CN110426032A - A kind of fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy - Google Patents
A kind of fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy Download PDFInfo
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Abstract
The invention discloses a kind of fault-tolerant navigation estimation methods of the aircraft of analytic expression redundancy.Firstly, being estimated using the kinetic model of rotor craft the acceleration of aircraft, angular acceleration information；Secondly, building angular accelerometer/kinetic model/IMU fault Detection Filter and IMU/ Magnetic Sensor/GPS/ barometer fault Detection Filter；Then, according to the output of two fault Detection Filters, fault location strategy is constructed, realizes the fault location to navigation sensor；Finally, according to failure detection result, isolated fault sensor is completed navigation information and is resolved.The present invention is by introducing kinetic model and angular accelerometer, realize the fault detection of angular acceleration meter, kinetic model, IMU, measuring sensor, failure IMU can be replaced to carry out navigation calculation completely with kinetic model simultaneously, improve the error resilience performance of navigation system.
Description
Technical field
The invention belongs to field of navigation technology, in particular to a kind of aircraft fault-tolerant navigation estimation method.
Background technique
Airborne navigation sensor is the data source that aircraft carries out navigation calculation, and the navigation information calculated is aircraft
Carry out the basis of stabilized flight.Currently used airborne navigation sensor includes inertial sensor --- IMU and measurement sensing
Device --- Magnetic Sensor, GPS, barometer etc..But in some special environment, the decline of airborne navigation sensor performance is even lost
Effect, such as sound wave interference can destroy the performance of IMU, and city, which is blocked, will lead to GPS signal loss etc..If using the sensing of failure
Device participates in navigation calculation, it is out of control to will lead to aircraft, or even crash.
Summary of the invention
In order to solve the technical issues of above-mentioned background technique is mentioned, the invention proposes a kind of aircraft of analytic expression redundancy
Fault-tolerant navigation estimation method, improves the error resilience performance of aircraft guidance system.
In order to achieve the above technical purposes, the technical solution of the present invention is as follows:
A kind of fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy, includes the following steps:
Step 1: the period read the rotor revolving speed measurement system of k moment carrier, angular accelerometer, IMU, Magnetic Sensor,
GPS and barometrical output, by the kinetic model of rotor craft, estimate rotor craft movement acceleration information and
Angular acceleration information；
Step 2: being exported using the airborne sensor data in step 1, constructs angular accelerometer/kinetic model/IMU
Fault Detection Filter S1 and IMU/ Magnetic Sensor/GPS/ barometer fault Detection Filter S2；It include three parallel in S1
The sub- detector D1 of sub- detector, respectively angular accelerometer/kinetic model, angular accelerometer/IMU detector D2 and power
Learn model/IMU detector D3；It include three parallel sub- detectors in S2, the respectively sub- detector D4 of IMU/ Magnetic Sensor,
The sub- detector D5 of the IMU/GPS and sub- detector D6 of IMU/ barometer；
Step 3: according to the failure detection result of each sub- detector in S1, constructing fault location strategy, realizes diagonal add
The fault location of speedometer, kinetic model and IMU exports positioning result；According to the fault location result of S1 with it is each in S2
The failure detection result of sub- detector constructs global fault's positioning strategy, positioning failure sensor；
Step 4: the fault location obtained according to step 3 is as a result, formulate the Fault Isolation Strategy, from state filtering estimation
Isolated fault sensor completes navigational state estimation；
When airborne sensor fault-free or kinetic model failure, use angular accelerometer, IMU accelerometer as
State equation inputs, and the gyro, Magnetic Sensor, GPS, barometer in IMU are as measuring sensor；
When IMU failure, the Aerodynamic Model of angular accelerometer and kinetic model is used to input as state equation, magnetic
Sensor, GPS, barometer are as measuring sensor；
When angular accelerometer failure, use accelerometer in the aerodynamic moment model and IMU of kinetic model as
State equation inputs, and the gyro, Magnetic Sensor, GPS, barometer in IMU are as measuring sensor；
When GPS or barometer or Magnetic Sensor failure, use the accelerometer of angular accelerometer, IMU as state side
Journey input, isolated fault sensor are allowed to be not involved in global fused filtering；
Step 5: the state estimation result obtained according to step 4 carries out school to the quantity of state in the S1 and S2 at k moment
Just.
Further, in step 1, the movement acceleration information and angular acceleration of rotor craft are estimated by following formula
Information:
fmz=kT0+kT1(ω1 2+ω2 2+ω3 2+ω4 2)
Wherein, fmx、fmy、fmzFor the acceleration for the body system x, y, z axis being calculated by kinetic model；For the angular acceleration for the body system x, y, z axis being calculated by kinetic model, ωmzFor the angle speed of z-axis
Degree；kHx0、kHx1、kHx2、kHx3、kHy0、kHy1、kHy2、kHy3、kT0、kT1、kx0、kx1、kx2、kx3、ky0、ky1、ky2、ky3、kz0、kz1、
kz2、kz3It is constant value for Rotarycraft power model parameter；Linear speed for body system relative to navigation system
Spend the component in body system x, y-axis；βx、βyFor body system x, the angular acceleration of y-axis, obtained by angular accelerometer；ωiFor
The revolving speed of i-th of rotor, i=1,2,3,4,For the angular acceleration of i-th of rotor revolving speed；fax、fayFor body system x, y-axis
Acceleration is obtained by the accelerometer in IMU.
Further, in step 2, the method for constructing the sub- detector D1 of angular accelerometer/kinetic model is as follows:
1, it is exported using angular accelerometer output and kinetic model, calculates z-axis angular acceleration residual error:
In above formula, r1(k) the z-axis angular acceleration residual error being calculated for angular accelerometer and kinetic model；βzIt (k) is k
Moment body system z-axis angular acceleration is exported by z-axis angular accelerometer and is obtained；It is calculated by kinetic model
The angular acceleration of body system z-axis；Abs (*) expression takes absolute value；
2, judge whether sub- detector D1 breaks down:
In above formula, τ1For breakdown judge threshold value；T1For failure detection result；If T1=1, then angular accelerometer or power
Learn model failure；If T1=0, then angular accelerometer and kinetic model fault-free；
It is as follows to construct angular accelerometer/IMU detector D2 method:
1, it is exported using angular accelerometer and calculates x, y, z axis angular rate:
In above formula, ωx(k)、ωy(k)、ωz(k) it is the body system x, y, z axis angular rate at k moment, passes through angular accelerometer
Output βx、βy、βzIntegral obtains；ωx(k-1)、ωy(k-1)、ωzIt (k-1) is the body system x, y, z shaft angle speed at k-1 moment
Degree is exported by k-1 moment Federated Kalman Filter and is obtained；Δ T is the discrete sampling time；
2, the statistical parameter of sub- detector D2 is calculated:
r2(k)=[ωgx(k) ωgy(k) ωgz(k)]T-[ωx(k) ωy(k) ωz(k)]T
A2(k)=H2(k)P2(k|k-1)H2(k)+R2(k)
λ2=r2(k)A2(k)-1r2(k)
P2(k | k-1)=F2(k)P2(k-1)F2 T(k)+G2(k-1)Q2(k-1)G2 T(k-1)
In above formula, r2(k) the x, y, z axis angular rate residual error being calculated is exported for k moment IMU and angular accelerometer；ωgx
(k)、ωgy(k)、ωgz(k) it is body system x, y, z axis angular rate, is exported and obtained by gyro；A2It (k) is that k moment IMU and angle add
Speedometer exports the residual variance being calculated；H2(k)=[I3×3] it is to measure coefficient matrix, I at the k moment3×3For 3 × 3 unit
Matrix；P2(k | k-1) it is k moment one-step prediction mean square error matrix；R2(k)=diag ([εωgx(k) εωgy(k) εωgz(k)]2)
Noise matrix, ε are measured for the k momentωgx(k)、εωgy(k)、εωgzIt (k) is body system x, y, z axis gyro noise；λ2It (k) is the k moment
IMU and angular accelerometer export the statistical parameter being calculated；F2(k)=[I3×3] it is k moment IMU and angular accelerometer detection
The coefficient of regime matrix of system；P2It (k-1) is the Square Error matrix at k-1 moment；G2(k-1)=[Δ T*I3×3] it is the k-1 moment
The state-noise coefficient matrix of IMU and angular accelerometer detection system；Q2(k-1)=diag ([εβx(k-1) εβy(k-1) εβz
(k-1)]2) be k-1 moment IMU and angular accelerometer detection system state-noise, εβx(k-1)、εβy(k-1)、εβz(k-1) it is
The x, y, z shaft angle accelerometer noise at k-1 moment；Subscript T indicates transposition；
3, judge whether sub- detector D2 breaks down:
In above formula, τ2For breakdown judge threshold value；T2For failure detection result；If T2=1, then angular accelerometer or IMU event
Barrier；If T3=0, then angular accelerometer and IMU fault-free；
Construction force model/IMU detector D3 method is as follows:
1, it is exported using kinetic model and calculates z-axis angular speed:
In above formula, ωmz(k-1) it is the angular speed at k-1 moment, passes through k-1 moment Federated Kalman Filter and export acquisition；
ωmzIt (k) is the angular speed at k moment；For the angular acceleration at k moment, obtained by kinetic model；Δ T is discrete adopts
The sample time；
2, the residual sum statistical parameter of sub- detector D3 is calculated:
r3a(k)=abs (faz(k)-fmz(k))
r3b(k)=ωgz(k)-ωmz(k)
A3b(k)=H3b(k)P3b(k|k-1)H3b(k)+R3b(k)
λ3b=r3b(k)A3b(k)-1r3b(k)
P3b(k | k-1)=F3b(k)P3b(k-1)F3b T(k)+G3b(k-1)Q3b(k-1)G3b T(k-1)
In above formula, r3a(k) the z-axis acceleration residual error being calculated for k moment IMU and kinetic model；faz(k) be k when
The acceleration for carving body system z-axis, is obtained by accelerometer；r3b(k) z-axis being calculated for k moment IMU and kinetic model
Angular speed residual error；A3b(k) the z-axis angular speed residual variance being calculated for k moment IMU and kinetic model；H3bIt (k)=1 is k
Moment measures coefficient matrix；P3b(k | k-1) it is k moment one-step prediction mean square error matrix；R3b(k)=diag ([εωgz(k)]2)
Noise matrix is measured for the k moment；λ3b(k) the z-axis angular speed statistical parameter being calculated for k moment IMU and kinetic model；For the coefficient of regime matrix of k moment z-axis gyro and kinetic model detection system；P3b(k-1) it is
The Square Error matrix at k-1 moment；G3b(k-1)=1 the state for k-1 moment z-axis gyro and kinetic model detection system is made an uproar
Sonic system matrix number；Q3b(k-1)=diag ([εωmz(k-1)]2) be k-1 moment z-axis gyro and kinetic model detection system shape
State noise, εωmzIt (k-1) is the body system z-axis angular speed noise obtained by kinetic model at the k-1 moment；
3, judge whether sub- detector D3 breaks down:
In above formula, τ3a、τ3bFor breakdown judge threshold value；T3a、T3bFor failure detection result；If T3aOr T (k)=13b(k)
=1, IMU or kinetic model failure；If T3aAnd T (k)=03b(k)=0, IMU and kinetic model fault-free.
Further, in step 2, the status predication of triplet detector D4, D5, D6 when calculating k:
q0(k)=q0(k-1)-0.5ωgx(k)q1(k-1)-0.5ωgy(k)q2(k-1)-0.5ωgz(k)q3(k-1)
q1(k)=0.5 ωgx(k)q0(k-1)+q1(k-1)+0.5ωgz(k)q2(k-1)-0.5ωgy(k)q3(k-1)
q2(k)=0.5 ωgy(k)q0(k-1)-0.5ωgz(k)q1(k-1)+q2(k-1)+0.5ωgx(k)q3(k-1)
q3(k)=0.5 ωgz(k)q0(k-1)+0.5ωgy(k)q1(k-1)-0.5ωgx(k)q2(k-1)+q3(k-1)
In above formula, q0(k)、q1(k)、q2(k)、q3(k) the k moment quaternary number to be obtained by state equation；q0(k-1)、
q1(k-1)、q2(k-1)、q3(k-1) it is k-1 moment quaternary number, is exported and obtained by k-1 moment Federated Kalman Filter；Component of the linear velocity in body system z-axis for the k moment body system that is obtained by state equation relative to navigation system；It is k-1 moment body system relative to component of the linear velocity for being in body system z-axis that navigate, is joined by the k-1 moment
The output of nation's Kalman filter obtains；G is acceleration of gravity；PN(k)、PE(k)、PD(k) for obtained by state equation k when
Carve north orientation, east orientation, to position；PN(k-1)、PE(k-1)、PD(k-1) for k-1 moment north orientation, east orientation, to position, pass through k-
The output of 1 moment Federated Kalman Filter obtains；
Construct the sub- detector D4 of IMU/ Magnetic Sensor:
1, the statistical parameter of sub- detector D4 is calculated:
Course is calculated using the status predication at k moment:
A4(k)=H4(k)P4(k|k-1)H4(k)+R4(k)
λ4(k)=r4(k)A4(k)-1r4(k)
P4(k | k-1)=F4(k)P4(k-1)F4 T(k)+G4(k-1)Q4(k-1)G4 T(k-1)
Q4(k-1)=diag ([εωgx(k-1) εωgy(k-1) εωgz(k-1) εvbx(k-1) εvby(k-1) εvbz(k-1)
εpn(k-1) εpe(k-1) εpd(k-1)]2)
In above formula,For the k moment course angle being calculated by status predication；r4It (k) is k moment D4 detector
Course angle residual error；ψmIt (k) is the course of k moment Magnetic Sensor output；A4It (k) is the residual variance of k moment D4 detector；H4(k)
For the measurement coefficient matrix of k moment D4 detector；P4(k | k-1) is the one-step prediction mean square error at k-1 moment to k moment；R4
(k)=diag ([εψm(k)]2) be the k moment noise matrix, εψmIt (k) is the height noise of k moment Magnetic Sensor；λ4It (k) is k
The statistical parameter at moment；0i×jFor the null matrix of i × j；Ii×jFor the unit matrix of i × j；F4(k) it is sensed for k moment IMU and magnetic
The coefficient of regime matrix of device detector；P4It (k-1) is the Square Error matrix at k-1 moment；G4It (k-1) is k-1 moment IMU and magnetic
The state-noise coefficient matrix of sensor detector；Q4It (k-1) is the state-noise of k-1 moment IMU and Magnetic Sensor detector,
εvbx(k-1)、εvby(k-1)、εvbzIt (k-1) is the x, y, z axial velocity noise at k moment；εpn(k-1)、εpe(k-1)、εpd(k-1)
For the north orientation at k-1 moment, east orientation, to position noise；
3, judge whether sub- detector D4 breaks down
In above formula, τ4For breakdown judge threshold value；T4For failure detection result；If T4=1, IMU or Magnetic Sensor failure；
If T4=0, IMU and Magnetic Sensor fault-free；
Construct the sub- detector D5 of IMU/GPS:
1, the statistical parameter of sub- detector D5 is calculated:
East orientation speed is calculated using the status predication at k moment:
North orientation speed is calculated using the status predication at k moment:
A5(k)=H5(k)P5(k|k-1)H5(k)+R5(k)
λ5(k)=r5(k)A5(k)-1r5(k)
H5(k)=[Υ2×4 Ω2×3 02×3]
In above formula,K moment east orientation speed to be calculated by status predication is predicted, north orientation is fast
Degree；r5It (k) is north orientation speed, the east orientation speed residual error of k moment IMU and GPS detection system；VNG(k)、VEGIt (k) is k moment GPS
North orientation speed, the east orientation speed of output；A5It (k) is the residual variance of k moment IMU and GPS detection system；H5It (k) is k moment IMU
With the measurement coefficient matrix of GPS detection system；P5(k | k-1)=P4(k | k-1) is that the one-step prediction at k-1 moment to k moment is square
Error；For the noise matrix at k moment,For the k moment
GPS north orientation, east orientation speed noise；λ5It (k) is the statistical parameter at k moment；
3, judge whether detector D5 breaks down:
In above formula, τ5For breakdown judge threshold value；T5For failure detection result；If T5=1, IMU or GPS failure；If T5
=0, IMU and GPS fault-free；
Construct the sub- detector D6 of IMU/ barometer:
1, the statistical parameter of detector D6:
Use the status predication computed altitude at k moment:
A6(k)=H6(k)P6(k|k-1)H6(k)+R6(k)
λ6(k)=r6(k)A6(k)-1r6(k)
In above formula,For the k moment height being calculated by status predication；r6It (k) is k moment IMU and barometer
The height residual error of detection system；hbIt (k) is the height of k moment barometer output；A6It (k) is k moment IMU and barometer detection system
The residual variance of system；H6It (k)=- 1 is the measurement coefficient matrix of k moment IMU and barometer detection system；P6(k | k-1)=P4(k
| k-1) be k-1 moment to the k moment one-step prediction mean square error；R6(k)=diag ([εhb(k)]2) be the k moment noise square
Battle array, εhbIt (k) is the barometrical height noise at k moment；λ6It (k) is the statistical parameter at k moment；
3, judge whether sub- detector D6 breaks down:
In above formula, τ6For breakdown judge threshold value；T6For failure detection result；If T6=1, IMU or barometer failure；Such as
Fruit T6=0, IMU and barometer fault-free.
Further, in step 3, fault location strategy is constructed according to S1 first:
Then according to the fault location strategy of the S1 and S2 building overall situation:
In above formula, FGACC、FDM、FIMU、Fmag、Fgps、FbaroRespectively angular accelerometer, kinetic model, IMU, magnetic sensing
Device, GPS, barometrical fault location function, fault location function are equal to " 1 ", indicate corresponding sensor failure, failure
Mapping function is equal to " 0 ", indicates that corresponding sensor does not break down；" | | " indicate logical operator "or"；" & " indicates logic
Operator "AND"；"-" indicates logical operator " non-".
Further, in step 4, fault condition is divided into following 7 kinds of situations:
Situation 1: airborne sensor fault-free:
(1) state and mean-square error forecast value at k moment are calculated:
q1(k)=0.5 ωgx(k)q0(k-1)+q1(k-1)+0.5ωgz(k)q2(k-1)-0.5ωgy(k)q3(k-1)
q2(k)=0.5 ωgy(k)q0(k-1)-0.5ωgz(k)q1(k-1)+q2(k-1)+0.5ωgx(k)q3(k-1)
q3(k)=0.5 ωgz(k)q0(k-1)+0.5ωgy(k)q1(k-1)-0.5ωgx(k)q2(k-1)+q3(k-1)
P (k | k-1)=F (k) P (k-1) FT(k)+G(k)Q(k)GT(k)
Q (k-1)=diag ([εβx(k-1) εβy(k-1) εβz(k-1) εωx(k-1) εωy(k-1) εωz(k-1) εvbx
(k-1) εvby(k-1) εvbz(k-1) εpn(k-1) εpe(k-1) εpd(k-1)]2)
In above formula,For k moment body system relative to the angular speed for being that navigates in body
It is the component on x, y, z axis；P (k | k-1) is the one-step prediction mean square error at k-1 moment to k moment；P (k-1) is the k-1 moment
Mean square error；F (k) is the coefficient of regime matrix at k moment；G (k-1) is the noise coefficient matrix at k-1 moment；Q (k-1) is k-
The noise coefficient matrix at 1 moment；εωx(k-1)、εωy(k-1)、εωzIt (k-1) is the x, y, z axis angular rate noise at k-1 moment；
(2) the gain K of the extended Kalman filter of i-th of subfilter of k moment is calculatedCi:
KCi(k)=P (k | k-1) HCi(k)T[HCi(k)P(k|k-1)HCi(k)T+RCi(k)]-1
In above formula,For the k moment GPS to velocity noise；For the k moment GPS make an uproar to speed
Sound；For the GPS east orientation position at k moment, north orientation position noise；
(3) the extended Kalman filter state estimation X of i-th of subfilter of k moment is calculatedCi(k) and mean square error
PCi(k):
XCi(k)=X (k | k-1)+KCi(k)[YCi(k)-hCi(XCi(k|k-1))]
PCi(k)=[I-KCi(k)HCi(k)]P(k|k-1)
YCi(k) it is i-th of subfilter measurement:
In above formula, VDG(k)、PNG(k)、PEG(k) for k moment GPS Xiang Sudu, north orientation position, east orientation position；hbaro(k)
For k moment barometer height；
(4) the global Fusion state estimation X of k moment subfilter is calculatedg(k) and mean square error estimates Pg(k):
Xg(k)=Pg(k)[PC1(k)-1XC1(k)+PC2(k)-1XC2(k)+PC3(k)-1XC3(k)+PC4(k)-1XC4(k)]-1
Pg(k)=[PC1(k)-1+PC2(k)-1+PC3(k)-1+PC4(k)-1]-1
X (k)=Xg(k)
P (k)=4Pg(k)
In above formula, X (k), P (k) are the state resetting and mean square error resetting of k moment each subfilter；
When situation 2:IMU failure, IMU is isolated, and is modified using the velocity differentials equation as input of accelerometer in IMU
It is as follows:
Use the gyro in IMU to be cut off as the subfilter of measurement information, is not involved in fused filtering；According to sensor
Isolated instances modify state estimation correlation matrix on the basis of situation 1；
Situation 3: when kinetic model failure, filtering renewal process is identical as situation 1；
Situation 4: when angular accelerometer failure, angular accelerometer is isolated, and uses angular accelerometer angle speed as input
The degree differential equation is amended as follows:
It is identical as situation 1 to measure renewal process；
Situation 5: when Magnetic Sensor failure, Magnetic Sensor is isolated, and uses the course information of redundancy as measurement；It measures
In renewal process, by the Y in the step (3) in situation 1C2(k)=ψm(k), it is revised as exporting using redundancy course transmitter
Data；
When situation 6:GPS failure, GPS is isolated, and uses the speed position information of redundancy as measurement；It measures updated
Cheng Zhong, by the Y in situation 1 in step (3)C3(k)=[VNG(k) VEG(k) VDG(k) PNG(k) PEG(k)]T, be revised as using
The data of redundancy velocity measurement sensor output；
Situation 7: when barometer failure, barometer is isolated, and uses the elevation information of redundancy as measurement；It measures and updates
In the process, by the Y in situation 1 in step (3)C4(k)=hbaro(k), it is revised as using the output of redundancy height-measuring sensor
Data.
By adopting the above technical scheme bring the utility model has the advantages that
The present invention by introducing kinetic model and angular accelerometer, may be implemented angular acceleration meter, kinetic model,
The fault detection of IMU, measuring sensor, while kinetic model can be used, failure IMU is replaced to carry out navigation calculation completely, it mentions
The error resilience performance of high navigation system.And in IMU failure, it is defeated as kinetic model that angular accelerometer output can be used
Enter, improves the kinetic model estimated accuracy under great-attitude angle variation.
Detailed description of the invention
Fig. 1 is the fault-tolerant block diagram of entirety of the invention；
Fig. 2 is fault diagnosis block diagram in the present invention；
Fig. 3 is to filter block diagram in the present invention；
Fig. 4 is flow chart of the invention；
Fig. 5 is failure detection result figure of the present invention in IMU failure；
Fig. 6 is x-axis Attitude rate estimator result figure of the present invention in IMU failure；
Fig. 7 is y-axis Attitude rate estimator result figure of the present invention in IMU failure；
Fig. 8 is z-axis Attitude rate estimator result figure of the present invention in IMU failure；
Fig. 9 is roll angle estimated result figure of the present invention in IMU failure；
Figure 10 is pitch angle estimated result figure of the present invention in IMU failure；
Figure 11 is course angle estimation result figure of the present invention in IMU failure；
Figure 12 is x-axis velocity estimation result figure of the present invention in IMU failure；
Figure 13 is y-axis velocity estimation result figure of the present invention in IMU failure.
Specific embodiment
Below with reference to attached drawing, technical solution of the present invention is described in detail.
The present invention devises a kind of fault-tolerant navigation estimation method of aircraft of analytic expression redundancy, as shown in Figure 1, including as follows
Step:
Step 1: the period read the rotor revolving speed measurement system of k moment carrier, angular accelerometer, IMU, Magnetic Sensor,
GPS and barometrical output, by the kinetic model of rotor craft, estimate rotor craft movement acceleration information and
Angular acceleration information；
Step 2: being exported using the airborne sensor data in step 1, constructs angular accelerometer/kinetic model/IMU
Fault Detection Filter S1 and IMU/ Magnetic Sensor/GPS/ barometer fault Detection Filter S2；It include three parallel in S1
The sub- detector D1 of sub- detector, respectively angular accelerometer/kinetic model, angular accelerometer/IMU detector D2 and power
Learn model/IMU detector D3；It include three parallel sub- detectors in S2, the respectively sub- detector D4 of IMU/ Magnetic Sensor,
The sub- detector D5 of the IMU/GPS and sub- detector D6 of IMU/ barometer；
Step 3: according to the failure detection result of each sub- detector in S1, constructing fault location strategy, realizes diagonal add
The fault location of speedometer, kinetic model and IMU exports positioning result；According to the fault location result of S1 with it is each in S2
The failure detection result of sub- detector constructs global fault's positioning strategy, positioning failure sensor；
Step 4: the fault location obtained according to step 3 is as a result, formulate the Fault Isolation Strategy, from state filtering estimation
Isolated fault sensor completes navigational state estimation；
When airborne sensor fault-free or kinetic model failure, use angular accelerometer, IMU accelerometer as
State equation inputs, and the gyro, Magnetic Sensor, GPS, barometer in IMU are as measuring sensor；
When IMU failure, the Aerodynamic Model of angular accelerometer and kinetic model is used to input as state equation, magnetic
Sensor, GPS, barometer are as measuring sensor；
When angular accelerometer failure, use accelerometer in the aerodynamic moment model and IMU of kinetic model as
State equation inputs, and the gyro, Magnetic Sensor, GPS, barometer in IMU are as measuring sensor；
When GPS or barometer or Magnetic Sensor failure, use the accelerometer of angular accelerometer, IMU as state side
Journey input, isolated fault sensor are allowed to be not involved in global fused filtering；
Step 5: the state estimation result obtained according to step 4 carries out school to the quantity of state in the S1 and S2 at k moment
Just.
In the present embodiment, step 1 is realized using following preferred embodiment:
In step 1, the movement acceleration information and angular acceleration information of rotor craft are estimated by following formula:
fmz=kT0+kT1(ω1 2+ω2 2+ω3 2+ω4 2)
Wherein, fmx、fmy、fmzFor the acceleration for the body system x, y, z axis being calculated by kinetic model；For the angular acceleration for the body system x, y, z axis being calculated by kinetic model, ωmzFor the angle speed of z-axis
Degree；kHx0、kHx1、kHx2、kHx3、kHy0、kHy1、kHy2、kHy3、kT0、kT1、kx0、kx1、kx2、kx3、ky0、ky1、ky2、ky3、kz0、kz1、
kz2、kz3It is constant value for Rotarycraft power model parameter；Linear speed for body system relative to navigation system
Spend the component in body system x, y-axis；βx、βyFor body system x, the angular acceleration of y-axis, obtained by angular accelerometer；ωiFor
The revolving speed of i-th of rotor, i=1,2,3,4,For the angular acceleration of i-th of rotor revolving speed；fax、fayFor body system x, y-axis
Acceleration is obtained by the accelerometer in IMU.
In the present embodiment, step 2 is realized using following preferred embodiment:
In step 2, the method for constructing the sub- detector D1 of angular accelerometer/kinetic model is as follows:
1, it is exported using angular accelerometer output and kinetic model, calculates z-axis angular acceleration residual error:
In above formula, r1(k) the z-axis angular acceleration residual error being calculated for angular accelerometer and kinetic model；βzIt (k) is k
Moment body system z-axis angular acceleration is exported by z-axis angular accelerometer and is obtained；It is calculated by kinetic model
The angular acceleration of body system z-axis；Abs (*) expression takes absolute value；
2, judge whether sub- detector D1 breaks down:
In above formula, τ1For breakdown judge threshold value；T1For failure detection result；If T1=1, then angular accelerometer or power
Learn model failure；If T1=0, then angular accelerometer and kinetic model fault-free；
It is as follows to construct angular accelerometer/IMU detector D2 method:
1, it is exported using angular accelerometer and calculates x, y, z axis angular rate:
In above formula, ωx(k)、ωy(k)、ωz(k) it is the body system x, y, z axis angular rate at k moment, passes through angular accelerometer
Output βx、βy、βzIntegral obtains；ωx(k-1)、ωy(k-1)、ωzIt (k-1) is the body system x, y, z shaft angle speed at k-1 moment
Degree is exported by k-1 moment Federated Kalman Filter and is obtained；Δ T is the discrete sampling time；
2, the statistical parameter of sub- detector D2 is calculated:
r2(k)=[ωgx(k) ωgy(k) ωgz(k)]T-[ωx(k) ωy(k) ωz(k)]T
A2(k)=H2(k)P2(k|k-1)H2(k)+R2(k)
λ2=r2(k)A2(k)-1r2(k)
P2(k | k-1)=F2(k)P2(k-1)F2 T(k)+G2(k-1)Q2(k-1)G2 T(k-1)
In above formula, r2(k) the x, y, z axis angular rate residual error being calculated is exported for k moment IMU and angular accelerometer；ωgx
(k)、ωgy(k)、ωgz(k) it is body system x, y, z axis angular rate, is exported and obtained by gyro；A2It (k) is that k moment IMU and angle add
Speedometer exports the residual variance being calculated；H2(k)=[I3×3] it is to measure coefficient matrix, I at the k moment3×3For 3 × 3 unit
Matrix；P2(k | k-1) it is k moment one-step prediction mean square error matrix；R2(k)=diag ([εωgx(k) εωgy(k) εωgz(k)]2)
Noise matrix, ε are measured for the k momentωgx(k)、εωgy(k)、εωgzIt (k) is body system x, y, z axis gyro noise；λ2It (k) is the k moment
IMU and angular accelerometer export the statistical parameter being calculated；F2(k)=[I3×3] it is k moment IMU and angular accelerometer detection
The coefficient of regime matrix of system；P2It (k-1) is the Square Error matrix at k-1 moment；G2(k-1)=[Δ T*I3×3] it is the k-1 moment
The state-noise coefficient matrix of IMU and angular accelerometer detection system；Q2(k-1)=diag ([εβx(k-1) εβy(k-1) εβz
(k-1)]2) be k-1 moment IMU and angular accelerometer detection system state-noise, εβx(k-1)、εβy(k-1)、εβz(k-1) it is
The x, y, z shaft angle accelerometer noise at k-1 moment；Subscript T indicates transposition；
3, judge whether sub- detector D2 breaks down:
In above formula, τ2For breakdown judge threshold value；T2For failure detection result；If T2=1, then angular accelerometer or IMU event
Barrier；If T3=0, then angular accelerometer and IMU fault-free；
Construction force model/IMU detector D3 method is as follows:
1, it is exported using kinetic model and calculates z-axis angular speed:
In above formula, ωmz(k-1) it is the angular speed at k-1 moment, passes through k-1 moment Federated Kalman Filter and export acquisition；
ωmzIt (k) is the angular speed at k moment；For the angular acceleration at k moment, obtained by kinetic model；Δ T is discrete adopts
The sample time；
2, the residual sum statistical parameter of sub- detector D3 is calculated:
r3a(k)=abs (faz(k)-fmz(k))
r3b(k)=ωgz(k)-ωmz(k)
A3b(k)=H3b(k)P3b(k|k-1)H3b(k)+R3b(k)
λ3b=r3b(k)A3b(k)-1r3b(k)
P3b(k | k-1)=F3b(k)P3b(k-1)F3b T(k)+G3b(k-1)Q3b(k-1)G3b T(k-1)
In above formula, r3a(k) the z-axis acceleration residual error being calculated for k moment IMU and kinetic model；faz(k) be k when
The acceleration for carving body system z-axis, is obtained by accelerometer；r3b(k) z-axis being calculated for k moment IMU and kinetic model
Angular speed residual error；A3b(k) the z-axis angular speed residual variance being calculated for k moment IMU and kinetic model；H3b(k)=1 it is
The k moment measures coefficient matrix；P3b(k | k-1) it is k moment one-step prediction mean square error matrix；R3b(k)=diag ([εωgz(k)]2) be
The k moment measures noise matrix；λ3b(k) the z-axis angular speed statistical parameter being calculated for k moment IMU and kinetic model；For the coefficient of regime matrix of k moment z-axis gyro and kinetic model detection system；P3b(k-1) it is
The Square Error matrix at k-1 moment；G3b(k-1)=1 the state for k-1 moment z-axis gyro and kinetic model detection system is made an uproar
Sonic system matrix number；Q3b(k-1)=diag ([εωmz(k-1)]2) be k-1 moment z-axis gyro and kinetic model detection system shape
State noise, εωmzIt (k-1) is the body system z-axis angular speed noise obtained by kinetic model at the k-1 moment；
3, judge whether sub- detector D3 breaks down:
In above formula, τ3a、τ3bFor breakdown judge threshold value；T3a、T3bFor failure detection result；If T3aOr T (k)=13b(k)
=1, IMU or kinetic model failure；If T3aAnd T (k)=03b(k)=0, IMU and kinetic model fault-free.
In step 2, the status predication of triplet detector D4, D5, D6 when calculating k:
q0(k)=q0(k-1)-0.5ωgx(k)q1(k-1)-0.5ωgy(k)q2(k-1)-0.5ωgz(k)q3(k-1)
q1(k)=0.5 ωgx(k)q0(k-1)+q1(k-1)+0.5ωgz(k)q2(k-1)-0.5ωgy(k)q3(k-1)
q2(k)=0.5 ωgy(k)q0(k-1)-0.5ωgz(k)q1(k-1)+q2(k-1)+0.5ωgx(k)q3(k-1)
q3(k)=0.5 ωgz(k)q0(k-1)+0.5ωgy(k)q1(k-1)-0.5ωgx(k)q2(k-1)+q3(k-1)
In above formula, q0(k)、q1(k)、q2(k)、q3(k) the k moment quaternary number to be obtained by state equation；q0(k-1)、
q1(k-1)、q2(k-1)、q3(k-1) it is k-1 moment quaternary number, is exported and obtained by k-1 moment Federated Kalman Filter；Component of the linear velocity in body system z-axis for the k moment body system that is obtained by state equation relative to navigation system；It is k-1 moment body system relative to component of the linear velocity for being in body system z-axis that navigate, is joined by the k-1 moment
The output of nation's Kalman filter obtains；G is acceleration of gravity；PN(k)、PE(k)、PD(k) for obtained by state equation k when
Carve north orientation, east orientation, to position；PN(k-1)、PE(k-1)、PD(k-1) for k-1 moment north orientation, east orientation, to position, pass through k-
The output of 1 moment Federated Kalman Filter obtains；
Construct the sub- detector D4 of IMU/ Magnetic Sensor:
1, the statistical parameter of sub- detector D4 is calculated:
Course is calculated using the status predication at k moment:
A4(k)=H4(k)P4(k|k-1)H4(k)+R4(k)
λ4(k)=r4(k)A4(k)-1r4(k)
P4(k | k-1)=F4(k)P4(k-1)F4 T(k)+G4(k-1)Q4(k-1)G4 T(k-1)
Q4(k-1)=diag ([εωgx(k-1) εωgy(k-1) εωgz(k-1) εvbx(k-1)εvby(k-1) εvbz(k-1)
εpn(k-1) εpe(k-1) εpd(k-1)]2)
In above formula,For the k moment course angle being calculated by status predication；r4It (k) is k moment D4 detector
Course angle residual error；ψmIt (k) is the course of k moment Magnetic Sensor output；A4It (k) is the residual variance of k moment D4 detector；H4(k)
For the measurement coefficient matrix of k moment D4 detector；P4(k | k-1) is the one-step prediction mean square error at k-1 moment to k moment；R4
(k)=diag ([εψm(k)]2) be the k moment noise matrix, εψmIt (k) is the height noise of k moment Magnetic Sensor；λ4It (k) is k
The statistical parameter at moment；0i×jFor the null matrix of i × j；Ii×jFor the unit matrix of i × j；F4(k) it is sensed for k moment IMU and magnetic
The coefficient of regime matrix of device detector；P4It (k-1) is the Square Error matrix at k-1 moment；G4It (k-1) is k-1 moment IMU and magnetic
The state-noise coefficient matrix of sensor detector；Q4It (k-1) is the state-noise of k-1 moment IMU and Magnetic Sensor detector,
εvbx(k-1)、εvby(k-1)、εvbzIt (k-1) is the x, y, z axial velocity noise at k moment；εpn(k-1)、εpe(k-1)、εpd(k-1)
For the north orientation at k-1 moment, east orientation, to position noise；
4, judge whether sub- detector D4 breaks down
In above formula, τ4For breakdown judge threshold value；T4For failure detection result；If T4=1, IMU or Magnetic Sensor failure；
If T4=0, IMU and Magnetic Sensor fault-free；
Construct the sub- detector D5 of IMU/GPS:
1, the statistical parameter of sub- detector D5 is calculated:
East orientation speed is calculated using the status predication at k moment:
North orientation speed is calculated using the status predication at k moment:
A5(k)=H5(k)P5(k|k-1)H5(k)+R5(k)
λ5(k)=r5(k)A5(k)-1r5(k)
H5(k)=[Υ2×4 Ω2×3 02×3]
In above formula,K moment east orientation speed to be calculated by status predication is predicted, north orientation is fast
Degree；r5It (k) is north orientation speed, the east orientation speed residual error of k moment IMU and GPS detection system；VNG(k)、VEGIt (k) is k moment GPS
North orientation speed, the east orientation speed of output；A5It (k) is the residual variance of k moment IMU and GPS detection system；H5It (k) is k moment IMU
With the measurement coefficient matrix of GPS detection system；P5(k | k-1)=P4(k | k-1) is that the one-step prediction at k-1 moment to k moment is square
Error；For the noise matrix at k moment,For the GPS at k moment
North orientation, east orientation speed noise；λ5It (k) is the statistical parameter at k moment；
4, judge whether detector D5 breaks down:
In above formula, τ5For breakdown judge threshold value；T5For failure detection result；If T5=1, IMU or GPS failure；If T5
=0, IMU and GPS fault-free；
Construct the sub- detector D6 of IMU/ barometer:
1, the statistical parameter of detector D6:
Use the status predication computed altitude at k moment:
A6(k)=H6(k)P6(k|k-1)H6(k)+R6(k)
λ6(k)=r6(k)A6(k)-1r6(k)
In above formula,For the k moment height being calculated by status predication；r6It (k) is k moment IMU and barometer
The height residual error of detection system；hbIt (k) is the height of k moment barometer output；A6It (k) is k moment IMU and barometer detection system
The residual variance of system；H6It (k)=- 1 is the measurement coefficient matrix of k moment IMU and barometer detection system；P6(k | k-1)=P4(k
| k-1) be k-1 moment to the k moment one-step prediction mean square error；R6(k)=diag ([εhb(k)]2) be the k moment noise square
Battle array, εhbIt (k) is the barometrical height noise at k moment；λ6It (k) is the statistical parameter at k moment；
4, judge whether sub- detector D6 breaks down:
In above formula, τ6For breakdown judge threshold value；T6For failure detection result；If T6=1, IMU or barometer failure；Such as
Fruit T6=0, IMU and barometer fault-free.
In the present embodiment, step 3 is realized using following preferred embodiment:
In step 3, fault location strategy is constructed according to S1 first:
Then according to the fault location strategy of the S1 and S2 building overall situation:
In above formula, FGACC、FDM、FIMU、Fmag、Fgps、FbaroRespectively angular accelerometer, kinetic model, IMU, magnetic sensing
Device, GPS, barometrical fault location function, fault location function are equal to " 1 ", indicate corresponding sensor failure, failure
Mapping function is equal to " 0 ", indicates that corresponding sensor does not break down；" | | " indicate logical operator "or"；" & " indicates logic
Operator "AND"；"-" indicates logical operator " non-".
Above-mentioned failure diagnostic process is as shown in Figure 2.
In the present embodiment, step 4 is realized using following preferred embodiment:
In step 4, fault condition is divided into following 7 kinds of situations:
Situation 1: airborne sensor fault-free:
(1) state and mean-square error forecast value at k moment are calculated:
q1(k)=0.5 ωgx(k)q0(k-1)+q1(k-1)+0.5ωgz(k)q2(k-1)-0.5ωgy(k)q3(k-1)
q2(k)=0.5 ωgy(k)q0(k-1)-0.5ωgz(k)q1(k-1)+q2(k-1)+0.5ωgx(k)q3(k-1)
q3(k)=0.5 ωgz(k)q0(k-1)+0.5ωgy(k)q1(k-1)-0.5ωgx(k)q2(k-1)+q3(k-1)
P (k | k-1)=F (k) P (k-1) FT(k)+G(k)Q(k)GT(k)
Q (k-1)=diag ([εβx(k-1) εβy(k-1) εβz(k-1) εωx(k-1) εωy(k-1) εωz(k-1) εvbx
(k-1) εvby(k-1) εvbz(k-1) εpn(k-1) εpe(k-1) εpd(k-1)]2) in above formula,It is k moment body system relative to point of the angular speed for being on body system x, y, z axis that navigate
Amount；P (k | k-1) is the one-step prediction mean square error at k-1 moment to k moment；P (k-1) is the mean square error at k-1 moment；F(k)
For the coefficient of regime matrix at k moment；G (k-1) is the noise coefficient matrix at k-1 moment；Q (k-1) is the noise coefficient at k-1 moment
Matrix；εωx(k-1)、εωy(k-1)、εωzIt (k-1) is the x, y, z axis angular rate noise at k-1 moment；
(2) the gain K of the extended Kalman filter of i-th of subfilter of k moment is calculatedCi:
KCi(k)=P (k | k-1) HCi(k)T[HCi(k)P(k|k-1)HCi(k)T+RCi(k)]-1
In above formula,For the k moment GPS to velocity noise；For the k moment GPS make an uproar to speed
Sound；For the GPS east orientation position at k moment, north orientation position noise；
(3) the extended Kalman filter state estimation X of i-th of subfilter of k moment is calculatedCi(k) and mean square error
PCi(k):
XCi(k)=X (k | k-1)+KCi(k)[YCi(k)-hCi(XCi(k|k-1))]
PCi(k)=[I-KCi(k)HCi(k)]P(k|k-1)
YCi(k) it is i-th of subfilter measurement:
In above formula, VDG(k)、PNG(k)、PEG(k) for k moment GPS Xiang Sudu, north orientation position, east orientation position；hbaro(k)
For k moment barometer height；
(4) the global Fusion state estimation X of k moment subfilter is calculatedg(k) and mean square error estimates Pg(k):
Xg(k)=Pg(k)[PC1(k)-1XC1(k)+PC2(k)-1XC2(k)+PC3(k)-1XC3(k)+PC4(k)-1XC4(k)]-1
Pg(k)=[PC1(k)-1+PC2(k)-1+PC3(k)-1+PC4(k)-1]-1
X (k)=Xg(k)
P (k)=4Pg(k)
In above formula, X (k), P (k) are the state resetting and mean square error resetting of k moment each subfilter；
When situation 2:IMU failure, IMU is isolated, such as using the velocity differentials equation modification as input of IMU accelerometer
Under:
Use the gyro in IMU to be cut off as the subfilter of measurement information, is not involved in fused filtering；According to sensor
Isolated instances modify state estimation correlation matrix on the basis of situation 1；
Situation 3: when kinetic model failure, filtering renewal process is identical as situation 1；
Situation 4: when angular accelerometer failure, angular accelerometer is isolated, and uses angular accelerometer angle speed as input
The degree differential equation is amended as follows:
It is identical as situation 1 to measure renewal process；
Situation 5: when Magnetic Sensor failure, Magnetic Sensor is isolated, and uses the course information of redundancy as measurement；It measures
Renewal process is modified on the basis of situation 1 according to measurement equation, by the Y in the step (3) in situation 1C2(k)=ψm
(k), the data exported using redundancy course transmitter are revised as；
When situation 6:GPS failure, GPS is isolated, and uses the speed position information of redundancy as measurement；It measures updated
Journey is modified on the basis of situation 1 according to measurement equation, by the Y in situation 1 in step (3)C3(k)=[VNG(k) VEG
(k) VDG(k) PNG(k) PEG(k)]T, it is revised as the data exported using redundancy velocity measurement sensor；
Situation 7: when barometer failure, barometer is isolated, and uses the elevation information of redundancy as measurement；It measures and updates
Process is modified on the basis of situation 1 according to measurement equation, by the Y in situation 1 in step (3)C4(k)=hbaro(k), it repairs
It is changed to the data exported using redundancy height-measuring sensor.
Above-mentioned filtering is as shown in Figure 3.
In the present embodiment, step 5 is realized using following preferred embodiment:
In step 5, state estimation result in step 4, the angle angular acceleration meter/IMU detector D2 speed are used
Degree, kinetic model/IMU detector D3 angular speed and IMU/ Magnetic Sensor/GPS/ barometer fault Detection Filter
The quaternary number of S2, is highly corrected update at linear velocity.
Above-mentioned whole process is as shown in Figure 4.
Hereafter illustrate effect of the invention by specific simulation example:
In such a way that actual flying quality carries out semi-physical simulation verifying, to using the fault-tolerant estimation after the present invention
It can be carried out verifying.To carrier fault detection performance verify, acquire data of the aircraft under the different motion of automobile: outstanding
Stop, roll motion, along the side of pitch axis fly to move, hover, pitching movement, along the side of pitch axis flying to move, course at a slow speed
The rotation of axis, the rotation of quick course axis.Since used unmanned plane lacks angular acceleration flowmeter sensor, so by using
Gyro data differential carries out the output of simulation angular accelerometer, and constant value simulated fault is injected in different sensing datas and is carried out
Fault detection verifying, fault detection and state estimation result when the present embodiment only provides IMU failure, remaining sensor fault knot
Fruit is seemingly.
Fig. 5 is when injecting zero bias failure in IMU data, using the failure detection result after the method for the present invention.By in figure
As can be seen that detection system can promptly and accurately detect that IMU breaks down.
Fig. 6 is when injecting zero bias failure in IMU data, using the x-axis Attitude rate estimator result after the method for the present invention.By
As can be seen that Attitude rate estimator error is less than 10 degrees seconds in figure.
Fig. 7 is when injecting zero bias failure in IMU data, using the y-axis Attitude rate estimator result after the method for the present invention.By
As can be seen that Attitude rate estimator error is less than 10 degrees seconds in figure.
Fig. 8 is when injecting zero bias failure in IMU data, using the z-axis Attitude rate estimator result after the method for the present invention.By
As can be seen that Attitude rate estimator error is less than 2 degrees seconds in figure.
Fig. 9 is when injecting zero bias failure in IMU data, using the roll angle estimated result after the method for the present invention.By scheming
In as can be seen that roll angle evaluated error less than 5 degree.
Figure 10 is when injecting zero bias failure in IMU data, using the pitch angle estimated result after the method for the present invention.By scheming
In as can be seen that pitch angle evaluated error less than 5 degree.
Figure 11 is when injecting zero bias failure in IMU data, using the course angle estimation result after the method for the present invention.By scheming
In as can be seen that course angle estimation error less than 1 degree.
Figure 12 is when injecting zero bias failure in IMU data, using the x-axis velocity estimation result after the method for the present invention.
As can be seen from Figure, x-axis speed estimation error is less than 0.2m/s.
Figure 13 is when injecting zero bias failure in IMU data, using the y-axis velocity estimation result after the method for the present invention.
As can be seen from Figure, y-axis speed estimation error is less than 0.2m/s.
Claims (6)
1. a kind of fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy, which comprises the steps of:
Step 1: the period read the rotor revolving speed measurement system of k moment carrier, angular accelerometer, IMU, Magnetic Sensor, GPS and
Barometrical output estimates that the movement acceleration information of rotor craft and angle add by the kinetic model of rotor craft
Velocity information；
Step 2: being exported using the airborne sensor data in step 1, constructs angular accelerometer/kinetic model/IMU failure
Fault detection filter S1 and IMU/ Magnetic Sensor/GPS/ barometer fault Detection Filter S2；It include three parallel son inspections in S1
Survey device, the respectively sub- detector D1 of angular accelerometer/kinetic model, angular accelerometer/IMU detector D2 and kinetic simulation
Type/IMU detector D3；It include three parallel sub- detectors, respectively IMU/ Magnetic Sensor detector D4, IMU/ in S2
The sub- detector D6 of GPS detector D5 and IMU/ barometer；
Step 3: according to the failure detection result of each sub- detector in S1, fault location strategy is constructed, realizes angular acceleration
The fault location of meter, kinetic model and IMU exports positioning result；It is examined according to height each in the fault location result of S1 and S2
The failure detection result of device is surveyed, global fault's positioning strategy, positioning failure sensor are constructed；
Step 4: the fault location obtained according to step 3 is as a result, formulation the Fault Isolation Strategy, is isolated from state filtering estimation
Fault sensor completes navigational state estimation；
When airborne sensor fault-free or kinetic model failure, use the accelerometer of angular accelerometer, IMU as state
Equation inputs, and the gyro, Magnetic Sensor, GPS, barometer in IMU are as measuring sensor；
When IMU failure, the Aerodynamic Model of angular accelerometer and kinetic model is used to input as state equation, magnetic sensing
Device, GPS, barometer are as measuring sensor；
When angular accelerometer failure, use accelerometer in the aerodynamic moment model and IMU of kinetic model as state
Equation inputs, and the gyro, Magnetic Sensor, GPS, barometer in IMU are as measuring sensor；
When GPS or barometer or Magnetic Sensor failure, use the accelerometer of angular accelerometer, IMU defeated as state equation
Enter, isolated fault sensor, is allowed to be not involved in global fused filtering；
Step 5: the state estimation result obtained according to step 4 is corrected the quantity of state in the S1 and S2 at k moment.
2. the fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy according to claim 1, which is characterized in that in step 1
In, the movement acceleration information and angular acceleration information of rotor craft are estimated by following formula:
fmz=kT0+kT1(ω1 2+ω2 2+ω3 2+ω4 2)
Wherein, fmx、fmy、fmzFor the acceleration for the body system x, y, z axis being calculated by kinetic model；For the angular acceleration for the body system x, y, z axis being calculated by kinetic model, ωmzFor the angle of z-axis
Speed；kHx0、kHx1、kHx2、kHx3、kHy0、kHy1、kHy2、kHy3、kT0、kT1、kx0、kx1、kx2、kx3、ky0、ky1、ky2、ky3、kz0、kz1、
kz2、kz3It is constant value for Rotarycraft power model parameter；Linear speed for body system relative to navigation system
Spend the component in body system x, y-axis；βx、βyFor body system x, the angular acceleration of y-axis, obtained by angular accelerometer；ωiFor
The revolving speed of i-th of rotor, i=1,2,3,4,For the angular acceleration of i-th of rotor revolving speed；fax、fayFor body system x, y-axis
Acceleration is obtained by the accelerometer in IMU.
3. the fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy according to claim 1, which is characterized in that in step 2
In, the method for constructing the sub- detector D1 of angular accelerometer/kinetic model is as follows:
1, it is exported using angular accelerometer output and kinetic model, calculates z-axis angular acceleration residual error:
In above formula, r1(k) the z-axis angular acceleration residual error being calculated for angular accelerometer and kinetic model；βzIt (k) is the k moment
Body system z-axis angular acceleration is exported by z-axis angular accelerometer and is obtained；For the body being calculated by kinetic model
It is the angular acceleration of z-axis；Abs (*) expression takes absolute value；
2, judge whether sub- detector D1 breaks down:
In above formula, τ1For breakdown judge threshold value；T1For failure detection result；If T1=1, then angular accelerometer or kinetic simulation
Type failure；If T1=0, then angular accelerometer and kinetic model fault-free；
It is as follows to construct angular accelerometer/IMU detector D2 method:
1, it is exported using angular accelerometer and calculates x, y, z axis angular rate:
In above formula, ωx(k)、ωy(k)、ωz(k) it is the body system x, y, z axis angular rate at k moment, passes through the defeated of angular accelerometer
β outx、βy、βzIntegral obtains；ωx(k-1)、ωy(k-1)、ωz(k-1) it is the body system x, y, z axis angular rate at k-1 moment, leads to
The output of k-1 moment Federated Kalman Filter is crossed to obtain；Δ T is the discrete sampling time；
2, the statistical parameter of sub- detector D2 is calculated:
r2(k)=[ωgx(k) ωgy(k) ωgz(k)]T-[ωx(k) ωy(k) ωz(k)]T
A2(k)=H2(k)P2(k|k-1)H2(k)+R2(k)
λ2=r2(k)A2(k)-1r2(k)
P2(k | k-1)=F2(k)P2(k-1)F2 T(k)+G2(k-1)Q2(k-1)G2 T(k-1)
In above formula, r2(k) the x, y, z axis angular rate residual error being calculated is exported for k moment IMU and angular accelerometer；ωgx(k)、
ωgy(k)、ωgz(k) it is body system x, y, z axis angular rate, is exported and obtained by gyro；A2It (k) is k moment IMU and angular acceleration
The residual variance that meter output is calculated；H2(k)=[I3×3] it is to measure coefficient matrix, I at the k moment3×3For 3 × 3 unit matrix；
P2(k | k-1) it is k moment one-step prediction mean square error matrix；R2(k)=diag ([εωgx(k) εωgy(k) εωgz(k)]2) it is k
Moment measures noise matrix, εωgx(k)、εωgy(k)、εωgzIt (k) is body system x, y, z axis gyro noise；λ2It (k) is k moment IMU
The statistical parameter being calculated is exported with angular accelerometer；F2(k)=[I3×3] it is k moment IMU and angular accelerometer detection system
Coefficient of regime matrix；P2It (k-1) is the Square Error matrix at k-1 moment；G2(k-1)=[Δ T*I3×3] it is k-1 moment IMU
With the state-noise coefficient matrix of angular accelerometer detection system；Q2(k-1)=diag ([εβx(k-1) εβy(k-1) εβz(k-
1)]2) be k-1 moment IMU and angular accelerometer detection system state-noise, εβx(k-1)、εβy(k-1)、εβzIt (k-1) is k-1
The x, y, z shaft angle accelerometer noise at moment；Subscript T indicates transposition；
3, judge whether sub- detector D2 breaks down:
In above formula, τ2For breakdown judge threshold value；T2For failure detection result；If T2=1, then angular accelerometer or IMU failure；
If T3=0, then angular accelerometer and IMU fault-free；
Construction force model/IMU detector D3 method is as follows:
1, it is exported using kinetic model and calculates z-axis angular speed:
In above formula, ωmz(k-1) it is the angular speed at k-1 moment, passes through k-1 moment Federated Kalman Filter and export acquisition；ωmz
It (k) is the angular speed at k moment；For the angular acceleration at k moment, obtained by kinetic model；When Δ T is discrete sampling
Between；
2, the residual sum statistical parameter of sub- detector D3 is calculated:
r3a(k)=abs (faz(k)-fmz(k))
r3b(k)=ωgz(k)-ωmz(k)
A3b(k)=H3b(k)P3b(k|k-1)H3b(k)+R3b(k)
λ3b=r3b(k)A3b(k)-1r3b(k)
P3b(k | k-1)=F3b(k)P3b(k-1)F3b T(k)+G3b(k-1)Q3b(k-1)G3b T(k-1)
In above formula, r3a(k) the z-axis acceleration residual error being calculated for k moment IMU and kinetic model；fazIt (k) is k moment machine
The acceleration of system z-axis, is obtained by accelerometer；r3b(k) the z-axis angle speed being calculated for k moment IMU and kinetic model
Spend residual error；A3b(k) the z-axis angular speed residual variance being calculated for k moment IMU and kinetic model；H3b(k)=1 when being k
It carves and measures coefficient matrix；P3b(k | k-1) it is k moment one-step prediction mean square error matrix；R3b(k)=diag ([εωgz(k)]2) be
The k moment measures noise matrix；λ3b(k) the z-axis angular speed statistical parameter being calculated for k moment IMU and kinetic model；For the coefficient of regime matrix of k moment z-axis gyro and kinetic model detection system；P3b(k-1)
For the Square Error matrix at k-1 moment；G3bIt (k-1)=1 is the state of k-1 moment z-axis gyro and kinetic model detection system
Noise coefficient matrix；Q3b(k-1)=diag ([εωmz(k-1)]2) it is k-1 moment z-axis gyro and kinetic model detection system
State-noise, εωmzIt (k-1) is the body system z-axis angular speed noise obtained by kinetic model at the k-1 moment；
3, judge whether sub- detector D3 breaks down:
In above formula, τ3a、τ3bFor breakdown judge threshold value；T3a、T3bFor failure detection result；If T3aOr T (k)=13b(k)=1,
IMU or kinetic model failure；If T3aAnd T (k)=03b(k)=0, IMU and kinetic model fault-free.
4. the fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy according to claim 3, which is characterized in that in step 2
In, the status predication of triplet detector D4, D5, D6 when calculating k:
q0(k)=q0(k-1)-0.5ωgx(k)q1(k-1)-0.5ωgy(k)q2(k-1)-0.5ωgz(k)q3(k-1)
q1(k)=0.5 ωgx(k)q0(k-1)+q1(k-1)+0.5ωgz(k)q2(k-1)-0.5ωgy(k)q3(k-1)
q2(k)=0.5 ωgy(k)q0(k-1)-0.5ωgz(k)q1(k-1)+q2(k-1)+0.5ωgx(k)q3(k-1)
q3(k)=0.5 ωgz(k)q0(k-1)+0.5ωgy(k)q1(k-1)-0.5ωgx(k)q2(k-1)+q3(k-1)
In above formula, q0(k)、q1(k)、q2(k)、q3(k) the k moment quaternary number to be obtained by state equation；q0(k-1)、q1(k-
1)、q2(k-1)、q3(k-1) it is k-1 moment quaternary number, is exported and obtained by k-1 moment Federated Kalman Filter；For
The k moment body system obtained by state equation is relative to component of the linear velocity for being in body system z-axis that navigate；
It is k-1 moment body system relative to component of the linear velocity for being in body system z-axis that navigate, passes through k-1 moment federation Kalman
Filter output obtains；G is acceleration of gravity；PN(k)、PE(k)、PDIt (k) is the k moment north orientation obtained by state equation, east
To, to position；PN(k-1)、PE(k-1)、PD(k-1) be k-1 moment north orientation, east orientation, to position, it is federal to pass through the k-1 moment
Kalman filter output obtains；
Construct the sub- detector D4 of IMU/ Magnetic Sensor:
1, the statistical parameter of sub- detector D4 is calculated:
Course is calculated using the status predication at k moment:
A4(k)=H4(k)P4(k|k-1)H4(k)+R4(k)
λ4(k)=r4(k)A4(k)-1r4(k)
P4(k | k-1)=F4(k)P4(k-1)F4 T(k)+G4(k-1)Q4(k-1)G4 T(k-1)
Q4(k-1)=diag ([εωgx(k-1) εωgy(k-1) εωgz(k-1) εvbx(k-1) εvby(k-1) εvbz(k-1) εpn
(k-1) εpe(k-1) εpd(k-1)]2)
In above formula,For the k moment course angle being calculated by status predication；r4It (k) is the course of k moment D4 detector
Angle residual error；ψmIt (k) is the course of k moment Magnetic Sensor output；A4It (k) is the residual variance of k moment D4 detector；H4It (k) is k
The measurement coefficient matrix of moment D4 detector；P4(k | k-1) is the one-step prediction mean square error at k-1 moment to k moment；R4(k)=
diag([εψm(k)]2) be the k moment noise matrix, εψmIt (k) is the height noise of k moment Magnetic Sensor；λ4It (k) is the k moment
Statistical parameter；0i×jFor the null matrix of i × j；Ii×jFor the unit matrix of i × j；F4(k) it is detected for k moment IMU and Magnetic Sensor
The coefficient of regime matrix of device；P4It (k-1) is the Square Error matrix at k-1 moment；G4It (k-1) is k-1 moment IMU and Magnetic Sensor
The state-noise coefficient matrix of detector；Q4It (k-1) is the state-noise of k-1 moment IMU and Magnetic Sensor detector, εvbx(k-
1)、εvby(k-1)、εvbzIt (k-1) is the x, y, z axial velocity noise at k moment；εpn(k-1)、εpe(k-1)、εpdIt (k-1) is k-1
The north orientation at moment, east orientation, to position noise；
2, judge whether sub- detector D4 breaks down
In above formula, τ4For breakdown judge threshold value；T4For failure detection result；If T4=1, IMU or Magnetic Sensor failure；If T4
=0, IMU and Magnetic Sensor fault-free；
Construct the sub- detector D5 of IMU/GPS:
1, the statistical parameter of sub- detector D5 is calculated:
East orientation speed is calculated using the status predication at k moment:
North orientation speed is calculated using the status predication at k moment:
A5(k)=H5(k)P5(k|k-1)H5(k)+R5(k)
λ5(k)=r5(k)A5(k)-1r5(k)
H5(k)=[γ2×4 Ω2×3 02×3]
In above formula,For be calculated by status predication k moment east orientation speed prediction, north orientation speed；r5
It (k) is north orientation speed, the east orientation speed residual error of k moment IMU and GPS detection system；VNG(k)、VEG(k) it is exported for k moment GPS
North orientation speed, east orientation speed；A5It (k) is the residual variance of k moment IMU and GPS detection system；H5(k) for k moment IMU and
The measurement coefficient matrix of GPS detection system；P5(k | k-1)=P4(k | k-1) is the one-step prediction mean square error at k-1 moment to k moment
Difference；For the noise matrix at k moment,For the north GPS at k moment
To, east orientation speed noise；λ5It (k) is the statistical parameter at k moment；
2, judge whether detector D5 breaks down:
In above formula, τ5For breakdown judge threshold value；T5For failure detection result；If T5=1, IMU or GPS failure；If T5=0,
IMU and GPS fault-free；
Construct the sub- detector D6 of IMU/ barometer:
1, the statistical parameter of detector D6:
Use the status predication computed altitude at k moment:
A6(k)=H6(k)P6(k|k-1)H6(k)+R6(k)
λ6(k)=r6(k)A6(k)-1r6(k)
In above formula,For the k moment height being calculated by status predication；r6It (k) is k moment IMU and barometer detection system
The height residual error of system；hbIt (k) is the height of k moment barometer output；A6It (k) is the residual of k moment IMU and barometer detection system
Poor variance；H6It (k)=- 1 is the measurement coefficient matrix of k moment IMU and barometer detection system；P6(k | k-1)=P4(k|k-1)
For the k-1 moment to the one-step prediction mean square error at k moment；R6(k)=diag ([εhb(k)]2) be the k moment noise matrix, εhb
It (k) is the barometrical height noise at k moment；λ6It (k) is the statistical parameter at k moment；
2, judge whether sub- detector D6 breaks down:
In above formula, τ6For breakdown judge threshold value；T6For failure detection result；If T6=1, IMU or barometer failure；If T6=
0, IMU and barometer fault-free.
5. the fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy according to claim 4, which is characterized in that in step 3
In, fault location strategy is constructed according to S1 first:
Then according to the fault location strategy of the S1 and S2 building overall situation:
In above formula, FGACC、FDM、FIMU、Fmag、Fgps、FbaroRespectively angular accelerometer, kinetic model, IMU, Magnetic Sensor,
GPS, barometrical fault location function, fault location function are equal to " 1 ", indicate corresponding sensor failure, failure is fixed
Bit function is equal to " 0 ", indicates that corresponding sensor does not break down；" | | " indicate logical operator "or"；" & " indicates logic fortune
Operator "AND"；"-" indicates logical operator " non-".
6. the fault-tolerant navigation estimation method of the aircraft of analytic expression redundancy according to claim 4, which is characterized in that in step 4
In, fault condition is divided into following 7 kinds of situations:
Situation 1: airborne sensor fault-free:
(1) state and mean-square error forecast value at k moment are calculated:
q1(k)=0.5 ωgx(k)q0(k-1)+q1(k-1)+0.5ωgz(k)q2(k-1)-0.5ωgy(k)q3(k-1)
q2(k)=0.5 ωgy(k)q0(k-1)-0.5ωgz(k)q1(k-1)+q2(k-1)+0.5ωgx(k)q3(k-1)
q3(k)=0.5 ωgz(k)q0(k-1)+0.5ωgy(k)q1(k-1)-0.5ωgx(k)q2(k-1)+q3(k-1)
P (k | k-1)=F (k) P (k-1) FT(k)+G(k)Q(k)GT(k)
Q (k-1)=diag ([εβx(k-1) εβy(k-1) εβz(k-1) εωx(k-1) εωy(k-1) εωz(k-1) εvbx(k-1)
εvby(k-1) εvbz(k-1) εpn(k-1) εpe(k-1) εpd(k-1)]2)
In above formula,For k moment body system relative to navigation system angular speed body system x,
Y, the component in z-axis；P (k | k-1) is the one-step prediction mean square error at k-1 moment to k moment；P (k-1) is the equal of k-1 moment
Square error；F (k) is the coefficient of regime matrix at k moment；G (k-1) is the noise coefficient matrix at k-1 moment；When Q (k-1) is k-1
The noise coefficient matrix at quarter；εωx(k-1)、εωy(k-1)、εωzIt (k-1) is the x, y, z axis angular rate noise at k-1 moment；
(2) the gain K of the extended Kalman filter of i-th of subfilter of k moment is calculatedCi:
KCi(k)=P (k | k-1) HCi(k)T[HCi(k)P(k|k-1)HCi(k)T+RCi(k)]-1
In above formula,For the k moment GPS to velocity noise；For the k moment GPS to velocity noise；For the GPS east orientation position at k moment, north orientation position noise；
(3) the extended Kalman filter state estimation X of i-th of subfilter of k moment is calculatedCi(k) and mean square error PCi
(k):
XCi(k)=X (k | k-1)+KCi(k)[YCi(k)-hCi(XCi(k|k-1))]
PCi(k)=[I-KCi(k)HCi(k)]P(k|k-1)
YCi(k) it is i-th of subfilter measurement:
In above formula, VDG(k)、PNG(k)、PEG(k) for k moment GPS Xiang Sudu, north orientation position, east orientation position；hbaro(k) be k when
Carve barometer height；
(4) the global Fusion state estimation X of k moment subfilter is calculatedg(k) and mean square error estimates Pg(k):
Xg(k)=Pg(k)[PC1(k)-1XC1(k)+PC2(k)-1XC2(k)+PC3(k)-1XC3(k)+PC4(k)-1XC4(k)]-1
Pg(k)=[PC1(k)-1+PC2(k)-1+PC3(k)-1+PC4(k)-1]-1
X (k)=Xg(k)
P (k)=4Pg(k)
In above formula, X (k), P (k) are the state resetting and mean square error resetting of k moment each subfilter；
When situation 2:IMU failure, IMU is isolated, such as using the velocity differentials equation modification as input of accelerometer in IMU
Under:
Use the gyro in IMU to be cut off as the subfilter of measurement information, is not involved in fused filtering；It is isolated according to sensor
Situation modifies state estimation correlation matrix on the basis of situation 1；
Situation 3: when kinetic model failure, filtering renewal process is identical as situation 1；
Situation 4: when angular accelerometer failure, angular accelerometer is isolated, micro- using angular accelerometer angular speed as input
Equation is divided to be amended as follows:
It is identical as situation 1 to measure renewal process；
Situation 5: when Magnetic Sensor failure, Magnetic Sensor is isolated, and uses the course information of redundancy as measurement；It measures and updates
In the process, by the Y in the step (3) in situation 1C2(k)=ψm(k), the data exported using redundancy course transmitter are revised as；
When situation 6:GPS failure, GPS is isolated, and uses the speed position information of redundancy as measurement；Measure renewal process
In, by the Y in situation 1 in step (3)C3(k)=[VNG(k) VEG(k) VDG(k) PNG(k) PEG(k)]T, it is revised as using superfluous
The data of remaining velocity measurement sensor output；
Situation 7: when barometer failure, barometer is isolated, and uses the elevation information of redundancy as measurement；Measure renewal process
In, by the Y in situation 1 in step (3)C4(k)=hbaro(k), the data exported using redundancy height-measuring sensor are revised as.
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| CN111024124B (en) * | 2019-12-25 | 2023-11-07 | 南京航空航天大学 | Combined navigation fault diagnosis method for multi-sensor information fusion |
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