Adaptive state noise compensation method for maneuvering spacecraft orbit determination

Abstract

The application discloses a kind of self-adapting state noise compensation's maneuverable spacecraft orbit determination method, belong to aerospace technology field.The application implementation method is: establishing the general dynamic model and only angle observation model of unknown mutation for random nonlinear dynamic system;System state and covariance matrix are predicted based on dynamic model;System measurement residual and covariance matrix are calculated at t k+1 Instant, the matching degree of the prediction result of system based on dynamic model and inorganic model is represented by index function Λ k+1 ;Index function at t k+1 Instant is compared with the average of index function from t1 to t k When the difference between evaluation index and average is greater than threshold value, it is determined that system state mutation occurs, and state noise compensation is made to system prediction result;Orbit determination algorithm gain is calculated based on the result of system state noise compensation, and system prediction result noise compensation result and measurement result z k+1 are weighted to output t k+1 System state and covariance estimate value, and state estimate value at each time is iteratively solved, so that high-precision orbit determination of maneuverable spacecraft is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of adaptive state noise compensation's maneuverable spacecraft orbit determination method, especially suitable for using impulse thrust to carry out maneuverable spacecraft tracking, belongs to aerospace technology field. BACKGROUND

[0002] Currently more than 1000 spacecrafts with maneuverability are in orbit, and the number of such spacecrafts is still increasing. This has triggered a strong demand for situation awareness of space targets, which requires accurate prediction of the trajectory of maneuverable spacecrafts, so as to perform tasks such as intention discrimination, function prediction, etc.

[0003] Many spacecraft maneuver tracking algorithms have been developed to estimate the position and velocity of maneuverable spacecrafts, and the traditional interactive multiple model (IMM) algorithm has been successfully applied to the tracking problem of maneuverable spacecrafts. However, the traditional IMM algorithm mixes the spacecraft state model under different motion states based on the given value of human experience, and the tracking and orbit determination accuracy of the spacecraft is low. The improved IMM algorithm assisted by prior knowledge establishes a state mutation probability model, so that the state mutation depends on the prior information of the system. Although this method effectively improves the tracking and orbit determination accuracy of the system, the covariance matrix and scaling coefficient in the probability model are given based on empirical values, which makes the tracking accuracy of the system near the state mutation area low, and for most maneuvering orbit problems, the prior knowledge of the system is difficult to obtain.

[0004] Based on this, the present application proposes a kind of adaptive state noise compensation's maneuverable spacecraft tracking method, first based on system dynamics equation carries out state prediction;Then, a model matching degree evaluation method based on system residual is proposed, and whether the system occurs state mutation is judged based on this result;Subsequently, for the case that the system occurs state mutation, the system state noise is solved according to the system model matching degree inversion, and the prediction result of the system based on dynamics equation is compensated for state noise;Finally, the final weighted result is output in combination with the measurement result, and the above process is iterated, to realize high-precision orbit determination of maneuverable spacecraft.

[0005] Among the developed maneuvering spacecraft tracking methods, see [1] (see: [1] LEE S, LEE J, HWANGI. Maneuvering Spacecraft Tracking via State-Dependent Adaptive Estimation[J / OL]. Journal of Guidance, Control, and Dynamics, 2016, 39(9):2034-2043.) proposed an improved IMM algorithm assisted by prior knowledge, established a probabilistic model describing the sudden change in system state, and then fused this state-based probabilistic model with the IMM algorithm, replacing the artificially given model transition probability that mixes spacecraft state models under different motion states in the traditional IMM algorithm. However, this method relies on the prior information of the system, and the covariance matrix and scaling factor of the probabilistic model are based on given empirical values, so the actual tracking accuracy is low near the sudden change in system state. Summary of the Invention

