CN112379373B - Space-borne SAR real-time imaging device - Google Patents

Abstract

The invention discloses a satellite-borne SAR real-time imaging device, which constructs SAR imaging by adopting two imaging modes of wide-area wide-range low-resolution general investigation and high-resolution detailed investigation, combines the satellite-borne SAR real-time imaging processing, target detection and a target classification recognition technology based on deep learning, realizes quick and effective reconnaissance on a sensitive area, and acquires information such as the number, the position, the type, the confidence and the slice of an interested target; the problems of large ground processing time delay, low timeliness and the like caused by directly downloading massive high-resolution SAR data are effectively solved, the bottleneck of data transmission is effectively broken through, the solid storage and load utilization rate is improved, and the combat efficiency of satellites is remarkably improved; the input data of the deep learning forward reasoning model is floating point complex data, the floating point complex data not only contains target characteristic information but also contains target phase information, the information dimension is increased, the information precision is high, and the classification accuracy is convenient to improve.

Description

A spaceborne SAR real-time imaging device

technical field

The invention belongs to the technical field of satellite load data processing and platform control, and in particular relates to a space-borne SAR real-time imaging device.

Background technique

Spaceborne SAR has the characteristics of high spatial resolution and large coverage area, which can realize the precise positioning of ship targets, and high-resolution SAR images provide the possibility for ship type identification, but it is difficult to effectively verify the ship extraction results, making the target The performance of extraction and type identification cannot be effectively improved. The deep learning technology emerging in recent years has been widely used in object classification, recognition, video analysis and natural language processing. With the help of deep learning technology, on-orbit target detection and identification based on remote sensing images can effectively improve the use efficiency of satellites, and timely discover land and sea salient targets. Compared with electronic reconnaissance methods, it has strong anti-interference ability and can translate target slice It has the advantages of high readability and wide scene adaptation.

Wang Guodong of Beijing Jiaotong University's paper "Deramp Chirp Scaling Imaging Algorithm for High-Resolution Spaceborne Spotlight SAR" proposed a Deramp ChirpScaling (DCS) algorithm suitable for high-resolution spaceborne spotlight synthetic aperture radar (SAR). This algorithm combines the advantages of spectral analysis (SPECAN) algorithm and Chirp Scaling algorithm. Firstly, the deramp processing with fixed Doppler modulation frequency is used to realize the coarse focusing of azimuth, which eliminates the azimuth spectrum aliasing phenomenon unique to spaceborne spotlight SAR. , and then apply the principle of Chirp Scaling to achieve precise focusing of the distance, and compensate the azimuth phase error caused by deramp processing to achieve fine azimuth focusing. The algorithm is suitable for accurate imaging processing of wide swath and high resolution spaceborne spotlight SAR.

The Institute of Electronics, Chinese Academy of Sciences CN103323828A/B proposes a super-high resolution spaceborne SAR imaging processing method and device. This invention establishes a two-dimensional spectrum model of the spaceborne SAR echo signal, and eliminates the range migration phase at the reference slant distance in the two-dimensional spectrum model by multiplying the two-dimensional spectrum model with its corresponding reference function term and the high-order coupling phase term, the range inverse Fourier transform is performed on the signal after the elimination process, the residual distance migration in the two-dimensional spectrum model is corrected by interpolation, and the two-dimensional spectrum model is finally eliminated by azimuth compression. The azimuth modulation phase term of the three-dimensional spectrum model is used to obtain the focused spaceborne SAR image. Utilizing the technical scheme of the invention, high-quality ultra-high-resolution space-borne SAR images can be obtained, and have good positioning capabilities.

CN201811280751.8 of Beijing Aerospace Vehicle Overall Design Department discloses an on-board autonomous imaging method guided by real-time information of on-board AIS. The main steps of the invention are as follows: (1) capture and confirm the ship target to be observed; (2) determine the satellite observation time and area; (3) calculate the observation attitude of the satellite to the ship; (4) adjust the satellite attitude to determine the SAR The payload can perform imaging work. The invention uses the spaceborne AIS information to match and screen the interested targets in real time, and determine the area where the target is located, realize the real-time calculation of the time, attitude, imaging parameters and other data required for SAR payload imaging, and automatically generate satellite attitude adjustment commands and SAR loads start-up imaging instructions, and completes the imaging task of selected areas and selected targets. The comprehensive utilization of AIS information and SAR imaging information improves the identification and confirmation efficiency of maritime ship targets.

