CN111551934A - Motion compensation self-focusing method and device for unmanned aerial vehicle SAR imaging - Google Patents
Motion compensation self-focusing method and device for unmanned aerial vehicle SAR imaging Download PDFInfo
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Abstract
The invention discloses a motion compensation self-focusing method and device for unmanned aerial vehicle-mounted SAR imaging, aiming at the problems that serious motion errors exist in echo data of an unmanned aerial vehicle-mounted synthetic aperture radar and high-resolution imaging cannot be carried out only by using an inertial navigation system. The improved sub-aperture correlation algorithm provided by the invention has higher Doppler frequency modulation rate estimation precision than the existing sub-aperture correlation algorithm under the condition of obtaining sufficient distance space-variant information, and can more effectively compensate the motion error with the distance space-variant. In order to avoid singular points generated by sub-aperture Doppler frequency modulation rate estimation due to the fact that a local scene is uniform or no strong scattering points exist, the method adopts a random consistency method to effectively inhibit the interference of the singular points on imaging results. The invention proves the effectiveness of the method through the simulation and reality verification of the measured data.
Description
Technical Field
The invention relates to a motion compensation self-focusing method and device for unmanned aerial vehicle SAR imaging, and belongs to the technical field of radar imaging.
Background
Synthetic Aperture Radars (SAR) can work under all weather conditions, high range resolution is obtained by adopting broadband signals, virtual large Aperture is formed through platform motion, azimuth resolution is improved, and finally high-resolution two-dimensional imaging is achieved. Synthetic aperture radar imaging requires that a platform motion vector is unchanged within synthetic aperture time, wherein a satellite-borne synthetic aperture radar generally meets requirements, but airborne synthetic aperture radars, especially unmanned airborne synthetic aperture radars, have serious motion errors due to deviation of a motion platform from a preset track, deviation of an antenna phase center and change of flight attitude caused by influences of unstable airflow and other factors in the atmosphere. How to carry out motion compensation on the unmanned aerial vehicle synthetic aperture radar system is a key technology of unmanned aerial vehicle synthetic aperture radar imaging. The mainstream synthetic aperture radar motion compensation imaging technology in foreign countries at present mainly utilizes a high-precision inertial navigation system and a global satellite positioning system to compensate the stationarity of a carrier and an antenna beam, and then the relatively ideal synthetic aperture radar image can be obtained through processing by a self-focusing imaging algorithm. And the existing domestic unmanned aerial vehicle synthetic aperture radar can only be provided with a small-size portable low-precision inertial navigation system, and the high-precision motion compensation of the unmanned aerial vehicle-mounted synthetic aperture radar cannot be realized. In addition, for breaching enemy radar defense line, unmanned aerial vehicle often adopts the means of low-altitude flight, and for obtaining wide scene synthetic aperture radar imaging this moment, the radar will work with low pitch angle, must lead to motion error to have very strong distance space variability, has further increased the degree of difficulty of high accuracy synthetic aperture radar imaging. Currently, in order to compensate the motion error of the airborne synthetic aperture radar platform, many scholars have proposed some motion compensation methods. Bezvesilnity et al [ Bezvesilnity, O.O., I.M.Gorovyi, and D.M.Vavriv. "Estimation of Phase Errors InSAR Data by Local-Quadratic Map-Drift Autofocus." International radial symposium 2012: 376-. M.xing et al [ m.xing, x.jiang, r.wu, f.zhou, and z.bao, "Motion compensation for uav SAR based on raw data," ieee trans. geosci.remote sens., vol.47, No.8, pp.2870-2883, and aug.2009] propose a new Motion compensation method to divide the azimuth into a plurality of sub-apertures, then block the distance directions in each sub-aperture, estimate the local Doppler modulation frequency (Doppler Rate, DR) by using the existing sub-aperture correlation (Map-Drift, MD) algorithm, and can compensate the Motion errors of the azimuth direction and the distance direction at the same time. However, the method may have many problems in that the doppler modulation frequency of each distance block is obtained after the distance block is segmented, and the doppler modulation frequencies are fitted to each doppler modulation frequency. If the number of distance direction blocks is small, each block has enough distance units, a more accurate solution can be obtained by using a sub-aperture correlation algorithm, but enough distance space-variant information cannot be provided; when the number of subblocks is large, sufficient distance space-variant information can be provided, but when only a small number of salient points are included in a subblock, it is difficult to ensure estimation accuracy.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problem of the number of distance direction blocks in the prior art, the invention aims to provide a novel method and a device for motion compensation self-focusing of synthetic aperture radar imaging based on the existing sub-aperture correlation algorithm. The method can solve the problem that good Doppler frequency modulation rate estimation precision and sufficient distance space-variant information are difficult to obtain simultaneously in the prior art, effectively inhibits the interference of singular points on imaging results, and can be suitable for the imaging of the synthetic aperture radar with small unmanned aerial vehicle volume, which cannot carry a high-precision inertial navigation system and has serious motion errors in synthetic aperture radar echo data.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a motion compensation self-focusing method for unmanned airborne SAR imaging, comprising the following steps:
