Unmanned aerial vehicle SAR real-time imaging system

Abstract

The invention relates to an SAR real-time imaging system of an unmanned aerial vehicle, belongs to the technical field of radar imaging, and solves the problem that SAR imaging is not real-time due to the fact that motion parameters cannot be compensated in time in the prior art. In the system, an unmanned aerial vehicle is provided with a motion sensor, a synthetic aperture radar, a synchronous control board card and a signal processing board card; the motion sensor collects first position data of the unmanned aerial vehicle; the synthetic aperture radar collects radar echo data; the synchronous control board card synchronizes the first position data and the radar echo data according to the timestamp and then sends the first position data and the radar echo data to the signal processing board card; the signal processing board card obtains second position data according to the radar echo data, calculates third position data according to the speed of the unmanned aerial vehicle, fuses the first position data, the second position data and the third position data for each radar sampling point according to three preset position weights to obtain corrected position data, and sends the corrected position data to the unmanned aerial vehicle; and obtaining an SAR image of the unmanned aerial vehicle according to the corrected position data and radar echo data. And SAR real-time imaging is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of radar imaging, and in particular to an unmanned aerial vehicle (UAV) SAR real-time imaging system. Background Art

[0002] With the continuous maturity of multi-rotor UAV technology and the miniaturization of synthetic aperture radar (SAR) systems, the combination of the two is used in more and more fields, such as national defense and military industry, geographical mapping, disaster monitoring, danger search and rescue, agriculture and forestry assessment, meteorology and hydrology, etc. Different from traditional optical images, SAR images can provide more physical information in the target scene. Therefore, the multi-rotor UAV SAR system has a very broad application prospect.

[0003] Multi-rotor UAVs mostly fly in the troposphere, and the flight is greatly affected by air flow disturbances. In addition, due to factors such as UAV performance and operation level, the UAV cannot move forward in a uniform linear motion state. However, the SAR imaging technology is based on a uniform linear flight state. The fluctuations in the flight trajectory and flight speed of the UAV cause the SAR imaging system to be unable to work directly. Therefore, motion compensation of the flight trajectory is required.

[0004] In the existing methods, the motion compensation based on radar echo data has a poor focusing effect in the case where there is no obvious reflector target in the imaging scene, and there is also a problem of inability to process in real time; when using the position and speed data of the multi-rotor UAV measured by motion sensors (such as inertial navigation, GPS, integrated navigation, etc.) for motion parameter compensation, due to problems such as low positioning accuracy and cumulative errors of motion sensors, it is impossible to meet the actual requirements of high-precision motion compensation for UAV SAR imaging. Summary of the Invention

[0005] In view of the above analysis, the embodiments of the present invention aim to provide an unmanned aerial vehicle (UAV) SAR real-time imaging system to solve the problem that the existing method cannot compensate motion parameters in time, resulting in non-real-time SAR imaging.

[0006] An embodiment of the present invention provides a UAV SAR real-time imaging system, including: a motion sensor, a synthetic aperture radar, a synchronous control board, and a signal processing board are installed on the UAV in the system; the motion sensor is used to collect the first position data of the UAV; the synthetic aperture radar is used to collect radar echo data; the synchronous control board is used to synchronize the first position data and the radar echo data according to the time stamp and then send them to the signal processing board; the signal processing board is used to obtain the second position data of the UAV according to the radar echo data, calculate the third position data of the UAV according to the speed of the UAV and the radar sampling point time, and fuse the corresponding first position data, second position data, and third position data for each radar sampling point according to three preset position weights to obtain the corrected position data, and perform SAR real-time imaging using the backscattering algorithm according to the corrected position data and the received radar echo data to obtain the SAR image of the UAV.

[0007] For a further improvement based on the above system, the three preset position weights are obtained through the following steps:

[0008] Through UAV flight experiments, obtain the position data from the motion sensor, synthetic aperture radar, and UAV at the experimental sampling points, and obtain the three position errors of each experimental sampling point in each experimental data according to the corresponding true position data; according to the three position errors of each experimental sampling point in multiple experimental data, obtain the variances of the three position errors of each experimental sampling point;

[0009] According to the three position weights, the three position errors, and the variances of the position errors, construct an objective function, and use a global optimization algorithm to calculate the optimal solutions of the three position weights in multiple experimental data respectively, and take the average to obtain the final three position weights, which are preset in the signal processing board.

