A transfer alignment method based on aircraft flight state detection

Abstract

The application discloses a transfer alignment method based on aircraft flight state detection, and steps of the method comprise the following steps: step S1: obtaining the flight state of an aircraft through an aircraft flight state detector according to the output of a main inertial navigation system and a sub-inertial navigation system; step S2: when the aircraft is in variable-speed flight, turning or oscillating-wing flight, a first Kalman filter is constructed through the main inertial navigation system and the sub-inertial navigation system to correct errors and estimate the installation error of the heading angle of the main inertial navigation system and the sub-inertial navigation system; when the aircraft is in uniform-speed flight, the installation error of the heading angle is directly used, and a second Kalman filter is constructed by using the output information of the main and sub-inertial navigation systems to correct errors and estimate the gyro zero offset. The application has the advantages of simple principle, high accuracy, fast alignment speed and the like.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of inertial navigation, and in particular to a transfer alignment method based on aircraft flight status detection. Background Art

[0002] Inertial Navigation System (INS) transfer alignment compares the output of a sub-INS with the high-precision main INS output, serving as a benchmark. Using filtering algorithms, the sub-INS error parameters are estimated and compensated, thereby minimizing the impact of misalignment angle errors. Transfer alignment is a key technology for rapid response and maneuverable launch systems. Transfer alignment offers significant advantages in alignment accuracy, speed, and disturbance tolerance. Its successful application in precision guidance systems can significantly improve the rapid response speed and responsiveness of various systems, and has been widely used in precision guidance systems.

[0003] Due to external disturbances, the system model exhibits varying degrees of uncertainty for different aircraft and flight conditions (such as constant speed, acceleration / deceleration, cornering, or flapping). During the transfer alignment process, the aircraft's engine operates at high speed, generating significant vibration. This can cause the aircraft to sway in flight due to gusts of wind, leading to significant system errors in these conditions. The ability to quickly and accurately complete the initial alignment of the precision guidance system's inertial navigation system (INS) significantly determines the guidance system's effectiveness and precision processing capabilities.

[0004] This shows that while aircraft maneuvers can improve system observability, thereby shortening alignment time and improving estimation accuracy, they also affect system linearity, negatively impacting the Kalman filter's estimation performance. Furthermore, specific maneuvers place higher demands on both the system and the pilot's overall performance. Therefore, effectively avoiding specific maneuvers and utilizing the aircraft's varying flight states to achieve transfer alignment is crucial. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: in response to the technical problems existing in the prior art, the present invention provides a transfer alignment method based on aircraft flight status detection, which has a simple principle, high precision and fast alignment speed.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A transfer alignment method based on aircraft flight status detection, the steps of which include:

[0008] Step S1: Obtain the flight status of the aircraft through the aircraft flight status detector based on the outputs of the main inertial navigation system and the sub-inertial navigation system;

[0009] Step S2: When the aircraft is flying at a variable speed, turning, or flapping, a first Kalman filter is constructed using the main inertial navigation system and the sub-inertial navigation system to perform error correction and estimate the heading angle installation error of the main inertial navigation system and the sub-inertial navigation system. When the aircraft is flying at a constant speed, the heading angle installation error is directly calculated, and a second Kalman filter is constructed using the output information of the main and sub-inertial navigation systems to perform error correction and estimate the gyro zero bias.

[0010] As a further improvement of the method of the present invention: Step S1 includes the following steps:

[0011] Step S11: Calculate the rate of change of the heading angular rate according to the heading angular rate information of the sub-INS;

[0012] Step S12: Calculate the rate of change of the heading angular velocity |δω z |0 median filtering to obtain |δω z |;

[0013] Step S13: Calculate the north and east rate change rates |δv based on the north and east rates of the main inertial navigation system N |0 and |δv E |0;

[0014] Step S14: Perform median filtering on the rate change rates of north and east to obtain |δv N | and |δv E |;

[0015] Step S15: Calculate the roll angle change |δγ|0 and the roll angular rate |ω of the sub-INS based on the roll angle γ of the main INS x |;

[0016] Step S16: Change of the roll angle |δγ|0 and the roll angular rate of the sub-INS |ω x |Perform median filtering to obtain |δγ| and |δω x |;

[0017] Step S17: Determine the flight status of the aircraft.

