An unmanned aerial vehicle cluster SAR span gap constraint center-scan imaging method

Abstract

The application provides a UAV cluster SAR span gap constraint center scanning imaging method, and belongs to the technical field of radar imaging. The method comprises the following steps: acquiring radar working, imaging tasks and receiver configuration parameters; under preset conditions, taking the formation center position as a variable to construct and a baseline set, and determining a baseline span threshold and a gap threshold according to parameters; taking the two thresholds as constraints, taking a center scanning sequence as an optimization variable, minimizing the maximum gap of adjacent baselines after sorting, and determining the center scanning sequence; controlling the formation to observe at each center position according to the sequence, obtaining multi-center multi-channel range compression echo data, and establishing a forward operator according to the path length of the transmitter and the receiver to the imaging pixel at each time to combine the echo data to inverse the SAR image. The application solves the problem that the existing large-span uniform UAV single-transmit multi-receive synthetic aperture radar imaging scheme cannot meet the requirements of imaging resolution and aliasing-free imaging.

Description

Technical Field

[0001] This invention relates to the field of radar imaging technology, and in particular to a method for constrained center sweep imaging of unmanned aerial vehicle (UAV) swarm SAR. Background Technology

[0002] Synthetic Aperture Radar (SAR), with its advantages of all-weather, all-day, and long-range detection, has been widely applied in various fields such as low-altitude remote sensing, disaster monitoring, ground feature identification, security reconnaissance, and environmental monitoring. With the rapid development of UAV technology, the use of UAVs carrying radar equipment in swarm formations for radar imaging operations is gradually becoming an important application of distributed SAR. Compared to single-unit UAV radar systems, UAV swarm SAR can expand the spatial sampling range through multi-platform collaborative operation, overcoming the performance limitations imposed by single-unit payload, antenna aperture, and trajectory stability, and possessing significant advantages in imaging complex low-altitude scenarios.

[0003] In UAV swarm SAR engineering practice, due to multiple constraints such as safe flight spacing, rotor aerodynamic interference, airframe size, and navigation and synchronization accuracy, it is difficult for UAVs to maintain a dense array layout with centimeter or sub-meter precision for extended periods. What is practically achievable is often a uniform formation with meter-level spacing, meaning the instantaneous receiving array exhibits a large-spaced, uniform structure. This large-spaced formation helps to increase the equivalent aperture and improve nominal resolution, but it also leads to large and periodically distributed gaps in the lateral spatial frequency sampling, resulting in severe increases in grating lobes and side lobes, as well as spatial aliasing problems in limited scenarios.

[0004] To address the aforementioned issues, existing technologies primarily focus on three directions. First, imaging methods relying on dense arrays or dense flight paths close the sampling intervals using a large number of closely spaced virtual array elements. However, this method struggles to meet the rigid constraints of safe flight spacing for UAVs. Second, uniform or random center scanning strategies increase the number of samples through multiple formation positions. However, these methods only focus on increasing the number of sampling points and do not explicitly constrain the maximum gap in the spatial frequency domain of the sampling sequence, thus failing to fundamentally guarantee aliasing-free sampling conditions in finite-width scenarios. Third, inversion algorithms such as sparse reconstruction, Bayesian learning, and total variation regularization are used for post-processing compensation in the image domain. However, when there are deterministically large gaps in the physical sampling geometry, algorithms alone cannot eliminate the risks of grating lobes and aliasing caused by sampling gaps, making it difficult to guarantee imaging reliability. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a UAV swarm SAR span-gap constrained center sweep imaging method, which aims to solve the problem that the existing UAV single-shot multi-receiver synthetic aperture radar imaging scheme with large spacing uniformity cannot simultaneously meet the requirements of imaging resolution and non-aliasing imaging, and the imaging quality is limited.

[0006] A method for constrained center-sweep imaging of unmanned aerial vehicle (UAV) swarm SAR span gap according to an embodiment of the present invention includes: The radar operating parameters, imaging mission parameters, and receiver configuration parameters of the UAV formation are obtained. The receiver configuration parameters include the fixed lateral offset of each receiver in the instantaneous UAV formation relative to the formation center. Under preset conditions, based on the receiver configuration parameters, and with the formation center position as a variable, a set of baselines is constructed, consisting of the sum of the transverse positions of the transmitters and the transverse positions of each receiver. Based on the radar operating parameters and the imaging mission parameters, the baseline span threshold and the baseline gap threshold are determined respectively. The center sweep sequence is determined by taking the baseline span threshold and the baseline gap threshold as constraints, the center sweep sequence composed of the formation center positions as optimization variables, and minimizing the maximum gap between adjacent baselines after the baseline set is sorted as the optimization objective. According to the center sweep sequence control or simulation formation, radar observations are performed sequentially at each center position to acquire multi-center, multi-channel range compressed echo data. At the same time, a forward operator is established based on the path length between the transmitter, receiver and imaging pixel at each observation time, so as to determine the SAR image based on the forward operator and the range compressed echo data.

[0007] In addition, the UAV swarm SAR span gap constrained center sweep imaging method according to the above embodiments of the present invention may also have the following additional technical features: Furthermore, under preset conditions, the step of constructing a set of baselines consisting of the sum of the transverse positions of the transmitters and the transverse positions of each receiver, using the formation center position as a variable, based on the receiver configuration parameters, includes: Position the transmitter at the center of the formation, ensuring that its lateral position is equal to the current center position of the formation. Each receiver is arranged at equal intervals with the formation center as the center of symmetry. According to the receiver configuration parameters, each receiver has a fixed lateral offset, so that the lateral position of each receiver is equal to the sum of the current formation center position and the corresponding receiver's fixed lateral offset. Using any possible formation center position as a variable, calculate the sum of the lateral positions of the transmitter and each receiver at each formation center position to obtain the corresponding baseline. The sum and baselines corresponding to the center positions of all formations and all receiver combinations are summarized to form a sum and baseline set.

