Satellite-borne sar radar target real-time guiding method based on ads-b signal

Abstract

The application discloses a satellite-borne SAR radar target real-time guiding method based on ADS-B signals and relates to the technical field of radar control, which comprises the following steps: receiving Beidou double-band signals and global ADS-B signals in real time, adopting a carrier phase double-difference ionosphere elimination combined algorithm to solve, and generating satellite platform position data containing satellite position component data, satellite speed component data and satellite attitude component data; inputting the spatial target point cloud data set into an adaptive spatial clustering algorithm, dynamically adjusting the clustering neighborhood radius by using the satellite speed component data, and outputting the central geographic coordinates of the target dense area; fusing the beam pointing vector and angular velocity data through a beam-attitude dynamic coupling controller to generate a phase shifter code value adjustment instruction; and dynamically adjusting the neighborhood recognition range through the real-time motion state of the satellite, so that the positioning accuracy and timeliness of the dynamic spatial target dense area are significantly improved. The continuous monitoring capability and guiding precision for time-sensitive targets are improved.

Description

Technical Field

[0001] This invention relates to the field of radar control technology, and in particular to a real-time target guidance method for spaceborne SAR radar based on ADS-B signals. Background Technology

[0002] Beamguidance technology for spaceborne SAR radar relies on high-precision platform pose calculation and real-time target localization. Current mainstream solutions employ GNSS point positioning combined with inertial navigation.

[0003] ADS-B signal analysis can enable airspace target surveillance, but it is not deeply coupled with radar beam control. In the field of beam pointing control, phased array radar achieves beam deflection by adjusting the phase shifter code value, and its accuracy is significantly affected by attitude measurement errors and dynamic environmental interference.

[0004] The existing technology has a core flaw: the real-time beam guidance accuracy of high-speed moving satellite platforms for dynamic airspace targets is insufficient.

[0005] Traditional clustering algorithms use a fixed neighborhood radius, which cannot adapt to the spatial distribution distortion of point clouds caused by changes in satellite velocity; attitude calculation does not compensate for geomagnetic field-temperature coupling error, resulting in beam pointing deviation.

[0006] Beam-attitude control closed-loop response delay makes it difficult to track high-speed target clusters;

[0007] Especially in the identification of dense target areas, fixed clustering parameters result in a large error in the target center positioning, which severely restricts the coverage capability of SAR radar for time-sensitive targets. Summary of the Invention

[0008] In view of the aforementioned existing problems, the present invention is proposed.

[0009] Therefore, this invention provides a real-time target guidance method for spaceborne SAR radar based on ADS-B signals to solve the problem of real-time and accurate beam guidance of dynamic airspace targets under high-speed conditions by spaceborne SAR radar.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0011] This invention provides a real-time target guidance method for spaceborne SAR radar 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 deionization combined algorithm to generate satellite platform attitude data containing satellite position component data, satellite velocity component data and satellite attitude component data.

[0012] Based on satellite position component data and satellite attitude component data, ADS-B signals are processed by celestial coordinate system transformation to generate a dynamic airspace target point cloud dataset.

[0013] The airspace target point cloud dataset is input into the adaptive airspace clustering algorithm, which dynamically adjusts the clustering neighborhood radius using satellite velocity component data and outputs the central geographic coordinates of the dense target area.

[0014] Based on satellite position component data, the geographic coordinates of the center of the dense target area are transformed and solved by WGS84 coordinate system and attitude projection to generate a beam pointing vector. The satellite attitude component data is used to drive the composite environmental field compensation model to generate angular velocity data after zero bias compensation.

[0015] The beam pointing vector and angular velocity data are fused by the beam-attitude dynamic coupling controller to generate phase shifter code value adjustment commands.

[0016] The phase shifter code value adjustment command is input to the phase shifter of the spaceborne SAR radar to drive the radar beam to cover the center geographic coordinates of the dense target area in real time.

[0017] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the specific steps for generating satellite platform pose data, including satellite position component data, satellite velocity component data, and satellite attitude component data, are as follows:

[0018] The dual-frequency carrier phase measurement values ​​are calculated by solving the dual-frequency carrier phase measurement values ​​through the Beidou dual-band signal. The dual-frequency carrier phase measurement values ​​are processed by inter-satellite single difference to generate single difference observation values. Then, the satellite-to-ground double difference processing is performed to generate double difference carrier phase observation values. At the same time, data from the onboard triaxial magnetometer and multi-channel temperature data are collected.

[0019] An ionospheric desaturation combination is constructed based on double-difference carrier phase observations, and the ionospheric desaturation double-difference carrier phase observations are output. The triaxial magnetometer data and multi-channel temperature data are fused together, and the satellite platform attitude data containing satellite position component data, satellite velocity component data and satellite attitude component data are output through least squares estimation.

[0020] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the specific steps for generating the dynamic airspace target point cloud dataset are as follows:

[0021] The latitude, longitude, and altitude coordinates in the ADS-B signal are converted to geocentric fixed rectangular coordinates using the WGS84 coordinate system transformation. Based on the satellite position component data, the geocentric fixed rectangular coordinates of the satellite are obtained.

[0022] The relative position vector from the satellite to the target is generated by calculating the difference between the geocentric fixed rectangular coordinates and the geocentric fixed rectangular coordinates of the satellite.

[0023] An inverse rotation matrix from the geocentric fixed coordinate system to the satellite body coordinate system is constructed using satellite attitude component data. The relative position vector is then transformed to satellite body rectangular coordinates using the inverse rotation matrix. All transformed satellite body rectangular coordinates are then integrated to generate a dynamic airspace target point cloud dataset.

[0024] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the specific steps of inputting the dynamic spatial target point cloud dataset into an adaptive spatial clustering algorithm, dynamically adjusting the clustering neighborhood radius using satellite velocity component data, and outputting the central geographic coordinates of the target dense area are as follows.

