CN112212860A - Distributed filtering micro-nano satellite attitude determination method with fault tolerance - Google Patents

Distributed filtering micro-nano satellite attitude determination method with fault tolerance Download PDF

Info

Publication number
CN112212860A
CN112212860A CN202010882166.6A CN202010882166A CN112212860A CN 112212860 A CN112212860 A CN 112212860A CN 202010882166 A CN202010882166 A CN 202010882166A CN 112212860 A CN112212860 A CN 112212860A
Authority
CN
China
Prior art keywords
fault
attitude
sensor
micro
coordinate system
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010882166.6A
Other languages
Chinese (zh)
Other versions
CN112212860B (en
Inventor
李乐宝
王菲
张众正
刘中伟
李明翔
王磊
姜连祥
占丰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong Institute of Space Electronic Technology
Original Assignee
Shandong Institute of Space Electronic Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shandong Institute of Space Electronic Technology filed Critical Shandong Institute of Space Electronic Technology
Priority to CN202010882166.6A priority Critical patent/CN112212860B/en
Publication of CN112212860A publication Critical patent/CN112212860A/en
Application granted granted Critical
Publication of CN112212860B publication Critical patent/CN112212860B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • 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
    • G01C21/24—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for cosmonautical navigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Astronomy & Astrophysics (AREA)
  • Navigation (AREA)

Abstract

According to the distributed filtering micro-nano satellite attitude determination method with fault tolerance, a fault detection and processing link is added based on the traditional Federal Kalman filtering algorithm, and the integral attitude determination method is enabled to have fault tolerance performance by adding fault detection factors and deducing given fault thresholds, so that the sensor fault type is effectively discriminated, and the attitude determination precision of the system is improved. Measurement residual using attitude sensor
Figure DDA0002654374140000011
Setting a fault detection factor
Figure DDA0002654374140000012
Fault detection factor
Figure DDA0002654374140000013
The following formula is satisfied:
Figure DDA0002654374140000014
wherein trace (·) represents the trace operation of matrix calculation; l is the dimension of the measuring vector of the sensor in the local filter;
Figure DDA0002654374140000015
an observed noise variance matrix representing a local filter; (.)TRepresents a transpose of a matrix; fault threshold gamma0Take a value of
Figure DDA0002654374140000016