[0018] As a further improvement of the method of the present invention: in step S11, the rate of change of the heading angular velocity |δω z |0 is:

[0019]

[0020] Where T is the window time, N is the total amount of data within the window time, ω z(i) is the output data of the Z-axis gyroscope.

[0021] As a further improvement of the method of the present invention: in step S13:

[0022]

[0023] As a further improvement of the method of the present invention: in step S17, the determination process is as follows:

[0024] Constant speed flight: |δv N | <K1,|δv E | <K2,|δω z | <K3;

[0025] Accelerated flight: |δv N |>K1 or |δv E |>K2;

[0026] Turning flight: |δω z |>K3;

[0027] Wing flapping flight: |δω x |>K4;

[0028] Among them, K1 and K2 are the north and east rate change rate thresholds respectively, K3 is the heading angular rate change rate threshold, and K4 is the roll angular rate change rate threshold.

[0029] As a further improvement of the method of the present invention: Step S2 includes the following steps:

[0030] Step S21: When the aircraft is in variable speed flight, turning flight, or flapping flight, selecting the state variable of the first Kalman filter;

[0031] Step S22: When the aircraft is flying at a constant speed, select the state variable of the second Kalman filter;

[0032] Step S23: Based on the Kalman filter, the continuous state equation is discretized, and then the state variable is estimated using the Kalman filter to obtain the estimated value of the state variable, and finally the real-time navigation information after the sub-INS transmission alignment is obtained.

[0033] As a further improvement of the method of the present invention: a mathematical model of the first Kalman filter is established, and the state equation is as follows:

[0034]

[0035] Among them, F1 is the state transfer matrix of the Kalman filter, G1 is the system noise driving matrix of the Kalman filter, X1 is the state variable vector of the Kalman filter, and W1 is the system noise vector of the Kalman filter.

[0036] As a further improvement of the method of the present invention, a mathematical model of the second Kalman filter is established, and the state equation is as follows:

[0037]

[0038]

[0039] The measurement equation of the second Kalman filter is as follows:

[0040]

[0041]

[0042] As a further improvement to the method of the present invention: the main inertial navigation system is a high-precision inertial navigation system installed on the aircraft, and the sub-inertial navigation system is an inertial navigation system or an inertial measurement unit installed on the equipment of the transfer alignment method; the high-precision inertial navigation system includes a flight status detector and a transfer alignment filter; the flight status detector is used to receive output data from the main inertial navigation system and the sub-inertial navigation system and detect the flight status of the aircraft; the transfer alignment filter is used to receive output data from the main inertial navigation system and the sub-inertial navigation system and calculate feedback reference correction parameters of the sub-inertial navigation system.

[0043] As a further improvement to the method of the present invention: the flight status detector includes a flight status detector and a microprocessor; the flight status detector is used to receive output information from the main inertial navigation system and the sub-inertial navigation system, and process the information through the microprocessor to obtain the flight status of the aircraft; the transfer alignment filter based on aircraft flight status detection includes a Kalman filter and a microprocessor; the Kalman filter is used to filter, enhance and differentiate the three-axis velocity information and heading angle information of the main and sub-inertial navigation systems, and the microprocessor is used to receive the output information of the main and sub-inertial navigation systems and calculate the reference correction parameters.

[0044] Compared with the prior art, the advantages of the present invention are:

[0045] 1. The present invention's transfer alignment method based on aircraft flight state detection features a simple principle, high accuracy, and rapid alignment. It can effectively detect the aircraft's normal flight state and utilize this state to perform transfer alignment, thereby effectively avoiding the impact of specific maneuvers on the aircraft, guidance system, and pilot. The present invention combines aircraft flight state detection with transfer alignment, completing transfer alignment under normal flight conditions by detecting the aircraft's flight state.