[0010] For a further improvement based on the above system, according to the three position weights, the three position errors, and the variances of the position errors, construct an objective function through the following formula:

[0011]

[0012] Where M represents the number of experimental sampling points, Q1, Q2, and Q3 respectively represent the first position weight from the motion sensor, the second position weight from the synthetic aperture radar, and the third position weight from the UAV, Δp ni , Δq ni , and Δs ni respectively represent the position errors from the motion sensor, synthetic aperture radar, and UAV at the i-th experimental sampling point in a single experimental data, R ci , R di , and R eirespectively represent the variances of the position errors from the motion sensor, synthetic aperture radar, and unmanned aerial vehicle at the \(i\)-th experimental sampling point.

[0013] Based on the further improvement of the above system, the first position data corresponding to the radar sampling point is that the signal processing board, according to the synchronized radar sampling point time, based on the received two adjacent first position data, fills in the missing first position data of the radar sampling point through linear interpolation, and then converts the first position data at each radar sampling point into the first line-of-sight position data and the first azimuth position data in the SAR image coordinate system according to the heading of the unmanned aerial vehicle.

[0014] Based on the further improvement of the above system, the second position data of the unmanned aerial vehicle obtained by the signal processing board according to the radar echo data includes the second line-of-sight position data and the second azimuth position data, which are obtained by calculating the displacement data of the received radar echo data in the line-of-sight and azimuth directions.

[0015] Based on the further improvement of the above system, calculating the displacement data of the received radar echo data in the line-of-sight and azimuth directions includes:

[0016] Segment the radar echo data in the azimuth direction, calculate the correlation peak of adjacent two azimuth spectra to obtain the frequency shift amount; use a high-pass filter and a low-pass filter to extract the frequency change values in the line-of-sight and azimuth directions from the frequency shift amount, and then multiply them by the corresponding line-of-sight and azimuth acceleration coefficients to obtain two accelerations, and perform double integration on the two accelerations respectively to obtain the displacement data of the radar echo data in the line-of-sight and azimuth directions.

[0017] Based on the further improvement of the above system, the third position data of the unmanned aerial vehicle calculated by the signal processing board according to the speed of the unmanned aerial vehicle and the radar sampling point time refers to the third azimuth position data; the third line-of-sight position data of each radar sampling point is 0.

[0018] Based on the further improvement of the above system, the corrected position data is obtained by calculating the weighted average of the first position data, the second position data, and the third position data corresponding to each radar sampling point in each dimension according to three preset position weights respectively in the two dimensions of the line-of-sight and azimuth directions.

[0019] Based on the further improvement of the above system, the corrected position data is obtained through the following formula:

[0020]

[0021] where \(P\) rj represents the corrected position data of the \(j\)-th radar sampling point, \(p\) j , \(q\) j and \(s\) jrespectively represent the first position data, the second position data, and the third position data of the same dimension at the j-th radar sampling point.

[0022] Based on the further improvement of the above system, the synchronization control board uses a single-chip microcomputer or an ARM board; the signal processing board uses an FPGA.

[0023] Compared with the prior art, the present invention can at least achieve one of the following beneficial effects:

[0024] 1. By presetting the weights of each position in the signal processing board, after obtaining the three positions during the flight of the unmanned aerial vehicle (UAV), motion compensation can be performed through simple multiplication, division, and addition calculations. The calculation speed is fast, and at the same time, the problem of only being able to achieve motion compensation through post-processing is avoided, enabling the UAV SAR imaging to have real-time imaging capabilities; moreover, it does not increase the hardware burden and is suitable for application on UAVs with relatively small load capacities.

[0025] 2. By fusing data from multiple sources to accurately estimate the motion data, the accuracy of the motion data is improved, thereby enhancing the motion compensation effect and obtaining high-precision images;

[0026] In the present invention, the above technical solutions can also be combined with each other to achieve more preferred combination schemes. Other features and advantages of the present invention will be described in the subsequent specification, and some advantages can be made obvious from the specification or understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained from the content specifically pointed out in the specification and the drawings. Description of the Drawings

[0027] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference signs represent the same components.