[0025] The dynamic spatial target point cloud dataset is input into the adaptive spatial clustering algorithm to extract the modulus of the satellite velocity component data. The dynamic scaling factor is calculated using the reciprocal proportional relationship. The preset benchmark neighborhood radius is scaled by the dynamic scaling factor, and the dynamically adjusted dynamic clustering neighborhood radius is output.

[0026] The average Euclidean distance standard deviation is calculated based on the spatial distribution of point clouds to generate a baseline density threshold.

[0027] A density peak search strategy is used to identify high-density feature points with a baseline density threshold. These high-density feature points are then merged with all neighboring points within the radius of the dynamic clustering neighborhood to generate a target dense region. The arithmetic mean of the rectangular coordinates of the satellite body within the target dense region is then calculated.

[0028] The arithmetic mean of the rectangular coordinates of the satellite body is projected onto the geocentric fixed coordinate system by the inverse rotation matrix. The geocentric fixed coordinate system is then inversely solved based on the WGS84 coordinate system to output the central geographic coordinates of the target dense area.

[0029] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the specific steps for generating the beam pointing vector are as follows:

[0030] Based on satellite location component data, the central geographic coordinates of the dense target area are converted to geocentric fixed rectangular coordinates using the WGS84 coordinate system.

[0031] Calculate the position vector of the satellite position component data in the geocentric fixed rectangular coordinate system, calculate the difference between the geocentric fixed rectangular coordinate system of the center of the target-dense area and the satellite position vector, and generate the direction vector from the satellite to the target.

[0032] A rotation matrix from the geocentric fixed rectangular coordinate system to the radar body coordinate system is constructed using satellite attitude component data. The rotation matrix is ​​then used to transform the direction vector to the radar body rectangular coordinate system.

[0033] The current radar beam pointing vector is generated by normalizing the direction vector in the radar's Cartesian coordinate system.

[0034] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the specific steps for generating the zero-bias compensated angular velocity data are as follows:

[0035] The vector magnitude calculation method is used to calculate the triaxial magnetometer data to generate magnetic field strength characteristics. The temperature change rate algorithm is used to calculate the multi-channel temperature data to output the temperature change characteristics.

[0036] The time stamp difference algorithm is used to calculate the acquisition timestamps of triaxial magnetometer data and multi-channel temperature data to generate time synchronization features. The magnetic field strength features, temperature change features and time synchronization features are integrated to generate an environmental interference feature vector.

[0037] The coupling error is output through the geomagnetic field-temperature coupling error mapping table pre-stored by the environmental interference feature vector. The initial compensated attitude value is obtained by subtracting the coupling error value from the satellite attitude component data. Gyro zero-bias Kalman filter compensation is performed on the initial compensated attitude value to generate zero-bias compensated angular velocity data.

[0038] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the pre-stored geomagnetic field-temperature coupling error mapping table refers to...

[0039] During the pre-orbit calibration phase, triaxial magnetometer data, multi-channel temperature data, satellite attitude component data, and attitude measurement true values ​​are collected at different orbital positions. The difference between the satellite attitude component data and the satellite attitude measurement true values ​​is calculated to generate the attitude measurement true value difference.

[0040] A coupled regression equation is established to measure the difference between the true values ​​of attitude measurements using triaxial magnetometer data and multi-channel temperature data. Based on the coupled regression equation, a geomagnetic field-temperature coupling error mapping table is generated and stored in the onboard memory.

[0041] As a preferred embodiment of the real-time target guidance method for spaceborne SAR radar based on ADS-B signals described in this invention, the specific steps for generating the phase shifter code value adjustment command are as follows:

[0042] Based on the central geographic coordinates of the dense target area, the target radar beam pointing vector is constructed through normalization calculation, and the spatial angle error between the current beam pointing vector projection and the target pointing vector is calculated in real time.

[0043] 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 the angular velocity correction, and the angular velocity is limited. The phase shifter code value adjustment command is generated by attitude integration using the second-order Runge-Kutta method.

[0044] The beneficial effects of this invention are:

[0045] An adaptive spatial clustering algorithm dynamically adjusts the neighborhood identification range based on the real-time motion state of satellites, significantly improving the positioning accuracy and timeliness of densely populated target areas in dynamic spatial domains. A dynamic scaling factor is generated based on the satellite velocity vector magnitude to adaptively scale the preset neighborhood radius: the neighborhood radius automatically shrinks when the satellite moves at high speed, accurately matching the spatially sparse distribution characteristics of the target point cloud; the neighborhood radius expands when the satellite moves at low speed, effectively avoiding target omissions. Simultaneously, a dynamic density judgment threshold is generated by combining the spatial dispersion characteristics of the target point cloud, achieving coordinated optimization of density peak search and neighborhood point merging. This invention significantly reduces the center positioning error of densely populated target areas, greatly compresses beam pointing delay, ensures continuous coverage of high-speed maneuvering target clusters by the synthetic aperture radar main lobe, and improves the continuous monitoring capability and guidance accuracy for time-sensitive targets. Attached Figure Description

[0046] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of a real-time target guidance method for spaceborne SAR radar based on ADS-B signals.

[0048] Figure 2 A flowchart for satellite platform pose calculation.

[0049] Figure 3 A flowchart for locating densely populated target areas.

[0050] Figure 4 This is a flowchart for beam pointing control. Detailed Implementation

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used 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 different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0054] Reference Figures 1-4 This is one embodiment of the present invention, which provides a real-time target guidance method for spaceborne SAR radar based on ADS-B signals, including the following steps:

[0055] S1: Real-time reception of BeiDou dual-band signals and global ADS-B signals, using carrier phase double-difference deionization combination algorithm to generate satellite platform attitude data containing satellite position component data, satellite velocity component data and satellite attitude component data.

[0056] Specifically, the satellite-borne BeiDou dual-frequency synchronous receiver is used to receive BeiDou dual-band signals in real time, referred to as L1 band and L2 band, and to synchronously receive global ADS-B signals. Multi-source data is synchronized using GPS pulse (1PPS) hard time synchronization.

[0057] It should be noted that the carrier phase measurement for each frequency band includes the satellite number, signal transmission time, reception time, carrier phase integer ambiguity, and measurement noise information.

