Space-time alignment method for fast capturing and steady-state tracking of high-dynamic target

Abstract

The invention discloses a time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target, and the method comprises the steps: carrying out the recursion of gyro information to a star-sensitive exposure moment, carrying out the filtering fusion of the gyro information and star-sensitive information, achieving the high-precision attitude determination of a satellite platform, and carrying out the recursion of the satellite attitude determined at the star-sensitive exposure moment to a miss distance timestamp; a satellite attitude corresponding to the miss distance timestamp is obtained, and time alignment of satellite attitude information and target observation information is completed; converting a target sight vector under a satellite camera coordinate system into a target sight vector of an inertial system by using the satellite attitude of the miss distance timestamp, and estimating the inertial position and speed of the miss distance timestamp target through Kalman filtering; recursion is carried out on the target inertial position of the miss distance timestamp and the inertial position of the satellite to the control implementation moment, and space alignment of the satellite and the target is completed. According to the method, the problem of misalignment of target pointing control caused by multi-information space-time inconsistency is effectively solved, and rapid capture and steady-state tracking of the high-dynamic target are realized.

Description

Technical Field

[0001] The invention relates to the field of satellite control, in particular to a time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target. Background Art

[0002] The satellite's attitude and orbit control subsystem is used to implement the satellite's attitude and orbit control. It is one of the most important subsystems of the satellite platform and plays a vital role in the successful completion of the satellite's flight mission. The attitude and orbit control subsystem is mainly composed of sensors, actuators, and controllers. Commonly used sensors include star sensors and gyroscopes. Gyroscopes are angular rate sensors of spacecraft, which can directly measure the satellite's three-axis inertial angular rate; star sensors are angle sensors that directly measure the satellite's three-axis inertial attitude. Both are the core units for the satellite to complete the on-orbit attitude determination, stability control, and attitude maneuvering functions. In addition, in order to capture and track highly dynamic targets, satellites are usually equipped with cameras (belonging to other subsystems other than the attitude and orbit control subsystem) to provide real-time target observation information (such as miss distance information).

[0003] There is a time difference between the timing of the various components within the attitude and orbit control subsystem and the timing with the external camera. For general targets, the existing technology mainly improves the accuracy of attitude determination by solving the problem of different information times, mainly by solving the asynchrony between star sensors and gyroscopes. The calculation of platform attitude pointing does not introduce real-time target observation information, and the sensor processing is not coupled with external target observation information.

[0004] For high-dynamic targets, compared with general targets, a high degree of information coupling between the attitude and orbit control subsystem and the camera is required. In the case of information misalignment, high-precision satellite position and attitude determination and rapid attitude maneuvers can be completed, thereby achieving rapid capture and steady-state tracking of the target. The higher the target dynamics, the greater the pointing control deviation caused by information misalignment. Therefore, in order to achieve rapid capture and steady-state tracking of high-dynamic targets, this problem must be solved, so as to accurately and quickly calculate the attitude adjustment control amount required by the attitude and orbit control subsystem based on the target observation information provided by the camera. Summary of the invention

[0005] The technical problem solved by the present invention is: in view of the technical difficulties in the prior art, a time-space alignment method for rapid capture and steady-state tracking of high-dynamic targets is provided, which ensures the rapid synchronization of the processing timing of each link within the attitude and orbit control subsystem and with other external subsystems, so as to obtain high-precision satellite attitude and target position in space, thereby realizing rapid and accurate satellite attitude adjustment, and achieving the purpose of rapid capture and steady-state tracking of high-dynamic targets.

