CN117268381B - Spacecraft state judging method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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Abstract
The invention discloses a method for judging the state of a spacecraft, which relates to the technical field of aerospace measurement and control.
Description
Technical Field
The invention relates to the technical field of aerospace measurement and control, in particular to a method for judging the state of a spacecraft.
Background
With the play of more and more important roles of the spacecraft in a plurality of fields of deep space exploration and navigation communication, the requirements of people on the autonomous running of the spacecraft on orbit are also higher and higher.
Autonomous navigation is one of core technologies for realizing autonomous operation of a spacecraft, and is a precondition for realizing autonomous orbit/state control, deep space exploration and on-orbit service space tasks of the spacecraft. The state estimation is a core means for realizing autonomous navigation of the spacecraft, and is a process of acquiring measurement data by utilizing self-carried equipment, analyzing and processing observation data with errors by combining a dynamics/kinematics model of the spacecraft, and acquiring the position, the speed and the state of the spacecraft in real time through recursive calculation.
However, the conventional spacecraft state judging method is generally based on a linear state transition and measurement model, and is limited when a nonlinear system or a sensor is processed, and meanwhile, when the nonlinear system is faced, the behavior of the nonlinear system cannot be accurately captured, so that errors occur, and in addition, the conventional spacecraft state judging method is limited by observability problems, namely, certain state parameters cannot be accurately estimated from sensor data, so that the state estimation is incomplete, the performance of the whole system is reduced, and therefore, a high-accuracy spacecraft state judging method capable of eliminating the limitation of the linear model is needed to solve the problems.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a spacecraft state judging method, which solves the problem that the behavior of a nonlinear system cannot be accurately captured when the state of the spacecraft is faced with the nonlinear system in the prior art, and the state judging method is limited by observability.
(II) technical scheme
In order to achieve the above object, the present invention provides a method for determining a state of a spacecraft, including:
step 1, estimating state parameters by adopting unscented Kalman filtering;
step 2, multi-sensor fusion is carried out, various sensor data are integrated, wherein the data comprise an inertial navigation sensor, a star sensor and a GPS sensor, and the multi-sensor fusion data are used as state estimation input;
step 3, expanding Kalman filtering optimization, optimizing based on unscented Kalman filtering state estimation, and modeling a noise model and a sensor error;
step 4, observability analysis is carried out, and partial state parameters are determined to be estimated from sensor data;
step 5, the state estimation and the control are cooperated, the state estimation and the controller are cooperatively designed, and the decision of the controller is determined based on the result of the state estimation;
and 6, adjusting the parameters of the real-time environment task, and automatically adjusting the parameters of the filter to adapt to the continuous real-time environment and task requirements based on the self-adaptive filtering algorithm.
The invention is further arranged to: in the step 1, the method for estimating the state parameters by adopting unscented kalman filter comprises the following steps:
initializing state estimation vectors，/>Representing the state of the spacecraft, including position, speed and attitude;
initializing a state covariance matrixFor describing uncertainty of state estimation;
setting a process noise covariance matrix Q for describing uncertainty in state transition;
setting a measurement noise covariance matrix R for describing uncertainty of sensor measurement;
the invention is further arranged to: in the step 1, the method for estimating the state parameters by adopting unscented kalman filter further includes:
step 1.1 predicts the next state using state transfer function f:
，
wherein the method comprises the steps ofRepresenting control input +.>Representing the predicted state vector at time step k, < >>Representing the state estimate at k-1, the state transfer function f will be the estimate of the current state +.>And control input +.>Transition to the next time step prediction state +.>；
Updating the state covariance matrix:
，
wherein the method comprises the steps ofRepresenting a state transition matrix, k and k-1 representing the next time step and the current time step, respectively, T being the matrix transpose operator, < >>Representing a state transition matrix->Is a transpose of (2);
the invention is further arranged to: in the step 1, the method for estimating the state parameters by adopting unscented kalman filter further includes:
step 1.2 acquiring sensor measurement data；
Step 1.3, calculating Kalman gain:
；
wherein the method comprises the steps ofFor measuring the matrix +.>Is->Is a transpose of (2);
the invention is further arranged to: in the step 1, the method for estimating the state parameters by adopting unscented kalman filter further includes:
step 1.4 updating the state estimation vector:
；