(1) after coarse compensation, RCM correction, distance matching filtering and De-tracking operation are carried out on the received echo signals by using inertial navigation data, azimuth blocking is carried out on the obtained signals with residual phase errors, and sub-aperture data are obtained;
(2) performing distance direction blocking on each sub-aperture, and selecting a special display point sample from all distance units;
(3) dividing a distance unit where the special display sample book is located into a front part and a rear part, performing correlation processing, estimating to obtain a second-order phase error of the distance unit where the special display sample book is located, and calculating to obtain a second-order phase error coefficient by using data of the distance unit where the special display sample book is located based on the linear change characteristic of the second-order error along the distance direction, so as to estimate to obtain the second-order error in the distance unit without the special display sample book;
(4) after second-order derivatives of residual phase errors of all sub-apertures of all distance units are obtained, eliminating abnormal values by adopting a random consistency algorithm;
(5) and obtaining residual phase errors by using a least square method and performing phase compensation to improve the imaging focusing effect of the synthetic aperture radar.
Preferably, in the step (3), the second-order phase error coefficient is calculated by the following formula:
wherein,
for fast time, f denotes the Doppler frequency, rkRepresents the pitch of the selected kth distance unit,
indicating that the correlation operation is carried out on the Doppler spectrum values of the echo sequence of the front half section and the back half section of the kth distance unit, wherein K indicates the total number of the distance units with special display points, and | represents the absolute value operation.
Preferably, the step (4) of removing the outliers by using a random consistency algorithm includes:
(4.1) approximating the phase error function to a Q (Q < N) order polynomial f (a)0,a1,…aQ) And N denotes the number of subapertures, the second derivative can be expressed as:
wherein t denotes a slow time, aqIs a polynomial f (a)0,a1,…aQ) Q-order item coefficients;
(4.2) corresponding to N sub-apertures, there are N second derivatives
N-1, wherein t is 0,2n＝(2n+1)TsAnd/2 represents the sub-aperture center time, and Q points in the N points are randomly selected as interior points to form an initial interior point set, and the corresponding polynomial coefficient a of the initial interior point set is obtainedq(ii) a Setting a threshold value G, respectively calculating the distance from the remaining N-Q points to the polynomial curve, and if the distance corresponding to a certain point is less than the threshold value, considering the point as an interior point and listing the interior point into an interior point set;
(4.3) counting the number of inner points in the inner point set;
(4.4) repeating the step (4.2) and the step (4.3) for S times, comparing and selecting the interior point set with the most interior points;
and (4.5) all the interior points in the interior point set with the most interior points are effective values.
Preferably, the number of repetitions S is selected according to the following formula:
wherein, P is confidence probability and data error rate.
Based on the same inventive concept, the invention discloses a motion compensation self-focusing device for unmanned aerial vehicle SAR imaging, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the motion compensation self-focusing method for unmanned aerial vehicle SAR imaging when being loaded to the processor.
Has the advantages that: the invention provides a novel motion compensation self-focusing method for unmanned aerial SAR imaging based on the existing sub-aperture correlation algorithm, which is characterized in that after distance is divided into blocks, the same number of special display points are respectively searched in all distance blocks of a certain azimuth block, and according to the principle that the Doppler frequency modulation frequency is in linear relation with the point target slant distance in a Doppler domain, the optimal Doppler frequency modulation rate which linearly changes along with the distance is matched through a distance unit where the special display points are located and is respectively used as the Doppler frequency modulation frequency corresponding to each distance gate of the azimuth block. The operation solves the problem that good Doppler frequency modulation rate estimation precision and sufficient distance space-variant information are difficult to obtain simultaneously in the prior art. Meanwhile, the azimuth block can cause that the sub-aperture does not contain strong scattering points or the scene is uniform, so that the estimation generates singular values, which can affect the imaging result, therefore, the method adopts a random consistency method to eliminate the singular values and utilizes the other effective values to estimate the Doppler frequency modulation of the range gate, thereby further improving the estimation precision of the Doppler frequency modulation.
