CN109001787A - A kind of method and its merge sensor of solving of attitude and positioning - Google Patents

A kind of method and its merge sensor of solving of attitude and positioning Download PDF

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CN109001787A
CN109001787A CN201810517173.9A CN201810517173A CN109001787A CN 109001787 A CN109001787 A CN 109001787A CN 201810517173 A CN201810517173 A CN 201810517173A CN 109001787 A CN109001787 A CN 109001787A
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information
gyroscope
data
fusion
attitude angle
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CN109001787B (en
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时广轶
黄亚华
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Peking University Shenzhen Graduate School
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Peking University Shenzhen Graduate School
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments

Abstract

A kind of method and its merge sensor of solving of attitude and positioning, the merge sensor include multiple IMU sensors, a magnetometer and GPS；This method comprises: correction magnetometer, accelerometer and gyroscope；It is utilized respectively redundancy blending algorithm and data fusion is carried out to the measurement data of each accelerometer and each gyroscope, obtain acceleration information and angular velocity information；Fusion attitude angle is obtained using the Magnetic Field of expanded Kalman filtration algorithm fusion acceleration information, angular velocity information and magnetometer；Location information is obtained to merge attitude angle auxiliary positioning information.Due to using more redundancy IMU sensors to carry out DATA REASONING and data fusion calculation, and each data being merged using expanded Kalman filtration algorithm and obtain fusion attitude angle, the attitude angle of degree of precision can be obtained under static and dynamic condition；The location information to be maked corrections simultaneously using the fusion attitude angle assistant GPS, is kept location information more accurate, improves the stability and reliability of solving of attitude and positioning.

Description

A kind of method and its merge sensor of solving of attitude and positioning
Technical field
The present invention relates to integrated navigation technology fields, and in particular to a kind of solving of attitude and the method for positioning and its merges Sensor.
Background technique
1970s rise, and airmanship obtains swift and violent development, with the development of information technology and economic construction, The fields such as communications and transportation, industry, agricultural, mapping propose more diversity and higher requirement to airmanship.
Currently, airmanship determine appearance and positioning in terms of, what is be most widely used is integrated navigation technology.Integrated navigation Refer to and utilizes GPS (Global Positioning System, global positioning system), radionavigation, celestial navigation, satellite One or several and INS (Inertial Navigation System, inertial navigation system) combination in the systems such as navigation exists Together, the integrated navigation system of formation.INS be using gyroscope and accelerometer as the navigational parameter resolving system of Sensitive Apparatus, The system establishes navigational coordinate system according to the output of gyroscope, calculates carrier in navigational coordinate system according to accelerometer output In speed and position.
In existing integrated navigation technology, the combination of GPS and INS are one of most common modes.But existing GPS and The integrated navigation technology of INS depend heavilys on GPS signal, and the concealment of GPS signal is very poor, and signal is easily disturbed and hides It covers, so as to cause the considerable decrease of navigation accuracy；Also, the work of GPS receiver is influenced by the motor-driven of aircraft, works as flight When the motor-driven dynamic range more than GPS receiver of device, receiver can losing lock and cannot capture and tracking satellite signal, thus nothing Method works, in this way, to the higher occasion of reliability requirement, GPS navigation will receive limitation.Meanwhile in existing GPS and In the integrated navigation technology of INS, INS be using gyroscope and accelerometer as Sensitive Apparatus, using complementary filter blending algorithm into Row solving of attitude, computation complexity is higher, and the error of the attitude angle obtained in a dynamic condition is larger.
Therefore, when carrying out the resolving and positioning of attitude angle using existing integrated navigation technology, the attitude angle information of acquisition Not high with the precision of location information, stability and the reliability for determining appearance and positioning are lower.
Summary of the invention
The application provides the method and its merge sensor of a kind of solving of attitude and positioning, steady with height to obtain high-precision Qualitative attitude angle information and location information, improve the stability and reliability of solving of attitude and positioning.
According in a first aspect, the method for a kind of solving of attitude and positioning is provided in a kind of embodiment, applied to attitude angle It resolving with the merge sensor of positioning, the merge sensor contains at least two IMU sensor, a magnetometer and GPS, One IMU sensor includes an accelerometer and a gyroscope, which comprises
Obtain the measurement data of each accelerometer and each gyroscope and the positioning letter of the Magnetic Field of magnetometer and GPS Breath；
Respectively according to setting between the measurement data of confidence level and each gyroscope between the measurement data of each accelerometer Reliability, using redundancy blending algorithm, the measurement data to each accelerometer and the measurement data of each gyroscope are counted respectively According to fusion, acceleration information and angular velocity information are obtained；
It is state variable and the acceleration information and the Magnetic Field as observed quantity using the angular velocity information, utilizes Fusion attitude angle is calculated in expanded Kalman filtration algorithm；
According to the acceleration information, the angular velocity information, the fusion attitude angle and the location information, expansion is utilized Exhibition Kalman filtering algorithm calculates location information.
According to second aspect, the merge sensor of a kind of solving of attitude and positioning is provided in a kind of embodiment, comprising: extremely Few two IMU sensors, a magnetometer, GPS and data processor, an IMU sensor include an accelerometer and one A gyroscope, at least two IMU sensor are connected using two-sided rotary-inversion axis type mode or uniaxial multisensor mode；
The two-sided rotary-inversion axis type mode is that 2f IMU sensor is divided into two groups, is respectively arranged in plate two sides, and two sides One axis of IMU sensor it is reversed, in addition two axis are in the same direction, the f is integer more than or equal to 1；
The single shaft multisensor mode is to install multiple IMU sensors respectively on each quadrature axis, and each IMU sensor Each axial direction is identical；
The accelerometer is used to measure the acceleration of carrier；
The gyroscope is used to measure the angular speed of carrier；
The magnetometer is for measuring Magnetic Field；
The GPS is used for measurement and positioning information；
The data processor is used to obtain the measurement data of each accelerometer and each gyroscope and the magnetic field of magnetometer The location information of information and GPS, respectively according to the measurement of confidence level and each gyroscope between the measurement data of each accelerometer Confidence level between data utilizes the survey of redundancy the blending algorithm measurement data to each accelerometer and each gyroscope respectively It measures data and carries out data fusion, obtain acceleration information and angular velocity information, using the angular velocity information as quantity of state and described Acceleration information and the Magnetic Field are observed quantity, and fusion attitude angle, root is calculated using expanded Kalman filtration algorithm According to the acceleration information, the angular velocity information, the fusion attitude angle and the location information, filtered using spreading kalman Wave algorithm calculates location information.