[0006] The technical solution of the present invention is: a time-space alignment method for rapid capture and steady-state tracking of high-dynamic targets, which is based on the star sensor, gyroscope and satellite camera of the satellite attitude and orbit control subsystem, and includes:

[0007] S1, using the method of combined filtering of star sensor and gyroscope to obtain the satellite inertial attitude quaternion estimation at the moment of star sensor exposure;

[0008] S2, estimating the target position and speed corresponding to the miss distance timestamp; the miss distance timestamp is the time corresponding to the target image captured by the satellite-borne camera;

[0009] S21, converting the satellite inertial attitude quaternion estimate at the star-sensitive exposure time calculated in S1 to the miss distance timestamp, to obtain the satellite inertial attitude quaternion corresponding to the miss distance timestamp;

[0010] S22, calculating the satellite inertial position corresponding to the miss distance timestamp;

[0011] S23, calculating the target sight line vector in the camera coordinate system, and then transforming it to obtain the target sight line vector in the geocentric inertial system;

[0012] S24, based on the calculation results of S21, S22, and S23, estimate the target inertial position and speed corresponding to the miss distance timestamp;

[0013] S3, recursively extrapolating the target inertial position and speed corresponding to the miss distance timestamp to the control implementation time to obtain the target inertial position at the control implementation time;

[0014] S4. Calculate the inertial position of the satellite at the time of control implementation; obtain the guidance amount required for the satellite to capture and track the target through the inertial position of the satellite and the inertial position of the target at the time of control implementation, and complete the time and space alignment of the satellite and the target.

[0015] Further, in S24, the target inertial position and speed corresponding to the miss distance timestamp are estimated by using Kalman filtering to estimate the state quantity X k Set as the target inertial position and speed corresponding to the timestamp of the miss distance to be estimated; the k-th filtering process is:

[0016] (1) System matrix update;

[0017]

[0018] Where μ is the Earth’s gravitational constant, ΔT pix is the time difference between the miss amount timestamp of the current shot and the miss amount timestamp of the previous shot, r t,k-1 is the target inertial system position corresponding to the system state quantity of the previous shot, I 3×3 is a 3×3 identity matrix;

[0019] (2) One-step state prediction;

[0020] X k|k-1 =A k X k-1

[0021] Among them, X k-1 is the state quantity of the previous shot, X k|k-1 is the state quantity predicted for one step;

[0022] (3) Update the measurement matrix and observation quantity;

[0023] The measurement matrix is:

[0024]

[0025] Where C1 is the star vector r st,k The corresponding Jacobian matrix, r st,k =r t,k|k-1 -r pix,k ; 0 3×3 is a 3×3 zero matrix; r t,k|k-1 is the target position vector predicted by the state one step ahead, r pix,k is the target position vector calculated based on the miss distance;

[0026] The observed quantity is:

[0027]

[0028] Among them, ΔS i,k is the target sight line vector increment in the inertial system, calculated as:

[0029]

[0030] Among them, S i,k is the target sight vector in the current inertial frame, S i,k-1 is the target sight vector in the inertial system of the previous shot;

[0031] (4) Residual calculation;

[0032] Δγ k =y k -C k X k|k-1

[0033] Among them, Δγ k is the residual;

[0034] (5) One-step prediction of error covariance matrix;

[0035]

[0036] Among them, P k|k-1 is the error covariance matrix of one-step prediction, P k-1 is the error covariance matrix of the previous shot; Q is the noise covariance matrix;

[0037] (6) Gain matrix calculation;

[0038]

[0039] Among them, K k is the gain matrix, δ0 ​​is the amplification parameter, and R is the measurement noise covariance matrix;

[0040] (7) State quantity estimation;

[0041] X k =X k|k-1 +K k Δγ k

[0042] (8) Error covariance matrix update;

[0043]

[0044] The state quantity X obtained in step (7) k That is, the target inertial position and speed corresponding to the timestamp of the miss distance to be calculated.

