CN113984042B - Series combined navigation method applicable to high-dynamic aircraft - Google Patents

Series combined navigation method applicable to high-dynamic aircraft Download PDF

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CN113984042B
CN113984042B CN202111012824.7A CN202111012824A CN113984042B CN 113984042 B CN113984042 B CN 113984042B CN 202111012824 A CN202111012824 A CN 202111012824A CN 113984042 B CN113984042 B CN 113984042B
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aircraft
filter
angular rate
navigation
carrier
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CN113984042A (en
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龙达峰
孙俊丽
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Huizhou University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
    • G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention provides a serial combined navigation method applicable to a high-dynamic aircraft, which adopts serial combination of two working modes of 'quick acquisition' and 'high-precision combined filtering', wherein in the initial stage of the aircraft flight, uncontrolled flight under external trajectory constraint is carried out, and three input values of carrier speed under a navigation coordinate system output by a satellite navigation system and three input values of carrier initial parameters are carried out through an initial attitude quick estimator according to the measured value of a triaxial geomagnetic sensor, the triaxial angular rate output by an angular rate filter and the three input values of the carrier speed under the navigation coordinate system output by a satellite navigation system respectively, so that the aerial quick acquisition of the carrier initial parameters is carried out, and the current carrier flight initial parameters are output; and when the aircraft enters a controlled flight stage, the high-precision filter estimation of the flight attitude is completed in real time by adopting a discrete EKF (extended Kalman Filter) filter algorithm through the high-precision filter of the flight attitude.

Description

Series combined navigation method applicable to high-dynamic aircraft
Technical Field
The invention relates to the technical field of flight attitude measurement of conventional ammunition, high-speed rotary ammunition, high-dynamic aircraft and the like, in particular to a serial combined navigation method applicable to high-dynamic aircraft.
Background
After the conventional ammunition is launched, the projectile flies by virtue of inertial force in the whole course under the external ballistic constraint, and has the characteristics of high initial speed, high overload, high-speed rotation and the like. In the conventional ammunition intelligent navigation reconstruction, the rolling angle rate of the ammunition shaft is as high as 14000 degrees/s, so that the measuring range and the precision of the existing gyro are difficult to meet the requirement of a missile-borne test at the same time, in addition, the large overload is launched up to 10000g, and the large measurement error, short-time failure, even damage and the like of an airborne sensor are extremely easy to occur. The existing mature airborne integrated navigation system is difficult to directly transplant and apply to intelligent modification of high-dynamic aircrafts such as conventional ammunition due to severe application environments such as large initial speed, high overload and high spin, and the problems of incomplete parameter test, lower measurement precision, poor system reliability and the like exist. Therefore, the search for a navigation system suitable for an aircraft with high overload, high spin and high dynamic motion characteristics is one of the key technologies needed to be solved in the intelligent reformation of conventional ammunition.
Disclosure of Invention
Aiming at the problems, the invention provides a serial combined navigation method applicable to a high-dynamic aircraft, and aiming at the problems, the invention adopts a serial combined scheme of two working modes of 'quick acquisition' and 'high-precision combined filtering' according to the motion characteristics of the high-dynamic aircraft, specifically, the method is used for completing the aerial quick acquisition of initial parameters of a carrier in the initial stage of the aircraft flight; and then the navigation system is switched to a high-precision combined filtering mode, and enters a controlled flight stage, so that the high-precision estimation of the controlled flight parameters of the high-dynamic aircraft is finally realized.
