Unmanned aerial vehicle SAR imaging method and system based on multi-data fusion motion compensation

Abstract

The invention relates to an unmanned aerial vehicle SAR imaging method and system based on multi-data fusion motion compensation, belongs to the technical field of radar imaging, and solves the problem of low SAR imaging precision caused by inaccurate motion compensation data in the prior art. Comprising the steps that multiple flight experiments are carried out on an unmanned aerial vehicle provided with a motion sensor and a synthetic aperture radar, position errors of three sources on each experiment sampling point are obtained, and the three sources comprise the motion sensor, the synthetic aperture radar and the unmanned aerial vehicle; establishing an objective function according to the position errors and the position weights of the three sources, and calculating an optimal solution of the position weights by using a global optimization algorithm; in the flight process of the unmanned aerial vehicle, the positions of the three sources on the radar sampling point are obtained, the compensated position of the radar sampling point is obtained according to the positions of the three sources and the optimal solution of the position weight, imaging operation is conducted through a back scattering algorithm, and an SAR image of the unmanned aerial vehicle is obtained. The SAR imaging precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar imaging, and in particular to a UAV SAR imaging method based on multi-data fusion motion compensation. 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 rescue, agricultural 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 straight-line motion state. However, SAR imaging technology is based on a uniform straight-line 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] Conventional solutions usually use the position and speed data of multi-rotor UAVs measured by motion sensors (inertial navigation, GPS, integrated navigation and other devices) to compensate for motion parameters. However, motion sensors usually have problems such as low positioning accuracy and cumulative errors, and cannot meet the actual needs of high-precision motion compensation for UAV SAR imaging systems.

[0005] Another motion compensation based on radar echo data has poor focusing effect in the case of no obvious reflector targets in the imaging scene and has problems that cannot be processed in real time. Summary of the Invention

[0006] In view of the above analysis, an embodiment of the present invention aims to provide a UAV SAR imaging method based on multi-data fusion motion compensation to solve the problem of low SAR imaging accuracy caused by inaccurate motion compensation data in the prior art.

[0007] On the one hand, an embodiment of the present invention provides a UAV SAR imaging method based on multi-data fusion motion compensation, including the following steps:

[0008] Conduct multiple flight experiments on a UAV equipped with a motion sensor and a synthetic aperture radar, and respectively obtain the position errors from three sources at each experimental sampling point. The three sources include a motion sensor, a synthetic aperture radar, and the UAV;

[0009] Based on the position errors and position weights from three sources, establish an objective function, and use a global optimization algorithm to calculate the optimal solutions of the position weights of the three sources in the objective function;

[0010] During the flight of the unmanned aerial vehicle (UAV), obtain the positions of the three sources at the radar sampling points. According to the positions of the three sources and the optimal solutions of the position weights, obtain the compensated positions of the radar sampling points. According to the radar echo data collected at the radar sampling points and the compensated positions, perform imaging operations using the backscattering algorithm to obtain the SAR image of the UAV.

[0011] Based on a further improvement of the above method, according to the position errors and position weights of the three sources, establish an objective function through the following formula:

[0012]

[0013] where M represents the number of experimental sampling points, Q1, Q2, and Q3 respectively represent the position weights from the motion sensor, synthetic aperture radar, and UAV, and Δp ni , Δq ni , and Δs ni respectively represent the position error of the motion sensor, the position error of the synthetic aperture radar, and the position error of the UAV at the i-th experimental sampling point, and R ci , R di , and R ei respectively represent the variance of the position error of the motion sensor, the variance of the position error of the synthetic aperture radar, and the variance of the position error of the UAV at the i-th experimental sampling point.

[0014] Based on a further improvement of the above method, according to the positions of the three sources and the optimal solutions of the position weights, obtain the compensated positions of the radar sampling points through the following formula:

[0015]

[0016] where P rj represents the compensated position of the j-th radar sampling point, and p j , q j , and s j respectively represent the positions from the motion sensor, the synthetic aperture radar, and the UAV at the j-th radar sampling point.

[0017] Based on a further improvement of the above method, the position includes the coordinate values in two dimensions of the line-of-sight direction and the azimuth direction in the SAR image coordinate system; the objective function is established and solved for the optimal solutions of the position weights in the corresponding dimensions according to the position errors and variances in the corresponding dimensions; the compensated positions are calculated for the corresponding dimensions of the compensated positions in the two dimensions according to the positions of the three sources in the corresponding dimensions.

[0018] Based on further improvements to the above method, the position of the radar sampling points from the synthetic aperture radar is obtained by calculating the displacement data of the collected radar echo data in the line-of-sight direction and the azimuth direction.