According to the solving of attitude of above-described embodiment and the method and its merge sensor of positioning, sensed by multiple IMU The redundancy blending algorithm of device carries out the calculating of acceleration information and angular velocity information so that calculated acceleration information and Angular velocity information is more accurate, basic herein, using magnetometer as auxiliary, merges the acceleration using expanded Kalman filtration algorithm The Magnetic Field of degree information, angular velocity information and magnetometer obtains fusion attitude angle, to greatly improve the essence of system Degree and stability can obtain the fusion attitude angle of degree of precision under static and dynamic condition；Meanwhile final location information It is to be obtained by the location information of the fusion attitude angle assistant GPS of degree of precision, is maked corrections, made to the location information of GPS The location information that must be obtained is more accurate；Thus the attitude angle information and location information of high-precision with high stability can be obtained, Improve the stability and reliability of solving of attitude and positioning.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of solving of attitude and the merge sensor of positioning in the embodiment of the present invention；
Fig. 2 is the structural schematic diagram that IMU sensor is connected by two-sided rotary-inversion axis type mode in the embodiment of the present invention；
Fig. 3 is the structural schematic diagram that IMU sensor is connected by uniaxial multisensor mode in the embodiment of the present invention；
Fig. 4 is the flow chart of solving of attitude and the method for positioning in the embodiment of the present invention；
Fig. 5 is the flow chart for correcting the method for gyroscope zero offset error in the embodiment of the present invention using Allan variance method；
Fig. 6 is to carry out data using measurement data of the redundancy blending algorithm to accelerometer in the embodiment of the present invention to melt The flow chart of the method for conjunction；
Fig. 7 is the flow chart that the method for fusion attitude angle is calculated in the embodiment of the present invention；
Fig. 8 is the flow chart for calculating fusion attitude angle in the embodiment of the present invention using Extended Kalman filter recurrence equation；
Fig. 9 is the flow chart that expanded Kalman filtration algorithm calculating location information is utilized in the embodiment of the present invention.
Specific embodiment
Below by specific embodiment combination attached drawing, invention is further described in detail.
In embodiments of the present invention, using the acceleration of more redundancies (at least two) IMU sensor measurement carrier and angle speed Degree, and data fusion is carried out to the measurement data of each accelerometer and each gyroscope respectively using redundancy blending algorithm, it obtains To the acceleration information and angular velocity information of fusion；Then using magnetometer as auxiliary, expanded Kalman filtration algorithm pair is utilized The Magnetic Field that the acceleration information, angular velocity information and magnetometer measure carries out information fusion, obtains fusion attitude angle；So The location information measured afterwards using the fusion attitude angle assistant GPS is calculated position using expanded Kalman filtration algorithm and believed Breath.
The embodiment of the present invention provides the merge sensor of a kind of solving of attitude and positioning, referring to FIG. 1, Fig. 1 is shown The structural schematic diagram of the solving of attitude and the merge sensor of positioning, as shown in Figure 1, the solving of attitude and positioning are merged Sensor includes 1, magnetometer 2 of N number of IMU sensor, GPS 3 and data processor 4, wherein N is more than or equal to 2 Integer；IMU sensor 1 therein includes an accelerometer 11 and a gyroscope 12, and accelerometer 11 is for measuring carrier Acceleration, gyroscope 12 is used to measure the angular speed of carrier, and N number of IMU sensor 1 is connected using two-sided rotary-inversion axis type mode, or Person is connected using uniaxial multisensor mode；Magnetometer 2 is used for measurement and positioning information, data for measuring Magnetic Field, GPS 3 Processor 4 is used to be filtered to IMU sensor 1, magnetometer 2 and GPS 3 signal detected, amplify, calculate, analyze etc. Reason；In the present embodiment, data processor 4 includes obtaining module 41, data fusion module 42 and computing module 43, wherein is obtained Modulus block 41 is for obtaining the measurement data of each accelerometer 11 and each gyroscope 12 and the Magnetic Field and GPS of magnetometer 2 3 location information；Data fusion module 42 is used to utilize redundancy according to the confidence level between the measurement data of each accelerometer 11 Information fusion algorithm carries out data fusion to the measurement data of each accelerometer 11, acceleration information is obtained, according to each gyroscope Confidence level between 12 measurement data carries out data using measurement data of the redundancy blending algorithm to each gyroscope 12 and melts It closes, obtains angular velocity information；Computing module 43 is used for by quantity of state of angular velocity information and acceleration information and Magnetic Field are Fusion attitude angle is calculated using expanded Kalman filtration algorithm in observed quantity, according to the fusion attitude angle, acceleration information, Angular velocity information and location information calculate location information using expanded Kalman filtration algorithm.Preferably, data processor 4 Further include correction module, which is used to carry out magnetometer 2 and accelerometer 11 error compensation, and to gyroscope 12 into Row zero bias amendment, so that magnetometer 2, accelerometer 11 and gyroscope 12 are when carrying out DATA REASONING not by the shadow of itself error It rings, the data of measurement are more accurate.