[0045] Furthermore, in S1, the satellite inertial attitude quaternion estimation at the moment of star-sensor exposure is obtained by using the combined filtering method of star-sensor and gyroscope. The specific method is as follows:

[0046] Based on the attitude quaternion of the star sensor relative to the geocentric inertial coordinate system obtained by the star sensor, the inertial attitude quaternion of the first satellite is calculated;

[0047] Based on the satellite inertial angular velocity obtained by the gyroscope, the inertial attitude quaternion of the second satellite is calculated;

[0048] The inertial attitude quaternion of the first satellite is compared with the inertial attitude quaternion of the second satellite to obtain the attitude deviation quaternion. The high-frequency terms in the attitude deviation quaternion are eliminated by using the Kalman filtering method to estimate the gyro constant drift and attitude deviation. The satellite inertial angular velocity output by the gyro is corrected by the gyro constant drift, and the inertial attitude quaternion of the second satellite is corrected by the attitude deviation. The corrected inertial attitude quaternion of the second satellite is the satellite inertial attitude quaternion estimate at the moment of star-sensitive exposure.

[0049] Furthermore, the first satellite inertial attitude quaternion and the second satellite inertial attitude quaternion are both attitude quaternions of the satellite system relative to the geocentric inertial system.

[0050] Furthermore, in S21, the satellite inertial attitude quaternion estimation conversion method is:

[0051] Using the satellite inertial angular velocity, the satellite inertial attitude quaternion estimate at the time of star-sensitive exposure is integrated to the miss distance timestamp to obtain the satellite inertial attitude quaternion corresponding to the miss distance timestamp.

[0052] Further, in S22, the calculation method of the satellite inertial position corresponding to the miss distance timestamp is any calculation method based on GNSS data or based on the above-mentioned parameters.

[0053] Further, in S23, the target sight line vector in the geocentric inertial system is calculated, specifically in the following manner:

[0054] First, the target sight line vector in the camera coordinate system is calculated; the target sight line vector in the camera coordinate system is converted to the satellite system using the installation matrix of the camera coordinate system relative to the satellite system; the target sight line vector in the satellite system is converted to the geocentric inertial system using the satellite inertial attitude quaternion corresponding to the miss distance timestamp obtained by S21, and the target sight line vector in the geocentric inertial system is obtained.

[0055] Furthermore, in S3, a non-powered recursive method is adopted to recursively obtain the target position at the time of control implementation.

[0056] Further, in S4, the direction cosine matrix A from the inertial system to the tracking reference system is ti As the guidance amount required for the satellite to capture and track the target, A ti Calculated by the following formula:

[0057]

[0058] A ti =[OY×OZ OY OZ] T

[0059] in, is the target inertial position vector at the time of control implementation; r exc It is the satellite inertial position vector at the time of control implementation.

[0060] The advantages of the present invention compared with the prior art are:

[0061] (1) The present invention first realizes high-precision attitude determination of the satellite platform by recursively extrapolating the gyro information to the star-sensitive exposure time and filtering and fusing it with the star-sensitive information, and then recursively extrapolates the satellite attitude determined at the star-sensitive exposure time to the target observation information time (miss distance timestamp), and obtains the satellite attitude corresponding to the miss distance timestamp. These two steps complete the time alignment of the satellite attitude information and the target observation information. Further, the satellite attitude of the miss distance timestamp is used to transform the target sight line vector in the satellite camera coordinate system to the target sight line vector in the inertial system, and the inertial system position vector and velocity vector of the miss distance timestamp target are estimated with the inertial system target sight line vector as the observation quantity. Further, the inertial system position vector of the miss distance timestamp target and the inertial system position vector of the satellite platform are both recursively extrapolated to the control implementation time of the satellite platform actuator. These two steps complete the spatial alignment of the satellite and the target.

[0062] (2) The present invention provides a specific implementation method for estimating the inertial system position vector and velocity vector of the miss distance timestamp target through Kalman filtering, and estimates the inertial system position and velocity of the miss distance timestamp target by taking the inertial system target sight vector as the observation quantity.

