estimating SPD:
setting monitoring target values in two consecutive images to
Figure 115977DEST_PATH_IMAGE108
Then SPD Δ φ may be expressed as:
Figure 552775DEST_PATH_IMAGE109
(10)
wherein
Figure 117749DEST_PATH_IMAGE110
Represents the calculated phase angle and represents the complex conjugate.
Considering that the SPD is perturbed by the system itself and the atmospheric phase, Δ φ may be expressed as:
Figure 94932DEST_PATH_IMAGE111
(11)
wherein
Figure 927496DEST_PATH_IMAGE112
Indicating the phase induced by the system itself,
Figure 738458DEST_PATH_IMAGE113
which represents the phase induced by the atmosphere,
Figure 790727DEST_PATH_IMAGE114
representing the noise phase. AIn general terms, it is preferred that,
Figure 306022DEST_PATH_IMAGE115
is constant. It is known that the temperature and humidity of the atmosphere vary with time, resulting in non-uniformity of electromagnetic properties, causing the propagation speed and direction of electromagnetic waves to vary continuously as they pass through the atmosphere. When the atmosphere changes independently along with the distance direction and the azimuth direction, a binary linear function model of the SPD can be established:
Figure 760137DEST_PATH_IMAGE116
(12)
wherein
Figure 538737DEST_PATH_IMAGE117
Is the wavelength of the carrier frequency and,
Figure 281565DEST_PATH_IMAGE118
is the ground range from the target to the radar,
Figure 325786DEST_PATH_IMAGE119
is the angle between the target and the initial scan direction of the radar.
Figure 899987DEST_PATH_IMAGE120
Are weighting coefficients.
As can be seen from equation (12), as long as three stable strong scattering points with high signal-to-noise ratio (SNR) are found, an equation set can be established by the phase shift thereof, and a weighting coefficient can be obtained by solving the equation set. The stable high signal-to-noise ratio strong scattering point is referred to herein as SPCP. In fact, we can obtain much more SPCP than 3. Therefore, the weighting coefficients will be estimated using the least squares method. Let
Figure 52751DEST_PATH_IMAGE121
The equation can be expressed as a matrix as follows
Figure 814034DEST_PATH_IMAGE122
(13)
Wherein
Figure 139973DEST_PATH_IMAGE123
Is a noise matrix. And is
Figure 303101DEST_PATH_IMAGE124
(14)
For multiple SPCP points, the equation can be expressed in a matrix
Figure 220241DEST_PATH_IMAGE125
(15)
Using a least squares algorithm, one can obtain
Figure 436197DEST_PATH_IMAGE126
(16)
Then
Figure 300247DEST_PATH_IMAGE127
Can be estimated by
Figure 583461DEST_PATH_IMAGE128
(17)
Due to the time continuity of the SPD caused by temperature and humidity changes, the estimated SPD may be filtered by a kalman filter. Establishing a state transition matrix, such as the formula:
Figure 140345DEST_PATH_IMAGE129
(18)
wherein
Figure 610640DEST_PATH_IMAGE130
Is a gaussian noise that is a function of the noise,
Figure 278382DEST_PATH_IMAGE131
is a state transition matrix. The observation equation is determined as:
Figure 150523DEST_PATH_IMAGE132
(19)
according to actual measurement, corresponding to
Figure 675045DEST_PATH_IMAGE133
Of the variance matrix
Figure 898216DEST_PATH_IMAGE134
Is 4, correspond to
Figure 805867DEST_PATH_IMAGE135
Of the variance matrix
Figure 63673DEST_PATH_IMAGE136
Is 9. And
Figure 962359DEST_PATH_IMAGE137
，
Figure 141667DEST_PATH_IMAGE138
. Time domain filtering of SPCP may then be implemented.
The further technical scheme of the invention is as follows: the SPCP screening method comprises the following steps: the selection of SPCP is very important in the SPD computation process. For amplitude images, we define three functions of screening SPCP: local image contrast
Figure 151212DEST_PATH_IMAGE139
Amplitude dispersion
Figure 263524DEST_PATH_IMAGE140
And correlation coefficient
Figure 67532DEST_PATH_IMAGE141
. The screening process flow of SPCP is shown in fig. 4.
Make the sequence image as
Figure 763830DEST_PATH_IMAGE142
Wherein
Figure 311486DEST_PATH_IMAGE143
Is the number of images.
Figure 278305DEST_PATH_IMAGE144
Representing pixel points
Figure 253215DEST_PATH_IMAGE145
Image of (2)
Figure 938274DEST_PATH_IMAGE146
An amplitude value. Then, the user can use the device to perform the operation,
Figure 555200DEST_PATH_IMAGE147
the definitions are as follows.
