阅读说明：本技术 一种基于惯性/卫星/大气组合的气象风测量方法 (Meteorological wind measurement method based on inertia/satellite/atmosphere combination ) 是由 王学运 从浩 张京娟 赵明 于 2020-06-19 设计创作，主要内容包括：本发明公开一种基于惯性/卫星/大气组合的气象风测量方法,尤其涉及风速风向的在线测量,在不改变飞行器结构外形、不增加测量装置和硬件设备的前提下,利用现有的机载惯性导航系统、卫星定位系统、大气数据系统的输出参数,得到惯性系统的加速度、角速度和姿态角,卫星测得地速、大气数据系统测得真空速,通过构建无迹卡尔曼滤波器,将风速风向测量模型和状态估计过程融合起来,实现飞行器机体周围风速风向的实时测量,并完成下一时刻风速风向的估计。本发明所提出的方法方案可以解决飞行器周围局域风速风向的实时测量和大区域连续飞行实时风速风向测量问题,为四维航迹飞行、飞机准点达到提供方法支持。(The invention discloses a meteorological wind measuring method based on inertia/satellite/atmosphere combination, in particular to on-line measurement of wind speed and direction, which obtains the acceleration, angular velocity and attitude angle of an inertia system by utilizing the output parameters of the existing airborne inertia navigation system, satellite positioning system and atmosphere data system on the premise of not changing the structural appearance of an aircraft and increasing the measuring device and hardware equipment, measures the ground speed by a satellite and measures the vacuum speed by the atmosphere data system, and integrates a wind speed and direction measuring model and a state estimation process by constructing an unscented Kalman filter to realize the real-time measurement of the wind speed and direction around the aircraft body and finish the estimation of the wind speed and direction at the next moment. The method scheme provided by the invention can solve the problems of real-time measurement of local wind speed and wind direction around the aircraft and real-time measurement of wind speed and wind direction of large-area continuous flight, and provides method support for four-dimensional flight path flight and aircraft punctuation.)

1. A meteorological wind measuring method based on inertia/satellite/atmosphere combination is characterized in that: the method comprises the following steps:

outputting parameters by using an avionics device onboard an aircraft, the parameters comprising:

(1) the inertial navigation system outputs the acceleration, the angular velocity and the attitude angle of the aircraft;

(2) the satellite positioning system outputs the ground speed of the aircraft;

(3) the air data system outputs the vacuum speed of the aircraft;

calculating a state transition matrix by using the acceleration, the angular velocity and the attitude angle output by the inertial navigation system;

solving the three-axis speed of the aircraft in the body coordinate system by using the ground speed output by the satellite positioning system through the body coordinate system and the state transfer matrix of the geographic coordinate system;

calculating the vacuum speed of the aircraft by using the vacuum speed output by the atmospheric data system;

calculating the wind speed and the wind direction around the fuselage of the aircraft by using the ground speed, the state transition matrix and the vacuum speed resolving result of the aircraft at the current moment, and accurately estimating the wind speed and the wind direction at the next moment in real time by using an unscented Kalman filter.

2. The method for measuring meteorological wind based on inertia/satellite/atmosphere combination as claimed in claim 1, wherein the acceleration, angular velocity and attitude angle of the aircraft are obtained by reading three angular velocities, three attitude angles and three linear acceleration information output by the inertial navigation system at a period △ T, and three angular velocities [ pqr ] are obtained]^TRoll angular velocity p, pitch angular velocity q and yaw angular velocity r in the X-axis, Y-axis and Z-axis directions under the machine system respectively; three attitude angles phi theta phi]^TRespectively a roll angle phi, a pitch angle theta and a yaw angle psi; three linear accelerations [ a ]_xa_ya_z]^TRespectively is the linear acceleration a of the body in the X-axis direction_xLinear acceleration a in the Y-axis direction_yAnd linear acceleration a in the Z-axis direction_zAnd the directions of the X axis, the Y axis and the Z axis of the body coordinate system are forward, rightward and downward respectively.