Drawings
Fig. 1 is a diagram of a distance space-variant synthetic aperture radar signal model according to an embodiment of the present invention.
FIG. 2 is a flow chart of a method according to an embodiment of the present invention.
Fig. 3 is a diagram of simulated motion error.
Fig. 4 is an image to be processed, 4(a) is an original image, and 4(b) is a defocused image with motion errors added.
Fig. 5 is a diagram of the result of the distance direction division by 4 blocks in the prior art method, wherein 5(a) does not remove singular values, and 5(b) removes singular values.
Fig. 6 is a diagram of the results of the distance direction 128 blocks of the prior art method.
FIG. 7 is a modified sub-aperture correlation algorithm auto-focusing an image, 7(a) without removing singular values, and 7(b) with removing singular values.
Fig. 8 is a partially enlarged view of an original image.
Fig. 9 is a partial enlarged view of the result of self-focusing. 9(a) partial enlargement of the existing method, and 9(b) partial enlargement of the modified subaperture correlation algorithm.
FIG. 10 is a diagram illustrating the results of entropy change with distance to partitions.
Detailed Description
The invention is further illustrated with reference to the following specific embodiments and the accompanying drawings.
The airborne synthetic aperture radar signal model with distance space-variant error related in the embodiment of the invention is shown in figure 1, wherein an X axisThe forward direction represents the direction of flight of the aircraft. The height of the carrier from the ground is H, the phase center of the antenna changes at a constant speed v along the X axis under an ideal condition, but the position deviation exists in the actual central flight path, R represents the shortest distance from the target to the ideal flight path, R (t) is the real distance from the target to the phase center of the antenna at the time of slow time t, R (t)0(t) is the ideal distance from the target to the antenna phase center at time t, and point P in the figure is the real position of the antenna phase center at time t, which is compared with the ideal position P0Instantaneous motion error of [ Δ x (t), [ Δ y (t), ] Δ z (t) ]]Where Δ X (t), Δ Y (t), and Δ Z (t) represent instantaneous positional deviations of the antenna phase center in the X-axis, Y-axis, and Z-axis directions, respectively. (x, y, z) is the ground certain target coordinates.
Assuming that the synthetic aperture radar transmits a chirp signal of
Wherein,
for fast time, j represents the unit of an imaginary number,
fcfor a signal carrier frequency, TpGamma is the modulation frequency for the pulse width of the transmitted signal. After coherent detection, the received echo signal is expressed as:
wherein c is the speed of light, R (t; R, x) is R in FIG. 1, and the specific expression is as follows:
firstly, carrying out coarse compensation processing on a received echo signal by using inertial navigation data, then carrying out Range Cell Migration (RCM) correction and distance matching filtering, and then carrying out azimuth deskewing (De-ramping) operation on the signal to eliminate a secondary frequency modulation component in an ideal slant Range, so as to obtain a signal with a residual phase error:
wherein r islIs the slant distance from the center of the L (1, 2.. L) distance unit to the ideal route, L represents the total distance unit number,
for residual phase error, sref(rlAnd t) is an ideal echo signal without residual phase error.
To utilize the sub-aperture correlation algorithm in the stripe mode, we block the azimuth direction, dividing the data of duration T into N data of duration TsCenter time of tn＝(2n+1)(TsN-1, N ═ 0, 1.
TsShould be small enough to ensure that the residual phase error in each sub-aperture can be approximated by a second order polynomial, typically on the order of one tenth of the coherent integration time, as follows:
wherein, tau ∈ -Ts/2＜τ＜Ts/2.