Specifically, Fig. 2 shows the structural schematic diagram that IMU sensor is connected by two-sided rotary-inversion axis type mode, such as Fig. 2 institute Show, N=2f identical IMU sensors constitute redundant sensor system, and f therein is the integer more than or equal to 1, which is Flat structure, N number of IMU sensor are divided into two groups, every group f, are separately mounted to the two sides of plate, the IMU of this two sides is passed One axis direction of sensor is on the contrary, the direction of other two axis is identical, for example, in Fig. 2, the coordinate side of all IMU sensors of the side A To identical, the coordinate direction of all IMU sensors of the side B is also identical, and is separately mounted to the IMU sensor of the side A and the side B, z Axis direction is on the contrary, and the direction of x-axis and y-axis difference is identical.
Fig. 3 shows the structural schematic diagram that IMU sensor is connected by uniaxial multisensor mode, as shown in figure 3, N=a + b+c identical IMU sensors constitute redundant sensor system, and N here is the integer more than or equal to 2；This is N number of identical IMU sensor be divided into a, b and c totally three groups, the quantity of every group of IMU sensor can be configured as needed, three Group IMU sensor is separately mounted on three quadrature axis of redundant sensor system, i.e. three in redundant sensor system A, b and c IMU sensor are installed respectively, also, each axial direction of this N number of IMU sensor is homogeneous on quadrature axis X, Y and Z Together, for example each axial direction is identical as the corresponding axial direction of xyz coordinate system.
Merge sensor based on above-mentioned solving of attitude and positioning, Fig. 4 show attitude angle solution in the embodiment of the present invention The flow chart with the method for positioning is calculated, as shown in figure 4, this method may include steps of:
Step S11: error compensation.
Correction module in data processor 4 carries out error compensation to magnetometer 2 and each accelerometer 11.Specifically, right Magnetometer 2 carries out error compensation can be using the method for 12 faces calibration, can also be using the method for ellipsoid fitting；To acceleration Meter 11 carries out the method that error compensation can use Kalman filtering, that is, establishes using acceleration error as the measurement side of state variable Journey, the error of the optimal estimation output calibration information in terms of compensated acceleration 11 by Kalman filter.
Step S12: zero bias amendment.
Correction module in data processor 4 carries out zero bias amendment to each gyroscope 12 simultaneously, specifically, can pass through Allan variance method corrects the zero offset error of gyroscope 12.In the present embodiment, gyroscope 12 can choose micromechanics (Micro Electro Mechanical systems, MEMS) gyroscope, the noise of MEMS gyroscope can be divided into 5 independent sectors, point Not Wei quantizing noise, angle random walk, deviation unstability, rate random walk and rate ramp, this 5 independent noises Size is respectively by quantizing noise coefficient Q, angle random walk coefficient N, deviation unstability coefficient B, rate random walk coefficient K And rate ramp coefficients R characterization.Since this 5 noises are mutually indepedent, so there are following relational expression (1):
Wherein, σ2(τ) is gyroscope overall noise, i.e. zero offset error,For quantizing noise,Angle random trip It walks,For deviation unstability,For rate random walk,For rate ramp.
Thus, it is possible to which the mean square deviation for defining Allan isIn this way, the double logarithmic curve of σ (τ) can Be explicitly described out gyroscope various error percentages and different error terms.Specifically, Fig. 5 is shown using Allan variance method The method for correcting gyroscope zero offset error, as shown in figure 5, this method may include following step S121~step S127, tool Body are as follows:
Step S121: acquisition static drift data.
When carrying out the amendment of gyroscope zero offset error using Allan variance method, sample rate takes the 3~6 of gyroscope bandwidth Times, it can guarantee the accuracy for handling data in this way, since the bandwidth of gyroscope is about 100Hz, so sample rate is set as 500Hz.When acquiring data, gyroscope is mounted in the high precision turntable with good vibration isolating effect, then continuous acquisition top The static drift data of spiral shell instrument, acquisition time can according to need determination, such as continuous acquisition 1 hour.
Step S122: handling averagely static drift data.
After the static drift data for having acquired gyroscope, the data of acquisition can be subjected to 1s to improve later period arithmetic speed Handling averagely rejects the data of preheating time section, for example rejects the warmable data of 0.1h, to obtain static drift to be processed Data.
Step S123: it averages.
After obtaining static drift data to be processed, the static drift data to be processed are divided according to pretreatment parameter Group, then seeks the mean value of each group static drift data to be processed, and pretreatment parameter therein may include sampling period, scale Factor etc..
Step S124: Allan mean square deviation is calculated.
After the mean value for obtaining static drift data to be processed, according to mean value computation Allan variance, then to Allan Variance carries out extracting operation, obtains Allan mean square deviation.
Step S125: the double logarithmic curve of Allan mean square deviation is drawn.
Step S126: matched curve is drawn.
After obtaining the double logarithmic curve of Allan mean square deviation, which is fitted, draws out the double-log The matched curve of curve.
Step S127: the zero offset error of gyroscope is sought.
The error coefficient of gyroscope, i.e., above-mentioned quantizing noise system are sought by the matched curve for the double logarithmic curve drawn out Number Q, angle random walk coefficient N, deviation unstability coefficient B, rate random walk coefficient K and rate ramp coefficients R, according to These error coefficients obtainWithTo be calculated according to relational expression (1) The overall noise σ of gyroscope2(τ) is to get the zero offset error for arriving gyroscope.
Step S128: the zero offset error of gyroscope is corrected.
The zero offset error of the gyroscope subtracted from the measurement data of gyroscope, thus to the zero offset error of gyroscope It is modified, obtains the measurement data of revised gyroscope.
In practical application, the processing of the above-mentioned static drift data to gyroscope can also carry out in MATLAB platform, The zero offset error of gyroscope is calculated by the Allan variance data processing algorithm write.
Step S13: measurement data is obtained.
Obtain the magnetic that module 41 obtains the measurement data of each accelerometer, the measurement data of each gyroscope, magnetometer measures The location information of field information and GPS measurement.