3. The method of claim 2, wherein the low speed of the aircraft is obtained by reading the three-axis ground speed [ V ] in the geographic coordinate system outputted from the onboard satellite positioning system at a period of △ T_xV_yV_z]^TIn which V is_xIs the speed component, V, of the aircraft on the X axis of the geographic coordinate system_yIs the velocity component, V, of the aircraft in the Y-axis of the geographic coordinate system_zIs the speed component of the aircraft in a geographic coordinate system, wherein the X axis and the Y axis of the geographic coordinate systemThe Z-axis points north, east and ground, respectively.

4. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 3, wherein: the state transition matrix is a state transition matrix from a geographic coordinate system to a body coordinate system and is obtained through attitude angle information, the attitude angle information comprises a roll angle phi, a pitch angle theta and a yaw angle psi, and the state transition matrix is calculated

Converting triaxial ground speed [ V ] under geographic coordinate system through state transition matrix_xV_yV_z]^TSwitching to a machine system:

[w_xw_yw_z]^Tthree axis wind speeds.

5. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 4, wherein: the vacuum speed of the aircraft is obtained by resolving pressure and temperature provided by a pressure sensor and a temperature sensor in an airborne atmospheric data system by combining a Bernoulli equation, and the calculation formula is as follows:

wherein, the delta P is dynamic pressure, the rho is air density, and the K is a correction factor;

ground speed measured by satellite positioning systemAttitude angle measured by an inertial navigation system, state transition matrix obtained by resolving attack angle α and sideslip angle β provided by an atmospheric data system, and vacuum speed measured by the atmospheric data system

Whereinw_xIs the wind speed in the X-axis direction, w_yIs the wind speed in the Y-axis direction, w_zIs the wind speed in the Z-axis direction.

6. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 5, wherein: the wind direction around the aircraft body comprises a wind direction angle, the wind direction angle comprises a wind direction azimuth angle and a wind direction elevation angle, and the included angle between the wind speed and the north direction is called the wind direction azimuth angleThe included angle between the wind speed and the horizontal plane is called wind direction elevation angle

7. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 6, wherein: the method for accurately estimating the wind speed and the wind direction at the next moment in real time comprises the following specific steps: with three axes of acceleration [ a ]_xa_ya_z]^TAnd angular rate [ p q r]^TAs input to the system; with three-axis speed [ uv w ] of aircraft body]^TRoll angle phi, pitch angle theta, yaw angle psi and IIIAxial wind velocity [ w ]_xw_yw_z]^TAs the state quantity, i.e., X ═ u v w Φ θ ψ w_xw_yw_z]^T(ii) a At triaxial ground speed [ V ]_xV_yV_z]^TVacuum speed V_aAngle of attack α and sideslip angle β are measured as quantities, i.e., Z ═ V_xV_yV_zV_aα β]^T(ii) a According to the current time t_kSystem input amount at time, current time t_kMeasurement information of time and current time t_kObtaining t by utilizing the unscented Kalman filter according to the state quantity information of the moment_k+1And the optimal estimation value of the state quantity at the moment is obtained, so that the on-line measurement and real-time accurate estimation of the wind speed and the wind direction are realized.

8. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 7, wherein: obtaining t by using unscented Kalman filter_k+1The optimal estimation value of the state quantity at the moment is obtained, so that the on-line measurement and real-time accurate estimation of the wind speed and the wind direction are realized, and the specific method comprises the following steps: establishing an unscented Kalman filter state equation: x_k＝f(X_k-1,u_k-1)+W_k-1Wherein X is_kAnd X_k-1State vectors at times k and k-1, W, respectively_k-1Is the state noise vector, W_k-1Three-axis speed change rate of aircraft bodyRate of change of three-axis attitude angle

8.1) selecting a filtering initial value:

wherein E refers to the mathematical expectation, X₀For the initial value of the state vector,

8.2) calculating the sigma sample point at the k-1 moment:

where n is the state dimension and λ is α²(n+κ)-n，α∈[10^-4,1]，κ＝3-n；

8.3) determining the weight:

wherein, W₀ ^(m)And W_i ^(m)Is the weight of the ith sigma point state vector, W₀ ^(c)And W_i ^(c)The weight of the covariance matrix of the ith sigma point-like state vector is β, which is a state distribution parameter, β is 2;

8.4) calculating a one-step prediction model value at the k moment:

to predict the state vector, P_k/k-1To predict the covariance matrix of the state vector, Q_k-1A noise matrix of a last state vector;

8.5) computing one-step predicted sample points at time k

8.6) measurement update:

wherein the content of the first and second substances,in order to predict the observation vector(s),

8.7) updating the filtering:

wherein, K_kIn order to be a matrix of gains, the gain matrix,to estimate the state vector, P_kIs a covariance matrix for estimating the state vector.

9. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 8, wherein: in the unscented Kalman filter, a roll angle phi, a pitch angle theta and a yaw angle psi of a state quantity are provided by an inertial navigation system, the triaxial speed of a body system is provided by a satellite positioning system, a three-dimensional wind speed initial value is provided by an external anemoscope, the three-dimensional wind speed change rate initial value is zero, the system initial state quantity is formed, and an initial state estimation error variance matrix P is formed₀Initial value Q of system noise variance array₀Sum measure noise variance matrix R₀The initial values of the matrix are directly input from the outside of the system, and the noise matrix Q of the system_kInitial value Q of system noise variance matrix₀Determining and measuring a noise matrix R_k+1By measuring the initial value R of the noise variance matrix₀And (4) determining.

10. The combined inertial/satellite/atmospheric meteorological wind measurement method of claim 9, wherein: knowing t from said state quantities_k+1And (3) taking the obtained three-dimensional wind speed to an included angle formula of the wind speed and the north direction and an included angle formula of the wind speed and the horizontal plane to obtain a wind direction angle.

Disclosure of Invention

The invention aims to make up for the defects of the existing method and provides a meteorological wind measuring method based on inertia/satellite/atmosphere combination.

The invention is realized by the following method scheme:

a meteorological wind measurement method based on inertia/satellite/atmosphere combination, which utilizes parameters output by an airborne avionics device of an aircraft, and comprises the following steps:

acceleration, angular velocity and attitude angle output by the inertial system;

the ground speed output by the satellite positioning system;

the vacuum speed output by the atmospheric data system;

calculating a state transition matrix according to output information of the inertial navigation system;

and solving the three-axis speed of the aircraft under the body coordinate system by the ground speed output by the satellite positioning system through the state transfer matrix of the body coordinate system and the geographic coordinate system.

And measuring data by an atmospheric data system, and calculating the vacuum speed of the aircraft.

Calculating the wind speed and the wind direction around the aircraft body according to the ground speed, the state transition matrix and the vacuum speed calculation result of the aircraft at the current moment, and accurately estimating the wind speed and the wind direction at the next moment in real time through an unscented Kalman filter.

Comprises the following steps:

in the first step, three angular velocities, three attitude angles and three linear acceleration information output by the inertial navigation system are read in a period △ T, and the three angular velocities [ p q r [ ]]^TRoll angular velocity p, pitch angular velocity q and yaw angular velocity r in the X-axis, Y-axis and Z-axis directions under the machine system respectively; three attitude angles phi theta phi]^TRespectively a roll angle phi, a pitch angle theta and a yaw angle psi; three linear accelerations [ a ]_xa_ya_z]^TRespectively is the linear acceleration a of the body in the X-axis direction_xLinear acceleration a in the Y-axis direction_yAnd linear acceleration a in the Z-axis direction_zAnd the directions of the X axis, the Y axis and the Z axis of the body coordinate system are forward, rightward and downward respectively.

Secondly, reading the three-axis ground speed [ V ] under the geographic coordinate system output by the airborne satellite positioning system in a period △ T_xV_yV_z]^TIn which V is_xFor the speed component, V, of the aircraft on the X-axis of the geographic coordinate system_yFor the speed component, V, of the aircraft in the Y-axis of the geographic coordinate system_zIs the speed component of the aircraft in a geographic coordinate system, wherein the X-axis, Y-axis and Z-axis of the geographic coordinate system are respectively oriented in the north direction, east direction and ground direction.

