Satellite-borne SAR radar target real-time guiding method based on ADS-B signals

Abstract

The invention discloses a satellite-borne SAR (Synthetic Aperture Radar) radar target real-time guiding method based on ADS-B (Automatic Dependent Surveillance-Broadcast) signals, which relates to the technical field of radar control, and comprises the following steps: receiving Beidou dual-band signals and global ADS-B signals in real time, resolving by adopting a carrier phase double-difference ionosphere elimination combinatorial algorithm, and obtaining a satellite-borne SAR generating satellite platform pose data including satellite position component data, satellite speed component data and satellite attitude component data; inputting the airspace target point cloud data set into an adaptive airspace clustering algorithm, dynamically adjusting a clustering neighborhood radius by using satellite speed component data, and outputting central geographic coordinates of a target dense area; fusing the beam pointing vector and the angular velocity data through a beam-attitude dynamic coupling controller, and generating a phase shifter code value adjustment instruction; the neighborhood recognition range is dynamically adjusted through the real-time motion state of the satellite, and the positioning precision and timeliness of the dynamic airspace target dense area are remarkably improved. And the continuous monitoring capability and the guiding precision of the time-sensitive target are improved.

Description

Technical Field

[0001] The present invention relates to the field of radar control technology, in particular to a real-time guidance method for spaceborne SAR radar targets based on ADS-B signals. Background Art

[0002] The beam steering technology of spaceborne SAR radar relies on high-precision platform pose calculation and real-time target positioning. The current mainstream solution uses GNSS single-point positioning combined with inertial navigation; ADS-B signal analysis can achieve airspace target surveillance, but it is not deeply coupled with radar beam steering. In the field of beam pointing control, phased array radar achieves beam deflection by adjusting the code value of the phase shifter. Its accuracy is significantly affected by attitude measurement errors and dynamic environmental interference. Existing technologies have core flaws: high-speed satellite platforms cannot accurately guide beams in real time to targets in dynamic airspace. Traditional clustering algorithms use a fixed neighborhood radius and cannot adapt to the spatial distribution distortion of point clouds caused by changes in satellite speed. The attitude solution does not compensate for the geomagnetic field-temperature coupling error, resulting in beam pointing deviation. The beam-attitude control closed-loop response delay makes it difficult to track high-speed target clusters; Especially in the identification link of dense target areas, fixed clustering parameters lead to large target center positioning errors, which seriously restricts the SAR radar's coverage capability for time-sensitive targets. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a real-time guidance method for spaceborne SAR radar targets based on ADS-B signals to solve the problem of real-time and precise beam guidance of spaceborne SAR radar on dynamic airspace targets under high-speed motion conditions.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a real-time guidance method for spaceborne SAR radar targets based on ADS-B signals, which includes receiving Beidou dual-band signals and global ADS-B signals in real time, and using a carrier phase double-difference ionospheric elimination combined algorithm to generate satellite platform posture data including satellite position component data, satellite velocity component data, and satellite attitude component data; Based on the satellite position component data and satellite attitude component data, the ADS-B signal is processed through the stellar coordinate system transformation to generate a dynamic airspace target point cloud dataset; The spatial target point cloud dataset is input into the adaptive spatial clustering algorithm, which uses the satellite velocity component data to dynamically adjust the clustering neighborhood radius and output the central geographic coordinates of the target dense area. Based on the satellite position component data, the geographic coordinates of the center of the target dense area are converted into the WGS84 coordinate system and the attitude projection is solved to generate the beam pointing vector. The satellite attitude component data is used to drive the composite environment field compensation model to generate the angular velocity data after zero bias compensation. The beam-attitude dynamic coupling controller fuses the beam pointing vector and angular velocity data to generate phase shifter code value adjustment instructions; The phase shifter code value adjustment instruction is input into the spaceborne SAR radar phase shifter to drive the radar beam to cover the central geographic coordinates of the target dense area in real time.

[0006] As a preferred solution of the real-time guidance method of spaceborne SAR radar targets based on ADS-B signals of the present invention, the satellite platform posture data of the satellite position component data, satellite velocity component data and satellite attitude component data are generated, and the specific steps are as follows: The dual-frequency carrier phase measurement values ​​are calculated using BeiDou dual-band signals. Inter-satellite single-difference processing is performed on the dual-frequency carrier phase measurement values ​​to generate single-difference observation values. Satellite-ground double-difference processing is then performed to generate double-difference carrier phase observation values. Simultaneously, onboard three-axis magnetometer data and multi-channel temperature data are collected. An ionospheric-free combination is constructed based on double-difference carrier phase observations, and the ionospheric-free double-difference carrier phase observations are output. The three-axis magnetometer data and multi-channel temperature data are integrated and solved through least squares estimation to output the satellite platform posture data including satellite position component data, satellite velocity component data and satellite attitude component data.

[0007] As a preferred solution of the spaceborne SAR radar target real-time guidance method based on ADS-B signals of the present invention, the specific steps of generating the dynamic airspace target point cloud dataset are as follows: Use WGS84 coordinate system conversion to convert the longitude and latitude coordinates in the ADS-B signal into geocentric rectangular coordinates, and obtain the geocentric rectangular coordinates of the satellite based on the satellite position component data; The relative position vector from the satellite to the target is generated by calculating the difference between the earth-centered rectangular coordinates and the earth-centered rectangular coordinates of the satellite; The satellite attitude component data is used to construct the inverse rotation matrix from the Earth-centered fixed coordinate system to the satellite body coordinate system. The inverse rotation matrix is ​​applied to transform the relative position vector into the satellite body rectangular coordinate. All the transformed satellite body rectangular coordinates are integrated to generate a dynamic airspace target point cloud dataset.