[0028] Figure 1 It is a block diagram of a UAV SAR real-time imaging system in an embodiment of the present invention;

[0029] Figure 2 It is a schematic diagram of SAR imaging during the flight of a UAV in an embodiment of the present invention. Detailed Embodiments

[0030] The following will specifically describe the preferred embodiments of the present invention with reference to the drawings. The drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, rather than to limit the scope of the present invention.

[0031] A specific embodiment of the present invention discloses a UAV SAR real-time imaging system, as Figure 1As shown in the figure, the UAV in the system is equipped with a motion sensor, a synthetic aperture radar, a synchronous control board, and a signal processing board. The motion sensor is used to collect the first position data of the UAV. The synthetic aperture radar is used to collect radar echo data. The synchronous control board is used to synchronize the first position data and the radar echo data according to the time stamp and then send them to the signal processing board. The signal processing board is used to obtain the second position data of the UAV based on the radar echo data, calculate the third position data of the UAV according to the speed of the UAV and the radar sampling point time, and fuse the corresponding first position data, second position data, and third position data for each radar sampling point according to three preset position weights to obtain the corrected position data. Based on the corrected position data and the received radar echo data, a backscattering algorithm is used for SAR real-time imaging to obtain the SAR image of the UAV.

[0032] During implementation, the first position data obtained by the motion sensor is acquired, the second position data of the UAV is estimated using the radar echo data, and the third position data calculated according to the preset value of the UAV speed are used for multi-data fusion calculation to obtain the corrected position data, and SAR imaging operation is performed based on this corrected position data to obtain the high-precision SAR image of the UAV.

[0033] It should be noted that the motion sensor can adopt an inertial navigation device with cumulative errors but relatively high short-term accuracy and fast update rate; it can also adopt positioning devices such as GPS and Beidou with slow update rate but no cumulative errors; it can also adopt a combined navigation device that combines the above two. The synchronous control board uses a single-chip microcomputer or an ARM board. The signal processing board has high requirements for computing power and uses an FPGA.

[0034] Since the sampling rate of the motion sensor is generally lower than that of the synthetic aperture radar, the number of sampling points of the motion sensor within the same time period is less than the number of radar sampling points of the radar echo data. The synchronous control board synchronizes the first position data collected by the motion sensor and the radar echo data according to the time stamp, that is, at the same time, the first position data and the radar echo data are corresponding to the same radar sampling point. After being sent to the signal processing board, the signal processing board fills in the first position data for the radar sampling points lacking the first position data, so as to realize the subsequent fusion of three position data for each radar sampling point.

[0035] Specifically, according to the time of the synchronized radar sampling points, the signal processing board fills in the data for the radar sampling points lacking the first position data by linear interpolation based on the two adjacent first position data received, and then converts the first position data at each radar sampling point into the first line-of-sight position data and the first azimuth position data in the SAR image coordinate system according to the heading of the UAV.

[0036] It should be noted that the first position data collected by the motion sensor is initially three-dimensional position data based on the geodetic coordinate system, that is, longitude, latitude, and altitude. Therefore, the signal processing board converts the three-dimensional position data into the SAR image coordinate system according to the heading of the UAV to obtain the first line-of-sight position data and the first azimuth position data.

[0037] It should be noted that the second position data of the UAV obtained by the signal processing board according to the radar echo data includes the second line-of-sight position data and the second azimuth position data, which are obtained by calculating the displacement data of the received radar echo data in the line-of-sight direction and the azimuth direction.

[0038] Since the radar echo data obtained by the synthetic aperture radar is collected during the continuous movement of the radar, the radar echo data contains the motion information of the radar. In this embodiment, the displacement data of the radar echo data in the line-of-sight direction and the azimuth direction is calculated through the following steps:

[0039] ① Take half of the synthetic aperture length as the segmentation length, and segment the radar echo data in the azimuth direction; calculate the azimuth spectrum of each segment of data according to the fast Fourier transform, that is, the azimuth frequency data.

[0040] ② According to the cross-correlation function, calculate the correlation peak of adjacent two segments of azimuth spectra to obtain the frequency shift amount.

[0041] It should be noted that the frequency shift amount consists of two parts, low-frequency information and high-frequency information. Among them, the frequency change in the azimuth direction is usually located in the low-frequency band, while the frequency change in the line-of-sight direction usually occurs in the high-frequency band.