Based on the airborne synthetic aperture radar signal model with the distance space-variant error, the embodiment of the invention discloses a motion compensation self-focusing method for unmanned airborne SAR imaging, after rough compensation, RCM correction, distance matching filtering and De-tracking operation are carried out on received echo signals by using inertial navigation data, azimuth blocking is carried out on the obtained signals with residual phase errors to obtain sub-aperture data, then distance blocking is carried out on the sub-apertures, and special display point samples are selected from all distance units; then dividing the distance unit where the sample is located into a front part and a rear part, performing correlation processing, and estimating to obtain a second-order phase error coefficient so as to obtain a second-order phase error of each distance unit; after the second derivative of the residual phase error of each sub-aperture of each distance unit is obtained, in order to avoid the situation that a few abnormal values exist, a random consistency algorithm is adopted to remove the abnormal values; and obtaining residual phase errors by using a least square method and performing phase compensation.
When the sub-aperture correlation algorithm is applied, if an orientation signal in a certain range bin has an explicit point, the estimation is more accurate than that of the range bin only having a stray point, and therefore, a sample is selected. It is usually sufficient to choose the one tenth range cell with the largest energy. Since the present invention takes into account the second order phase error from the space-variant, the samples chosen should provide sufficient distance space-variant information. The method is to select samples of each sub-aperture data, divide each sub-aperture data into distance blocks, and select required sample data from the distance units of each distance block according to an energy criterion.
In the following, taking the nth sub-aperture as an example, the same processing is performed on the N sub-apertures by applying a sub-aperture correlation algorithm, respectively. Dividing the nth sub-aperture into front and back halves can be expressed as:
wherein,
rkk is 1,2.. K, K is less than L, and K is selected as a specially displayed pointTotal number of distance cells. Constant term
Has no influence on the estimation and can be ignored at the same time
Only the left and right images are translated in the same direction, and as shown in equations (7), (8) and (9), the following equations are considered equivalently:
wherein,
fourier transform in the azimuth direction is performed on equations (9) and (10) to obtain:
wherein f represents the Doppler frequency,
is composed of
The Fourier transform is carried out on the data to obtain a Fourier transform,
is composed of
And performing Fourier transform.
The doppler shift frequency error is considered here to vary linearly along the distance, i.e.:
wherein k is0,k1Is the second order phase error coefficient.
Then, the following equations (12) and (13) can be obtained:
wherein, Δ frRepresenting Doppler spectra
And
at a distance rkThe frequency shift can be estimated by performing correlation processing on the front and back half echo sequence Doppler spectrum values. This correlation process can be expressed as:
where φ represents the correlation of two functions, Δ frIs equivalent to
The position of the correlation peak of (a).
In the traditional sub-aperture correlation algorithm, the Doppler frequency modulation rate is assumed to be a constant, and the accuracy of Doppler frequency modulation rate estimation can be effectively improved by discontinuously adding distance unit cross-correlation functions with strong scattering points (special display points). In practical applications, this is important if there are strong clutter or no salient points in the scene. In the prior art, after distance blocks are partitioned, distance units with strong scattering points in each distance block are directly added to calculate the Doppler modulation frequency of the distance block, and then the Doppler modulation frequency obtained by each distance block is subjected to fitting operation, so that many problems are caused. When the number of the distance blocks is large, each distance block has a middle partThe distance units with strong scattering points are few, and the Doppler frequency modulation rate cannot be accurately estimated; although the doppler modulation frequency per block can be accurately estimated when the distance is small, the doppler modulation frequency has distance space variation, which cannot provide enough distance space variation information, as will be clearly seen in the simulation experiment in section 3. Therefore, in order to make the algorithm robust by using sufficient samples, overcome the influence of noise and realize high precision and quick convergence, the correlation peak position delta f of the cross-correlation function is usedrAnd estimating the parameter k by combining the characteristics of linear movement along the distance unit0、k1And further obtained by the formula (13)
L ═ 1,2,. L. The above process can be expressed by the following formula:
wherein,
represents the accumulation of the correlation spectrum, and the value is shown by the parameter k in the formulas (12) and (13)0、k1Influence. Meanwhile, argmax represents a position Δ f at which the maximum peak of the accumulated spectrum is takenrAnd k can be estimated from (14)0、k1. In order to reduce the calculation amount while ensuring the accuracy, the document [ Zhang L, Duan J, Qiao Z J, et al. phase adjustment and island imaging of manufacturing targets with spark apertures [ J ] can be applied].IEEE Transactions on Aerospace&Electronic Systems,2014,50(3):1955-1973.]The method of (1) or other acceleration algorithm.