Description

Distributed filtering micro-nano satellite attitude determination method with fault tolerance
Technical Field
The invention relates to a distributed filtering micro-nano satellite attitude determination method with fault tolerance, and belongs to the technical field of satellite attitude determination and control.
Background
With the rapid development of the domestic microelectronic technology and chip process design technology, the satellite attitude determination and control level is increasingly improved. The attitude determination and control technology is a basic guarantee for normal in-orbit work of a satellite platform, and is used for dynamically adjusting and controlling the in-orbit attitude of a satellite according to coordinate information of the satellite relative to a certain reference coordinate system (such as an orbit coordinate system or an inertia coordinate system) so as to improve the accuracy and efficiency of data transmission. The attitude determination and control system generally comprises an on-board brain (spaceborne computer), an attitude sensor and an attitude determination algorithm, wherein the accuracy of the result of the attitude determination algorithm directly influences the accuracy of satellite attitude control and adjustment, so that the improvement of the accuracy of the satellite attitude determination algorithm is always the core subject of the attitude determination and control field.
Due to the limitations of mass, volume and power consumption, micro-nano satellites commonly adopt miniaturized attitude sensors, such as MEMS gyroscopes, magnetometers, micro sun sensors and the like. Although the miniaturized attitude sensor is light and small and has low power consumption, the single sensor has the problems of incapability of determining the attitude, low accuracy of acquiring attitude information and the like. The conventional general improvement mode is to combine a micro attitude sensor with a multi-source information fusion algorithm to expect to acquire attitude information with higher precision. However, the multi-source information fusion algorithm such as the federal kalman filter only provides a distributed information fusion structure, and does not consider the detection and processing link of sensor faults, so that a corresponding fault-tolerant link is lacked. On the premise that the sensor attitude determination and attitude information acquisition precision is not high, targeted fault detection and isolation cannot be realized, sufficient compensation for satellite attitude adjustment cannot be provided through fault analysis, and the conventional attitude determination and control system needs to be further improved.
In view of this, the present patent application is specifically proposed.
Disclosure of Invention
The method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance aims to solve the problems in the prior art, and a fault detection and processing link is added based on the traditional Federal Kalman filtering algorithm, namely a fault detection factor and a fault threshold given by derivation are added, so that the integral attitude determination method has fault tolerance, the sensor fault type is effectively discriminated, and the attitude determination precision of the system is improved.
In order to achieve the above design objective, the present application proposes the following solutions:
a distributed filtering micro-nano satellite attitude determination method with fault tolerance is based on a distributed Federal Kalman filtering algorithm and utilizes a measurement value residual error of an attitude sensor
Figure BDA0002654374120000021
Setting a fault detection factor
Figure BDA0002654374120000022
Failure detection factor
Figure BDA0002654374120000023
The following formula is satisfied:
Figure BDA0002654374120000024
wherein: trace (·) represents the trace operation of the matrix; l is the dimension of the measuring vector of the sensor in the local filter;
Figure BDA0002654374120000025
an observed noise variance matrix representing a local filter; (.)TRepresents a transpose of a matrix;
fault threshold gamma0Take a value of
Figure BDA0002654374120000026
Further, the method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance comprises the following steps:
1) acquiring the angular velocity omega of the micro-nano satellite body coordinate system b relative to the inertial coordinate system i by using the measurement value of the MEMS gyroscopemFor estimation of angular velocity;
2) the mounting shaft of the magnetometer is consistent with the coordinate system of the micro-nano satellite body, and the geomagnetic field vector measurement value B under the coordinate system of the satellite body is obtainedb；
3) The aiming axis of the sun sensor is consistent with the coordinate system-Y axis of the micro-nano satellite body to obtain the sun vector measurement value S under the coordinate system of the satellite bodyb；
4) Selecting an error quaternion Δ qboThe vector component Δ q and the gyro angular rate drift estimation error Δ b of (1) are state quantities
Figure BDA0002654374120000027
Giving a state equation of a satellite attitude determination system;
5) utilizing the geomagnetic field vector under the satellite body coordinate system obtained in the step 2)Measured value BbAnd the sun vector measurement value S under the satellite body coordinate system obtained in the step 3)bCombined with the reference value B of the earth magnetic field vector in the orbital coordinate systemoAnd the sun vector reference value S under the orbital coordinate systemoProviding observation information of two local filters in a distributed Federal Kalman filtering algorithm;
6) and calculating to obtain the attitude of the micro-nano satellite by utilizing a distributed Federal Kalman filtering algorithm, and realizing satellite attitude calculation based on a plurality of low-cost, small-volume and low-power consumption micro sensors.
Further, the fault detection and processing and state fusion process is implemented according to the following steps:
(1) fault detection and handling
Setting a fault detection factor
Figure BDA0002654374120000028
Fault threshold gamma of0Namely:
Figure BDA0002654374120000029
when the sensor is normal;
Figure BDA00026543741200000210
when the sensor fails, the sensor fails;
based on the fault detection factor and the fault threshold, when the sensor is detected to be in fault, if the local filter S1 is failed, the local filter S1 is failed
Figure BDA00026543741200000211
Is incorrect and thus not input to the main filter, at which point the overall estimate of the system error state may be modified to