[0006] The technical problem to be solved by the adaptive state noise compensation-based orbit determination method for maneuvering spacecraft disclosed in this invention is: to track and determine the orbit of a spacecraft that uses pulse thrust for maneuvering, so that the target spacecraft can still achieve high-precision tracking and orbit determination even when the prior maneuvering information is unknown. This invention has the following advantages: (1) high tracking accuracy and good convergence; (2) compared with the parallel operation of two different state models in the traditional IMM algorithm and its improved algorithms, this method is based on state noise compensation based on evaluation index, and the tracking and orbit determination efficiency is high; (3) wide applicability, and can still achieve high-precision tracking and orbit determination even when the prior maneuvering information of the target spacecraft is unknown.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] This invention discloses an adaptive state noise compensation method for maneuvering spacecraft orbit determination, which establishes a general dynamic model and an angle-only observation model for unknown mutations in stochastic nonlinear dynamic systems; predicts the system state and covariance matrix based on the dynamic model; and calculates t. k+1 The system measures the residuals and covariance matrix at each time step, and then uses the index function Λ k+1 The degree of matching between the prediction results of the characterization system based on the dynamic model and the non-maneuvering model; t k+1 The index function at time t1 to t k The system compares the performance index with the mean of the evaluation index function. When the difference between the evaluation index and the mean exceeds a threshold, a state change is determined, and state noise compensation is applied to the system prediction results. Based on the system state noise compensation results, the orbit determination algorithm gain is calculated, and the gain is then used to adjust the system prediction results. Noise compensation results and measurement results z k+1 weighted output t k+1 System state and covariance estimates are iteratively solved to achieve high-precision orbit determination of a maneuvering spacecraft.

[0009] The application discloses a maneuvering spacecraft orbit determination method based on adaptive state noise compensation, and comprises the following steps:

[0010] Step 1, a general dynamic model for unknown mutations of a random nonlinear dynamic system and an angle-only observation model are established;

[0011] Step 1.1, a general dynamic model for unknown mutations of a random nonlinear dynamic system is established;

[0012] The state variable differential equation of the space target is expressed as:

[0013]

[0014] In the formula, r represents a system state vector; r k represents a position vector of the space target at the k moment, [x k y k z k ] T v represents a scalar form thereof; v k represents a velocity vector of the space target at the k moment, is a scalar form thereof;

[0015] A general dynamic model for unknown mutations of a random nonlinear dynamic system is established:

[0016] x k+1 = F(x k + δ t,k Gu) + Bw k (2)

[0017] Wherein, δ t,k represents a Kronecker symbol, δ t,k = 0 when t≠k, and δ t,k = 1 when t=k; is a constant matrix; represents an unknown input vector of the system; is a constant matrix; represents white noise with an expected value of 0; δ t,k Gu represents random maneuvers of the system; F(x k + δ t,k Gu) represents state recursion based on the state variable differential equation (1) of the space target, and the specific process is to perform state recursion on the system state x k + δt,k Gu as the initial value, the numerical integrator is used to integrate equation (1) to obtain the recursive result of the k+1 time based on dynamics x k+1 ;

[0018] Step 1.2, establish an angle-only observation model;

[0019] The angle information of the maneuvering target relative to the observation satellite is taken as the measurement:

[0020] z k+1 = [α k+1 β k+1 ] T (3)

[0021] In the formula, z k+1 represents the measurement, α k+1 and β k+1 are the pitch angle α k+1 and azimuth angle β k+1 of the maneuvering target relative to the observation satellite respectively;

[0022] The observation model is represented as:

[0023] z k+1 = h(x k+1 )+ν k+1 (4)

[0024] In the formula:

[0025]

[0026] [x k+1 y k+1 z k+1 ] T is the position vector of the maneuvering target, [X k+1 Y k+1 Z k+1 ] T is the position vector of the observation satellite; ν k+1 is a two-dimensional zero-expectation white noise, R k+1 is the measurement noise covariance matrix, σ z is the error standard deviation of the measurement; h(x k+1 ) and ρ k+1 are intermediate variables.

[0027] Step two, predict the system state and covariance matrix based on the dynamics model;

[0028] Predict the system state:

[0029]

[0030] In the formula, denotes the state prediction value at time k+1; denotes the state estimation value at time k; denotes the state prediction based on the state variable differential equation (1) of the space target, and the specific process is to use the state estimation value at time k As an initial value, the numerical integrator is used to integrate equation (1) to obtain the state prediction value at time k+1

[0031] The system covariance matrix is predicted:

[0032]

[0033] wherein, denotes the covariance matrix prediction value at time k+1; denotes the covariance matrix estimation value at time k; Φ k denotes the system state transition matrix from time k to time k+1, and its differential equation is represented as:

[0034]

[0035] In the formula, denotes the Jacobian matrix, and is specifically represented as:

[0036]

[0037] Step three: calculate the system measurement residual and covariance matrix at time t k+1 The index function Λ k+1 characterizes the matching degree of the prediction result of the system based on the dynamic model with the non-maneuvering model; t k+1 The index function at time t1 is compared with the average of the index functions from t1 to t k When the evaluation index is greater than the threshold value, it is considered that the system has a state mutation, and the state noise compensation is made to the system prediction result;

[0038] Step 3.1: calculate the system measurement residual γ k+1 and the covariance matrix S k+1 at time t k+1 , and the index function Λ k+1 characterizes the matching degree of the prediction result of the system based on the dynamic model with the non-maneuvering model;

[0039] The system measurement residual γ k+1 and the covariance matrix S k+1 are represented as:

[0040]

[0041] wherein, the matrix H k+1 is represented as:

[0042]

[0043] t k+1 Instantaneous indicator function Λ k+1 is expressed as:

[0044]

[0045] wherein tr(S k+1 ) represents the trace of the measurement residual covariance matrix;

[0046] Step 3.2: Obtain the indicator function at time t k+1 by formula (15); calculate the average of the indicator functions from t1 to t k ; when the difference between the indicator function and the average is greater than a threshold value, the system state mutates, and the system prediction covariance matrix is compensated for state noise;

[0047] The average of the indicator functions from time t1 to time t k is expressed as:

[0048] vale k = (vale k-1 × (k-1) + Λ k ) / k (16)

[0049] wherein the initial value of the average of the indicator functions is set to 0;

[0050] State noise compensation Q k+1 is expressed as:

[0051]

[0052] wherein η is a threshold value for judging whether the system mutates; D k+1 represents state noise compensation for the position under the condition of system state mutation; V k+1 represents state noise compensation for the position under the condition of system state mutation;

[0053] State noise compensation D k+1 for the position under the condition of system state mutation is expressed as:

[0054]

[0055] In the formula, the intermediate variable dr k+1 is expressed as:

[0056]

[0057] In the formula, the intermediate variable Ω k+1 may be expressed as:

[0058]

[0059] State transition matrix Φ k+1 is expressed as:

[0060]

[0061] State noise compensation for velocity under system state mutation V k+1 is expressed as:

[0062]

[0063] In the formula, intermediate variables dv1 k+1 , dv2 k+1 and dv3 k+1 are expressed as:

[0064]

[0065] In the formula, △v represents the position deviation estimation value, which is specifically expressed as:

[0066]

[0067] State noise compensation is performed on the system prediction result:

[0068]

[0069] Step four: based on the state noise compensation result of the system, calculate the orbit determination algorithm gain; based on the system prediction result noise compensation result and the measurement result z k+1 weighted output t k+1 System state and covariance estimation value, the estimation result is brought into step two, and the system state variable estimation value at each time is solved iteratively, and high-precision orbit determination of the maneuverable spacecraft is realized according to the system state variable estimation value at each time.

[0070] Step 4.1 Orbit determination algorithm gain:

[0071]

[0072] Step 4.2 State noise compensation result based on the system prediction result noise compensation result and the measurement result z k+1 weighted output t k+1 System state and covariance estimation value:

[0073]

[0074] will Substitute P into equation (8) k+1 Substituting into equation (9), the estimated values ​​of the system state variables at each moment are obtained by iterative solution. The observation stop time is set as the orbit determination termination time, thereby realizing high-precision orbit determination of the maneuvering spacecraft.

[0075] Beneficial effects:

[0076] 1. Compared with the traditional IMM maneuvering spacecraft tracking method, the adaptive state noise compensation maneuvering spacecraft tracking method disclosed in this invention first evaluates the measurement residual of the system state model under non-maneuvering conditions, and then constructs a step function based on the evaluation result to fuse the system state noise and compensate for the state change under maneuvering conditions. This method can improve the tracking and orbit determination accuracy of the system while effectively identifying the state change under maneuvering conditions.

[0077] 2. Compared with the parallel computation of two different state models in the traditional IMM algorithm and its improved algorithms, the adaptive state noise compensation maneuvering spacecraft tracking method disclosed in this invention only compensates for state noise based on evaluation indicators, thereby improving the computational efficiency of maneuvering spacecraft tracking.

[0078] 3. The present invention discloses an adaptive state noise compensation method for tracking maneuvering spacecraft. Based on the measurement residual evaluation results of the system state model under non-maneuvering conditions, state noise compensation is performed. It does not rely on prior information of system state abrupt changes and can still achieve high-precision tracking and orbit determination even when the prior maneuvering information of the target spacecraft is unknown.

[0079] 4. The adaptive state noise compensation method for tracking maneuvering spacecraft disclosed in this invention, compared with the traditional IMM algorithm and its improved algorithms, solves the system state noise by inverting the system state noise based on the system model matching degree, which can more accurately fit the actual sudden changes in the system state and improve the orbit determination accuracy of the spacecraft maneuvering. Attached Figure Description

[0080] Figure 1 This is a flowchart of an adaptive state noise compensation method for tracking maneuvering spacecraft disclosed in this invention.

[0081] Figure 2 This is the constellation distribution diagram observed under the CRTBP model in Example 1.

[0082] Figure 3 This is a diagram of the target spacecraft maneuvering to maintain its orbit at the apogee in Example 1.

[0083] Figure 4 This is the root mean square error of the position obtained from 100 Monte Carlo simulations in Example 1.

[0084] Figure 5 is the result of 100 Monte Carlo simulation speed root mean square error in this example 1. DETAILED DESCRIPTION

[0085] In order to better illustrate the purposes and advantages of the present application, the following simulation analysis is carried out on the scenario of far point maneuver maintenance of NRHO orbit in the earth-moon space, and the present application is explained in detail.

[0086] The tracking of the target spacecraft is carried out under the CRTBP model, and the observation orbit is set as L1 point Halo orbit (orbit amplitude is 23200km, and orbit initial phase is 240deg) and L2 point plane Lyapunov orbit (orbit amplitude is 5000km, and orbit initial phase is 120deg). The observation scenario is shown in FIG. 1. Figure 2 The target spacecraft orbit is set as NRHO. Due to the fast speed of the spacecraft near the perigee, the fuel consumption is less, and the maneuver is applied near the apogee. The nominal orbit of the target orbit is obtained by using the differential correction method, and the initial data of the orbit is shown in Table 1. The nominal orbit of the target spacecraft is shown in FIG. 2. Figure 3 The observation interval is selected as 0.1 hour, and the target is continuously observed for 6 hours.

[0087] Table 1 Initial data of far point orbit maintenance

[0088]

[0089] Example 1:

[0090] As shown in FIG. 1, the adaptive state noise compensation maneuver spacecraft tracking method disclosed in the present embodiment is specifically implemented as follows: Figure 1 Step one, establish a general dynamic model for unknown mutations of random nonlinear dynamic system and an angle-only observation model;

[0091] Step 1.1, establish a general dynamic model for unknown mutations of random nonlinear dynamic system;

[0092] The state variable differential equation of the space target is expressed as:

[0093]

[0094]

[0095] In the formula, Table The system state vector; r k represents the position vector of the space target at time k, [x k y k z k ] T is its scalar form; v​k a velocity vector of the space target at time k, is its scalar form;

[0096] A general dynamic model for unknown abrupt changes of a stochastic nonlinear dynamic system is established:

[0097] x k+1 = F(x k + δ t,k Gu) + Bw k (30)

[0098] where δ t,k is the Kronecker symbol, δ t,k = 0 when t≠ k, and δ t,k = 1 when t = k; is a constant matrix, and is set to G = [O 3×3 ; I 3×3 ] in order to change only the velocity without changing the position; is an unknown input vector of the system; is a constant matrix, and is set to B = O 6×6 in this embodiment without considering the system state noise from other sources; is white noise with an expected value of 0; δ t,k Gu represents a random maneuver of the system; F(x k + δ t,k Gu) represents state recursion based on the state variable differential equation (29) of the space target, and the specific process is to use a numerical integrator to integrate equation (29) by taking x k + δ t,k Gu at time k as the initial value to obtain the recursion result x k+1 at time k+1 based on dynamics, and ode45 integrator in MATLAB is selected as the preferred numerical integrator;

[0099] Step 1.2. Establish an angle-only observation model;

[0100] The angle information of the maneuvering target relative to the observation satellite is taken as the measurement:

[0101] z k+1 = [α k+1 β k+1 ] T (31)

[0102] In the formula, z k+1 represents the measurement, α k+1 and β k+1 are the pitch angle α k+1 and azimuth angle β of the maneuvering target relative to the observation satellitek+1 ;

[0103] The observation model is represented as:

[0104] z k+1 = h(x k+1 ) + v k+1 (32)

[0105] where:

[0106]

[0107] [x k+1 y k+1 z k+1 ] T is the position vector of the maneuvering target, [X k+1 Y k+1 Z k+1 ] T is the position vector of the observed star; v k+1 is a two-dimensional zero-mean white noise, R k+1 is the measurement noise covariance matrix, σ z is the error standard deviation of the measurement, which is set to σ z = 10 -6 rad; h(x k+1 ) and p k+1 are intermediate variables.

[0108] Step two, predict the system state and covariance matrix based on the dynamics model;

[0109] Predict the system state:

[0110]

[0111] where, represents the state prediction value at time k+1; represents the state estimation value at time k; represents the state prediction based on the state variable differential equation (29) of the space target, the specific process is to use the numerical integrator to integrate equation (29) by taking the state estimation value at time k as the initial value, to obtain the state prediction value at time k+1

[0112] Predict the system covariance matrix:

[0113]

[0114] where, represents the covariance matrix prediction value at time k+1; represents the covariance matrix estimation value at time k; Φk Let represent the system state transition matrix from time k to time k+1. Its differential equation is expressed as follows: the initial prior covariance matrix of the system is set as a diagonal matrix, the 1-σ position standard deviation is set as 100km, and the velocity standard deviation is set as 0.1m / s.

[0115]

[0116] In the formula, The Jacobian matrix is ​​specifically represented as follows:

[0117]

[0118] Step 3: Calculate t k+1 The system measures the residuals and covariance matrix at each time step, and then uses the index function Λ k+1 The degree of matching between the prediction results of the characterization system based on the dynamic model and the non-maneuvering model; t k+1 The index function at time t1 to t k The index function is compared with the mean. When the difference between the evaluation index and the mean is greater than the threshold value, it is considered that the system has undergone a state change, and state noise compensation is performed on the system prediction results.

[0119] Step 3.1: Calculate t k+1 Time system measurement residual γ k+1 S and covariance matrix k+1 And through the index function Λ k+1 The matching degree between the prediction results of the characterization system based on the dynamic model and the non-maneuvering model;

[0120] System measurement residual γ k+1 S and covariance matrix k+1 Represented as:

[0121]

[0122] Wherein, matrix H k+1 Represented as:

[0123]

[0124] t k+1 Time index function Λ k+1 Represented as:

[0125]

[0126] Where, tr(S) k+1 ) represents the trace of the measurement residual covariance matrix;

[0127] Step 3.2: Obtain t using equation (43) k+1 The index function at time t1; calculate the time interval from t1 to t2.k the mean of the indicator function; when the difference between the indicator function and the mean is greater than a threshold value, the system undergoes a state mutation, and the system prediction covariance matrix is compensated for state noise;

[0128] The mean of the indicator function from time t1 to time t k is expressed as:

[0129] vale k = (vale k-1 × (k-1) + Λ k ) / k (44)

[0130] wherein the initial value of the mean of the indicator function is set to 0;

[0131] The state noise compensation Q k+1 for the position under the system state mutation is expressed as:

[0132]

[0133] wherein η is a threshold value for judging whether the system undergoes a mutation, and in this embodiment, the threshold value is set to η = 20; D k+1 represents the state noise compensation for the position under the system state mutation; k+1 represents the state noise compensation for the position under the system state mutation;

[0134] The state noise compensation D k+1 for the position under the system state mutation is expressed as:

[0135]

[0136] In the formula, the intermediate variable dr k+1 is expressed as:

[0137]

[0138] In the formula, the intermediate variable Ω k+1 can be expressed as:

[0139]

[0140] The scalar form of the state transition matrix Φ k is expressed as:

[0141]

[0142] The state noise compensation V k+1 for the velocity under the system state mutation is expressed as:

[0143]

[0144] In the formula, the intermediate variable dv1 k+1 dv2 k+1 and dv3 k+1 Represented as:

[0145]

[0146] In the formula, Δv represents the estimated position deviation, specifically expressed as:

[0147]

[0148] State noise compensation is applied to the system prediction results:

[0149]

[0150] Step 4: Calculate the orbit determination algorithm gain based on the system state noise compensation results; calculate the orbit determination algorithm gain based on the system prediction results. Noise compensation results and measurement results z k+1 Weighted output t k+1 The system state and covariance estimates are then substituted into step two for iterative solution to achieve high-precision orbit determination of the maneuvering spacecraft.

[0151] Step 4.1 Orbit Determination Algorithm Gain:

[0152]

[0153] Step 4.2 Based on system prediction results Noise compensation results and measurement results z k+1 Weighted output t k+1 System state and covariance estimates:

[0154]

[0155] Will Substitute P into equation (8) k+1 Substituting into equation (9), the estimated value of the system state variable at each moment is solved iteratively. In this embodiment, the termination time is set to k = 60, thereby achieving high-precision orbit determination of the maneuvering spacecraft.

[0156] Following the above settings, the steps proposed in this invention are used, and the results are subjected to 100 Monte Carlo simulations to obtain statistical results. Figure 4 This represents the root mean square error of the position in the 100 Monte Carlo simulation results. Figure 5Root mean square error of velocity representing 100 Monte Carlo simulation results. In the figure, the red solid line represents the traditional IMM algorithm for maneuvering spacecraft orbit determination, the green dotted line represents the improved IMM algorithm assisted by prior knowledge, and the blue dashed line represents the orbit determination result using the method of the present patent. The method has small limitations and higher orbit determination accuracy, and can achieve state estimation of the maneuvering target in various scenarios in space, thereby accurately predicting the trajectory of the maneuvering spacecraft, and performing intention discrimination, function prediction and other tasks on the target spacecraft.

[0157] The above specific description further details the purpose, technical solutions and beneficial effects of the application. It should be understood that the above description is only a specific embodiment of the application, which is used to explain the application and does not limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application should be included in the protection scope of the application.

Claims

1. A method of orbit determination for a maneuvering spacecraft with adaptive compensation for state noise, the method comprising: Comprising the following steps: Step one, establishing a general dynamic model for unknown mutations of random nonlinear dynamic system and an angle-only observation model; Step two, predict the system state and covariance matrix based on the dynamic model at time t k+1 t Step three: Calculate t k+1 The system measures the residual and covariance matrix at every time instant, and compares the index function Λ k+1 The index function represents the matching degree between the prediction result of the system based on the dynamic model and the non-mechanical model; t k+1 The index function at time t is compared with the average of the index functions from t1 to t k When the difference between the evaluation index and the average is greater than the threshold value, it is determined that the system has a state mutation, and the state noise compensation is made to the prediction result of the system. Step four: calculate the orbit determination algorithm gain based on the result of system state noise compensation; calculate the system prediction result based on the gain Noise compensation result And the measurement result z k+1 Weighted output t k+1 System state and covariance estimates, bring the estimates into step two, iteratively solve the state estimates of each time of the system, and realize high-precision orbit determination of the maneuverable spacecraft according to the state estimates of each time.

2. A self-adapting state noise compensation method for orbit determination of a maneuverable space vehicle as recited in claim 1, characterized in that: The implementation method of step one is, Step 1.1, establishing a general dynamic model for unknown mutations of random nonlinear dynamic system; The state variable differential equation of the space target is expressed as: wherein Table System state vector; r k represents the position vector of the spatial target at time k, [x k y k z k ] T is its scalar form; v k represents the velocity vector of the spatial target at time k, is its scalar form; Establish a general dynamic model for unknown mutations of random nonlinear dynamic system: x k+1 = F(x k + δ t,k Gu) + Bw k (2) where δ t,k denotes the Kronecker symbol, δ t,k = 0 when t≠ k, and δ t,k = 1 when t = k; is a constant matrix; denotes the input vector of the system which is unknown; is a constant matrix; denotes white noise with zero mean; δ t,k Gu denotes a random maneuver of the system; F(x k + δ t,k Gu) denotes state propagation based on the state variable differential equation (1) of the space target, and the specific process is that the system state x k + δ t,k Gu at time k is used as the initial value, and the integral of equation (1) is performed by using a numerical integrator to obtain the k+1 time recursive result x k+1 based on dynamics; Step 1.2, establish an angle-only observation model; The angle information of the maneuvering target relative to the observation satellite is taken as the measurement: z k+1 ＝[α k+1 β k+1 ] T (3) where z k+1 represents the measurement, a k+1 and β k+1 are the pitch angle a k+1 and azimuth angle β k+1 of the maneuvering target with respect to the observation satellite, respectively. The observation model is expressed as: z k+1 = h(x k+1 ) + v k+1 (4) In the formula: [x k+1 y k+1 z k+1 ] T is the position vector of the maneuvering target, [X k+1 Y k+1 Z k+1 ] T is the position vector of the observed star; v k+1 is a two-dimensional zero-mean white noise, R k+1 is the measurement noise covariance matrix, σ z is the error standard deviation of the measurement; h(x k+1 ) and p k+1 are intermediate variables.

3. A method of adaptive state noise compensation for orbit determination of a maneuvered space vehicle as recited in claim 2, characterized by: The implementation method of step two is, The system state is predicted: wherein denotes the state prediction value at time k+1; denotes the state estimate at time k; denotes the state prediction based on the state variable differential equation (1) of the space object, and the specific process is to use the state estimate at time k As the initial value, the numerical integrator is used to integrate equation (1) to obtain the state prediction value at time k+1 The system covariance matrix is predicted: wherein denotes the covariance matrix prediction at time k + 1 ; denotes the covariance matrix estimate at time k ; Φ k denotes the system state transition matrix from time k to time k + 1, whose differential equation is expressed as: wherein denotes the Jacobian matrix, which is specified as:

4. A method of adaptive state noise compensation for orbit determination of a maneuvered space vehicle as recited in claim 3, characterized by: The implementation method of step three is, Step 3.1: Calculate t k+1 System measurement residual γ k+1 And covariance matrix S k+1 And the index function Λ k+1 Characterize the matching degree between the system prediction results based on the dynamic model and the non-mechanical model Systematic measurement residuals γ k+1 and covariance matrix S k+1 is represented as: where the matrix H k+1 is expressed as: t k+1 time indicator function Λ k+1 is represented as: where tr(S k+1 ) denotes the trace of the measurement residual covariance matrix; Step 3.2: Obtain t by formula (15) k+1 the indicator function at time t; calculate the mean of the indicator function from t1 to t k ; when the difference between the indicator function and the mean is greater than a threshold value, the system undergoes a state mutation, and the system prediction covariance matrix is compensated for state noise; the indicator function at time t1to t k The mean of the indicator function at time t1to t vale k = (vale k-1 × (k - 1) + Λ k ) / k (16) Wherein, the initial value of the index function mean is set to 0; State noise compensation Q k+1 is represented as: wherein, η is a threshold value for determining whether the system has a mutation; D k+1 represents the state noise compensation for the position in the case of a system state mutation; V k+1 represents the state noise compensation for the position in the case of a system state mutation; System state mutation case of position state noise compensation D k+1 is expressed as: In the formula, the intermediate variable dr k+1 is expressed as: where the intermediate variable Ω k+1 may be expressed as: State transition matrix Φ k+1 is expressed in scalar form as: System state mutation case of speed state noise compensation V k+1 is expressed as: where the intermediate variables dv1 k+1 dv2 k+1 and dv3 k+1 are expressed as: In the formula, △v represents the position deviation estimation value, which is specifically expressed as: The state noise compensation is performed on the system prediction result:

5. A method of adaptive state noise compensation for orbit determination of a maneuvered space vehicle as recited in claim 4, characterized by: The implementation method of step four is, Step 4.1, orbit determination algorithm gain: Step 4.2 Based on system prediction results Noise compensated results And measurement results z k+1 Weighted output t k+1 System state and covariance estimates: Will Substitute P into equation (8) k+1 Substituting into equation (9), the estimated system state value at each moment is obtained by iterative solution. The observation stop time is set as the termination time of orbit determination. Based on the estimated system state value at each moment, high-precision orbit determination of the maneuvering spacecraft is achieved.

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