CN118112566A - SAR imaging method of unmanned aerial vehicle - Google Patents
SAR imaging method of unmanned aerial vehicle Download PDFInfo
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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
- G01S13/9004—SAR image acquisition techniques
- G01S13/9019—Auto-focussing of the SAR signals
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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
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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Abstract
The invention discloses an unmanned aerial vehicle SAR imaging method, which belongs to the technical field of SAR imaging and is used for imaging an unmanned aerial vehicle SAR system, and comprises the steps of estimating the speed of an unmanned aerial vehicle by utilizing the existing radar imaging result, re-imaging by utilizing the estimated speed, and removing a focusing stray point near a peak value of a target profile by utilizing a self-focusing algorithm; removing focus stray points near the peak value of the target profile by using a self-focusing algorithm comprises center shift, windowing, phase gradient estimation and phase compensation, and then continuously performing loop iteration to complete the self-focusing function of the radar image. The accuracy of the speed estimation algorithm is very high, wherein the speed estimation value of the iterative method is completely equal to the actual speed of the radar by 2 m/s; the imaging result point after the self-focusing treatment is very prominent in target, and clutter is suppressed to a great extent.
Description
Technical Field
The invention discloses an unmanned aerial vehicle SAR imaging method, and belongs to the technical field of SAR imaging.
Background
In view of the advantages of the FMCW radar in the aspects of miniaturization, light weight and the like, the SAR system is carried on the unmanned aerial vehicle, so that the unmanned aerial vehicle SAR system with low cost and high flexibility is constructed. Because the fixed wing unmanned aerial vehicle is limited by a field, the safety and the flexibility are very poor, and the carrying capacity is limited, the rotor unmanned aerial vehicle is more suitable for being used as a carrier of an SAR system. The SAR system is carried on the rotor unmanned aerial vehicle, so that the flexibility and the simplicity of the SAR system are greatly expanded, and the SAR system has very wide application prospects in the aspects of remote sensing mapping, resource exploration, disaster prediction and the like. The GPS of a single unmanned aerial vehicle has limited precision, and the highest speed measurement precision is 0.1m/s, so that the speed estimation effect is poor. Due to the high-frequency vibration of the unmanned aerial vehicle, the non-ideal components of the radar system and other reasons, a high-order phase error is inevitably introduced in the SAR imaging process, which slightly causes problems such as image defocusing and contrast reduction.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle SAR imaging method, which aims to solve the problem of poor unmanned aerial vehicle SAR imaging effect in the prior art.
An unmanned aerial vehicle SAR imaging method utilizes the existing radar imaging result to estimate the speed of the unmanned aerial vehicle, utilizes the estimated speed to re-image, and utilizes a self-focusing algorithm to remove focusing stray points near the peak value of a target profile;
Removing focus stray points near the peak value of the target profile by using a self-focusing algorithm comprises center shift, windowing, phase gradient estimation and phase compensation, and then continuously performing loop iteration to complete the self-focusing function of the radar image.
Re-imaging with the estimated velocity includes:
s1, setting the speed measured by a GPS of an unmanned aerial vehicle as According to/>The result of radar imaging is/>：
；
In the method, in the process of the invention,Representing amplitude/>Is a window width of/>Fourier transform of rectangular window of/(Sampling time for analog-to-digital converter,/>Is the frequency modulation rate of the linear frequency modulation signal,/>Is the speed of light,/>Is a distance unit,/>Representing the nearest distance from the center of the scene to the flight trajectory of the rotorcraft, and/>To broaden the deformed antenna azimuth weight function,/>In imaginary units,/>Is the signal wavelength,/>Representing an exponential function,/>Error phase introduced for frequency modulation mismatch.
Re-imaging with the estimated velocity includes:
s2, at Distance direction unit with strongest energy extractedThe azimuthal data of the location is recorded as/>：
；
For a pair ofFourier transform and/>Dividing the constructed matched filter, and performing inverse Fourier transform to obtain a range-direction compressed signal/>, wherein the range-direction compressed signal is subjected to range migration correction：
；
In the method, in the process of the invention,Is the fast fourier transform result of the function after the azimuth antenna pattern is widened.