[0008] Furthermore, the steps of determining the baseline span threshold and the baseline gap threshold based on the radar operating parameters and the imaging mission parameters respectively include: The radar wavelength and reference slant range are obtained from the radar operating parameters, and the target lateral resolution and the lateral width of the scene to be imaged are obtained from the imaging mission parameters. The baseline span threshold is determined based on the radar wavelength, reference slant range, and target lateral resolution, wherein the baseline span threshold is directly proportional to the product of the radar wavelength and the reference slant range and inversely proportional to the target lateral resolution. The baseline gap threshold is determined based on the radar wavelength, reference slant range, and lateral width of the scene to be imaged, wherein the baseline gap threshold is directly proportional to the product of the radar wavelength and the reference slant range, and inversely proportional to the lateral width of the scene to be imaged.

[0009] Furthermore, constrained by the baseline span threshold and the baseline gap threshold, and with the center sweep sequence composed of the formation center positions as optimization variables, and with minimizing the maximum gap between adjacent baselines after sorting the baseline set as the optimization objective, the steps for determining the center sweep sequence include: Construct a candidate center sweep sequence as an optimization variable, wherein the candidate center sweep sequence contains at least one formation center position; Sort all the sums and baselines in the set corresponding to the current candidate center sweep sequence in ascending order of their numerical values ​​to obtain an ordered sum and baseline sequence, and calculate the maximum gap between adjacent sums and baselines after sorting. If the sum-baseline span of the ordered and baseline sequences is less than the sum-baseline span threshold, then the coverage of the candidate center sweep sequence is expanded until the sum-baseline span meets the requirement; If the maximum gap is greater than the baseline gap threshold, then the maximum gap priority iterative optimization strategy is adopted. The iterative optimization strategy is to identify the maximum gap between adjacent elements in the ordered and baseline sequences each time, add a formation center at the midpoint of the maximum gap, add the new center to the candidate center sweep sequence, recalculate and sort the baseline set, and update the ordered and baseline sequences, the total span of the baselines and the maximum gap. Repeat the iterative optimization strategy, where the total span of the ordered and baseline sequences is not less than the baseline span threshold, and the gap between all adjacent and baseline sequences is not greater than the baseline gap threshold. Then, output the current candidate center sweep sequence as the optimization result.

[0010] Furthermore, the steps of controlling or simulating the formation to sequentially perform radar observations at each center position according to the center sweep sequence to acquire multi-center, multi-channel range compressed echo data include: According to the center sweep sequence, the drone formation is controlled or simulated to move to the center position of each formation corresponding to the sequence. At the center of each formation, the instantaneous formation shape under the preset conditions and the relative offset of the receiver remain unchanged throughout the entire process. The transmitter is controlled to transmit radar detection signals, and all receivers are controlled to synchronously receive the original radar echo reflected from the scene. The raw radar echoes collected by each receiver are uniformly preprocessed with range compression. The processing results of all center locations and all receiving channels are integrated to obtain multi-center, multi-channel range compressed echo data.

[0011] Furthermore, the steps of establishing the forward operator based on the path lengths between the transmitter, receiver, and imaging pixels at each observation time include: Collect and record the real-time spatial positions of the transmitter and each receiver at each observation moment, and retrieve the preset global imaging pixel position information; Calculate the length of the complete bistatic propagation path of the radar signal from the transmitter to the imaging pixel and then from the imaging pixel to the receiver under a single observation time and a single receiving channel. By combining the bistatic propagation path length, radar signal propagation characteristics, and imaging geometry, the correspondence between echo data and target reflectivity is mapped to construct the forward operator corresponding to range-compressed echoes.

[0012] Furthermore, the step of determining the SAR image based on the forward operator and the range compressed echo data includes: Using the range-compressed echo data as the observation vector and the target reflectivity to be reconstructed as the unknown vector, a linear observation model is established, making the observation vector equal to the forward operator multiplied by the target reflectivity vector plus noise. The target reflectivity vector is recovered from the observation vector using a preset method; The recovered target reflectivity vector is rearranged according to the imaging pixel grid to obtain the final SAR image.

[0013] Another objective of this invention is to provide a UAV swarm SAR span gap constrained center sweep imaging system for implementing the aforementioned UAV swarm SAR span gap constrained center sweep imaging method, the system comprising: The parameter acquisition module is used to acquire radar operating parameters, imaging mission parameters, and receiver configuration parameters of the UAV formation. The receiver configuration parameters include the fixed lateral offset of each receiver in the instantaneous UAV formation relative to the formation center. The data construction module is used to construct a set of baselines consisting of the sum of the transverse positions of the transmitter and each receiver, based on the receiver configuration parameters under preset conditions and with the formation center position as the variable. The module also determines the baseline span threshold and baseline gap threshold based on the radar operating parameters and the imaging mission parameters. The sweep sequence determination module is used to determine the center sweep sequence by taking the baseline span threshold and the baseline gap threshold as constraints, the center sweep sequence composed of the formation center position as the optimization variable, and minimizing the maximum gap between adjacent baselines after the baseline set is sorted as the optimization objective. The image determination module is used to control or simulate the formation to perform radar observations at each center position in sequence according to the center sweep sequence, acquire multi-center multi-channel range compressed echo data, and establish a forward operator based on the path length between the transmitter, receiver and imaging pixel at each observation time, so as to determine the SAR image based on the forward operator and the range compressed echo data.

[0014] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described UAV swarm SAR span gap constrained center sweep imaging method.

[0015] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described UAV swarm SAR span gap constrained center sweep imaging method.