Thirdly, the state transition matrix is a state transition matrix from the geographic coordinate system to the body coordinate system and is obtained through attitude angle information, the attitude angle information comprises a roll angle phi, a pitch angle theta and a yaw angle psi, and the state transition matrix is calculated

Fourthly, the three-axis ground speed [ V ] under the geographic coordinate system can be realized through the state transition matrix_xV_yV_z]^TSwitching to a machine system:

[w_xw_yw_z]^Tthree axis wind speeds.

Fifthly, the airborne vacuum speed is generally obtained by resolving pressure and temperature provided by a pressure sensor and a temperature sensor in an airborne atmospheric data system by combining a Bernoulli equation, and the calculation formula is as follows:

wherein, Δ P is dynamic pressure, ρ is air density, and K is a correction factor.

Sixthly, measuring the ground speed by using a satellite positioning system

Attitude angle measured by an inertial navigation system, attitude transition matrix obtained by resolving an attack angle α and a sideslip angle β provided by an atmospheric data system, and vacuum speed measured by the atmospheric data system

Obtaining:

whereinw_xIs the wind speed in the X-axis direction, w_yIs the wind speed in the Y-axis direction, w_zIs ZWind speed in the axial direction.

Seventhly, measuring a wind field around the aircraft to obtain a wind direction angle, wherein the wind direction angle comprises a wind direction azimuth angle and a wind direction elevation angle, and an included angle between the wind speed and the north direction is called the wind direction azimuth angleThe included angle between the wind speed and the horizontal plane is called wind direction elevation angle

Eighthly, using the three-axis acceleration [ a ] measured by the inertial measurement unit of the inertial navigation system_xa_ya_z]^TAnd angular rate [ pqr]^TAs input to the system; selecting three-axis speed [ uv w ] of aircraft body]^TRoll angle phi, pitch angle theta, yaw angle psi and triaxial wind speed w_xw_yw_z]^TAs the state quantity, i.e., X ═ u v w Φ θ ψ w_xw_yw_z]^TAnd establishing an unscented Kalman filter state equation: x_k＝f(X_k-1,u_k-1)+W_k-1Wherein X is_kAnd X_k-1State vectors at times k and k-1, W, respectively_k-1Is the state noise vector, W_k-1Three-axis speed change rate of aircraft body

Rate of change of three-axis attitude angleAnd three axis wind speed rate of changeCausing; selecting the three-axis ground speed [ V ] measured by a satellite positioning system_xV_yV_z]^TVacuum speed V_aAngle of attack α and sideslip angle β are measured as quantities, i.e., Z ═ V_xV_yV_zV_aα β]^TAnd establishing an unscented Kalman filter measurement equation Z_k＝h(X_k)+V_kMeasuring the noise V_kCaused by measurement noise of a satellite positioning system and an atmospheric data system; obtaining the current time t according to the first step_kThe system input quantity of the time is obtained according to the second step and the fifth step_kThe measurement information of the time is obtained according to the fourth step and the fifth step_kObtaining t by utilizing the unscented Kalman filter according to the state quantity information of the moment_k+1And the optimal estimation value of the state quantity at the moment is obtained, so that the on-line measurement and real-time accurate estimation of the wind speed and the wind direction are realized.

And ninthly, performing unscented Kalman filtering:

8.1) selecting a filtering initial value:

wherein E refers to the mathematical expectation, X₀For the initial value of the state vector,

is the mean of the initial values of the state vector, P₀Covariance matrix as initial value of state vector

8.2) calculating the sigma sample point at the k-1 moment:

where n is the state dimension and λ is α²(n+κ)-n，α∈[10^-4,1]，κ＝3-n；

8.3) determining the weight:

wherein the content of the first and second substances,and W_i ^(m)The weight of the ith sigma point state vector,

and W_i ^(c)The weight of the covariance matrix of the ith sigma point-like state vector is β, which is a state distribution parameter, β is 2;

8.4) calculating a one-step prediction model value at the k moment:

to predict the state vector, P_k/k-1To predict the covariance matrix of the state vector, Q_k-1A noise matrix of a last state vector;

8.5) calculating one-step predicted sample point at time k:

8.6) measurement update:

wherein the content of the first and second substances,in order to predict the observation vector(s),

is a covariance matrix of the prediction vector and the observation vector,to predict covariance matrix of observations and state vectors, R_kTo observe the noise matrix;

8.7) Filter update

Wherein, K_kIn order to be a matrix of gains, the gain matrix,to estimate the state vector, P_kIs a covariance matrix for estimating the state vector.