Figure BDA00026543741200000212
Similarly, if the local filter S2 fails, the overall estimate of the system error state is
Figure BDA0002654374120000031
When the sensor is not detected to have faults, directly executing the following step (2);
(2) state fusion
State estimation for local filters S1 and S2
Figure BDA0002654374120000032
Sum estimation error covariance matrix
Figure BDA0002654374120000033
Performing data fusion to obtain final state estimation
Figure BDA0002654374120000034
Sum estimation error covariance matrix Pg,k；
Figure BDA0002654374120000035
Figure BDA0002654374120000036
In conclusion, the method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance has the following advantages:
1. through the fault tolerance link provided by the application, the accuracy of the sensor for acquiring the attitude information of the micro-nano satellite can be improved, and the integral fault tolerance performance of the attitude determination system can be improved by detecting and processing the fault of the sensor.
2. A fault detection factor is innovatively provided, a value reference and a strict mathematical analysis process are provided for a fault threshold, and a large amount of data debugging work in engineering application is remarkably reduced, so that the efficient design of the fault-tolerant attitude determination method is realized.
3. When the satellite is at the end of the service life, if the attitude sensor fails (such as failure of a magnetometer), the existing attitude determination system can still normally acquire and adjust attitude information based on the application, and accordingly the service life of the satellite is prolonged.
Drawings
The following drawings are illustrative of specific embodiments of the present application.
FIG. 1 is a schematic diagram of a prior art multi-source information distributed fusion pose determination method;
FIG. 2 is a schematic diagram of a distributed Federal Kalman filtering algorithm based on fault tolerance described in the present application;
FIG. 3 is a flow chart of a fault detection and handling process described herein;
FIG. 4 is a waveform of satellite attitude information determined based on the fault tolerant distributed filtering algorithm described herein when the local filter sensor is fault-free;
FIG. 5 is a waveform of satellite angular velocity information determined based on the fault tolerant distributed filtering algorithm described herein when the local filter sensor is fault-free;
FIG. 6 is a waveform diagram of satellite roll angle information determined based on the fault tolerant distributed filtering algorithm described herein when a local filter magnetometer or sun sensor fails;
FIG. 7 is a waveform diagram of satellite pitch angle information determined based on the fault-tolerant distributed filtering algorithm described in the present application when a local filter magnetometer or sun sensor fails;
FIG. 8 is a waveform diagram of satellite yaw angle information determined based on the fault tolerant distributed filtering algorithm described herein when a local filter magnetometer or sun sensor fails.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
Embodiment 1, the application provides a novel method for determining a micro/nano satellite attitude with a fault tolerance link on the basis of the existing distributed federal kalman filtering algorithm.
As shown in fig. 1 and 2, the existing and improved distributed fusion micro-nano satellite attitude determination methods are based on the existing multi-source information distributed fusion structure, an MEMS gyroscope, a magnetometer and a sun sensor are used as attitude sensors, and the distributed federal kalman filtering algorithm is composed of two local filters and a main filter.
The local filter S1 and the local filter S2 respectively complete the measurement updating of the estimated error covariance and the state of the magnetometer and the sun sensor according to the feedback attitude and the error covariance of the adaptive distribution; the state variables of the two local filters are the same, and the two local filters perform parallel operation.
The main filter realizes the prediction of the attitude quaternion, the one-step prediction of the state and the covariance and the information distribution, detects the sensor fault according to the fault detection factor or performs state fusion according to the output information of the local filter, and corrects and finally outputs the attitude and the gyro drift.
As shown in fig. 2, the method for determining the attitude of the distributed filtering micro/nano satellite with fault tolerance according to the present application can complete satellite attitude determination and detect and process sensor faults at the same time, and includes the following implementation steps:
1) acquiring the angular velocity omega of the micro-nano satellite body coordinate system b relative to the inertial coordinate system i by using the measurement value of the MEMS gyroscopemFor estimation of angular velocity;
2) when the mounting shaft of the magnetometer is consistent with the coordinate system of the micro-nano satellite body, the geomagnetic field vector measurement value B under the coordinate system of the satellite body is obtainedb；
3) The aiming axis of the sun sensor is consistent with the coordinate system-Y axis of the micro-nano satellite body to obtain the sun vector measurement value S under the coordinate system of the satellite bodyb；
4) Selection error quaternion Δ qboThe vector component Δ q and the gyro angular rate drift estimation error Δ b of (1) are state quantities
Figure BDA0002654374120000041
The discrete state equation for the satellite attitude determination system is given as:
Xk＝Φk,k-1Xk-1+Γk,k-1Wk-1
wherein,
Figure BDA0002654374120000042
t is the calculation step size,
Figure BDA0002654374120000051
ωbiis the angular velocity vector, I, of the satellite body coordinate system relative to the Earth's center inertial coordinate system3×3The gyro angular velocity random walk noise is a 3 x 3-order unit matrix, upsilon is gyro angular velocity random walk noise, zeta is gyro angular velocity random walk noise, k represents the kth time, and k-1 represents the kth time.
5) Utilizing the geomagnetic field vector measurement value B under the satellite body coordinate system obtained in the step 2)bAnd the sun vector measurement value S under the satellite body coordinate system obtained in the step 3)bCombined with the reference value B of the earth magnetic field vector in the orbital coordinate systemoAnd the sun vector reference value S under the orbital coordinate systemoAnd providing observation information of two local filters in the distributed Federal Kalman filtering algorithm:
