CN108827313A - Multi-mode rotor craft Attitude estimation method based on extended Kalman filter - Google Patents
Multi-mode rotor craft Attitude estimation method based on extended Kalman filter Download PDFInfo
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Abstract
Multi-mode rotor craft Attitude estimation method based on extended Kalman filter, is related to the flight control method of small-sized rotor craft, and the estimated accuracy in order to solve the problems, such as existing rotor craft Attitude estimation method is low.The method of the present invention includes：Step 1: making extended Kalman filter be in different operating modes according to the new breath vector that the output of three-axis gyroscope and three axis accelerometer, extended Kalman filter are fed back；Step 2: different working modes correspond to different measurement models, rotor craft posture is estimated based on corresponding measurement model extended Kalman filter, obtains Attitude estimation result.The present invention is suitable for estimation rotor craft posture.
Description
Technical field
The present invention relates to the flight control methods of small-sized rotor craft.
Background technique
Attitude estimation algorithm is the indispensable important component of small-sized quadrotor drone flight control system, inertia
Measuring unit is integrated with three-axis gyroscope and three axis accelerometer, can measure the angular speed of three body axis directions of unmanned plane and remove
The acceleration that other outer strong resultant forces of gravity generate sensor.When the acceleration of motion of carrier aircraft is smaller, three axis accelerate
Degree meter can be with the component of reversed each axis in the case where carrying body coordinate system of approximate measure gravitational vectors, and pitch angle and roll angle can be by
This is directly calculated.Therefore, many existing algorithms are based on small acceleration it is assumed that using gravitational vectors observation model as acceleration
The measurement model of meter.But this kind of algorithm could can only play a role well under the conditions of nearly hovering.When there are numerical value for carrier aircraft
In the case where the biggish acceleration of motion that can not ignore, due to the unreliable information that accelerometer provides, the estimation essence of algorithm
It spends poor.And for quadrotor drone, accelerate flight very universal, in addition, the interference of wind can also cause movement to accelerate
Degree.Therefore, how accurately to estimate that the posture of quadrotor drone is a big research hotspot in the presence of acceleration of motion.
In general, there are two types of methods：First method is such as to utilize GPS metrical information solution using additional sensor
The acceleration of motion for calculating unmanned plane estimates UAV position and orientation etc. by fusion vision measurement information.But increase additional biography
Sensor can make system it is more complicated, reduce navigation information updating efficiency, be not suitable for using in small drone；Second method
Reasonably to adapt to different flying conditions using the output of accelerometer as far as possible, method be acceleration measuring magnitude
Be used for Attitude estimation in carrier aircraft smooth motion, when carrier aircraft accelerate when, do not merge acceleration measuring magnitude, method be plus
Weight of the measurement of velocity magnitude in fusion is adjusted according to the size adaptation of acceleration of motion, which measured by changing
Noise matrix is realized；Although this method improves the Attitude estimation precision of high maneuver acceleration in-flight to a certain extent,
It is to be realized by abandoning acceleration measuring magnitude to a certain extent, therefore estimated accuracy is low.
Summary of the invention
The purpose of the present invention is to solve the low problem of the estimated accuracy of existing rotor craft Attitude estimation method, from
And provide the multi-mode rotor craft Attitude estimation method based on extended Kalman filter.
Multi-mode rotor craft Attitude estimation method of the present invention based on extended Kalman filter, this method
Including：
Step 1: the new breath fed back according to the output of three-axis gyroscope and three axis accelerometer, extended Kalman filter
Vector makes extended Kalman filter be in different operating modes；
Step 2: different working modes correspond to different measurement models, based on corresponding measurement model spreading kalman
Filter estimates rotor craft posture, obtains Attitude estimation result.
Preferably, step 2 includes：
Step 2 one, the state prior estimate vector that the K moment is calculated according to state renewal equation
Wherein,For the state Posterior estimator vector at k-1 moment,
Δ t is sampling period, ωkFor k moment gyroscope measurement obtain with noisy angular speed,
Step 2 two, the prior uncertainty covariance matrix for calculating the k moment
Wherein, Pk-1For the posteriori error covariance matrix at k-1 moment, QkFor process noise matrix；
Step 2 three, the measurement matrix H for calculating the k momentk,
The operating mode of extended Kalman filter includes gyroscopic mode, acceleration mode and balanced mode, when for gyro mould
σ when formulak=0, ξk=0, accelerate mode when σk=1, ξk=0, σ when balanced modek=1, ξk=1；
σkFor the coefficient that accelerometer measurement information is added, ξkFor the handoff factor between balanced mode and acceleration mode；
Step 2 four, the kalman gain matrix K for calculating the k momentk,
Wherein R is to measure noise matrix；
Step 2 five, the measurement vector Z for calculating the k momentk,
Wherein, Accx,k、Accy,kAnd Accz,kThe respectively measured value of k moment three axis accelerometer x-axis, y-axis and z-axis, μ
For the linear resistance coefficient of rotor craft；
Step 2 six, the measurement vector estimated value for calculating the k moment
Step 2 seven, the new breath vector r for calculating the k momentk,
Step 2 eight, the posteriori error covariance matrix for calculating the k moment,
Step 2 nine, the state Posterior estimator vector for calculating the k momentThat is attitude quaternion,
Then it is normalized, then by the attitude quaternion after extended Kalman filter output normalization, completes posture
Estimation.
