Compression method and device of spaceborne SAR data granularity based on sub-aperture fusion, storage medium and equipment

Abstract

The invention provides a compression method and device for spaceborne SAR data granularity based on sub-aperture fusion, a storage medium and equipment, and the method comprises the steps: obtaining spaceborne SAR echo data; performing direction position sub-aperture division on the spaceborne SAR echo data based on the number of sub-aperture points to obtain a plurality of pieces of sub-aperture data; performing unambiguous spectrum reconstruction on each piece of sub-aperture data to obtain an unambiguous sub-aperture spectrum; performing clutter filtering on the unambiguous sub-aperture spectrum to filter clutter bandwidth outside a scene in the unambiguous sub-aperture spectrum; and fusing the unambiguous sub-aperture spectrum after clutter filtering in a frequency domain to obtain a complete bandwidth signal, and obtaining echo data after data granularity compression based on the complete bandwidth signal. According to the method and the device, the spaceborne SAR data granularity can be compressed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of spaceborne echo data processing, in particular to a spaceborne SAR data granularity compression method and device based on sub-aperture fusion, a storage medium and equipment. BACKGROUND

[0002] Spaceborne synthetic aperture radar is a radar carried on a satellite platform, which realizes two-dimensional high-resolution imaging by emitting and receiving electromagnetic waves to the ground. It has all-weather and all-day imaging capability and plays a huge advantage in environmental monitoring, disaster prediction, and geographic mapping. With the development of spaceborne SAR imaging technology, satellites are required to simultaneously achieve high resolution and wide imaging, so multi-channel technology is emerging. By arranging multiple antennas in the satellite motion direction to receive echo signals simultaneously and then performing non-ambiguous spectrum reconstruction, the low-PRF echo data of multiple channels is reconstructed into non-ambiguous high-PRF echo data, which improves the resolution of the imaging while greatly improving the imaging width. However, the high-resolution and wide-width mode will cause a sharp increase in echo data volume, which brings difficulties and challenges to on-board single-machine processing. Therefore, data granularity compression of single-machine processed echo data is needed to adapt to the on-board single-machine processing performance SUMMARY The purpose of the embodiments of the present application is to provide a spaceborne SAR data granularity compression method and device based on sub-aperture fusion, a storage medium and equipment to compress the spaceborne SAR data granularity.

[0003] In a first aspect, the present application provides a spaceborne SAR data granularity compression method based on sub-aperture fusion, the method comprising: acquiring spaceborne SAR echo data; performing directional bit sub-aperture division on the spaceborne SAR echo data based on the number of sub-aperture points to obtain a plurality of sub-aperture data; performing non-ambiguous spectrum reconstruction on each of the sub-aperture data to obtain non-ambiguous sub-aperture spectrum; filtering out clutter in the non-ambiguous sub-aperture spectrum to filter out the scene external clutter bandwidth in the non-ambiguous sub-aperture spectrum; fusing the non-ambiguous sub-aperture spectrum after clutter filtering in the frequency domain to obtain a complete bandwidth signal, and obtaining echo data after data granularity compression based on the complete bandwidth signal.

[0004] The method described in this application can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single machine to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the entire echo data at once. This reduces the performance requirements of a single machine per processing cycle and improves single-machine processing efficiency. Furthermore, by compressing the data granularity of the echo data, the upper limit of the performance requirements for a single machine can be lowered.

[0005] In an optional implementation, the method further includes: The target scene's azimuth swath width, the synthetic aperture radar's pulse repetition frequency, and the synthetic aperture radar's beam ground velocity are obtained. The number of sub-aperture points is determined based on the azimuth swath width of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the synthetic aperture radar beam.

[0006] This optional implementation obtains the azimuth swath of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the synthetic aperture radar beam, and then determines the number of sub-aperture points based on the azimuth swath of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the synthetic aperture radar beam.

[0007] In an optional implementation, the formula for calculating the number of sub-aperture points is: ; in, Indicates the number of sub-aperture points. This indicates the azimuth width of the target scene. This represents the ground velocity of the synthetic aperture radar beam. This indicates the pulse repetition frequency of the synthetic aperture radar. This represents the power of 2 operation. This indicates the rounding up operation.

[0008] This optional implementation can accurately calculate the number of sub-aperture points based on the above calculation formula, thereby making full use of the performance of a single machine while reducing the performance requirements of a single machine for single-processing.

[0009] In an optional implementation, before performing unambiguous spectral reconstruction on each of the sub-aperture data to obtain an unambiguous sub-aperture spectrum, the method further includes: The sub-aperture data is padded with zeros in the azimuth direction so that the number of points in the sub-aperture data covers twice the scene width.

[0010] This optional implementation can perform azimuth zero-padding on the sub-aperture data before using inverse filtering to process the unambiguous sub-aperture spectrum, in order to avoid the folding effect of unfiltered clutter caused by scene clutter that cannot be completely filtered out in the subsequent clutter filtering process.

[0011] In an optional implementation, the step of performing unambiguous spectral reconstruction on each of the sub-aperture data to obtain an unambiguous sub-aperture spectrum includes: The sub-aperture data is subjected to inverse filtering to obtain an unambiguous sub-aperture spectrum.

[0012] Alternatively, an unambiguous sub-aperture spectrum can be obtained by performing inverse filtering on the sub-aperture data.

[0013] In an optional implementation, the clutter filtering of the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum includes: The satellite orbit coordinates, target point coordinates, signal wavelength, and satellite velocity are obtained, and the oblique angle between the sub-aperture edge and the two edge points of the azimuth scene is determined based on the satellite orbit coordinates and the target point coordinates. The Doppler spectral range of the target scene is determined based on the oblique angle, the signal wavelength, and the satellite velocity; The scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum is filtered out based on the Doppler spectrum range of the target scene.