wherein the method comprises the steps ofI.e. state estimation of time step k, +.>Representing the predicted state of time step k, +.>For Kalman gain, ++>For measuring the matrix +.>Monitoring data for the sensor measured at time step k;
step 1.5, updating a state covariance matrix:
wherein I is an identity matrix;
repeating steps 1.1 to 1.5 for continuous state estimation and updating;
the invention is further arranged to: in the multi-sensor fusion step, the method for combining the data of the multiple sensors specifically comprises the following steps:
step 2.1, measuring data of each sensor including the measuring data of an inertial navigation sensor, a star sensor and a GPS sensor are taken, and the measuring data of each sensor is expressed as vectors which are respectively；
Step 2.2, fusing the measurement data of each sensor into an overall measurement vector:
，/>the whole measuring vector is;
the invention is further arranged to: in the multi-sensor fusion step, the method for merging the multiple sensor data further comprises the following steps:
step 2.3, constructing an integrated measurement noise covariance matrix, wherein the block diagonal matrix form of the measurement noise covariance matrix of each sensor is as follows:
wherein->、/>And->Respectively represent noise of the sensorA covariance matrix;
step 2.4 Using the measurement vector integrated in step 1And the integrated measurement noise covariance matrix R is subjected to fused state estimation update:
；
；
repeating steps 2.1 to 2.4 for continuous multi-sensor fusion and state estimation;
the invention is further arranged to: in the step 3, the Kalman filtering expansion method specifically comprises the following steps:
based on the process noise covariance matrix Q and the measurement noise covariance matrix R defined in the step 1 and the step 2, performing state transition and measurement by adopting a nonlinear function;
and (3) predicting:
；
；
measurement update:
；
；
；
wherein f and h are represented as nonlinear state transitions and measurementsThe function of the quantity is that,for state transition matrix>Is a measurement matrix;
the invention also provides a terminal device, which comprises: the control program of the spacecraft state judging method is executed by the processor to realize the spacecraft state judging method;
the invention also provides a storage medium which is applied to a computer, wherein the storage medium is stored with a control program of the spacecraft state judging method, and the spacecraft state judging method is realized when the control program of the spacecraft state judging method is executed by the processor.
(III) beneficial effects
The invention provides a method for judging the state of a spacecraft. The beneficial effects are as follows:
the spacecraft state judging method provided by the application optimizes from a state estimating angle, adopts unscented Kalman filtering to estimate the state of the spacecraft, comprises the steps of position, speed and gesture, sets a process noise and measurement noise covariance matrix by initializing a state estimating vector and a state covariance matrix, describes the uncertainty of state transition and sensor measurement, and integrates the information of a plurality of sensors into the state estimation more effectively by nonlinear prediction and measurement updating of a state transition function to provide a more accurate state estimating result.
In the multi-sensor fusion step, data of an inertial navigation sensor, a star sensor and a GPS sensor are integrated, measurement data of each sensor is obtained, the monitoring data are expressed as vectors, the vectors are integrated into an integral measurement vector, an integrated measurement noise covariance matrix is constructed, and the information of the multi-sensors is fused together through state estimation updating of Kalman filtering to provide stronger state estimation.
In the extended Kalman filtering optimization step, the extended Kalman filtering is adopted to perform nonlinear system state estimation, the noise model and the sensor error are accurately modeled, so that estimation errors are reduced, meanwhile, state transition and measurement are performed through a nonlinear function, parameters of the noise model and the sensor error are adjusted, state estimation is optimized, which state parameters are determined to be estimated from sensor data in the predictability analysis, the decision of a controller is determined based on the result of the state estimation in the state estimation and control collaborative design step, and navigation and control performance of the spacecraft is improved, so that good coordination between the state estimation and control is ensured.
In summary, the spacecraft state judging method provided by the application adopts unscented Kalman filtering to perform state estimation, performs more accurate state estimation on a nonlinear system, eliminates the limitation of a linear model, utilizes a nonlinear state transfer function to better predict the next state, improves the accuracy of state estimation, simultaneously, better fuses sensor data through Kalman gain and nonlinear measurement updating, improves the quality of state estimation, and further improves observability and the accuracy of state estimation through integrating various sensors, inertial navigation sensors, star sensor and GPS sensor data, accommodates the noise characteristics of different sensors based on an integrated measurement noise covariance matrix, improves the consistency of state estimation, further optimizes the state estimation through expanding Kalman filtering, eliminates the limitation of the linear model in the traditional method, and improves the accuracy of state estimation.