[0008] As a preferred solution of the real-time guidance method of spaceborne SAR radar targets based on ADS-B signals described in the present invention, the following specific steps are used: inputting the dynamic airspace target point cloud dataset into the adaptive airspace clustering algorithm, dynamically adjusting the clustering neighborhood radius using satellite velocity component data, and outputting the central geographic coordinates of the target dense area. The dynamic airspace target point cloud dataset is input into the adaptive airspace clustering algorithm, the modulus value of the satellite velocity component data is extracted, the dynamic scaling factor is calculated using the inverse proportional relationship, the preset reference neighborhood radius is scaled by the dynamic scaling factor, and the dynamically adjusted dynamic clustering neighborhood radius is output; Calculate the average Euclidean distance standard deviation based on the spatial distribution of the point cloud and generate a benchmark density threshold; A density peak search strategy is used to identify high-density feature points within the baseline density threshold. The high-density feature points are combined with all neighboring points within the dynamic clustering neighborhood radius to generate a target dense area. The arithmetic mean of the rectangular coordinates of the satellite bodies within the target dense area is calculated. The arithmetic mean of the satellite's rectangular coordinates is projected into the geocentric fixed coordinate system through the inverse rotation matrix. The geocentric fixed coordinate system is inversely solved based on the WGS84 coordinate system to output the central geographic coordinates of the target dense area.

[0009] As a preferred solution of the spaceborne SAR radar target real-time guidance method based on ADS-B signals of the present invention, the beam pointing vector is generated, and the specific steps are as follows: Based on the satellite position component data, the central geographic coordinates of the target dense area are converted into geocentric rectangular coordinates through the WGS84 coordinate system; Calculate the position vector of the satellite position component data in the geocentric rectangular coordinates, calculate the difference between the geocentric rectangular coordinates of the center of the target dense area and the satellite position vector, and generate the direction vector from the satellite to the target; Use the satellite attitude component data to construct a rotation matrix from the Earth-centered rectangular coordinate system to the radar body coordinate system, and apply the rotation matrix to transform the direction vector to the radar body rectangular coordinate system; The current radar beam pointing vector is generated by normalizing the direction vector in the radar body rectangular coordinate system.

[0010] As a preferred solution of the spaceborne SAR radar target real-time guidance method based on ADS-B signals of the present invention, the specific steps of generating the angular velocity data after zero bias compensation are as follows: The three-axis magnetometer data is calculated using the vector modulus calculation method to generate magnetic field intensity characteristics, and the temperature change rate algorithm is used to calculate the multi-channel temperature data to output the temperature change characteristics; The time synchronization feature is generated by calculating the time stamps of the triaxial magnetometer data and the multi-channel temperature data by a time stamp difference algorithm. The coupling error is outputted by a pre-stored geomagnetic field-temperature coupling error mapping table based on the environmental interference feature vector, and the satellite attitude component data is subtracted by the coupling error value to obtain initial compensation attitude quantity.

[0011] As a preferred scheme of the satellite SAR radar target real-time guiding method based on the ADS-B signal, the pre-stored geomagnetic field-temperature coupling error mapping table refers to, The triaxial magnetometer data, the multi-channel temperature data, the satellite attitude component data and the attitude measurement true value at different orbit positions are collected in the pre-orbit calibration stage, the difference between the satellite attitude component data and the satellite attitude measurement true value is calculated, and the attitude measurement true value difference is generated. The coupling regression equation of the triaxial magnetometer data and the multi-channel temperature data on the attitude measurement true value difference is established, and the geomagnetic field-temperature coupling error mapping table is generated based on the coupling regression equation and stored in the on-board memory.

[0012] As a preferred scheme of the satellite SAR radar target real-time guiding method based on the ADS-B signal, the phase shifter code value adjustment instruction is generated, and the specific steps are as follows, Based on the central geographic coordinates of the target dense area, the target radar beam pointing vector is constructed by normalization operation, and the spatial angle error between the current beam pointing vector projection and the target pointing vector is calculated in real time. Based on the zero-offset compensated angular velocity data, the dynamic feedback control law is constructed, the spatial angle error is inputted into the dynamic feedback control law to generate the angular velocity correction quantity, the angular velocity limiting is performed, and the phase shifter code value adjustment instruction is generated through the second-order Runge-Kutta method attitude integration.

[0013] The present application has the following advantages: The adaptive spatial clustering algorithm dynamically adjusts the neighbor identification range through the real-time motion state of the satellite, significantly improving the positioning accuracy and timeliness of the dynamic spatial target dense area. A dynamic scaling factor is generated based on the satellite speed vector module value to adaptively scale the preset neighborhood radius: the neighborhood radius is automatically reduced when the satellite moves at high speed, accurately matching the spatial sparsification distribution characteristics of the target point cloud; the neighborhood radius is expanded when the satellite moves at low speed, effectively avoiding target identification. At the same time, a dynamic density determination threshold is generated in combination with the spatial dispersion characteristics of the target point cloud, realizing the collaborative optimization of density peak search and neighborhood point merging. The present application significantly reduces the positioning error of the target dense area center, greatly compresses the beam pointing delay, ensures the continuous coverage of the main lobe of the synthetic aperture radar on the high-speed maneuvering target cluster, and improves the continuous monitoring capability and guidance accuracy of the time-sensitive target. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0015] Fig. 1 The flowchart of the satellite-borne SAR radar target real-time guidance method based on ADS-B signal.

[0016] Fig. 2 The flowchart of satellite platform pose solution.

[0017] Fig. 3 The flowchart of target dense area positioning.

[0018] Fig. 4 The flowchart of beam pointing control. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail in conjunction with the drawings of the specification.

[0020] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0022] Reference Figs. 1-4 , is an embodiment of the present invention, which provides a real-time guidance method for spaceborne SAR radar targets based on ADS-B signals, comprising the following steps: S1: Receives Beidou dual-band signals and global ADS-B signals in real time, and uses the carrier phase double-difference ionospheric elimination combined algorithm to generate satellite platform posture data including satellite position component data, satellite velocity component data and satellite attitude component data.