Specifically, the method for serial combined navigation of applicable high-dynamic aircrafts comprises the following steps:
in the initial stage of the flight of the aircraft, the aircraft flies uncontrollably under the external trajectory constraint, and the initial attitude fast estimator is respectively used for respectively measuring values of the triaxial geomagnetic sensorsThree-axial angular rate of the angular rate filter output>Carrier speed +.>Three input values are used for quickly acquiring initial parameters of the carrier in the air and outputting the initial parameters of the current carrier in the air
In the stage of the aircraft entering the controlled flight, the high-precision filter of the flight attitude is respectively based on the initial flight parametersThe measured value H of the triaxial geomagnetic sensor b The triaxial angular rate +.>Said carrier speed>Four input values, and adopting a discrete EKF filtering algorithm to complete high-precision filtering estimation of flying attitude in real time
Wherein, the input data of the angular rate filter is the measured value of the MEMS gyroscopeAnd->Measurement value H of triaxial geomagnetic sensor b The method comprises the steps of carrying out a first treatment on the surface of the The MEMS gyroscope comprises O-X arranged on the carrier coordinate system b Y b Z b A single-axis gyro Gx on the X-axis and a single-axis gyro Gz on the Z-axis; the sensitive directions of all the three axes of the triaxial geomagnetic sensor are completely coincident with the carrier coordinate system and are used for measuring geomagnetic field vector information under the carrier coordinate system; the receiver antenna of the satellite navigation system is mounted on the surface of the aircraft shell.
Further, calculating initial flight parameters by the initial attitude fast estimatorThe method also comprises the following calculation process:
estimating initial pitch angle θ of carrier from velocity information 0 The pitch angle solution formula is:
in the method, in the process of the invention,and->Respectively expressed as initial speed components of the aircraft under a navigation coordinate system, and are measured by a satellite navigation system;
calculating yaw angle of carrier from magnetic measurement informationAnd a roll angle gamma 0 Initial attitude information, the formula is:
in the method, in the process of the invention,and->The measured output values of the triaxial geomagnetic sensor X, Y and the Z-axis geomagnetic sensor are respectively represented as +.> Is the north geomagnetic component of the geomagnetic field under the navigation system.
Further, the three axial angular rateThe angular rate filtering equation of (2) consists of a state equation and an observation equation together, and the Kalman filtering algorithm is adopted to complete the optimal filtering estimation of the state variable X (k)>Providing accurate aircraft carrier angular rate information for a high-precision flight attitude filter>
Further, the state equation further includes:
selecting an angular rate as a state of the systemVariable X (k) = [ ω ] x ,ω y ,ω z ] T The state equation of the system can be expressed as:
X(k)＝Φ k，k-1 X(k-1)+w(k)；
in the state transition matrixMeasurement noise w (k) = [ n ] x ,n y ,n z ] T The satisfied mean value is E [ w (k)]=0, variance E [ w (k), w T (j)]=q (k), Q (k) is a systematic noise sequence variance matrix.
Further, the observation equation further includes:
selectingAs observables of the filter system for each axial angular rate measurementThe observation equation for the angular rate filter can be expressed as:
Z(k)＝H(k)X(k)+v(k)；
in the measuring arrayv (k) is the measured noise of the system, and the average value is E [ v (k)]=0, variance E [ v (k), v T (j)]R (k), R (k) is the measurement noise sequence variance matrix.
The angular rate filter equation of the angular rate filter further comprises the following two updating processes:
the time updating process comprises the following steps:
and a measurement update process:
in the middle of，One-step prediction for state; p (P) (k,k-1) Predicting a mean square error for one step; k (K) k Representing the filter gain; r (k) is a measurement noise array; q (k-1) is a system noise variance matrix at the moment k-1; i is a unit matrix; p (P) k To estimate the mean square error; />Is a state estimate.