the observed information of the local filter S1 is
Z1k＝(Bb(k)-Bb(k/k-1))/2
Wherein, Bb(k)The measured value of the geomagnetic field vector under a satellite body coordinate system at the moment k is obtained; b isb(k/k-1)＝Tbo(qbo(k/k-1))Bo(k)The earth magnetic field vector under the body coordinate system is calculated according to the attitude estimation value;
Bo(k)a ground magnetic field vector of a satellite orbit coordinate system at the moment k; q. q.sbo(k/k-1)Is the predicted value of the attitude four-element number from the time k-1 to the time k.
The local filter S2 observes the information as
Z2k＝(Sb(k)-Sb(k/k-1))/2
Wherein S isb(k)The measured value of the sun vector is the measured value of the satellite body coordinate system at the moment k; sb(k/k-1)＝Tbo(qbo(k/k-1))So(k)The sun vector under the body coordinate system calculated according to the attitude estimation value; so(k)The solar vector is the solar vector under the satellite orbit coordinate system at the moment k.
6) And calculating to obtain the attitude of the micro-nano satellite by utilizing a distributed Federal Kalman filtering algorithm, and realizing satellite attitude calculation based on a plurality of low-cost, small-volume and low-power consumption micro sensors.
As shown in fig. 3, the present application provides an improvement of a distributed fusion micro-nano satellite attitude determination method, namely, a fault detection and processing link is added. The method for determining the attitude of the distributed Federal Kalman filtering micro-nano satellite with fault tolerance is implemented according to the following steps:
(1) prediction of attitude quaternion
Predicting attitude quaternion by using RungeKutta equation according to attitude kinematics equation and estimated angular velocity
Figure BDA0002654374120000052
Quaternion estimation from attitude at time k-1
Figure BDA0002654374120000053
And estimating angular velocity
Figure BDA0002654374120000054
Predicting the angular speed of the body coordinate system relative to the track coordinate system at the moment k as follows:
Figure BDA0002654374120000061
according to the kinematic equation of satellite attitude, adopting RungeKutta equation to predict attitude quaternion at k moment
Figure BDA0002654374120000062
Is composed of
Figure BDA0002654374120000063
Wherein k is1、k2、k3And k4Intermediate variables for the longge-kuta method.
(2) One step prediction of state and covariance
According to the discrete state information, the state estimation value is estimated from the k-1 time
Figure BDA0002654374120000064
One step state prediction for calculating time k
Figure BDA0002654374120000065
Sum estimation error covariance matrix Pg,k/k-1；
Figure BDA0002654374120000066
Figure BDA0002654374120000067
Wherein, Pg,k-1An estimation error covariance matrix at the time of k-1; q is the process noise variance matrix of the main filter.
(3) Information distribution
Information distribution factor alpha of main filteriAnd resets the estimation error covariance matrix to the local filter S1 and the local filter S2
Figure BDA0002654374120000068
Calculating an information distribution factor alphaiThe following were used:
Figure BDA0002654374120000069
wherein,
Figure BDA00026543741200000610
Figure BDA00026543741200000611
is the error covariance matrix of the local filter i,
Figure BDA00026543741200000612
is a matrix
Figure BDA00026543741200000613
The trace of (c). Resetting the estimation error covariance matrix to the local filter S1 and the local filter S2
Figure BDA00026543741200000614
Therefore, the temperature of the molten steel is controlled,
Figure BDA00026543741200000615
(4) and updating the measurement
Each local filter carries out measurement updating according to the observation information of the magnetometer and the sun sensor respectively, and the local filter Kalman gain at the moment k is calculated
Figure BDA00026543741200000616
Local filter state estimation
Figure BDA00026543741200000617
And error covariance matrix
Figure BDA00026543741200000618
Figure BDA00026543741200000619
Wherein,
Figure BDA00026543741200000620
an observation matrix representing the local filter at time k;
Figure BDA00026543741200000621
and ZikRespectively representing an observation noise variance matrix and observation information of a local filter;
Figure BDA00026543741200000622
(5) fault detection and handling and state fusion
And detecting the sensor fault according to the fault detection factor or performing state fusion according to the output information of the local filter.
(5.1) Fault detection and handling
Based on distributed Federal Kalman filtering algorithm, utilizing measurement value residual error of attitude sensor
Figure BDA0002654374120000071
Setting a fault detection factor
Figure BDA0002654374120000072
To detect attitude to determine if the system has a sudden fault or a slowly accumulating fault.
Fault detection factor
Figure BDA0002654374120000073
The following formula is satisfied:
Figure BDA0002654374120000074
wherein: trace (·) represents the trace operation of the matrix; l is the dimension of the measuring vector of the sensor in the local filter;
Figure BDA0002654374120000075
an observed noise variance matrix representing a local filter; (.)TRepresenting the transpose of the matrix.
In conjunction with FIG. 2, then
Figure BDA0002654374120000076
And
Figure BDA0002654374120000077
namely the fault detection factors of the local filter S1, the local filter S2.
When the attitude sensor has no fault, the corresponding measurement value residual error
Figure BDA0002654374120000078
Should be oneAnd the noise is very small, and theoretically meets the characteristics of zero mean Gaussian white noise. When the attitude sensor has sudden change fault or slowly changing fault accumulated to a certain degree, the residual error of the measured value
Figure BDA0002654374120000079
Will become significantly larger, applying the above described fault detection factor
Figure BDA00026543741200000710
The calculation result can directly judge whether the sensor in the attitude determination system has a fault.
Therefore, the following failure detection factor needs to be set
Figure BDA00026543741200000711
Fault threshold gamma of0Namely:
Figure BDA00026543741200000712
the sensor is normal;
Figure BDA00026543741200000713
when the sensor fails, the sensor fails.