[0023] Specifically, a satellite-borne Beidou dual-frequency synchronous receiver is used to receive Beidou dual-band signals in real time, denoted as L1 band and L2 band, and synchronously receive global ADS-B signals, and hard time synchronize multi-source data through GPS pulse (1PPS).

[0024] It should be noted that the carrier phase measurement value of each frequency band includes the satellite number, signal transmission time, reception time, carrier phase integer ambiguity and measurement noise information.

[0025] S1.1: Calculate dual-frequency carrier phase measurements using BeiDou dual-band signals, perform inter-satellite single-difference processing on these dual-frequency carrier phase measurements to generate single-difference observations, and perform satellite-ground double-difference processing to generate double-difference carrier phase observations. Simultaneously, collect onboard three-axis magnetometer data and multi-channel temperature data. Specifically, based on the BeiDou dual-band signal, the carrier phase measurement values ​​of each frequency band are analyzed, and inter-satellite single-difference processing is performed. The BeiDou satellite with the highest elevation angle is selected as the reference satellite (for example, PRN-C01). The dual-frequency carrier phase measurement values ​​of the non-reference satellite are then differentially calculated with those of the reference satellite to eliminate the influence of the satellite-side clock error and generate single-difference observation values. Satellite-ground double-difference processing is used to perform time series difference calculation on two continuously output time-aligned single-difference observation arrays to completely eliminate the receiver clock error and generate double-difference carrier phase observations.

[0026] The onboard three-axis magnetometer collects X-axis, Y-axis, and Z-axis three-axis magnetometer data, and simultaneously collects multi-channel temperature data of key areas of the satellite platform.

[0027] It should be noted that the carrier phase measurement value of each frequency band includes the satellite number, signal transmission time, reception time, carrier phase integer ambiguity and measurement noise information.

[0028] S1.2: Construct an ionospheric-free combination based on double-difference carrier phase observations, output the ionospheric-free double-difference carrier phase observations, and fuse the three-axis magnetometer data and multi-channel temperature data. Through least squares estimation, output the satellite platform posture data including satellite position component data, satellite velocity component data and satellite attitude component data.

[0029] Specifically: Based on the double-difference carrier phase observations, an ionospheric elimination combination is constructed, and the double-difference carrier phase observations are weighted to generate the ionospheric elimination double-difference carrier phase observations.

[0030] Among them, the construction of the deionospheric combination is expressed as: ; Where, Φ i IF is the ionospheric-free double-difference carrier phase observation value of the i-th satellite, is the L1 ionospheric double-difference carrier phase observation value of the i-th satellite, is the L2 deionospheric double-difference carrier phase observation value of the i-th satellite, is the double difference operator, Φ is the carrier phase observation, IF is the ionospheric elimination, and i is the satellite index.

[0031] The temperature coefficient of the magnetometer is used to perform temperature drift compensation calculation on the original data of the three-axis magnetometer to generate the three-axis magnetometer compensation data.

[0032] Among them, the three-axis magnetometer compensation data is generated as follows: B comp =B raw ·(1+α·ΔT); Where B comp is the compensation data of the three-axis magnetometer, B raw is the raw data of the three-axis magnetometer, α is the temperature drift coefficient of the magnetometer (example: 0.001% / °C), ΔT is the difference between the current temperature and the calibration reference temperature, comp is the compensated value, and raw is the original value.

[0033] Perform sliding average filtering on the multi-channel temperature raw data (for example, a time window of 30 seconds) to generate temperature smoothed data.

[0034] By constructing a state vector containing satellite position components, satellite velocity components and satellite attitude quaternion, the geometric range observation equation is constructed based on the ionospheric-free double-difference carrier phase observation value; Among them, the geometric distance observation equation is expressed as: ; Where, ρ calc To calculate the geometric distance, calc is the calculated value, r satis the satellite ECEF position vector, r ref is the reference star ECEF position vector, c is the speed of light, δt rcc is the receiver clock error, is the ionospheric residual, sat is the satellite position, ref is the reference satellite position, and iono is the ionosphere.

[0035] The geomagnetic field projection constraint equation is constructed using the three-axis magnetometer compensation data; the attitude thermal deformation compensation constraint equation is constructed using the temperature smoothing data.

[0036] Among them, the geomagnetic field projection constraint equation is expressed as: ; Where B nom To calibrate the three-axis magnetometer data, is the magnetic field constraint threshold, and nom is the rated value.

[0037] Among them, the attitude thermal deformation compensation constraint equation constructed by temperature smoothing data is expressed as: ; Where, T smooth,k is the temperature smoothing data, T nom is the rated temperature value, c k is the thermal sensitivity coefficient, is the temperature tolerance threshold, k is the channel index, T is the temperature, and smooth is the smoothing process.

[0038] It should be noted that B nom The calibration data of the three-axis magnetometer is obtained through pre-orbit static calibration (for example, measuring the magnetic field strength of each axis in the satellite attitude reference state in a magnetic field shielding room). The magnetic field constraint threshold is calibrated by the pre-orbit dynamic magnetic environment simulation (Example: minimum magnetic constraint threshold = 800 nT 2 , maximum magnetic confinement threshold = 5,000 nT 2 ), T nom The rated temperature value is obtained through the pre-track thermal balance (for example: the rated temperature of the star sensor mounting surface = 20°C, the rated temperature of the payload compartment = 25°C, the rated temperature of the solar wing hinge = 15°C). Temperature tolerance thresholds are calibrated by pre-orbit thermal cycling (Example: minimum tolerance for star sensor region = ±0.5°C, maximum tolerance = ±3.0°C (typical) =2.0℃)).

[0039] The least squares estimation is used to fuse and solve the multi-constraint equations to obtain the satellite platform posture data including satellite position component data, satellite velocity component data and satellite attitude component data.