Re-imaging with the estimated velocity includes:
S3, setting the actual speed of the unmanned aerial vehicle as According to/>Building an azimuthal matched filter/>：
；/>；
In the method, in the process of the invention,For frequency adjustment;
S4, carrying out matching filtering on a distance-oriented azimuth fast Fourier transform result, and then carrying out inverse fast Fourier transform to obtain an imaging result ：
；
In the method, in the process of the invention,Is the residual phase error,/>A refocusing form of azimuth antenna weight;
S5, arranging The solution range of [/>Solving actual speed/>, of unmanned aerial vehicle according to cyclic iteration method or optimization。
The center shifting operation moves the strongest scattering point of each range gate unit to the azimuth center through cyclic shift, and when the strongest scattering point is moved to the center of the image, the subsequent operation is performed.
After center shifting, windowing azimuth data in each range gate unit, reserving a high-energy area and inhibiting a low-energy area;
And calculating azimuth energy to obtain azimuth energy distribution, taking twice the azimuth width of the position 10dB below the peak value as window length, gradually reducing the window length along with the increase of iteration times, and finally converging the window length to a plurality of azimuth sampling points.
The phase gradient estimation includes:
performing inverse fast Fourier transform on the azimuth windowed data, and recording an inverse fast Fourier transform result as Pair/>Correlation yields gradient of phase error/>，/>Represents the/>Distance gate units,/>Representing an azimuth unit;
First, the The azimuthal phase error gradient of each range bin is/>：
；
First, theThe azimuthal phase error of each range bin is/>：
。
The phase compensation comprises the steps of estimating a phase error, carrying out reverse direction fast Fourier transform on an original defocused radar image, carrying out conjugate multiplication on the original defocused radar image and the solved phase error, and carrying out reverse direction fast Fourier transform to obtain a radar refocused image after the phase compensation.
The loop iteration includes: repeating center shift, windowing, phase gradient estimation and phase compensation until the lifting effect of each iteration is insufficient or the number of iterations reaches a threshold, ending the iteration, and obtaining a radar image at the moment, namely a final radar image after self-focusing and phase compensation.
Compared with the prior art, the invention has the following beneficial effects: the accuracy of the speed estimation algorithm is very high, wherein the speed estimation value of the iterative method is completely equal to the actual speed of the radar by 2 m/s; the clutter in the imaging result before the self-focusing treatment is very obvious, the actual point target result is almost covered, the imaging result after the self-focusing treatment is very prominent in point target, and the clutter is suppressed to a great extent.
Drawings
FIG. 1 is a graph of the direction compression of a special display point without speed estimation in an iteration simulation result point target section;
FIG. 2 is a graph showing the result of compressing the azimuth of a point of interest in the target profile of the point of the simulation result of the iterative method;
FIG. 3 is a graph showing the result of compressing the azimuth of a point without speed estimation in the target profile of the simulation result point by the optimization method;
FIG. 4 is a graph showing the compression result of the azimuth of the special display point for speed estimation in the target profile of the simulation result point of the optimization method;
FIG. 5 is a point target imaging result before PGA processing by the self-focusing algorithm;
FIG. 6 is a point target imaging result after PGA processing by the self-focusing algorithm;
fig. 7 is a cross-section of the azimuth of the point target actually imaged before and after PGA.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An unmanned aerial vehicle SAR imaging method utilizes the existing radar imaging result to estimate the speed of the unmanned aerial vehicle, utilizes the estimated speed to re-image, and utilizes a self-focusing algorithm to remove focusing stray points near the peak value of a target profile;
Removing focus stray points near the peak value of the target profile by using a self-focusing algorithm comprises center shift, windowing, phase gradient estimation and phase compensation, and then continuously performing loop iteration to complete the self-focusing function of the radar image.
Re-imaging with the estimated velocity includes:
s1, setting the speed measured by a GPS of an unmanned aerial vehicle as According to/>The result of radar imaging is/>：
；
In the method, in the process of the invention,Representing amplitude/>Is a window width of/>Fourier transform of rectangular window of/(Sampling time for analog-to-digital converter,/>Is the frequency modulation rate of the linear frequency modulation signal,/>Is the speed of light,/>Is a distance unit,/>Representing the nearest distance from the center of the scene to the flight trajectory of the rotorcraft, and/>To broaden the deformed antenna azimuth weight function,/>In imaginary units,/>Is the signal wavelength,/>Representing an exponential function,/>Error phase introduced for frequency modulation mismatch.
Re-imaging with the estimated velocity includes:
s2, at Distance direction unit with strongest energy extractedThe azimuthal data of the location is recorded as/>：
；
For a pair ofFourier transform and/>Dividing the constructed matched filter, and performing inverse Fourier transform to obtain a range-direction compressed signal/>, wherein the range-direction compressed signal is subjected to range migration correction：
；
In the method, in the process of the invention,Is the fast fourier transform result of the function after the azimuth antenna pattern is widened.