[0019] Based on further improvements to the above method, calculating the displacement data of the collected radar echo data in the line-of-sight direction and the azimuth direction includes:

[0020] Segment the radar echo data in the azimuth direction, calculate the correlation peak of adjacent two-segment 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, and perform double integration on the two accelerations respectively to obtain the displacement data of the radar echo data in the line-of-sight direction and the azimuth direction.

[0021] Based on further improvements to the above method, the acceleration coefficients in the line-of-sight direction and the azimuth direction are calculated through the following formula:

[0022]

[0023] Among them, a1 represents the acceleration coefficient in the line-of-sight direction, a2 represents the acceleration coefficient in the azimuth direction, λ represents the wavelength of the radar echo signal, Δτ represents the time interval of non-overlapping data of two adjacent segments in the azimuth direction, and R0 represents the slant range of the radar from the target center.

[0024] Based on further improvements to the above method, the position of the radar sampling points from the motion sensor is based on the three-dimensional position coordinates of two adjacent points collected by the motion sensor. After obtaining the corresponding three-dimensional position coordinates of the radar sampling points through linear interpolation, the three-dimensional position coordinates are converted into the line-of-sight coordinates and azimuth coordinates in the SAR image coordinate system according to the heading of the unmanned aerial vehicle.

[0025] Based on further improvements to the above method, the position of the radar sampling points from the unmanned aerial vehicle is obtained by calculating the line-of-sight coordinates and azimuth coordinates according to the speed of the unmanned aerial vehicle and the time corresponding to each radar sampling point.

[0026] On the other hand, an embodiment of the present invention provides a UAV SAR imaging system based on multi-data fusion motion compensation, including:

[0027] An experimental data acquisition module, configured to perform multiple flight experiments on a UAV equipped with a motion sensor and a synthetic aperture radar, and respectively obtain the position errors of three sources at each experimental sampling point, where the three sources include a motion sensor, a synthetic aperture radar, and a UAV;

[0028] A position weight calculation module, configured to establish an objective function according to the position errors and position weights from three sources, and use a global optimization algorithm to calculate the optimal solutions of the position weights from the three sources in the objective function;

[0029] An SAR imaging processing module, configured to, during the actual flight of the unmanned aerial vehicle, obtain the positions from three sources at the radar sampling points, obtain the compensated positions of the radar sampling points according to the positions from the three sources and the optimal solutions of the position weights, and perform imaging operations using the backscattering algorithm according to the radar echo data collected at the radar sampling points and the compensated positions to obtain the SAR image of the unmanned aerial vehicle.

[0030] Compared with the prior art, the present invention can at least achieve one of the following beneficial effects:

[0031] 1. By fusing data from multiple sources to accurately estimate motion data, the accuracy of motion data is improved, thereby enhancing the motion compensation effect and obtaining high-precision images;

[0032] 2. During the flight of the unmanned aerial vehicle, motion compensation can be performed through simple calculations, avoiding the problem that motion compensation can only be achieved through post-processing methods, enabling the unmanned aerial vehicle SAR imaging to have quasi-real-time imaging capabilities; moreover, it does not increase the hardware burden and is suitable for application on unmanned aerial vehicles with small load capacities.

[0033] 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 through the content specifically pointed out in the specification and the drawings. Description of the Drawings

[0034] 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.

[0035] Figure 1 It is a flowchart of a method for unmanned aerial vehicle SAR imaging based on multi-data fusion motion compensation in Embodiment 1 of the present invention;

[0036] Figure 2 It is a schematic diagram of SAR imaging during the flight of the unmanned aerial vehicle in Embodiment 1 of the present invention. Detailed Embodiments

[0037] 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.

[0038] Embodiment 1

[0039] A specific embodiment of the present invention discloses a UAV SAR imaging method based on multi-data fusion motion compensation, as Figure 1 shown, including the following steps:

[0040] S1. Conduct multiple flight experiments on a UAV equipped with a motion sensor and a synthetic aperture radar, and respectively obtain the position errors from three sources at each experimental sampling point. The three sources include the motion sensor, the synthetic aperture radar, and the UAV.

[0041] It should be noted that when conducting the flight experiment, an experimental scene with standard position identifiers is selected. The experimental object in the experimental scene is preferably a UAV, or a vehicle can be used to replace the UAV, or a long guide rail can be used to let the motion sensor and the synthetic aperture radar move on the long guide rail.

[0042] Steps S11 - S15 are refined to illustrate the steps for obtaining the position errors from three sources at each experimental sampling point.

[0043] S11. Obtain the experimental sampling points according to the radar sampling frequency, and obtain the true position values of each experimental sampling point.