[0058] S1.1: The dual-frequency carrier phase measurement value is calculated by solving the dual-frequency carrier phase measurement value through the Beidou dual-frequency signal, the inter-satellite single difference processing is performed on the dual-frequency carrier phase measurement value to generate single difference observation value, and the satellite-ground double difference processing is performed to generate double difference carrier phase observation value. At the same time, the on-board triaxial magnetometer data and multi-channel temperature data are collected.

[0059] Specifically, based on the BeiDou dual-band signal, the carrier phase measurement value of each band is analyzed, inter-satellite single-difference processing is performed, and the BeiDou satellite with the highest elevation angle is selected as the reference satellite (example: PRN-C01). The dual-frequency carrier phase measurement value of the non-reference satellite is differentially calculated with the dual-frequency carrier phase measurement value of the reference satellite to eliminate the influence of satellite clock error and generate single-difference observation value.

[0060] Using satellite-ground double-difference processing, time series difference calculations are performed on two time-aligned single-difference observation arrays that are continuously output, completely eliminating receiver clock errors and generating double-difference carrier phase observations.

[0061] The satellite collects triaxial magnetometer data along the X, Y, and Z axes using an onboard triaxial magnetometer, and simultaneously acquires multi-channel temperature data for key areas of the satellite platform.

[0062] It should be noted that the carrier phase measurement for each frequency band includes the satellite number, signal transmission time, reception time, carrier phase integer ambiguity, and measurement noise information.

[0063] S1.2: Construct an ionospheric de-spheric combination based on double-difference carrier phase observations, output ionospheric double-difference carrier phase observations, and fuse triaxial magnetometer data and multi-channel temperature data. Through least squares estimation, output satellite platform attitude data containing satellite position component data, satellite velocity component data and satellite attitude component data.

[0064] Specifically: Based on the double-difference carrier phase observations, an ionosphere-depleting combination is constructed, and the double-difference carrier phase observations are weighted and calculated to generate ionosphere-depleting double-difference carrier phase observations.

[0065] The construction of the deionization layer combination is represented as follows:

[0066] ;

[0067] In the formula, Φ i IF The ionospheric double-difference carrier phase observation value of the i-th satellite. This represents the L1 deionized double-difference carrier phase observation value for the i-th satellite. The L2 deionized double-difference carrier phase observation value for the i-th satellite. Φ is the double difference operator, IF is the carrier phase observation, i is the deionization operator, and i is the satellite index.

[0068] Temperature drift compensation calculations are performed on the raw data of the triaxial magnetometer using the magnetometer temperature coefficient to generate triaxial magnetometer compensation data.

[0069] The generated triaxial magnetometer compensation data is represented as follows:

[0070] B comp =B raw ·(1+α·ΔT)

[0071] In the formula, B comp For triaxial magnetometer compensation data, B raw The values ​​are the raw data from the triaxial magnetometer, where α is the magnetometer temperature drift coefficient (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.

[0072] Perform a moving average filter calculation on the raw temperature data from multiple channels (example: time window of 30 seconds) to generate smoothed temperature data.

[0073] By constructing a state vector containing satellite position components, satellite velocity components, and satellite attitude quaternions, a geometric distance observation equation is constructed based on the deionized ionospheric double-difference carrier phase observations.

[0074] The geometric distance observation equation is expressed as follows:

[0075] ;

[0076] In the formula, ρ calc To calculate the geometric distance, calc is the calculated value, and r is the calculated value. sat r is the satellite ECEF position vector. ref Let c be the position vector of the reference star ECEF, and δt be the speed of light. rcc For receiver clock bias, , where is the ionospheric residual, sat is the satellite position, ref is the reference satellite position, and iono is the ionosphere.

[0077] Geomagnetic field projection constraint equations were constructed using triaxial magnetometer compensation data; attitude thermal deformation compensation constraint equations were constructed using temperature smoothing data.

[0078] The geomagnetic field projection constraint equation is expressed as follows:

[0079] ;

[0080] In the formula, B nom To calibrate the triaxial magnetometer data, nom is the magnetic field constraint threshold, and nom is the rated value.

[0081] The attitude thermal deformation compensation constraint equation constructed using temperature smoothing data is expressed as follows:

[0082] ;

[0083] In the formula, T smooth,k For temperature smoothing data, T nom The rated temperature value, c k The thermal sensitivity coefficient, Here, k is the temperature tolerance threshold, k is the channel index, T is the temperature, and smooth is the smoothing process.

[0084] It should be noted that B nom The calibration data of the triaxial magnetometer is obtained through pre-orbit static calibration (example: measuring the magnetic field strength of each axis in the satellite's attitude reference state in a magnetic field shielded room). The magnetic field confinement threshold is calibrated through pre-orbit dynamic magnetic environment simulation (Example: minimum magnetic confinement threshold = 800 nT). 2 Maximum magnetic confinement threshold = 5,000 nT 2 ), T nom The rated temperature values ​​are obtained through on-orbit thermal balance (example: rated temperature of star sensor mounting surface = 20℃, rated temperature of payload bay = 25℃, rated temperature of solar panel hinge = 15℃). Temperature tolerance thresholds are calibrated through pre-orbit thermal cycling (Example: minimum tolerance = ±0.5℃, maximum tolerance = ±3.0℃ for the star sensor region (typical value)). =2.0℃).

[0085] Least squares estimation is used to fuse and solve the multi-constraint equations, resulting in satellite platform attitude data that includes satellite position components, satellite velocity components, and satellite attitude components.

[0086] S2: Based on satellite position and attitude component data, ADS-B signals are processed through stellar coordinate system transformation to generate a dynamic spatial target point cloud dataset.

[0087] S2.1: Use WGS84 coordinate system transformation to convert the latitude, longitude and altitude coordinates in the ADS-B signal into geocentric fixed rectangular coordinates, and obtain the geocentric fixed rectangular coordinates of the satellite based on the satellite position component data.