[0046] 2. The transfer alignment method based on aircraft flight state detection of the present invention detects the aircraft flight state and adopts corresponding alignment strategies according to different flight states. When the aircraft is in variable speed flight, turning or flapping flight, a first Kalman filter is constructed to perform error correction and estimate the sub-inertial navigation system heading angle installation error; when the aircraft is in uniform speed flight, the azimuth angle installation error is directly bound, and a second Kalman filter is constructed based on the heading angle and speed information of the main and sub-inertial navigation systems to perform error correction and estimate the gyro zero bias, thereby shortening the transfer alignment time and improving the transfer alignment accuracy. The present invention has simple steps and is easy to implement. The aircraft does not need to perform difficult maneuvers, and can effectively avoid the impact of maneuvers on the aircraft, guidance system and pilot. It can effectively improve the rapid response capability and processing accuracy of the guidance system without increasing additional hardware costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic flow diagram of the method of the present invention.

[0048] Figure 2 It is a detailed flow chart of the present invention in a specific application example. DETAILED DESCRIPTION

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1 and Figure 2 As shown, the transfer alignment method based on aircraft flight state detection of the present invention comprises the following steps:

[0051] Step S1: Obtain the flight status of the aircraft through the aircraft flight status detector according to the output of the master and slave inertial navigation systems;

[0052] Step S2: When the aircraft is flying at a variable speed, turning, or flapping, a first Kalman filter is constructed through the master and slave inertial navigation systems to perform error correction and estimate the heading angle installation error of the master and slave inertial navigation systems. When the aircraft is flying at a constant speed, the heading angle installation error is directly bound, and the output information of the master and slave inertial navigation systems is further used to construct a second Kalman filter to perform error correction and estimate the gyro zero bias.

[0053] In a specific application example, step S1 includes the following steps:

[0054] Step S11: Calculate the rate of change of the heading angular rate |δω based on the heading angular rate information of the sub-INS z |0:

[0055]

[0056] Where T is the window time, N is the total amount of data within the window time, ω z (i) is the output data of the Z-axis gyroscope.

[0057] Step S12: Calculate the rate of change of the heading angular velocity |δω z |0 median filtering to obtain |δω z |;

[0058] Step S13: Calculate the north and east rate change rates |δv based on the north and east rates of the main inertial navigation system N |0 and |δv E |0, where:

[0059]

[0060] Step S14: Perform median filtering on the rate change rates of north and east to obtain |δv N | and |δv E |;

[0061] Step S15: Calculate the roll angle change |δγ|0 and the roll angular rate |ω of the sub-INS based on the roll angle γ of the main INS x |, where:

[0062]

[0063] Step S16: Change of the roll angle |δγ|0 and the roll angular rate of the sub-INS |ω x |Perform median filtering to obtain |δγ| and |δω x |;

[0064] Step S17: Determine the flight status of the aircraft:

[0065] Constant speed flight: |δv N | <K1,|δv E | <K2,|δω z | <K3;

[0066] Accelerated flight: |δv N |>K1 or |δv E |>K2;

[0067] Turning flight: |δω z |>K3;

[0068] Wing flapping flight: |δω x |>K4;

[0069] Among them, K1 and K2 are the north and east rate change rate thresholds respectively, K3 is the heading angular rate change rate threshold, and K4 is the roll angular rate change rate threshold.

[0070] In a specific application example, step S2 includes the following steps:

[0071] Step S21: When the aircraft is in variable speed flight, turning flight, or flapping flight, the state variables of the first Kalman filter are selected:

[0072] X1=[δv N δv E (Φ n ) T μ z (ε b ) T ] T ,

[0073] Among them, δv N and δv E are the north and east velocity errors of the sub-INS, (Φ n ) T is the misalignment angle of the sub-INS, μ z is the installation angle error of the sub-INS in the z-axis direction, (ε b ) T is the constant drift of the gyro in the sub-INS, (W a ) T is the accelerometer noise in the sub-INS, (W g ) T is the gyroscope noise in the inertial navigation system, is the installation angle noise of the sub-INS in the z-axis direction.