For the
Figure 376526DEST_PATH_IMAGE148
First of all, obtain
Figure 522336DEST_PATH_IMAGE149
Each pixel in
Figure 927647DEST_PATH_IMAGE150
Neighborhood image slice of
Figure 817106DEST_PATH_IMAGE151
Then calculating the average value
Figure 758517DEST_PATH_IMAGE152
And standard deviation of
Figure 871967DEST_PATH_IMAGE153
Then calculate
Figure 469301DEST_PATH_IMAGE154
(20)
Figure 959188DEST_PATH_IMAGE155
The larger the SNR, the more stable for the corresponding pixel.
For the
Figure 958368DEST_PATH_IMAGE156
We calculate the average value of the amplitude of each pixel in the radar sequence image
Figure 711561DEST_PATH_IMAGE157
And standard deviation of
Figure 91464DEST_PATH_IMAGE158
Then, it is calculated using the following formula:
Figure 322726DEST_PATH_IMAGE159
(21)
Figure 973150DEST_PATH_IMAGE160
the larger the amplitude information, the more stable it is. Due to the fact that
Figure 897243DEST_PATH_IMAGE160
The method only uses amplitude information for measurement, and has the advantages of small calculated amount, convenient extraction and the like.
For the
Figure 265908DEST_PATH_IMAGE161
The correlation coefficient of each pixel in the sequence image needs to be calculated according to the following formula
Figure 300860DEST_PATH_IMAGE162
(22)
Wherein
Figure 805791DEST_PATH_IMAGE163
Sliding the neighborhood window size. Then calculate each
Figure 900786DEST_PATH_IMAGE164
Average of (2)
Figure 553484DEST_PATH_IMAGE165
And calculate
Figure 625083DEST_PATH_IMAGE166
Figure 453361DEST_PATH_IMAGE167
(23)
Figure 719258DEST_PATH_IMAGE166
The larger the correlation, the stronger the correlation.
Figure 593673DEST_PATH_IMAGE166
The neighborhood of the involved pixels has a large number of computations, but is functionally stable and immune to noise.
Each of the extracted features is formed into a vector,
Figure 236007DEST_PATH_IMAGE168
. Considering that the value of each component of the feature vector is not uniform, it has different effects on the linear classifier, and thus it is necessary to normalize it.
Here, the feature vector is normalized according to the mahalanobis distance principle. Since the covariance matrix is a real symmetric matrix, the unitary matrix can be diagonalized, so
Figure 715530DEST_PATH_IMAGE169
(24)
Wherein
Figure 355590DEST_PATH_IMAGE170
Is a unitary matrix of the matrix,
Figure 419098DEST_PATH_IMAGE171
is the average of the sample feature vectors, T is the transpose,
Figure 333965DEST_PATH_IMAGE172
a covariance eigenvalue matrix. The normalized feature vector
Figure 730311DEST_PATH_IMAGE173
Comprises the following steps:
Figure 338010DEST_PATH_IMAGE174
(25)
in the classification process, a linear classification decision criterion is established:
Figure 593542DEST_PATH_IMAGE175
(26)
wherein
Figure 312099DEST_PATH_IMAGE176
Are weight coefficients, which can be trained using known calibration target feature vectors. The classification process is broadly applicable when the radar performance is stable and consistent, and the SNR is large and meets the monitoring conditions.
Finally, the SPCP within a certain distance range from the runway is selected through distance constraint, and the accuracy of SPD estimation is further improved.
The correction method comprises the following steps: fig. 1 shows a complete flow of a phase drift correction method for a high-resolution runway foreign object detection system. It consists of four stages:
SPD modeling: observing the runway through an AS-SAR system, generating a high-resolution image, and modeling the SPD;
screening of SPCP: this stage can be done in real time or offline. To prevent FOD targets from being screened as SPCP, it requires data of empty scenes. It processes the sequence magnitude image by feature extraction and classification and sends the obtained SPCP to the second stage.
Estimation of SPD: in the actual processing, the SPCP provided in the first stage is used and based on the SPD model, the SPD between two complex images is estimated in real time by using an LS algorithm. And finally, performing a Kalman filter according to the multi-frame SPD (greater than 2) to acquire the SPD for correction.
SPD correction: the prospective correction is achieved by subtracting the SPD estimate, and a phase corrected sequence complex image is generated and output.
SPD correction is carried out on 8 cylindrical targets of scene actual measurement data by using the algorithm; the diameter and height of the target are both 4cm, and the AS-SAR image result is shown in FIG. 5.
Fig. 6 shows the phase change history of the cylindrical target 1 in fig. 5 before and after SPD correction. By contrast, before SPD correction, the phase change of the target 1 is severe and gradually increased; after SPD correction, the phase of target 1 tends to be consistent and is kept in a certain range. Therefore, the SPD correction algorithm can effectively inhibit phase drift and stabilize the target phase.