[0088] The 1090ES data link protocol parsing method is used to perform coordinate transformation calculations on the latitude, longitude, and altitude coordinates (longitude, latitude, and altitude values) of the ADS-B signal to obtain the geocentric fixed rectangular coordinates of the target.

[0089] The longitude, latitude, and altitude values ​​of a single target are obtained by parsing the ADS-B signal. Based on the WGS84 ellipsoid parameters, geocentric coordinate calculation is performed to obtain the geocentric fixed rectangular coordinates of the target's X-axis, Y-axis, and Z-axis.

[0090] Based on the satellite platform pose data, the satellite X-axis coordinates, satellite Y-axis coordinates, and satellite Z-axis coordinates are extracted from the satellite position component data to obtain the satellite's geocentric fixed rectangular coordinates.

[0091] S2.2: By calculating the difference between the geocentric fixed rectangular coordinates and the geocentric fixed rectangular coordinates of the satellite, the relative position vector of the satellite to the target is generated.

[0092] The X, Y, and Z coordinates corresponding to the target's geocentric fixed rectangular coordinates and the satellite's geocentric fixed rectangular coordinates are calculated using difference calculations. Through three-dimensional vector integration, a relative position vector from the satellite to the target is generated.

[0093] S2.3: Construct an inverse rotation matrix from the geocentric fixed coordinate system to the satellite body coordinate system using satellite attitude component data. Apply the inverse rotation matrix to transform the relative position vector to the satellite body rectangular coordinates. Integrate all the transformed satellite body rectangular coordinates to generate a dynamic airspace target point cloud dataset.

[0094] Using satellite attitude component data, an inverse rotation matrix from the geocentric fixed coordinate system to the satellite body coordinate system is constructed through coordinate system orthogonal transformation;

[0095] The inverse rotation matrix from the geocentric fixed coordinate system to the satellite body coordinate system, constructed through orthogonal transformation of the coordinate systems, is expressed as:

[0096] ;

[0097] In the formula, R -1 ECEF→SAT Let q be the inverse rotation matrix from the geocentric fixed coordinate system to the satellite body coordinate system. x For the imaginary part of the quaternion of satellite attitude component data along the x-axis, q y For the quaternion y-axis imaginary part of the satellite attitude component data, q z For the imaginary part of the quaternion along the z-axis of the satellite attitude component data, q w Let q be the real part of the attitude quaternion for satellite attitude component data, and q be the satellite attitude quaternion.

[0098] The generated inverse rotation matrix is ​​used to linearly transform the relative position vectors of the satellite geocentric fixed rectangular coordinates and the target geocentric fixed rectangular coordinates to the satellite body rectangular coordinate system. The satellite body rectangular coordinate systems of all targets at the same time are integrated to generate a dynamic airspace target point cloud dataset.

[0099] 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 dense target area.

[0100] S3.1: Input the dynamic spatial target point cloud dataset into the adaptive spatial clustering algorithm, extract the modulus of the satellite velocity component data, calculate the dynamic scaling factor using the reciprocal proportional relationship, scale the preset benchmark neighborhood radius using the dynamic scaling factor, and output the dynamic clustering neighborhood radius.

[0101] Specifically, the dynamic airspace target point cloud dataset is input into the adaptive airspace clustering algorithm. Based on the satellite platform pose data, satellite velocity component data is extracted, and velocity clamping protection is performed. The Euclidean modulus formula is used to calculate the modulus of the satellite velocity component data to obtain the modulus of the satellite velocity component data.

[0102] The modulus expression for obtaining the satellite velocity component data is as follows:

[0103] ;

[0104] In the formula, v mag v is the magnitude of the satellite velocity component data. x v is the satellite velocity component along the x-axis. y v is the satellite velocity component along the y-axis. z Here, mag represents the satellite velocity component along the z-axis, and mag is the magnitude.

[0105] The execution speed clamp is expressed as:

[0106] ;

[0107] In the formula, v clamp v represents the magnitude of the clamped satellite velocity component data. min The minimum speed clamping threshold, v max is the maximum speed clamping threshold, and clamp is the value after clamping.

[0108] It should be noted that v min With v max Dynamic calibration based on track type is achieved through high-speed pre-track operating condition simulation.

[0109] (Example: Low-Earth orbit minimum speed clamping threshold = 100 m / s, maximum speed clamping threshold = 8000 m / s; Medium-Earth orbit minimum threshold = 50 m / s, maximum threshold = 6000 m / s; High-Earth orbit minimum threshold = 10 m / s, maximum threshold = 3500 m / s).

[0110] By using the reciprocal proportional relationship between the satellite velocity modulus and the preset reference velocity, a dynamic scaling factor is obtained. The preset reference neighborhood radius (obtained through simulation calibration of the target distribution in the pre-orbit space) is then scaled using the dynamic scaling factor, and the dynamically adjusted dynamic clustering neighborhood radius is output.

[0111] The dynamically adjusted clustering neighborhood radius is expressed as follows:

[0112] ;

[0113] In the formula, r adj r is the dynamic clustering neighborhood radius. base The preset baseline neighborhood radius is defined by adj, where adj is the cluster radius, base is the preset baseline radius, and v is the base radius. ref The base speed is denoted as 'adj', and the adjusted speed is denoted as 'adj'.

[0114] It should be noted that the preset reference speed is obtained through pre-rail calibration (example: 7800 m / s).

[0115] S3.2: Calculate the average Euclidean distance standard deviation based on the spatial distribution of point cloud and generate a baseline density threshold.

[0116] By analyzing the spatial distribution of point clouds, the standard deviation of Euclidean distance is calculated for the three-dimensional coordinates. A baseline density threshold is generated based on the spatial dispersion characteristics. The satellite velocity modulus is extracted, and a dynamic baseline density threshold is generated using an inverse proportional relationship.

[0117] The expression for generating the dynamic baseline density threshold is as follows:

[0118] ;

[0119] In the formula, ρ th σ is the dynamic baseline density threshold. dist is the standard deviation of the Euclidean distance, k is the density scaling factor (example: 0.8), th is the threshold, and dist is the distance distribution.

[0120] It should be noted that the density scaling factor is an empirical parameter calibrated through simulation of the target distribution in the pre-orbit airspace (example: 0.8).

[0121] S3.3: The density peak search strategy is used to identify high-density feature points with a baseline density threshold. The high-density feature points are merged with all neighborhood points within the radius of the dynamic clustering neighborhood to generate the target dense area. The arithmetic mean of the rectangular coordinates of the satellite body within the target dense area is then calculated.

[0122] Specifically, a density peak search strategy is used to count the number of neighborhood points within the radius of its dynamic clustering neighborhood; based on the density threshold, target points that meet the target number of neighborhood points are selected and marked as high-density feature points.

[0123] Merge all high-density feature points and their neighboring points within the radius of their dynamic clustering neighborhood to generate a target dense region. Calculate the arithmetic mean of the satellite body rectangular coordinates of all points within the target dense region and output the center coordinates of the satellite body coordinate system.

[0124] S3.4: Project the arithmetic mean of the rectangular coordinates of the satellite body to the geocentric fixed coordinate system through the inverse rotation matrix, perform inverse calculation on the geocentric fixed coordinate system based on the WGS84 coordinate system, and output the central geographic coordinates of the target dense area.

[0125] A rotation matrix is ​​constructed based on satellite attitude component data, and the relative position vector is obtained by projecting the center coordinates of the satellite body coordinate system through matrix multiplication.

[0126] Based on the output satellite position component data, the relative position vectors are superimposed and calculated to output the geocentric fixed coordinate system;

[0127] Using the WGS84 coordinate system standard inverse calculation formula, the geocentric fixed rectangular coordinate system is converted into latitude, longitude and altitude, and the central geographic coordinates of the target dense area are output.

[0128] The standard inverse solution formula using the WGS84 coordinate system is expressed as follows:

[0129] ;

[0130] In the formula, λ represents the longitude coordinates of the center of the target-dense area on the Earth's surface. Let X be the latitude coordinate of the center of the target dense area on the Earth's surface. c Let Y be the absolute position of the center of the target dense area on the X-axis in the WGS84 geocentric fixed coordinate system. c Let Z be the absolute position of the center of the target dense area on the Y-axis in the WGS84 geocentric fixed coordinate system. c Let e ​​be the absolute position of the center of the target dense area on the Z-axis in the WGS84 geocentric fixed coordinate system. 2 The square of the first eccentricity of WGS84.

[0131] S4: Based on satellite position component data, the geographic coordinates of the center of the dense target area are transformed and attitude projection is calculated through WGS84 coordinate system transformation to generate a beam pointing vector. The satellite attitude component data is used to drive the composite environmental field compensation model to generate angular velocity data after zero bias compensation.

[0132] S4.1: Based on satellite position component data, the central geographic coordinates of the target-dense area are converted into geocentric fixed rectangular coordinates using the WGS84 coordinate system.

[0133] Specifically, using the WGS84 coordinate system transformation formula, based on the central geographic coordinates (longitude, latitude, and altitude) of the dense target area, the geocentric fixed rectangular coordinates of the target center are calculated by combining the ramidal radius of curvature formula with the geocentric coordinate transformation formula.

[0134] S4.2: Calculate the position vector of the satellite position component data in the geocentric fixed rectangular coordinate system, calculate the difference between the geocentric fixed rectangular coordinate system of the center of the target-dense area and the satellite position vector, and generate the direction vector from the satellite to the target.

[0135] Specifically, based on satellite position component data, the geocentric fixed rectangular coordinates of the satellite are obtained, and the direction vector from the satellite to the target is generated by coordinate difference calculation.

[0136] S4.3: Use satellite attitude component data to construct a rotation matrix from the geocentric Cartesian coordinate system to the radar body coordinate system, and apply the rotation matrix to transform the direction vector to the radar body Cartesian coordinate system.

[0137] Specifically, using satellite attitude component data, a rotation matrix is ​​constructed to transform the geocentric fixed rectangular coordinate system into the radar body coordinate system. The rotation matrix is ​​then used to perform matrix multiplication on the direction vector from the satellite to the target, transforming it into the radar body rectangular coordinate system.

[0138] S4.4: The current radar beam pointing vector is generated by normalizing the direction vector in the radar body's Cartesian coordinate system.

[0139] Specifically, the vector magnitude is calculated using the Euclidean mode length method, and a normalization operation is performed on the radar body coordinate system to generate the current radar beam pointing vector.

[0140] S4.5: The vector magnitude calculation method is used to calculate the triaxial magnetometer data to generate magnetic field strength characteristics. The temperature change rate algorithm is used to calculate the multi-channel temperature data and output the temperature change characteristics.

[0141] Using raw data from a triaxial magnetometer, magnetic field strength characteristics are generated through vector magnitude calculation.

[0142] The characteristics of the generated magnetic field strength are expressed as follows:

[0143] ;

[0144] In the formula, M mag B is a characteristic of magnetic field strength. x 2 B represents the magnetic field along the x-axis of the satellite's body coordinate system. y 2 B represents the magnetic field along the y-axis of the satellite's body coordinate system. z 2 The magnetic field is located along the z-axis of the satellite's coordinate system.

[0145] Based on multi-channel temperature data, a first-order difference algorithm (fixed time interval) is used to calculate and output the temperature change rate of each channel (unit: ℃ / second), generating temperature change characteristics.

[0146] The generation temperature change characteristic is represented as follows:

[0147] ;

[0148] In the formula, ΔT ＇ max Characteristic of temperature change The maximum value across all channels, Δt is the sampling time interval (example: fixed value 0.1s), t n For the current time, t n-1 For the previous moment, T k This represents the temperature value of the k-th temperature channel.

[0149] S4.6: The time stamp difference algorithm is used to calculate the acquisition timestamps of triaxial magnetometer data and multi-channel temperature data to generate time synchronization features. The magnetic field strength features, temperature change features and time synchronization features are integrated to generate an environmental interference feature vector.

[0150] The time synchronization deviation between the magnetometer data and the multi-channel temperature data is calculated by using a timestamp difference algorithm, and a time synchronization feature is generated.

[0151] The generation time synchronization feature is represented as follows:

[0152] τ sync =|t mag -t temp |;

[0153] In the formula, τ sync For time synchronization features, t mag For the timestamp of the magnetometer data, t temp τ represents the timestamp of the multi-channel temperature data, τ represents the time deviation, sync represents the time synchronization characteristic, mag represents the magnetometer, and temp represents the multi-channel temperature data.

[0154] By integrating magnetic field strength characteristics, temperature change characteristics, and time synchronization characteristics, an environmental disturbance feature vector is generated.

[0155] S4.6.1: During the pre-orbit calibration phase, triaxial magnetometer data, multi-channel temperature data, satellite attitude component data, and attitude measurement true values ​​are collected at different orbital positions. The difference between the satellite attitude component data and the satellite attitude measurement true values ​​is calculated, and the attitude measurement true value difference is generated.

[0156] During the pre-orbit calibration phase, the satellite is collected at different orbital positions (e.g., equator, mid-latitude, polar region) and the original data from the triaxial magnetometer, multi-channel temperature data, satellite attitude component data, and attitude measurement true values ​​(output from the star sensor and GPS differential positioning, unit: radians) are collected. All data timestamps are aligned to the same epoch (e.g., time synchronization accuracy ≤ 1 millisecond).

[0157] Based on satellite attitude component data (quaternion format), pitch angle, roll angle and yaw angle are calculated using quaternion-to-Euler angle function conversion.

[0158] The difference between the satellite attitude component data and the true attitude measurement value is calculated by subtraction, and the attitude measurement true value difference is generated.

[0159] S4.6.2: Establish a coupled regression equation for the difference between the true value of attitude measurement and the data from the triaxial magnetometer and the multi-channel temperature data. Based on the coupled regression equation, generate a geomagnetic field-temperature coupling error mapping table and store it in the onboard memory.

[0160] Based on triaxial magnetometer data and multi-channel temperature data, a vector magnitude calculation method is used to generate magnetic field strength characteristics. The difference between the true values ​​of attitude measurement is used as the dependent variable to construct a coupled regression equation.

[0161] The magnetic field strength characteristics and temperature change characteristics are partitioned by 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 a geomagnetic field-temperature coupling error mapping table. The geomagnetic field-temperature coupling error mapping table is stored in the on-board memory in binary format.

[0162] The geomagnetic field-temperature coupling error mapping table is represented as follows:

[0163] ;

[0164] In the formula, g(fcnv) is the geomagnetic field-temperature coupling error, and ε θ For pitch angle error, ε Φ For roll angle error, ε Ψ Let g be the yaw angle error, and g be the coupling error mapping function.

[0165] 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 compensated attitude value, perform gyro zero-bias Kalman filter compensation on the initial compensated attitude value, and generate zero-bias compensated angular velocity data.

[0166] Using a pre-stored geomagnetic field-temperature coupling error mapping table, the environmental disturbance feature vector is input and the coupling error value (unit: radians) is output. Based on satellite attitude component data, the initial compensation attitude quantity is generated through subtraction.

[0167] The system employs zero-bias Kalman filtering compensation based on gyroscopes. The input is the initial compensated attitude value, and the output is the angular velocity data after zero-bias compensation (unit: radians / second).

[0168] S5: The beam pointing vector and angular velocity data are fused by the beam-attitude dynamic coupling controller to generate phase shifter code value adjustment commands.

[0169] S5.1: Based on the central geographic coordinates of the dense target area, construct the target radar beam pointing vector through normalization operation, and calculate the spatial angle error between the current beam pointing vector projection and the target pointing vector in real time.

[0170] Specifically, the beam pointing vector (radar body coordinate system) and the center coordinates (unit: meters) of the target dense area are used. The center coordinates of the target dense area are processed by Euclidean modulus normalization operation to construct the target pointing vector.

[0171] Based on the current beam pointing vector and the target pointing vector, the cosine value of the angle between them is calculated using the dot product, and the spatial angle error is generated using the inverse cosine function.

[0172] S5.2: Based on the angular velocity data after zero bias compensation, a dynamic feedback control law is constructed. The spatial angle error is input into the dynamic feedback control law to generate the angular velocity correction, angular velocity limiting is executed, and the phase shifter code value adjustment command is generated through second-order Runge-Kutta attitude integration.

[0173] A dynamic feedback control law framework is constructed based on the angular velocity data after zero bias compensation. A proportional-derivative (PD) control law structure is used to calculate the spatial angle error and output the angular velocity correction (unit: radians / second).

[0174] Among them, the proportional-derivative control law is adopted, and the angular velocity correction is generated by calculating the error rate of change difference, as expressed as:

[0175] ;

[0176] In the formula, Δω cmd K is the angular velocity correction factor. P For proportional gain, K D For differential gain, Ψ crr This is the spatial angle error. ω is the time differential operator, ω is the angular velocity vector, crr is the spatial pointing error, and cmd is the command value.

[0177] Specifically, the time differential operator Represented as:

[0178] ;

[0179] In the formula, For the attitude control cycle, Ψ crr (t n ) represents the spatial angle error at the current time, (t) n-1 ) represents the spatial angle error at the previous moment.

[0180] It should be noted that the proportional gain and derivative gain in the proportional-derivative control law are determined through frequency domain stability analysis of satellite attitude control, and the attitude control period Δt is the fixed execution period of the onboard computer's attitude control task (example value: 0.01s).

[0181] Using a preset maximum velocity clamping threshold, the generated angular velocity correction is limited and calculated. Then, through a clamping function, an updated angular velocity correction is generated.

[0182] Based on the angular velocity data after zero bias compensation, the updated angular velocity correction is superimposed to calculate and output the updated satellite angular velocity vector;

[0183] By using the second-order Runge-Kutta attitude integration method, 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 then output using the results of the two slope calculations.

[0184] Specifically, the slope calculation, performed using the second-order Runge-Kutta attitude integral, is expressed as follows: (The slope is calculated in two steps between the current attitude quaternion and the updated satellite angular velocity vector.)

[0185] ;

[0186] In the formula, Let ω be the rate of change of the satellite's attitude quaternion. x Let ω be the angular velocity component along the satellite's x-axis. y Let ω be the angular velocity component along the satellite's y-axis. z Let ω be the z-axis angular velocity component of the satellite, and Ω(ω) be the oblique symmetric matrix function.

[0187] Based on the updated satellite attitude quaternion, the new beam pointing vector in the radar body coordinate system is obtained by calculation through the coordinate system projection relationship. The pre-stored calibration function (established through ground experiments) is used to generate a phase shifter code value adjustment command (example: 12-bit binary digital quantity) for the new beam pointing vector.

[0188] S6: Input the phase shifter code value adjustment command to the phase shifter of the spaceborne SAR radar to drive the radar beam to cover the center geographic coordinates of the dense target area in real time.

[0189] S6.1: The phase shifter code value adjustment command is used and transmitted to the phase shifter of the spaceborne SAR radar through the spaceborne high-speed data bus.

[0190] Based on the instruction parsing protocol, the phase shifter code value adjustment instruction is decoded to generate the phase control word for each phase shifter unit (example: 8-bit control word corresponds to 256 levels of phase quantization).

[0191] S6.2: Using a digital-to-analog converter circuit, the phase control word is converted into an analog voltage signal (example: 0~5V range). Based on the pre-stored voltage-phase response curve (example: established through a ground microwave anechoic chamber calibration experiment), the phase shifter unit is driven to adjust the phase offset and output a phase-modulated radio frequency carrier signal.

[0192] The voltage-phase response curve is represented as follows:

[0193] ;

[0194] In the formula, ΔΦ m ps Let k be the phase offset of the m-th unit. v V is the voltage-phase conversion coefficient. m ctrl Φ is the phase offset of the m-th unit. m 0 This represents the zero-bias phase of the m-th unit, where m is the phase shifter unit index, and ctrl is the control attribute identifier.

[0195] Vector synthesis is performed on the phase-modulated radio frequency carrier signal through the antenna array feed network to generate a synthesized beam electric field intensity distribution.

[0196] Based on the array factor theory, the spatial energy distribution of the electric field intensity distribution of the synthetic beam is controlled to precisely point the radar beam main lobe to the center geographic coordinates of the dense target area.

[0197] In summary, this invention significantly improves the positioning accuracy and timeliness of densely populated target areas in dynamic airspace by using an adaptive spatial clustering algorithm to dynamically adjust the neighborhood recognition range based on the real-time motion state of the satellite. A dynamic scaling factor is generated based on the satellite velocity vector magnitude to adaptively scale the preset neighborhood radius: the neighborhood radius automatically shrinks when the satellite moves at high speed to accurately match the spatially sparse distribution characteristics of the target point cloud; the neighborhood radius expands when the satellite moves at low speed to effectively avoid missing target identification. Simultaneously, a dynamic density judgment threshold is generated by combining the spatial dispersion characteristics of the target point cloud, achieving coordinated optimization of density peak search and neighborhood point merging. This invention significantly reduces the center positioning error of densely populated target areas, greatly compresses beam pointing delay, ensures continuous coverage of high-speed maneuvering target clusters by the synthetic aperture radar main lobe, and improves the continuous monitoring capability and guidance accuracy for time-sensitive targets.