The method solves the problem that the behavior of the nonlinear system cannot be accurately captured when the nonlinear system is faced in the prior art, and the state judgment method is limited by the observability problem.
Drawings
Fig. 1 is a flowchart of a method for determining the state of a spacecraft according to the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 1, the present invention provides a method for determining a state of a spacecraft, including:
s1, estimating state parameters by adopting unscented Kalman filtering;
in step 1, the method for estimating the state parameters by adopting unscented Kalman filtering comprises the following steps:
initializing state estimation vectors，/>Representing the state of the spacecraft, including position, speed and attitude;
initializing a state covariance matrixFor describing uncertainty of state estimation;
setting a process noise covariance matrix Q for describing uncertainty in state transition;
setting a measurement noise covariance matrix R for describing uncertainty of sensor measurement;
in step 1, the method for estimating the state parameters by adopting unscented kalman filtering further comprises:
step 1.1 predicts the next state using state transfer function f:
，
wherein the method comprises the steps ofRepresentation controlBrake input (I)>Representing the predicted state vector at time step k, < >>Representing the state estimate at k-1, the state transfer function f will be the estimate of the current state +.>And control input +.>Transition to the next time step prediction state +.>；
Updating the state covariance matrix:
，
wherein the method comprises the steps ofRepresenting a state transition matrix, k and k-1 representing the next time step and the current time step, respectively, T being the matrix transpose operator, < >>Representing a state transition matrix->Is a transpose of (2);
in step 1, the method for estimating the state parameters by adopting unscented kalman filtering further comprises:
step 1.2 acquiring sensor measurement data；
Step 1.3, calculating Kalman gain:
；
wherein the method comprises the steps ofFor measuring the matrix +.>Is->Is a transpose of (2);
in step 1, the method for estimating the state parameters by adopting unscented kalman filtering further comprises:
step 1.4 updating the state estimation vector:
；
wherein the method comprises the steps ofI.e. state estimation of time step k, +.>Representing the predicted state of time step k, +.>For Kalman gain, ++>For measuring the matrix +.>Monitoring data for the sensor measured at time step k;
step 1.5, updating a state covariance matrix:
wherein I is an identity matrix;
repeating steps 1.1 to 1.5 for continuous state estimation and updating;
s2, multi-sensor fusion is carried out, various sensor data including an inertial navigation sensor, a star sensor and a GPS sensor are integrated, the multi-sensor fusion data are used as state estimation input, and observability and accuracy of state estimation are improved;
in the multi-sensor fusion step, the method for combining the data of the multiple sensors specifically comprises the following steps:
step 2.1, measuring data of each sensor including the measuring data of an inertial navigation sensor, a star sensor and a GPS sensor are taken, and the measuring data of each sensor is expressed as vectors which are respectively；
Step 2.2, fusing the measurement data of each sensor into an overall measurement vector:
，/>the whole measuring vector is;
in the multi-sensor fusion step, the method for combining the multiple sensor data further comprises the following steps:
step 2.3, constructing an integrated measurement noise covariance matrix, wherein the block diagonal matrix form of the measurement noise covariance matrix of each sensor is as follows:
wherein->、/>And->Respectively representing noise covariance matrixes of the sensors;
step 2.4 Using the measurement vector integrated in step 1And the integrated measurement noise covariance matrix R is subjected to fused state estimation update:
；
；
repeating steps 2.1 to 2.4 for continuous multi-sensor fusion and state estimation;
s3, expanding Kalman filtering optimization, optimizing based on unscented Kalman filtering state estimation, modeling a noise model and a sensor error, and reducing estimation error;
in the step 3, the Kalman filtering expansion method specifically comprises the following steps:
based on the process noise covariance matrix Q and the measurement noise covariance matrix R defined in the step 1 and the step 2, performing state transition and measurement by adopting a nonlinear function;
and (3) predicting:
；
；
measurement update:
；
；
；
where f and h are represented as nonlinear state transfer and measurement functions,for state transition matrix>Is a measurement matrix;
reducing the estimation error by optimizing parameters of the noise model and the sensor error;
s4, observability analysis is carried out, and partial state parameters are determined to be estimated from sensor data;
s5, the state estimation and the control are cooperated, the state estimation and the controller are cooperatively designed, the decision of the controller is determined based on the result of the state estimation, and the navigation and control performance of the spacecraft is improved;
s6, adjusting the parameters of the real-time environment task, and automatically adjusting the parameters of the filter to adapt to the continuous real-time environment and task requirements based on the self-adaptive filtering algorithm.