Re-imaging with the estimated velocity includes:
S3, setting the actual speed of the unmanned aerial vehicle as According to/>Building an azimuthal matched filter/>：
；/>；
In the method, in the process of the invention,For frequency adjustment;
S4, carrying out matching filtering on a distance-oriented azimuth fast Fourier transform result, and then carrying out inverse fast Fourier transform to obtain an imaging result ：
；
In the method, in the process of the invention,Is the residual phase error,/>A refocusing form of azimuth antenna weight;
S5, arranging The solution range of [/>Solving actual speed/>, of unmanned aerial vehicle according to cyclic iteration method or optimization。
The center shifting operation moves the strongest scattering point of each range gate unit to the azimuth center through cyclic shift, and when the strongest scattering point is moved to the center of the image, the subsequent operation is performed.
After center shifting, windowing azimuth data in each range gate unit, reserving a high-energy area and inhibiting a low-energy area;
And calculating azimuth energy to obtain azimuth energy distribution, taking twice the azimuth width of the position 10dB below the peak value as window length, gradually reducing the window length along with the increase of iteration times, and finally converging the window length to a plurality of azimuth sampling points.
The phase gradient estimation includes:
performing inverse fast Fourier transform on the azimuth windowed data, and recording an inverse fast Fourier transform result as Pair/>Correlation yields gradient of phase error/>，/>Represents the/>Distance gate units,/>Representing an azimuth unit;
First, the The azimuthal phase error gradient of each range bin is/>：
；
First, theThe azimuthal phase error of each range bin is/>：
。
The phase compensation comprises the steps of estimating a phase error, carrying out reverse direction fast Fourier transform on an original defocused radar image, carrying out conjugate multiplication on the original defocused radar image and the solved phase error, and carrying out reverse direction fast Fourier transform to obtain a radar refocused image after the phase compensation.
The loop iteration includes: repeating center shift, windowing, phase gradient estimation and phase compensation until the lifting effect of each iteration is insufficient or the number of iterations reaches a threshold, ending the iteration, and obtaining a radar image at the moment, namely a final radar image after self-focusing and phase compensation.
In the invention, the iterative method has the advantages that the method is clear and simple, and the azimuth focusing result index used for solving the optimal speed can be easily set, thereby obtaining a more accurate speed estimation result; the method has the defects that the algorithm efficiency is low, one azimuth focusing is required to be carried out for each iteration, and the step precision limits the solving precision. The optimization method has the advantages of high algorithm efficiency and fast operation; the method has the disadvantages that the construction of the optimization function is clumsy and inflexible, and the optimization solution is easy to fall into local optimum.
MATLAB simulation is performed on the speed estimation algorithm to verify the effectiveness of the speed estimation algorithm. The actual speed of the radar is made to take 2m/s as the center, the amplitude is 0.1m/s, sinusoidal fluctuation is carried out at the angular speed of 80rad/s, the radar speed which is roughly measured by the GPS is assumed to fluctuate up and down with 2.1m/s as the center, and the rest parameters simulated by the speed estimation algorithm are consistent with the parameters simulated by the RD algorithm, so that simulation operation is carried out. The result of compressing the directions of the special display points which are not subjected to speed estimation in the point target profile of the iterative simulation result is shown in fig. 1, the result of compressing the directions of the special display points which are not subjected to speed estimation in the point target profile of the iterative simulation result is shown in fig. 2, the result of compressing the directions of the special display points which are not subjected to speed estimation in the point target profile of the optimization simulation result is shown in fig. 3, and the result of compressing the directions of the special display points which are not subjected to speed estimation in the point target profile of the optimization simulation result is shown in fig. 4.
The speed stepping precision is 0.01m/s, and the speed stepping interval is 1.8 m/s-2.2 m/s when the iteration method is simulated; the speed estimation interval of the simulation optimization method is 1.8 m/s-2.2 m/s. It can be seen that the accuracy of the speed estimation algorithm is very high, both in the iterative method and the optimization method, wherein the speed estimation value of the iterative method is completely equal to the actual radar speed of 2m/s, and the optimization method has very small speed deviation, but can still be considered to be effective.