However, the above method mainly has the following problems:

(1) The existing technical means use AIS information as a means of verification and guidance to assist in the completion of SAR imaging or to detect and identify ship targets in SAR images. The core algorithms are all based on traditional image segmentation and morphological targets The detection and recognition methods have limited processing accuracy, correct rate, and performance improvement capabilities, and cannot meet the ability requirements for target extraction and type identification of subsequent high-resolution SAR images.

(2) If only relying on AIS to detect, locate and identify ship targets, not only the technical means are single, but also the behaviors such as AIS underreporting, non-cooperative targets (warships, etc.) or intentional evasion (AIS is not turned on) cannot be effectively monitored. The mature spaceborne SAR image ship target detection and identification algorithm can make up for the lack of AIS in ship target monitoring.

(3) The existing method mainly considers the application on the ground or the participation of ground equipment to complete the functional closed loop. It must rely on the space-ground data transmission system and occupy a large amount of satellite-ground communication bandwidth, which will inevitably consume a lot of processing time and reduce the communication efficiency of the satellite-ground network. Further affecting the effectiveness of remote sensing satellites, the processing efficiency and real-time performance will not be able to meet the needs of modern remote sensing satellites.

Contents of the invention

In view of this, the object of the present invention is to provide a spaceborne SAR real-time imaging device, which can realize rapid acquisition of information of the region of interest and detailed reconnaissance of key targets.

A spaceborne SAR real-time imaging device, comprising a satellite attitude orbit control unit, a central processing unit, a SAR payload, a SAR imaging processing unit, an intelligent information processing unit, a data processing unit, and a payload control unit;

The central processing unit determines control parameters including imaging resolution, width, load pointing and satellite attitude according to the target information, and sends the control parameters to the satellite attitude orbit control unit and the load control unit;

The satellite attitude orbit control unit adjusts the satellite attitude to the specified state of the mission according to the control parameters, the load control unit calculates the load imaging parameters according to the control parameters to control the SAR load work, and the SAR load starts to detect the designated area according to the imaging parameters Scan, and down-convert the received radio frequency echo signal into an intermediate frequency analog signal and send it to the SAR imaging processing unit;

The SAR imaging processing unit combines the radar parameters sent by the load control unit to process the echo signal into a SAR image, and then sends the imaging data to the intelligent information processing unit through the optical fiber;

The intelligent information processing unit first performs image quantization on the SAR image data, that is, quantizes the original single-precision floating-point complex data into 8-bit fixed-point data, and then performs target detection processing: if no target is detected, the task ends; if detected Target, judge and calculate the coordinate position of the target, determine the center position of the densest target as the central imaging point of the detailed inspection imaging; then calculate the detailed inspection including imaging resolution, width, The parameter information of viewing angle, satellite orbit and attitude under imaging is sent to the central processing unit to calculate the SAR imaging parameters in the detailed inspection mode, and the central processing unit distributes the above parameter information to the load control unit, attitude orbit control unit and SAR imaging processing unit;

The attitude orbit control unit maneuvers to a designated position within a designated time according to the orbit and attitude parameter information and adjusts the attitude to a designated state, which is convenient for subsequent SAR loads to detect targets; the load control unit controls the beam pointing of the SAR load according to the beam control signal to perform Detection and scanning of the target of interest; the SAR imaging processing unit performs SAR imaging processing according to the collected SAR payload echo signal and radar parameters to obtain high-resolution SAR image data, and sends it to the intelligent information processing unit through the optical fiber module;