Step S14: acceleration information and angular velocity information are calculated.
Obtain module 41 get the measurement data of each accelerometer, the measurement data of each gyroscope, magnetometer magnetic field After information and the location information of GPS, by data fusion module 42 according to the confidence between the measurement data of each accelerometer Degree carries out data fusion using measurement data of the redundancy blending algorithm to each accelerometer, obtains acceleration information, simultaneously According to the confidence level between the measurement data of each gyroscope, using redundancy blending algorithm to the measurement data of each gyroscope into Row data fusion, obtains angular velocity information.
In the measurement data using redundancy the blending algorithm measurement data to each accelerometer and each gyroscope respectively When carrying out data fusion, thus the advantage for making full use of the subordinating degree function range in Fuzzy Set Theory to determine defines one The fuzzy type index confidence function of kind, with the mutual confidence level of each sensor in quantification treatment same type sensor, and by setting Matrix is believed to measure the synthesis confidence level of each sensor output data, reasonably distributes each sensor in same type sensor Measurement data weight shared in data fusion process, obtains the final expression formula of data fusion, realizes and senses to more redundancies The data fusion of the measurement data of device, to obtain acceleration information and angular velocity information.
Specifically, set multiple identical sensor measurement same parameters, what i-th of sensor and j-th of sensor measured Data are respectively ciAnd cj, work as ciAuthenticity it is higher when, ciThe degree trusted by remainder data is higher.So-called ciBy cjLetter The degree appointed, refers to from cjFrom the point of view of ciThis trust for the possibility degree of truthful data, between the measurement data of more redundant sensors Degree is referred to as confidence level.In order to which the trusting degree the measurement data between more redundant sensors carries out further unified quantization Processing, can define a confidence level function kij, for indicating ciBy cjThe degree of trust.Meanwhile a upper limit value S is set, And S > 0, when | ci﹣ cj| value when being more than upper limit value S, it is believed that the two data are no longer trusted each other, then available Confidence level function kijExpression formula are as follows:
According to expression formula (2), if kij=0, then it is assumed that i-th of sensor and j-th of sensor phase mutual mistrust；If kij =1, then it is assumed that i-th of sensor trusts j-th of sensor.When a sensor is not trusted by other sensors, or only lacked When number sensor is trusted, the measurement data of the sensor is removed when carrying out data fusion, in this way, to each sensor Measurement data carry out data fusion when can automatic rejection fall abnormal data, it will be reliable for participating in the measurement data of data fusion Data.
Data processing is carried out to multiple sensors by redundancy blending algorithm as a result, increases target feature vector Dimension overcome list so that entire measuring system obtains the unobtainable independent characteristic information of any single sensor The uncertainty and limitation of a sensor, significantly improve the performance of system.
On the basis of the theory, Fig. 6 shows the measurement data using redundancy blending algorithm to N number of accelerometer The flow chart of the method for data fusion is carried out, as shown in fig. 6, may include step S141~step S143, specifically:
Step S141: confidence level matrix K is established.
N number of accelerometer measures the acceleration of carrier simultaneously, according to the confidence level between the measurement data of each accelerometer Function establishes confidence level matrix K, the expression formula of K are as follows:
Wherein, n=N, kijFor the confidence level function between the measurement data of each accelerometer, i-th of accelerometer is represented The degree trusted by j-th of accelerometer, i and j are integer.
Step 142: calculating weight matrix E.
Use EiIndicate the measurement data a of i-th of accelerometeriShared weight, E in data fusion processiSize it is anti- Measurement data a of the measurement data to i-th of accelerometer of other accelerometers is reflectediSynthesis trusting degree.In degree of belief In matrix K, confidence level function kijIt is merely representative of measurement data ajTo aiTrusting degree, can not reflect all N number of acceleration The measurement data of meter is spent to aiTrusting degree, and aiReally degree actually should be by ki1, ki2..., kinIt integrates to embody, At this moment, measurement data a of the measurement data of other accelerometers to i-th of accelerometeriTrusting degree may be expressed as:
Ei=m1ki1+m2ki2+…+mnkin (3)
Wherein, m1, m2..., mnFor one group of nonnegative number, M=[m can be expressed as with the form of matrix1,m2,…,mn]T, together Sample, by EiThe form for being expressed as matrix is E=[e1,e2,…,en]T, in this way, (3) formula can be indicated with the form of matrix are as follows:
E=KM
Again because of kij>=0, so confidence level matrix K is a nonnegative matrix, and there is maximum model in symmetrical matrix K Value indicative λ (λ > 0), this feature are worth the solution that corresponding feature vector is M, thus can by confidence level matrix K and it is non-negative to Amount M obtains weight matrix E.
Step S143: acceleration information is calculated.
Obtaining weight matrix E=[e1,e2,…,en]TAfterwards, according to data fusion formula (4), E pairs of weight matrix is utilized The measurement data of accelerometer is weighted summation, so that acceleration information a is obtained, data fusion formula (4) therein are as follows:
Similarly, the process and benefit of data fusion are carried out using measurement data of the redundancy blending algorithm to N number of gyroscope The process for carrying out data fusion with measurement data of the redundancy blending algorithm to N number of accelerometer is identical, i.e., first according to each top Confidence level function between the measurement data of spiral shell instrument establishes confidence level matrixWherein, n=N, k′ijFor the confidence level function between the measurement data of each gyroscope, the journey that i-th of gyroscope is trusted by j-th of gyroscope is represented Degree, i and j are integer；Further according to matrix multiplication formula E '=K ' M ' calculating weight matrix E '=[e '1, e '2…e′i..., e 'n]T, M ' therein=[m '1, m '2…m′j..., m 'n]TFor confidence level matrix K ' the corresponding feature vector of maximum norm characteristic value, and M ' For non-negative vector；Then according to data fusion formulaUsing weight matrix E ' to the measurement data of gyroscope into Row weighted sum obtains angular velocity information ω, ω thereiniFor the measurement data of i-th of gyroscope, e 'iRepresent ωiMelt in data Shared weight in conjunction.
