CN107014371A - UAV integrated navigation method and apparatus based on the adaptive interval Kalman of extension - Google Patents
UAV integrated navigation method and apparatus based on the adaptive interval Kalman of extension Download PDFInfo
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
Abstract
The invention discloses a kind of UAV integrated navigation method and apparatus based on the adaptive interval Kalman of extension, the present invention measures the inertial navigation parameter of unmanned plane using micro electro mechanical inertia measuring unit, and vision navigation system measures the vision guided navigation parameter of unmanned plane；Using Digital Signal Processing processing module as navigational computer, it is used as combined method fusion inertia, vision guided navigation parameter by extending adaptive Interval Kalman filter technology, so as to obtain the optimal estimation of system navigational parameter.Apparatus of the present invention are easily achieved, with low cost, and quality is smaller, and power consumption is relatively low, are adapted to the popularization of civil area；By extending adaptive Interval Kalman filter technology, the system model that can be linearized；And be that in temperature change, carrier, frequently attitudes vibration, illumination condition are undesirable so as to improve navigation system for adaptive interval model by the time-varying parameter modelling of system, or vision sensor focuses the navigator fix task under the complexity such as difficulty, severe situation.
Description
Technical field
The present invention relates to a kind of inertia/vision/ultrasonic wave based on the adaptive interval Kalman of extension for unmanned plane
Combinated navigation method and device.
Background technology
Be miniaturized with navigation system, intellectuality, autonomy-oriented, the development trend of low cost, it is and autonomous in navigation system
Change under the background that becomes increasingly conspicuous of importance, inertia/visual combination airmanship is with its good mutual supplement with each other's advantages and autonomous nature,
Through becoming one important development direction of navigation field.In the spot hover of unmanned plane, autonomous flight and landing and robot
In the task such as target recognition and tracking and environment measuring, inertia/vision guided navigation Technological expression, which goes out, can interact with external environment, move
State property can the various advantages such as good, real-time is high.In civil inertia/vision navigation system, due to device or equipment in itself
The deficiency of precision and stability, systematic parameter and noise statisticses are often with external environments such as temperature, illumination conditions
Change and significantly change.For in actual engineering specifications, system model is often nonlinear, noise statisticses
Because some conditions can be fluctuated in certain interval range, such as in temperature change, carrier frequently attitudes vibration, illumination condition
It is undesirable, or vision sensor focused under the situations such as difficulty, the measured value of inertial sensor and vision sensor is often wrapped
Random outlier even occurs containing the complex noise component(s) of composition, and the composition of noise generally also can be with external condition
Change and change.Traditional Kalman filter will be unable to the optimal estimation of acquisition system under these conditions, even
The phenomenon that can occur filter divergence and cause navigation system to be collapsed.
Researchers are general in the nonlinear problem of system model in handling Kalman filtering process, often by non-thread
Sexual system obtains system linearity equation by way of approximation method is linearized, and this kind of method mainly includes spreading kalman and filtered
Ripple, Unscented kalman filtering etc..Expanded Kalman filtration algorithm is near system function characteristic point by way of Taylor expansion
Eliminate system model higher order term to obtain system linearity model, effectively increase the robustness and filtering essence of wave filter
Degree.But when systematic parameter or noise statisticses time-varying or it is imprecise known when, EKF is helpless to this.
Interval Kalman Filtering Algorithm is on the basis of compartmention, by being area by the uncertainties model of system or noise parameter
Between model a kind of processing can not accurately to system or noise modeling when Robust filtering algorithms.Interval Kalman Filtering Algorithm exists
Do not increase can effectively reduce on the basis of extra computation amount model it is inaccurate under the conditions of the influence that is caused to wave filter.
The content of the invention
The technology of the present invention solves problem：Kalman Filter Technology is expanded and merged, with reference to spreading kalman
The advantage of method and interval Kalman's two kinds of filtering algorithms of method, establishes the novel compositions based on inertia/vision/ultrasonic wave and leads
Boat method.This method effectively overcomes conventional method due to navigation accuracy under the influence of nonlinear system model and uncertain factor
And the shortcoming of robustness, obtain that confidence level is higher, information more complete navigational parameter, effectively increase navigation system unknown
Stability and adaptability in environment, enhances the real-time of navigation system, can make that carrier complete process is more complicated, environment more
Severe navigation task.