[0016] This invention acquires radar operating parameters, imaging mission parameters, and receiver configuration parameters to construct a set of baselines consisting of the sum of the transverse positions of the transmitter and receiver. Simultaneously, it determines the baseline span threshold to ensure resolution and the baseline gap threshold to avoid aliasing, thus transforming the two core requirements of imaging quality into numerical indicators that directly constrain the sampling geometry. Then, using these two thresholds as constraints, the center sweep sequence composed of the formation center positions as optimization variables, and minimizing the maximum gap between adjacent baselines after sorting as the optimization objective, the optimal center sweep sequence is obtained. Because this optimization process directly controls the span and maximum gap of the baselines sampled in the spatial frequency domain, it can simultaneously ensure that the span of the baseline set meets resolution requirements and the maximum gap meets the non-aliasing sampling requirements, while maintaining the engineering constraint of a large-spaced uniform structure in the instantaneous formation. This fundamentally closes the deterministic sampling gap caused by large spacing, avoiding the inherent problems of traditional methods such as the difficulty in achieving dense arrays, the inability to guarantee the maximum gap meets the non-aliasing requirement simply by increasing the number of samples, and the difficulty of post-processing algorithms to compensate for physical sampling defects. Finally, based on this sequence, the formation is controlled to perform multi-center radar observations, and a forward operator is established using precise bistatic path lengths for inversion, creating a closed loop between sampling design and imaging reconstruction, further ensuring the resolution and fidelity of the final SAR image. Thus, without changing the physical configuration of the UAV formation or adding additional hardware overhead, both imaging resolution and scene aliasing requirements can be simultaneously achieved through optimization of only one center sweep sequence. Therefore, this invention solves the problem in existing technologies where large-spacing uniform UAV single-shot multi-receiver synthetic aperture radar imaging schemes cannot simultaneously achieve both imaging resolution and aliasing-free imaging requirements, resulting in limited imaging quality. Attached Figure Description

[0017] Figure 1 This is a flowchart of the UAV swarm SAR span gap constrained center sweep imaging method in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the UAV swarm SAR span gap constrained center sweep imaging system in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention; Figure 4 This is a comparison chart of the normalized point spread function magnitudes of different center sweep strategies in one embodiment of the present invention. Figure 5 This is a comparison diagram of the distribution of baseline sampling positions for different center sweep strategies in one embodiment of the present invention; Figure 6 This is a comparison diagram of the maximum and baseline gaps and required gaps for different center sweep strategies in one embodiment of the present invention; Figure 7This is a comparison chart of peak sidelobe ratios for different center sweep strategies in one embodiment of the present invention; Figure 8 This is a precise range-compressed echo inversion diagram for three types of reflectivity scenarios in one embodiment of the present invention, wherein, Figure 8 (a) in the image is a precise distance-compressed echo inversion image for an urban road scene. Figure 8 (b) in the image is a precise distance-compressed echo inversion diagram for an industrial facility scenario. Figure 8 (c) in the image is the accurate distance-compressed echo inversion image for a dark and weak urban texture scene; Figure 9 This is the experimental result of precise echo error closure in one embodiment of the present invention, wherein, Figure 9 (a) in the figure represents the reference range compressed echo result generated based on the accurate bistatic range history model; Figure 9 (b) in the figure represents the closed echo result obtained by reprojecting the precise echo forward operator and inversion reconstruction result described in this invention; Figure 9 (c) in the figure is the amplitude error distribution diagram between the reference echo and the closed echo; Figure 9 (d) in the figure is the phase error distribution diagram between the reference echo and the closed echo; Figure 9 (e) in the figure represents the statistical curve of the normalized echo error or residual norm as a function of the number of iterations. Figure 10 This is a robustness analysis diagram of the center sweep scheme under position, phase, and noise perturbations in one embodiment of the present invention. Figure 10 (a) in the figure is a schematic diagram of the robustness of the sidelobe under position, phase and noise perturbation. Figure 10 (b) in the figure is a robustness diagram of the maximum gap under position, phase and noise disturbances. Figure 10 (c) in the figure is a schematic diagram of the robustness of frequency coverage under position, phase and noise disturbance.

[0018] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0019] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1 Please see Figure 1 The figure shows the UAV cluster SAR span gap constrained center sweep imaging method in the first embodiment of the present invention, which specifically includes S01-S04.

[0022] S01, acquire radar operating parameters, imaging mission parameters, and receiver configuration parameters of the UAV formation. The receiver configuration parameters include the fixed lateral offset of each receiver in the instantaneous UAV formation relative to the formation center.

[0023] In practical implementation, radar operating parameters, including radar carrier frequency, wavelength, and reference slant range, determine the propagation characteristics of electromagnetic waves and the spatial frequency scale. Imaging mission parameters, including target lateral resolution and the lateral width of the scene to be imaged, determine the core indicators of imaging quality. Receiver configuration parameters determine the instantaneous geometry of the UAV formation. Acquiring these parameters uniformly and incorporating them into a single mathematical framework provides a consistent data foundation for subsequent baseline construction and threshold calculations, avoiding design deviations caused by missing parameters or inconsistent apertures.

[0024] S02, under preset conditions, based on the receiver configuration parameters, and using the formation center position as a variable, construct a set of baselines consisting of the sum of the transverse positions of the transmitters and the transverse positions of each receiver, and determine the baseline span threshold and baseline gap threshold respectively based on the radar operating parameters and the imaging mission parameters.

[0025] In practical implementation, after acquiring the parameters, the imaging quality requirements need to be transformed into numerical constraints that can be directly used for optimization design. On the one hand, by constructing a baseline set, the sampling positions of multiple centers and multiple channels are uniformly mapped onto the baseline axis, allowing sampling points that were originally scattered across different center positions and receiving channels to be sorted and analyzed in the same dimension. On the other hand, based on the radar wavelength, reference slant range R, target lateral resolution, and scene lateral width, the baseline span threshold and baseline gap threshold are calculated respectively. These two thresholds correspond to the resolution requirement and the non-aliasing requirement, respectively. The former specifies the minimum total range that the baseline must cover, and the latter specifies the maximum allowable interval between adjacent sampling points of the baseline. Thus, the abstract imaging quality index is quantified into two specific numerical boundaries, providing clear constraints for subsequent center sweep sequence optimization. The preset condition is to maintain the instantaneous formation as a uniform large-space formation. A uniform large-space formation is a formation method in a UAV swarm where each UAV maintains a relatively uniform and large spatial interval, where the large spatial interval is larger than that of traditional airborne SAR.