Step S15: fusion attitude angle is calculated.
After obtaining acceleration information and angular velocity information, computing module 43 using the angular velocity information as quantity of state and with The Magnetic Field that acceleration information and magnetometer measure is observed quantity, and fusion appearance is calculated using expanded Kalman filtration algorithm State angle, specifically, Fig. 7 show calculate fusion attitude angle process, as shown in fig. 7, the process may include step S151~ Step S155, specifically:
Step S151: initial attitude angle is calculated.
Before calculating fusion attitude angle using expanded Kalman filtration algorithm, it is necessary to first there is no the case where filtering method Under obtain attitude angle, that is, the Magnetic Field of the acceleration information and magnetometer measures that are directly based upon accelerometer measures calculates just Beginning attitude angle.
Specifically, by the solving of attitude that three axis accelerometer and magnetometer directly carry out being carried out in carrier coordinate system , it is therefore desirable to it will be converted by accelerometer and the calculated posture amount of magnetometer into navigational coordinate system；By carrier coordinate system It is defined as b, navigational coordinate system is defined as d, then the conversion is realized by the transition matrix from b to d, and the sequence with X-Y-Z axis is Standard is rotated, and obtained transition matrix can indicate are as follows:
Wherein,θ and ψ respectively represents pitch angle, roll angle and yaw angle,For obtained transition matrix.
When carrier is in static or uniform linear movement state, other than gravity accelerates, it has small add Speed can calculate the transition matrix by the acceleration information of accelerometer and the Magnetic Field of magnetometerIt is calculated Formula are as follows:
Wherein, g is acceleration of gravity, ax、ayAnd azFor the acceleration information that accelerometer measures, acceleration is respectively represented Spend the 3-axis acceleration of meter.
It is calculated by formula (6)Initial pitch angle can be obtained in conjunction with formula (5)With initial roll angle θ0Calculating Formula is respectively as follows:
θ0=arctan (ax/g)
Further, according to calculatedθ0The Magnetic Field measured with magnetometer is counted using formula one and formula two Calculate initial yaw angle Ψ0, formula one therein are as follows:
Wherein, mx、myAnd mzFor the Magnetic Field that magnetometer measures, the magnetic field value of three axis of magnetometer is respectively represented.
Formula two are as follows: Ψ0=arctan (Hy/Hx)。
Thus initial attitude angle can be calculatedθ0And Ψ0。
Step S152: four element of initial attitude is calculated.
According to calculated initial attitude angleθ0And Ψ0, it is calculated using attitude angle and the corresponding relationship of four elements The four element q of posture of initial time, wherein define q=q0+q1i+q2j+q3K, q0、q1、q2And q3Represent four elements.
In the resolving of attitude angle, in order to avoid the unusual problem of Eulerian angles, attitude angle is completed usually using quaternary number Resolving, according to unit rotate four element q=q0+q1i+q2j+q3K, the transition matrix from b to d can indicate are as follows:
The formula is subjected to the corresponding corresponding relationship that can obtain attitude angle Yu four elements with formula (5), thus right using this It should be related to and four element q are calculated0、q1、q2And q3, and then obtain the four element q of posture of initial time.
Step S153: four element of posture is updated.
When carrying out the resolving of attitude angle using four element of posture, need to constantly update four element of posture, it can It is updated by the quaternion differential equation based on gyro data.Specifically, the angular speed that can be first measured according to gyroscope Information establishes angular velocity matries W, the expression formula of W are as follows:
Wherein, ωx、ωyAnd ωzFor the angular velocity information of gyroscope, the angular speed of three axis of gyroscope is respectively represented.
Then, renewal equation is utilizedFour element of posture is updated, updated posture four is obtained Element q '.
Step S154: observational equation is established.
Using the four element q ' of posture of continuous renewal as state variable X, and the acceleration letter measured with redundant accelerometers The Magnetic Field that breath and magnetometer measure is observed quantity Z, establishes observational equation, the observational equation of foundation are as follows:
Z=H (X)+V
Wherein, observed quantity Z=[ax ay az Ψ]T,G is gravity Acceleration, ax、ayAnd azFor acceleration information, the 3-axis acceleration of accelerometer is respectively represented, Ψ is according to Magnetic Field meter The course angle of calculating, V are measurement noise.
Sliding-model control is carried out to the observational equation, obtains the observational equation of discretization are as follows:
Z (k)=H (k) X (k)+V (k)
Wherein, X (k) is the state value at k moment, and V (k) is the measurement noise at k moment,For H's (X) Jacobian matrix indicates that the H (X (k)) at k moment seeks local derviation to X (k).
Step S155: fusion attitude angle is calculated.
According to the observational equation Z (k) of state variable X and discretization, calculated using Extended Kalman filter recurrence equation To fusion attitude angle, specifically, Fig. 8 shows the process for calculating fusion attitude angle using Extended Kalman filter recurrence equation Figure, as shown in figure 8, may include steps of:
Step SA1: state one-step prediction.
According to formulaStatus predication is carried out, the status predication value at k moment is obtainedWherein, F (k-1) is the state-transition matrix at k-1 moment, and X (k-1) is the state value at k-1 moment, and U (k-1) is k-1 The state-noise at moment.Wherein, the state-transition matrix at k moment can indicate are as follows:
Wherein, ωx(k)、ωy(k) and ωzIt (k) is the angular velocity information of k moment gyroscope.
Step SA2: error covariance prediction.
According to formulaThe estimation for carrying out error covariance, obtains error The predicted value of covariance matrixWherein, P (k-1) is the error co-variance matrix at k-1 moment, FTIt (k-1) is F (k-1) Transposition, Q (k-1) are the system noise covariance matrix at k-1 moment.
Step SA3: filtering gain matrix are calculated.