For fault threshold gamma0When γ is analyzed0When a smaller value is set, fault detection is easier to be carried out on the attitude determination system, but error judgment is also easier to be caused; when gamma is0With a larger value set, it is relatively difficult to detect a fault.
The present application proposes the following fault threshold γ0And the calculation process comprises the following steps:
assuming that the dimension of the attitude sensor measurement vector of a certain local filter Si is l,
order to
Figure RE-GDA00028059050500000714
Then
Figure RE-GDA00028059050500000715
There is a solution procedure as follows,
Figure BDA0002654374120000081
wherein,
Figure BDA0002654374120000082
L
Figure BDA0002654374120000083
as vectors
Figure BDA0002654374120000084
The elements of (1); sigma(i)The standard deviation of the measurement noise of the sensor in the local filter Si.
Due to the order of l matrix
Figure BDA0002654374120000085
The sum of each element on the main diagonal is the trace of the matrix, then there is
Figure BDA0002654374120000086
Figure BDA0002654374120000087
Make the single-dimensional measurement residual error of local filter Si
Figure BDA0002654374120000088
Then
Figure BDA0002654374120000089
Wherein,
Figure BDA00026543741200000810
measuring residual error delta in one dimension when the sensor is normal(i)The information of (a); d is a single-dimensional measurement residual error delta when the sensor fails(i)The information of (1).
Further, from the above plurality of publicationsDerived from the formula, the fault detection factor
Figure BDA00026543741200000811
Standard deviation sigma of sensor measuring noise in local filter Si(i)Are closely related.
As described above, when the attitude sensor is not faulty, the measurement residual
Figure BDA00026543741200000812
Similar to the noise measured by the sensor, the sensor meets the characteristics of zero-mean Gaussian white noise. Typically, the upper bound on the measurement noise of the attitude sensor is taken to be 3 σ(i)Therefore, the residual error of one-dimensional measurement value when the attitude sensor in the local filter Si is not in fault
Figure BDA00026543741200000813
The upper limit of (3 a) may be taken to be 3 a(i)I.e. by
Figure BDA00026543741200000814
When the attitude sensor has no fault, | d | -, 0,
Figure BDA00026543741200000815
so that the fault threshold gamma0Is taken as
Figure BDA00026543741200000816
Based on the fault detection factor and the fault threshold, when the sensor is detected to be in fault, if the local filter S1 is failed, the local filter S1 is failed
Figure BDA0002654374120000091
Is incorrect and thus not input to the main filter, at which point the overall estimate of the system error state may be modified to
Figure BDA0002654374120000092
Similarly, if the local filter S2 fails, the overall estimate of the system error state is
Figure BDA0002654374120000093
When the sensor is not detected to be in fault, the following step (5.2) is directly executed.
(5.2) State fusion
State estimation for local filters S1 and S2
Figure BDA0002654374120000094
Sum estimation error covariance matrix
Figure BDA0002654374120000095
Performing data fusion to obtain final state estimation
Figure BDA0002654374120000096
Sum estimation error covariance matrix Pg,k；
Figure BDA0002654374120000097
Figure BDA0002654374120000098
(6) Attitude and gyro drift correction
Using state estimation
Figure BDA0002654374120000099
Modifying estimated attitude quaternion
Figure BDA00026543741200000910
And random drift estimate
Figure BDA00026543741200000911
And combined with the measured value omega of the MEMS gyroscopemObtaining the final estimation attitude quaternion
Figure BDA00026543741200000912
And estimating angular velocity
Figure BDA00026543741200000913
Figure BDA00026543741200000914
Figure BDA00026543741200000915
Figure BDA00026543741200000916
Figure BDA00026543741200000917
The following simulation experiment results are given in fig. 4 to 8 to verify the accuracy and validity of the fault detection and state fusion.
Taking a micro-nano satellite as an example, the micro-nano satellite runs on a sun synchronous orbit with the orbit height of 520km, and the rotational inertia of the satellite is diag [ 0.088450.14220.07518 ]]kg·m2The descending intersection point is 7:30am, and the track inclination angle is 97.62 degrees. When the satellite is stable on the three axes of the earth, the expected attitude is [0,0 ]]Angle random walk sigma of MEMS gyroυ＝0.03°/s12Angular velocity random walk σζ＝0.0001°/s32The measuring noise of the magnetometer is 100nT, and the measuring noise of the sun sensor is 0.1 degree. The distributed filtering micro-nano satellite attitude determination method for fault tolerance is utilized to carry out simulation experiments. When the local filter sensor has no fault, the attitude information of the micro/nano satellite determined based on the improved distributed filtering algorithm is shown in fig. 4; when the local filter sensor has no fault, the angular velocity information of the micro-nano satellite determined based on the improved distributed filtering algorithm provided by the application is shown in fig. 5. When a local filter magnetometer fails or a sun sensor fails, the fault tolerant sub-system based on the present applicationThe roll angle, pitch angle and yaw angle information of the micro-nano satellite determined by the distributed filter algorithm are respectively shown in fig. 6, fig. 7 and fig. 8.
As can be seen from fig. 4 to 5, when the local filter sensor has no fault, the attitude angle and the angular velocity of the micro/nano satellite estimated based on the distributed filtering algorithm of the present application have higher estimation accuracy. As can be seen from fig. 6 to 8, when the sun sensor of the local filter fails, the attitude angle of the micro/nano satellite determined based on the fault-tolerant distributed filtering algorithm of the present application is within a range of ± 0.5 °; when the local filter magnetometer has a fault, the rolling angle and the yaw angle of the micro-nano satellite determined based on the distributed filtering algorithm of fault tolerance are in a range of +/-0.4 degrees, and the estimated pitch angle is in a range of +/-1.25 degrees. According to comparison of all simulation results, the distributed filtering micro-nano satellite attitude determination method with fault tolerance has high practicability and good fault tolerance.
In summary, the embodiments presented in connection with the figures are only preferred. Those skilled in the art can derive other alternative structures according to the design concept of the present invention, and the alternative structures also fall within the scope of the present invention.