Wherein, the high accuracy filter of flight gesture still includes:
filter state equation: selecting three-dimensional attitude angle of aircraftAs a system state variableDeducing Euler angle differential equation of the aircraft under the northeast navigation coordinate system according to the inertial navigation principle as a state equation of the system, wherein the formula is as follows:
wherein f [ X (t), t ] is a system of nonlinear equations for X (t):
where w (t) is the system process noise and satisfies the mean value E [ w (t)]=0, variance E [ w (t), w T (τ)]＝Q(t).Is the filtered aircraft carrier angular rate information;
filter observation equation: selecting geomagnetic sensor measurement outputAndcalculating pitch angle (θ) from velocity information measured by a satellite navigation system v ) Observed quantity as systemThe observation equation of the system is as follows:
Z(t)＝h[X(t)，t]+v(k)；
where h [ X (t), t ] is a system of nonlinear equations for X (t):
wherein v (t) is the measured noise and satisfies the average value E [ v (t) ]]=0, variance E [ v (t), v T (τ)]＝Q(t)。
Further, the adoption of the discrete EKF filtering algorithm completes the high-precision filtering estimation of the flight attitude in real timeFurther comprises:
building an aircraft attitude filtering model:
and discretizing to obtain:
wherein the state transition matrix Φ k,k-1 The method is calculated according to the following formula:
in phi, phi k,k-1 In the form of a jacobian matrix,expressed as a set of equations f [ X (t), t]For each state variableIs the first partial derivative of (a); />One-step prediction for state;
wherein the observation matrix H (k) is a jacobian:
in (1) the->Expressed as a system of equations h [ X (t), t]For each state variable->Is the first partial derivative of (a);one-step prediction for state; v (k) is the measurement noise.
Furthermore, the discrete EKF filtering algorithm also includes the following two filtering processes:
the time updating process comprises the following steps:
P k，k-1 ＝Φ k，k-1 P k-1 (Φ k，k-1 ) T +Q k-1 ；
and a measurement update process:
K k ＝P (k，k-1) (H(k)) T [H(k)P k，k-1 (H(k)) T +R(k)] -1
P k ＝(I-K k H(k))P k，k-1 (I-K k H(k)) T +K k R(K)(K k ) T ；
wherein K is k Is a filtering gain; phi k，k-1 Is a state transition array; h (k) is a measurement matrix; r (k) is a measurement noise array; q (k-1) is a system noise variance matrix at the moment k-1; p (P) k-1 Estimating a variance matrix for the system at the previous moment; p (P) k，k-1 Predicting a mean square error for one step; p (P) k To estimate the mean square error;is a state estimate.
In summary, the invention provides a serial combined navigation method applicable to a high-dynamic aircraft, which adopts serial combination of two working modes of fast acquisition and high-precision combined filtering, wherein in the initial stage of the aircraft flight, uncontrolled flight under external trajectory constraint is carried out, and three input values of carrier speed under a navigation coordinate system output by a satellite navigation system and three input values of carrier initial parameters are carried out through an initial attitude fast estimator according to the measured values of a triaxial geomagnetic sensor, the triaxial angular rate output by an angular rate filter and the three input values of the carrier speed under the navigation coordinate system output by a satellite navigation system respectively, so that the aerial fast acquisition of the carrier initial parameters is carried out, and the flight initial parameters of the current carrier are output; and when the aircraft enters a controlled flight stage, the flight attitude high-precision filter estimation is completed in real time by adopting a discrete EKF (extended Kalman Filter) filtering algorithm through the flight attitude high-precision filter.
Drawings
Fig. 1 is a schematic diagram of a combined filter in series with a flying gesture.
FIG. 2 is a schematic view of a navigation sensor installation.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the invention provides a serial combined navigation method applicable to a high-dynamic aircraft, which comprises the following specific steps:
in the initial stage of the flight of the aircraft, the aircraft flies uncontrollably under the external trajectory constraint, and the initial attitude fast estimator is respectively used for respectively measuring values of the triaxial geomagnetic sensorsThree-axial angular rate of the angular rate filter output>Carrier speed +.>Three input values are used for quickly acquiring initial parameters of the carrier in the air and outputting the initial parameters of the current carrier in the air
In the stage of the aircraft entering the controlled flight, the high-precision filter of the flight attitude is respectively based on the initial flight parametersThe measured value H of the triaxial geomagnetic sensor b The triaxial angular rate +.>Said carrier speed>Four input values, and adopting a discrete EKF filtering algorithm to complete high-precision filtering estimation of flying attitude in real time
The high-dynamic aircraft on-board strapdown navigation sensor mainly comprises a triaxial geomagnetic sensor, a tourbillon and a satellite navigation receiver, and the measurement and installation modes of the sensors are shown in figure 2.
Wherein, the input data of the angular rate filter is the measured value of the MEMS gyroscopeAnd->Measurement value H of triaxial geomagnetic sensor b The method comprises the steps of carrying out a first treatment on the surface of the The MEMS gyroscope comprises a single-axis gyroscope Gx and a single-axis gyroscope Gz which are respectively arranged on the X axis of the carrier coordinate system O-XbYbZb; the sensitive directions of all axes of the triaxial geomagnetic sensor are completely overlapped with the carrier coordinate system and are used for measuring geomagnetic field vector information under the carrier coordinate system; the receiver antenna of the satellite navigation system is mounted on the surface of the aircraft shell.
The combined filter for the series connection of the flying postures adopted by the invention mainly comprises three completely different modules, namely an angular rate filter, an initial posture rapid estimator and a flying posture high-precision filter. The method comprises the following steps:
initial attitude fast estimator
According to the invention, the northeast and north day coordinate system (ENU) is selected as the navigation coordinate system (n-system), and after the high-dynamic aircraft is transmitted, the real-time speed information of the aircraft can be measured and obtained when the onboard satellite receiver works normally. Considering that the angle of attack of the aircraft is small during uncontrolled flight, the initial pitch angle theta of the carrier 0 The pitch angle can be estimated through speed information, and a pitch angle calculation formula is as follows:
equation 1:
in the above-mentioned (1),and->Respectively expressed as initial velocity components of the aircraft in the navigation coordinate system, measured by the satellite navigation system.
The initial pitch angle of the aircraft can be calculated from the formula (1), and thus the depression angle θ is calculated from the formula (1) 0 In the case of a yaw angle of the carrier is then calculated from the magnetic measurement informationAnd a roll angle gamma 0 The initial attitude information comprises the following magnetic measurement attitude calculation formulas:
equation 2:
equation 3:
in the above formulas (2) and (3),and->The measured output values of the triaxial geomagnetic sensor X, Y and the Z-axis geomagnetic sensor are respectively represented as +.> Is the north geomagnetic component of the geomagnetic field under the navigation system.
Therefore, by combining the formulas (1) - (3), the aircraft in the air can be quickly calculatedThree-dimensional initial attitude parameters during uncontrolled flight: yaw anglePitch angle theta 0 And a roll angle gamma 0 And a relatively accurate filtering initial parameter is provided for the serial secondary flight attitude high-precision filter.
Angular rate filter
Due to the measurement output of the airborne gyroscopeAnd->) And calculating the rolling speed by magnetic measurement information>There is a measurement error, and the error model can be expressed as:
equation 4:
in the above-mentioned (4),for each axial angular rate measurement, ω x ,ω y ,ω z For each axial ideal angular rate, n x ,n y ,n z Noise is measured for each axial angular rate.
The invention selects the angular rate as the state variable X (k) = [ omega ] of the system x ,ω y ,ω z ] T The state equation of the system can be expressed as:
equation 5:
X(k)＝Φ k，k-1 X(k-1)+w(k)；
in the above (5), the state transition matrixMeasurement noise w (k) = [ n ] x ,n y ,n z ] T The average value is E w (k)]=0, variance E [ w (k), w T (j)]=q (k), Q (k) is a systematic noise sequence square difference matrix.
SelectingAs observables of the filter system for each axial angular rate measurementThe observation equation for the angular rate filter can be expressed as:
equation 6:
Z(k)＝H(k)X(k)+v(k)；
in the above (6), the measuring arrayv (k) is the measurement noise of the system, and the average value is E [ v (k)]=0, variance E [ v (k), v T (j)]R (k), R (k) is the measurement noise sequence variance matrix.
Therefore, the invention forms an angular rate filter equation by the state equation (5) and the observation equation (6) together, and adopts a Kalman filter algorithm to complete the optimal filter estimation of the state variable X (k)Providing accurate aircraft carrier angular rate information for a high-precision flight attitude filter>
The filtering algorithm of the angular rate filter comprises the following two updating processes:
(1) Time update process, equation 7:
(2) Measurement update process, equation 8:
in the above formulas (7) and (8),one-step prediction for state; p (P) (k,k-1) Predicting a mean square error for one step; k (K) k Representing the filtering gain; r (k) is a measurement noise array; q (k-1) is a system noise square difference array at the moment k-1; i is an identity matrix; p (P) k To estimate the mean square error; />Is a state estimate.
High-precision filter for flight attitude
1. Filter state equation
According to the principle of inertial navigation, the Euler angle differential equation of the aircraft under the northeast navigation coordinate system can be deduced as formula 9:
in the above-mentioned (9),for carrier posture change rate, +.>Is the filtered aircraft carrier angular rate information.
Selecting three-dimensional attitude angle of aircraftAs a system state variable->And the above formula (9) is selected as a state equation of the system, which can be abbreviated as general form formula 10:
in the above formula (10), fX (t), t is a nonlinear equation set concerning X (t),
w (t) is the system process noise and satisfies the mean value E [ w (t)]=0, variance E [ w (t), w T (τ)]＝Q(t)。
2. Filter observation equation
According to the principle of magnetic measurement, the measurement output of a geomagnetic sensor can be expressed as formula 11:
in the above-mentioned (11),is the geomagnetic field vector under the northeast navigation coordinate system; />Is a pose transformation matrix.
As previously described, the pitch angle can be calculated from the velocity information measured by the satellite navigation system, equation 12:
in the above-mentioned method, the step of,is the speed of the carrier in the navigational coordinate system.
Selecting geomagnetic sensor measurement outputAnd calculating a pitch angle (θ) from velocity information measured by the satellite navigation system v ) Observed quantity as system->And a magnetic measurement formula (11) and a pitch angle calculation formula (12) are selected as observation equations of the system, which can be abbreviated as general form as formula 13:
Z(t)＝h[X(t),t]+v(t)；
in the above formula (13), v (t) is the measured noise and satisfies the average value E [ v (t) ]]=0, variance E [ v (t), v T (τ)]＝Q(t)。h[X(t)，t]A system of nonlinear equations for X (t):
3. EKF filtering algorithm
The comprehensive filter state equation (10) and the filter observation equation (13) form an aircraft attitude filtering model, which is a nonlinear continuous system and can be abbreviated as a general form, and the model is shown as a formula 14:
performing state estimation by using a discrete EKF algorithm, wherein the EKF is a filtering method based on linearization processing of Taylor series expansion, and performing linearization processing on a filtering model first, and then performing discretization on a formula (15) to obtain a formula (15):
in the above (15), the state transition matrix Φ k,k-1 Calculated as follows in equation 16:
Φ k,k-1 in the form of a jacobian matrix,expressed as a set of equations f [ X (t), t]For each state variableIs the first partial derivative of (a); />
One-step prediction of the state.
In the above formula (15), the observation matrix H (k) is a jacobian,
expressed as a system of equations h [ X (t), t]For each state variable->Is used for the first order partial derivative of (a),
one-step prediction for state; v (k) is the measurement noise.
Thus, the discrete EKF-based filtering algorithm includes two filtering processes:
(1) The time update process uses equation 17:
P k,k-1 ＝Φ k,k-1 P k-1 (Φ k,k-1 ) T +Q k-1 ；
(2) The measurement update procedure uses equation 18:
K k ＝P (k,k-1) (H(k)) T [H(k)P k,k-1 (H(k)) T +R(k)] -1 ；
/>
P k ＝(I-K k H(k))P k,k-1 (I-K k H(k)) T +K k R(k)(K k ) T ；
in the above filter equations (17) - (18), K k Is a filtering gain; phi k,k-1 Is a state transition array; h (k) is a measurement matrix; r (k) is a measurement noise array; q (k-1) is a system noise variance matrix at the moment k-1; p (P) k-1 Estimating a variance matrix for the system at the previous moment; p (P) k,k-1 Predicting a mean square error for one step; p (P) k To estimate the mean square error;is a state estimate.
It can be seen that the initial pose parameters provided at the initial pose fast estimatorThrough the series combination scheme, the on-board navigation system has different problems and algorithm requirements at different stages. In the uncontrolled flight stage with unknown initial parameters, the navigation algorithm is required to quickly estimate the initial parameters and estimate the accuracy; the initial parameters are known (low in precision) in the subsequent controlled flight stage, but the on-board navigation system has strong nonlinearity, large calculation error and the like, and the requirement of the stage on the navigation algorithm is to improve the precision of filter estimation and consider the problem of real-time performance.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (7)

1. The serial combined navigation method for the high-dynamic aircraft is characterized by comprising the following steps of:
in the initial stage of the flight of the aircraft, the aircraft flies uncontrollably under the external trajectory constraint, and the initial attitude fast estimator is respectively used for respectively measuring values of the triaxial geomagnetic sensorsThree axial angular rate of the angular rate filter outputAnd carrier speed +.>Three input values are used for quickly acquiring initial parameters of the carrier in the air and outputting the initial parameters of the current carrier in the air>
In the stage of the aircraft entering the controlled flight, the high-precision filter of the flight attitude is respectively based on the initial flight parametersThe measured value H of the triaxial geomagnetic sensor b The triaxial angular rate +.>The carrier speedFour input values, and adopting a discrete EKF filtering algorithm to complete high-precision filtering estimation of the flight attitude in real time
The input data of the angular rate filter is the measured value of the MEMS gyroscopeAnd->Measurement value H of triaxial geomagnetic sensor b The method comprises the steps of carrying out a first treatment on the surface of the The MEMS gyroscope comprises O-X arranged on the carrier coordinate system b Y b Z b A single-axis gyro Gx on the X-axis and a single-axis gyro Gz on the Z-axis; the sensitive directions of all the three axes of the triaxial geomagnetic sensor are completely coincident with the carrier coordinate system and are used for measuring geomagnetic field vector information under the carrier coordinate system; the receiver antenna of the satellite navigation system is arranged on the surface of the aircraft shell;
calculating initial flight parameters by the initial attitude fast estimatorThe method also comprises the following calculation process:
estimating initial pitch angle θ of carrier from velocity information 0 And yaw angleThe pitch angle solution formula is:
in the method, in the process of the invention,and->Respectively expressed as initial speed components of the aircraft under a navigation coordinate system, and are measured by a satellite navigation system;
from magnetic information resolving carriersRoll angle gamma 0 Initial attitude information, the formula is:
in the method, in the process of the invention,and->The measured output values of the triaxial geomagnetic sensor X, Y and the Z-axis geomagnetic sensor are respectively represented;geomagnetic field component under navigation system;
the high-precision filter for the flying gesture further comprises:
filter state equation: selecting three-dimensional attitude angle of aircraftAs a system state variableDeducing Euler angle differential equation of the aircraft under the northeast navigation coordinate system according to the inertial navigation principle as a state equation of the system, wherein the formula is as follows:
wherein f [ X (t), t ] is a system of nonlinear equations for X (t):
where w (t) is the system process noise and satisfies the mean value E [ w (t)]=0, variance E [ w (t), w T (τ)]＝Q(t)；Is the filtered aircraft carrier angular rate information;
filter observation equation: selecting geomagnetic sensor measurement outputAnd calculating a pitch angle (θ) from velocity information measured by the satellite navigation system v ) Observed quantity as system->The observation equation of the system is as follows:
Z(t)＝h[X(t),t]+v(t)；
where h [ X (t), t ] is a system of nonlinear equations for X (t):
wherein v (t) is the measured noise and satisfies the average value E [ v (t) ]]=0, variance E [ v (t), v T (τ)]＝Q(t)；The output values are measured for each axis of the triaxial geomagnetic sensor.
2. The method for integrated serial navigation of high dynamic aircraft according to claim 1, wherein the three axial angular ratesThe angular rate filtering equation of (2) consists of a state equation and an observation equation together, and the Kalman filtering algorithm is adopted to complete the optimal filtering estimation of the state variable X (k)>Providing accurate aircraft carrier angular rate information for a high-precision flight attitude filter>
3. The method for integrated serial navigation of applicable high dynamic aircraft according to claim 2, wherein the state equation further comprises:
angular rate is chosen as the state variable X (k) = [ ω ] of the system x ,ω y ,ω z ] T The state equation of the system is expressed as:
X(k)＝Φ k,k-1 X(k-1)+w(k)；
in the state transition matrixMeasurement noise w (k) = [ n ] x ,n y ,n z ] T The average value is E w (k)]=0, variance E [ w (k), w T (j)]=q (k), Q (k) is a systematic noise sequence variance matrix.
4. The method for integrated serial navigation of applicable high dynamic vehicles according to claim 2, wherein the observation equation further comprises:
selectingFor each axial angular rate measurement as an observation of the filter system +.>The observation equation for the angular rate filter can be expressed as:
Z(k)＝H(k)X(k)+v(k)；
in the measuring arrayv (k) is the measurement noise of the systemThe sound satisfies the mean value E v (k)]=0, variance E [ v (k), v T (j)]R (k), R (k) is the measurement noise sequence variance matrix.
5. The method for integrated navigation of a highly dynamic aircraft series of claim 2, wherein the angular rate filter equation of the angular rate filter further comprises two updating processes:
the time updating process comprises the following steps:
and a measurement update process:
in the method, in the process of the invention,one-step prediction for state; p (P) (k,k-1) Predicting a mean square error for one step; k (K) k Representing the filtering gain; r (k) is a measurement noise array; q (k-1) is a system noise variance matrix at the moment k-1; i is an identity matrix; p (P) k To estimate the mean square error;is a state estimate; phi k,k-1 Is a state transition matrix; z (k) is the observed quantity of the system; h (k) is the observation matrix of the system; p (P) k-1 To predict the mean square error.
6. The method for serial integrated navigation of high dynamic aircraft according to claim 1, wherein the discrete EKF filtering algorithm is adopted to complete the high-precision filtered estimation of the flight attitude in real timeFurther comprises:
building an aircraft attitude filtering model:
and discretizing to obtain:
wherein Z (k) is the observed quantity of the system; h (k) is the observation matrix; v (k) is measurement noise;
wherein the state transition matrix Φ k,k-1 The method is calculated according to the following formula:
in phi, phi k,k-1 In the form of a jacobian matrix,expressed as a set of equations f [ X (t), t]For each state variableIs the first partial derivative of (a); />One-step prediction for state;
wherein the observation matrix H (k) is a jacobian:
in the method, in the process of the invention,expressed as a system of equations h [ X (t), t]For each state variable->Is the first partial derivative of (a); />One-step prediction for state; v (k) is the measurement noise.
7. The method for integrated navigation of a highly dynamic aircraft series of claim 6, wherein the discrete EKF filtering algorithm further comprises two filtering processes:
the time updating process comprises the following steps:
P k，k-1 ＝Φ k，k-1 P k-1 (Φ k，k-1 ) T +Q k-1 ；
and a measurement update process:
K k ＝P k，k-1 (H(k)) T [H(k)P k，k-1 (H(k)) T +R(k)] -1 ；
P k ＝(I-K k H(k))P k，k-1 (I-K k H(k)) T +K k R(k)(K k ) T ；
wherein K is k Is a filtering gain; phi k，k-1 Is a state transition array; h (k) is a measurement matrix; r (k) is a measurement noise array; q (k-1) is a system noise variance matrix at the moment k-1; p (P) k-1 Estimating a variance matrix for the system at the previous moment; p (P) k，k-1 Predicting a mean square error for one step; p (P) k To estimate the mean square error;is a state estimate; />One-step prediction for state; i is an identity matrix.
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