According to formulaIt calculates filtering gain matrix K (k), wherein HT(k) transposition for being H (k), R (k) are the measurement noise covariance at k moment.
Step SA4: fusion attitude angle is calculated.
First according to formulaThe state value X (k) for calculating the k moment, obtains k Then four element of posture at moment merges posture according to what four element of posture and the corresponding relationship of attitude angle were calculated the k moment Angle.
Step SA5: error covariance is updated.
According to formulaError covariance is updated, the k moment is obtained Error co-variance matrix, to calculate for the update of subsequent time, wherein A=I-K (k) H (k), I are unit matrix.
So constantly fusion attitude angle can be updated in a dynamic condition, obtain the fusion posture under dynamic condition Angle.
Step S16: fusion attitude angle assists calculating location information.
The positioning that computing module 43 is measured according to calculated fusion attitude angle, acceleration information, angular velocity information and GPS Information calculates location information using expanded Kalman filtration algorithm.Specifically, Fig. 9 shows the process of calculating location information, As shown in figure 9, the process may include following step S161~step S165:
Step S161: state variable is determined.
Computing module 43 gets the velocity component υ of three axial directions from acceleration information firstx、υyAnd υz, then with this The location information of velocity component, GPSWithAnd merge sensor is in the parameter error of three axial directionsWithState variable X ' is established, it is thereinWithLongitude, latitude and height are respectively represented, the state variable of foundation is
Step S162: state-transition matrix is established.
Computing module 43 also sets up state-transition matrix after the velocity component for getting three axial directions F thereinNFor the sytem matrix comprising velocity component, fusion attitude angle and location information, FSFor unit matrix, FMFor by gyro The matrix of the output frequency of instrument and accelerometer composition.
Step S163: carrier location information is calculated.
Computing module 43 calculates carrier positioning letter according to the acceleration information and angular velocity information that obtain after data fusion Breath.Specifically, computing module 43 integrates acceleration information, carrier movement distance, while angular velocity information can be obtained Double integral, available carrier movement direction are carried out, in this way, computing module 43 can be transported according to carrier movement distance and carrier Dynamic direction obtains carrier location information Lt、λtAnd htTo get longitude, latitude and the height for arriving carrier.
Step S164: observational equation is established.
Calculating carrier location information Lt、λtAnd htAfterwards, computing module 43 is first according to calculated carrier location information The location information measured with GPSWithUtilize formulaEstablish position detection equation Z′p(t), wherein RMFor ellipsoid radius of curvature of meridian, RNFor fourth of the twelve Earthly Branches tenth of the twelve Earthly Branches radius of curvature of meridian；Then, carrier is obtained by GPS Velocity information υGx、υGyAnd υGz, further according to velocity component υx、υyAnd υzWith the velocity information υ of carrierGx、υGyAnd υGzBetween difference Value establishes speed observational equation Z 'v(t), the speed observational equation Z ' of foundationv(t) it is
Establishing position detection equation Z 'p(t) and speed observational equation Z 'v(t) after, by Z 'p(t) and Z 'v(t) it ties It is combined and total observational equation Z ' (t) can be obtained, specifically:
Wherein, X ' (t) is the corresponding state equation of quantity of state X ', and V ' (t) is measurement noise.
Sliding-model control is carried out to observational equation Z ' (t), obtains the observational equation Z ' (k) of discretization, Z ' (k) can be indicated Are as follows:
Z ' (k)=H ' (k) X ' (k)+V ' (k)
Wherein, H ' (k) is Jacobian matrix, and X ' (k) and V ' (k) are respectively the state value and measurement noise at k moment.
Step S165: calculating location information.
According to the observational equation Z ' (k) of state variable X ', state-transition matrix F ' and discretization, filtered using spreading kalman Location information is calculated in wave recurrence equation.Specifically, be state variable with X ', F ' be state-transition matrix and Z ' (k) be from The observational equation of dispersion, using Extended Kalman filter recurrence equation calculate fusion attitude angle when, process and using extend The process that fusion attitude angle is calculated in Kalman filtering recurrence equation is identical, reference can be made to step SA1~step SA5, here not It repeats again.
So constantly location information can be updated in a dynamic condition, obtain the location information under dynamic condition. Moreover, during calculating location information, merge what attitude angle measured GPS with calculated high-precision and high stability Location information is maked corrections, therefore, finally obtained location information also precision with higher and higher stability.
The method and its merge sensor of solving of attitude provided in an embodiment of the present invention and positioning use multiple IMU Sensor carries out the measurement of acceleration and angular speed, increases the dimension of target feature vector, so that entire measuring system obtains Any single unobtainable independent characteristic information of sensor.Further, right first in the solution process of attitude angle Magnetometer and each accelerometer carry out error compensation, and carry out zero bias amendment to each gyroscope, to avoid magnetometer, accelerometer Influence with gyroscope itself error to measurement data；Pass through measurement of the redundancy blending algorithm to multiple IMU sensors again Data are coordinated, complementary and combination, fusion obtains final acceleration information and angular velocity information, in automatic rejection measurement Abnormal data, overcome the uncertainty and limitation of single sensor, greatly improve the precision of solving of attitude and steady It is qualitative；Then the magnetic that the acceleration information, angular velocity information and magnetometer obtain is merged using expanded Kalman filtration algorithm Field information has abandoned conventional complementary filtering blending algorithm and gradient descent method, and the complexity of calculating is relatively low, static and dynamic The attitude angle that degree of precision can be obtained under the conditions of state, significantly reduces the error of attitude angle；The finally fusion posture by obtaining Final location information is calculated in the location information of angle assistant GPS, is compensated the location information of GPS, so that obtaining Location information it is more accurate, stability is more preferable.Therefore, the method for solving of attitude provided in an embodiment of the present invention and positioning and Its merge sensor can obtain high-precision in the solution process of attitude angle and believe with the attitude angle information of high stability and position Breath, improves the stability and reliability of solving of attitude and positioning.