[0026] Specifically, the transmitter is positioned at the center of the formation, with its lateral position equal to the current formation center position. Receivers are arranged at equal intervals with the formation center as the center of symmetry. Each receiver has a fixed lateral offset according to its configuration parameters, ensuring its lateral position equals the sum of the current formation center position and its corresponding fixed lateral offset. Using any possible formation center position as a variable, the sum of the lateral positions of the transmitter and receivers at each formation center position is calculated to obtain the corresponding baseline. All possible formation center positions and all receiver combinations are combined to form a set of baselines. In practical implementation, by placing the transmitter at the formation center and arranging the receivers with fixed offsets, the sum of the transmit and receive positions is equivalent to the sampling coordinates of the lateral spatial frequency, effectively transforming the formation geometry into a frequency domain sampling distribution. This allows for flexible adjustment of the overall spatial sampling distribution by adjusting the formation center position, providing an operable adjustment variable for subsequently filling sampling gaps by moving the formation center.

[0027] Furthermore, the radar wavelength and reference slant range are obtained from the radar operating parameters, and the target lateral resolution and the lateral width of the scene to be imaged are obtained from the imaging task parameters. A baseline span threshold is determined based on the radar wavelength, reference slant range, and target lateral resolution, wherein the baseline span threshold is directly proportional to the product of the radar wavelength and reference slant range and inversely proportional to the target lateral resolution. A baseline gap threshold is determined based on the radar wavelength, reference slant range, and the lateral width of the scene to be imaged, wherein the baseline gap threshold is directly proportional to the product of the radar wavelength and reference slant range and inversely proportional to the lateral width of the scene to be imaged.

[0028] In practical implementation, the instantaneous formation consists of 1 transmitter and M receivers. The transmitter is an unmanned aerial vehicle (UAV) platform equipped with a radar transmitting module, responsible for transmitting detection signals to the target area. The receiver is an UAV platform equipped with a radar receiving module, responsible for receiving the echo signals scattered by the target. The lateral position of the transmitters meets the following requirements. The receiver's lateral position satisfies ,in For the first The center position of the formation For the first Each receiver is laterally offset relative to the center of the formation, and Arranged at equal intervals. Baseline is... And the baseline is used to represent the local lateral spatial frequency sampling location, the local lateral spatial frequency satisfies ,in For radar wavelength, For reference slope distance, For the first The center position of the formation, the The sum and baseline of each receiving channel. The formula for calculating the baseline span threshold is as follows: The formula for calculating the baseline gap threshold is as follows: ,in For the target horizontal resolution or horizontal pixel interval. Let be the horizontal width of the scene to be imaged. Critical conditions are derived from two dimensions: total sampling span and sampling interval. The span threshold corresponds to the resolution requirement, and the gap threshold corresponds to the requirement of no aliasing in the scene, thus clarifying the acceptable boundary of imaging performance. This provides clear and rigid constraints for subsequent optimization and ensures that the optimized sampling scheme can match the preset imaging task indicators.

[0029] S03, with the baseline span threshold and the baseline gap threshold as constraints, the center sweep sequence composed of the formation center positions as optimization variables, and minimizing the maximum gap between adjacent baselines after the baseline set is sorted as the optimization objective, the center sweep sequence is determined.

[0030] In practical implementation, the formation center position is used as an adjustable variable, the core optimization direction is to minimize the maximum gap between adjacent and baseline units, and the span threshold, gap threshold and flight range are used as constraints to solve the problem. This plays a role in actively optimizing the sampling distribution from the sampling geometry level. As a result, without increasing the number of receivers or reducing the formation safety distance, the imaging resolution can be guaranteed and the scene aliasing can be suppressed from the root of the sampling, thus minimizing the sampling defects of large-space formations.

[0031] Specifically, a candidate center sweep sequence is constructed as an optimization variable, wherein the candidate center sweep sequence contains at least one formation center position; all the sums and baselines in the set corresponding to the current candidate center sweep sequence are sorted in ascending order of numerical value to obtain an ordered sum and baseline sequence, and the maximum gap between adjacent sums and baselines after sorting is calculated; if the total span of the sums and baselines in the ordered sum and baseline sequence is less than the sum and baseline span threshold, the coverage of the candidate center sweep sequence is expanded until the total span of the sums and baselines meets the requirement; if the maximum gap is greater than the sum and baseline gap threshold, iterative optimization prioritizing the maximum gap is adopted. The strategy, the iterative optimization strategy, involves identifying the maximum gap between adjacent elements in the ordered and baseline sequences each time, adding a formation center at the midpoint of the maximum gap, adding the new center to the candidate center sweep sequence, recalculating and sorting the baseline set, updating the ordered and baseline sequences, the total span of the baselines, and the maximum gap; repeating the iterative optimization strategy until the total span of the ordered and baseline sequences is not less than the baseline span threshold, and the gaps between all adjacent baselines are not greater than the baseline gap threshold, at which point the current candidate center sweep sequence is output as the optimization result.

[0032] In practice, an iterative logic prioritizing maximum gaps is adopted. Each time, the most prominent sampling gap is located, and a new formation center is added at its midpoint. The maximum gap is compressed by successively filling the gap until all constraints are simultaneously satisfied. This method eliminates the need for complex global optimization solutions, has a clear iterative direction, and converges quickly, achieving the optimization goal with minimal computational cost. Ultimately, it satisfies all sampling constraints with the fewest possible formation center positions, effectively controlling the amount of observation work and flight costs while ensuring imaging quality. More specifically, the optimization objective of the center sweep sequence is... ,in , C The set of central locations Indicates the location from the center C The set of sum and baseline positions, generated together with the relative position of the receiver and sorted by size. For the reason The baseline set formed by the relative offset of the receiver and sorted by size. This indicates the interval between two adjacent sampling points. in The total number of baseline sampling points; optimization is achieved while maintaining the relative offset of the receiver, ensuring the center position is within the preset flight range, and ensuring the baseline span is not less than [amount missing]. It is performed under constraints. When uniform center sweep or random center sweep cannot satisfy... When the set of center positions C is used as the optimization variable, and the relative position of the receiver remains unchanged, the center position is within the preset flight range and the distance from the baseline is not less than 1 / 3. Under the constraint of maximum gap, the center position is iteratively adjusted to generate a result that satisfies the condition. The optimized center sweep sequence ensures that the union of multiple uniform receiving channel trajectories on the baseline axis satisfies the sampling conditions of finite scenarios.