The present invention to achieve the above object, is adopted the following technical scheme that：
A kind of UAV integrated navigation method based on the adaptive interval Kalman of extension, including：
Velocity information and positional information of the unmanned plane under navigational coordinate system are obtained by inertial navigation, integrated navigation is used as
The status information of parameter；By vision guided navigation obtain unmanned plane under camera coordinate system in the horizontal direction on optical flow velocity
Information and flying height information, are used as the observation information of integrated navigation parameter；
Nonlinear system is linearized by EKF, and utilizes adaptive Interval Kalman filter by time-varying
Systematic parameter be modeled as adaptive interval model, build integrated navigation Filtering Model, fusion inertia, vision guided navigation parameter,
Optimal estimation is made to system navigational parameter.
Preferably, the integrated navigation Filtering Model is：
Wherein,WithThe respectively optimal estimation and prior estimate of k moment system mode vectors,During for k-1
The optimal estimation of etching system state vector；KkFor k moment Kalman filtering gains；With PkRespectively k moment one-step prediction is missed
Poor variance matrix and estimation error variance matrix, Pk-1It is then the estimation error variance matrix at k-1 moment；F represents system mode letter
Number；BkFor the control allocation matrix of system；UkFor the dominant vector of system；AkFor the Jacobian matrix of state equation；HkFor observation
The Jacobian matrix of equation；WkFor state-noise allocation matrix；VkFor observation noise allocation matrix；ZkFor observation vector；WithThe respectively interval model of state-noise matrix and observation noise matrix；Subscript I identifies for interval matrix, represents the matrix
Vector is interval matrix vector；When expression observation noise is zero, on prior estimateSystematic observation function.
Preferably,WithIt is expressed as：
Wherein, Δ QkWith Δ RkThe respectively excursion of state-noise matrix and observation noise matrix；
By calculating residual errorActual measurement varianceWith theoretical varianceThe ratio between the mark of matrixMake and commenting come the accuracy to Filtering Model
Valency；When model estimated bias is larger, according to Δ Rk=(τ e|ε-1|- 1) C corrects observation noise, wherein, akFor smoothing factor,
τ is the scale factor of auto-adaptive function, and C is the Criterion-matrix of dynamic observation noise.
Preferably, system mode vector and observation vector are respectively：
Zk=[vx vy Zc]T
Wherein, Xn,Yn,ZnRepresent the unmanned plane obtained by inertial navigation under navigational coordinate system along x, y, z tri- respectively
The position of axle,Represent the unmanned plane obtained by inertial navigation under navigational coordinate system along x, y, z tri- respectively
The speed of axle；vx,vyThe unmanned plane that expression is obtained by vision guided navigation respectively is along x under camera coordinate system, and two axles of y are water
Square upward optical flow velocity information, ZcRepresent the flying height of unmanned plane.
Preferably, the Jacobian matrix of state equation and observational equation is respectively
Wherein, dt is the state update cycle；L is the focal length of vision sensor in vision guided navigation.
A kind of UAV integrated navigation dress based on the adaptive interval Kalman of extension of use combinations thereof air navigation aid
Put, including MEMS Inertial Measurement Unit, ultrasonic sensor, vision sensor and digital signal processing module；It is described
MEMS Inertial Measurement Unit is used for the angular speed and linear velocity information for obtaining carrier；The vision sensor is used to obtain
Image information around carrier；The ultrasonic sensor is used to obtain unmanned plane during flying elevation information；The Digital Signal Processing
Module, including：Inertial navigation resolves unit, and the appearance of unmanned plane is obtained for being resolved according to carrier angular speed and linear velocity information
State, speed and positional information；Vision guided navigation solving unit, is obtained for being resolved according to image information around carrier and flying height
The velocity information of unmanned plane in the horizontal direction；And filtering algorithm unit, for according to acquired navigational parameter combination group
Close Navigation model execution extension adaptive Interval Kalman Filtering Algorithm and optimal estimation is made to system navigational parameter.
Preferably, the MEMS Inertial Measurement Unit uses model MPU9250 sensor, include in it
Three axis accelerometer, three axis accelerometer, three axle magnetometer；The digital signal processing module is using model TMS320C5509's
DSP；The vision sensor model MT9V034；The ultrasonic sensor model MB1043.