The invention also provides a terminal device, which comprises: the control program of the spacecraft state judging method is executed by the processor to realize the spacecraft state judging method;
the invention also provides a storage medium which is applied to a computer, wherein the storage medium is stored with a control program of the spacecraft state judging method, and the spacecraft state judging method is realized when the control program of the spacecraft state judging method is executed by a processor.
In combination with the above, in the present application:
the spacecraft state judging method provided by the application optimizes from a state estimating angle, adopts unscented Kalman filtering to estimate the state of the spacecraft, comprises the steps of position, speed and gesture, sets a process noise and measurement noise covariance matrix by initializing a state estimating vector and a state covariance matrix, describes the uncertainty of state transition and sensor measurement, and integrates the information of a plurality of sensors into the state estimation more effectively by nonlinear prediction and measurement updating of a state transition function to provide a more accurate state estimating result.
In the multi-sensor fusion step, data of an inertial navigation sensor, a star sensor and a GPS sensor are integrated, measurement data of each sensor is obtained, the monitoring data are expressed as vectors, the vectors are integrated into an integral measurement vector, an integrated measurement noise covariance matrix is constructed, and the information of the multi-sensors is fused together through state estimation updating of Kalman filtering to provide stronger state estimation.
In the extended Kalman filtering optimization step, the extended Kalman filtering is adopted to perform nonlinear system state estimation, the noise model and the sensor error are accurately modeled, so that estimation errors are reduced, meanwhile, state transition and measurement are performed through a nonlinear function, parameters of the noise model and the sensor error are adjusted, state estimation is optimized, which state parameters are determined to be estimated from sensor data in the predictability analysis, the decision of a controller is determined based on the result of the state estimation in the state estimation and control collaborative design step, and navigation and control performance of the spacecraft is improved, so that good coordination between the state estimation and control is ensured.
In summary, the spacecraft state judging method provided by the application adopts unscented Kalman filtering to perform state estimation, performs more accurate state estimation on a nonlinear system, eliminates the limitation of a linear model, utilizes a nonlinear state transfer function to better predict the next state, improves the accuracy of state estimation, simultaneously, better fuses sensor data through Kalman gain and nonlinear measurement updating, improves the quality of state estimation, and further improves observability and the accuracy of state estimation through integrating various sensors, inertial navigation sensors, star sensor and GPS sensor data, accommodates the noise characteristics of different sensors based on an integrated measurement noise covariance matrix, improves the consistency of state estimation, further optimizes the state estimation through expanding Kalman filtering, eliminates the limitation of the linear model in the traditional method, and improves the accuracy of state estimation.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (9)
1. The method for judging the state of the spacecraft is characterized by comprising the following steps of:
step 1, estimating state parameters by adopting unscented Kalman filtering;
step 2, multi-sensor fusion is carried out, various sensor data are integrated, wherein the data comprise an inertial navigation sensor, a star sensor and a GPS sensor, and the multi-sensor fusion data are used as state estimation input;
step 3, expanding Kalman filtering optimization, optimizing based on unscented Kalman filtering state estimation, and modeling a noise model and a sensor error;
step 4, observability analysis is carried out, and partial state parameters are determined to be estimated from sensor data;
step 5, the state estimation and the control are cooperated, the state estimation and the controller are cooperatively designed, and the decision of the controller is determined based on the result of the state estimation;
and 6, adjusting the parameters of the real-time environment task, and automatically adjusting the parameters of the filter to adapt to the continuous real-time environment and task requirements based on the self-adaptive filtering algorithm.
2. The method for determining the state of a spacecraft according to claim 1, wherein in the step 1, the method for estimating the state parameters by using unscented kalman filter comprises:
initializing state estimation vectors，/>Representing the state of the spacecraft, including position, speed and attitude;
initializing a state covariance matrixFor describing uncertainty of state estimation;
setting a process noise covariance matrix Q for describing uncertainty in state transition;
a measurement noise covariance matrix R is set to describe the uncertainty of the sensor measurements.