[0198] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for real-time guidance of space-borne SAR radar targets based on ADS-B signals, characterized in that: Comprising, Real-time receiving Beidou dual-band signals and global ADS-B signals, using carrier phase double-difference ionosphere elimination algorithm to generate double-difference carrier phase observations eliminating ionosphere, and constructing geometric distance observation equation; Collecting satellite-borne three-axis magnetometer data and multi-channel temperature data and respectively preprocessing to construct geomagnetic field projection constraint equation and attitude thermal deformation compensation constraint equation; Fusing the geometric distance observation equation, the geomagnetic field projection constraint equation and the attitude thermal deformation compensation constraint equation by least square estimation to generate satellite platform position and attitude data containing satellite position component data, satellite velocity component data and satellite attitude component data; Processing ADS-B signals through satellite coordinate system transformation based on satellite position component data and satellite attitude component data to generate dynamic airspace target point cloud data set; Inputting the dynamic airspace target point cloud data set into adaptive airspace clustering algorithm, dynamically adjusting clustering neighborhood radius using satellite velocity component data, and outputting center geographic coordinates of target dense area; Based on satellite position component data, converting and projecting the center geographic coordinates of target dense area through WGS84 coordinate system to generate beam pointing vector, and using satellite attitude component data, three-axis magnetometer data and multi-channel temperature data to drive composite environment field compensation model to generate zero-offset compensated angular velocity data, the specific steps are as follows, Using three-axis magnetometer data, calculating through vector modulus value calculation method to generate magnetic field intensity feature; Using first-order difference algorithm to calculate multi-channel temperature data to output temperature change feature; Calculating three-axis magnetometer data and multi-channel temperature data through time stamp difference algorithm to obtain time synchronization deviation of magnetometer and multi-channel temperature data to generate time synchronization feature; Integrating magnetic field intensity feature, temperature change feature and time synchronization feature to generate environment interference feature vector; Inputting the environment interference feature vector into the pre-stored geomagnetic field-temperature coupling error mapping table to output coupling error value, and generating initial compensation attitude based on satellite attitude component data through subtraction operation; Using gyro zero-offset Kalman filter compensation, inputting initial compensation attitude to output zero-offset compensated angular velocity data; Fusing beam pointing vector and angular velocity data through beam-attitude dynamic coupling controller to generate phase shifter code value adjustment instruction; Inputting the phase shifter code value adjustment instruction into the satellite-borne SAR radar phase shifter to drive the radar beam to cover the center geographic coordinates of the target dense area in real time. 2.The ADS-B signal based space-borne SAR radar target real-time guidance method according to claim 1, wherein: Generating the satellite platform position and attitude data of the satellite position component data, satellite velocity component data and satellite attitude component data, the specific steps are as follows, Calculating double-frequency carrier phase measurement value through Beidou dual-band signals, performing inter-satellite single-difference processing on double-frequency carrier phase measurement value to generate single-difference observation value, and performing double-difference processing to generate double-difference carrier phase observation value, while collecting satellite-borne three-axis magnetometer data and multi-channel temperature data; Based on the double-difference carrier phase observation value, an ionosphere-free combination is constructed, and the ionosphere-free double-difference carrier phase observation value is outputted, and the three-axis magnetometer data and the multi-channel temperature data are fused, and the satellite platform position data containing the satellite position component data, the satellite speed component data and the satellite attitude component data are outputted through least square estimation solution. 3.The ADS-B signal based space-borne SAR radar target real-time guidance method of claim 1, wherein: The dynamic airspace target point cloud data set is generated, and the specific steps are as follows, The WGS84 coordinate system conversion is used to convert the longitude, latitude and height coordinates in the ADS-B signal into the earth-fixed rectangular coordinates, the satellite position component data are used to obtain the earth-fixed rectangular coordinates of the satellite, the 1090ES data link protocol analysis method is used to calculate the coordinate transformation of the longitude, latitude and height coordinates in the ADS-B signal, and the earth-fixed rectangular coordinates of the target are obtained; The relative position vector from the satellite to the target is generated by calculating the difference between the earth-fixed rectangular coordinates of the target and the earth-fixed rectangular coordinates of the satellite; The inverse rotation matrix from the earth-fixed rectangular coordinate system to the satellite body coordinate system is constructed by using the satellite attitude component data, the inverse rotation matrix is applied to convert the relative position vector to the satellite body rectangular coordinates, and the dynamic airspace target point cloud data set is generated by integrating all the converted satellite body rectangular coordinates. 4.The ADS-B signal based space-borne SAR radar target real-time guidance method of claim 1, wherein: The dynamic airspace target point cloud data set is inputted into the adaptive airspace clustering algorithm, the modulus of the satellite speed component data is extracted, the dynamic scaling factor is calculated by using the reciprocal proportion relationship, the preset reference neighborhood radius is scaled by using the dynamic scaling factor, and the dynamically adjusted dynamic clustering neighborhood radius is outputted. The average Euclidean distance standard deviation is calculated based on the spatial distribution of the point cloud, and the reference density threshold is generated. The high-density feature points of the reference density threshold are identified by using the density peak value search strategy, all the neighborhood points in the high-density feature points and the dynamic clustering neighborhood radius range are merged, the target dense area is generated, and the arithmetic mean of the satellite body rectangular coordinates in the target dense area is calculated. The arithmetic mean of the satellite body rectangular coordinates in the target dense area is projected to the earth-fixed rectangular coordinate system by using the inverse rotation matrix, the earth-fixed rectangular coordinate system is inversely calculated based on the WGS84 coordinate system, and the center geographic coordinates of the target dense area are outputted. The beam pointing vector is generated, and the specific steps are as follows, 5.The ADS-B signal based space-borne SAR radar target real-time guidance method of claim 1, wherein: The center geographic coordinates of the target dense area are converted into the earth-fixed rectangular coordinates by using the WGS84 coordinate system based on the satellite position component data; The position vector of the satellite position component data in the earth-fixed rectangular coordinates is calculated, the difference between the center earth-fixed rectangular coordinates of the target dense area and the satellite position vector is calculated, and the direction vector from the satellite to the target is generated; The rotation matrix from the earth-fixed rectangular coordinate system to the radar body coordinate system is constructed by using the satellite attitude component data, and the direction vector is converted to the radar body rectangular coordinate system by using the rotation matrix; The current radar beam pointing vector is generated by performing normalization processing on the direction vector in the radar body rectangular coordinate system. The zero-offset compensated angular velocity data are generated, and the specific steps are as follows, 6.The ADS-B signal based space-borne SAR radar target real-time guidance method of claim 1, wherein: ​ The triaxial magnetometer data is calculated by using a vector modulus calculation method to generate a magnetic field strength feature, and the multi-channel temperature data is calculated by using a temperature change rate algorithm to output a temperature change feature; The triaxial magnetometer data and the multi-channel temperature data are calculated by using a time stamp difference algorithm to generate a time synchronization feature, and the magnetic field strength feature, the temperature change feature and the time synchronization feature are integrated to generate an environmental interference feature vector; The coupling error is output by using a pre-stored geomagnetic field-temperature coupling error mapping table, and the satellite attitude component data is subtracted by the coupling error value to obtain an initial compensation attitude quantity, and the gyro zero offset Kalman filter compensation is performed on the initial compensation attitude quantity to generate zero offset compensated angular velocity data.

7. The method of claim 6, wherein the method further comprises: determining a target location based on the ADS-B signal; and determining a target velocity 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 through the pre-launch calibration stage, the difference between the satellite attitude component data and the satellite attitude measurement true value is calculated, and an attitude measurement true value difference is generated; Based on the triaxial magnetometer data and the multi-channel temperature data, a vector modulus calculation method is used to generate a magnetic field strength feature, and the attitude measurement true value difference is used as a dependent variable to construct a coupling regression equation; The magnetic field strength feature and the temperature change feature are partitioned by using a discretization method, and based on the coupling regression equation, the coupling error mapping table is calculated for each discrete partition to generate a geomagnetic field-temperature coupling error mapping table and store it in the on-board memory. 8.The ADS-B signal based space-borne SAR radar target real-time guidance method of claim 1, wherein: 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, a 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, a dynamic feedback control law is constructed, the spatial angle error is input into the dynamic feedback control law to generate an angular velocity correction amount, the angular velocity is limited, and the phase shifter code value adjustment instruction is generated by using a second-order Runge-Kutta method for attitude integration.

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