[0063] (3) The present invention effectively solves the problem of inaccurate target observation benchmark calculation caused by spatiotemporal inconsistency of multiple input information when the satellite platform attitude is coupled to the target observation estimation through the above method without adding redundant products, effectively improves the dynamic adaptability and robustness of the target observation, and realizes reliable rapid capture and steady-state tracking of high-dynamic targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The figure is a schematic diagram of the method process involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] The present invention aims at the situation that the attitude sensors (star sensors, gyroscopes) of the attitude and orbit control subsystem of the satellite platform and the target observation information (miss distance information) obtained from the outside of the attitude and orbit control subsystem are inconsistent in information timing, resulting in different information times, and the corresponding moments of the inertial position vectors of the target to be observed and the satellite platform are inconsistent. In order to realize real-time target capture and tracking, the above-mentioned time differences and spatial inconsistencies are aligned in time and space through the method of the present invention, thereby ensuring high-precision high-dynamic target pointing control.

[0067] First, some time relationships involved in this method and the meaning of different moments are explained. Figure 1In this paper, ETR refers to the periodic time unified reference generated by external hardware and input to the attitude and orbit control subsystem; the satellite angular velocity data output by the gyro at the ETR k time is recorded as gyro data k; the satellite attitude data output by the star sensor at the ETR k time is recorded as star sensor data k, and the corresponding star sensor detector exposure time is T k-2 +Ams; The miss distance information output by the camera at the ETR k moment is recorded as miss distance information k, and the corresponding camera detector exposure time is T k-2 +Bms; A represents the deviation of each beat star-sensitive exposure time relative to the ETR time, and B represents the deviation of the miss amount timestamp relative to the ETR time.

[0068] Taking a certain on-orbit model as an example, this method includes the following processes:

[0069] Step 1: Use the combined filtering method of star sensor and gyroscope to obtain the satellite inertial attitude quaternion estimation at the time of star sensor exposure

[0070] The details are as follows:

[0071] (1) Based on star sensor measurement, the attitude quaternion q of the star sensor relative to the geocentric inertial coordinate system is obtained si According to the on-board calibration information, the installation and thermal deformation of the star sensor are corrected, and then the star positions of the two star sensor heads are integrated through coordinate transformation to calculate the attitude quaternion of the satellite system relative to the geocentric inertial system. Its error includes the star sensor measurement noise;

[0072] (2) Obtaining satellite inertial angular velocity ω based on gyroscope m , using the attitude kinematics equation based on the given initial value, calculate the attitude quaternion of the satellite system relative to the geocentric inertial system at the current moment Its error includes gyro constant drift and measurement noise;

[0073] (3) Compare attitude quaternions and Get the attitude deviation quaternion (Contains gyro constant drift, measurement noise and star sensor measurement noise). An error model is established based on the principles of star sensors and gyroscopes, and the Kalman filter method is used to eliminate The high-frequency terms in (the noise of the star sensor and the gyro) are used to estimate the gyro constant drift b and attitude deviation q e , use b to correct the satellite inertial angular velocity output of the gyro, and use q e Correct the gyro-calculated attitude quaternion Corrected That is, the satellite inertial attitude quaternion estimate is obtained

[0074] Step 2: Estimate the target position and speed corresponding to the miss distance timestamp

[0075] Considering the different update cycles of sensor information, miss distance information and orbit information, they should be unified to the control implementation time, such as Figure 1 The miss distance information is provided by the camera to the attitude and orbit control subsystem, including the miss distance obtained by the camera shooting the target and the miss distance timestamp, that is, the shooting time when the target is shot.

[0076] In order to estimate the target position and velocity corresponding to the miss distance timestamp, it is necessary to obtain the satellite inertial attitude quaternion at the same time. Satellite inertial position vector r pix , combined with the target sight line vector in the inertial system for solution.

[0077] (1) Calculate the satellite inertial attitude quaternion corresponding to the miss distance timestamp

[0078] The time corresponding to the k-th ETR is recorded as T k There is a certain time difference between the miss distance timestamp and the beat of the gyro obtaining the satellite inertial angular velocity. The relationship can be expressed as: T k-2 ±Bms, where B represents the miss time stamp in each beat relative to the ETR time T of the previous 2 beats. k-2 Due to the variation in satellite platform timing, B is different in each beat, but is usually controlled within 50ms.