The intelligent information processing unit first performs image quantization on the high-resolution SAR image data sent by the SAR imaging processing unit, and quantizes the original single-precision floating-point complex data into 8-bit fixed-point data for target detection processing; the target detection is completed Finally, on the one hand, the target slice is obtained from the original floating-point complex image data according to the detection results, and the target slice is sent to the deep learning forward reasoning model for target identification processing to obtain the target type and confidence information; on the other hand, according to The coordinate position of the target detection result and the SAR payload beam pointing, satellite orbit and attitude information are processed for spatial geometric positioning, the longitude and latitude information of the target location is determined, and the result information such as the target position, target slice, target type, and target confidence is passed through the SpaceWire bus sent to the data processing unit;

The data processing unit receives the result information, compresses and encodes the result information, and quickly downloads the information to form high-quality intelligence information. At the same time, the data processing unit can download the original echo data or the processed SAR image data transmitted by the SAR imaging processing unit through the optical fiber according to the task requirements and the command control method, so as to facilitate data analysis on the ground.

Preferably, the calculation method of the position of the central imaging point of the target is as follows: the coordinates of target i are (X _i , Y _i ), the total number of targets is k, and the coordinates of target i and other k-1 target coordinates are respectively calculated. The square difference δ _i , then the position coordinate corresponding to the minimum value of δ _i is the central imaging point.

Preferably, the process of quantizing the image data is: divide the 32-bit unsigned fixed-point data into 6 intervals, which are respectively: the first interval is [0, 256), the second interval is [256, 4096), the third interval The interval is [4096, 16384), the fourth interval is [16384, 65535), the fifth interval is [65536, 262143), the sixth interval is [262144, 4294967296), the six intervals correspond to generate 6 grayscale mapping tables, The size of each table is 256, and 6 grayscale tables are synthesized into a table with a size of 1536. The method of generating the first interval grayscale table is as follows: Assume that the input data is n, where n=0,1,2,3,… ,255, after grayscale mapping, the obtained grayscale table value is 10×log(n), and the corresponding address index value is n;

The gray scale generation method for the second interval is as follows: assuming that the input data is n, where n=256, 257, 258, 259, ..., 4094, 4095, after the gray scale mapping, the obtained gray scale value is 10×log(round(n/16 )×16), the corresponding address index value is round(n/16);

The gray scale generation method of the third interval is as follows: assuming that the input data is n, where n=4096, 4097, 4098, 4099, ..., 16382, 16383, after the gray scale mapping, the obtained gray scale value is 10×log (round(n/64)×64), the corresponding address index value is round(n/64);

The gray scale generation method for the fourth interval is as follows: assuming that the input data is n, where n=16384, 16385, 16386, ..., 65534, 65535, after gray scale mapping, the obtained gray scale value is 10×log(round (n/256)×256), the corresponding address index value is round(n/256);

The grayscale generation method of the fifth interval is as follows: assuming that the input data is n, where n=65536, 65537, 65538, 65539, ..., 262142, 262143, after grayscale mapping, the obtained grayscale value is 10×log (round(n/1024)×1024), the corresponding address index value is round(n/1024);

The gray scale generation method of the sixth interval is as follows: assuming that the input data is n, where n=262144, 262145, 262146, ..., 4294967294, 4294967295, after gray scale mapping, the obtained gray scale value is 10×log(round (n/4096)×4096), the corresponding address index value is round(n/4096).

For the data in the input table, first determine which interval it belongs to: if it belongs to the first interval, then directly use the input value to look up the table, that is, directly use the input value as the address index value to look up the table;

If it belongs to the second interval, divide the input value by 16, and add 256 to the result as the address index value for table lookup; if it belongs to the third interval, divide the input value by 64, and add 512 to the result Look up the table as the address index value; if it belongs to the 4th interval, divide the input value by 256, and add 768 to the result to look up the table as the address index value; if it belongs to the 5th interval, divide the input value by 1024, add 1024 to the obtained result as the address index value for table lookup; if it belongs to the sixth interval, then divide the input value by 4096, and add 1280 to the obtained result as the address index value for table lookup;

The 8-bit grayscale data is obtained by looking up the grayscale mapping table, which is the quantized image data.