[0074] Through the selection of Kalman filter state variables, we can know that the error equation of Kalman filter includes velocity error equation, attitude error equation, installation angle error equation and inertial instrument error equation. They are:

[0075]

[0076]

[0077]

[0078]

[0079] Among them, f n is the accelerometer output in the navigation coordinate system, is the Earth's rotation angular velocity in the navigation system, is the projection of the angular velocity relative to the earth caused by the motion of the carrier on the navigation system, v n is the speed under the navigation system, is the projection of the angular velocity of the geographic coordinate system on the navigation system, is the gyro output, f b is the accelerometer output under load, is the direction cosine matrix.

[0080] The difference between the north and east velocities of the master inertial navigation system and the difference in heading angle are selected as observation quantities to establish the measurement equation.

[0081] Establish the mathematical model of the first Kalman filter:

[0082] The state equation is as follows:

[0083]

[0084] Among them, F1 is the state transfer matrix of the Kalman filter, G1 is the system noise driving matrix of the Kalman filter, X1 is the state variable vector of the Kalman filter, and W1 is the system noise vector of the Kalman filter.

[0085] in:

[0086]

[0087]

[0088]

[0089] The measurement equation of the first Kalman filter is as follows:

[0090]

[0091]

[0092] Step S22: When the aircraft is flying at a constant speed, the state variable of the second Kalman filter is selected as:

[0093] X2=[δv N δv E (Φ n ) T (ε b ) T ] T ,W2=[(W a ) T (W g ) T ] T

[0094] Establish the mathematical model of the second Kalman filter:

[0095] The state equation is as follows:

[0096]

[0097]

[0098] The measurement equation of the second Kalman filter is as follows:

[0099]

[0100]

[0101] Step S23: Based on the Kalman filter, the continuous state equation is discretized, and then the state variable is estimated using the Kalman filter to obtain the estimated value of the state variable, and finally the real-time navigation information after the sub-INS transmission alignment is obtained.

[0102] The present invention's transfer alignment method based on aircraft flight state detection involves installing a high-precision inertial navigation system (INS) as the primary inertial navigation system (INS) on the aircraft, and an inertial navigation system or inertial measurement unit (IMU) as the secondary INS on the equipment employing the transfer alignment method. The high-precision INS includes a flight state detector and a transfer alignment filter. The flight state detector receives output data from the primary and secondary INSs and detects the aircraft's flight state; the transfer alignment filter receives output data from the primary and secondary INSs and calculates feedback reference correction parameters for the secondary INS systems.

[0103] In the present invention, the main inertial navigation information includes position, velocity, attitude, and other information. The sub-insertion navigation system includes a three-axis MEMS gyroscope and an accelerometer. The three-axis MEMS gyroscope is used to sense the aircraft's three-dimensional angular velocity information, and the three-axis MEMS accelerometer is used to sense the aircraft's three-dimensional acceleration information. The aircraft's flight status detector detects the aircraft's flight status based on the position, velocity, attitude, three-dimensional angular velocity information, and motion acceleration information.

[0104] In the present invention, the flight status detector includes a flight status detector and a microprocessor; the flight status detector is used to receive output information of the master and slave inertial navigation systems, and process the information through the microprocessor to obtain the flight status of the aircraft.

[0105] In the present invention, the transfer alignment filter based on aircraft flight status detection includes a Kalman filter and a microprocessor; the Kalman filter is used to filter, enhance and differentiate the three-axis velocity information and heading angle information of the master and slave inertial navigation systems, and the microprocessor is used to receive the output information of the master and slave inertial navigation systems and calculate the reference correction parameters.

[0106] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A transfer alignment method based on aircraft flight status detection, characterized in that: The steps include: Step S1: Obtain the flight status of the aircraft through the aircraft flight status detector based on the outputs of the main inertial navigation system and the sub-inertial navigation system; Step S2: When the aircraft is flying at a variable speed, turning, or flapping, a first Kalman filter is constructed using the main inertial navigation system and the sub-inertial navigation system to perform error correction and estimate the heading angle installation error of the main inertial navigation system and the sub-inertial navigation system. When the aircraft is flying at a constant speed, the heading angle installation error is directly calculated, and a second Kalman filter is constructed using the output information of the main and sub-inertial navigation systems to perform error correction and estimate the gyro zero bias.