3. The method for determining the state of a spacecraft according to claim 2, wherein in the step 1, the method for estimating the state parameter by using unscented kalman filter further comprises:
step 1.1 predicts the next state using state transfer function f:
，
wherein the method comprises the steps ofRepresenting control input +.>Representing the predicted state vector at time step k, < >>Representing the state estimate at k-1, the state transfer function f will be the estimate of the current state +.>And control input +.>Transition to the next time step prediction state +.>；
Updating the state covariance matrix:
，
wherein the method comprises the steps ofRepresenting a state transition matrix, k and k-1 representing the next time step and the current time step, respectively, T being the matrix transpose operator, < >>Representing a state transition matrix->Is a transpose of (2); />Covariance matrix representing current time step, +.>Representing the updated covariance matrix, wherein the process noise covariance matrix is Q;
step 1.2 acquiring sensor measurement data；
Step 1.3 calculation of Kalman gain：
；
Wherein the method comprises the steps ofFor measuring the matrix +.>Is->Transpose of->For the updated covariance matrix, R represents the covariance matrix of the observed noise.
4. The method for determining a state of a spacecraft according to claim 3, wherein in the step 1, the method for estimating the state parameter by using unscented kalman filter further comprises:
step 1.4 updating the state estimation vector:
；
wherein the method comprises the steps ofI.e. state estimation of time step k, +.>Representing the predicted state of time step k, +.>In order for the kalman gain to be achieved,for measuring the matrix +.>Is a time stepSensor monitoring data measured at k;
step 1.5, updating a state covariance matrix:
wherein I is an identity matrix,>for an updated covariance matrix +.>The covariance matrix at the moment k of the time step;
steps 1.1 to 1.5 are repeated for continuous state estimation and updating.
5. The method for determining a state of a spacecraft according to claim 4, wherein in the multi-sensor fusion step, the method for merging the plurality of sensor data is specifically as follows:
step 2.1, measuring data of each sensor including the measuring data of an inertial navigation sensor, a star sensor and a GPS sensor are taken, and the measuring data of each sensor is expressed as vectors which are respectively；
Step 2.2, fusing the measurement data of each sensor into an overall measurement vector:
，/>i.e. the overall measurement vector.
6. The method for determining a state of a spacecraft according to claim 5, wherein in the multi-sensor fusion step, the method for merging the plurality of sensor data further comprises:
step 2.3, constructing an integrated measurement noise covariance matrix, wherein the block diagonal matrix form of the measurement noise covariance matrix of each sensor is as follows:
wherein->、/>And->Respectively representing noise covariance matrixes of the sensors;
step 2.4 Using the measurement vector integrated in step 1And the integrated measurement noise covariance matrix R is subjected to fused state estimation update:
；
；
steps 2.1 to 2.4 are repeated for successive multi-sensor fusion and state estimation.
7. The method for determining the state of a spacecraft according to claim 6, wherein in the step 3, the kalman filter expansion method specifically comprises:
based on the process noise covariance matrix Q and the measurement noise covariance matrix R defined in the step 1 and the step 2, performing state transition and measurement by adopting a nonlinear function;
and (3) predicting:
；
；
measurement update:
；
；
；
wherein the method comprises the steps ofRepresenting control input +.>Representing the predicted state vector at time step k, < >>Representing the state estimate at k-1, the state transfer function f will be the estimate of the current state +.>And control input +.>Transition to the next time step prediction state +.>，/>Representing a state transition matrix, k and k-1 representing the next time step and the current time step, respectively, T being the matrix transpose operator, < >>Representing a state transition matrix->Transpose of->For an updated covariance matrix, the process noise covariance matrix is Q, < >>For Kalman gain, ++>For measuring the matrix +.>Is thatR represents the covariance matrix of the observed noise, < ->I.e. state estimation of time step k, +.>Representing the predicted state of time step k, +.>For the sensor monitoring data measured at time step k, f and h are denoted as nonlinear state transitions and measurement functions, I is an identity matrix, +.>Is a time stepCovariance matrix at time k>Representing the covariance matrix of the current time step.
8. A terminal device, characterized in that the device comprises: memory, processor and control program for a method of determining a state of a spacecraft stored on the memory and executable on the processor, which when executed by the processor implements the method of determining a state of a spacecraft as claimed in any one of claims 1 to 7.
9. A storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the method of determining the state of a spacecraft according to any of claims 1-7.
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