Synthetic aperture radar imaging system and unmanned aerial vehicle array sar image detection method

Abstract

The present application belongs to the technical field of radar imaging and unmanned system integration, and particularly relates to a synthetic aperture radar imaging system and a UAV array SAR image detection method. The UAV array SAR image detection method comprises: S1, issuing an array arrangement form, trajectory design parameters and an imaging mode to a master control UAV; S2, the master control UAV calculates the position, spacing, flight path and beam direction parameters of each UAV; S3, baseband excitation parameters, synchronization signals and attitude control instructions are issued to the UAV detection array; S4, the UAV detection array synchronously transmits radar beams and receives target echoes, and the echo data is returned to a signal processing node; S5, the signal processing node performs coherent processing and SAR image signal processing, and returns the target image to the ground system. The present application realizes coherent synthesis of multiple front-end UAV received echoes and high-resolution image generation, and the image result can be used for tactical identification or geographic situation awareness display.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar imaging and unmanned system integration, and particularly relates to a synthetic aperture radar imaging system and a UAV array SAR image detection method. BACKGROUND

[0002] With the development of unmanned aerial vehicle technology, a distributed array is formed by using multiple unmanned aerial vehicles to perform radar detection and imaging tasks, which has become an important development direction of air-ground cooperative detection and battlefield perception. Traditional synthetic aperture radar systems (SAR) are mostly dependent on single-platform large-size phased array antennas, which have poor deployment capability in a multi-target environment, high rigidity of trajectory, and limited radar resource scheduling, and are difficult to meet the requirements of rapid deployment and fine imaging in a high dynamic and complex environment.

[0003] In recent years, researchers have attempted to form a virtual antenna array with multiple small unmanned aerial vehicles, and to realize radar signal transmission, echo reception and image reconstruction through cooperative flight. However, the existing technology still has many bottlenecks in the following aspects:

[0004] 1) insufficient formation control accuracy and poor array structure stability: there is a lack of deep integration of SAR imaging geometry requirements and mobile flight control, and it is difficult to ensure the continuity of synthetic aperture and spatial resolution;

[0005] 2) decoupling of beam pointing and path planning, and inability to adapt in real time: most systems use fixed flight paths or manually preset parameters, which are difficult to adapt to dynamic tasks and obstacle environments;

[0006] 3) lack of algorithm adaptive mechanism in radar signal processing: in the face of different target scenes, platform jitter or interference, it is difficult to intelligently switch imaging algorithms, resulting in a decline in image quality;

[0007] 4) weak damage robustness and poor system formation recovery capability: after part of the nodes fail, the system imaging quality is severely degraded, and there is a lack of damage reconstruction mechanism.

[0008] Based on this, the application provides a synthetic aperture radar imaging system and a UAV array SAR image detection method to obtain the best synthetic aperture trajectory and image resolution. SUMMARY

[0009] To solve at least one of the above technical problems in the prior art, the application provides a synthetic aperture radar imaging system and a UAV array SAR image detection method. The synthetic aperture radar imaging system is equivalent to a range-increasing sensor, which improves the flexibility and reconnaissance radius of single combat, enhances the combat capability of entering high-risk environments, and can provide a multifunctional sensing system solution for single combat, including long-range detection, fine identification, and multi-dimensional information integration. It meets the needs of single-portable situation awareness and tactical combat.

[0010] To achieve the above object, the technical scheme of the present application is as follows:

[0011] In the first aspect, the present application provides a UAV array SAR image detection method, comprising:

[0012] S1. According to the imaging task planning instruction, the array arrangement form, the trajectory design parameter and the imaging mode are issued to the master control UAV;

[0013] S2. The master control UAV calculates the position, spacing, flight path and beam direction parameter of each UAV in the UAV detection array;

[0014] S3. The baseband excitation parameter, the synchronization signal and the attitude control instruction are issued to the UAV detection array;

[0015] S4. The UAV detection array synchronously transmits the radar beam and receives the target echo, and the echo data is returned to the signal processing node after local preliminary processing;

[0016] S5. The signal processing node coherently processes the returned data, performs SAR image signal processing, and returns the target image to the ground system;

[0017] S6. The ground system monitors the state of each UAV in real time, and if there is an abnormal UAV, the reconstruction planning is executed.