Fig. 7 shows an SNR comparison diagram after cylindrical target coherent accumulation before and after SPD correction, and it can be seen that after SPD correction, the target coherent accumulation SNR is significantly improved. Since the basis of coherent accumulation is the phase stability of the target, it is also shown from the side that the algorithm of the present invention can effectively correct the SPD to obtain a stable target phase.
In a word, through comparison of target phase change processes before and after SPD correction and comparison of SNR after target coherence accumulation, the method and the device can realize SPD correction of the target in the AS-SAR system image, obtain stable target phase and lay a foundation for subsequent SNR enhancement and target identification.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A phase drift correction method of a high-resolution runway foreign object detection system is characterized by comprising the following steps: the method comprises the following steps:
step one, modeling of a phase drift SPD: observing the runway through an AS-SAR system, generating a high-resolution image, and modeling a phase drift SPD to obtain a phase drift SPD model;
step two, screening stable phase control points SPCP: forming a complex image sequence with a fixed length on a complex image acquired through an AS-SAR system by adopting a first-in first-out principle, and carrying out classification processing after feature extraction;
estimating a phase drift SPD through a stable phase control point SPCP; estimating a phase drift SPD by adopting a least square algorithm based on the phase drift SPD model for the stable phase control point SPCP obtained in the step two to obtain an estimated value of the phase drift SPD;
step four, correcting the phase drift SPD: subtracting the estimated value of the phase drift SPD from the phase of the current complex image to realize the SPD correction of the phase drift of the image, and outputting the AS-SAR complex image with high resolution after the phase correction;
the modeling step of the phase drift SPD in the first step is as follows: observing the runway through an AS-SAR system to obtain two continuous images, considering the influence of phase drift SPD on phase difference by the system and atmospheric phase disturbance, and establishing a binary linear function model of the phase drift SPD when the atmosphere changes independently along with the distance direction and the azimuth direction;
the binary linear function model is as follows:
Figure DEST_PATH_IMAGE001
(12)
wherein Δ is the phase difference,
Figure 479473DEST_PATH_IMAGE002
is the wavelength of the carrier frequency and,
Figure DEST_PATH_IMAGE003
is the ground range from the target to the radar,
Figure 301674DEST_PATH_IMAGE004
is the angle between the target and the initial scan direction of the radar,
Figure DEST_PATH_IMAGE005
the phase of the noise is represented by,
Figure 143728DEST_PATH_IMAGE006
、
Figure DEST_PATH_IMAGE007
、
Figure 883145DEST_PATH_IMAGE008
are weighting coefficients.
2. The method according to claim 1, wherein the step two is followed by a secondary Stable Phase Control Point (SPCP) screening step: and selecting a stable phase control point closer to the runway through distance constraint, then extracting a stable phase control point SPCP, and performing step three processing.
3. The method according to claim 1, wherein the phase drift SPD estimated value obtained in the third step is filtered by a Kalman filter to obtain a more accurate phase drift SPD estimated value.
4. The phase drift correction method of the high-resolution runway foreign object detection system according to claim 1, characterized in that Stable Phase Control Points (SPCP) are screened out, weighting coefficients in the binary linear function model are calculated through the Stable Phase Control Points (SPCP), and a phase drift (SPD) model is obtained.
5. The phase drift correction method of the high-resolution runway foreign object detection system according to any one of claims 1-3, wherein the second step of performing feature extraction comprises: for the amplitude image, three functions of screening the stable phase control points SPCP are defined: local image contrast
Figure DEST_PATH_IMAGE009
Amplitude dispersion
Figure 9101DEST_PATH_IMAGE010
And correlation coefficient
Figure DEST_PATH_IMAGE011
。
6. The phase drift correction method of the high-resolution runway foreign object detection system according to claim 5, characterized in that the classification in the step two is to normalize the extracted features, then train linear classifier parameters by combining feature vectors of stable phase control points of known scenes, and obtain the stable phase control points SPCP through linear classification screening.
7. The method for correcting the phase drift of the high-resolution runway foreign object detection system according to any one of claims 1-3, wherein the phase drift SPD is estimated through a Stable Phase Control Point (SPCP) in the third step, and the phase drift SPD between two complex images is estimated in real time by using the Stable Phase Control Point (SPCP) obtained in the second step and based on the phase drift SPD model obtained in the first step and a least square algorithm; and finally, filtering the Kalman filter according to the multi-frame phase drift SPD.
8. A high resolution runway foreign object detection system, wherein the detection system is an AS-SAR system in the phase drift correction method of any of claims 1 to 7.
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