Both the iterative method and the optimization method can predict the speed value with the best focusing effect, but the speed is sinusoidal fluctuation with the speed being 2m/s as the center and the amplitude being 0.1m/s and the angular speed being 80rad/s, and the speed is not a constant value, so that although the speed estimation is accurate, the estimated value is a constant value finally and cannot reflect the actual speed change condition, and the final azimuth compression result has some defocusing. Two small focus spurs are located on either side of the peak, which can ultimately be removed by processing with a self-focusing algorithm.
The MATLAB is utilized to simulate the self-focusing algorithm for verifying the effectiveness of the self-focusing algorithm. The speed is still set to be 2m/s as the center, 0.1m/s as the amplitude, the sinusoidal fluctuation is carried out at the angular speed of 80rad/s, and meanwhile, random uniform phase noise with the range of 0-1.8 is overlapped in echo data, so that high-frequency phase errors caused by speed change, unmanned aerial vehicle vibration and the like are simulated, and other parameters are consistent with RD algorithm simulation parameters. A final focusing result obtained by six iterations of the self-focusing algorithm is utilized, and a point target imaging result is obtained before the PGA processing of the self-focusing algorithm; as shown in fig. 5, the point target imaging result after the PGA processing by the self-focusing algorithm is shown in fig. 6. The azimuth profile of the actually imaged point target before and after PGA is shown in fig. 7, and the clutter in the imaging result before the self-focusing process is very obvious, almost masking the actual point target result. The imaging result point after the self-focusing treatment is very prominent in target, and clutter is suppressed to a great extent.
The architecture of the rotor unmanned aerial vehicle SAR system used by the invention is introduced below, and the rotor unmanned aerial vehicle SAR system is composed of components such as an IWR1443Boost radar evaluation board, a DCA1000EVM data acquisition board, a six-rotor unmanned aerial vehicle, an unmanned aerial vehicle-mounted GPS/IMU, a self-stabilizing cradle head, miniPC, a battery, a voltage conversion module, a master control PC, a gigabit bridge, a router, a carry-on WIFI and the like.
IWR1443Boost radar evaluation board and DCA1000EVM data acquisition board are the foremost core part in rotor unmanned aerial vehicle SAR system, and IWR1443Boost radar evaluation board is TI company towards single chip IWR1443 millimeter wave sensor's 77Ghz millimeter wave radar evaluation board. The processor core of the IWR1443Boost radar evaluation board is low-power-consumption arm@cortex R4F, an onboard USB interface and a high-density connector interface, and the IWR1443Boost radar evaluation board can be conveniently programmed and controlled by a PC.
The DCA1000EVM data acquisition board is a special data acquisition board card designed for a radar evaluation board by TI company, the IWR1443Boost radar evaluation board can transmit data to the DCA1000EVM data acquisition board in real time through multi-channel LVDS and store the data in on-board DDR of the DCA1000EVM data acquisition board in real time, and the DCA1000EVM data acquisition board is used for dispatching the PC to read and control radar data.
TI provides software mmwave studio for convenient control of IWR1443Boost radar evaluation board and DCA1000EVM data acquisition board, and parameters such as radar signal parameters, distance sampling frequency and distance sampling point number, azimuth sampling frequency and azimuth sampling point number can be conveniently and rapidly configured by using the mmwave studio, so that complicated script configuration process is avoided. After data reading by using mmwave studio, the data can be imported into MATLAB, and then radar data is focused and imaged by using RD imaging algorithm.
The invention respectively develops imaging tests of three point targets and six point targets. The common parameters in each experiment are shown in table 1, and different parameters will be described separately in each experiment.
Table 1 common parameters for each trial
；
In the imaging test of three point targets, the azimuth sampling point is 20000 points, the flying height of the unmanned aerial vehicle is 4m, the imaging result of the three corner reflectors is clear and definite, the width of the corner reflectors in the experiment is 0.3m, the main lobe width of the three targets in the imaging result is about 0.3m, the distance between the targets in the SAR image and the actual distance between the corner reflectors have better anastomosis, and the imaging effect is good.
In the imaging test of six point targets, the azimuth sampling point is 20000 points, the flying height of the unmanned aerial vehicle is 4.5m, the width of the corner reflector is 0.3m, the main lobe width of each target in the imaging result is also about 0.3m, the distance between each target in the SAR image and the actual distance between the corner reflectors have better consistency, and the imaging effect is good.