2. The transfer alignment method based on aircraft flight status detection according to claim 1, characterized in that: The step S1 comprises the following steps: Step S11: Calculate the rate of change of the heading angular rate according to the heading angular rate information of the sub-INS; Step S12: Calculate the rate of change of the heading angular velocity |δω z |0 median filtering to obtain |δω z |; Step S13: Calculate the north and east rate change rates |δv based on the north and east rates of the main inertial navigation system N |0 and |δv E |0; Step S14: Perform median filtering on the rate change rates of north and east to obtain |δv N | and |δv E |; Step S15: Calculate the roll angle change |δγ|0 and the roll angular rate |ω of the sub-INS based on the roll angle γ of the main INS x |; Step S16: Change of the roll angle |δγ|0 and the roll angular rate of the sub-INS |ω x |Perform median filtering to obtain |δγ| and |δω x |; Step S17: Determine the flight status of the aircraft.

3. The transfer alignment method based on aircraft flight status detection according to claim 2, characterized in that: In step S11, the rate of change of the heading angular velocity |δω z |0 is: Where T is the window time, N is the total amount of data within the window time, ω z (i) is the output data of the Z-axis gyroscope.

4. The transfer alignment method based on aircraft flight status detection according to claim 2, characterized in that: In step S13:

5. The transfer alignment method based on aircraft flight status detection according to claim 2, characterized in that: In step S17, the determination process is as follows: Constant speed flight: |δv N | <K1,|δv E | <K2,|δω z | <K3; Accelerated flight: |δv N |>K1 or |δv E |>K2; Turning flight: |δω z |>K3; Wing flapping flight: |δω x |>K4; Among them, K1 and K2 are the north and east rate change rate thresholds respectively, K3 is the heading angular rate change rate threshold, and K4 is the roll angular rate change rate threshold.

6. The transfer alignment method based on aircraft flight status detection according to any one of claims 1 to 5, characterized in that: The step S2 comprises the following steps: Step S21: When the aircraft is in variable speed flight, turning flight, or flapping flight, selecting the state variable of the first Kalman filter; Step S22: When the aircraft is flying at a constant speed, select the state variable of the second Kalman filter; Step S23: Based on the Kalman filter, the continuous state equation is discretized, and then the state variable is estimated using the Kalman filter to obtain the estimated value of the state variable, and finally the real-time navigation information after the sub-INS transmission alignment is obtained.

7. The transfer alignment method based on aircraft flight status detection according to claim 6, characterized in that: The mathematical model of the first Kalman filter is established, and the state equation is as follows: Among them, F1 is the state transfer matrix of the Kalman filter, G1 is the system noise driving matrix of the Kalman filter, X1 is the state variable vector of the Kalman filter, and W1 is the system noise vector of the Kalman filter.

8. The transfer alignment method based on aircraft flight status detection according to claim 6, characterized in that: The mathematical model of the second Kalman filter is established, and the state equation is as follows: The measurement equation of the second Kalman filter is as follows:

9. The transfer alignment method based on aircraft flight status detection according to any one of claims 1 to 5, characterized in that: The main inertial navigation system is a high-precision inertial navigation system installed on the aircraft, and the sub-inertial navigation system is an inertial navigation system or inertial measurement unit installed on the equipment of the transfer alignment method; the high-precision inertial navigation system includes a flight status detector and a transfer alignment filter; the flight status detector is used to receive the output data of the main inertial navigation system and the sub-inertial navigation system and detect the flight status of the aircraft; the transfer alignment filter is used to receive the output data of the main inertial navigation system and the sub-inertial navigation system and calculate the feedback reference correction parameters of the sub-inertial navigation system.

10. The transfer alignment method based on aircraft flight status detection according to claim 9, characterized in that: The flight status detector includes a flight status detector and a microprocessor; the flight status detector is used to receive output information from the main inertial navigation system and the sub-inertial navigation system, and process the information through the microprocessor to obtain the flight status of the aircraft; the transfer alignment filter based on aircraft flight status detection includes a Kalman filter and a microprocessor; the Kalman filter is used to filter, enhance and differentiate the three-axis velocity information and heading angle information of the main and sub-inertial navigation systems, and the microprocessor is used to receive the output information of the main and sub-inertial navigation systems and calculate the reference correction parameters.

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