Tenth, in the unscented kalman filter, the roll angle phi, the pitch angle theta and the yaw angle psi of the state quantity are provided by an inertial navigation system, the three-axis speed of the body system is provided by a satellite positioning system, an external anemoscope provides a three-dimensional wind speed initial value, the three-dimensional wind speed change rate initial value is zero, the system initial state quantity is formed, and an initial state estimation error variance matrix P is formed₀Initial value Q of system noise variance array₀Measuring the noise variance matrix R₀The initial values of the matrix are directly input from the outside of the system, and the noise matrix Q of the system_kInitial value Q of systematic noise variance array₀Determining and measuring a noise matrix R_k+1By measuring the initial value R of the noise variance matrix₀And (4) determining.

The tenth step, the last three items of the state quantity can be used to obtain t_k+1And (5) taking the obtained three-dimensional wind speed to the seventh step to obtain the corresponding wind direction angle.

The invention has the advantages that: according to the invention, no additional airborne avionics equipment is required to be added, the structural appearance of the aircraft is not required to be changed, the real-time wind speed and wind direction around the aircraft are obtained by resolving by utilizing the output of the existing inertial navigation system, the existing satellite positioning system and the existing atmospheric data system, the large-area continuous wind measurement on the flight track can be realized, and the method has great significance for four-dimensional track flight and the accurate point arrival of the aircraft.

Drawings

FIG. 1 is a schematic block diagram of a method for measuring meteorological wind based on a combination of inertia/satellite/atmosphere;

FIG. 2 is a schematic diagram of a measurement method of a meteorological wind measurement method based on a combination of inertia/satellite/atmosphere;

FIG. 3 is a forward wind speed estimate and a true error plot from a combined inertial/satellite/atmospheric meteorological wind measurement method in one embodiment;

FIG. 4 is a plot of estimated right-hand wind speed and true error based on a combined inertial/satellite/atmospheric meteorological wind measurement method in one embodiment;

FIG. 5 is a graph of estimated values and true errors of the wind speed in the sky based on a combined inertial/satellite/atmospheric meteorological wind measurement method in one embodiment;

FIG. 6 is a wind direction azimuth plot of a combined inertial/satellite/atmospheric weather wind measurement method according to one embodiment;

FIG. 7 is a wind direction altitude and altitude graph obtained by a meteorological wind measurement method based on a combination of inertia/satellite/atmosphere in one embodiment.

Detailed Description

For a more clear explanation of the present application, the following description is further provided in conjunction with the embodiments and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons of ordinary skill in the art that the following descriptions are intended to be illustrative, but not limiting, and are not intended to limit the scope of the present disclosure.

It is clear that the above examples are given solely for the purpose of illustrating the present invention in a clear and non limitative way, and that on the basis of the above description, other variants and modifications are possible for a person skilled in the art of the method, and that not all embodiments are exhaustive, and that all obvious variants and modifications are still within the scope of the invention, as they are introduced in the method variant of the present application.

In one embodiment, referring to fig. 1, the present invention utilizes the output parameters of the existing airborne inertial navigation system, satellite positioning system, and atmospheric data system to realize the on-line measurement of wind speed and direction at the current moment and the real-time accurate estimation of wind speed and direction at the next moment by designing the kalman filter.

In order to realize the on-line measurement and estimation of wind speed and wind direction, the following steps are required:

firstly, acquiring output information of an inertial navigation system, and reading three angular velocities, three attitude angles and three linear acceleration information output by the inertial navigation system at a period △ T, wherein the three angular velocities are [ p q r ]]^TRoll angular velocity p, pitch angular velocity q and yaw angular velocity r in the X-axis, Y-axis and Z-axis directions under the machine system respectively; three attitude angles phi theta phi]^TRespectively a roll angle phi, a pitch angle theta and a yaw angle psi; three linear accelerations [ a ]_xa_ya_z]^TRespectively is the linear acceleration a of the body in the X-axis direction_xLinear acceleration a in the Y-axis direction_yAnd linear acceleration a in the Z-axis direction_zAnd the directions of the X axis, the Y axis and the Z axis of the body coordinate system are forward, rightward and downward respectively.