[0042] ③ Use a high-pass filter and a low-pass filter to extract the frequency change values in the line-of-sight direction and the azimuth direction from the frequency shift amount, and then multiply them by the corresponding acceleration coefficients in the line-of-sight direction and the azimuth direction to obtain two accelerations. Integrate the two accelerations twice to obtain the displacement data of the radar echo data in the line-of-sight direction and the azimuth direction.

[0043] Specifically, the acceleration coefficient a1 in the line-of-sight direction and the acceleration coefficient a2 in the azimuth direction are calculated through the following formula:

[0044]

[0045] Among them, λ represents the wavelength of the radar echo signal, Δτ represents the time interval between two adjacent non-overlapping segments of data in the azimuth direction, and R0 represents the slant range of the radar from the target center.

[0046] It should be noted that since the heading of the UAV is the direction of forward movement along the azimuth direction, the third position data of the UAV calculated by the signal processing board according to the speed of the UAV and the radar sampling point time refers to the third azimuth position data; the third line-of-sight position data of each radar sampling point is 0.

[0047] After the signal processing board obtains the three types of position data of each radar sampling point, since the position data includes two dimensions, namely the line-of-sight direction and the azimuth direction, when performing data fusion, it also calculates the weighted average of the first position data, the second position data, and the third position data corresponding to each radar sampling point in each dimension according to the preset three position weights in the two dimensions of the line-of-sight direction and the azimuth direction, respectively, to obtain the position data corrected in the two dimensions.

[0048] Specifically, the position data corrected in any dimension is calculated through the following formula:

[0049]

[0050] Among them, P rj represents the corrected position data of the jth radar sampling point, p j , q j and s j respectively represent the first position data, the second position data, and the third position data in the same dimension on the jth radar sampling point; Q1, Q2, and Q3 respectively represent the first position weight from the motion sensor, the second position weight from the synthetic aperture radar, and the third position weight from the unmanned aerial vehicle.

[0051] It should be noted that the three preset position weights in this embodiment are not subjectively set by humans, but are obtained through statistical analysis of multiple flight experiment data and calculated using a global optimization algorithm, including the following steps:

[0052] S1. Through the unmanned aerial vehicle flight experiment, obtain the first position data, the second position data, and the third position data of the experimental sampling points, and obtain the three position errors of each experimental sampling point in each experiment data according to the corresponding true position data; according to the three position errors of each experimental sampling point in multiple experiment data, obtain the variances of the three position errors of each experimental sampling point.

[0053] It should be noted that when conducting the flight experiment, select an experimental scenario with standard position markings. The experimental object of the experimental scenario is preferably an unmanned aerial vehicle, and a vehicle can also be used to replace the unmanned aerial vehicle, or a long guide rail can be used to move the motion sensor and the synthetic aperture radar on the long guide rail.

[0054] Step S1 is further divided into steps S11 - S15 to illustrate the acquisition steps of the three position errors on each experimental sampling point.

[0055] S11. Obtain the experimental sampling points according to the radar sampling frequency, and obtain the true position values of each experimental sampling point.

[0056] In the experimental scenario, three sets of true position data of each experimental sampling point can be accurately obtained according to the standard position identification, including data in two dimensions: the line-of-sight direction and the azimuth direction.

[0057] S12. Obtain the measured position data from the motion sensors at each experimental sampling point.

[0058] It should be noted that in the experiment, the motion sensors did not collect measured position data at some radar sampling points. To approximate the actual flight scenario of the UAV, the measured position data corresponding to the radar sampling points can be obtained by linear interpolation between two adjacent sampling points of the motion sensors. Or, to approximate the experimental scenario, the true position data corresponding to the experimental sampling points where no measured position data was collected can also be used as the measured position data.

[0059] Furthermore, according to the motion direction of the test object, the collected measured position data is converted into line-of-sight position data and azimuth position data in the SAR image coordinate system to obtain the measured position data from the motion sensors at each experimental sampling point.

[0060] S13. Obtain the measured position data from the synthetic aperture radar at each experimental sampling point.

[0061] It should be noted that the measured position data from the synthetic aperture radar at each experimental sampling point is also obtained by calculating the displacement data of the radar echo data at each experimental sampling point in the line-of-sight direction and the azimuth direction. The calculation method has been described above and will not be elaborated here.

[0062] S14. Obtain the measured position data from the UAV at each experimental sampling point.