[0018] Further, the flight path in step S2 is constructed by a quadratic Bezier curve:

[0019]

[0020] wherein, , , are the starting point coordinates, the control point coordinates and the ending point coordinates respectively, t represents time, represents the position coordinates of the UAV on the flight path at time t;

[0021] Multiple UAVs are arranged in a horizontal direction with a spacing , each UAV flies along its own Bezier trajectory to form a dynamic virtual array. Wherein, represents the actual spacing of the th UAV in the horizontal arrangement, is the spacing fine tuning amount for the th UAV, the spacing fine tuning amount is error compensation according to the real-time position of the UAV, and the real-time position of the UAV is obtained by a positioning system; is the reference spacing design value, which is 1 / 2 of the radar operating wavelength.

[0022] Further, the heading angle and speed of the UAV are:

[0023] ;

[0024] ;

[0025] represents the flight angle of the i-th UAV, i.e. the heading angle; represents the flight speed of the i-th UAV; , is the coordinate value of the i-th UAV at time t. Further, the flight path of the UAV is determined by the control points

[0026] , then a multi-objective optimization function is constructed:

[0027]

[0028] wherein, is the aperture consistency (variance of the directional position); x is the imaging coverage rate; η is the obstacle avoidance re-planning rate; is the real-time azimuth resolution, represents the resolution threshold, i.e. the minimum azimuth resolution required by the system; , , , , is a coefficient;

[0029] Each sub-item in the multi-objective optimization function has a clear functional dependence on the path trajectory function , and is uniquely determined by the control point coordinates , therefore, the optimization function has the following mathematical relationship chain with the control points:

[0030]

[0031] If there is an obstacle area in the motion observation area range, the multi-objective optimization function is iterated, so that the multi-objective optimization function reaches the minimum or meets the requirements, and then the optimal position of the control points is searched, so that the trajectory completely avoids the obstacle area (tends to 0), while meeting the SAR imaging performance requirements.

[0032] Further, the SAR image signal processing algorithm used in step S5 is selected in the following manner:

[0033] The trajectory error ​​The deviation between the actual UAV flight path and the preset imaging trajectory is measured:

[0034]

[0035] wherein, N is the number of UAVs participating in imaging; is the number of trajectory sampling time points; is the actual position of the i-th UAV at the j-th time point; is the target trajectory position of the i-th UAV at the j-th time point; is the target trajectory position of the i-th UAV at the j-th time point; If the deviation is large (covering an area greater than 300 or more than 10 key pixel units), the RD algorithm is used, wherein

[0036] represents the range of the imaging area; If the deviation is small (covering an area less than 300 or less than 10 key pixel units), the BP algorithm is used; wherein, represents the estimated error threshold, and the empirical estimated error threshold is set to .

[0037] If there is auxiliary registration information and the scene is regular, the PFA algorithm is used. Auxiliary information mainly refers to external auxiliary input required by non-autonomous registration algorithms, including but not limited to prior geographic coordinate information, high-precision trajectory sample data obtained in historical imaging tasks, externally provided DEM (Digital Elevation Map) data or reference images, etc.

[0038] In the second aspect, the application provides a synthetic aperture radar imaging system, comprising:

[0039] a plurality of UAV detection arrays, each UAV detection array comprising a UAV, an antenna, a transceiver component, and a space-time synchronization module; the UAV detection array is used to transmit radar signals and receive echo signals, and the echo signals are initially processed and then transmitted back to the comprehensive information processing platform;

[0040] a comprehensive information processing platform, used to control the time-frequency synchronization of all UAV detection arrays, and based on the initially processed echo signals, to generate target detection information; and based on task planning configuration, to control the flight parameters of the UAV detection array;

[0041] a ground comprehensive display control system, used for task planning configuration, and sending the task planning configuration to the comprehensive information processing platform, and based on the target detection information, to generate and display a SAR image and output a target recognition result.

[0042]

[0043] ​​​​​

[0044] Compared with the prior art, the present application has the following beneficial effects:

[0045] (1) Dynamically reconfigured virtual array structure and standardized interface design: A flexible array is formed by multiple front-end unmanned vehicles carrying transceiver components, and the number and structure can be reconfigured according to task requirements, so that the system has the advantages of flexible task scheduling and strong anti-destroying recovery capability.

