Method and system for calculating aircraft anemometer data

Abstract

The present invention provides a method and system for solving aircraft anemometer data, which can be applied to the fields of aviation measurement and control technology and aviation navigation technology. The method includes: collecting the pressure difference between the two ends of a rigid rotating rod to obtain the kth pressure difference signal; inputting the k-1th wind speed vector to be estimated, the kth pressure difference signal and the kth motor angle signal into a linear regression model, outputting the kth wind speed related vector at the kth moment, the k-1th wind speed vector to be estimated is obtained according to the linear regression model, and the regression parameters of the linear regression model are determined by the pressure difference signal model, and the regression parameters include wind speed parameters and angle parameters. The pressure difference signal model is established based on the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information; solving the kth wind speed related vector and the kth motor angle signal to obtain the kth anemometer data at the kth moment, and the kth anemometer data is used to determine the position and heading of the aircraft.

Description

Technical Field

[0001] The present invention relates to the fields of aviation measurement and control technology and aviation navigation technology, and in particular to a method and system for calculating aircraft anemometer data. Background Art

[0002] An anemometer is an instrument used to measure the speed of air movement. It plays a key role in the aerospace industry. An anemometer needs to accurately measure wind speed and direction during flight. Accurate wind speed and direction information can help flight planning and determine the best route and altitude.

[0003] However, the differential pressure sensor will be affected by various disturbance factors such as the pneumatic rod disturbance caused by the high-frequency vibration of the electromechanical system after rotation, the drift of the differential pressure sensor itself, and dynamic measurement noise. There is a large distortion between the differential pressure measurement signal and the theoretical cosine signal, and the burrs of the interference signal are large, resulting in large errors in extracting wind speed / wind direction information from the differential pressure signal, making it difficult to accurately determine the optimal route and altitude of the aircraft. Summary of the invention

[0004] In view of the above problems, the present invention provides a method and system for calculating aircraft anemometer data.

[0005] According to a first aspect of the present invention, a method for solving aircraft anemometer data is provided, characterized in that the method comprises: collecting the pressure difference between the two ends of a rigid rotating rod to obtain a kth pressure difference signal, the rigid rotating rod is installed on the rotating shaft of a motor, the aircraft anemometer is driven by the motor, k is an integer greater than or equal to 2, and the kth pressure difference signal is the pressure difference signal at the kth moment; inputting the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor rotation angle signal into a linear regression model, and outputting the kth wind speed related vector at the kth moment, the k-1th wind speed vector to be estimated is based on the linear regression model. The k-th wind speed related vector and the k-th motor angle signal are estimated by a linear regression model, the regression parameters of the linear regression model are determined by a pressure difference signal model, the regression parameters include wind speed parameters and angle parameters corresponding to the k-th wind speed related vector, the pressure difference signal model is established based on the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information, the k-th motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the k-th moment; the k-th wind speed related vector and the k-th motor angle signal are solved to obtain the k-th anemometer data at the k-th moment, and the k-th anemometer data is used to determine the position and heading of the aircraft.

[0006] According to an embodiment of the present invention, the kth wind speed related vector includes a kth DC noise element and at least one kth denoised wind-induced pressure difference component element, the kth DC noise element represents the DC noise information in the kth pressure difference signal, and the kth denoised wind-induced pressure difference component element represents the component information of the kth pressure difference signal after denoising.

[0007] According to an embodiment of the present invention, the kth angle-related vector corresponding to the angle parameter includes at least one kth motor angle component element, and the kth motor angle component element represents component information of the kth motor angle.

[0008] According to an embodiment of the present invention, the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal are input into a linear regression model to output the kth wind speed related vector at the kth moment, and also includes: determining the k-1th covariance matrix according to the estimation accuracy of the k-1th wind speed vector to be estimated; inputting the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal, the kth motor angle signal and the k-1th covariance matrix into a recursive least squares model, and outputting the kth denoised wind-induced pressure difference component element in the kth wind speed related vector.

[0009] According to an embodiment of the present invention, the above-mentioned k-th wind speed related vector and the above-mentioned k-th motor angle signal are solved to obtain the k-th anemometer data at the k-th moment, and also include: calculating the atmospheric density according to the atmospheric pressure and the atmospheric temperature, the above-mentioned atmospheric pressure and the above-mentioned atmospheric temperature are respectively obtained by collecting environmental information during the flight of the aircraft through an atmospheric pressure sensor and an atmospheric temperature sensor; the above-mentioned k-th denoised wind-induced pressure difference component element, the above-mentioned k-th motor angle information and the above-mentioned atmospheric density are solved to obtain at least one of the above-mentioned k-th wind speed and the above-mentioned k-th wind direction angle, and the above-mentioned k-th anemometer data includes at least one of the k-th wind speed and the k-th wind direction angle during the flight of the aircraft.

[0010] According to an embodiment of the present invention, the kth pressure difference signal includes a first pressure difference sub-signal and a second pressure difference sub-signal, and the rigid rotating rod includes a main rigid rotating rod and a spare rigid rotating rod; the pressure difference between the two ends of the rigid rotating rod is collected to obtain the kth pressure difference signal, including: collecting the pressure difference between the two ends of the main rigid rotating rod to obtain the first pressure difference sub-signal; collecting the pressure difference between the two ends of the spare rigid rotating rod to obtain the second pressure difference sub-signal, and the main rigid rotating rod and the spare rigid rotating rod are symmetrically installed on the rotating shaft of the motor.