A kind of inertia/vision/ultrasound based on the unmanned plane for extending adaptive Interval Kalman filter proposed by the present invention
Ripple Combinated navigation method and device, wherein new filtering method has merged EKF and Interval Kalman filter two
The advantage of person, mainly solves two problems, one is being by spreading kalman method by nonlinear system model
The linear model of system；The second is the interval model of state-noise and observation noise is set up by compartmention, and by adaptive
Method dynamically adjusts interval excursion, to reduce influence of the noise statisticses change to filter effect.It is of the invention and existing
Have the advantages that technology is compared to be：
(1) the Inertial Measurement Unit MPU9250 based on MEMS, vision sensor MT9V034, ultrasound applied to unmanned plane
Wave sensor MB1043, digital signal processing module TMS320C5509 combined navigation device are easily achieved, with low cost, matter
Amount is smaller, and power consumption is relatively low, is adapted to the popularization of civil area；
(2) advantage of two kinds of filtering algorithms of spreading kalman and interval Kalman has been merged, not only can be to nonlinear system
System is filtered noise reduction, can also be handled the uncertain method by interval modeling of systematic parameter；
(3) the Complex Modeling process optimized for particular surroundings is avoided, the noise statisticses of system are described as
The interval model of fluctuation in certain limit, and interval scope is dynamically changed by adaptive method, it is a kind of dynamic filter
Ripple algorithm, it is to avoid various uncertain or random influences of the outlier to algorithm, can effectively improve navigation system to uncertain factor
Adaptability so that frequently attitudes vibration, illumination condition are undesirable in temperature change, carrier, or vision sensor without
There is provided that precision is higher, the more structurally sound navigator fix service of data under the situations such as legal Jiao.
Brief description of the drawings
Fig. 1 is the integrated navigation system principle schematic for unmanned plane of the invention；
Fig. 2 is vision guided navigation parameter computation model schematic diagram of the invention.
Embodiment
With reference to specific embodiment, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate the present invention
Rather than limitation the scope of the present invention, after the present invention has been read, various equivalences of the those skilled in the art to the present invention
The modification of form falls within the application appended claims limited range.
A kind of UAV integrated navigation method based on the adaptive interval Kalman of extension is disclosed in the embodiment of the present invention：
Velocity information and positional information of the unmanned plane under navigational coordinate system are obtained by inertial navigation, the shape of integrated navigation parameter is used as
State information；By vision guided navigation obtain unmanned plane under navigational coordinate system in the horizontal direction on velocity information and flying height
Information, is used as the observation information of integrated navigation parameter；Nonlinear system is linearized by EKF, and using certainly
Adapt to Interval Kalman filter and the systematic parameter of time-varying is modeled as to adaptive interval model, build integrated navigation filtering mould
Type, fusion inertia, vision guided navigation parameter, optimal estimation is made to system navigational parameter.
As shown in figure 1, a kind of unmanned plane combination based on the adaptive interval Kalman of extension disclosed in the embodiment of the present invention
Guider, including MEMS Inertial Measurement Unit, ultrasonic sensor, vision sensor and Digital Signal Processing mould
Block；Digital signal processing module includes：Inertial navigation resolves unit, vision guided navigation solving unit, and filtering algorithm unit.This
MEMS Inertial Measurement Unit uses model MPU9250 sensor in example, and it is interior including three axis accelerometer, and three axles accelerate
Degree meter, three axle magnetometer, digital signal processing module uses model TMS320C5509 DSP, vision sensor model
MT9V034, ultrasonic sensor model MB1043.The Combinated navigation method for unmanned plane of the present invention is first with micro electronmechanical
System inertia measuring unit obtains the angular movement of unmanned plane and line movement rate passes through spreading kalman method linearized system again
Model, so as to calculate the status information of the system navigational parameter of unmanned plane；Reuse vision sensor and obtain the unmanned plane external world
The image information of environment, the elevation information provided by pin-hole imaging model and with reference to ultrasonic sensor calculates unmanned plane
Vision guided navigation parameter；Finally using adaptive interval modeling method, on-line tuning state-noise and observation noise, and merge and be
Unite navigational parameter status information and observation information, obtain the optimal estimation of system navigational parameter, thus realize temperature change,
Frequently attitudes vibration, illumination condition are undesirable for carrier, or vision sensor is focused under the complexity such as difficulty, severe situation