[0014] This optional implementation can determine the Doppler spectrum range of the sub-aperture data for the target scene based on the oblique angle, the signal wavelength, and the satellite velocity, and then filter out the scene clutter bandwidth in the unambiguous sub-aperture spectrum according to the Doppler spectrum range of the target scene.

[0015] In an optional implementation, the Doppler spectral range of the target scene is calculated as follows: ; in, Indicates the speed of the satellite. This refers to the oblique angle. Indicates the wavelength of the signal. This indicates the Doppler spectrum range of the target scene.

[0016] This optional implementation can accurately calculate the Doppler spectrum range of the target scene using the above calculation formula.

[0017] In an optional implementation, the method further includes: The unambiguous sub-aperture spectrum after clutter filtering is multiplied by the phase factor of the sub-aperture data so that the time-frequency relationship of the sub-aperture data is restored to the time-frequency relationship of the full aperture.

[0018] This optional implementation can restore the time-frequency relationship of the sub-aperture data to the time-frequency relationship of the full aperture, avoiding the introduction of new ambiguity factors during fusion.

[0019] In an optional implementation, the phase factor of the sub-aperture data is calculated as follows: ; in, This indicates the length of the sub-aperture data before zero-padding in the azimuth direction. This represents the frequency components corresponding to the retained spectrum after the unambiguous sub-aperture spectral filtering. , The phase factor representing the sub-aperture data, This indicates the total number of sub-aperture data. This represents the i-th sub-aperture data.

[0020] This optional implementation can accurately calculate the phase factor of the sub-aperture data using the above calculation formula.

[0021] Secondly, the present invention provides a compression device for spaceborne SAR data granularity based on sub-aperture fusion, the device comprising: The acquisition module is used to acquire spaceborne SAR echo data; The partitioning module is used to partition the spaceborne SAR echo data into directional sub-apertures based on the number of sub-aperture points, thereby obtaining several sub-aperture data. The spectrum reconstruction module is used to perform unambiguous spectrum reconstruction on each of the sub-aperture data to obtain an unambiguous sub-aperture spectrum. The clutter filtering module is used to filter out clutter from the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum. The fusion module is used to fuse the unambiguous sub-aperture spectrum after clutter filtering in the frequency domain to obtain a complete bandwidth signal, and to obtain echo data with data granularity compression based on the complete bandwidth signal.

[0022] The device described in this application can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single unit to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the entire echo data at once. This reduces the performance requirements of a single unit per processing cycle and improves its processing efficiency. Furthermore, by compressing the data granularity of the echo data, the performance ceiling requirements for a single unit can be lowered.

[0023] Thirdly, the present invention provides an electronic device, comprising: Processor; and The memory is configured to store machine-readable instructions that, when executed by the processor, perform the method as described in any of the foregoing embodiments.

[0024] The electronic device of this application, by implementing a sub-aperture fusion-based compression method for spaceborne SAR data granularity, can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single unit to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the entire echo data at once, thus reducing the performance requirements for a single unit's processing and improving its processing efficiency. Simultaneously, by compressing the data granularity of the echo data, the upper limit of the performance requirements for a single unit can be lowered.

[0025] Fourthly, the present invention provides a storage medium storing a computer program, the computer program being executed by a processor as described in any of the foregoing embodiments.

[0026] The storage medium of this application, by implementing a sub-aperture fusion-based compression method for spaceborne SAR data granularity, can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single machine to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the entire echo data at once, thus reducing the performance requirements for a single machine's processing and improving its processing efficiency. Simultaneously, by compressing the data granularity of the echo data, the upper limit of the performance requirements for a single machine can be lowered. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating a method for compressing spaceborne SAR data granularity based on sub-aperture fusion, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of a compression device for spaceborne SAR data granularity based on sub-aperture fusion, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 4 This is a flowchart illustrating another method for compressing spaceborne SAR data granularity based on sub-aperture fusion, provided in an embodiment of this application. Figure 5 This is a schematic diagram of a sub-aperture data unambiguous spectrum recovery process provided in an embodiment of this application; Figure 6 This is a schematic diagram of zero-padding at both ends of sub-aperture data according to an embodiment of this application; Figure 7 This is a schematic diagram of scene clutter filtering provided in an embodiment of this application; Figure 8 This is a schematic diagram illustrating the calculation of the scene external clutter filtering range provided in an embodiment of this application; Figure 9 This is a schematic diagram of sub-aperture fusion and aliasing provided in an embodiment of this application; Figure 10 This is a schematic diagram of a simulated dot matrix imaging result provided in an embodiment of this application; Figure 11 yes Figure 10 A schematic diagram of the imaging quality assessment of points A, B, and C in the middle; Figure 12 This is a schematic diagram of a surface target simulation imaging result provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0030] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for compressing spaceborne SAR data granularity based on sub-aperture fusion, as provided in an embodiment of this application. Figure 1 As shown, the method in this application embodiment includes the following steps: 101. Acquire spaceborne SAR echo data; 102. Based on the number of sub-aperture points, the directional position sub-aperture of the spaceborne SAR echo data is divided to obtain several sub-aperture data. 103. Perform unambiguous spectrum reconstruction on each sub-aperture data to obtain the unambiguous sub-aperture spectrum; 104. Perform clutter filtering on the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum; 105. The unambiguous sub-aperture spectrum after clutter filtering is fused in the frequency domain to obtain a complete bandwidth signal, and the echo data after data granular compression is obtained based on the complete bandwidth signal.

[0031] In this embodiment, spaceborne SAR echo data refers to the raw data formed by converting the electromagnetic waves reflected from the target after the synthetic aperture radar (SAR) on a satellite transmits electromagnetic waves to the target scene. It is the fundamental data source for SAR imaging processing. This spaceborne SAR echo data can be L-band spaceborne SAR echo data, X-band spaceborne SAR echo data, or other forms of echo data.

[0032] In the embodiments of this application, the number of sub-aperture points refers to the number of azimuth data units used to divide the spaceborne SAR echo data, that is, the number of azimuth sampling points contained in the sub-interval.

[0033] In the embodiments of this application, azimuth sub-aperture division refers to the process of dividing the complete spaceborne SAR echo data into several independent data segments in the azimuth direction according to a preset number of sub-aperture points.

[0034] In this embodiment, unambiguous spectrum reconstruction refers to the operation of filtering and other processing of sub-aperture data to eliminate ambiguity phenomena such as spectrum folding and aliasing, and to restore the true spectrum characteristics of the target scene. Unambiguous spectrum reconstruction can be either inverse filtering unambiguous spectrum reconstruction or adaptive filtering unambiguous spectrum reconstruction.

[0035] In the embodiments of this application, clutter filtering refers to the processing step of removing the bandwidth components corresponding to irrelevant signals (such as noise, interference waves, etc.) outside the scene from the unambiguous sub-aperture spectrum.

[0036] In this embodiment of the application, frequency domain fusion refers to the process of splicing and synthesizing multiple unambiguous sub-aperture spectra after clutter filtering in the frequency domain to form a signal covering the complete frequency range of the target scene.

[0037] In this embodiment of the application, the data granularity compressed echo data refers to SAR echo data that has been reduced in size and retains effective information of the target scene after sub-aperture division, clutter filtering, and frequency domain fusion.

[0038] In this application embodiment, a specific method for obtaining spaceborne SAR echo data is as follows: the SAR data receiving module on the satellite collects the target scene reflection signal in real time, converts it into a digital signal and stores it in the spaceborne memory, and then reads it directly from the memory.

[0039] In this embodiment of the application, a specific method for dividing the azimuth sub-aperture data based on the number of sub-aperture points is as follows: the azimuth data are arranged in order, and the azimuth sub-aperture data is divided equally in sequence with the number of sub-aperture points as a fixed length to obtain several sub-aperture data of the same length.

[0040] In this embodiment of the application, a specific method for dividing the azimuth sub-aperture data of spaceborne SAR echo data based on the number of sub-aperture points is as follows: combining the azimuth complexity of the target scene, within the preset range of the number of sub-aperture points, a smaller step size is used to divide complex areas and a larger step size is used to divide simple areas to complete the azimuth sub-aperture division.

[0041] In this application embodiment, a specific method for filtering clutter from the unambiguous sub-aperture spectrum can be: setting a fixed clutter frequency threshold, identifying frequency components in the unambiguous sub-aperture spectrum that exceed the threshold range as scene clutter, and filtering them out.

[0042] In this application embodiment, a specific method for fusing the unambiguous sub-aperture spectrum after clutter filtering in the frequency domain can be: using a weighted average fusion algorithm, assigning weights according to the signal strength of each sub-aperture spectrum, and obtaining the complete bandwidth signal after weighted superposition.

[0043] In this application embodiment, a specific method for fusing the unambiguous sub-aperture spectrum after clutter filtering in the frequency domain can be: first, perform phase alignment processing on each sub-aperture spectrum, and then directly splice them in frequency order to form a complete bandwidth signal, thereby obtaining echo data after data granularity compression.

[0044] The method in this application can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single machine to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the complete echo data at once. This reduces the performance requirements of a single machine per processing run and improves processing efficiency. Simultaneously, by compressing the data granularity of the echo data, the performance ceiling requirements for a single machine can be lowered. For example, a complete SAR echo data processing task that previously required high-end dedicated processing equipment can now be completed by a regular single machine by processing sub-aperture data sequentially, without relying on high-configuration hardware. Furthermore, when processing SAR data, a single machine does not need to wait for the complete data transmission to finish before starting processing; it can receive and process sub-aperture data simultaneously, reducing overall processing wait time. Moreover, when batch processing spaceborne SAR data, it is unnecessary to purchase top-performance single-machine equipment to meet processing requirements; medium- to low-performance single machines can complete the task, reducing hardware investment costs.

[0045] In this application embodiment, as an optional implementation, the method of this application embodiment further includes the following: The target scene's azimuth swath width, the synthetic aperture radar's pulse repetition frequency, and the synthetic aperture radar's beam ground velocity are obtained. The number of sub-aperture points is determined based on the azimuth swath width of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the synthetic aperture radar beam.

[0046] In the above implementation, the azimuth swath width refers to the spatial width of the target scene in the azimuth direction (satellite flight direction) of SAR imaging, and is a key parameter describing the size of the target scene. The azimuth swath width can be the azimuth swath width of a mountain scene or the azimuth swath width of a plain scene.

[0047] In the above embodiments, the pulse repetition frequency (PRF) refers to the number of electromagnetic pulses emitted by the synthetic aperture radar per unit time, directly affecting the sampling density and imaging resolution of SAR data. The pulse repetition frequency can be a fixed frequency or an adaptively adjusted frequency. In the above implementation, the ground velocity of the beam refers to the speed at which the electromagnetic wave beam emitted by the synthetic aperture radar moves on the ground of the target scene, and is related to the satellite flight speed and the beam pointing angle.

[0048] In the above embodiments, a specific way to obtain the azimuth swath of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the beam can be to obtain the azimuth swath of the target scene through the mapping parameter preset module of the spaceborne SAR, read the preset pulse repetition frequency from the radar transmission control unit, and calculate the ground velocity of the beam by combining it with satellite orbit data.

[0049] In the above implementation, a specific way to determine the number of sub-aperture points based on the azimuth swath width of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the synthetic aperture radar beam can be to construct a mapping relationship model between parameters and the number of sub-aperture points, input the three obtained parameters into the model, and output the corresponding number of sub-aperture points after model analysis and matching.

[0050] This optional implementation obtains the azimuth swath of the target scene, the pulse repetition frequency of the synthetic aperture radar (SAR), and the ground velocity of the SAR beam. Based on these parameters, the number of sub-aperture points can be determined. In one example, this method can obtain a suitable number of sub-aperture points for target scenes of different sizes and terrains, avoiding the problems of too many or too few points. In another example, the determined number of sub-aperture points ensures that when processing sub-aperture data, a single machine avoids excessive processing pressure due to too many points or resource waste due to too few points, achieving efficient resource utilization.

[0051] In this embodiment of the application, as an optional implementation, the formula for calculating the number of sub-aperture points is: ; in, Indicates the number of sub-aperture points. Indicates the azimuth width of the target scene. This represents the ground velocity of the synthetic aperture radar beam. This indicates the pulse repetition frequency of the synthetic aperture radar. This represents the power of 2 operation. This indicates the rounding up operation.

[0052] In the above implementation, the ceil operation is a mathematical operation that rounds the calculation result up to the nearest integer. When the result is a decimal, it takes the smallest integer greater than that decimal. The ceil operation can be either single-precision or double-precision.

[0053] In the above implementation, the power of 2 operation (log2 related) refers to the logarithmic or exponential operation with base 2, which is used to adjust the calculation result to the form of an integer power of 2 to adapt to the binary characteristics of data processing.

[0054] This optional implementation can accurately calculate the number of sub-aperture points based on the above formula, thereby fully utilizing the performance of a single machine while reducing the performance requirements for single-processing. In one example, regardless of how the input parameter values ​​change, the formula can obtain the number of sub-aperture points that meet the data processing requirements, avoiding errors from manual calculation.

[0055] In this embodiment of the application, as an optional implementation, before performing unambiguous spectral reconstruction on each sub-aperture data to obtain the unambiguous sub-aperture spectrum, the method of this embodiment of the application further includes the following steps: Zeros are padded to the sub-aperture data in the azimuth direction so that the number of sub-aperture data points covers twice the scene width.

[0056] In the above embodiments, azimuth zero-padding refers to a preprocessing operation that adds zero-value data points at the azimuth end or specific positions of the sub-aperture data to increase the total number of sub-aperture data points. Azimuth zero-padding can be either fixed-length or adaptive-length.

[0057] In the above implementation, twice the scene width refers to twice the value corresponding to the azimuth width of the target scene, which is the standard number of points that need to be achieved after zeroing the sub-aperture data.

[0058] In the above implementation, a specific way to zero-patch the sub-aperture data in the azimuth direction is as follows: first calculate the number of points corresponding to the target scene width, then calculate the total number of points required for twice the scene width, subtract the current number of sub-aperture data points from the total number of points to obtain the number of zero-patch points to be added, and continuously add the corresponding number of zero-value points at the end of the sub-aperture data in the azimuth direction.

[0059] In the above implementation, a specific way to zero-padded sub-aperture data in the azimuth direction can be: using an interval zero-padded method, zero points are evenly inserted between the azimuth data points of the sub-aperture data, and the remaining zero points are supplemented at the end, so that the number of sub-aperture data points reaches twice the scene width.

[0060] This optional implementation can perform azimuth zero-padding on the sub-aperture data before using inverse filtering to process the unambiguous sub-aperture spectrum. This avoids the folding effect of unfiltered clutter caused by outside clutter that cannot be completely filtered out in the subsequent clutter filtering process. In one example, the zero-padding sub-aperture data can completely cover the target scene and surrounding areas where clutter may exist. During subsequent clutter filtering, outside clutter can be completely removed, and clutter will not fold into the target spectrum.

[0061] In this embodiment of the application, as an optional implementation, unambiguous spectral reconstruction is performed on each sub-aperture data to obtain an unambiguous sub-aperture spectrum, including the following sub-steps: The sub-aperture data is subjected to inverse filtering to obtain an unambiguous sub-aperture spectrum.

[0062] In the above embodiments, inverse filtering refers to a processing method that designs a filter function based on the reverse process of signal distortion to correct the distorted sub-aperture data and restore the original signal spectrum. Inverse filtering can be linear inverse filtering or nonlinear inverse filtering.

[0063] In the above embodiments, the unambiguous sub-aperture spectrum refers to the spectral data that, after inverse filtering, eliminates ambiguity phenomena such as spectral aliasing and distortion, and can truly reflect the characteristics of the target region corresponding to the sub-aperture. The unambiguous sub-aperture spectrum can be a high-resolution unambiguous sub-aperture spectrum or a low-noise unambiguous sub-aperture spectrum.

[0064] Alternatively, unambiguous sub-aperture spectra can be obtained by performing inverse filtering on the sub-aperture data. In one example, to address the spectral distortion caused by sub-aperture data segmentation, inverse filtering can quickly restore the authenticity of the spectrum, ensuring that each sub-aperture spectrum accurately corresponds to the target region. In another example, it reduces data processing complexity: the logic of inverse filtering is relatively simple, requiring no complex model construction, and while ensuring processing effectiveness, it does not significantly increase the overall computational load of data processing.

[0065] In this embodiment of the application, as an optional implementation, clutter filtering is performed on the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum, including the following steps: Obtain satellite orbit coordinates, target point coordinates, signal wavelength, and satellite velocity, and determine the oblique angle between the sub-aperture edge and the two edge points of the azimuth scene based on the satellite orbit coordinates and target point coordinates; The Doppler spectral range of the target scene is determined based on the oblique angle, signal wavelength, and satellite velocity; Based on the Doppler spectrum range of the target scene, filter out the scene clutter bandwidth in the unambiguous sub-aperture spectrum.

[0066] In the above embodiments, satellite orbit coordinates refer to the spatial coordinates corresponding to the trajectory of a satellite in space, used to determine the relative positional relationship between the satellite and the target point. These satellite orbit coordinates can include low Earth orbit satellite orbit coordinates and geostationary orbit satellite orbit coordinates.

[0067] In the above implementation, the target point coordinates refer to the spatial coordinates of a specific reference point in the target scene, which are the basic data for calculating the oblique angle. The target point coordinates can be the coordinates of the scene center point or the coordinates of a scene edge point.

[0068] In the above implementation, the oblique angle refers to the angle between the line connecting the edge of the sub-aperture to the edge points of the two ends of the azimuth scene and the central axis of the satellite beam, which reflects the relative attitude of the satellite and the target point.

[0069] In the above implementation, the signal wavelength refers to the wavelength of the electromagnetic waves emitted by the synthetic aperture radar, which is a key parameter affecting the calculation of the Doppler frequency.

[0070] In the above implementation, the Doppler spectrum range refers to the Doppler frequency distribution range of the reflected signal from the target scene, which is the core basis for distinguishing the target signal from clutter signals.

[0071] In the above implementation, a specific way to obtain relevant parameters and determine the oblique angle can be: obtain the real-time satellite orbit coordinates from the satellite's orbit control system, determine the target point coordinates through the target detection module of the onboard SAR, and calculate the oblique angle through geometric operations by combining the sub-aperture edge position data.

[0072] In the above implementation, a specific way to obtain relevant parameters and determine the oblique angle can be: using satellite orbit prediction data from ground stations to obtain satellite orbit coordinates, determining the target point coordinates through scene mapping maps, and substituting them into a preset oblique angle calculation formula to obtain the oblique angle between the sub-aperture edge and the scene edge point.

[0073] In the above implementation, one specific way to determine the Doppler spectrum range is to directly substitute the values ​​of the oblique angle, signal wavelength, and satellite velocity into the Doppler spectrum range calculation formula, and then quickly calculate the result through the hardware computing unit.

[0074] In the above implementation, one specific way to determine the Doppler spectrum range is to construct a Doppler spectrum range prediction model, input three parameters into the model, and output the Doppler spectrum range of the target scene through the mapping relationship after model training.

[0075] In the above implementation, one specific way to filter out clutter bandwidth outside the scene can be: constructing a bandpass filter based on the Doppler spectrum range, allowing only the spectral components within that range to pass through, and filtering out clutter bandwidth outside the scene that exceeds the range.

[0076] In the above implementation, a specific way to filter out clutter bandwidth outside the scene can be: to segment the unambiguous sub-aperture spectrum by frequency, mark the frequency bands corresponding to the Doppler spectrum range, and delete the clutter components of the remaining frequency bands to achieve clutter filtering.

[0077] This optional implementation can determine the Doppler spectral range of the sub-aperture data for the target scene based on the oblique viewing angle, signal wavelength, and satellite velocity. It can then filter out the scene-related clutter bandwidth in the unambiguous sub-aperture spectrum based on the Doppler spectral range of the target scene. In one example, the Doppler spectral range accurately defines the frequency range of the target signal, avoiding misclassification as clutter while completely filtering out irrelevant clutter. In another example, the sub-aperture spectrum after clutter filtering retains only the effective signal components of the target scene, significantly improving spectral purity and laying a good foundation for subsequent frequency domain fusion.

[0078] In this embodiment of the application, as an optional implementation, the formula for calculating the Doppler spectrum range of the target scene is: ; in, Indicates satellite speed. Indicates an oblique angle. Indicates the signal wavelength. Indicates the Doppler spectrum range of the target scene.

[0079] This optional implementation can accurately calculate the Doppler spectrum range of the target scene using the above calculation formula.

[0080] In this embodiment of the application, as an optional implementation, the method further includes the following steps: The unambiguous sub-aperture spectrum after clutter filtering is multiplied by the phase factor of the sub-aperture data to restore the time-frequency relationship of the sub-aperture data to the time-frequency relationship of the full aperture.

[0081] In the above implementation, the phase factor refers to a mathematical factor used to correct the time-frequency relationship of the sub-aperture data. Its core function is to compensate for the phase shift caused by sub-aperture segmentation.

[0082] In the above implementation, the time-frequency relationship refers to the corresponding relationship between the signal in the time domain and the frequency domain.

[0083] In the above implementation, the time-frequency relationship at full aperture refers to the inherent time-domain and frequency-domain correspondence of the signal when the complete SAR echo data has not been segmented into sub-apertures.

[0084] In the above implementation, a specific way to multiply the unambiguous sub-aperture spectrum after clutter filtering by the phase factor of the sub-aperture data can be: first, obtain the value of the phase factor through calculation, and then use complex multiplication to multiply each frequency component of the unambiguous sub-aperture spectrum with the phase factor to complete the time-frequency relationship correction.

[0085] In the above implementation, one specific way to multiply the unambiguous sub-aperture spectrum after clutter filtering by the phase factor of the sub-aperture data is to construct a phase factor correction module, input the unambiguous sub-aperture spectrum after clutter filtering and the calculated phase factor, and the module realizes the phase factor multiplication correction of batch spectra through parallel operation.