Secondly, obtaining the output information of the satellite positioning system, and reading the triaxial ground speed [ V ] under the geographic coordinate system output by the airborne satellite positioning system according to the period delta T_xV_yV_z]^TIn which V is_xFor the speed component, V, of the aircraft on the X-axis of the geographic coordinate system_yFor the speed component, V, of the aircraft in the Y-axis of the geographic coordinate system_zIs the speed component of the aircraft in a geographic coordinate system, wherein the X-axis, Y-axis and Z-axis of the geographic coordinate system are respectively oriented in the north direction, east direction and ground direction.

Thirdly, obtaining a state transition matrix of the system, wherein the state transition matrix is a state transition matrix from a geographic coordinate system to a body coordinate system, obtaining the state transition matrix through attitude angle information, the attitude angle information comprises a roll angle phi, a pitch angle theta and a yaw angle psi, and calculating the state transition matrix

Fourthly, the three-axis ground speed [ V ] under the geographic coordinate system can be realized through the state transition matrix_xV_yV_z]^TSwitching to a machine system:

[w_xw_yw_z]^Tthree axis wind speeds.

And fifthly, acquiring output data of the airborne atmospheric data system, wherein the airborne vacuum speed is generally obtained by resolving pressure and temperature provided by a pressure sensor and a temperature sensor in the airborne atmospheric data system by combining a Bernoulli equation, and the calculation formula is as follows:

wherein, Δ P is dynamic pressure, ρ is air density, and K is a correction factor.

Sixthly, measuring the ground speed by using a satellite positioning system

Attitude angle measured by an inertial navigation system, attitude transition matrix obtained by resolving an attack angle α and a sideslip angle β provided by an atmospheric data system, and vacuum speed measured by the atmospheric data systemObtaining:

whereinw_xIs the wind speed in the X-axis direction, w_yIs the wind speed in the Y-axis direction, w_zIs the wind speed in the Z-axis direction.

Seventhly, obtaining the three-axis wind speed from the sixth step and substituting the three-axis wind speed into the wind directionCornerWind direction high and low angle

And obtaining the wind direction angle of the corresponding point.

Eighthly, using the three-axis acceleration [ a ] measured by the inertial measurement unit of the inertial navigation system_xa_ya_z]^TAnd angular rate [ pqr]^TAs input to the system; selecting three-axis speed [ uv w ] of aircraft body]^TRoll angle phi, pitch angle theta, yaw angle psi and triaxial wind speed w_xw_yw_z]^TAs the state quantity, i.e., X ═ u v w Φ θ ψ w_xw_yw_z]^TAnd establishing an unscented Kalman filter state equation: x_k＝f(X_k-1,u_k-1)+W_k-1Wherein X is_kAnd X_k-1State vectors at times k and k-1, W, respectively_k-1Is the state noise vector, W_k-1Three-axis speed change rate of aircraft body

Rate of change of three-axis attitude angleAnd three axis wind speed rate of change

Causing; selecting the three-axis ground speed [ V ] measured by a satellite positioning system_xV_yV_z]^TVacuum speed V_aAngle of attack α and sideslip angle β are measured as quantities, i.e., Z ═ V_xV_yV_zV_aα β]^TAnd establishing an unscented Kalman filter measurement equation Z_k＝h(X_k)+V_kMeasuring the noise V_kCaused by measurement noise of a satellite positioning system and an atmospheric data system; obtaining the current time t according to the first step_kThe system input amount of the time is based on the secondStep five, obtaining the current time t_kThe measurement information of the time is obtained according to the fourth step and the fifth step_kObtaining t by utilizing the unscented Kalman filter according to the state quantity information of the moment_k+1And the optimal estimation value of the state quantity at the moment is obtained, so that the on-line measurement and real-time accurate estimation of the wind speed and the wind direction are realized.