[0046] (2) Obstacle avoidance mechanism combining Bezier curve trajectory planning and particle swarm optimization: The flight control software of the system constructs the trajectory of each unmanned vehicle based on a Bezier curve, and intelligently optimizes the control points by combining a PSO algorithm, so as to automatically generate an efficient synthetic aperture path that can avoid obstacles, with imaging coverage, azimuth resolution, array consistency and obstacle avoidance rate as the joint target.

[0047] (3) Heterogeneous signal processing module combining multiple algorithms: The system supports automatic selection of RD, BP or PFA SAR imaging algorithms in different imaging scenarios, thereby improving the robustness and image focusing quality of the system. The algorithm selection is dynamically adjusted according to the trajectory error, target features and system state, and has intelligent and adaptive characteristics.

[0048] (4) Task closed-loop control and system situation adaptive capability: The system has complete closed-loop control capability from task configuration, state perception, target identification to image feedback, and adapts to the requirements of high mobility deployment in complex environments and tactical reconfiguration, by combining the high-speed wireless communication link between the ground display control system, the main control unmanned vehicle node and the front array platform. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a system block diagram of the present application.

[0050] Figure 2 is a Bezier detection trajectory planning diagram of the unmanned vehicle of the present application in the case of no obstacle.

[0051] Figure 3 is a Bezier detection trajectory planning diagram of the unmanned vehicle of the present application based on PSO optimization in the case of obstacle.

[0052] Figure 4 is a diagram of imaging coverage and obstacle avoidance rate changing with time in the case of obstacle.

[0053] Figure 5 is a diagram of SAR imaging azimuth resolution and range resolution changing with time in the case of obstacle. DETAILED DESCRIPTION

[0054] The technical solutions of the present application will be described clearly below with the accompanying drawings. Obviously, the described embodiments are not all the embodiments of the present application, and all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0055] It should be noted that, unless otherwise specified, the relative arrangement of components and steps, numerical expressions set forth in these embodiments should not be understood as limiting the scope of the present application.

[0056] The following description of the exemplary embodiments is merely illustrative in nature and is in no way intended to limit the application or its application or use. Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail herein, but where applicable, such techniques, methods, and devices should be considered as part of the present specification.

[0057] Embodiment one

[0058] The present embodiment provides a UAV array SAR image detection method, comprising:

[0059] S1. According to the imaging task planning instruction, the array arrangement form, the trajectory design parameter and the imaging mode are issued to the main control UAV.

[0060] The ground comprehensive display control system issues the array arrangement form, the trajectory design parameter and the imaging mode to the main control UAV through the display control software according to the imaging task planning instruction.

[0061] S2. The main control UAV calculates the position, spacing, flight path and beam direction parameter of each UAV in the UAV detection array.

[0062] The flight control software of the ground comprehensive display control system generates initial trajectory control points according to the terrain features and imaging parameters of the target area in the initial stage of the task. The flight path (flight trajectory) of each UAV is constructed by a quadratic Bezier curve:

[0063]

[0064] wherein, , , are the starting point coordinates, the control point coordinates and the ending point coordinates respectively, t represents time, represents the position coordinates of the UAV on the flight path at time t;

[0065] Multiple UAVs are arranged horizontally at a spacing , and each UAV flies along its own Bezier trajectory to form a dynamic virtual array. This represents the actual spacing of the i-th UAV in the horizontal arrangement. This refers to the spacing fine-tuning amount for the i-th drone. Error compensation is performed based on the drone's real-time location, which is obtained through a positioning system. The reference spacing design value is taken as 1 / 2 of the radar operating wavelength.

[0066] The drone's heading angle and speed are:

[0067] ;

[0068] ;

[0069] Indicates the first The flight angle of the drone, i.e., the heading angle; Indicates the first The flight speed of the drone; , For the first The coordinates of a UAV at time t;

[0070] If there are obstacle zones within the motion observation area, a Bezier control point optimization strategy based on optimization (PSO) is adopted.