Preferably, in step 2 four,
Wherein, ΣaFor acceleration of gravity scalar g measurement noise covariance matrix,For acceleration of gravity scalar g's
Measure the variance of noise.
Three axis accelerometer output when preferably, using rotor craft hovering flight is made an uproar to calculate the measurement of each axis
The variance of sound.
Preferably, in step 2 two,
Wherein, wg,kFor the process noise of system,qkFor the k moment
The vector section of attitude quaternion, q4,kFor the scalar component of k moment attitude quaternion, ΣgThe association side of noise is measured for gyroscope
Poor matrix,The variance of noise is measured for gyroscope.
Preferably, step 1 is specially：
The mould of carrier aircraft angular velocity vector is obtained according to the output of three-axis gyroscope | | ω | |, judgement | | ω | | whether it is greater than pre-
First given angular speed threshold value δω, if it is judged that be it is yes, then extended Kalman filter is placed in gyroscopic mode；
Otherwise further judge that three axis accelerometer measures the mould of vector Acc | | Acc | | the difference with acceleration of gravity scalar g
Value | | Acc | |-g | whether it is less than previously given acceleration rate threshold δgAnd three axis accelerometer measures the change rate of vector
MouldWhether rate of acceleration change threshold value is less thanIf it is judged that being to be, then extended Kalman filter is placed in
Balanced mode；
Otherwise further judge the new breath vector of extended Kalman filter feedbackMouldWhether it is greater than and gives in advance
Fixed new breath threshold value δr, if it is judged that be it is yes, then extended Kalman filter is placed in gyroscopic mode, otherwise spreading kalman
Filter is placed in acceleration mode.
Multi-mode rotor craft Attitude estimation method based on extended Kalman filter of the invention can be neatly
Switching filtering operation mode, Attitude estimation precision is high, the strong antijamming capability in wind disturbance, especially under acceleration conditions,
Precision is with the obvious advantage compared to existing method.
Detailed description of the invention
Fig. 1 is the schematic diagram for the decision tree for aircraft flight state of classifying；
Fig. 2 is the functional block diagram of multi-mode attitude estimator；
Fig. 3 is the pitch angle result figure that the distinct methods that emulation obtains obtain；
Fig. 4 is the roll angle result figure that the distinct methods that emulation obtains obtain；
Fig. 5 is the pitch angle result figure that the distinct methods that experiment obtains obtain；
Fig. 6 is the roll angle result figure that the distinct methods that experiment obtains obtain.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art without creative labor it is obtained it is all its
His embodiment, shall fall within the protection scope of the present invention.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.
The present invention will be further explained below with reference to the attached drawings and specific examples, but not as the limitation of the invention.
Multi-mode rotor craft Attitude estimation method based on extended Kalman filter, this method include:
Step 1: the new breath fed back according to the output of three-axis gyroscope and three axis accelerometer, extended Kalman filter
Vector makes extended Kalman filter be in different operating modes；
Using decision tree, will be flown according to the new breath vector of sensor output and extended Kalman filter (EKF) feedback
State is classified, and devises suitable filter patterns for each state of flight.