[0033] S04, according to the center sweep sequence control or simulation formation, radar observations are performed sequentially at each center position to acquire multi-center, multi-channel range compressed echo data. At the same time, a forward operator is established based on the path length between the transmitter, receiver and imaging pixel at each observation time, so as to determine the SAR image based on the forward operator and the range compressed echo data.

[0034] Specifically, the UAV formation is controlled or simulated to move sequentially to the center position of each formation according to the center sweep sequence. At each formation center position, the instantaneous formation shape and relative offset to the receiver remain unchanged under the preset conditions throughout the process. The transmitter is controlled to emit radar detection signals, while all receivers are simultaneously controlled to synchronously receive the raw radar echoes reflected from the scene. Range compression preprocessing is uniformly performed on the raw radar echoes collected by each receiver, and the processing results from all center positions and all receiving channels are integrated to obtain multi-center, multi-channel range-compressed echo data. In practical implementation, by strictly moving the formation according to the sequence and maintaining the instantaneous relative position of the formation throughout the process, the actual sampling distribution is ensured to perfectly match the theoretical optimization results. Simultaneously, the range compression preprocessing of the echoes is completed, effectively transforming the optimized scheme into measured data. This avoids sampling errors introduced by formation deviations, ensuring that the effect of sampling optimization is accurately reflected in the echo data. It should be noted that before SAR imaging, the ground target area is divided into many small grids. Each small grid represents a target location that may generate radar echoes. These small grids are reconstructed to form pixels in the SAR image. The imaging pixels are spatial sampling units of the area to be imaged after discretization, i.e., target scattering units, which are the basic spatial units in the SAR image reconstruction process.

[0035] Furthermore, the real-time spatial positions of the transmitter and each receiver are collected and recorded at each observation moment, and the preset global imaging pixel position information is retrieved. The complete bistatic propagation path length of the radar signal from the transmitter to the imaging pixel and then from the imaging pixel to the receiver is calculated one by one at a single observation moment and under a single receiving channel. Combining the bistatic propagation path length, radar signal propagation characteristics, and imaging geometry, the correspondence between echo data and target reflectivity is mapped to construct the forward operator corresponding to the range-compressed echo. Here, the bistatic propagation path length is the sum of the distance from the transmitter to the target and the distance from the target to the receiver, i.e., the total geometric path of the signal round trip; the target at this point is the imaging pixel.

[0036] In practical implementation, the precise bistatic echo forward operator is based on path length. Establish, among which, Let be the bistatic propagation path length between the q-th transmitting node and the m-th receiving node. For the position of the imaging pixel, For the q-th drone to launch in slow time Spatial location at a given time, and For the m-th receiving drone in the q-th formation, in slow time Spatial location at a given time "Slow time" represents the azimuth sampling time corresponding to different observation moments during the movement of the UAV platform. Range compression echo satisfies... .

[0037] in, N The total contribution to the echoes from all scattering points; The complex scattering coefficient at the nth scattering point is used to describe the reflection intensity and phase characteristics of the target and is a key parameter reflecting the differences in electromagnetic scattering of the target. The impulse response envelope after range compression has its main lobe position determining the focusing position of the scattering point in the range direction, while the side lobes reflect the range compression residual spread. B The bandwidth of the transmitted signal directly affects the range resolution; the larger the bandwidth, the stronger the range resolution. This represents the matching relationship between fast time and propagation delay, used to determine the delay position of the scattering point echo in the range direction; It represents a fast time variable, which can also be understood as a range-to-time or echo propagation delay coordinate, used to describe the arrival position of the radar transmitted pulse echo in the range direction; At the speed of light, This could be noise or modeling error. The phase modulation term describes the phase changes caused by radar platform motion and target range history variations, and is fundamental to SAR azimuth synthetic aperture imaging and coherent accumulation; symbol In The imaginary unit satisfies , Appearing in the exponent, it represents the complex phase modulation of the radar echo; the negative sign reflects the commonly used echo phase delay convention, that is, changes in target distance will cause changes in the echo phase along the propagation path; where The radar wavelength determines the phase sensitivity to range changes; the shorter the wavelength, the more sensitive the phase is to range changes. Therefore, these annotations characterize the formation mechanism of SAR echo signals from aspects such as scattering intensity, range compression, propagation delay, and phase history.

[0038] Additionally, using the range-compressed echo data as the observation vector and the target reflectivity to be reconstructed as the unknown vector, a linear observation model is established, making the observation vector equal to the forward operator multiplied by the target reflectivity vector plus noise. The target reflectivity vector is then recovered from the observation vector using a preset method. The recovered target reflectivity vector is rearranged according to the imaging pixel grid to obtain the final SAR image. In specific implementations, the preset method can employ a non-negative least squares model, a maximum a posteriori estimation model with total variation regularization, or an unconstrained least squares model for target reflectivity recovery. Specifically, when the target reflectivity is defined as non-negative real-valued reflectivity, the reconstruction result is obtained by solving the non-negative least squares model shown in the following equation:

[0039] in, This is the target reflectivity estimation vector obtained through a non-negative least squares model. For accurate bistatic distance compression echo forward operator; The scattering coefficient vector of the scene to be estimated reflects the electromagnetic scattering intensity distribution of the target or scattering unit; This refers to the actual received range-compressed echo observation data. It represents the N-dimensional nonnegative real space, which is a set of N-dimensional real vectors whose components are all greater than or equal to zero. It is used to represent the nonnegative constraint of the reflectivity vector of the target to be reconstructed, where N represents the dimension of the reflectivity vector of the target to be reconstructed.

[0040] Alternatively, the reconstruction result can be obtained by solving the maximum a posteriori estimation model with total variation regularization, i.e., the TV-MAP model:

[0041] in, This is the target reflectivity estimation vector obtained through the maximum a posteriori estimation model with total variation regularization. To smooth the total variation regularization term, To smooth parameters, it can effectively suppress noise, sidelobe artifacts and unstable oscillations, while preserving the target edge and local structural features; This is the regularization weight parameter, used to adjust the balance between data fidelity terms and prior constraint terms.