It will be understood by those skilled in the art that all or part of function of various methods can pass through in above embodiment The mode of hardware is realized, can also be realized by way of computer program.When function all or part of in above embodiment When being realized by way of computer program, which be can be stored in a computer readable storage medium, and storage medium can To include: read-only memory, random access memory, disk, CD, hard disk etc., it is above-mentioned to realize which is executed by computer Function.For example, program is stored in the memory of equipment, when executing program in memory by processor, can be realized State all or part of function.In addition, when function all or part of in above embodiment is realized by way of computer program When, which also can store in storage mediums such as server, another computer, disk, CD, flash disk or mobile hard disks In, through downloading or copying and saving into the memory of local device, or version updating is carried out to the system of local device, when logical When crossing the program in processor execution memory, all or part of function in above embodiment can be realized.
Use above specific case is illustrated the present invention, is merely used to help understand the present invention, not to limit The system present invention.For those skilled in the art, according to the thought of the present invention, can also make several simple It deduces, deform or replaces.

Claims (10)

1. a kind of method of solving of attitude and positioning, which is characterized in that merge sensing applied to solving of attitude and positioning In device, the merge sensor contains at least two IMU sensor, a magnetometer and GPS, an IMU sensor packet Containing an accelerometer and a gyroscope, which comprises
Obtain the location information of the measurement data of each accelerometer and each gyroscope and the Magnetic Field of magnetometer and GPS；
Respectively according to the confidence level between the measurement data of confidence level and each gyroscope between the measurement data of each accelerometer, Using redundancy blending algorithm, the measurement data to each accelerometer and the measurement data of each gyroscope progress data are melted respectively It closes, obtains acceleration information and angular velocity information；
It is quantity of state and the acceleration information and the Magnetic Field as observed quantity using the angular velocity information, utilizes expansion card Fusion attitude angle is calculated in Kalman Filtering algorithm；
According to the acceleration information, the angular velocity information, the fusion attitude angle and the location information, expansion card is utilized Kalman Filtering algorithm calculates location information.
2. the method as described in claim 1, which is characterized in that described in the measurement number for obtaining each accelerometer and each gyroscope Accordingly and before the Magnetic Field of magnetometer and the location information of GPS, the method also includes:
Error compensation is carried out to magnetometer and accelerometer, and zero bias amendment is carried out to gyroscope.
3. method according to claim 2, which is characterized in that described to carry out zero bias amendment to gyroscope, comprising:
Acquire the static drift data of gyroscope；
1s handling averagely is carried out to the static drift data, rejects the data of preheating time section, obtains static drift to be processed Move data；
The static drift data to be processed are grouped according to pretreatment parameter, seek each group static drift data to be processed Mean value, the pretreatment parameter includes sampling period and scale factor；
According to the mean value computation Allan variance；
Allan mean square deviation is calculated according to the Allan variance；
Draw the double logarithmic curve of the Allan mean square deviation；
The double logarithmic curve is fitted, matched curve is obtained；
Seek the error coefficient of gyroscope according to the matched curve, the error coefficient include quantizing noise coefficient, angle with Machine migration coefficient, deviation unstability coefficient, rate random walk coefficient and rate ramp coefficient；
According to the overall noise of the error coefficient computing gyroscope of the gyroscope, the zero offset error of gyroscope is obtained；
The zero offset error is subtracted from the measurement data of gyroscope to correct the zero offset error of gyroscope.
4. the method as described in claim 1, which is characterized in that the redundancy blending algorithm includes:
Weight matrix E=[e is calculated according to matrix multiplication formula E=KM1, e2…ei..., en]T, wherein it is describedFor the confidence level matrix established according to the confidence level function between each measurement data, kijIt is each Confidence level function between measurement data, represents the degree that ith measurement data are trusted by j-th of measurement data, and n represents measurement The number of data, i and j are integer, the M=[m1, m2…mj..., mn]TIt is corresponding for the maximum norm characteristic value of confidence level matrix K Feature vector, and M is non-negative vector；
According to data fusion formula, summation is weighted to measurement data using weight matrix E, obtains fused data a；
The data fusion formula are as follows:
Wherein, aiFor ith measurement data, eiRepresent aiThe shared weight in data fusion.
5. the method as described in claim 1, which is characterized in that described using the angular velocity information as quantity of state and the acceleration Spending information and the Magnetic Field is observed quantity, and fusion attitude angle is calculated using expanded Kalman filtration algorithm, comprising:
Initial attitude angle is calculated according to the acceleration information and the Magnetic Field；
According to the initial attitude angle, the posture quaternary of initial time is calculated using the corresponding relationship of attitude angle and four elements Plain q, wherein q=q0+q1i+q2j+q3K, q0、q1、q2And q3Represent four elements；
Angular velocity matries W is established according to the angular velocity information；
According to the angular velocity matries W, renewal equation is utilizedFour element of posture is updated, is updated Four element q ' of posture afterwards；
Using q ' as state variable X, and acceleration information and Magnetic Field are observed quantity Z, establish observational equation；
Sliding-model control is carried out to the observational equation, obtains the observational equation Z (k) of discretization；
According to the X and Z (k), fusion attitude angle is calculated using Extended Kalman filter recurrence equation；
The angular velocity matries W isWherein, ωx、ωyAnd ωzFor angular speed letter Breath, respectively represents the angular speed of three axis of gyroscope；
The observational equation is Z=H (X)+V, wherein observed quantity Z=[ax ay az Ψ]T,G is acceleration of gravity, ax、ayAnd azFor the acceleration information, point The 3-axis acceleration of accelerometer is not represented, and Ψ is according to the calculated course angle of the Magnetic Field, and V is measurement noise；
The observational equation of the discretization is Z (k)=H (k) X (k)+V (k), wherein X (k) is the state value at k moment, and V (k) is The measurement noise at k moment,For the Jacobian matrix of H (X), indicate that the H (X (k)) at k moment asks inclined to X (k) It leads.