[0071] Assume the flight path of the UAV is based on the control point Decision, with To optimize the variables, a multi-objective optimization function is constructed:

[0072]

[0073] in, For consistent aperture ( x (Directional position variance) η For imaging coverage; To avoid obstacles, the planning ratio should be adjusted. For real-time azimuth resolution, This represents the resolution threshold, i.e., the minimum azimuth resolution required by the system. , , , For coefficients;

[0074] Each sub-term in the multi-objective optimization function is related to the path trajectory function. There is a clear functional dependency, and And from the coordinates of the control points The only decision, therefore, is the optimization function. The following mathematical relationship exists between the control points and the control points:

[0075]

[0076] If there are obstacle zones within the motion observation area, then iterate the multi-objective optimization function to make the multi-objective optimization function... To achieve the minimum or meet the requirements, the particle swarm optimization algorithm, under the constraints of the number of iterations and velocity, searches for the optimal position of the control point, ensuring that the trajectory completely avoids the obstacle area. (Approaching 0), while meeting SAR imaging performance requirements.

[0077] S3. Send the baseband excitation parameters, synchronization signal, and attitude control commands to the UAV detection array;

[0078] S4. The UAV detection array synchronously transmits radar beams and receives target echoes. The echo data is processed locally and then transmitted back to the signal processing node.

[0079] S5. The signal processing node performs coherent processing on the returned data, performs SAR image signal processing, and returns the target image to the ground system.

[0080] Multi-algorithm fusion SAR image reconstruction mechanism UAV heterogeneous signal processing software through imaging area range Trajectory error The imaging algorithm selection is based on a comprehensive assessment. Trajectory error is a key factor. Used to measure the deviation between the actual drone flight path and the preset imaging trajectory:

[0081]

[0082] in, N The number of drones involved in imaging; This represents the number of time points used for trajectory sampling. For the first The drone in The actual location at that moment; For the first The drone in The position of the target trajectory at any given moment;

[0083] like and Larger (coverage area greater than 300) If there are more than 10 key pixel units, the RD algorithm is used, where A t Indicates the range of the imaging area;

[0084] like Then the BP algorithm is used; where, represents the estimated error threshold, the empirical estimated error threshold is set as .

[0085] If there is auxiliary registration information and the scene rule, the PFA algorithm is used.

[0086] The auxiliary information mainly refers to the external auxiliary input required by the non-autonomous registration algorithm, including but not limited to prior geographic coordinate information, high-precision trajectory sample data obtained in historical imaging tasks, externally provided DEM (Digital Elevation Map) data or reference images, etc.

[0087] Figure 2 As shown in the figure, the five front unmanned aerial vehicles are arranged in the form of Bezier trajectory planning in an obstacle-free environment, and the lateral symmetry distribution forms a regular synthetic aperture path.

[0088] Figure 3 It is shown that under the condition of containing obstacles, the system generates multiple unmanned aerial vehicle Bezier trajectories based on PSO optimization. For Figure 2 It can be seen that the central control point height is raised to form obvious obstacle avoidance effect, and the overall shape of the trajectory is raised above the obstacle area, ensuring that all paths are continuous and do not cross the obstacles in the SAR imaging window.

[0089] Figure 4 and Figure 5 respectively show the dynamic change curves of the imaging coverage η , obstacle avoidance re-planning ratio and real-time azimuth resolution , range resolution with time. Figure 4 It can be observed in the middle section of the SAR imaging window (t interval is the imaging window) that the peak value is reached and stabilized at a high level, the obstacle avoidance re-planning ratio always remains 0, indicating that the optimized trajectory realizes complete avoidance.

[0090] Figure 5 It is shown that the range resolution remains constant, while the azimuth resolution always remains below the system set limit of 5 meters, meeting the high-resolution SAR imaging index.

[0091] S6, the ground system monitors the state of each unmanned aerial vehicle in real time, and if there is an abnormal unmanned aerial vehicle, reconfiguration planning is performed.

[0092] Embodiment Two

[0093] The embodiment provides a synthetic aperture radar imaging system, as shown in the figure, which comprises: Figure 1

[0094] ​A plurality of unmanned aerial vehicle detection arrays, each unmanned aerial vehicle detection array comprising an unmanned aerial vehicle, an antenna, a transceiver assembly and a space-time synchronization module; the unmanned aerial vehicle detection arrays constitute a flexible networking virtual phased array, and the unmanned aerial vehicle detection arrays radiate radar signals through distributed optimization arraying, receive echo signals, and transmit the echo signals to a comprehensive information processing platform after initial processing of the echo signals.