Claims (3)

1. A distributed filtering micro-nano satellite attitude determination method with fault tolerance is characterized by comprising the following steps: based on distributed Federal Kalman filtering algorithm, utilizing measurement value residual error of attitude sensor
Figure FDA0002654374110000011
Setting a fault detection factor
Figure FDA0002654374110000012
Fault detection factor
Figure FDA0002654374110000013
The following formula is satisfied:
Figure FDA0002654374110000014
wherein: trace (·) represents the trace operation of the matrix; l is the dimension of the measuring vector of the sensor in the local filter;
Figure FDA0002654374110000015
an observed noise variance matrix representing a local filter; (.)TRepresents a transpose of a matrix;
fault threshold gamma0Take a value of
Figure FDA0002654374110000016
2. The method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance according to claim 1, wherein the method comprises the following steps: comprises the following implementation steps of the following steps of,
1) acquiring the angular velocity omega of the micro-nano satellite body coordinate system b relative to the inertial coordinate system i by using the measurement value of the MEMS gyroscopemFor estimation of angular velocity;
2) the mounting shaft of the magnetometer is consistent with the coordinate system of the micro-nano satellite body, and the geomagnetic field vector measurement value B under the coordinate system of the satellite body is obtainedb；
3) The aiming axis of the sun sensor is consistent with the coordinate system-Y axis of the micro-nano satellite body to obtain the sun vector measurement value S under the satellite body coordinate systemb；
4) Selecting an error quaternion Δ qboThe vector component Δ q and the gyro angular rate drift estimation error Δ b of (1) are state quantities
Figure FDA0002654374110000017
Giving a state equation of a satellite attitude determination system;
5) utilizing the geomagnetic field vector measurement value B under the satellite body coordinate system obtained in the step 2)bAnd the sun vector measurement value S under the satellite body coordinate system obtained in the step 3)bCombined with the earth-magnetic field vector in the orbital coordinate systemQuantity reference value BoAnd the sun vector reference value S under the orbital coordinate systemoProviding observation information of two local filters in a distributed Federal Kalman filtering algorithm;
6) and calculating to obtain the attitude of the micro-nano satellite by utilizing a distributed Federal Kalman filtering algorithm, and realizing satellite attitude calculation based on a plurality of low-cost, small-volume and low-power consumption micro sensors.
3. The method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance according to claim 2, wherein the method comprises the following steps: the fault detection and processing and state fusion process is implemented as follows,
(1) fault detection and handling
Setting a fault detection factor
Figure FDA0002654374110000021
Fault threshold gamma of0Namely:
Figure FDA0002654374110000022
when the sensor is normal;
Figure FDA0002654374110000023
when the sensor fails, the sensor fails;
based on the fault detection factor and the fault threshold, when the sensor is detected to be in fault, if the local filter S1 is failed, the local filter S1 is failed
Figure FDA0002654374110000024
Is incorrect and thus not input to the main filter, at which point the overall estimate of the system error state may be modified to
Figure FDA0002654374110000025
Similarly, if the local filter S2 fails, the overall estimate of the system error state is
Figure FDA0002654374110000026
When the sensor is not detected to have faults, directly executing the following step (2);
(2) state fusion
State estimation for local filters S1 and S2
Figure FDA0002654374110000027
Sum estimation error covariance matrix
Figure FDA0002654374110000028
Performing data fusion to obtain final state estimation
Figure FDA0002654374110000029
Sum estimation error covariance matrix Pg,k；
Figure FDA00026543741100000210
Figure FDA00026543741100000211
CN202010882166.6A 2020-08-28 2020-08-28 Distributed filtering micro-nano satellite attitude determination method with fault tolerance Active CN112212860B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010882166.6A CN112212860B (en) 2020-08-28 2020-08-28 Distributed filtering micro-nano satellite attitude determination method with fault tolerance