[0086] This optional implementation restores the time-frequency relationship of the sub-aperture data to the time-frequency relationship of the full aperture, avoiding the introduction of new ambiguities during fusion. In one example, the corrected sub-aperture spectrum accurately restores the time-frequency characteristics of the full aperture data, ensuring consistency in the time-frequency attributes of each sub-aperture data point. In another example, during frequency domain fusion, the consistent time-frequency relationship of each sub-aperture spectrum prevents phase conflicts or spectral misalignments, avoiding the introduction of new ambiguities and ensuring the quality of the complete bandwidth signal after fusion.

[0087] In this embodiment of the application, as an optional implementation, the phase factor of the sub-aperture data is calculated as follows: ; in, This indicates the length of the sub-aperture data before zero-padding in the azimuth direction. This represents the frequency components corresponding to the retained spectrum after unambiguous sub-aperture spectral filtering. , The phase factor representing the sub-aperture data, This indicates the total number of sub-aperture data. This represents the data for the i-th sub-aperture.

[0088] In the above implementation, the length before azimuth zeroing refers to the original number of azimuth data points before the sub-aperture data is zeroed.

[0089] In the above implementation, the frequency component corresponding to the retained spectrum refers to the frequency component corresponding to the remaining effective signal of the target scene after the unambiguous sub-aperture spectrum is filtered out by clutter.

[0090] In the above implementation, the total number of sub-aperture data refers to the total number of sub-aperture data obtained after the spaceborne SAR echo data is divided into directional sub-apertures.

[0091] This optional implementation can accurately calculate the phase factor of the sub-aperture data using the above formula. In one example, the formula comprehensively considers key factors such as the length, frequency components, and total number of sub-aperture data, and the calculated phase factor can accurately match the time-frequency correction requirements of the sub-aperture data. In another example, regardless of how the parameters of the sub-aperture data change, the formula can obtain a consistent and reliable phase factor, ensuring uniform time-frequency relationship correction effects for different sub-aperture data and providing stable support for subsequent fusion.

[0092] Please see Figure 2 , Figure 2 This is a schematic diagram of a spaceborne SAR data granularity compression device based on sub-aperture fusion, provided in an embodiment of this application. Figure 2 As shown, the device includes the following functional modules: Acquisition module 201 is used to acquire spaceborne SAR echo data; The partitioning module 202 is used to partition the spaceborne SAR echo data into directional sub-apertures based on the number of sub-aperture points, thereby obtaining several sub-aperture data. The spectrum reconstruction module 203 is used to perform unambiguous spectrum reconstruction on each sub-aperture data to obtain the unambiguous sub-aperture spectrum; The clutter filtering module 204 is used to filter out clutter from the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum. The fusion module 205 is used to fuse the unambiguous sub-aperture spectrum after clutter filtering in the frequency domain to obtain a complete bandwidth signal, and to obtain echo data with data granularity compression based on the complete bandwidth signal.

[0093] The apparatus in this application embodiment can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single machine to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the complete echo data at once. This reduces the performance requirements of a single machine per processing cycle and improves single-machine processing efficiency. Simultaneously, by compressing the data granularity of the echo data, the upper limit of the performance requirements for a single machine can be lowered.

[0094] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 3 As shown, the electronic device includes: Processor 301; and The memory 302 is configured to store machine-readable instructions that, when executed by the processor 301, perform the method as described in any of the foregoing embodiments.

[0095] The electronic device in this application embodiment, by implementing a compression method for spaceborne SAR data granularity based on sub-aperture fusion, can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single device to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the entire echo data at once, thus reducing the performance requirements for a single processing cycle and improving single-device processing efficiency. Simultaneously, by compressing the data granularity of the echo data, the upper limit of the performance requirements for a single device can be lowered.

[0096] Furthermore, embodiments of this application also provide a storage medium storing a computer program, which is executed by a processor using the method described in any of the foregoing embodiments.

[0097] The storage medium in this embodiment of the application, by implementing a compression method based on sub-aperture fusion for spaceborne SAR data granularity, can divide SAR echo data into several sub-aperture data and remove clutter, resulting in smaller data granularity of the fused echo data. This allows a single machine to process the smaller-granularity sub-aperture data sequentially, eliminating the need to process the entire echo data at once, thus reducing the performance requirements for a single machine's processing and improving its processing efficiency. Simultaneously, by compressing the data granularity of the echo data, the upper limit of the performance requirements for a single machine can be lowered.

[0098] For a specific example of an embodiment of this application, please refer to [link / reference]. Figure 4 , Figure 4 This is a flowchart illustrating another method for compressing spaceborne SAR data granularity based on sub-aperture fusion, provided in an embodiment of this application. Figure 4 As shown, the compression method for spaceborne SAR data granularity based on sub-aperture fusion includes: Sub-aperture segmentation of echo data: Azimuth sub-aperture segmentation of the echo data aims to divide the complete Doppler spectrum of the target scene into different locations across multiple sub-apertures. This allows a single satellite to process small-granularity sub-aperture data sequentially. Therefore, the satellite must be able to illuminate the entire target scene within a single sub-aperture. Furthermore, to ensure maximum processing efficiency, the number of sub-aperture points is generally taken as an integer power of 2. In summary, the number of sub-aperture points is:

[0099] in, Indicates the number of sub-aperture points, This indicates the azimuth width of the target scene. This represents the ground velocity of the synthetic aperture radar beam. This indicates the pulse repetition frequency of the synthetic aperture radar. This represents the power of 2 operation. This indicates the rounding up operation.