As shown in Figure 1, firstly, the mould of the carrier aircraft angular velocity vector obtained according to three-axis gyroscope measurement | | ω | |, judgement
Whether carrier aircraft attitude is quickly changing, if carrier aircraft is carrying out attitude maneuver, i.e., | | ω | | greater than previously given angle speed
Spend threshold value δω, then extended Kalman filter is placed in gyroscopic mode (G mode).If carrier aircraft angular speed is little, further
Judge that three axis accelerometer measures the mould of vector Acc | | Acc | | the difference with acceleration of gravity scalar g | | Acc | |-g | and three axis
The change rate of accelerometer measurement vectorMouldIf both they are respectively smaller than previously given acceleration threshold
Value δgWith rate of acceleration change threshold valueThen think that carrier aircraft is in hovering or the state that flies at a constant speed, by extended Kalman filter
It is placed in balanced mode (Balance mode, B-mode).If the two has at least one to be greater than threshold value, expansion card is further judged
The new breath vector (innovation vector) of Thalmann filterMouldIf it is greater than previously given new breath threshold value δr,
Then show to measure vector actual value and estimated value gap is larger, aircraft has been most likely subject to external disturbance (disturbing as air-dried), adds
Speedometer measurement model is no longer applicable in, and an integrating gyroscope measured value seeks posture, and extended Kalman filter is placed in gyroscopic mode
(Gyroscope mode, G mode).If newly breath vector mould it is smaller, then it is assumed that aircraft be in without external interference have compared with
In-flight, extended Kalman filter is placed in acceleration mode (Acceleration mode, mode A) to big acceleration of motion.
Multi-mode posture of the multi-mode rotor craft Attitude estimation method based on Fig. 2 based on extended Kalman filter
Estimator is realized；As shown in Fig. 2, decision tree is in hub location in multi-mode attitude estimator, by reading sensor number
State of flight classification is carried out according to the new breath vector fed back with EKF, and by changing two two-value filter state parameter ξkAnd σk(such as
Table 1) change the mode of EKF.Since three axis accelerometer measures the change rate of vectorSeek need to acceleration measuring
Magnitude does discrete time difference, measures noise and is amplified, thus using low-pass filter carry out noise reduction obtain it is filtered
I.e.
The working principle of extended Kalman filter：
Core of the invention content is the extended Kalman filter with multiple filter patterns.The state vector X of EKF is
Attitude quaternion is defined as
X=[q1 q2 q3 q4]T
Wherein q=[q1 q2 q3]TFor the vector section of quaternary number, q4For the scalar component of quaternary number.
The state of time discrete more new model is：
Xk+1=ΦkXk+wg,k
Wherein,
Wherein, Δ t is the sampling period；I3×3Indicate the unit matrix of 3 rows 3 column；ωkIt is obtained for k moment gyroscope measurement
With noisy angular speed；wg,kFor the process noise of system, noise v is measured by gyroscopeg,kWith square relevant to current pose
Battle array Ξg,kIt determines；qkFor the vector section of k moment attitude quaternion；vg,kIt is the three-dimensional noise vector of three-axis gyroscope.For reality
Existing Extended Kalman filter, needs to set process noise matrix QkValue, QkValue represent the confidence level to system model.?
In process model proposed by the present invention, QkRelated to the noise characteristic of gyroscope and k moment posture, gyroscope measures the association of noise
Variance matrix isWhereinThe variance of noise is measured for gyroscope, then QkIt is represented by
The measurement equation of extended Kalman filter is
Zk=h (Xk)+va,k
Wherein ZkIndicate k moment three-dimensional measuring vector, XkIndicate k moment state vector, h indicates to measure function, va,kIt indicates
Measurement noise vector relevant to accelerometer measurement noise and filter patterns, it is believed that the noise is Gaussian noise.
Multi-mode extended Kalman filter of the invention has different measurement vector sums under different filter patterns
Measurement model.The measurement vector of balanced mode (B-mode) is ZB,k=Acc=[Accx Accy Accz]T, Acc indicate three axis add
The original measurement vector of speedometer measures function hBFor
Measurement matrix HBFor
Wherein,Indicate k moment state prior estimate vector.
In the case where accelerating mode (mode A), the difference of acceleration measuring magnitude is introduced into measurement vector, under the conditions of discrete time
Measurement vector definition be
Wherein, μ is the linear resistance coefficient of rotor craft, and physical significance is flying speed and body coordinate system horizontal plane
The linear ratio relation of interior acceleration measuring magnitude, can be by acquiring flying speed true value and acceleration measuring in flight experiment
Magnitude calculation obtains.
In order to facilitate pattern switching, the measurement vector Z of mode will be acceleratedA,kThe significant trivector of bidimensional before being designed as.
Measure function h (Xk) form is similar with balanced mode, third dimension zero setting
Similarly, measurement matrix HAIt is defined as
Since gyroscopic mode (G mode) does not use the measurement information of accelerometer, therefore without corresponding measurement model, measure
Model is 0 matrix.
Such as table 1, by the judgement of decision tree, EKF is determined, and then determines filter state parameter σkAnd ξkValue, σkWith
ξkThe switching to filter patterns can be realized by changing matrix form.
1 extended Kalman filter mode of table and filter state parameter corresponding relationship
| Filter mode | Filter parameter |
| Gyroscopic mode | σk=0, ξk=0 |
| Acceleration mode | σk=1, ξk=0 |
| Balanced mode | σk=1, ξk=1 |
In present embodiment, the amount side noise that two amounts surveys model is approximately Gaussian noise, is represented to metric data confidence
The measurement noise covariance matrix of degree is defined as：In addition to using low-pass filter to reduce acceleration measuring magnitude
Contained noise, the vibration isolation measure that in-flight vibration of carrier aircraft body and carrier aircraft may use, can all make acceleration measuring
The data that the real noise characteristic of magnitude is provided with sensing data handbook differ widely.Therefore flown by acquisition aircraft hovering
Three axis accelerometer when row exports to calculate the variance of the measurement noise of each axis
Based on above-mentioned process model and measurement model, step 2 is carried out；
Step 2: extended Kalman filter estimates rotor craft posture, Attitude estimation result is obtained.
Step 2 one, the state prior estimate vector that the K moment is calculated according to state renewal equation
Wherein,For the state Posterior estimator vector at k-1 moment,
Δ t is sampling period, ωkFor k moment gyroscope measurement obtain with noisy angular speed,
Step 2 two, the prior uncertainty covariance matrix for calculating the k moment
Wherein, Pk-1For the posteriori error covariance matrix at k-1 moment, QkFor process noise matrix；
Step 2 three, the measurement matrix H for calculating the k momentk,
Step 2 four, the kalman gain matrix K for calculating the k momentk,
Wherein R is to measure noise matrix；
Step 2 five, the measurement vector Z for calculating the k momentk,
Wherein, Accx,k、Accy,kAnd Accz,kThe respectively measured value of k moment three axis accelerometer x-axis, y-axis and z-axis, μ
For the linear resistance coefficient of rotor craft；
Step 2 six, the measurement vector estimated value for calculating the k moment
Step 2 seven, the new breath vector r for calculating the k momentk,
Step 2 eight, the posteriori error covariance matrix for calculating the k moment,
Step 2 nine, the state Posterior estimator vector for calculating the k momentThat is attitude quaternion,
Then it is normalized, then by the attitude quaternion after extended Kalman filter output normalization, completes posture
Estimation.
In order to indicate that effective rotation relationship, the mould of attitude quaternion are necessary for 1.Although state-transition matrix ΦkIt is just
Hand over, but the unit norm property of posteriority quaternary number must be guaranteed by normalization step, therefore pass through byDivided by it
Euclideam normTo realize normalization.
Verifying of this method by emulation data and practical flight data, shows excellent performance.
As shown in Figure 3 and Figure 4, fine line represent wind speed (for unit as m/s), wide chain-dotted line indicates posture true value (angle),
Narrow chain-dotted line indicates the Attitude estimation value (angle) of the existing EKF based on gravitational vectors observation model, and dotted line indicates non-linear
The Attitude estimation value (angle) of complementary filter, heavy line indicate the Attitude estimation value (angle) that method of the invention obtains, and put table
Show that (degree is that 10 extended Kalman filters are in G mould to each moment multi-mode extended Kalman filter state in which
Formula, degree are that 20 expression extended Kalman filters are in mode A, and degree is 30 expression B-modes).
In emulation, Eulerian angles that method proposed by the present invention estimation obtains are than existing algorithm closer to true value.Work as rotor
For aircraft when hovering or with constant speed movement (the about the 8th second and the 22nd second), extended Kalman filter is in B-mode.
When attitudes vibration quickly when (the 15th second and the 40th second front and back), extended Kalman filter is in G mode.Just complete posture
In the motor-driven translational acceleration significant period, such as the 4th second and the 44th second front and back, extended Kalman filter are switched to A mould
Formula.When there is wind disturbance within the 45th second, conventional filter error is larger.The variation of wind velocity gradient makes newly to cease vector displaced from zero,
Therefore extended Kalman filter is switched to G mode by decision tree, to avoid insecure acceleration measuring magnitude is introduced.
As shown in Figure 5 and Figure 6, chain-dotted line is the posture true value (angle) measured by automatic Optic Motion Capture System, and solid line is
The Attitude estimation value (angle) that method proposed by the present invention obtains, point indicate each moment multi-mode extended Kalman filter institute
The state at place.Although introducing biggish measurement noise, method energy proposed by the present invention with the accelerometer of the connected installation of fuselage
It is enough quickly to change in posture, accelerate the posture for relatively accurately estimating unmanned plane under flying condition.
Claims (6)
1. the multi-mode rotor craft Attitude estimation method based on extended Kalman filter, which is characterized in that this method packet
It includes：
Step 1: the new breath vector fed back according to the output of three-axis gyroscope and three axis accelerometer, extended Kalman filter
Extended Kalman filter is set to be in different operating modes；
Step 2: different working modes correspond to different measurement models, based on corresponding measurement model Extended Kalman filter
Device estimates rotor craft posture, obtains Attitude estimation result.
2. the multi-mode rotor craft Attitude estimation method according to claim 1 based on extended Kalman filter,
It is characterized in that, step 2 includes：
Step 2 one, the state prior estimate vector that the K moment is calculated according to state renewal equation
Wherein,For the state Posterior estimator vector at k-1 moment,Δt
It is sampling period, ωkFor k moment gyroscope measurement obtain with noisy angular speed,
Step 2 two, the prior uncertainty covariance matrix for calculating the k moment
Wherein, Pk-1For the posteriori error covariance matrix at k-1 moment, QkFor process noise matrix；
Step 2 three, the measurement matrix H for calculating the k momentk,
The operating mode of extended Kalman filter includes gyroscopic mode, acceleration mode and balanced mode, σ when for gyroscopic modek
=0, ξk=0, accelerate mode when σk=1, ξk=0, σ when balanced modek=1, ξk=1；
σkFor the coefficient that accelerometer measurement information is added, ξkFor the handoff factor between balanced mode and acceleration mode；
Step 2 four, the kalman gain matrix K for calculating the k momentk,
Wherein, R is to measure noise matrix；
Step 2 five, the measurement vector Z for calculating the k momentk,
Wherein, Accx,k、Accy,kAnd Accz,kThe respectively measured value of k moment three axis accelerometer x-axis, y-axis and z-axis, μ are rotation
The linear resistance coefficient of rotor aircraft；
Step 2 six, the measurement vector estimated value for calculating the k moment
Step 2 seven, the new breath vector r for calculating the k momentk,
Step 2 eight, the posteriori error covariance matrix for calculating the k moment,
Step 2 nine, the state Posterior estimator vector for calculating the k momentThat is attitude quaternion,
Then it is normalized, then by the attitude quaternion after extended Kalman filter output normalization, completes estimating for posture
Meter.
3. the multi-mode rotor craft Attitude estimation method according to claim 2 based on extended Kalman filter,
It is characterized in that, in step 2 four,
Wherein, ΣaFor acceleration of gravity scalar g measurement noise covariance matrix,For the measurement of acceleration of gravity scalar g
The variance of noise.
4. the multi-mode rotor craft Attitude estimation method according to claim 3 based on extended Kalman filter,
It is characterized in that, three axis accelerometer when using rotor craft hovering flight exports to calculate the acceleration of gravity mark of each axis
Measure the variance of the measurement noise of g.
5. the multi-mode rotor craft Attitude estimation method according to claim 2 based on extended Kalman filter,
It is characterized in that, in step 2 two,
Wherein, wg,kFor the process noise of system,qkFor k moment posture
The vector section of quaternary number, q4,kFor the scalar component of k moment attitude quaternion, ΣgThe covariance square of noise is measured for gyroscope
Battle array, The variance of noise is measured for gyroscope.
6. the multi-mode rotor craft Attitude estimation method according to claim 1 based on extended Kalman filter,
It is characterized in that, step 1 is specially：
The mould of carrier aircraft angular velocity vector is obtained according to the output of three-axis gyroscope | | ω | |, judgement | | ω | | whether it is greater than and gives in advance
Fixed angular speed threshold value δω, if it is judged that be it is yes, then extended Kalman filter is placed in gyroscopic mode；
Otherwise further judge that three axis accelerometer measures the mould of vector Acc | | Acc | | the difference with acceleration of gravity scalar g | |
Acc | |-g | whether it is less than previously given acceleration rate threshold δgAnd three axis accelerometer measures the change rate of vectorMouldWhether rate of acceleration change threshold value is less thanIf it is judged that being to be, then extended Kalman filter is placed in balance
Mode；
Otherwise further judge the new breath vector of extended Kalman filter feedbackMouldWhether it is greater than previously given
New breath threshold value δr, if it is judged that be it is yes, then extended Kalman filter is placed in gyroscopic mode, otherwise Extended Kalman filter
Device is placed in acceleration mode.
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Inventor after: Guo Jifeng Inventor after: Bai Chengchao Inventor after: Zheng Hongxing Inventor after: Xu Yingfu Inventor before: Guo Jifeng Inventor before: Xu Yingfu Inventor before: Bai Chengchao Inventor before: Zheng Hongxing |
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Application publication date: 20181116 |