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010882166.6A CN112212860B (en) 2020-08-28 2020-08-28 Distributed filtering micro-nano satellite attitude determination method with fault tolerance

Publications (2)

Publication Number Publication Date
CN112212860A true CN112212860A (en) 2021-01-12
CN112212860B CN112212860B (en) 2023-03-03

Family

ID=74058977

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010882166.6A Active CN112212860B (en) 2020-08-28 2020-08-28 Distributed filtering micro-nano satellite attitude determination method with fault tolerance

Country Status (1)

Country Link
CN (1) CN112212860B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116149186A (en) * 2023-02-16 2023-05-23 大连交通大学 Kalman filtering method for fault estimation of satellite attitude control system

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101216319A (en) * 2008-01-11 2008-07-09 南京航空航天大学 Low orbit satellite multi-sensor fault tolerance autonomous navigation method based on federal UKF algorithm
CN102735259A (en) * 2012-06-18 2012-10-17 北京控制工程研究所 Satellite control system fault diagnosis method based on multiple layer state estimators
CN102778888A (en) * 2012-07-11 2012-11-14 江苏科技大学 Fault detecting device and method of cable-free bathyscaph track control system
CN103323007A (en) * 2013-06-17 2013-09-25 南京航空航天大学 Robust federated filtering method based on time-variable measurement noise
CN103676941A (en) * 2013-12-24 2014-03-26 北京控制工程研究所 Satellite control system fault diagnosis method based on kinematics and dynamics model
CN106679693A (en) * 2016-12-14 2017-05-17 南京航空航天大学 Fault detection-based vector information distribution adaptive federated filtering method
CN108279010A (en) * 2017-12-18 2018-07-13 北京时代民芯科技有限公司 A kind of microsatellite attitude based on multisensor determines method
CN109373999A (en) * 2018-10-23 2019-02-22 哈尔滨工程大学 Combinated navigation method based on failure tolerant Kalman filtering
CN109813342A (en) * 2019-02-28 2019-05-28 北京讯腾智慧科技股份有限公司 A kind of fault detection method and system of inertial navigation-satellite combined guidance system
CN109829938A (en) * 2019-01-28 2019-05-31 杭州电子科技大学 A kind of self-adapted tolerance volume kalman filter method applied in target following
CN110196443A (en) * 2019-06-06 2019-09-03 中国人民解放军战略支援部队信息工程大学 A kind of fault-tolerance combined navigation method and system of aircraft
CN110285815A (en) * 2019-05-28 2019-09-27 山东航天电子技术研究所 It is a kind of can in-orbit whole-process application micro-nano satellite multi-source information attitude determination method