[0063] Based on the velocity set for the test object along the azimuth direction and the time corresponding to each experimental sampling point, the measured position data of the test object at each experimental sampling point (this value corresponds to the azimuth position data, and the line-of-sight position data is 0) is calculated as the measured position data of the UAV.

[0064] S15. Obtain the three position errors at each experimental sampling point.

[0065] Based on the three sets of measured position data at each experimental sampling point obtained in steps S12 - S14 and the three sets of true position data at each experimental sampling point obtained in step S11, the three position errors at each experimental sampling point are obtained, that is, the position errors in two dimensions: the line-of-sight direction and the azimuth direction.

[0066] Conduct multiple flight experiments according to steps S11 - S15. Based on the position errors in two dimensions at each experimental sampling point in the multiple flight experiment data, calculate the variance of the position errors in two dimensions at each experimental sampling point.

[0067] S2. Based on three position weights, three position errors, and the variance of the position error, construct an objective function, and use a global optimization algorithm to calculate the optimal solutions of the three position weights in multiple sets of experimental data respectively, and take the average to obtain the final three position weights, which are preset in the signal processing board.

[0068] It should be noted that in order to accurately fuse the three position data and improve the accuracy of the motion data, position weights are set for each type of position data, and the position weights are obtained by constructing an objective function and solving the optimal solution.

[0069] Specifically, the objective function represents that the sum of the errors after fusing the three position errors at each experimental sampling point is minimized, and the objective function is constructed by the following formula:

[0070]

[0071] where M represents the number of experimental sampling points, and Δp ni , Δq ni and Δs ni respectively represent the position errors from the motion sensor, synthetic aperture radar, and unmanned aerial vehicle at the i-th experimental sampling point in a set of experimental data, and R ci , R di and R ei respectively represent the variances of the position errors from the motion sensor, synthetic aperture radar, and unmanned aerial vehicle at the i-th experimental sampling point.

[0072] It can be understood that since the position data includes two dimensions, the line-of-sight direction and the azimuth direction in the SAR image coordinate system, the objective function is established and solved according to the position errors and variances of the corresponding dimensions for each of the two dimensions to obtain the optimal solutions of the position weights for the corresponding dimensions.

[0073] It should be noted that after using the quantum particle swarm optimization algorithm or genetic algorithm to solve the optimal solutions of the position weights for multiple sets of experimental data according to formula (3), the average value is taken as the final position weight, which is preset in the signal processing board.

[0074] After the signal processing board obtains the corrected position data of each radar sampling point, according to the corrected position data and the received radar echo data, the backscattering algorithm is used for SAR real-time imaging. That is to say, during the flight of the unmanned aerial vehicle, according to the preset time period, when the corrected position data and radar echo data of this time period are obtained, the backscattering algorithm is called to generate the SAR image of this time period. The SAR images of each time period can be processed in real time or stored in the memory card of the unmanned aerial vehicle for facilitating other operations.

[0075] Preferably, the system of this embodiment further includes a wireless data transmission radio station and a host computer. The signal processing board regularly transmits the SAR images of each time period to the host computer through the wireless data transmission radio station, and splices and displays the SAR images in the order of time periods in the host computer to achieve real-time observation.

[0076] Compared with the prior art, the UAV SAR real-time imaging system provided in this embodiment accurately estimates the motion data by fusing data from multiple sources, improves the accuracy of the motion data, thereby enhancing the motion compensation effect and obtaining high-precision images; by presetting the weights of each position in the signal processing board, after obtaining three positions during the flight of the UAV, motion compensation can be performed through simple multiplication, division, and addition calculations, with a fast calculation speed, and at the same time, avoiding the problem that motion compensation can only be achieved through post-processing methods, enabling UAV SAR imaging to have real-time imaging capabilities; moreover, it does not increase the hardware burden and is suitable for application on UAVs with a small load capacity.

[0077] Those skilled in the art can understand that all or part of the processes for implementing the methods of the above embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a magnetic disk, an optical disc, a read-only memory, or a random access memory, etc.

[0078] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A UAV SAR real-time imaging system, characterized in that, In the system, the unmanned aerial vehicle (UAV) is equipped with a motion sensor, a synthetic aperture radar (SAR), a synchronous control board, and a signal processing board. The motion sensor is used to collect the first position data of the UAV. The synthetic aperture radar is used to collect radar echo data. The synchronous control board is used to synchronize the first position data and the radar echo data according to the time stamp and then send them to the signal processing board. The signal processing board is used to obtain the second position data of the UAV according to the radar echo data, calculate the third position data of the UAV according to the speed of the UAV and the radar sampling point time, and fuse the corresponding first position data, second position data, and third position data for each radar sampling point according to three preset position weights to obtain the corrected position data. According to the corrected position data and the received radar echo data, the backscattering algorithm is used for SAR real-time imaging to obtain the SAR image of the UAV.