[0079] It can be based on the previous beat (ie T k-1 ) The inertial angular velocity ω obtained by the gyroscope bi,k-1 The satellite inertial attitude quaternion estimate obtained in step 1 Integrate to the miss distance timestamp to obtain the satellite inertial attitude quaternion corresponding to the miss distance timestamp

[0080] (2) Calculate the satellite inertial position r corresponding to the miss distance timestamp pix

[0081] The on-board orbit calculation method is divided into two types: based on GNSS data calculation method or based on the above-mentioned parameter calculation method. Either method can be selected as needed. According to the current satellite orbit calculation method, the satellite inertial position r corresponding to the miss distance timestamp is obtained. pix .

[0082] (3) Calculate the target sight vector S in the geocentric inertial system i

[0083] The coordinates of the center of the camera image plane are (C x ,C y ), define the camera coordinate system Oc X c Y c Z c , the origin is at the center of the image plane, Z c The axis is perpendicular to the image plane and outward, X c Y c The plane definition is consistent with the image plane coordinate axis definition.

[0084] First, calculate the target sight vector S in the camera coordinate system c The installation matrix of the camera coordinate system relative to the satellite system is A bc , get the target sight vector under this system, and then the satellite inertial attitude quaternion corresponding to the miss distance timestamp Convert the target sight vector to the inertial system S i .

[0085] (4) Estimate the target position and speed corresponding to the miss distance timestamp

[0086] After the target sight vector is solved in the inertial system, the Kalman filter method is used to estimate the target inertial position corresponding to the miss distance timestamp according to the target's dynamic characteristics. speed

[0087] The state quantity X k Set as the target inertial position and speed corresponding to the timestamp of the miss distance to be estimated; the k-th filtering process is:

[0088] (1) System matrix update;

[0089]

[0090] Where μ is the Earth's gravitational constant, which is 3.986005e14, ΔT pix is the time difference between the miss amount timestamp of the current shot and the miss amount timestamp of the previous shot, r t,k-1 is the target inertial system position corresponding to the system state quantity of the previous shot, I 3×3 is a 3×3 identity matrix;

[0091] (2) One-step state prediction;

[0092] X k|k-1 =A k X k-1

[0093] Among them, X k-1 is the state quantity of the previous shot, X k|k-1 is the predicted value of the current shot state;

[0094] (3) Update the measurement matrix and observation quantity;

[0095] The measurement matrix is:

[0096]

[0097] Where C1 is the star vector r st,k The corresponding Jacobian matrix, r st,k =r t,k|k-1 -r pix,k ; 0 3×3 is a 3×3 zero matrix; r t,k|k-1 For r t,k|k-1 The target position vector predicted for one step of state:

[0098] r t,k|k-1 =X k|k-1 (1:3)

[0099] r pix,k is the target position vector constructed according to the miss distance;

[0100] The observed quantity is:

[0101]

[0102] Among them, ΔS i,k is the target sight line vector increment of the inertial system, calculated as:

[0103]

[0104] Among them, S i,k is the target sight vector in the current inertial frame, S i,k-1 is the target sight line vector in the previous shot inertial system, which is calculated by the target miss distance;

[0105] (4) Residual calculation;

[0106] Δγ k =y k -C k X k|k-1

[0107] Among them, Δγ k is the residual;

[0108] (5) One-step prediction of error covariance matrix;

[0109]

[0110] Among them, P k|k-1 is the error covariance matrix of one-step prediction, P k-1 is the error covariance matrix of the previous shot; Q is the noise covariance matrix;

[0111] (6) Gain matrix calculation;

[0112]

[0113] Among them, K k is the gain matrix, δ0 ​​is the amplification parameter, and δ0 can be taken as 10 14 , R is the measurement noise covariance matrix;

[0114] (7) State quantity estimation;

[0115] X k =X k|k-1 +K k Δγ k

[0116] (8) Error covariance matrix update;

[0117]

[0118] The state quantity X obtained in step (7) k That is, the target inertial position corresponding to the time stamp of the miss distance to be calculated speed Specifically:

[0119]

[0120] Step 3: Target position at the time of recursive control implementation

[0121] The control implementation time is a time set artificially. The target inertial position and speed corresponding to the miss amount timestamp have been estimated by Kalman filtering. In order to solve the control attitude angle at the control implementation time, the target inertial position at the control implementation time needs to be obtained. Therefore, the target position and speed corresponding to the miss amount timestamp are recursively deduced to the control implementation time to obtain the target position at the control implementation time; preferably, the calculation can be performed by unpowered recursion.