Navigator fix task.Comprise the following steps that：
(1) angular movement and the line movement rate of unmanned plane are obtained using MEMS Inertial Measurement Unit, is thus set up
The mathematical computations platform of inertial navigation parameter based on expanded Kalman filtration algorithm, so as to calculate the system navigation of unmanned plane
The status information (posture, speed, position) of parameter.By being sampled to gyroscope, the turning rate fortune of unmanned plane is obtained
Dynamic information, and the attitude information of unmanned plane is resolved using Quaternion Algorithm, specific method is as follows：
Note q represents rotation relation of the carrier coordinate system (b systems) relative to navigational coordinate system (n systems) to rotate quaternary number,
Then have using quaternary number multiplication and in view of quaternion differential equation：
WhereinIt is angular velocity vector of the carrier system relative to navigation system, q*For quaternary number q conjugate complex number.By above formula
The form of matrix is expanded into, and is deployed, is had：
WhereinAngular speed of the carrier system relative to navigation system is represented in x, y, the component in z-axis, q0,q1,q2,q3Respectively
For quaternary number q four-dimensional component (q=q0+q1i+q2j+q3k)。
Quaternary number is tied to the attitude matrix of navigation system with carrierThere is transformation relation, have：
So, carrier system can be described as relative to attitude vectors of the navigation system comprising three attitude angles：
The differential equation that speed updates can be expressed as：
Wherein fbTo pass through the specific force sampled to accelerometer, gnFor the acceleration of gravity under navigational coordinate system.By formula
(5) deploy in three directions, have：
WhereinRepresent respectively unmanned plane in navigational coordinate system speed along x, y, the component of tri- axles of z；For three components of rotational-angular velocity of the earth；For the position speed of carrier, by carrier
Determined in the position of navigational coordinate system.
The position differential equation of unmanned plane is represented by
Wherein λ, L, h represent the longitude of unmanned plane current location, dimension, height respectively；RM,RNRespectively along the earth fourth of the twelve Earthly Branches tenth of the twelve Earthly Branches
The radius of curvature of circle and meridian circle, and the three-dimensional velocity of carrier can then be obtained by the speed more new algorithm described before.
(2) image information of unmanned plane external environment is obtained using vision sensor, by the method for Gray-scale Matching, is seen
Examine, calculate apparent motion in different frame objects in images grayscale mode, therefrom extract the vision guided navigation parameter of object, and combine
Ultrasonic wave calculate unmanned plane during flying elevation information as system navigational parameter observation information.Specific method is as follows：
Pin-hole imaging model as shown in Figure 2 is considered, if Pc=[Xc,Yc,Zc]TIt is in vision sensor coordinate system FcUnder
One point, then this point p=[x, y, l] is expressed as under vision sensor imaging planeT, wherein l represents vision sensor
Focal length.Unmanned plane is in flight course, point PcThere is translational motion and rotary motion relative to vision sensor, then point Pc
Relative to FcMotion can be expressed as
V=-T- ω × Pc (8)
T in formula, ω are respectively point PcLine motion and angular movement.To formula both sides differential and arrange can obtain light stream vector
vx、vyIt is component of the light stream vector on x directions and y directions, can be by the side of Block- matching minimal error definitely sum
Method is calculated；Z in formulacIt can be drawn by ultrasonic sensor；Magnitude of angular velocity ω in formulax、ωy、ωz, can be by gyro
Instrument is drawn.Unmanned plane can be just calculated in the horizontal direction under navigational coordinate system thus according to formula (9) and formula (10)
Speed.
(3) uncertainty of state equation and the noise statisticses of observational equation is built as interval mould using compartmention
Type, and interval scope is dynamically adjusted using adaptive model, pass through kalman filter method emerging system again on this basis
The status information and observation information of navigational parameter, so as to obtain the optimal estimation of system navigational parameter.Specially：
If the state equation and observational equation of system are respectively：
Xk=f [Xk-1,k]+BkUk+WkQk (11)
Zk=h [Xk,k]+VkRk (12)
In formula：
It is state vector, it includes unmanned plane under navigational coordinate system
Velocity information and positional information, wherein Xn,Yn,ZnRepresent unmanned plane under navigational coordinate system along x, y, the position of tri- axles of z respectively
Confidence ceases,Represent unmanned plane under navigational coordinate system along x, y, the velocity information of tri- axles of z respectively.
Zk=[vx vy Zc]TIt is observation vector, vx,vyRepresent unmanned plane under camera coordinate system along x, y two respectively
Axle is the optical flow velocity information in horizontal direction, ZcThe reading for representing ultrasonic sensor is the flying height information of unmanned plane.