[0040] S2: Based on the satellite position component data and satellite attitude component data, the ADS-B signal is processed through the stellar coordinate system transformation to generate a dynamic airspace target point cloud dataset. S2.1: Use the WGS84 coordinate system to convert the longitude and latitude coordinates in the ADS-B signal into geocentric rectangular coordinates. Based on the satellite position component data, obtain the geocentric rectangular coordinates of the satellite.

[0041] The 1090ES data link protocol parsing method is used to perform coordinate transformation calculation on the longitude and latitude high coordinates (longitude, latitude and altitude) of the ADS-B signal to obtain the geocentric rectangular coordinates of the target.

[0042] By parsing the ADS-B signal, the longitude, latitude, and altitude of a single target are obtained. Geocentric coordinate calculation is performed based on the WGS84 ellipsoid parameters to obtain the geocentric rectangular coordinates of the target's X-axis coordinate, target's Y-axis coordinate, and target's Z-axis coordinate. Based on the satellite platform posture data, the satellite X-axis coordinate, satellite Y-axis coordinate, and satellite Z-axis coordinate in the satellite position component data are extracted to obtain the satellite's geocentric rectangular coordinates.

[0043] S2.2: Generate a relative position vector from the satellite to the target by calculating the difference between the Earth-centered rectangular coordinates and the Earth-centered rectangular coordinates of the satellite.

[0044] Perform difference calculation on the X-axis coordinate, Y-axis coordinate and Z-axis coordinate corresponding to the target geocentric rectangular coordinate and the satellite geocentric rectangular coordinate, and generate the relative position vector from the satellite to the target through three-dimensional vector integration. S2.3: Use the satellite attitude component data to construct the inverse rotation matrix from the Earth-centered fixed coordinate system to the satellite body coordinate system. Apply the inverse rotation matrix to transform the relative position vector to the satellite body rectangular coordinate. Integrate all the transformed satellite body rectangular coordinates to generate a dynamic airspace target point cloud dataset.

[0045] Using satellite attitude component data, the inverse rotation matrix from the Earth-centered fixed coordinate system to the satellite body coordinate system is constructed through coordinate system orthogonal transformation; Among them, the inverse rotation matrix from the Earth-centered fixed coordinate system to the satellite body coordinate system constructed by the orthogonal transformation of the coordinate system is expressed as: ; Where R -1 ECEF→SAT is the inverse rotation matrix from the Earth-centered fixed coordinate system to the satellite body coordinate system, q x is the x-axis imaginary part of the satellite attitude component data quaternion, q y is the y-axis imaginary part of the satellite attitude component data quaternion, q z is the z-axis imaginary part of the satellite attitude component data quaternion, qw is the real part of the attitude quaternion of the satellite attitude component data, and q is the satellite attitude quaternion.

[0046] The generated inverse rotation matrix is ​​used to linearly transform the relative position vectors of the satellite's geocentric rectangular coordinates and the target's geocentric rectangular coordinates into the satellite's body rectangular coordinate system. The satellite's body rectangular coordinate systems of all targets at the same time are integrated to generate a dynamic airspace target point cloud dataset.

[0047] S3: Input the dynamic airspace target point cloud dataset into the adaptive airspace clustering algorithm, dynamically adjust the clustering neighborhood radius using satellite velocity component data, and output the central geographic coordinates of the target dense area.

[0048] S3.1: Input the dynamic airspace target point cloud dataset into the adaptive airspace clustering algorithm, extract the modulus value of the satellite velocity component data, calculate the dynamic scaling factor using the inverse proportional relationship, scale the preset benchmark neighborhood radius by the dynamic scaling factor, and output the dynamic clustering neighborhood radius.

[0049] Specifically, the dynamic airspace target point cloud dataset is input into the adaptive airspace clustering algorithm. Based on the satellite platform posture data, the satellite velocity component data is extracted, and velocity clamping protection is performed. The satellite velocity component data is calculated using the Euclidean modulus formula to obtain the modulus value of the satellite velocity component data.

[0050] Among them, the modulus expression of the satellite velocity component data is: ; Where, v mag is the modulus of the satellite velocity component data, v x is the x-axis component of the satellite velocity, v y is the y-axis component of the satellite velocity, v z is the satellite velocity component on the z-axis, and mag is the modulus.

[0051] The execution speed clamp is expressed as: ; Where, v clamp is the modulus of the satellite velocity component data after clamping, v min is the minimum speed clamp threshold, v max is the maximum speed clamping threshold, and clamp is after clamping.

[0052] It should be noted that v min With v max It is dynamically calibrated according to the track type through the simulation of high-speed working conditions before the track (Example: Low orbit minimum speed clamp threshold = 100 m / s, maximum speed clamp threshold = 8000 m / s; medium orbit minimum threshold = 50 m / s, maximum threshold = 6000 m / s; high orbit minimum threshold = 10 m / s, maximum threshold = 3500 m / s).

[0053] The dynamic scaling factor is obtained by comparing the satellite velocity modulus with the preset reference velocity using the inverse proportional relationship. The preset reference neighborhood radius (obtained through pre-orbit airspace target distribution simulation calibration) is scaled using the dynamic scaling factor to output the dynamically adjusted dynamic clustering neighborhood radius.

[0054] Among them, the dynamic clustering neighborhood radius of the output dynamic adjustment is expressed as: ; Where r adj is the dynamic clustering neighborhood radius, r base is the preset benchmark neighborhood radius, adj is the cluster radius, base is the preset benchmark radius, v ref is the reference speed, and adj is the adjusted speed.

[0055] It should be noted that the preset reference speed is obtained through pre-track calibration (example: 7800 m / s).

[0056] S3.2: Calculate the average Euclidean distance standard deviation based on the spatial distribution of the point cloud and generate a benchmark density threshold.

[0057] Through point cloud spatial distribution analysis and calculation, the Euclidean distance standard deviation calculation is performed on the three-dimensional coordinates, the benchmark density threshold is generated based on the spatial discreteness characteristics, and the satellite velocity modulus value is extracted, and the dynamic benchmark density threshold is generated using the inverse proportional relationship.

[0058] The expression for generating the dynamic baseline density threshold is: ; Where, ρ th is the dynamic reference density threshold, σ dist is the Euclidean distance standard deviation, k is the density scaling factor (example: 0.8), th is the threshold, and dist is the distance distribution.

[0059] It should be noted that the density scaling factor is an empirical parameter (for example: 0.8) calibrated through simulation of target distribution in the pre-orbit airspace.

[0060] S3.3: Use the density peak search strategy to identify high-density feature points within the baseline density threshold, merge the high-density feature points with all neighboring points within the dynamic clustering neighborhood radius, generate a target dense area, and calculate the arithmetic mean of the rectangular coordinates of the satellite body within the target dense area.

[0061] Specifically, the density peak search strategy is used to count the number of neighborhood points within the dynamic clustering neighborhood radius; based on the density threshold, the target points with the required number of neighborhood points are screened and marked as high-density feature points.

[0062] Merge all high-density feature points and their neighboring points within the dynamic clustering neighborhood radius to generate a target dense area. Calculate the arithmetic mean of the satellite body rectangular coordinates of all points in the target dense area and output the center coordinates of the satellite body coordinate system.

[0063] S3.4: Project the arithmetic mean of the satellite's rectangular coordinates to the Earth-centered fixed coordinate system using the inverse rotation matrix. Perform an inverse solution of the Earth-centered fixed coordinate system based on the WGS84 coordinate system to output the central geographic coordinates of the target dense area.

[0064] Construct a rotation matrix based on the satellite attitude component data, and calculate the projection of the center coordinate of the satellite body coordinate system through matrix multiplication to obtain the relative position vector; According to the output satellite position component data, the relative position vector is superimposed and calculated to output the geocentric fixed coordinate system; The standard inverse solution formula of the WGS84 coordinate system is used to convert the geocentric rectangular coordinate system into longitude, latitude and altitude, and output the central geographic coordinates of the target dense area.

[0065] Among them, the standard inverse solution formula of the WGS84 coordinate system is expressed as: ; Where λ is the longitude coordinate of the center of the target dense area on the earth’s surface, is the latitude coordinate of the center of the target dense area on the earth’s surface, X c is the absolute X-axis position of the center of the target dense area in the WGS84 geocentric coordinate system, c is the Y-axis absolute position of the center of the target dense area in the WGS84 geocentric fixed coordinate system, c is the absolute Z-axis position of the center of the target dense area in the WGS84 geocentric fixed coordinate system, e 2 is the square of the first eccentricity of WGS84.

[0066] S4: Based on the satellite position component data, the geographic coordinates of the center of the target dense area are converted to the WGS84 coordinate system and the attitude projection is solved to generate the beam pointing vector. The satellite attitude component data is used to drive the composite environmental field compensation model to generate the angular velocity data after zero bias compensation.

[0067] S4.1: Based on the satellite position component data, the central geographic coordinates of the target dense area are converted into geocentric rectangular coordinates using the WGS84 coordinate system.

[0068] Specifically, the WGS84 coordinate system conversion formula is used. Based on the central geographic coordinates (longitude, latitude and altitude) of the target dense area, the geocentric coordinate conversion formula is combined with the curvature radius formula of the azimuth circle to obtain the geocentric rectangular coordinates of the target center.

[0069] S4.2: Calculate the position vector of the satellite position component data in the geocentric rectangular coordinates, calculate the difference between the geocentric rectangular coordinates of the center of the target dense area and the satellite position vector, and generate a direction vector from the satellite to the target.

[0070] Specifically, based on the satellite position component data, the Earth-centered rectangular coordinates of the satellite are obtained, and the direction vector from the satellite to the target is generated by calculating the coordinate difference operation.

[0071] S4.3: Use the satellite attitude component data to construct a rotation matrix from the Earth-centered rectangular coordinate system to the radar body coordinate system, and apply the rotation matrix to transform the direction vector to the radar body rectangular coordinate system.

[0072] Specifically, the satellite attitude component data is used to construct a rotation matrix from the Earth-centered rectangular coordinate system to the radar body coordinate system. The rotation matrix is ​​applied to perform matrix multiplication operations on the direction vector from the satellite to the target and convert it to the radar body rectangular coordinate system.

[0073] S4.4: Generate a current radar beam pointing vector by normalizing the direction vector in the radar body rectangular coordinate system.

[0074] Specifically, the vector modulus is calculated using the Euclidean modulus method, and a normalization operation is performed on the radar body coordinate system to generate the current radar beam pointing vector.

[0075] S4.5: Use the vector modulus calculation method to calculate the three-axis magnetometer data to generate the magnetic field intensity characteristics, and use the temperature change rate algorithm to calculate the multi-channel temperature data and output the temperature change characteristics; The original data of the three-axis magnetometer is used to generate the magnetic field intensity characteristics through the vector modulus calculation method.

[0076] Among them, the generated magnetic field strength characteristics are expressed as: ; Where M mag is the magnetic field intensity characteristic, B x 2 is the magnetic field in the x-axis direction of the satellite body coordinate system, B y 2 is the magnetic field in the y-axis direction of the satellite body coordinate system, B z 2 is the magnetic field in the z-axis direction of the satellite body coordinate system.

[0077] Based on multi-channel temperature data, a first-order difference algorithm (fixed time interval) is used for calculation, and the temperature change rate (unit: ℃ / s) of each channel is output to generate temperature change characteristics.

[0078] Among them, the generation of temperature change characteristics is represented as: ; In the formula, ΔT ＇ max is the temperature change characteristic, is the maximum value of all channels, Δt is the sampling time interval (example: fixed value 0.1s), t n is the current time, t n-1 is the last time, T k is the temperature value of the kth temperature channel.

[0079] S4.6: Calculate the time stamps of the three-axis magnetometer data and the multi-channel temperature data by the time stamp difference algorithm, generate the time synchronization characteristics, integrate the magnetic field strength characteristics, the temperature change characteristics and the time synchronization characteristics to generate the environmental interference feature vector; Through the time stamp difference algorithm, the three-axis magnetometer data and the multi-channel temperature data are calculated to obtain the time synchronization deviation of the magnetometer and the multi-channel temperature data, and the time synchronization characteristics are generated.

[0080] Among them, the generation of time synchronization characteristics is represented as: τ sync =|t mag -t temp |; In the formula, τ sync is the time synchronization characteristic, t mag is the magnetometer data time stamp, t temp is the multi-channel temperature data time stamp, τ is the time deviation, sync is the time synchronization characteristic, mag is the magnetometer, and temp is the multi-channel temperature data.

[0081] Integrate the magnetic field strength characteristics, the temperature change characteristics and the time synchronization characteristics to generate the environmental interference feature vector.

[0082] S4.6.1: Collect three-axis magnetometer data, multi-channel temperature data, satellite attitude component data and attitude measurement true value at different orbit positions through pre-track calibration, calculate the difference between satellite attitude component data and satellite attitude measurement true value, and generate attitude measurement true value difference.

[0083] During the pre-orbit calibration phase, the satellite collects three-axis magnetometer raw data, multi-channel temperature data, satellite attitude component data, and attitude measurement true values ​​(output by star sensor and GPS differential positioning, unit: radians) at different orbital positions (for example, equator, mid-latitude, and polar regions) through the timestamp alignment method. All data timestamps are aligned to the same epoch (for example, time synchronization accuracy ≤ 1 millisecond).

[0084] Based on the satellite attitude component data (quaternion format), the pitch angle, roll angle and yaw angle are calculated using the quaternion to Euler angle function.

[0085] The difference between the satellite attitude component data and the attitude measurement true value is calculated by subtraction operation to generate the attitude measurement true value difference.

[0086] S4.6.2: Establish a coupled regression equation for the difference between the true value of attitude measurement of the three-axis magnetometer data and the multi-channel temperature data, and generate a geomagnetic field-temperature coupling error mapping table based on the coupled regression equation and store it in the onboard memory.

[0087] Based on the three-axis magnetometer data and multi-channel temperature data, the vector modulus calculation method is adopted to generate the magnetic field intensity characteristics. The true value difference of attitude measurement is used as the dependent variable to construct a coupled regression equation.

[0088] The magnetic field intensity characteristics and temperature change characteristics are partitioned by the discretization method. Based on the coupled regression equation, the pitch angle error, roll angle error and yaw angle error of each discrete partition are calculated to generate the geomagnetic field-temperature coupling error mapping table. The mapping table is stored in the onboard memory in binary format.

[0089] Among them, the geomagnetic field-temperature coupling error mapping table is expressed as: ; Where g(fcnv) is the geomagnetic field-temperature coupling error, ε θ is the pitch angle error, ε Φ is the roll angle error, ε Ψ is the yaw angle error, and g is the coupling error mapping function.

[0090] S4.7: Output the coupling error through the geomagnetic field-temperature coupling error mapping table pre-stored by the environmental interference feature vector, subtract the coupling error value from the satellite attitude component data to obtain the initial compensation attitude value, perform gyro zero bias Kalman filter compensation on the initial compensation attitude value, and generate angular velocity data after zero bias compensation.

[0091] Using the pre-stored geomagnetic field-temperature coupling error mapping table, the environmental interference feature vector is input, the coupling error value (unit: radian) is output, and the initial compensation attitude value is generated through subtraction operation based on the satellite attitude component data.

[0092] Adopting gyro zero offset Kalman filter compensation, input initial compensation attitude quantity, output zero offset compensated angular velocity data (unit: rad / s).

[0093] S5: Fuse beam pointing vector and angular velocity data through beam-attitude dynamic coupling controller to generate phase shifter code value adjustment instruction.

[0094] S5.1: Based on the central geographic coordinates of the target dense area, construct the target radar beam pointing vector through normalization operation, and calculate the spatial included angle error of the current beam pointing vector projection and the target pointing vector in real time.

[0095] Specifically, the beam pointing vector (radar body coordinate system) and the central coordinates (unit: meters) of the target dense area are processed through Euclidean modulus normalization operation to construct the target pointing vector.

[0096] Based on the current beam pointing vector and the target pointing vector, the spatial included angle error is generated through the cosine value of dot product operation and the inverse cosine function.

[0097] S5.2: Based on the zero offset compensated angular velocity data, construct a dynamic feedback control law, input the spatial included angle error into the dynamic feedback control law to generate angular velocity correction, execute angular velocity limiting, and through second-order Runge-Kutta method attitude integration, generate phase shifter code value adjustment instruction.

[0098] Based on the zero offset compensated angular velocity data, construct a dynamic feedback control law framework, adopt proportional-differential (PD) control law structure to calculate the spatial included angle error, and output angular velocity correction. (Unit: rad / s).

[0099] Among them, the proportional-differential control law is adopted, the angular velocity correction is generated through error change rate difference calculation, and is expressed as: ; In the formula, Δω cmd is the angular velocity correction, K P is the proportional gain, K D is the differential gain, Ψ crr is the spatial included angle error, is the time differential operator, ω is the angular velocity vector, crr is the spatial pointing error, and cmd is the command value.

[0100] Specifically, the time differential operator is expressed as: ; In the formula, is the attitude control period, Ψ crr (t n) is the spatial angle error at the current moment, (t n-1 ) is the spatial angle error at the previous moment.

[0101] It should be noted that the proportional gain and differential gain in the proportional-differential control law are determined through frequency-domain stability analysis of satellite attitude control, and the Δt attitude control period is the fixed execution period of the onboard computer attitude control task (example value: 0.01 s).

[0102] The generated angular velocity correction value is limited by the preset maximum velocity clamping threshold, and the updated angular velocity correction value is generated through the clamping function processing.

[0103] Based on the angular velocity data after zero bias compensation, the updated angular velocity correction value is superimposed and calculated to output the updated satellite angular velocity vector; Through the second-order Runge-Kutta attitude integration, a two-step slope calculation is performed on the current attitude quaternion and the updated satellite angular velocity vector. The updated satellite attitude quaternion is output using the results of the two slope calculations.

[0104] Among them, the two-step slope calculation of the current attitude quaternion and the updated satellite angular velocity vector is performed through the second-order Runge-Kutta method attitude integration and is expressed as: ; Where, is the satellite attitude quaternion change rate, ω x is the satellite x-axis angular velocity component, ω y is the satellite y-axis angular velocity component, ω z is the angular velocity component of the satellite along the z-axis, and Ω(ω) is a skew-symmetric matrix function.

[0105] Based on the updated satellite attitude quaternion, calculations are performed through the coordinate system projection relationship to obtain the new beam pointing vector in the radar body coordinate system. The pre-stored calibration function (established through ground experiments) is used to generate the phase shifter code value adjustment instruction (for example: 12-bit binary digital value) for the new beam pointing vector.

[0106] S6: Input the phase shifter code value adjustment instruction to the spaceborne SAR radar phase shifter to drive the radar beam to cover the central geographic coordinates of the target dense area in real time.

[0107] S6.1: Use the phase shifter code value adjustment instruction and transmit it to the onboard SAR radar phase shifter through the onboard high-speed data bus.

[0108] Based on the instruction parsing protocol, the phase shifter code value adjustment instruction is decoded and processed to generate the phase control word of each phase shifter unit (for example, an 8-bit control word corresponds to 256 levels of phase quantization).

[0109] S6.2: Use the digital-to-analog conversion circuit to convert the phase control word into an analog voltage signal (for example, in the range of 0 to 5 V). Based on a pre-stored voltage-phase response curve (for example, established through a ground-based microwave anechoic chamber calibration experiment), drive the phase shifter unit to adjust the phase offset and output a phase-modulated RF carrier signal.

[0110] Among them, the voltage-phase response curve is expressed as: ; Where, ΔΦ m ps is the phase offset of the mth unit, k v is the voltage-phase conversion coefficient, V m ctrl is the phase offset of the mth unit, Φ m 0 is the zero-bias phase of the mth unit, m is the phase shifter unit index, and ctrl is the control attribute identifier.

[0111] A vector synthesis operation is performed on the phase-modulated RF carrier signal through the antenna array feeding network to generate a synthetic beam electric field intensity distribution.

[0112] Based on the array factor theory, the spatial energy distribution of the electric field intensity distribution of the synthetic beam is regulated, and the main lobe of the radar beam is controlled to accurately point to the central geographic coordinates of the target dense area.

[0113] In summary, the present invention achieves significant improvements in positioning accuracy and timeliness in dynamic airspace target-dense areas by: using an adaptive airspace clustering algorithm to dynamically adjust the neighborhood recognition range through the real-time motion state of the satellite. A dynamic scaling factor is generated based on the satellite velocity vector modulus, and the preset neighborhood radius is adaptively scaled: when the satellite is moving at high speed, the neighborhood radius is automatically reduced to accurately match the spatial sparse distribution characteristics of the target point cloud; when the satellite is moving at low speed, the neighborhood radius is expanded to effectively avoid missed target recognition. At the same time, a dynamic density determination threshold is generated in combination with the spatial discreteness characteristics of the target point cloud to achieve collaborative optimization of density peak search and neighborhood point merging. The present invention significantly reduces the center positioning error in target-dense areas, greatly compresses beam pointing delay, ensures that the main lobe of the synthetic aperture radar continuously covers the high-speed maneuvering target cluster, and improves the continuous monitoring capability and guidance accuracy of time-sensitive targets.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A real-time guidance method for spaceborne SAR radar targets based on ADS-B signals, characterized by: include, Receive Beidou dual-band signals and global ADS-B signals in real time, and use the carrier phase double-difference ionospheric elimination combined algorithm to generate satellite platform posture data including satellite position component data, satellite velocity component data and satellite attitude component data; Based on the satellite position component data and satellite attitude component data, the ADS-B signal is processed through the stellar coordinate system transformation to generate a dynamic airspace target point cloud dataset; The spatial target point cloud dataset is input into the adaptive spatial clustering algorithm, which uses the satellite velocity component data to dynamically adjust the clustering neighborhood radius and output the central geographic coordinates of the target dense area. Based on the satellite position component data, the geographic coordinates of the center of the target dense area are converted into the WGS84 coordinate system and the attitude projection is solved to generate the beam pointing vector. The satellite attitude component data is used to drive the composite environment field compensation model to generate the angular velocity data after zero bias compensation. The beam-attitude dynamic coupling controller fuses the beam pointing vector and angular velocity data to generate phase shifter code value adjustment instructions; The phase shifter code value adjustment instruction is input into the spaceborne SAR radar phase shifter to drive the radar beam to cover the central geographic coordinates of the target dense area in real time.

2. The method for real-time guidance of a spaceborne SAR radar target based on ADS-B signals according to claim 1, wherein: The satellite platform posture data of the satellite position component data, satellite velocity component data and satellite attitude component data are generated in the following specific steps: The dual-frequency carrier phase measurement values ​​are calculated using BeiDou dual-band signals. Inter-satellite single-difference processing is performed on the dual-frequency carrier phase measurement values ​​to generate single-difference observation values. Satellite-ground double-difference processing is then performed to generate double-difference carrier phase observation values. Simultaneously, onboard three-axis magnetometer data and multi-channel temperature data are collected. An ionospheric-free combination is constructed based on double-difference carrier phase observations, and the ionospheric-free double-difference carrier phase observations are output. The three-axis magnetometer data and multi-channel temperature data are integrated and solved through least squares estimation to output the satellite platform posture data including satellite position component data, satellite velocity component data and satellite attitude component data.