[0122] Step 4: Calculate guidance information

[0123] First, the satellite inertial position at the time of control implementation is calculated, and the calculation method is similar to step 2 (2);

[0124] Then, according to the target position and satellite inertial position at the time of control implementation, the target-geocentric dual vector reference is used to obtain the direction cosine matrix A from the inertial system to the tracking reference system: ti , that is, get the guidance information.

[0125] Among them, the direction cosine matrix A from the inertial system to the reference system is obtained ti The specific method is:

[0126]

[0127] A ti =[OY×OZ OY OZ] T

[0128] Preferably, the direction cosine matrix can be converted into a quaternion q ti , thereby calculating the guidance attitude angle, angular velocity and angular acceleration.

[0129] It is to be understood that the present invention is described by way of embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and embodiments that can fall within the scope of the claims of this application all fall within the scope protected by the present invention.

[0130] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A method for time-space alignment of rapid capture and steady-state tracking of high-dynamic targets, characterized by: The satellite attitude and orbit control subsystem is based on the star sensor, gyroscope, and satellite camera, including: S1, using the combined filtering method of star sensor and gyroscope to obtain the satellite inertial attitude quaternion estimation at the moment of star sensor exposure; S2, estimating the target position and speed corresponding to the miss distance timestamp; the miss distance timestamp is the time corresponding to the target image captured by the satellite-borne camera; S21, converting the satellite inertial attitude quaternion estimate at the star-sensitive exposure time calculated in S1 to the miss distance timestamp, to obtain the satellite inertial attitude quaternion corresponding to the miss distance timestamp; S22, calculating the satellite inertial position corresponding to the miss distance timestamp; S23, calculating the target sight line vector in the camera coordinate system, and then transforming it to obtain the target sight line vector in the geocentric inertial system; S24, based on the calculation results of S21, S22, and S23, estimate the target inertial position and speed corresponding to the miss distance timestamp; S3, recursively extrapolating the target inertial position and speed corresponding to the miss distance timestamp to the control implementation time to obtain the target inertial position at the control implementation time; S4. Calculate the inertial position of the satellite at the time of control implementation; obtain the guidance amount required for the satellite to capture and track the target through the inertial position of the satellite and the inertial position of the target at the time of control implementation, and complete the time and space alignment of the satellite and the target.

2. The time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target according to claim 1, characterized in that: In S24, the target inertial position and speed corresponding to the miss distance timestamp are estimated by using Kalman filtering to estimate the state quantity X k Set to the target inertial position and velocity corresponding to the timestamp of the miss distance to be estimated; the k-th filtering process is: (1) System matrix update; Where μ is the Earth’s gravitational constant, ΔT pix is the time difference between the miss amount timestamp of the current shot and the miss amount timestamp of the previous shot, r t,k-1 is the target inertial system position corresponding to the system state of the previous shot, I 3×3 is a 3×3 identity matrix; (2) One-step state prediction; X k|k-1 =A k X k-1 Among them, X k-1 is the state quantity of the previous shot, X k|k-1 is the state quantity predicted for one step; (3) Update the measurement matrix and observation quantity; The measurement matrix is: Where C1 is the star vector r st,k The corresponding Jacobian matrix, r st,k =r t,k|k-1 -r pix,k ; 0 3×3 is a 3×3 zero matrix; r t,k|k-1 is the target position vector predicted by the state one step ahead, r pix,k is the target position vector calculated based on the miss distance; The observed quantity is: Among them, ΔS i,k is the target sight line vector increment in the inertial system, calculated as: Among them, S i,k is the target sight vector in the current inertial frame, S i,k-1 is the target sight vector in the inertial system of the previous shot; (4) Residual calculation; Dg k =y k -C k X k|k-1 Among them, Δγ k is the residual; (5) One-step prediction of error covariance matrix; Among them, P k|k-1 is the error covariance matrix of one-step prediction, P k-1 is the error covariance matrix of the previous shot; Q is the noise covariance matrix; (6) Gain matrix calculation; Among them, K k is the gain matrix, δ0 ​​is the amplification parameter, and R is the measurement noise covariance matrix; (7) State quantity estimation; X k =X k|k-1 +K k Dg k (8) Error covariance matrix update; The state quantity X obtained in step (7) k That is, the target inertial position and speed corresponding to the timestamp of the miss distance to be calculated.