It is the dominant vector of system, coordinate system is passed through by the numerical value of accelerometer
Matrixing draws,Wherein represent respectively under navigational coordinate system along x, y, the unmanned plane acceleration letter of tri- axles of z
Breath.
Bk=[dt of 0 dt, 0 dt 0] is control allocation matrix, and dt is sampling time interval.
Wk=[1 1111 1] are state-noise allocation matrixs.
Qk=diag [dx dvx dy dvy dz dvz] ^2 is state-noise matrix, dx, dvx, dy, dvy, dz, dvz points
It is not the standard deviation of each state-noise, they are corresponded with the element in state vector.
Vk=[1 1 1] are observation noise allocation matrixs.
Rk=diag [dfx dfy dsz] ^2 is measurement noise matrix, and dfx, dfy, dsz are the mark of each measurement noise respectively
Accurate poor, they are corresponded with the element in observation vector.
f[Xk-1, k] to inscribe when representing k, system is on Xk-1The function of state of this point.
h[Xk, k] to inscribe when representing k, system is on XkThe observation function of this point.
It can be passed through according to the inertial navigation accounting equation and vision guided navigation accounting equation obtained by step (1) and step (2)
The method of Taylor expansion obtains the Jacobian matrix of state model and observation model.
For the Jacobian matrix of state equation；
For the Jacobian matrix of observational equation.
Assuming that state-noise battle array and observation noise battle array change within the specific limits：
In formulaWithThe respectively interval model of state-noise matrix and observation noise matrix, Δ QkWith Δ RkIt is to make an uproar
The excursion of sound matrix.Referred to as residual vector, it represents the accuracy of Kalman filter model.Interval mould in the present invention
The adaptive realization principle of type is the actual measurement variance N by calculating residual errorkWith theoretical variance TkThe ratio between the mark of matrix ε
Made an appraisal come the accuracy to Filtering Model.When model is accurate, ε should be near 1；The ε when model estimated bias is larger
1 is will deviate from, observation noise now should be suitably corrected.akFor smoothing factor, in order to weaken noise jamming and measured to actual
Variance NkInfluence, make data more smooth.τ is that the scale factor C of auto-adaptive function is the Criterion-matrix of dynamic observation noise.
So complete integrated navigation Filtering Model can be represented by formula (18)-formula (22)：
Wherein,WithThe respectively optimal estimation and prior estimate of k moment system mode vectors,During for k-1
The optimal estimation of etching system state vector；KkFor k moment Kalman filtering gains；With PkRespectively k moment one-step prediction is missed
Poor variance matrix and estimation error variance matrix, Pk-1It is then the estimation error variance matrix at k-1 moment；F represents system mode letter
Number；BkIt is the control allocation matrix of system；UkIt is the dominant vector of system；AkFor the Jacobian matrix of state equation；HkFor observation
The Jacobian matrix of equation；WkIt is state-noise allocation matrix；VkIt is observation noise allocation matrix；ZkIt is observation vector；WithThe respectively interval model of state-noise matrix and observation noise matrix；Subscript I identifies for interval matrix, represents the matrix
Vector is interval matrix vector；When expression observation noise is zero, on prior estimateSystematic observation function.
The Combinated navigation method of the present invention, fusion EKF and adaptive Interval Kalman filter scheduling algorithm are excellent
Point, the Kalman integrated navigation model more single than tradition has stronger robustness and environmental suitability, can obtain system shape
The optimal estimation of state.
Claims (10)
1. a kind of UAV integrated navigation method based on the adaptive interval Kalman of extension, it is characterised in that including：
Velocity information and positional information of the unmanned plane under navigational coordinate system are obtained by inertial navigation, integrated navigation parameter is used as
Status information；By vision guided navigation obtain unmanned plane under camera coordinate system in the horizontal direction on optical flow velocity information and
Flying height information, is used as the observation information of integrated navigation parameter；
Nonlinear system is linearized by EKF, and is by time-varying using adaptive Interval Kalman filter
System parameter model is adaptive interval model, builds integrated navigation Filtering Model, fusion inertia, vision guided navigation parameter, to being
System navigational parameter makes optimal estimation.