The present invention has following beneficial effects:

1) The present invention adopts two imaging modes of wide-area low-resolution survey and high-resolution detailed survey to construct SAR imaging, and combines spaceborne SAR real-time imaging processing, target detection and target classification and recognition technology based on deep learning to realize rapid detection of sensitive areas. Effective reconnaissance, obtaining intelligence information such as the number, location, type, confidence level, and slice of the target of interest. It effectively solves the problems of large ground processing delay and low timeliness caused by the direct download of massive high-resolution SAR data, effectively breaks through the bottleneck of data transmission, improves solid storage and load utilization, and significantly improves the combat effectiveness of satellites .

2) The spaceborne SAR real-time imaging method involved in the present invention adopts the on-orbit autonomous closed-loop working mode of actively discovering targets and autonomously guiding high-resolution imaging, which will greatly improve the intelligence level of the satellite and enhance the ease of use and easy-to-use experience of users. At the same time, it simplifies the use of satellites, improves the flexibility and timeliness of sensitive target imaging in the application process, and meets the application needs of users.

3) The input data of the deep learning forward reasoning model involved in the present invention is floating-point complex data. The floating-point complex data not only contains target feature information but also contains target phase information, which increases the information dimension and has high information accuracy, which is convenient for improving classification. accuracy.

Description of drawings

Figure 1 is the imaging task timeline;

Figure 2 is the observation model of the front and side view strips;

Figure 3 is the rear squint sliding spotlight observation model.

Detailed ways

The present invention will be described in detail below with reference to the accompanying drawings and examples.

A spaceborne SAR real-time imaging device includes a satellite attitude orbit control unit, a central processing unit, a SAR payload, a SAR imaging processing unit, an intelligent information processing unit, a data processing unit, and a payload control unit, and the above modules cooperate to complete satellite SAR imaging tasks. The task flow is set to firstly carry out low-resolution wide-area census SAR imaging of frontal and side-looking strips, conduct preliminary detection of interested targets on the obtained SAR images, and organize rear-squinting spotlight high-resolution detailed observations based on the detection results and satellite-related information. Check SAR imaging, and perform interest target detection, slice extraction, and target identification based on deep learning on high-resolution SAR image data. After processing, information such as target position, target slice, target type, and confidence level are packaged and transmitted to the ground for reception Station, quickly generate high-quality intelligence information.

As shown in Figure 1, the SAR imaging is divided into two working modes: general survey and detailed survey. The domain imaging search results are used to obtain detailed imaging parameters, and satellite platforms and payloads are organized to conduct detailed imaging for the target of interest to determine the target type and latitude and longitude position. Among them, the census adopts the low-resolution imaging mode of frontal and side-view strips, and the detailed investigation adopts the high-resolution imaging of posterior squint beam. Imaging parameters and modes such as width and resolution can be flexibly selected according to task requirements and target characteristics.

As shown in Figure 2, the low-resolution imaging process of the front-view and side-view strips is as follows: the central processing unit determines the control parameters such as imaging resolution, width, load pointing, and satellite attitude according to the target size and target area, and Send the control parameters to the satellite attitude and orbit control unit and the load control unit, the satellite attitude and orbit control unit adjusts the satellite attitude to the specified state of the mission according to the control parameters, and the load control unit calculates the load imaging parameters according to the control parameters to control the SAR load work, The SAR payload starts to detect and scan the designated area according to the imaging parameters, and down-converts the received radio frequency echo signal into an intermediate frequency analog signal and sends it to the SAR imaging processing unit. The SAR imaging processing unit combines the radar parameters sent by the payload control unit. The wave signal is processed into a SAR image.

The target detection process of the low-resolution SAR image is as follows: the SAR imaging processing unit sends the low-resolution imaging data to the intelligent information processing unit through the optical fiber, and the intelligent information processing unit first performs image quantization on the SAR image data, and converts the original single-precision The floating-point complex data is quantized into 8-bit fixed-point data for target detection processing. If the target is not detected, the task ends; if the target is detected, the coordinate position of the target is judged and calculated, and the center position with the densest target is determined as the central imaging point of the detailed inspection imaging.