[0095] The initial processing of the echo signals is down-conversion to generate baseband data.

[0096] The comprehensive information processing platform controls the time-frequency synchronization of all unmanned aerial vehicle detection arrays according to position information, and performs cross-platform coherent signal processing based on the initial processed echo signals to generate target detection information; the comprehensive information processing platform comprises a main control module, which is a centralized or distributed signal processing node, i.e., a main control unmanned aerial vehicle, which is responsible for coordination and scheduling between the front-end unmanned aerial vehicles, task instruction issuing, data fusion and coherent signal processing, such as controlling the flight parameters of the unmanned aerial vehicle detection arrays.

[0097] The ground comprehensive display control system is used for task planning and configuration, beam control instruction generation, sending of the task planning and configuration to the comprehensive information processing platform, generation and display of SAR images based on the target detection information, and output of target recognition results.

[0098] The control signals, baseband signals, state signals and time-frequency synchronization signals between the plurality of unmanned aerial vehicle detection arrays, the comprehensive information processing platform and the ground comprehensive display control system are transmitted at high speed in a wireless manner.

[0099] The ground comprehensive display control system comprises display control software for task planning and configuration, working mode control, image fusion display and comprehensive display of monitoring states, beam control software for phase shift, weighting and power amplification control of the TR assembly to achieve adaptive adjustment of the beam direction, and heterogeneous signal processing software for the unmanned aerial vehicles, which is mainly used for SAR image related signal processing of the baseband data obtained by the front-end unmanned aerial vehicle detection arrays to obtain SAR image information.

[0100] The ground integrated display control system is based on unmanned aerial vehicle heterogeneous signal processing software, has the ability to fuse multiple algorithms, and supports automatic selection of appropriate SAR imaging algorithms according to imaging scene conditions: when the platform trajectory is stable and the target area is wide, the RD algorithm is preferred to obtain higher operation efficiency; when the target is close, the attitude changes greatly or the path is nonlinear, the BP algorithm is automatically selected to obtain stronger anti-trajectory deviation capability; when the imaging area has a regular structure or geographical auxiliary registration requirements, the PFA (Polar Format Algorithm) is preferred; the system can automatically switch algorithms based on environmental complexity, path error evaluation and imaging real-time indicators to improve imaging quality and robustness.

[0101] The integrated information processing platform further includes a digital beam forming module, a timing module, and a frequency source module.

[0102] The main control unmanned aerial vehicle includes unmanned aerial vehicle main control software, which arranges the structure and spacing distribution of the unmanned aerial vehicle array according to the planning and configuration of the task, and completes the node control information issuance.

[0103] In addition, the main control unmanned aerial vehicle further includes time-frequency synchronization software, which mainly synchronizes the time and frequency of all unmanned aerial vehicles using wireless networks and synchronization protocols. It also includes unmanned aerial vehicle flight control software, which is mainly used for trajectory dynamic planning, adaptive formation and path obstacle avoidance of each front unmanned aerial vehicle detection array.

[0104] The main control unmanned aerial vehicle uses flight control software to generate a synthetic aperture path for the front unmanned aerial vehicle based on the terrain features of the target area, the observation bandwidth and resolution requirements, using a Bezier curve fitting trajectory planning algorithm. The algorithm constructs a smooth and differentiable flight trajectory according to the specified control points, so that the synthetic aperture length and the observation angle range meet the SAR imaging accuracy requirements at the same time, and automatically adjusts the heading, speed and attitude of each unmanned aerial vehicle in combination with the aircraft dynamics and formation constraints to optimize the range and azimuth imaging resolution.

[0105] The digital beam forming module is used to generate LFM or nonlinear frequency modulation excitation waveform parameters that meet the SAR imaging requirements, and synchronously transmit them to each unmanned aerial vehicle through wireless broadcasting.