[0042] Without applying nonnegativity constraints and total variation regularization, target reflectivity reconstruction can be expressed as an unconstrained least squares problem:

[0043] The conjugate gradient least squares method (CGLS) is then used to iteratively solve the aforementioned least squares problem. This is the target reflectivity estimation vector obtained through an unconstrained least squares model.

[0044] Finally, the recovered target reflectivity vector is rearranged according to the preset imaging pixel grid to form a target reflectivity image; when the recovered amount is a complex scattering coefficient, its amplitude or power is further calculated and its logarithmic dynamic range is compressed to obtain the final SAR image.

[0045] For the linear least squares subproblem in the reconstruction process, the conjugate gradient least squares algorithm, i.e., the CGLS algorithm, can be used for iterative solution. Finally, the reconstructed target reflectivity vector is rearranged according to the imaging pixel grid to obtain the final SAR image. By transforming the imaging problem into a mature optimization problem, various inversion algorithms can be flexibly adapted to different imaging requirements, playing a core role in recovering scene information from observation data. Ultimately, based on the optimized sampled echo data, a high-resolution synthetic aperture radar image without significant aliasing and grating lobes can be stably reconstructed, achieving high-quality overall target imaging.

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below in conjunction with specific scenarios. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Any scheme that makes equivalent substitutions or conventional improvements to the number of centers, the number of receivers, the receiver spacing, the carrier frequency, the scene width, the reconstruction algorithm, or the flight constraints under the spirit of this invention should fall within the protection scope of this invention.

[0047] In this scenario, the carrier frequency 5.7 GHz, wavelength The reference slant distance is 0.0526316m; the UAV's flight altitude is 300m, the incident angle is 65 degrees, and the reference slant distance is... The horizontal width of the scene to be imaged is 709.86m. The target's horizontal resolution is 25.6m. The value is 0.2m. The lateral offset of the receiver relative to the center is [-10, -5, 5, 10]m, corresponding to a uniform receiving spacing of 5m and a physical receiving aperture of 20m.

[0048] The required baseline span is calculated using the formula. =186.805m, maximum allowable gap between baseline and ground. =1.459m. A single-center 5m uniform formation only has a 20m baseline span and a maximum gap of 10m, which cannot meet the sampling requirements of 0.2m lateral resolution and 25.6m scene width.

[0049] Table 1

[0050] Comparison of sampling gap and reconstruction performance for different strategies, for example Figures 4 to 7 As shown in the figure, Table 1 is derived from the data in the figure. Table 1 shows that the single-center uniform subject scheme configures only one phase center for each subject and arranges multiple subjects uniformly at fixed, equal intervals. This scheme does not rely on the combination of multiple phase centers on a single subject, but rather forms spatial sampling through the regular distribution of subjects. Its advantages are simple structure, regular array configuration, and ease of engineering implementation; however, due to the strong periodicity of the center spacing, it may generate many duplicate baselines, limiting the diversity of effective baseline intervals and coverage uniformity. The traditional 7-center sweep scheme uses 7 phase centers from the original or traditional system and is usually used as a benchmark scheme to compare the performance improvement after adding phase centers. Although it meets the baseline span requirements, the maximum gap is 21.701m; the uniform 65-center scheme arranges 65 phase centers at fixed, equal intervals. The advantages of the first method are its regular structure and ease of implementation, but it may not achieve the most ideal baseline spacing distribution. Its maximum gap is 2.606m, still greater than the 1.459m threshold. The random 65-center scheme randomly sets 65 phase centers within an allowable range. It can be used to test the effect of irregular arrangements, but performance fluctuations may exist between different random results. Therefore, multiple random experiments and averaging are usually required. Its maximum gap is 4.454m, and even this cannot guarantee the worst-case gap. The interval-optimized 65-center scheme of this invention also includes 65 phase centers, but their positions are not uniformly or randomly determined; instead, they are configured based on the baseline spacing distribution index. Its goal is usually to improve the coverage and uniformity of the effective baseline spacing, reduce interval duplication, gaps, or clustering, thereby improving SAR imaging, reconstruction, or parameter estimation performance. After adopting the interval-optimized 65-center scheme of this invention, the baseline span is 186.805m, and the maximum gap is 1.435m, less than the 1.459m threshold, satisfying the finite scene sampling conditions.

[0051] After obtaining the center sweep sequence, the three-dimensional positions of the transmitter and receiver are established for each center location, and the precise bistatic path length is calculated based on each imaging pixel. The range-compressed echo is obtained by superimposing the reflectance of all pixels through the bistatic propagation phase and the range compression kernel. During reconstruction, the forward operator A and the adjoint operator A^H, consistent with the observation geometry, are used to solve the nonnegative least squares, TV-MAP, or CGLS subproblems.

[0052] Table 2

[0053] Figure 8To obtain accurate distance-compressed echo inversion maps for different reflectivity scenarios, the physical echo simulation results for the corresponding scenarios are shown in Table 2. As can be seen from Table 2, in the three sets of physical echo simulation experiments driven by reflectivity maps, the 65-center interval optimization scheme of this invention achieved NRMSE ranging from 0.001456 to 0.002354, PSNR ranging from 62.95 dB to 73.66 dB, and SSIM all above 0.99996 under TV-MAP reconstruction. This invention first establishes an imaging model for the bistatic SAR sweep imaging process of UAV swarms, determined by the formation spatial layout, observation geometry, and echo propagation path. Based on this, by analyzing the influence of different sampling positions on the bistatic echo signal propagation distance, phase change, and spatial spectrum coverage, a mapping relationship between sampling parameters and imaging quality indicators is established. Furthermore, the resolution constraint, baseline coverage constraint, and non-aliasing sampling constraint are transformed into optimizable numerical conditions, realizing the design optimization of the UAV sampling trajectory and formation parameters during the sweep imaging process. Therefore, the sampling design results can be directly applied to the precise bistatic echo imaging link, improving the quality of the reconstructed image.