3. The time-space alignment method for rapid capture and steady-state tracking of high-dynamic targets according to claim 1, characterized in that: In S1, the satellite inertial attitude quaternion estimation at the moment of star-sensor exposure is obtained by using the combined filtering of star-sensor and gyroscope. The specific method is as follows: Based on the attitude quaternion of the star sensor relative to the geocentric inertial coordinate system obtained by the star sensor, the inertial attitude quaternion of the first satellite is calculated; Based on the satellite inertial angular velocity obtained by the gyroscope, the inertial attitude quaternion of the second satellite is calculated; The inertial attitude quaternion of the first satellite and the inertial attitude quaternion of the second satellite are compared to obtain the attitude deviation quaternion; the high-frequency terms in the attitude deviation quaternion are eliminated by using the Kalman filter method to estimate the gyro constant drift and attitude deviation; The satellite inertial angular velocity output by the gyro is corrected by the gyro constant drift, and the inertial attitude quaternion of the second satellite is corrected by the attitude deviation. The corrected inertial attitude quaternion of the second satellite is the estimated inertial attitude quaternion of the satellite at the moment of star-sensitive exposure.

4. The time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target according to claim 3, characterized in that: The first satellite inertial attitude quaternion and the second satellite inertial attitude quaternion are both attitude quaternions of the satellite system relative to the geocentric inertial system.

5. The time-space alignment method for rapid capture and steady-state tracking of high-dynamic targets according to claim 1, characterized in that: In S21, the satellite inertial attitude quaternion estimation conversion method is: Using the satellite inertial angular velocity, the satellite inertial attitude quaternion estimate at the time of star-sensitive exposure is integrated to the miss distance timestamp to obtain the satellite inertial attitude quaternion corresponding to the miss distance timestamp.

6. The time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target according to claim 1, characterized in that: In S22, the satellite inertial position corresponding to the miss distance timestamp is calculated based on GNSS data or based on the above parameters.

7. The time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target according to claim 1, characterized in that: In S23, the target sight line vector in the geocentric inertial system is calculated as follows: First, the target sight line vector in the camera coordinate system is calculated; the target sight line vector in the camera coordinate system is converted to the satellite system using the installation matrix of the camera coordinate system relative to the satellite system; the target sight line vector in the satellite system is converted to the geocentric inertial system using the satellite inertial attitude quaternion corresponding to the miss distance timestamp obtained by S21, and the target sight line vector in the geocentric inertial system is obtained.

8. The time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target according to claim 1, characterized in that: In S3, a non-powered recursive method is used to recursively obtain the target position at the time of control implementation.

9. The time-space alignment method for rapid capture and steady-state tracking of a high-dynamic target according to claim 1, characterized in that: In S4, the direction cosine matrix A from the inertial system to the tracking reference system is ti As the guidance amount required for the satellite to capture and track the target, A ti Calculated by the following formula: A ti =[OY×OZ OY OZ] T in, is the target inertial position vector at the time of control implementation; r exc It is the satellite inertial position vector at the time of control implementation.

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