2. The UAV SAR real-time imaging system according to claim 1, characterized in that, The three preset position weights are obtained through the following steps: Through the UAV flight experiment, the position data from the motion sensor, the synthetic aperture radar, and the UAV at the experimental sampling points are obtained. According to the corresponding true position data, the three position errors of each experimental sampling point in each experiment data are obtained. According to the three position errors of each experimental sampling point in multiple experiment data, the variances of the three position errors of each experimental sampling point are obtained. According to the three position weights, the three position errors, and the variances of the position errors, an objective function is constructed. The global optimization algorithm is used to calculate the optimal solutions of the three position weights in multiple experiment data respectively, and the average is taken to obtain the final three position weights, which are preset in the signal processing board.

3. The UAV SAR real-time imaging system according to claim 2, wherein The objective function is constructed according to the three position weights, the three position errors, and the variances of the position errors through the following formula: Where M represents the number of experimental sampling points, Q1, Q2, and Q3 represent the first position weight from the motion sensor, the second position weight from the synthetic aperture radar, and the third position weight from the unmanned aerial vehicle respectively, and Δp ni , Δq ni , and Δs ni represent the position errors from the motion sensor, the synthetic aperture radar, and the unmanned aerial vehicle at the i-th experimental sampling point in a single experiment data respectively, and R ci , R di , and R ei represent the variances of the position errors from the motion sensor, the synthetic aperture radar, and the unmanned aerial vehicle at the i-th experimental sampling point respectively.

4. The UAV SAR real-time imaging system according to claim 1 or 3, characterized in that The first position data corresponding to the radar sampling point is that the signal processing board, according to the synchronized radar sampling point time, based on the received two adjacent first position data, fills in the missing first position data of the radar sampling point through linear interpolation, and then converts the first position data at each radar sampling point into the first line-of-sight position data and the first azimuth position data in the SAR image coordinate system according to the heading of the UAV.

5. The UAV SAR real-time imaging system according to claim 1 or 3, characterized in that, The second position data of the UAV obtained by the signal processing board according to the radar echo data, including the second line-of-sight position data and the second azimuth position data, is obtained by calculating the displacement data of the received radar echo data in the line-of-sight direction and the azimuth direction.

6. The UAV SAR real-time imaging system according to claim 5, wherein The calculation of the displacement data of the received radar echo data in the line-of-sight direction and the azimuth direction includes: Segment the radar echo data in the azimuth direction, calculate the correlation peak of adjacent two azimuth spectra to obtain the frequency shift amount. Use a high-pass filter and a low-pass filter to extract the frequency change values in the line-of-sight direction and the azimuth direction from the frequency shift amount, and then multiply them by the corresponding acceleration coefficients in the line-of-sight direction and the azimuth direction to obtain two accelerations. Integrate the two accelerations twice respectively to obtain the displacement data of the radar echo data in the line-of-sight direction and the azimuth direction.

7. The UAV SAR real-time imaging system according to claim 1 or 3, characterized in that, The third position data of the UAV calculated by the signal processing board based on the speed of the UAV and the radar sampling point time refers to the third azimuth position data; the third line-of-sight position data of each radar sampling point is 0.

8. The UAV SAR real-time imaging system according to claim 3, characterized in that, The corrected position data is obtained by calculating the weighted average of the first position data, the second position data, and the third position data corresponding to each radar sampling point in the corresponding dimension according to three preset position weights respectively in two dimensions of line-of-sight and azimuth.

9. The UAV SAR real-time imaging system according to claim 8, wherein, The corrected position data is obtained through the following formula: Among them, P rj represents the corrected position data of the j-th radar sampling point, p j , q j and s j respectively represent the first position data, the second position data, and the third position data of the same dimension at the j-th radar sampling point.

10. The UAV SAR real-time imaging system according to claim 3, wherein, The synchronization control board uses a single-chip microcomputer or an ARM board; the signal processing board uses an FPGA.

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