The second-order phase error coefficient obtained by current estimation is used for compensating the distance sample in the time domain, so that the peak value of the cross-correlation function is narrower, a more accurate second-order phase error coefficient is obtained, and an image with better focus is obtained. By repeating the iteration, convergence can be finally achieved. In practice, 2-5 iterations will result in a sufficiently accurate estimate of the second order phase error.
Due to the fact that the scene azimuth direction is subjected to blocking processing, when the sub-blocks do not contain strong scattering points or the scene is uniform, due to inherent limitation of a sub-aperture correlation algorithm, even if the estimation method cannot obtain accurate Doppler frequency modulation rate estimation values in the sub-apertures, the singular values can affect imaging results without any processing. Therefore, after obtaining L distance units, the second derivative of the residual phase error of N sub-apertures
N-1, to avoid the presence of a few outliers, we use RANdom consensus (RANSAC) to reject outliers. The same operation is performed on each distance unit, taking the ith distance unit as an example, the specific steps are as follows:
1) approximating a phase error function to a Q (Q < N) order polynomial f (a)0,a1,…aQ) Then its second derivative can be expressed as:
wherein t denotes a slow time, aqIs a polynomial f (a)0,a1,…aQ) Q-order item coefficients;
2) in that
N-1, randomly selecting Q number of inliers to form an initial inlier set, calculating according to equation (17), and obtaining the corresponding polynomial coefficient aq. Setting a threshold value G, respectively calculating the distance from the remaining N-Q points to the polynomial curve, and if the distance corresponding to a certain point is less than the threshold value, considering the point as an interior point and listing the interior point into an interior point set;
3) counting the number of the inner points in the inner point set;
4) repeating the step 2) and the step 3) for S times, comparing and selecting the interior point set with the most interior points;
5) all inliers in the set of inliers are considered valid values.
Obviously, if the Q points used to calculate the polynomial coefficients include outliers, then the curve will not have the highest number of corresponding interior points. Meanwhile, the selection of the threshold G is important, if the threshold G is too small, some interior points to be selected can be lost, and if the threshold G is too large, some abnormal points can be wrongly judged as interior points. The threshold is chosen in relation to the size of the error.
The number of repetitions S is selected according to the following formula:
wherein, P is confidence probability and data error rate.
After the above processing, each distance unit r can be obtainedl(L1, 2.. L)
Is given by the effective value of1With M significant values in a range unit
M is 1,2.. M, and a is obtained from the formula (17)qLeast squares solution of Q2
Thus we obtain the polynomial f (a)0,a1,…aQ) The residual phase error function of the distance unit can be compensated by using the analytic expression without zero-order terms and primary terms and substituting the expression into the expression (7), so that the singularity of Doppler frequency modulation estimated values caused by the fact that strong scattering points do not exist in some sub-apertures or scenes are uniform can be effectively avoided, and the focusing result is improved.
In order to further verify the effect of the method of the present invention, the following is a result of processing the measured data from the synthetic aperture radar system of unmanned aerial vehicle in Sandia laboratory by using the method of the present invention, and specific system parameters thereof are shown in table 1.
TABLE 1 System simulation parameters
In the experiment, the raw data of the synthetic aperture radar is firstly transformed to a range pulse pressure domain, and the motion errors in three coordinate directions are assumed to be ten-order polynomials of slow time, as shown in fig. 3.
To illustrate the applicability of the improved sub-aperture correlation algorithm, the observation scene is chosen to be a field, and only a few range bins containing strong scattering points are used as the phase gradient estimation samples. In order to verify the self-focusing performance of the algorithm, the image entropy is used as a quantitative evaluation index of imaging focusing. The entropy of the two-dimensional synthetic aperture radar image is:
wherein N isaIs the total number of picture pulses, NtD (q, k) is the scattering intensity density of the image for the total number of distance elements, expressed as:
where s (I) is the total energy of the image and I (q, k) is the complex reflection intensity of the synthetic aperture radar image. The image with the phase error is fuzzy, large in uncertainty and larger in entropy value, so that the entropy value of the image can well reflect the self-focusing performance of the algorithm.