2. a kind of UAV integrated navigation method based on the adaptive interval Kalman of extension according to claim 1, its
It is characterised by, the integrated navigation Filtering Model is：
Wherein,WithThe respectively optimal estimation and prior estimate of k moment system mode vectors,It is for the k-1 moment
The optimal estimation for state vector of uniting；KkFor k moment Kalman filtering gains；With PkRespectively k moment one-step predictions error side
Poor matrix and estimation error variance matrix, Pk-1It is then the estimation error variance matrix at k-1 moment；F represents system function of state；
BkFor the control allocation matrix of system；UkFor the dominant vector of system；AkFor the Jacobian matrix of state equation；HkFor observation side
The Jacobian matrix of journey；WkFor state-noise allocation matrix；VkFor observation noise allocation matrix；ZkFor observation vector；With
The respectively interval model of state-noise matrix and observation noise matrix；Subscript I identifies for interval matrix, represents the matrix-vector
For interval matrix vector；When expression observation noise is zero, on prior estimateSystematic observation function.
3. a kind of UAV integrated navigation method based on the adaptive interval Kalman of extension according to claim 2, its
It is characterised by,WithIt is expressed as：
Wherein, Δ QkWith Δ RkThe respectively excursion of state-noise matrix and observation noise matrix；
By calculating residual errorActual measurement varianceWith theoretical varianceThe ratio between the mark of matrixMade come the accuracy to Filtering Model
Evaluate；When model estimated bias is larger, according to Δ Rk=(τ e|ε-1|- 1) RC corrects observation noise, wherein, akFor it is smooth because
Son, τ is the scale factor of auto-adaptive function, and C is the Criterion-matrix of dynamic observation noise.
4. a kind of UAV integrated navigation method based on the adaptive interval Kalman of extension according to claim 2, its
It is characterised by, system mode vector and observation vector are respectively：
Zk=[vx vy Zc]T
Wherein, Xn,Yn,ZnRepresent respectively the unmanned plane that is obtained by inertial navigation under navigational coordinate system along x, y, tri- axles of z
Position,Represent respectively the unmanned plane that is obtained by inertial navigation under navigational coordinate system along x, y, tri- axles of z
Speed；vx,vyThe unmanned plane that expression is obtained by vision guided navigation respectively is along x under camera coordinate system, and two axles of y are level side
Upward optical flow velocity information, ZcRepresent the flying height of unmanned plane.
5. a kind of UAV integrated navigation method based on the adaptive interval Kalman of extension according to claim 4, its
It is characterised by, the Jacobian matrix of state equation and observational equation is respectively
Wherein, dt is the state update cycle；L be vision guided navigation in vision sensor focal length.
6. a kind of use according to any one of the claim 1-5 Combinated navigation methods based on the adaptive interval Kalman of extension
UAV integrated navigation device, it is characterised in that：Including MEMS Inertial Measurement Unit, ultrasonic sensor, vision
Sensor and digital signal processing module；
The MEMS Inertial Measurement Unit is used for the angular speed and linear velocity information for obtaining carrier；The vision sensor
For obtaining image information around carrier；The ultrasonic sensor is used to obtain unmanned plane during flying elevation information；
The digital signal processing module, including：
Inertial navigation resolves unit, and posture, the speed of unmanned plane are obtained for being resolved according to carrier angular speed and linear velocity information
And positional information；
Vision guided navigation solving unit, unmanned plane is obtained in level side for being resolved according to image information around carrier and flying height
Upward velocity information；
And filtering algorithm unit, it is adaptive for performing extension according to acquired navigational parameter combination integrated navigation Filtering Model
Interval Kalman Filtering Algorithm is answered to make optimal estimation to system navigational parameter.
7. a kind of UAV integrated navigation device based on the adaptive interval Kalman of extension according to claim 6, its
It is characterised by, the MEMS Inertial Measurement Unit uses model MPU9250 sensor, it is interior including three axle tops
Spiral shell, three axis accelerometer, three axle magnetometer.
8. a kind of UAV integrated navigation device based on the adaptive interval Kalman of extension according to claim 6, its
It is characterised by, the digital signal processing module uses model TMS320C5509 DSP.
9. a kind of UAV integrated navigation device based on the adaptive interval Kalman of extension according to claim 6, its
It is characterised by the vision sensor model MT9V034.
10. a kind of UAV integrated navigation device based on the adaptive interval Kalman of extension according to claim 6, its
It is characterised by, the ultrasonic sensor model MB1043.
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