The calculation method of the position of the central imaging point of the detailed investigation imaging is: the coordinates of target i are (X _i , Y _i ), the total number of targets is k, and the square difference between target i and other k-1 target coordinates is calculated respectively δ _i , then the position coordinate corresponding to the minimum value of δ _i is the central imaging point. in, The square difference of coordinate values of the central imaging point δ=min(δ _i ), i=1, 2...k.

As shown in Figure 3, the calculation and configuration process of the rear squint spotlight high-resolution detailed survey SAR imaging parameters is as follows: calculate the detailed survey imaging resolution, width, and imaging angle of view according to the distribution of the central imaging point and the target , satellite orbit and attitude and other parameter information, and send it to the central processing unit to calculate the SAR imaging parameters in the detailed inspection mode, and the central processing unit distributes the above parameter information to the load control unit, attitude orbit control unit and SAR imaging processing unit.

The high-resolution SAR imaging process is as follows: After the imaging parameters are calculated and configured, the attitude-orbit control unit maneuvers to the designated position within a designated time according to the orbit and attitude parameter information and adjusts the attitude to a designated state, which is convenient for subsequent SAR payloads to carry out Target detection; the payload control unit controls the beam pointing of the SAR payload according to the beam control signal to detect and scan the target of interest; the SAR imaging processing unit performs SAR imaging processing according to the collected SAR payload echo signals and radar parameters to obtain high-resolution SAR image data , and send it to the intelligent information processing unit through the optical fiber module.

The intelligent information processing unit sends the high-resolution SAR image data sent by the SAR imaging processing unit, and the intelligent information processing unit performs image quantization on the SAR image data first, and quantizes the original single-precision floating-point complex data into 8-bit fixed-point data , for object detection processing. After the target detection is completed, on the one hand, the target slice is obtained from the original floating-point complex image data according to the detection result, and the target slice is sent to the deep learning forward reasoning model for target identification processing to obtain the target type and confidence information; the other On the one hand, according to the coordinate position of the target detection result and the SAR payload beam pointing, satellite orbit and attitude and other information, the spatial geometric positioning process is performed to determine the latitude and longitude information of the target location, and the target position (latitude and longitude), target slice, target type, and target confidence The result information such as the degree is sent to the data processing unit a/b through the SpaceWire bus (a new generation of on-board high-speed data bus standard).

The data processing unit receives the result information, compresses and encodes the result information, and quickly downloads the information to form high-quality intelligence information. At the same time, the data processing unit can download the original echo data or the processed SAR image data transmitted by the SAR imaging processing unit through the optical fiber according to the task requirements and the command control method, so as to facilitate data analysis on the ground.

The floating-point complex data sent to the intelligent information processing unit is quantified for target detection based on morphology. Specifically: Calculate the absolute value of the received floating-point complex data, convert the absolute value result from single-precision floating-point data to 32-bit unsigned fixed-point data, and use the result as input to obtain 8-bit gray by looking up the gray-scale mapping table The degree data is the quantized image data. Among them, the specific generation process of the grayscale mapping table is as follows:

Divide the 32-bit unsigned fixed-point data into 6 intervals, namely: the first interval is [0, 256), the second interval is [256, 4096), the third interval is [4096, 16384), and the fourth interval is [ 16384,65535), the fifth interval is [65536,262143), the sixth interval is [262144,4294967296), the six intervals correspond to generate 6 grayscale mapping tables, each table size is 256, 6 grayscale table synthesis It is a table with a size of 1536, and the method for generating the first interval grayscale table is as follows: Assuming that the input data is n, where n=0,1,2,3,...,255, after performing grayscale mapping, the obtained grayscale The value of the degree table is 10×log(n), and the corresponding address index value is n;

The gray scale generation method for the second interval is as follows: assuming that the input data is n, where n=256, 257, 258, 259, ..., 4094, 4095, then after gray scale mapping, the obtained gray scale value is 10×log(round(n/16 )×16), the corresponding address index value is round(n/16);

The gray scale generation method of the third interval is as follows: assuming that the input data is n, where n=4096, 4097, 4098, 4099, ..., 16382, 16383, after the gray scale mapping, the obtained gray scale value is 10×log (round(n/64)×64), the corresponding address index value is round(n/64);