FIG. 4(a) shows raw image data with an entropy value of 13.9467, and with the addition of a distance space-variant motion error, a defocused image is shown in FIG. 4(b) with an entropy value of 14.2484, which is about 0.3 different from the original image, when roads and trees are not clearly identified. About the first eighth of the original image data direction unit in fig. 4(a) contains a very small number of strong scattering points, the scene is relatively uniform, singular values are generated when the doppler modulation frequency is estimated by using the sub-aperture correlation algorithm, and the imaging result is poor, so that the singular values are necessarily removed by the aforementioned random consistency method.
The processing method using the sub-aperture correlation algorithm in the prior art is adopted to perform self-focusing on the defocused image added with the distance space-variant motion error, and the number of distance blocks can seriously affect the imaging result. If the number of distance direction blocks is small, each block has enough distance units, a more accurate solution can be obtained by using a sub-aperture correlation algorithm, but enough distance space-variant information cannot be provided; when the number of blocks is large, sufficient distance space-variant information can be provided, but this reduces the number of salient points included in the sub-block, and decreases the estimation accuracy. When the distance is less in the direction of blocking, the result of the block division into 4 blocks is shown in fig. 5, and fig. 5(a) is a result of not removing singular values, and its entropy value is 14.0830; fig. 5(b) shows the result of removing singular values by combining the method proposed by the present invention with the existing method, and the entropy value is 14.0205, so it can be seen that the image can be further focused by removing the singular values by using the method proposed by the present invention, and the image quality can be effectively improved. Fig. 5(b) the difference between the entropy value and the original image is 0.0738, the entropy value of the defocused image is obviously reduced, the doppler modulation frequency can be estimated more accurately, but the image entropy value has a certain difference compared with the original image, because the algorithm can effectively correct the non-space-variant part, but the influence caused by the distance space-variant is not well eliminated, and the residual distance space-variant phase error causes the image to have obvious defocusing; when the distance blocks are more, the entropy value of the result obtained by dividing the image into 128 blocks without using the random consistency to remove the singular value is shown in fig. 6, which is 14.4093, and since fig. 6 is 0.1636 higher than the entropy value of the defocused image before compensation, it shows that at this time, because the distance blocks are too many, the distance units containing strong scattering points in each distance block are few, an accurate estimation value cannot be obtained by using the existing sub-aperture correlation algorithm, the existing processing method has failed, the obtained estimation values are singular values, and therefore, the reuse of the random consistency has no significance.
In order to solve the above problem, under the condition of the same distance space-variant error, the improved sub-aperture correlation algorithm provided by the invention is adopted for self-focusing, the result of dividing the distance direction into 128 blocks is shown in fig. 7, and fig. 7(a) is the result without removing singular values, and the entropy value is 14.0035; fig. 7(b) shows the result after removing singular values, and its entropy value is 13.9897. It can be seen from fig. 7(b) that the image is already well focused after the processing of the method of the present invention, and the difference between the entropy value of the image and the entropy value of the original image is only 0.043.
To further illustrate the effectiveness of the modified sub-aperture correlation algorithm for space-variant phase error correction, fig. 8 shows a partial enlarged view of the original image fig. 4(a), and fig. 9(a) and 9(b) show partial enlarged views of the self-focusing image fig. 5(b) and the modified sub-aperture correlation algorithm self-focusing image fig. 7(b) with less blocks, respectively, in the prior art method. As can be seen from the figure, the method provided by the invention can accurately focus on the strong scattering points in the figure, while the neutron aperture correlation method in the prior art cannot accurately focus, which fully illustrates that the method provided by the invention can better compensate the motion error with the distance space variation.
Fig. 10 shows the variation of the difference between the entropy obtained by the self-focusing of the existing sub-aperture correlation algorithm and the elimination of the singular value in combination with the improved sub-aperture correlation algorithm proposed by the present invention and the original image entropy with respect to the number of distance blocks. It can be seen from the figure that, no matter how many the number of the blocks is, the method provided by the invention has very stable effect and is superior to the existing sub-aperture correlation algorithm, when the number of the blocks is small, the two methods have similar performance, but gradually show the advantage of the method provided by the invention for effectively utilizing the distance space-variant information along with the increase of the distance blocks, and the existing method is gradually invalid and cannot perform effective self-focusing processing on the image. Fig. 10 fully demonstrates the effectiveness of the proposed method for motion errors with distance space variation.