3. The method for real-time guidance of a spaceborne SAR radar target based on ADS-B signals according to claim 1, wherein: The specific steps for generating the dynamic airspace target point cloud dataset are as follows: Use WGS84 coordinate system conversion to convert the longitude and latitude coordinates in the ADS-B signal into geocentric rectangular coordinates, and obtain the geocentric rectangular coordinates of the satellite based on the satellite position component data; The relative position vector from the satellite to the target is generated by calculating the difference between the earth-centered rectangular coordinates and the earth-centered rectangular coordinates of the satellite; The satellite attitude component data is used to construct the inverse rotation matrix from the Earth-centered fixed coordinate system to the satellite body coordinate system. The inverse rotation matrix is ​​applied to transform the relative position vector into the satellite body rectangular coordinate. All the transformed satellite body rectangular coordinates are integrated to generate a dynamic airspace target point cloud dataset.

4. The method for real-time guidance of a spaceborne SAR radar target based on ADS-B signals according to claim 1, wherein: The dynamic airspace target point cloud dataset is input into the adaptive airspace clustering algorithm, the clustering neighborhood radius is dynamically adjusted using the satellite velocity component data, and the central geographic coordinates of the target dense area are output. The specific steps are as follows: The dynamic airspace target point cloud dataset is input into the adaptive airspace clustering algorithm, the modulus value of the satellite velocity component data is extracted, the dynamic scaling factor is calculated using the inverse proportional relationship, the preset reference neighborhood radius is scaled by the dynamic scaling factor, and the dynamically adjusted dynamic clustering neighborhood radius is output; Calculate the average Euclidean distance standard deviation based on the spatial distribution of the point cloud and generate a benchmark density threshold; A density peak search strategy is used to identify high-density feature points within the baseline density threshold. The high-density feature points are combined with all neighboring points within the dynamic clustering neighborhood radius to generate a target dense area. The arithmetic mean of the rectangular coordinates of the satellite bodies within the target dense area is calculated. The arithmetic mean of the satellite's rectangular coordinates is projected into the geocentric fixed coordinate system through the inverse rotation matrix. The geocentric fixed coordinate system is inversely solved based on the WGS84 coordinate system to output the central geographic coordinates of the target dense area.

5. The method for real-time guidance of spaceborne SAR radar targets based on ADS-B signals according to claim 1, wherein: The beam pointing vector is generated in the following steps: Based on the satellite position component data, the central geographic coordinates of the target dense area are converted into geocentric rectangular coordinates through the WGS84 coordinate system; Calculate the position vector of the satellite position component data in the geocentric rectangular coordinates, calculate the difference between the geocentric rectangular coordinates of the center of the target dense area and the satellite position vector, and generate the direction vector from the satellite to the target; Use the satellite attitude component data to construct a rotation matrix from the Earth-centered rectangular coordinate system to the radar body coordinate system, and apply the rotation matrix to transform the direction vector to the radar body rectangular coordinate system; The current radar beam pointing vector is generated by normalizing the direction vector in the radar body rectangular coordinate system.

6. The method for real-time guidance of a spaceborne SAR radar target based on ADS-B signals according to claim 1, wherein: The angular velocity data after the zero bias compensation is generated in the following specific steps: The three-axis magnetometer data is calculated using the vector modulus calculation method to generate magnetic field intensity characteristics, and the temperature change rate algorithm is used to calculate the multi-channel temperature data to output the temperature change characteristics; The timestamp difference algorithm is used to calculate the acquisition timestamps of the three-axis magnetometer data and the multi-channel temperature data to generate time synchronization features. The environmental interference feature vector is generated by integrating the magnetic field intensity features, temperature change features and time synchronization features. The coupling error is output through the geomagnetic field-temperature coupling error mapping table pre-stored in the environmental interference feature vector, and the coupling error value is subtracted from the satellite attitude component data to obtain the initial compensation attitude value. The gyro zero bias Kalman filter compensation is performed on the initial compensation attitude value to generate the angular velocity data after zero bias compensation.

7. The method for real-time guidance of a spaceborne SAR radar target based on ADS-B signals according to claim 6, wherein: The pre-stored geomagnetic field-temperature coupling error mapping table refers to: During the pre-orbit calibration phase, three-axis magnetometer data, multi-channel temperature data, satellite attitude component data, and attitude measurement true values ​​at different orbital positions are collected, and the difference between the satellite attitude component data and the satellite attitude measurement true value is calculated to generate the attitude measurement true value difference. Based on the three-axis magnetometer data and multi-channel temperature data, the vector modulus calculation method is used to generate the magnetic field intensity characteristics. The true value difference of the attitude measurement is used as the dependent variable to construct a coupled regression equation. The magnetic field intensity characteristics and temperature change characteristics are partitioned by the discretization method. Based on the coupled regression equation, each discrete partition is calculated to generate a geomagnetic field-temperature coupling error mapping table and store it in the onboard memory.

8. The method for real-time guidance of spaceborne SAR radar targets based on ADS-B signals according to claim 1, wherein: Generate the phase shifter code value adjustment instruction, the specific steps are as follows: Based on the central geographic coordinates of the target dense area, the target radar beam pointing vector is constructed through normalization operation, and the spatial angle error between the current beam pointing vector projection and the target pointing vector is calculated in real time; A dynamic feedback control law is constructed based on the angular velocity data after zero bias compensation. The spatial angle error is input into the dynamic feedback control law to generate an angular velocity correction. The angular velocity is limited, and the phase shifter code value adjustment instruction is generated through the second-order Runge-Kutta method attitude integration.

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