[0054] To verify robustness in practical applications, perturbations can be introduced into the center position, receiver position, channel phase, and observation noise to construct an error closure experiment. The results of the precise echo error closure experiment are as follows: Figure 9 As shown, the robustness analysis results of the center sweep scheme under position, phase, and noise perturbations are as follows: Figure 10 As shown in the figure. Under the baseline conditions without introducing position error, phase error, and additional noise, this embodiment achieves an NRMSE of 0.001107; with a position error of 0.02m, a phase error of 10 degrees, and a signal-to-noise ratio of 30dB, the NRMSE is 0.0433; and with a position error of 0.10m, a phase error of 30 degrees, and a signal-to-noise ratio of 20dB, the NRMSE is 0.3072. These results demonstrate that the present invention can quantitatively assess the impact of position, phase, and noise mismatch on imaging quality and provide a basis for the design of UAV formation positioning and synchronization accuracy.

[0055] In summary, this invention acquires radar operating parameters, imaging task parameters, and receiver configuration parameters to construct a set of baselines consisting of the sum of the transverse positions of the transmitter and receiver. Simultaneously, it determines the baseline span threshold to ensure resolution and the baseline gap threshold to avoid aliasing, thus transforming the two core requirements of imaging quality into numerical indicators that directly constrain the sampling geometry. Furthermore, using these two thresholds as constraints, the center sweep sequence composed of the formation center positions as optimization variables, and minimizing the maximum gap between adjacent baselines after sorting as the optimization objective, the optimal center sweep sequence is obtained. Since this optimization process directly controls the span and maximum gap of the baselines sampled in the spatial frequency domain, it can simultaneously ensure that the span of the baseline set meets resolution requirements and the maximum gap meets the non-aliasing sampling requirements, while maintaining the engineering constraint of a large-spaced uniform structure in the instantaneous formation. This fundamentally closes the deterministic sampling gap caused by large spacing, avoiding the inherent problems of traditional methods such as the difficulty in achieving dense arrays, the inability to guarantee the maximum gap meets the non-aliasing requirement simply by increasing the number of samples, and the difficulty of post-processing algorithms to compensate for physical sampling defects. Finally, based on this sequence, the formation is controlled to perform multi-center radar observations, and a forward operator is established using precise bistatic path lengths for inversion, creating a closed loop between sampling design and imaging reconstruction, further ensuring the resolution and fidelity of the final SAR image. Thus, without changing the physical configuration of the UAV formation or adding additional hardware overhead, both imaging resolution and scene aliasing requirements can be simultaneously achieved through optimization of only one center sweep sequence. Therefore, this invention solves the problem in existing technologies where large-spacing uniform UAV single-shot multi-receiver synthetic aperture radar imaging schemes cannot simultaneously achieve both imaging resolution and aliasing-free imaging requirements, resulting in limited imaging quality.

[0056] Example 2 Please see Figure 2 The diagram shows a structural block diagram of the UAV swarm SAR span gap constrained center sweep imaging system proposed in the second embodiment of the present invention. The UAV swarm SAR span gap constrained center sweep imaging system 200 includes: a parameter acquisition module 21, a data construction module 22, a sweep sequence determination module 23, and an image determination module 24, wherein: The parameter acquisition module 21 is used to acquire radar operating parameters, imaging mission parameters and receiver configuration parameters of UAV formation. The receiver configuration parameters include the fixed lateral offset of each receiver in the instantaneous UAV formation relative to the formation center. The data construction module 22 is used to construct a set of baselines consisting of the sum of the transverse positions of the transmitter and each receiver, based on the receiver configuration parameters under preset conditions and with the formation center position as the variable, and to determine the baseline span threshold and baseline gap threshold respectively based on the radar operating parameters and the imaging mission parameters. The sweep sequence determination module 23 is used to determine the center sweep sequence by taking the baseline span threshold and the baseline gap threshold as constraints, the center sweep sequence composed of the formation center position as the optimization variable, and minimizing the maximum gap between adjacent baselines after the baseline set is sorted as the optimization objective. The image determination module 24 is used to control or simulate the formation to perform radar observations at each center position in sequence according to the center sweep sequence, acquire multi-center multi-channel range compressed echo data, and establish a forward operator based on the path length between the transmitter, receiver and imaging pixel at each observation time, so as to determine the SAR image based on the forward operator and the range compressed echo data.

[0057] Example 3 In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device in the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the above-described UAV swarm SAR span gap constrained center sweep imaging method.

[0058] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0059] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0060] It should be pointed out that, Figure 3The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0061] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described UAV swarm SAR span gap constrained center sweep imaging method.

[0062] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0063] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0064] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0065] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for constrained center-sweep imaging of unmanned aerial vehicle (UAV) swarm SAR, characterized in that, The method includes: The radar operating parameters, imaging mission parameters, and receiver configuration parameters of the UAV formation are obtained. The receiver configuration parameters include the fixed lateral offset of each receiver in the instantaneous UAV formation relative to the formation center. Under preset conditions, based on the receiver configuration parameters, and with the formation center position as a variable, a set of baselines is constructed, consisting of the sum of the transverse positions of the transmitters and the transverse positions of each receiver. Based on the radar operating parameters and the imaging mission parameters, the baseline span threshold and the baseline gap threshold are determined respectively. The center sweep sequence is determined by taking the baseline span threshold and the baseline gap threshold as constraints, the center sweep sequence composed of the formation center positions as optimization variables, and minimizing the maximum gap between adjacent baselines after the baseline set is sorted as the optimization objective. According to the center sweep sequence control or simulation formation, radar observations are performed sequentially at each center position to acquire multi-center, multi-channel range compressed echo data. At the same time, a forward operator is established based on the path length between the transmitter, receiver and imaging pixel at each observation time, so as to determine the SAR image based on the forward operator and the range compressed echo data.

2. The UAV swarm SAR span-gap constrained center sweep imaging method according to claim 1, characterized in that, Under preset conditions, based on the receiver configuration parameters, the step of constructing a set of baselines consisting of the sum of the transverse positions of the transmitters and the transverse positions of each receiver, with the formation center position as the variable, includes: Position the transmitter at the center of the formation, ensuring that its lateral position is equal to the current center position of the formation. Each receiver is arranged at equal intervals with the formation center as the center of symmetry. According to the receiver configuration parameters, each receiver has a fixed lateral offset, so that the lateral position of each receiver is equal to the sum of the current formation center position and the corresponding receiver's fixed lateral offset. Using any possible formation center position as a variable, calculate the sum of the lateral positions of the transmitter and each receiver at each formation center position to obtain the corresponding baseline. The sum and baselines corresponding to the center positions of all formations and all receiver combinations are summarized to form a sum and baseline set.