In summary, when the sub-aperture correlation algorithm is used for estimating the doppler modulation frequency, strong scattering points need to exist in the selected range cells, the number of the strong scattering points is reduced due to more range blocks, the estimation accuracy is reduced, and the range blocks are fewer and cannot provide enough range space-variant information. The improved sub-aperture correlation algorithm provided by the invention can effectively solve the problem by utilizing the characteristic that the Doppler frequency modulation frequency linearly changes along the range gate. When the scene only contains a small number of strong scattering points or the scene is uniform, singular values are removed by adopting a random consistency method, and the focusing effect of the image can be improved.
Based on the same inventive concept, the embodiment of the invention discloses a motion compensation self-focusing device for unmanned aerial vehicle SAR imaging, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the motion compensation self-focusing method for unmanned aerial vehicle SAR imaging when being loaded to the processor.
Claims (6)
1. A motion compensated autofocus method for unmanned on-board SAR imaging, the method comprising the steps of:
(1) after coarse compensation, RCM correction, distance matching filtering and De-tracking operation are carried out on the received echo signals by using inertial navigation data, azimuth blocking is carried out on the obtained signals with residual phase errors, and sub-aperture data are obtained;
(2) performing distance direction blocking on each sub-aperture, and selecting a special display point sample from all distance units;
(3) dividing a distance unit where the special display sample book is located into a front part and a rear part, performing correlation processing, estimating to obtain a second-order phase error of the distance unit where the special display sample book is located, and calculating to obtain a second-order phase error coefficient by using data of the distance unit where the special display sample book is located based on the linear change characteristic of the second-order error along the distance direction, so as to estimate to obtain the second-order error in the distance unit without the special display sample book;
(4) after second-order derivatives of residual phase errors of all sub-apertures of all distance units are obtained, eliminating abnormal values by adopting a random consistency algorithm;
(5) and obtaining residual phase errors by using a least square method and performing phase compensation to improve the imaging focusing effect of the synthetic aperture radar.
2. The motion-compensated self-focusing method for unmanned airborne SAR imaging according to claim 1, wherein the distance-wise blocking of each sub-aperture and the selection of the samples in step (2) are performed by: and (4) considering the second-order phase error of the distance space-variant, respectively selecting samples of each sub-aperture data, dividing each sub-aperture data into a plurality of distance blocks, and selecting required sample data from the distance units of each distance block by an energy criterion.
3. The motion compensated self-focusing method for unmanned airborne SAR imaging according to claim 1, wherein in said step (3) the second order phase error coefficient is calculated by the following formula:
wherein,
for fast time, f denotes the Doppler frequency, rkRepresents the pitch of the selected kth distance unit,
indicating that the correlation operation is carried out on the Doppler spectrum values of the echo sequence of the front half section and the back half section of the kth distance unit, wherein K indicates the total number of the distance units with special display points, and | represents the absolute value operation.
4. The motion-compensated self-focusing method for unmanned airborne SAR imaging according to claim 1, wherein the step (4) of rejecting outliers by using a random consistency algorithm comprises:
(4.1) approximating the phase error function to a Q (Q < N) order polynomial f (a)0,a1,…aQ) And N denotes the number of subapertures, the second derivative can be expressed as:
wherein t denotes a slow time, aqIs a polynomial f (a)0,a1,…aQ) Q-order item coefficients;
(4.2) corresponding to N sub-apertures, there are N second derivatives
Wherein t isn＝(2n+1)TsAnd/2 represents the sub-aperture center time, and Q points in the N points are randomly selected as interior points to form an initial interior point set, and the corresponding polynomial coefficient a of the initial interior point set is obtainedq(ii) a Setting a threshold value G, respectively calculating the distance from the remaining N-Q points to the polynomial curve, and if the distance corresponding to a certain point is less than the threshold value, considering the point as an interior point and listing the interior point into an interior point set;
(4.3) counting the number of inner points in the inner point set;
(4.4) repeating the step (4.2) and the step (4.3) for S times, comparing and selecting the interior point set with the most interior points;
and (4.5) all the interior points in the interior point set with the most interior points are effective values.
5. The motion-compensated autofocus method for unmanned-airborne SAR imaging according to claim 4, wherein the number of repetitions S is selected according to the following equation:
wherein, P is confidence probability and data error rate.
6. A motion compensated autofocus apparatus for unmanned on-board SAR imaging, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program, when loaded into the processor, implements the motion compensated autofocus method for unmanned on-board SAR imaging according to any of claims 1 to 5.
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