The gray scale generation method for the fourth interval is as follows: assuming that the input data is n, where n=16384, 16385, 16386, ..., 65534, 65535, after gray scale mapping, the obtained gray scale value is 10×log(round (n/256)×256), the corresponding address index value is round(n/256);

The grayscale generation method of the fifth interval is as follows: assuming that the input data is n, where n=65536, 65537, 65538, 65539, ..., 262142, 262143, after grayscale mapping, the obtained grayscale value is 10×log (round(n/1024)×1024), the corresponding address index value is round(n/1024);

The gray scale generation method of the sixth interval is as follows: assuming that the input data is n, where n=262144, 262145, 262146, ..., 4294967294, 4294967295, after gray scale mapping, the obtained gray scale value is 10×log(round (n/4096)×4096), the corresponding address index value is round(n/4096).

For the data in the input table, first determine which interval it belongs to: if it belongs to the first interval, then directly use the input value to look up the table, that is, directly use the input value as the address index value to look up the table;

If it belongs to the second interval, divide the input value by 16, and add 256 to the result as the address index value for table lookup; if it belongs to the third interval, divide the input value by 64, and add 512 to the result Look up the table as the address index value; if it belongs to the 4th interval, divide the input value by 256, and add 768 to the result to look up the table as the address index value; if it belongs to the 5th interval, divide the input value by 1024, add 1024 to the obtained result as the address index value for table lookup; if it belongs to the sixth interval, divide the input value by 4096, add 1280 to the obtained result as the address index value for table lookup.

The target slice data sent to the deep learning model for target classification and recognition. Specifically, the target slice generation module notifies the floating-point complex image data storage module to center on the coordinate position according to the target coordinate position passed by the target detection module, extract the target slice according to the set size information, and extract the extracted The floating-point complex number slice data is sent to the deep learning model for forward inference calculation, and the target type and confidence information are obtained.

To sum up, the above are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (3)