And ninthly, performing unscented Kalman filtering:

8.1) selecting a filtering initial value:

wherein E refers to the mathematical expectation, X₀For the initial value of the state vector,is the mean of the initial values of the state vector, P₀Covariance matrix as initial value of state vector

8.2) calculating the sigma sample point at the k-1 moment:

where n is the state dimension and λ is α²(n+κ)-n，α∈[10^-4,1]，κ＝3-n；

8.3) determining the weight:

wherein the content of the first and second substances,and W_i ^(m)The weight of the ith sigma point state vector,

and W_i ^(c)The weight of the covariance matrix of the ith sigma point-like state vector is β, which is a state distribution parameter, β is 2;

8.4) calculating a one-step prediction model value at the k moment:

to predict the state vector, P_k/k-1To predict the covariance matrix of the state vector, Q_k-1A noise matrix of a last state vector;

8.5) calculating one-step predicted sample point at time k:

8.6) measurement update:

wherein the content of the first and second substances,in order to predict the observation vector(s),

is a covariance matrix of the prediction vector and the observation vector,

to predict covariance matrix of observations and state vectors, R_kTo observe the noise matrix;

8.7) Filter update

Wherein, K_kIn order to be a matrix of gains, the gain matrix,

to estimate the state vector, P_kIs a covariance matrix for estimating the state vector.

Tenth, in the unscented kalman filter, the roll angle phi, the pitch angle theta and the yaw angle psi of the state quantity are provided by an inertial navigation system, the three-axis speed of the body system is provided by a satellite positioning system, an external anemoscope provides a three-dimensional wind speed initial value, the three-dimensional wind speed change rate initial value is zero, the system initial state quantity is formed, and an initial state estimation error variance matrix P is formed₀Initial value Q of system noise variance array₀Measuring the noise variance matrix R₀The initial values of the matrix are directly input from the outside of the system, and the noise matrix Q of the system_kInitial value Q of systematic noise variance array₀Determining and measuring a noise matrix R_k+1By measuring the initial value R of the noise variance matrix₀And (4) determining.

The tenth step, the last three items of the state quantity can be used to obtain t_k+1The three-dimensional wind speed at the moment is taken to the seventh step to obtain t_k+1The wind direction angle corresponding to the moment.

Therefore, the online measurement of the wind speed and the wind direction around the aircraft body and the real-time accurate estimation of the wind speed and the wind direction are completed.

FIG. 1 is a basic principle block diagram of the method, and an unscented Kalman filter is used for resolving and obtaining real-time wind speed and wind direction around an aircraft body and a wind speed and wind direction estimated value at the next moment by utilizing the outputs of an airborne inertial navigation system, a satellite positioning system and an atmospheric data system. Solid arrows between the various modules represent the basic logical connections.

FIG. 2 is a further refinement of the principle in FIG. 1, which represents a specific flow of a meteorological wind measurement method based on a combination of inertia/satellite/atmosphere, and more clearly shows the data source, data transmission and wind speed and direction calculation of the system.

FIG. 3 is a graph of forward wind speed estimate and true error in seconds on the horizontal axis and in meters/second on the vertical axis for forward error in meters/second for wind speed estimate and true wind speed in the range of [ -0.4m/s,0.4m/s ] obtained in an embodiment of the method of the present invention.

FIG. 4 is a plot of the estimated and true values of the right wind speed in seconds on the horizontal axis and the error in meters/second on the right hand axis in the range of [ -0.5m/s,0.5m/s ] on the vertical axis for the estimated and true values of the wind speed obtained in an embodiment of the method of the present invention.

FIG. 5 is a graph of estimated and true values of the wind speed in the direction of the day in seconds on the horizontal axis and the error in the direction of the day in meters per second on the vertical axis for the estimated and true values of the wind speed in the range of [ -0.4m/s,0.4m/s ] in one embodiment of the method of the present invention.

FIG. 6 is a plot of wind direction azimuth angle in seconds on the horizontal axis and wind direction azimuth angle in degrees on the vertical axis for a range of [6, 16 ] obtained in one embodiment of the method of the present invention.

FIG. 7 is a graph of wind direction elevation angles obtained in an embodiment of the method of the present invention, with time in seconds on the horizontal axis and wind direction elevation angle values in degrees on the vertical axis, in the range of [17, 27 ].