[0106] In the process of synthetic aperture radar imaging task of the leading UAV detection array, if there is complex terrain or obstacle environment in the combat area, the Bezier trajectory control point optimization method based on particle swarm optimization (PSO) is used to dynamically generate a flight path that meets the task requirements. The height of the middle control point of the Bezier curve of each leading UAV is taken as the optimization variable, and the imaging coverage, azimuth resolution, array consistency and obstacle avoidance replanning ratio are taken as the joint target to construct the optimization function. Through iterative search by the particle swarm algorithm, the system can automatically adjust the flight path without crossing the obstacles, so that the UAV array forms a continuous and efficient synthetic aperture structure in the target area. This method is suitable for imaging scenes with dense obstacles, multiple path constraints and the need for dynamic reconstruction of formation, and can effectively improve the resolution and integrity of SAR images, and enhance the task adaptability and anti-destroying reconstruction ability of the system in complex terrain conditions.

[0107] The above detailed description is only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the examples, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the scope of the technical solutions of the present application, which should be covered by the scope of the claims of the present application.

Claims

1. A method for detecting SAR images using an unmanned aerial vehicle array, characterized in that, include: S1. Based on the imaging mission planning instructions, the array arrangement, trajectory design parameters and imaging mode are sent to the main control UAV. S2. The main control UAV calculates the position, spacing, flight path, and beam direction parameters of each UAV in the UAV detection array; S3. Send the baseband excitation parameters, synchronization signal, and attitude control commands to the UAV detection array; S4. The UAV detection array synchronously transmits radar beams and receives target echoes. The echo data is processed locally and then transmitted back to the signal processing node. S5. The signal processing node performs coherent processing on the returned data, performs SAR image signal processing, and returns the target image to the ground system. The flight path of the drone is based on control points If a decision is made, a multi-objective optimization function is constructed: in, For consistent aperture; η For imaging coverage; To avoid obstacles, the planning ratio should be adjusted. R a For real-time azimuth resolution, Indicates the resolution threshold; , , , For coefficients; If there are obstacle areas within the motion observation area, the multi-objective optimization function is iterated to search for the optimal position of the control point, so that the trajectory completely avoids the obstacle area while meeting the SAR imaging performance requirements.

2. The UAV array SAR image detection method according to claim 1, characterized in that, Also includes: S6. The ground system monitors the status of each UAV in real time. If any UAV is found to be abnormal, a reconfiguration plan will be executed.

3. The UAV array SAR image detection method according to claim 1, characterized in that, The flight path in step S2 is constructed using a quadratic Bezier curve.

4. The UAV array SAR image detection method according to claim 1, characterized in that, In step S2, the drones are arranged horizontally with spacing between them. ; in, This represents the actual spacing of the i-th UAV in the horizontal arrangement. It is the spacing adjustment amount for the i-th drone; This is the design value for the reference spacing.

5. The UAV array SAR image detection method according to claim 1, characterized in that, The drone's heading angle and speed are: ； ； This represents the flight angle of the i-th UAV, i.e., the heading angle; Let represent the flight speed of the i-th drone; , For the first The coordinates of a UAV at time t.

6. The UAV array SAR image detection method according to claim 1, characterized in that, In step S5, the SAR image signal processing algorithm is selected using the following method: use Indicates trajectory error; if and Greater than 300 Then the RD algorithm is used, where Indicates the imaging area range. Indicates the estimated error threshold; like If so, then the BP algorithm is used; If auxiliary registration information exists and the scene is regular, then the PFA algorithm is used.

7. The UAV array SAR image detection method according to claim 6, characterized in that, The estimation error threshold is set to 0.

5. , R a This is the real-time azimuth resolution.

8. A synthetic aperture radar imaging system, characterized in that, The method for performing the UAV array SAR image detection method according to any one of claims 1-7 includes: Multiple UAV detection arrays, each containing a UAV, an antenna, a transceiver component, and a spatiotemporal synchronization module; the UAV detection arrays are used to transmit radar signals, receive echo signals, and transmit the echo signals back to the integrated information processing platform after initial processing; The integrated information processing platform is used to control the time and frequency synchronization of all UAV detection arrays and generate target detection information based on the pre-processed echo signals; at the same time, it controls the flight parameters of the UAV detection arrays based on the mission planning configuration. The ground-based integrated display and control system is used for mission planning and configuration, and sends the mission planning and configuration to the integrated information processing platform. Based on the target detection information, it generates and displays SAR images and outputs target identification results.

Images