1. A spaceborne SAR real-time imaging device, characterized in that, comprises a satellite attitude orbit control unit, a central processing unit, a SAR load, a SAR imaging processing unit, an intelligent information processing unit, a data processing unit and a load control unit; The central processing unit determines control parameters including imaging resolution, width, load pointing and satellite attitude according to the target information, and sends the control parameters to the satellite attitude orbit control unit and the load control unit; The satellite attitude orbit control unit adjusts the satellite attitude to the specified state of the mission according to the control parameters, the load control unit calculates the load imaging parameters according to the control parameters to control the SAR load work, and the SAR load starts to detect the designated area according to the imaging parameters Scan, and down-convert the received radio frequency echo signal into an intermediate frequency analog signal and send it to the SAR imaging processing unit; The SAR imaging processing unit combines the radar parameters sent by the load control unit to process the echo signal into a SAR image, and then sends the imaging data to the intelligent information processing unit through the optical fiber; The intelligent information processing unit first performs image quantization on the SAR image data, that is, quantizes the original single-precision floating-point complex data into 8-bit fixed-point data, and then performs target detection processing: if no target is detected, the mission ends; if detected Target, judge and calculate the coordinate position of the target, determine the center position of the densest target as the central imaging point of the detailed inspection imaging; then calculate the detailed inspection including imaging resolution, width, The parameter information of viewing angle, satellite orbit and attitude under imaging is sent to the central processing unit to calculate the SAR imaging parameters in the detailed inspection mode, and the central processing unit distributes the above parameter information to the load control unit, attitude orbit control unit and SAR imaging processing unit; The attitude orbit control unit maneuvers to a designated position within a designated time according to the orbit and attitude parameter information and adjusts the attitude to a designated state, which is convenient for subsequent SAR loads to detect targets; the load control unit controls the beam pointing of the SAR load according to the beam control signal to perform Detection and scanning of the target of interest; the SAR imaging processing unit performs SAR imaging processing according to the collected SAR payload echo signal and radar parameters to obtain high-resolution SAR image data, and sends it to the intelligent information processing unit through the optical fiber module; The intelligent information processing unit first performs image quantization on the high-resolution SAR image data sent by the SAR imaging processing unit, and quantizes the original single-precision floating-point complex data into 8-bit fixed-point data for target detection processing; the target detection is completed Finally, on the one hand, the target slice is obtained from the original floating-point complex image data according to the detection results, and the target slice is sent to the deep learning forward reasoning model for target identification processing to obtain the target type and confidence information; on the other hand, according to The coordinate position of the target detection result and the SAR payload beam pointing, satellite orbit and attitude information are processed for spatial geometric positioning, the longitude and latitude information of the target location is determined, and the target position, target slice, target type, and target confidence result information are sent through the SpaceWire bus to the data processing unit; The data processing unit receives the result information, performs data compression and encoding processing on it, and then quickly downloads the information to form high-quality intelligence information; at the same time, the data processing unit, according to the task requirements and the way of command control, The raw echo data or the processed SAR image data transmitted by the SAR imaging processing unit through the optical fiber is downloaded to facilitate data analysis on the ground. 2. A kind of space-borne SAR real-time imaging device as claimed in claim 1, is characterized in that, the calculation method of the central imaging point position of described target is: the coordinate of target i is (X _i , Y _i ), and the target total The number is k, respectively calculate the square difference δ _i of target i and other k-1 target coordinates, then the position coordinate corresponding to the minimum value of δ _i is the central imaging point. 3. A kind of space-borne SAR real-time imaging device as claimed in claim 1, is characterized in that, the process that image data is quantified is: 32 unsigned fixed-point data are divided into 6 intervals, are respectively: the first interval is [0, 256), the second interval is [256, 4096), the third interval is [4096, 16384), the fourth interval is [16384, 65535), the fifth interval is [65536, 262143), the sixth interval is [262144,4294967296), the six intervals correspond to generate six grayscale mapping tables, each table size is 256, and the six grayscale tables are synthesized into a table with a size of 1536. The first interval grayscale table generation method is as follows: Assume The input data is n, where n=0,1,2,3,...,255, after the grayscale mapping, the obtained grayscale table value is 10×log(n), and the corresponding address index value is n; The gray scale generation method for the second interval is as follows: assuming that the input data is n, where n=256, 257, 258, 259, ..., 4094, 4095, after the gray scale mapping, the obtained gray scale value is 10×log(round(n/16 )×16), the corresponding address index value is round(n/16); The gray scale generation method of the third interval is as follows: assuming that the input data is n, where n=4096, 4097, 4098, 4099, ..., 16382, 16383, after the gray scale mapping, the obtained gray scale value is 10×log (round(n/64)×64), the corresponding address index value is round(n/64); The gray scale generation method for the fourth interval is as follows: assuming that the input data is n, where n=16384, 16385, 16386, ..., 65534, 65535, after gray scale mapping, the obtained gray scale value is 10×log(round (n/256)×256), the corresponding address index value is round(n/256); The grayscale generation method of the fifth interval is as follows: assuming that the input data is n, where n=65536, 65537, 65538, 65539, ..., 262142, 262143, after grayscale mapping, the obtained grayscale value is 10×log (round(n/1024)×1024), the corresponding address index value is round(n/1024); The gray scale generation method of the sixth interval is as follows: assuming that the input data is n, where n=262144, 262145, 262146, ..., 4294967294, 4294967295, after gray scale mapping, the obtained gray scale value is 10×log(round (n/4096)×4096), the corresponding address index value is round(n/4096); For the data in the input table, first judge which interval it belongs to: if it belongs to the first interval, directly use the input value to look up the table, that is, directly use the input value as the address index value to look up the table; If it belongs to the second interval, divide the input value by 16, and add 256 to the obtained result as the address index value for table lookup; if it belongs to the third interval, divide the input value by 64, and add 512 to the obtained result as the address Index value for table lookup; if it belongs to the 4th interval, divide the input value by 256, and add 768 to the result obtained as the address index value for table lookup; if it belongs to the 5th interval, divide the input value by 1024 to get Add 1024 to the result as the address index value for table lookup; if it belongs to the sixth interval, divide the input value by 4096, and add 1280 to the obtained result as the address index value for table lookup; The 8-bit grayscale data is obtained by looking up the grayscale mapping table, which is the quantized image data.