[0100] Furthermore, unambiguous spectral recovery of sub-aperture data: see [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of a sub-aperture data unambiguous spectrum recovery process provided in an embodiment of this application. For example... Figure 5 As shown, phase operations need to be performed on the sub-aperture spectra one by one. For example, phase operation is performed on the sub-aperture 1 spectrum and the sub-aperture 2 spectrum to obtain a new level sub-aperture spectrum. Then, phase operation is performed on the new level sub-aperture spectrum and the sub-aperture 3 spectrum to obtain a new level sub-aperture spectrum, and so on, until the phase operation of the sub-aperture N spectrum is completed. Finally, a clear and complete spectrum is obtained. In addition, after the sub-aperture data is divided, the sub-aperture data needs to be zero-paved in the azimuth direction to the number of points corresponding to twice the scene size. The number of sub-aperture points after zero-padding is:

[0101] in, This refers to the number of points after zero-padding in the azimuth direction of the sub-aperture. Zero-padding is necessary because during clutter filtering, it's impossible to completely remove clutter outside the scene. Only clutter extending beyond one scene width at each end of the scene edge, centered on the target scene, can be completely filtered out. The remaining clutter outside the scene will remain in the incomplete accumulation region. If the azimuth granularity of the final imaging result is insufficient to cover three scene widths, the spectrum of the incompletely filtered clutter will fold inwards into the scene. Considering that if the folded portion only appears in the incomplete accumulation region, it can be removed after imaging to obtain a high-quality imaging result, the number of sub-aperture points after zero-padding only needs to cover twice the scene width to meet the requirements for blur-free imaging. After zero-padding, inverse filtering is performed on the sub-aperture data to reconstruct the blur-free spectrum, obtaining the blur-free sub-aperture spectrum. It should be noted that... (Please refer to...) Figure 6 , Figure 6 This is a schematic diagram illustrating zero-padding at both ends of sub-aperture data azimuth according to an embodiment of this application. For example... Figure 6 As shown, zero padding refers to padding both ends of the echo data with zeros.

[0102] Furthermore, clutter filtering: After unambiguous spectrum recovery via sub-apertures, the spectrum is no longer blurred. The target scene's spectral information is located at different positions in the spectra of different sub-apertures, which also include the spectra of targets outside the scene. If sub-aperture fusion is performed directly, the clutter spectrum outside the scene will be superimposed into the spectrum of the target scene. Therefore, it is necessary to filter out the clutter outside the scene to avoid aliasing after fusion. Please refer to [link to relevant documentation]. Figure 7 , Figure 7 This is a schematic diagram of scene clutter filtering provided in an embodiment of this application. During clutter filtering, the oblique angle between the sub-aperture edge and the two edge points of the azimuth scene is first calculated jointly based on the satellite orbit coordinates and the target point coordinates, such as... Figure 7 As shown, the Doppler frequency range of the sub-aperture for the target scene is calculated according to the Doppler frequency calculation formula, ensuring that all spectra of the scene are retained within the intercept range. For details on calculating the clutter filtering range outside the scene, please refer to [link to relevant documentation]. Figure 8 , Figure 8 This is a schematic diagram illustrating the calculation of the external clutter filtering range provided in an embodiment of this application. The Doppler frequency calculation formula is as follows: ; in, Indicates the speed of the satellite. This refers to the oblique angle. Indicates the wavelength of the signal. This indicates the Doppler spectrum range of the target scene.

[0103] Furthermore, sub-aperture fusion: After clutter filtering, the residual clutter spectrum can no longer affect subsequent imaging, and the sub-aperture spectra can be fused to obtain the full aperture spectrum. However, in step two, the sub-aperture data were reconstructed separately without ambiguity. Therefore, from a time domain perspective, the energy of their echo signals is concentrated around time 0, such as... Figure 9 As shown, direct fusion will introduce new ambiguities into the echo data, in which... Figure 9 This is a schematic diagram of aliasing after sub-aperture fusion provided in an embodiment of this application. Therefore, the sub-aperture data needs to be shifted in the time domain to the correct time-frequency position to restore the time-frequency relationship when the full aperture is present before fusion. According to the Fourier transform, signal time-domain shifting is equal to frequency-domain multiplication by the corresponding phase. Therefore, the clutter-filtered data obtained in step three needs to be multiplied by the corresponding phase, and its expression is: ; in, This indicates the length of the sub-aperture data before zero-padding in the azimuth direction. This represents the frequency components corresponding to the retained spectrum after the unambiguous sub-aperture spectral filtering. , The phase factor representing the sub-aperture data, This indicates the total number of sub-aperture data. This represents the i-th sub-aperture data.

[0104] Finally, after the above steps, the azimuth data granularity is reduced to its original value. The compressed data can then undergo further steps such as distance compression, distance migration correction, and azimuth compression.

[0105] Explanation of the technical effects: The scenario is set as a 10km x 10km area, and the imaging scenario is a 5km x 5km area in the middle region. Several point targets are evenly distributed along the azimuth and range directions in each area, with a 1km left-right interval between each point target. Based on the radar system parameters and flight platform parameters in Table 1, the azimuth data granularity of each channel can be calculated to be 8k. Direct processing, after spectrum synthesis, will result in 128k azimuth data granularity, significantly impacting the timeliness of onboard single-unit imaging processing.

[0106] Using the sub-aperture fusion data compression technology proposed in the embodiments of this application, the size of each sub-aperture is set to 1k, with a total of 8 sub-apertures. Before spectrum synthesis, zeros are padded to 2k before and after each sub-aperture in the time domain. After spectrum synthesis, the number of points in each sub-aperture is 32k, and the data granularity is reduced to 1 / 4 of the original, as shown in Table 1 below.