[0044] In the experimental scene, the true position values of each experimental sampling point from three sources can be accurately obtained according to the standard position identifiers, including the coordinate values in two dimensions of the line-of-sight direction and the azimuth direction.

[0045] S12. Obtain the measured position values from the motion sensor at each experimental sampling point.

[0046] It should be noted that the motion sensor can adopt an inertial navigation device with cumulative error but relatively high short-term accuracy and fast update rate; it can also adopt a positioning device such as GPS or Beidou with a slow update rate but no cumulative error; it can also adopt a combined navigation device that combines the above two.

[0047] Considering that the sampling rate of the motion sensor is generally lower than the sampling rate of the synthetic aperture radar, and the motion sensor does not collect measured position values at some radar sampling points. In order to approximate the actual flight scenario of the UAV, the measured position values corresponding to the radar sampling points can be obtained by linear interpolation between two adjacent sampling points of the motion sensor. If in order to approximate the experimental scenario, the true position values corresponding to the uncollected sampling points can also be used as the measured position values.

[0048] Furthermore, since the measured position values of the motion sensor are three-dimensional position coordinates based on the geodetic coordinate system, that is, longitude, latitude, and altitude, so according to the motion direction, each three-dimensional position coordinate is converted into the line-of-sight direction coordinate and the azimuth direction coordinate in the SAR image coordinate system to obtain the measured position values from the motion sensor at each experimental sampling point.

[0049] S13. Obtain the measurement position values from the synthetic aperture radar at each experimental sampling point.

[0050] It should be noted that the radar echo data obtained by the synthetic aperture radar is collected during the continuous movement of the radar. Therefore, the radar motion information is contained in the radar echo data. In this embodiment, the measurement position values from the synthetic aperture radar at each test sampling point are obtained by calculating the displacement data of the radar echo data in the line-of-sight direction and the azimuth direction.

[0051] Specifically, it includes the following steps:

[0052] 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.

[0053] ② According to the cross-correlation function, calculate the correlation peak of adjacent two segments of azimuth spectra to obtain the frequency shift amount.

[0054] 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.

[0055] 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 respectively to obtain the displacement data of the radar echo data in the line-of-sight direction and the azimuth direction.

[0056] Specifically, calculate the acceleration coefficient a1 in the line-of-sight direction and the acceleration coefficient a2 in the azimuth direction through the following formula:

[0057]

[0058] 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.

[0059] S14. Obtain the measurement position values from the unmanned aerial vehicle at each experimental sampling point.

[0060] According to the velocity set for the experimental object in the azimuth direction and the time corresponding to each experimental sampling point, calculate the measurement position value of the experimental object at each experimental sampling point (this value corresponds to the azimuth coordinate, and the line-of-sight coordinate is 0), which is used as the measurement position value of the unmanned aerial vehicle.

[0061] S15. Obtain the position errors from three sources at each experimental sampling point.

[0062] Based on the measured position values of the three sources at each experimental sampling point obtained in steps S12 - S14 and the true position values of the three sources at each experimental sampling point obtained in step S11, the position errors of the three sources at each experimental sampling point are obtained, that is, the position errors in two dimensions of the line - of - sight direction and the azimuth direction are obtained.

[0063] It should be noted that multiple flight experiments are carried out according to steps S11 - S15. According to the position errors in two dimensions at each experimental sampling point in the multiple flight experiment data, the variances of the position errors in two dimensions at each experimental sampling point are calculated.

[0064] S2. Based on the position errors and position weights of the three sources, establish an objective function, and use a global optimization algorithm to calculate the optimal solutions of the position weights of the three sources in the objective function.

[0065] It should be noted that in order to accurately fuse the three types of data and improve the accuracy of the motion data, position weights are set for each source of data, and the position weights are obtained by establishing an objective function and solving the optimal solutions.

[0066] Specifically, the objective function represents that the sum of the errors after fusing the position errors of the three sources at each experimental sampling point is minimized. Based on the position errors and position weights of the three sources, the objective function is established by the following formula:

[0067]

[0068] where M represents the number of experimental sampling points, Q1, Q2, and Q3 respectively represent the position weights from the motion sensor, synthetic aperture radar, and unmanned aerial vehicle, Δp ni , Δq ni and Δs ni respectively represent the position error of the motion sensor, the position error of the synthetic aperture radar, and the position error of the unmanned aerial vehicle at the i - th experimental sampling point, and R ci , R di and R ei respectively represent the variances of the position errors of the motion sensor, the synthetic aperture radar, and the unmanned aerial vehicle at the i - th experimental sampling point.