3. The UAV swarm SAR span-gap constrained center sweep imaging method according to claim 2, characterized in that, The steps for determining the baseline span threshold and the baseline gap threshold based on the radar operating parameters and the imaging mission parameters include: The radar wavelength and reference slant range are obtained from the radar operating parameters, and the target lateral resolution and the lateral width of the scene to be imaged are obtained from the imaging mission parameters. The baseline span threshold is determined based on the radar wavelength, reference slant range, and target lateral resolution, wherein the baseline span threshold is directly proportional to the product of the radar wavelength and the reference slant range and inversely proportional to the target lateral resolution. The baseline gap threshold is determined based on the radar wavelength, reference slant range, and lateral width of the scene to be imaged, wherein the baseline gap threshold is directly proportional to the product of the radar wavelength and the reference slant range, and inversely proportional to the lateral width of the scene to be imaged.

4. The UAV swarm SAR span-gap constrained center sweep imaging method according to claim 1, characterized in that, The steps for determining the center sweep sequence, constrained by the baseline span threshold and the baseline gap threshold, with the center sweep sequence composed of the formation center positions as the optimization variable and minimizing the maximum gap between adjacent baselines after sorting the baseline set as the optimization objective, include: Construct a candidate center sweep sequence as an optimization variable, wherein the candidate center sweep sequence contains at least one formation center position; Sort all the sums and baselines in the set corresponding to the current candidate center sweep sequence in ascending order of their numerical values ​​to obtain an ordered sum and baseline sequence, and calculate the maximum gap between adjacent sums and baselines after sorting. If the sum-baseline span of the ordered and baseline sequences is less than the sum-baseline span threshold, then the coverage of the candidate center sweep sequence is expanded until the sum-baseline span meets the requirement; If the maximum gap is greater than the baseline gap threshold, then the maximum gap priority iterative optimization strategy is adopted. The iterative optimization strategy is to identify the maximum gap between adjacent elements in the ordered and baseline sequences each time, add a formation center at the midpoint of the maximum gap, add the new center to the candidate center sweep sequence, recalculate and sort the baseline set, and update the ordered and baseline sequences, the total span of the baselines and the maximum gap. Repeat the iterative optimization strategy, where the total span of the ordered and baseline sequences is not less than the baseline span threshold, and the gap between all adjacent and baseline sequences is not greater than the baseline gap threshold. Then, output the current candidate center sweep sequence as the optimization result.

5. The UAV swarm SAR span-gap constrained center sweep imaging method according to claim 1, characterized in that, The steps for acquiring multi-center, multi-channel range-compressed echo data by controlling or simulating the formation to perform radar observations sequentially at each center position according to the center sweep sequence include: According to the center sweep sequence, the drone formation is controlled or simulated to move to the center position of each formation corresponding to the sequence. At the center of each formation, the instantaneous formation shape under the preset conditions and the relative offset of the receiver remain unchanged throughout the entire process. The transmitter is controlled to transmit radar detection signals, and all receivers are controlled to synchronously receive the original radar echo reflected from the scene. The raw radar echoes collected by each receiver are uniformly preprocessed with range compression. The processing results of all center locations and all receiving channels are integrated to obtain multi-center, multi-channel range compressed echo data.

6. The UAV swarm SAR span-gap constrained center sweep imaging method according to claim 5, characterized in that, The steps for establishing the forward operator based on the path length between the transmitter, receiver, and imaging pixels at each observation time include: Collect and record the real-time spatial positions of the transmitter and each receiver at each observation moment, and retrieve the preset global imaging pixel position information; Calculate the length of the complete bistatic propagation path of the radar signal from the transmitter to the imaging pixel and then from the imaging pixel to the receiver under a single observation time and a single receiving channel. By combining the bistatic propagation path length, radar signal propagation characteristics, and imaging geometry, the correspondence between echo data and target reflectivity is mapped to construct the forward operator corresponding to range-compressed echoes.

7. The UAV swarm SAR span-gap constrained center sweep imaging method according to claim 6, characterized in that, The steps for determining a SAR image based on the forward operator and the range compressed echo data include: Using the range-compressed echo data as the observation vector and the target reflectivity to be reconstructed as the unknown vector, a linear observation model is established, making the observation vector equal to the forward operator multiplied by the target reflectivity vector plus noise. The target reflectivity vector is recovered from the observation vector using a preset method; The recovered target reflectivity vector is rearranged according to the imaging pixel grid to obtain the final SAR image.

8. A UAV swarm SAR span-gap constrained center-sweep imaging system, characterized in that, The system for implementing the UAV swarm SAR span gap constrained center sweep imaging method according to any one of claims 1 to 7, the system comprising: The parameter acquisition module is used to acquire radar operating parameters, imaging mission parameters, and receiver configuration parameters of the UAV formation. The receiver configuration parameters include the fixed lateral offset of each receiver in the instantaneous UAV formation relative to the formation center. The data construction module is used to construct a set of baselines consisting of the sum of the transverse positions of the transmitter and each receiver, based on the receiver configuration parameters under preset conditions and with the formation center position as the variable. The module also determines the baseline span threshold and baseline gap threshold based on the radar operating parameters and the imaging mission parameters. The sweep sequence determination module is used to determine the center sweep sequence by taking the baseline span threshold and the baseline gap threshold as constraints, the center sweep sequence composed of the formation center position as the optimization variable, and minimizing the maximum gap between adjacent baselines after the baseline set is sorted as the optimization objective. The image determination module is used to control or simulate the formation to perform radar observations at each center position in sequence according to the center sweep sequence, acquire multi-center multi-channel range compressed echo data, and establish a forward operator based on the path length between the transmitter, receiver and imaging pixel at each observation time, so as to determine the SAR image based on the forward operator and the range compressed echo data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the UAV swarm SAR span gap constrained center sweep imaging method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the UAV swarm SAR span gap constrained center sweep imaging method as described in any one of claims 1 to 7.

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