[0107] Table 1: Comparison of Data Granularity

[0108] Furthermore, the compressed data was compressed using traditional imaging methods for two-dimensional data compression, and the imaging results are as follows: Figure 10 As shown, a clear 5x5 dot matrix can be seen. Figure 10 This is a schematic diagram of a simulated dot matrix imaging result provided in an embodiment of this application. Furthermore, several points were selected for evaluation, and the evaluation results are shown in Table 2 below. Figure 11 As shown, the imaging results are of good quality. Figure 11 yes Figure 10 A schematic diagram of the imaging quality assessment of points A, B, and C.

[0109] Table 2: Schematic diagram of image quality assessment at points A, B, and C

[0110] To further verify the data compression algorithm of the embodiments of this application, a simulation verification of area target echo data was performed, wherein the imaging results are as follows: Figure 12 As shown, where, Figure 12 This is a schematic diagram of a simulated imaging result of a surface target provided in an embodiment of this application. Figure 12 As shown, the imaging results are clear and of good quality. This demonstrates that the method proposed in this invention can effectively reduce data granularity while maintaining image quality.

[0111] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0112] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0114] It should be noted that if a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0116] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A compression method for spaceborne SAR data granularity based on sub-aperture fusion, characterized in that, The method includes: Acquire spaceborne SAR echo data; The spaceborne SAR echo data is divided into directional sub-apertures based on the number of sub-aperture points, resulting in several sub-aperture data. Unambiguous spectral reconstruction is performed on each of the sub-aperture data to obtain the unambiguous sub-aperture spectrum; Clutter filtering is performed on the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum; The unambiguous sub-aperture spectrum after clutter filtering is fused in the frequency domain to obtain a complete bandwidth signal, and the echo data after data granular compression is obtained based on the complete bandwidth signal.

2. The method as described in claim 1, characterized in that, The method further includes: The target scene's azimuth swath width, the synthetic aperture radar's pulse repetition frequency, and the synthetic aperture radar's beam ground velocity are obtained. The number of sub-aperture points is determined based on the azimuth swath width of the target scene, the pulse repetition frequency of the synthetic aperture radar, and the ground velocity of the synthetic aperture radar beam.

3. The method as described in claim 2, characterized in that, The formula for calculating the number of sub-aperture points is: ； in, Indicates the number of sub-aperture points, This indicates the azimuth width of the target scene. This represents the ground velocity of the synthetic aperture radar beam. This indicates the pulse repetition frequency of the synthetic aperture radar. This represents the power of 2 operation. This indicates the rounding up operation.

4. The method as described in claim 1, characterized in that, Before performing unambiguous spectral reconstruction on each of the sub-aperture data to obtain the unambiguous sub-aperture spectrum, the method further includes: The sub-aperture data is padded with zeros in the azimuth direction so that the number of points in the sub-aperture data covers twice the scene width.

5. The method as described in claim 4, characterized in that, The step of performing unambiguous spectral reconstruction on each of the sub-aperture data to obtain an unambiguous sub-aperture spectrum includes: The sub-aperture data is subjected to inverse filtering to obtain an unambiguous sub-aperture spectrum.

6. The method as described in claim 1, characterized in that, The step of filtering out clutter from the unambiguous sub-aperture spectrum to remove out-of-scene clutter bandwidth in the unambiguous sub-aperture spectrum includes: The satellite orbit coordinates, target point coordinates, signal wavelength, and satellite velocity are obtained, and the oblique angle between the sub-aperture edge and the two edge points of the azimuth scene is determined based on the satellite orbit coordinates and the target point coordinates. The Doppler spectral range of the target scene is determined based on the oblique angle, the signal wavelength, and the satellite velocity; The scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum is filtered out based on the Doppler spectrum range of the target scene.

7. The method as described in claim 5, characterized in that, The formula for calculating the Doppler spectrum range of the target scene is: ； in, Indicates the speed of the satellite. This indicates the oblique angle. Indicates the wavelength of the signal. This indicates the Doppler spectrum range of the target scene.

8. The method as described in claim 1, characterized in that, The method further includes: The unambiguous sub-aperture spectrum after clutter filtering is multiplied by the phase factor of the sub-aperture data so that the time-frequency relationship of the sub-aperture data is restored to the time-frequency relationship of the full aperture.

9. The method as described in claim 8, characterized in that, The formula for calculating the phase factor of the sub-aperture data is: ； in, This indicates the length of the sub-aperture data before zero-padding in the azimuth direction. This represents the frequency components corresponding to the retained spectrum after the unambiguous sub-aperture spectral filtering. , The phase factor representing the sub-aperture data, This indicates the total number of sub-aperture data. This represents the i-th sub-aperture data.

10. A compression device for spaceborne SAR data granularity based on sub-aperture fusion, characterized in that, The device includes: The acquisition module is used to acquire spaceborne SAR echo data; The partitioning module is used to partition the spaceborne SAR echo data into directional sub-apertures based on the number of sub-aperture points, thereby obtaining several sub-aperture data. The spectrum reconstruction module is used to perform unambiguous spectrum reconstruction on each of the sub-aperture data to obtain an unambiguous sub-aperture spectrum. The clutter filtering module is used to filter out clutter from the unambiguous sub-aperture spectrum to remove the scene-outside clutter bandwidth in the unambiguous sub-aperture spectrum. The fusion module is used to fuse the unambiguous sub-aperture spectrum after clutter filtering in the frequency domain to obtain a complete bandwidth signal, and to obtain echo data with data granularity compression based on the complete bandwidth signal.

11. An electronic device, characterized in that, include: processor; as well as A memory configured to store machine-readable instructions that, when executed by the processor, perform the method as described in any one of claims 1-9.

12. A storage medium, characterized in that, The storage medium stores a computer program, which is executed by a processor according to any one of claims 1-7.

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