6. method as claimed in claim 5, which is characterized in that described according to the acceleration information and the Magnetic Field meter Calculate initial attitude angle, comprising:
According toAnd θ0=arctan (ax/ g) calculate separately out initial pitch angleWith initial roll angle θ0；
According to describedθ0And Magnetic Field, initial yaw angle Ψ is calculated using formula one and formula two0；
The formula one are as follows:Wherein, mx、myAnd mzIt is described Magnetic Field respectively represents the magnetic field value of three axis of magnetometer；
The formula two are as follows: Ψ0=arctan (Hy/Hx)。
7. method as claimed in claim 5, which is characterized in that it is described according to the X and the Z (k), utilize spreading kalman Fusion attitude angle is calculated in filtering recurrence equation, comprising:
According to formulaStatus predication is carried out, the status predication value at k moment is obtained Wherein, F (k-1) is the state-transition matrix at k-1 moment, and X (k-1) is the state value at k-1 moment, and U (k-1) is the k-1 moment State-noise；
According to formulaThe estimation for carrying out error covariance, obtains error association side The predicted value of poor matrixWherein, P (k-1) is the error co-variance matrix at k-1 moment, FT(k-1) turn for F (k-1) It sets, Q (k-1) is the system noise covariance matrix at k-1 moment；
According to formulaIt calculates filtering gain matrix K (k), wherein HT(k) For the transposition of H (k), R (k) is the measurement noise covariance at k moment；
According to formulaThe state value X (k) for calculating the k moment, obtains the appearance at k moment Four element of state；
Attitude angle is merged according to what four element of posture and the corresponding relationship of attitude angle were calculated the k moment；
According to formulaError covariance is updated, the error at k moment is obtained Covariance matrix, wherein A=I-K (k) H (k), I are unit matrix.
8. the method as described in claim 1, which is characterized in that it is described according to the acceleration information, the angular velocity information, The fusion attitude angle and the location information, are calculated location information using expanded Kalman filtration algorithm, comprising:
The velocity component in the acceleration information is obtained, with the velocity component, the location information and merge sensor Parameter error is as state variable X '；
State-transition matrix F ' is established according to formula one；
Carrier location information is calculated according to the acceleration information and the angular velocity information；
According to the location information of the carrier location information and GPS, position detection equation Z ' is established using formula twop(t)；
The velocity information of carrier is obtained by GPS, and speed is established according to the difference of the velocity component and the velocity information Observational equation Z 'v(t)；
According to the Z 'p(t) and the Z 'v(t), observational equation Z ' (t) is obtained using formula three；
Sliding-model control is carried out to the Z ' (t), obtains the observational equation Z ' (k) of discretization；
According to the observational equation Z ' (k) of the state variable X ', the state-transition matrix F ' and the discretization, extension is utilized Location information is calculated in Kalman filtering recurrence equation；
It is describedWherein, υx、υyAnd υzFor the velocity component,WithFor the location information, longitude, latitude and height, ▽ are respectively representedx、▽yAnd ▽zFor fusion sensing The parameter error of device；
The formula one are as follows:Wherein, FNTo include the velocity component, the fusion attitude angle and described fixed The sytem matrix of position information, the FSFor unit matrix, the FMFor the square being made of the output frequency of gyroscope and accelerometer Battle array；
The formula two are as follows:Wherein, Lt、λtAnd htFor the carrier location information, RMIt is ellipse Ball radius of curvature of meridian, RNFor fourth of the twelve Earthly Branches tenth of the twelve Earthly Branches radius of curvature of meridian, L is the initial latitude of carrier；
The formula three are as follows:Wherein, V ' (t) is measurement noise；
The Z ' (k)=H ' (k) X ' (k)+V ' (k), wherein H ' (k) is Jacobian matrix, when X ' (k) and V ' (k) are respectively k The state value and measurement noise at quarter.
9. method according to claim 8, which is characterized in that described according to the acceleration information and the angular velocity information Calculate carrier location information, comprising:
The acceleration information is integrated, carrier movement distance is obtained；
Double integral is carried out to the angular velocity information, obtains carrier movement direction；
Carrier location information is obtained according to the carrier movement distance and the carrier movement direction.
10. the merge sensor of a kind of solving of attitude and positioning characterized by comprising at least two IMU sensors, one A magnetometer, GPS and data processor, IMU sensor include an accelerometer and a gyroscope, and described at least two A IMU sensor is connected using two-sided rotary-inversion axis type mode or uniaxial multisensor mode；
The two-sided rotary-inversion axis type mode is that 2f IMU sensor is divided into two groups, is respectively arranged in plate two sides, and two sides One axis of IMU sensor is reversed, in addition two axis are in the same direction, and the f is the integer more than or equal to 1；
The single shaft multisensor mode is to install multiple IMU sensors, and each axis of each IMU sensor respectively on each quadrature axis To identical；
The accelerometer is used to measure the acceleration of carrier；
The gyroscope is used to measure the angular speed of carrier；
The magnetometer is for measuring Magnetic Field；
The GPS is used for measurement and positioning information；
The data processor is for obtaining the measurement data of each accelerometer and each gyroscope and the Magnetic Field of magnetometer With the location information of GPS, respectively according to the measurement data of confidence level and each gyroscope between the measurement data of each accelerometer Between confidence level, utilize the measurement number of redundancy the blending algorithm measurement data to each accelerometer and each gyroscope respectively According to data fusion is carried out, acceleration information and angular velocity information are obtained, using the angular velocity information as quantity of state and the acceleration Spending information and the Magnetic Field is observed quantity, fusion attitude angle is calculated using expanded Kalman filtration algorithm, according to institute Acceleration information, the angular velocity information, the fusion attitude angle and the location information are stated, is calculated using Extended Kalman filter Method calculates location information.
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