[0069] It can be understood that since the position includes coordinate values in two dimensions of the line - of - sight direction and the azimuth direction in the SAR image coordinate system, the objective function is established separately for the two dimensions and solved for the optimal solutions of the position weights corresponding to the two dimensions according to the position errors and variances of the corresponding dimensions.

[0070] It should be noted that after using the quantum particle swarm algorithm or genetic algorithm to solve the optimal solutions of the position weights for the multiple historical test data according to formula (2), the average value is taken as the final optimal solution of the position weights.

[0071] S3. During the flight of the unmanned aerial vehicle (UAV), obtain the positions of three sources at the radar sampling points. According to the positions of the three sources and the optimal solution of the position weights, obtain the compensated position of the radar sampling points. According to the radar echo data collected at the radar sampling points and the compensated position, perform imaging operations using the backscattering algorithm to obtain the SAR image of the UAV.

[0072] It should be noted that, as Figure 2 shown, during the flight of the UAV, obtain the positions of three sources at the radar sampling points according to the method in steps S12 - S14. Among them, for some radar sampling points that are not collected by the motion sensor, the position from the motion sensor is obtained by linear interpolation.

[0073] According to the positions of the three sources and the optimal solution of the position weights, fuse the positions of the same dimension of the three sources through the following formula to obtain the compensated position of the corresponding dimension of the radar sampling points, improving the motion compensation effect:

[0074]

[0075] Among them, P rj represents the compensated position of the j-th radar sampling point, and p j , q j and s j respectively represent the positions of the same dimension from the motion sensor, synthetic aperture radar, and UAV at the j-th radar sampling point.

[0076] Finally, according to the radar echo data collected at the radar sampling points and the compensated position, perform imaging operations using the backscattering algorithm to obtain the SAR image of the UAV.

[0077] Compared with the prior art, the UAV SAR imaging method based on multi-data fusion motion compensation 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 a high-precision image; during the flight of the UAV, motion compensation can be performed through simple calculations, avoiding the problem that motion compensation can only be achieved through post-processing methods, enabling the UAV SAR imaging to have quasi-real-time imaging capabilities; moreover, it does not increase the hardware burden and is suitable for application on UAVs with a small payload capacity.

[0078] Embodiment 2

[0079] Another embodiment of the present invention discloses a UAV SAR imaging system based on multi-data fusion motion compensation to implement the UAV SAR imaging method based on multi-data fusion motion compensation in Embodiment 1. The specific implementation methods of each module refer to the corresponding descriptions in Embodiment 1. The system includes:

[0080] An experimental data acquisition module is used to conduct multiple flight experiments on an unmanned aerial vehicle (UAV) equipped with a motion sensor and a synthetic aperture radar (SAR), and respectively obtain the position errors from three sources at each experimental sampling point. The three sources include the motion sensor, the synthetic aperture radar, and the UAV.

[0081] A position weight calculation module is used to establish an objective function based on the position errors and position weights from three sources, and use a global optimization algorithm to calculate the optimal solutions of the position weights from the three sources in the objective function.

[0082] The SAR imaging processing module is used to obtain the positions from three sources at the radar sampling points during the actual flight of the UAV, obtain the compensated positions of the radar sampling points according to the positions from the three sources and the optimal solutions of the position weights, and perform imaging operations using the backscattering algorithm based on the radar echo data collected at the radar sampling points and the compensated positions to obtain the SAR image of the UAV.

[0083] It should be noted that during the experiment, the optimal solutions of the position weights from the three sources are obtained according to the experimental data acquisition module and the position weight calculation module, and are set as preset weights in the SAR imaging processing module.

[0084] A motion sensor, a synthetic aperture radar, a synchronous control board, and a signal processing board are installed on the UAV. The synchronous control board is used to synchronize the data collected by the motion sensor and the synthetic aperture radar according to the same time stamp. Exemplarily, a single-chip microcomputer or an ARM board is used. The signal processing board is used to implement the functions of the SAR imaging processing module. Exemplarily, an FPGA is used.

[0085] Since the relevant parts of this embodiment and the foregoing method for UAV SAR imaging based on multi-data fusion motion compensation can be mutually referred to, and to avoid repeated description, they will not be elaborated here. Since the principle of this system embodiment is the same as that of the above method embodiment, this system embodiment also has the corresponding technical effects of the above method embodiment.

[0086] 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. The program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0087] The above is only a preferred specific embodiment 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 within the protection scope of the present invention.