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101216319A (en) * 2008-01-11 2008-07-09 南京航空航天大学 Low orbit satellite multi-sensor fault tolerance autonomous navigation method based on federal UKF algorithm
CN102735259A (en) * 2012-06-18 2012-10-17 北京控制工程研究所 Satellite control system fault diagnosis method based on multiple layer state estimators
CN102778888A (en) * 2012-07-11 2012-11-14 江苏科技大学 Fault detecting device and method of cable-free bathyscaph track control system
CN103323007A (en) * 2013-06-17 2013-09-25 南京航空航天大学 Robust federated filtering method based on time-variable measurement noise
CN103676941A (en) * 2013-12-24 2014-03-26 北京控制工程研究所 Satellite control system fault diagnosis method based on kinematics and dynamics model
CN106679693A (en) * 2016-12-14 2017-05-17 南京航空航天大学 Fault detection-based vector information distribution adaptive federated filtering method
CN108279010A (en) * 2017-12-18 2018-07-13 北京时代民芯科技有限公司 A kind of microsatellite attitude based on multisensor determines method
CN109373999A (en) * 2018-10-23 2019-02-22 哈尔滨工程大学 Combinated navigation method based on failure tolerant Kalman filtering
CN109829938A (en) * 2019-01-28 2019-05-31 杭州电子科技大学 A kind of self-adapted tolerance volume kalman filter method applied in target following
CN109813342A (en) * 2019-02-28 2019-05-28 北京讯腾智慧科技股份有限公司 A kind of fault detection method and system of inertial navigation-satellite combined guidance system
CN110285815A (en) * 2019-05-28 2019-09-27 山东航天电子技术研究所 It is a kind of can in-orbit whole-process application micro-nano satellite multi-source information attitude determination method
CN110196443A (en) * 2019-06-06 2019-09-03 中国人民解放军战略支援部队信息工程大学 A kind of fault-tolerance combined navigation method and system of aircraft

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
F.N. PIRMORADI, ET AL: "Fault detection and diagnosis in a spacecraft attitude determination system", 《ACTA ASTRONAUTICA》 *
田晓春: "组合导航***的故障检测与重构", 《组合导航***的故障检测与重构》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116149186A (en) * 2023-02-16 2023-05-23 大连交通大学 Kalman filtering method for fault estimation of satellite attitude control system
CN116149186B (en) * 2023-02-16 2023-08-11 大连交通大学 Kalman filtering method for fault estimation of satellite attitude control system

Also Published As

Publication number Publication date
CN112212860B (en) 2023-03-03

Similar Documents

Publication Publication Date Title
CN107525503B (en) Adaptive cascade Kalman filtering method based on combination of dual-antenna GPS and MIMU
CN103278837B (en) Adaptive filtering-based SINS/GNSS (strapdown inertial navigation system/global navigation satellite system) multistage fault-tolerant integrated navigation method
CN103676941B (en) Satellite control system method for diagnosing faults based on kinematics and dynamics modeling
EP2973127B1 (en) Heading confidence interval estimation
US9073648B2 (en) Star tracker rate estimation with kalman filter enhancement
CN100356139C (en) Miniature assembled gesture measuring system for mini-satellite
CN102692225B (en) Attitude heading reference system for low-cost small unmanned aerial vehicle
CN112505737B (en) GNSS/INS integrated navigation method
CN109916395B (en) Gesture autonomous redundant combined navigation algorithm
CN102654404A (en) Method for improving resolving precision and anti-jamming capability of attitude heading reference system
CN104698485A (en) BD, GPS and MEMS based integrated navigation system and method
CN108168549B (en) A kind of satellite communication in moving attitude detecting method
CN102735259A (en) Satellite control system fault diagnosis method based on multiple layer state estimators
CN103968844B (en) Big oval motor-driven Spacecraft Autonomous Navigation method based on low rail platform tracking measurement
CN110954102A (en) Magnetometer-assisted inertial navigation system and method for robot positioning
CN103697894A (en) Multi-source information unequal interval federated filtering method based on filter variance matrix correction
CN113670314B (en) Unmanned aerial vehicle attitude estimation method based on PI self-adaptive two-stage Kalman filtering
WO2022222938A1 (en) Motion state monitoring-based adaptive horizontal attitude measurement method
CN112212860B (en) Distributed filtering micro-nano satellite attitude determination method with fault tolerance
CN109916399B (en) Carrier attitude estimation method under shadow
CN112284388B (en) Unmanned aerial vehicle multisource information fusion navigation method
CN101769743B (en) Distributed filtering device for MIMU and GPS combined navigation system
CN110375773B (en) Attitude initialization method for MEMS inertial navigation system
CN112762925A (en) Low-orbit satellite attitude determination method based on geomagnetism meter and gyroscope
Zhao et al. Information Monitoring and Adaptive Information Fusion of Multi-Source Fusion Navigation Systems in Complex Environments

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant