CN108534783A - A kind of aircraft navigation method based on Beidou navigation technology - Google Patents
A kind of aircraft navigation method based on Beidou navigation technology Download PDFInfo
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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
- G01C21/20—Instruments for performing navigational calculations
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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
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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
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Abstract
The invention discloses a kind of aircraft navigation methods based on Beidou navigation technology, include the following steps：S100, the target information extraction based on SINS/BDS systems will carry out information extraction by the Federated Filters of no reconfiguration structure to SINS/BDS integrated navigation systems；S200, the target information extraction based on SINS/CNS systems；S300, setting local filter, carry out conventional Kalman filtering to existing integrated navigation information, calculate two groups of local optimum estimated values for obtaining system mode；S400, setting fault detection module, examine the validity of each partial estimation；S500, primary filter structure is established, effective partial estimation value is sent into senior filter and carries out global optimum's information fusion, obtain the global best estimates of system common condition, this method had both absorbed SINS/BDS integrated navigations and had tested the speed the advantage high with positioning accuracy, the high advantage of SINS/CNS integrated navigation accuracy of attitude determination is absorbed again, to obtain it is very high determine appearance, test the speed and positioning accuracy, realize comprehensive optimization of each navigational parameter.
Description
Technical Field
The invention relates to the field of aircraft navigation, in particular to an aircraft navigation method based on a Beidou navigation technology.
Background
In modern war, high informatization enables attackers and defenders to quickly acquire battlefield information, but the speed of a platform for putting in a weapon system and weapons is relatively low, the platform cannot quickly reach a target area and destroy the target, and a warplane is lost in the short term. The slow speed and easy interception are the biggest defects of the current cruise aircraft, and the hypersonic aircraft has come up against the problems.
Eyes as hypersonic aircrafts: navigation systems are clearly key. The navigation system is the most basic link of a generalized flight control system (navigation, guidance and control system), is the basis of a guidance and control loop, is a data source of the guidance and control loop, and is one of the key components of the aircraft, but the existing method for aircraft navigation has the following defects:
for example, the invention patent with application number of 201611032966.9 is a microminiature unmanned aerial vehicle positioning and navigation method based on the Beidou navigation system:
the method comprises the steps that an optical flow sensor installed at the bottom of a four-rotor unmanned aerial vehicle is used for obtaining speed information of the unmanned aerial vehicle, an airborne inertial navigation device is used for obtaining acceleration information, an airborne visual system is used for obtaining speed information, and original measurement values of the position of a Beidou system are combined to obtain estimation of the position and the speed through fusion filtering; and further, the position control of the aircraft is realized through a nonlinear position control algorithm. The invention is mainly applied to unmanned aerial vehicles and flight control occasions.
However, the existing aircraft navigation method based on the Beidou navigation technology has the following defects:
(1) at present, certain research and application have been carried out on the combined guidance of inertia and satellites and inertia and astronomy in China, but the research on the combined navigation technology of inertia, satellites, astronomy and other systems based on multi-sensor information fusion is still in the stages of theoretical research and experimental exploration;
(2) at present, in the navigation systems of strategic weapons such as intercontinental missiles and the like in China, an inertia/astronomical combined navigation mode is adopted, so that the outstanding problem that a stable platform continuously deviates from a reference position along with the increase of flight time is effectively solved, and the speed of accumulation of positioning errors of an inertial navigation system along with the time is slowed down to a certain extent, but the navigation precision is still not high in flight environments such as long-endurance flight and high-speed flight, and the outstanding defects of complex structure, large size, heavy weight, poor reliability, poor fault tolerance, difficult maintenance and the like exist;
(3) at present, a navigation system combining a platform inertial navigation system, a star tracker and a Global Positioning System (GPS) is researched domestically, two star trackers with mutually orthogonal aiming lines are configured in the system, and the star tracker is installed on the inertial navigation platform, so that the coupling among subsystems is too tight, and the estimation effect of system errors is influenced to a certain extent.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an aircraft navigation method based on the Beidou navigation technology, which can effectively solve the problems in the background art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an aircraft navigation method based on the Beidou navigation technology comprises the following steps:
s100, extracting target information based on the SINS/BDS system, and extracting information of the SINS/BDS combined navigation system through a federal filter without a reset structure;
s200, extracting target information based on the SINS/CNS system, and extracting information of the SINS/CNS integrated navigation system through a federal filter without a reset structure;
s300, setting a local filter, performing conventional Kalman filtering on the existing integrated navigation information, and calculating to obtain two groups of local optimal estimated values of the system state;
s400, setting a fault detection module, and respectively enabling the two groups of local optimal estimated values to pass through respective fault detection modules to check the effectiveness of each local estimation value;
s500, establishing a main filter structure, and sending the effective local estimation value to the main filter for global optimal information fusion to obtain global optimal estimation of the system public state.
Further, in step S100, the specific algorithm for extracting the target information based on the SINS/BDS system is as follows:
s101, solving a differential equation of the attitude, and setting a quaternion describing the attitude of the carrier asIt satisfies the differential equation:wherein, referred to as the pose velocity;
s102, obtaining a posture speed formula and utilizingWherein,is the carrier angular rate output by a gyroscope in the strapdown inertial navigation,the determined attitude matrix is updated for the attitude in step S101,is the commanded angular velocity of the mathematical platform;
s103, solving a differential equation, and combining the steps S101 and S102 to obtain the attitude quaternion in real timeFrom the real-time values of the vector, a vector attitude matrix can be determined
Is composed of
Thereby obtaining the heading angle, the pitch angle and the roll angle of the carrier.
Further, the velocity update algorithm of the federal filter in steps S100 and S200 is as follows:
s201, obtaining a speed updating equation, and obtaining a speed updating differential equation of the strapdown inertial navigation system according to a specific force equation of the inertial navigation system
S202, determining the speed in a navigation coordinate system at a certain moment, and setting the speed updating period of the strapdown inertial navigation to be Tv＝tm-tm-1In the above formula [ t ]m-1，tm]Integrating in time period, and obtaining t after finishingmThe velocity of the time carrier in the navigation coordinate system is
S203, determining a rotation effect compensation term in the speed updating,where Δ θmAt an angular velocity of [ tm-1，tm]An angular increment generated over a period of time;
and S204, determining a rowing effect compensation item in the speed updating.
Further, in step S400, the specific correction method in the fault detection module is as follows:
s401, setting a carrier conversion matrix, and setting a conversion matrix from a carrier coordinate system (b system) to a navigation coordinate system (n system) as
S402, calculating a matrix conversion error angle, and setting a navigation coordinate system n actually obtained by navigation calculation1The corresponding coordinate transformation matrix isWherein n is1Is at an error angle with respect to n
S403, determining a coefficient conversion matrix, and according to the mathematical platform attitude error angle estimation value output by the Kalman filterCalculating from n1Conversion matrix tied to n seriesNamely:
thus, the true strapdown attitude matrix
Further, in step S500, the combined navigation information fusion algorithm of the main filter is as follows:
s501, setting the local optimal estimated value of the system public state of SINS/BDS combined navigation as XBIts corresponding estimated mean square error is PB(ii) a System public state bureau with SINS/CNS integrated navigationThe optimum estimated value is XcIts corresponding estimated mean square error is PC；
S502, setting XcFor common states of combined navigation systems, Xci(i-1, 2 … N) is the locally optimal estimate of the common state for the local filter i, with the covariance matrix of the estimate PCi，δXciThe estimation error for each locally optimal estimate, namely: delta Xci＝Xci-Xc；
S503, obtaining a global optimal estimated value X and an estimated mean square error P of the public state of the SINS/BDS/CNS integrated navigation system according to a global information fusion algorithm without a reset federal filter, namely:
further, in step S503, the estimated value X of the system state according to the local kalman filter at the previous time is obtainedK-1And its mean square error PK-1And performing standard Kalman filtering by using the system state equation, the output of the strapdown inertial navigation system and the output of other auxiliary systems.
Further, in step S500, a computer mathematical simulation is performed on the combined navigation information rapid compensation algorithm.
Further, in step S500, the main filter outputs an optimal estimation of the system error to a main control system, where the main control system includes an attitude resolution quaternion number module, a pitch angle control module, and a PID control module;
the attitude resolution quaternion quantity module receives an acceleration signal of the three-axis accelerometer, and a signal end of the attitude resolution quaternion quantity module is interactively connected with the three-axis gyroscope;
the output end of the attitude resolving quaternion quantity module is connected with a pitch angle control module, and the control end of the pitch angle control module is connected with a PID control module;
and the output end of the PID control module is used for adjusting the tail wing attitude through PWM waves.
Compared with the prior art, the invention has the beneficial effects that:
(1) the aircraft navigation method not only absorbs the advantages of high precision of SINS/BDS integrated navigation speed measurement and positioning, but also absorbs the advantages of high precision of SINS/CNS integrated navigation attitude determination, thereby obtaining high attitude determination, speed measurement and positioning precision and realizing the comprehensive optimization of each navigation parameter;
(2) the invention adopts the integrated navigation system and the information fusion algorithm thereof, takes the strapdown inertial navigation system as a public reference system, takes the Beidou and astronomical navigation systems as an auxiliary navigation system, adopts the federal filtering structure to design the strapdown inertial navigation/Beidou/astronomical high-precision integrated navigation system, not only has very high attitude determination, positioning and speed measurement precision, but also can effectively estimate the error of an inertial device, fully absorbs the advantages of each navigation subsystem, realizes the optimization of each navigation parameter and comprehensively improves the comprehensive performance of the navigation system.
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FIG. 1 is an overall flow chart of the present invention;
fig. 2 is a schematic structural diagram of a master control system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the invention provides an aircraft navigation method based on the Beidou navigation technology, which comprises the following steps:
s100, extracting target information based on the SINS/BDS system, and extracting information of the SINS/BDS combined navigation system through a federal filter without a reset structure;
s200, extracting target information based on the SINS/CNS system, and extracting information of the SINS/CNS integrated navigation system through a federal filter without a reset structure;
s300, setting a local filter, performing conventional Kalman filtering on the existing integrated navigation information, and calculating to obtain two groups of local optimal estimated values of the system state;
s400, setting a fault detection module, and respectively enabling the two groups of local optimal estimated values to pass through respective fault detection modules to check the effectiveness of each local estimation value;
s500, establishing a main filter structure, and sending the effective local estimation value to the main filter for global optimal information fusion to obtain global optimal estimation of a system public state (SINS error state).
In this embodiment, the SINS and BDS, and the SINS and CNS are combined in pairs to form an SINS/BDS, SINS/CNS combined navigation local kalman filter, and the two local filters calculate two sets of local optimal estimates X of the system stateB，XC(ii) a Then, the two groups of local optimal estimates pass through respective fault detection modules respectively to check the effectiveness of each local estimate; then, the effective local estimation value is sent to a main filter for global optimal information fusion to obtain global optimal estimation of a system public state (SINS error state); and finally, immediately carrying out error correction on the SINS by using the obtained SINS error optimal estimated value, and taking the corrected SINS output as the output of the SINS/BDS/CNS integrated navigation system.
In step S100, a specific algorithm for extracting target information based on the SINS/BDS system is as follows:
s101, solving a differential equation of the attitude, and setting a quaternion describing the attitude of the carrier asIt satisfies the differential equation:wherein, referred to as the pose velocity;
s102, obtaining a posture speed formula and utilizingWherein,is the carrier angular rate output by a gyroscope in the strapdown inertial navigation,the determined attitude matrix is updated for the attitude in step S101,is the commanded angular velocity of the mathematical platform;
s103, solving a differential equation, and combining the steps S101 and S102 to obtain the attitude quaternion in real timeFrom the real-time values of the vector, a vector attitude matrix can be determined
Is composed of
Thereby obtaining the heading angle, the pitch angle and the roll angle of the carrier.
The velocity update algorithm of the federal filter in steps S100, S200 is as follows:
s201, obtaining a speed updating equation, and obtaining a speed updating differential equation of the strapdown inertial navigation system according to a specific force equation of the inertial navigation system
S202, determining the speed in a navigation coordinate system at a certain moment, and setting the speed updating period of the strapdown inertial navigation to be Tv＝tm-tm-1In the above formula [ t ]m-1，tm]Integrating in time period, and obtaining t after finishingmThe velocity of the time carrier in the navigation coordinate system is
S203, determining a rotation effect compensation term in the speed updating,where Δ θmAt an angular velocity of [ tm-1，tm]An angular increment generated over a period of time;
and S204, determining a rowing effect compensation item in the speed updating.
In step S400, the specific correction method in the fault detection module is as follows:
s401, setting a carrier conversion matrix, and setting a conversion matrix from a carrier coordinate system (b system) to a navigation coordinate system (n system) as
S402, calculating a matrix conversion error angle, and setting a navigation coordinate system n actually obtained by navigation calculation1The corresponding coordinate transformation matrix isWherein n is1Is at an error angle with respect to n
S403, determining a coefficient conversion matrix, and according to the mathematical platform attitude error angle estimation value output by the Kalman filterCalculating from n1Conversion matrix tied to n seriesNamely:
thus, the true strapdown attitude matrix
In step S500, the combined navigation information fusion algorithm of the main filter is as follows:
s501, setting the local optimal estimated value of the system public state of SINS/BDS combined navigation as XBIts corresponding estimated mean square error is PB(ii) a The local optimal estimated value of the system public state of SINS/CNS integrated navigation is XcIts corresponding estimated mean square error is PC；
S502, setting XcFor common states of combined navigation systems, Xci(i-1, 2 … N) is the locally optimal estimate of the common state for the local filter iThe estimated covariance matrix is PCi，δXciThe estimation error for each locally optimal estimate, namely: delta Xci＝Xci-Xc；
S503, obtaining a global optimal estimated value X and an estimated mean square error P of the public state of the SINS/BDS/CNS integrated navigation system according to a global information fusion algorithm without a reset federal filter, namely:
in this embodiment, after obtaining the global optimal estimated value X of the error state of the strapdown inertial navigation system, the error correction of the strapdown inertial navigation system needs to be performed in time according to the optimal estimated value, and finally, the output of the strapdown inertial navigation system subjected to the system error correction is used as the output of the SINS/BDS/CNS high-precision integrated navigation system, which specifically includes navigation information such as the attitude, speed, position, angular velocity, and acceleration of the carrier.
In the embodiment, each local filter in the non-resetting federal filtering structure does not interfere with each other, filtering is performed independently and parallelly, mutual influence caused by feedback resetting is avoided, and when a certain subsystem breaks down, normal work of other navigation subsystems and the whole integrated navigation system is not influenced, so that the accuracy of the integrated navigation system is ensured, and better fault tolerance and reliability are realized. Therefore, the system design based on the non-reset federal filter structure and the redundant configuration of various navigation subsystems with different performances provide effective guarantee for the reliability and fault tolerance of the combined navigation system under the complex and severe environment.
In step S503, the estimated value X of the system state of the local Kalman filter at the previous moment is usedK-1And its mean square error PK-1And performing standard Kalman filtering by using the system state equation, the output of the strapdown inertial navigation system and the output of other auxiliary systems.
In this embodiment, taking the normal condition as an example, according to step S503, the following steps can be obtained:
XK＝XK/K-1+KK*(ZK-HK*XK/K-1)
PK＝(I-KK*HK)PK/K-1(I-KK*HK)T+KKRKKK T
according to the above formula, when the navigation system works in normal environment, it can construct the measurement information Z in real time through the output of each navigation subsystemKSo that the standard Kalman filtering algorithm passes through the constructed measurement information ZKThe influence of factors such as initial value errors, system noise, system modeling errors and the like on the state recurrence estimation value is continuously eliminated, so that the recurrence estimation value of the system state is more and more approximate to a true value, namely the filtering precision of the system is more and more high.
In step S500, performing computer mathematical simulation on the combined navigation information fast compensation algorithm, in this embodiment, the SINS/BDS/CNS high-precision combined navigation system utilizes the non-resetting federal kalman filtering technique, fully exerts the advantages of the SINS, BDS, CNS navigation subsystems, has very high attitude determination, speed measurement, and positioning precision, can also effectively estimate the errors of inertial devices, comprehensively improves the comprehensive performance of the combined navigation system, and realizes the optimization of the navigation parameters of the system.
As shown in fig. 2, in step S500, the main filter outputs an optimal estimation of the system error to a main control system, where the main control system includes an attitude resolution quaternion number module, a pitch angle control module, and a PID control module; the attitude resolution quaternion quantity module receives an acceleration signal of the three-axis accelerometer, and a signal end of the attitude resolution quaternion quantity module is interactively connected with the three-axis gyroscope; the output end of the attitude resolving quaternion quantity module is connected with a pitch angle control module, and the control end of the pitch angle control module is connected with a PID control module; and the output end of the PID control module is used for adjusting the tail wing attitude through PWM waves.
In the embodiment, the PID control module adopts a complementary filtering algorithm to have a low-frequency filtering function on the information of the acceleration sensor with low-frequency characteristics, so that the obtained attitude information is smoother, and meanwhile, the interference of high-frequency information on the attitude information is reduced; the low-frequency noise obtained by the gyroscope sensor has a high-pass filtering function, so that the interference of gyroscope drift is reduced by the obtained attitude information, and the gyroscope can stably work for a long time.
In this embodiment, the complementary filtering method utilizes the characteristics of the two sensors to complement each other, thereby improving the measurement accuracy of the sensors and the dynamic performance of the system.
In this embodiment, in the process of solving the attitude of the flapping wing aircraft by using the complementary filtering algorithm, the data is initialized, and the data obtained by the accelerometer is the three-axis acceleration ax，ay，azThe data obtained by the gyroscope is the three-axis angular velocity wx，wy，wzThe initial angular velocity is integrated to obtain the three-axis attitude angle at the initial time, and the three-axis attitude angle at the initial state is usually 0, so that under the initial condition, the three-axis attitude angle needs to be the same as the specified initial direction of the coordinate of the navigation system, and the solution of the quaternion at the initial state is as follows:
before the flapping wing aircraft flies, the initial position needs to be adjusted for representation convenience, and under the initial condition, theta,set to zero degrees. Get the initial quaternion q ═ 1000]。
In this embodiment, the acceleration values are generally unitized for convenience in the calculation process. And correcting parameters of the gyroscope by using a complementary filtering method, performing cross multiplication on a representation of the gravity acceleration in an inertial coordinate system and a gravity acceleration value represented in the body measured by the accelerometer to obtain an error value, wherein the error value is called a compensation correction error, and then correcting the gyroscope by using the error.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. An aircraft navigation method based on the Beidou navigation technology is characterized in that: the method comprises the following steps:
s100, extracting target information based on the SINS/BDS system, and extracting information of the SINS/BDS combined navigation system through a federal filter without a reset structure;
s200, extracting target information based on the SINS/CNS system, and extracting information of the SINS/CNS integrated navigation system through a federal filter without a reset structure;
s300, setting a local filter, performing conventional Kalman filtering on the existing integrated navigation information, and calculating to obtain two groups of local optimal estimated values of the system state;
s400, setting a fault detection module, and respectively enabling the two groups of local optimal estimated values to pass through respective fault detection modules to check the effectiveness of each local estimation value;
s500, establishing a main filter structure, and sending the effective local estimation value to the main filter for global optimal information fusion to obtain global optimal estimation of the system public state.
2. The aircraft navigation method based on the Beidou navigation technology according to claim 1, wherein: in step S100, a specific algorithm for extracting target information based on the SINS/BDS system is as follows:
s101, solving a differential equation of the attitude, and setting a quaternion describing the attitude of the carrier asIt satisfies the differential equation:wherein, referred to as the pose velocity;
s102, obtaining a posture speed formula and utilizingWherein,is the carrier angular rate output by a gyroscope in the strapdown inertial navigation,the determined attitude matrix is updated for the attitude in step S101,is the commanded angular velocity of the mathematical platform;
s103, solving a differential equation, and combining the steps S101 and S102 to obtain the attitude quaternion in real timeFrom the real-time values of the vector, a vector attitude matrix can be determined
Is composed of
Thereby obtaining the heading angle, the pitch angle and the roll angle of the carrier.
3. The aircraft navigation method based on the Beidou navigation technology according to claim 1, wherein: the velocity update algorithm of the federal filter in steps S100, S200 is as follows:
s201, obtaining a speed updating equation, and obtaining a speed updating differential equation of the strapdown inertial navigation system according to a specific force equation of the inertial navigation system
S202, determining the speed in a navigation coordinate system at a certain moment, and setting the speed updating period of the strapdown inertial navigation to be Tv＝tm-tm-1In the above formula [ t ]m-1，tm]Integrating in time period, and obtaining t after finishingmThe velocity of the time carrier in the navigation coordinate system is
S203, determining rotation in speed updatingThe term of the effect compensation is used,where Δ θmAt an angular velocity of [ tm-1，tm]An angular increment generated over a period of time;
and S204, determining a rowing effect compensation item in the speed updating.
4. The aircraft navigation method based on the Beidou navigation technology according to claim 1, wherein: in step S400, the specific correction method in the fault detection module is as follows:
s401, setting a carrier conversion matrix, and setting a conversion matrix from a carrier coordinate system (b system) to a navigation coordinate system (n system) as
S402, calculating a matrix conversion error angle, and setting a navigation coordinate system n actually obtained by navigation calculation1The corresponding coordinate transformation matrix isWherein n is1Is at an error angle with respect to n
S403, determining a coefficient conversion matrix, and according to the mathematical platform attitude error angle estimation value output by the Kalman filterCalculating from n1Conversion matrix tied to n seriesNamely:
thus, the true strapdown attitude matrix
5. The aircraft navigation method based on the Beidou navigation technology according to claim 1, wherein: in step S500, the combined navigation information fusion algorithm of the main filter is as follows:
s501, setting the local optimal estimated value of the system public state of SINS/BDS combined navigation as XBIts corresponding estimated mean square error is PB(ii) a The local optimal estimated value of the system public state of SINS/CNS integrated navigation is XcIts corresponding estimated mean square error is PC；
S502, setting XcFor common states of combined navigation systems, Xci(i-1, 2 … N) is the locally optimal estimate of the common state for the local filter i, with the covariance matrix of the estimate PCi，δXciThe estimation error for each locally optimal estimate, namely: delta Xci＝Xci-Xc；
S503, obtaining a global optimal estimated value X and an estimated mean square error P of the public state of the SINS/BDS/CNS integrated navigation system according to a global information fusion algorithm without a reset federal filter, namely:
6. the aircraft navigation method based on the Beidou navigation technology according to claim 5, wherein: in step S503, the estimated value X of the system state of the local Kalman filter at the previous moment is usedK-1And its mean square error PK-1Using system equation of state, strapdown inertial navigation system and other auxiliary systemsAnd outputting and carrying out standard Kalman filtering.
7. The aircraft navigation method based on the Beidou navigation technology according to claim 1, wherein: in step S500, a computer mathematical simulation is performed on the combined navigation information rapid compensation algorithm.
8. An aircraft navigation method based on the Beidou navigation technology is characterized in that: in step S500, the main filter outputs an optimal estimation of the system error to a main control system, where the main control system includes an attitude resolution quaternion number module, a pitch angle control module, and a PID control module;
the attitude resolution quaternion quantity module receives an acceleration signal of the three-axis accelerometer, and a signal end of the attitude resolution quaternion quantity module is interactively connected with the three-axis gyroscope;
the output end of the attitude resolving quaternion quantity module is connected with a pitch angle control module, and the control end of the pitch angle control module is connected with a PID control module;
and the output end of the PID control module is used for adjusting the tail wing attitude through PWM waves.
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| CN109813299A (en) * | 2019-03-06 | 2019-05-28 | 南京理工大学 | A Combined Navigation Information Fusion Method Based on Interactive Multiple Models |
| CN110057382A (en) * | 2019-04-23 | 2019-07-26 | 西北工业大学 | A kind of inertial navigation numerical value update method based on launching coordinate system |
| CN111649744A (en) * | 2020-05-15 | 2020-09-11 | 北京自动化控制设备研究所 | A Combined Navigation and Positioning Method Based on Dynamic Model Aid |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109813299A (en) * | 2019-03-06 | 2019-05-28 | 南京理工大学 | A Combined Navigation Information Fusion Method Based on Interactive Multiple Models |
| CN110057382A (en) * | 2019-04-23 | 2019-07-26 | 西北工业大学 | A kind of inertial navigation numerical value update method based on launching coordinate system |
| CN110057382B (en) * | 2019-04-23 | 2021-07-09 | 西北工业大学 | A Numerical Update Method of Strapdown Inertial Navigation Based on Transmitting Coordinate System |
| CN111649744A (en) * | 2020-05-15 | 2020-09-11 | 北京自动化控制设备研究所 | A Combined Navigation and Positioning Method Based on Dynamic Model Aid |
| CN111649744B (en) * | 2020-05-15 | 2023-08-15 | 北京自动化控制设备研究所 | Combined navigation positioning method based on dynamic model assistance |
| CN111947654A (en) * | 2020-08-13 | 2020-11-17 | 杭州北斗东芯科技有限公司 | Navigation and control integrated chip and control method thereof |
| CN112629538A (en) * | 2020-12-11 | 2021-04-09 | 哈尔滨工程大学 | Ship horizontal attitude measurement method based on fusion complementary filtering and Kalman filtering |
| CN113048987A (en) * | 2021-03-12 | 2021-06-29 | 湘潭大学 | Vehicle navigation system positioning method |
| CN114689054A (en) * | 2022-02-24 | 2022-07-01 | 中国电子科技集团公司第十研究所 | High-precision navigation method and device for Takang system, flight equipment and storage medium |
| CN114689054B (en) * | 2022-02-24 | 2023-06-20 | 中国电子科技集团公司第十研究所 | Takang system high-precision navigation method and device, flight equipment and storage medium |
| CN118089700A (en) * | 2024-02-28 | 2024-05-28 | 中国人民解放军95795部队 | Accurate air-drop integrated navigation data self-adaptive filtering method |
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