Claims

1. An unmanned aerial vehicle (UAV) SAR imaging method based on multi-data fusion motion compensation, characterized in that It includes the following steps: Conduct multiple flight experiments on an unmanned aerial vehicle (UAV) equipped with a motion sensor and a synthetic aperture radar (SAR), and respectively obtain the position errors from three sources at each experimental sampling point. The three sources include the motion sensor, the synthetic aperture radar, and the UAV. Based on the position errors and position weights from the three sources, establish an objective function, and use a global optimization algorithm to calculate the optimal solutions of the position weights of the three sources in the objective function. During the flight of the UAV, obtain the positions of the three sources at the radar sampling points. According to the positions of the three sources and the optimal solutions of the position weights, obtain the compensated positions of the radar sampling points. According to the radar echo data collected at the radar sampling points and the compensated positions, perform imaging operations using the backscattering algorithm to obtain the SAR image of the UAV.

2. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 1, wherein The objective function is established based on the position errors and position weights from the three sources through the following formula: Among them, M represents the number of experimental sampling points, Q1, Q2, and Q3 respectively represent the position weights from the motion sensor, synthetic aperture radar, and unmanned aerial vehicle, and Δp ni , Δq ni , and Δs ni respectively represent the position error of the motion sensor, the position error of the synthetic aperture radar, and the position error of the unmanned aerial vehicle at the i-th experimental sampling point. R ci , R di , and R ei respectively represent the variance of the position error of the motion sensor, the variance of the position error of the synthetic aperture radar, and the variance of the position error of the unmanned aerial vehicle at the i-th experimental sampling point.

3. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 2, wherein, The compensated positions of the radar sampling points are obtained based on the positions of the three sources and the optimal solutions of the position weights through the following formula: Among them, P rj represents the compensated position of the j-th radar sampling point, p j , q j and s j respectively represent the positions from the motion sensor, synthetic aperture radar, and unmanned aerial vehicle at the j-th radar sampling point.

4. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 3, wherein The positions include the coordinate values in two dimensions, the line-of-sight direction and the azimuth direction, in the SAR image coordinate system. The objective function is established and solved separately for each dimension according to the position errors and variances in the corresponding dimension to obtain the optimal solutions of the position weights in the corresponding dimension. The compensated positions are calculated separately for each dimension according to the positions of the three sources in the corresponding dimension in each dimension to obtain the compensated positions in the corresponding dimension.

5. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 3, characterized in that, The position of the radar sampling point from the synthetic aperture radar is obtained by calculating the displacement data of the collected radar echo data in the line-of-sight direction and the azimuth direction.

6. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 5, characterized in that, The calculation of the displacement data of the collected 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-segment 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 method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 6, wherein, The acceleration coefficients in the line-of-sight direction and the azimuth direction are calculated through the following formula: Where, a1 represents the acceleration coefficient in the line-of-sight direction, a2 represents the acceleration coefficient in the azimuth direction, λ represents the wavelength of the radar echo signal, Δτ represents the time interval between two adjacent non-overlapping data segments in the azimuth direction, and R0 represents the slant range of the radar from the target center.

8. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 3, wherein, The position of the radar sampling point from the motion sensor is obtained by linearly interpolating the three-dimensional position coordinates of two adjacent points collected by the motion sensor to obtain the corresponding three-dimensional position coordinates of the radar sampling point, and then converting each three-dimensional position coordinate into the line-of-sight coordinate and the azimuth coordinate in the SAR image coordinate system according to the heading of the UAV.

9. The method for UAV SAR imaging based on multi-data fusion motion compensation according to claim 3, wherein, The position of the radar sampling point from the UAV is obtained by calculating the line-of-sight coordinate and the azimuth coordinate according to the speed of the UAV and the time corresponding to each radar sampling point.

10. An unmanned aerial vehicle (UAV) SAR imaging system based on multi-data fusion motion compensation, characterized in that It includes: An experimental data acquisition module is used to conduct multiple flight experiments on an unmanned aerial vehicle (UAV) equipped with a motion sensor and a synthetic aperture radar (SAR), and respectively obtain the position errors from three sources at each experimental sampling point. The three sources include the motion sensor, the synthetic aperture radar, and the UAV. A position weight calculation module is used to establish an objective function based on the position errors and position weights from the three sources, and use a global optimization algorithm to calculate the optimal solutions of the position weights of the three sources in the objective function. An SAR imaging processing module is used to obtain the positions from the three sources at the radar sampling points during the actual flight of the UAV, obtain the compensated positions of the radar sampling points according to the positions from the three sources and the optimal solutions of the position weights, and perform imaging operations using the backscattering algorithm based on the radar echo data collected at the radar sampling points and the compensated positions to obtain the SAR image of the UAV.

Images