The above embodiments are only for illustrating the technical aspects of the present invention, not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some or all of the technical features may be replaced with other technical solutions, which do not depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The unmanned aerial vehicle SAR imaging method is characterized in that an existing radar imaging result is utilized to estimate the speed of an unmanned aerial vehicle, the estimated speed is utilized to re-image, and a self-focusing algorithm is utilized to remove focusing stray points near a peak value of a target profile;
Removing focus stray points near the peak value of the target profile by using a self-focusing algorithm comprises center shift, windowing, phase gradient estimation and phase compensation, and then continuously performing loop iteration to complete the self-focusing function of the radar image.
2. The unmanned aerial vehicle SAR imaging method of claim 1, wherein re-imaging with the estimated velocity comprises:
s1, setting the speed measured by a GPS of an unmanned aerial vehicle as According to/>The result of radar imaging is/>：
；
In the method, in the process of the invention,Representing amplitude/>Is a window width of/>Fourier transform of rectangular window of/(Sampling time for analog-to-digital converter,/>Is the frequency modulation rate of the linear frequency modulation signal,/>Is the speed of light,/>Is a distance unit,/>Representing the nearest distance from the center of the scene to the flight trajectory of the rotorcraft, and/>To broaden the deformed antenna azimuth weight function,/>In imaginary units,/>As a function of the wavelength of the signal,Representing an exponential function,/>Error phase introduced for frequency modulation mismatch.
3. A method of unmanned aerial vehicle SAR imaging according to claim 2, wherein re-imaging with the estimated velocity comprises:
s2, at Distance direction unit with strongest energy extractedThe azimuthal data of the location is recorded as/>：
；
For a pair ofFourier transform and/>Dividing the constructed matched filter, and performing inverse Fourier transform to obtain a range-direction compressed signal/>, wherein the range-direction compressed signal is subjected to range migration correction：
；
In the method, in the process of the invention,Is the fast fourier transform result of the function after the azimuth antenna pattern is widened.
4. A method of unmanned aerial vehicle SAR imaging according to claim 3, wherein re-imaging using the estimated velocity comprises:
S3, setting the actual speed of the unmanned aerial vehicle as According to/>Building an azimuthal matched filter/>：
；/>；
In the method, in the process of the invention,For frequency adjustment;
S4, carrying out matching filtering on a distance-oriented azimuth fast Fourier transform result, and then carrying out inverse fast Fourier transform to obtain an imaging result ：
；
In the method, in the process of the invention,Is the residual phase error,/>A refocusing form of azimuth antenna weight;
S5, arranging The solution range of [/>Solving the actual speed of the unmanned aerial vehicle according to a cyclic iteration method or optimization。
5. The unmanned aerial vehicle SAR imaging method according to claim 4, wherein the center shift moves the strongest scattering point of each range gate unit to the azimuth center by cyclic shift, and when the strongest scattering point is moved to the image center, the subsequent operation is performed.
6. The unmanned aerial vehicle SAR imaging method according to claim 5, wherein after center shifting, the azimuth data is windowed in each range gate unit, and the high energy region is preserved and the low energy region is suppressed;
And calculating azimuth energy to obtain azimuth energy distribution, taking twice the azimuth width of the position 10dB below the peak value as window length, gradually reducing the window length along with the increase of iteration times, and finally converging the window length to a plurality of azimuth sampling points.
7. The unmanned aerial vehicle SAR imaging method of claim 6, wherein the phase gradient estimation comprises:
performing inverse fast Fourier transform on the azimuth windowed data, and recording an inverse fast Fourier transform result as Pair/>Correlation yields gradient of phase error/>，/>Represents the/>Distance gate units,/>Representing an azimuth unit;
First, the The azimuthal phase error gradient of each range bin is/>：
；
First, theThe azimuthal phase error of each range bin is/>：
。
8. The unmanned aerial vehicle SAR imaging method of claim 7, wherein the phase compensation comprises estimating the phase error, performing an azimuthal inverse fast Fourier transform on the original defocused radar image, performing conjugate multiplication on the original defocused radar image and the solved phase error, and performing an azimuthal inverse fast Fourier transform to obtain the radar refocused image after the phase compensation.
9. The unmanned aerial vehicle SAR imaging method of claim 8, wherein the loop iteration comprises: repeating center shift, windowing, phase gradient estimation and phase compensation until the lifting effect of each iteration is insufficient or the number of iterations reaches a threshold, ending the iteration, and obtaining a radar image at the moment, namely a final radar image after self-focusing and phase compensation.
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