[0011] According to an embodiment of the present invention, the kth wind speed related vector includes a first wind speed related sub-vector corresponding to the main rigid rotating rod and a second wind speed related sub-vector corresponding to the spare rigid rotating rod; the kth wind speed related vector and the kth motor angle signal are solved to obtain the kth anemometer data at the kth moment, and also includes: according to the first wind speed related sub-vector and the kth motor angle signal, the kth wind speed and the kth wind direction angle of the main rigid rotating rod are solved; according to the second wind speed related sub-vector and the kth motor angle signal, the kth wind speed and the kth wind direction angle of the spare rigid rotating rod are solved; according to the kth wind speed of the main rigid rotating rod and the kth wind speed of the spare rigid rotating rod, the kth wind speed of the aircraft during flight is solved; according to the kth wind direction angle of the main rigid rotating rod and the kth wind direction angle of the spare rigid rotating rod, the kth wind direction angle of the aircraft during flight is solved.

[0012] According to an embodiment of the present invention, the formula of the pressure difference signal model is as follows:

[0013]

[0014] ε represents the above random noise signal, θ represents the above motor position, represents the pressure difference between the two ends of the above rigid rotating rod; , , b 1 is the pressure difference signal model parameter corresponding to the above wind speed parameter;

[0015] The k-th wind speed related vector corresponding to the above wind speed parameter is expressed as , represents the kth DC noise element mentioned above, , b 1( k ) represent the kth denoised wind-induced pressure difference component element; the kth rotation angle related vector corresponding to the rotation angle parameter is expressed as , , They all represent the above-mentioned k-th motor angle component elements.

[0016] A second aspect of the present invention provides a system for calculating aircraft anemometer data, the system comprising: a pressure sensor for collecting the pressure difference between the two ends of a rigid rotating rod to obtain a kth pressure difference signal, the rigid rotating rod being mounted on a rotating shaft of a motor, the aircraft anemometer being driven by the motor, k being an integer greater than or equal to 2, and the kth pressure difference signal being a pressure difference signal at the kth moment;

[0017] A rotary encoder, used to collect the motor position of the aircraft anemometer to obtain a k-th motor angle signal, wherein the k-th motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the k-th moment;

[0018] A microprocessor is used to input the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal into a linear regression model, and output the kth wind speed related vector at the kth moment, wherein the k-1th wind speed vector to be estimated is estimated according to the linear regression model, and the regression parameters of the linear regression model are determined by the pressure difference signal model, and the regression parameters include wind speed parameters and angle parameters, and the pressure difference signal model is established according to the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information; the kth wind speed related vector and the kth motor angle signal are solved to obtain the kth anemometer data at the kth moment, and the kth anemometer data is used to determine the position and heading of the aircraft.

[0019] According to an embodiment of the present invention, the system further includes:

[0020] An atmospheric pressure sensor is used to collect the atmospheric pressure during the flight of the above-mentioned aircraft;

[0021] An atmospheric temperature sensor is used to collect the atmospheric temperature during the flight of the above-mentioned aircraft;

[0022] The microprocessor is used to calculate the atmospheric density according to the atmospheric pressure and the atmospheric temperature;

[0023] The microprocessor is also used to solve the k-th wind speed related vector and the k-th motor rotation angle signal to obtain the k-th anemometer data at the k-th moment, including:

[0024] The kth anemometer data at the kth moment is calculated based on the kth wind speed related vector, the kth motor rotation angle signal and the atmospheric density. The kth anemometer data includes at least one of the kth wind speed and the kth wind direction angle during the flight of the aircraft.

[0025] According to a method and system for solving aircraft anemometer data provided by the present invention, the pressure difference between the two ends of the rigid rotating rod is collected to obtain the kth pressure difference signal; the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal are input into a linear regression model, and the kth wind speed related vector at the kth moment is output, the k-1th wind speed vector to be estimated is obtained according to the linear regression model, and the regression parameters of the linear regression model are determined by the pressure difference signal model, and the regression parameters include wind speed parameters and angle parameters corresponding to the kth wind speed related vector, and the pressure difference signal model is established based on the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information. Since the kth wind speed related vector is not obtained by sensor collection, it not only reduces the influence of various disturbance factors on the kth wind speed related vector, but also realizes the real-time acquisition of the wind speed related vector, thereby improving the accuracy of the kth wind speed related vector.

[0026] By solving the kth wind speed related vector and the kth motor angle signal, the kth anemometer data at the kth moment during the flight of the aircraft can be accurately solved, thereby improving the accuracy of determining the position and heading of the aircraft based on the kth anemometer data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0028] Figure 1 A schematic diagram of the overall structure of an aircraft anemometer according to an embodiment of the present invention is shown;

[0029] Figure 2 A flow chart showing a method for calculating aircraft anemometer data according to an embodiment of the present invention is shown;

[0030] Figure 3 shows a pressure difference signal image without interference according to an embodiment of the present invention;

[0031] Figure 4 shows a pressure difference signal image in the case of interference according to an embodiment of the present invention;

[0032] Figure 5 The pressure difference signal image obtained by the method for solving the aircraft anemometer data according to the embodiment of the present invention is shown;

[0033] Figure 6 A schematic diagram showing signal processing of a system for solving aircraft anemometer data according to an embodiment of the present invention; and

[0034] Figure 7 The structure block diagram of the device for calculating aircraft anemometer data according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0036] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the above-mentioned features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0037] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0038] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0039] High-altitude, low-speed atmospheric aircraft include stratospheric airships, stratospheric solar aircraft, near-space floating integrated aircraft, high-altitude balloons, etc. In recent years, driven by the needs of regional atmospheric environment monitoring, high-resolution real-time surveillance, early warning, missile defense, regional communications, etc., high-altitude stationary aircraft represented by stratospheric airships have attracted widespread attention from various countries.

[0040] The motion characteristics of high-altitude, low-speed atmospheric aircraft are greatly affected by the environmental wind field. Therefore, real-time and accurate measurement of wind speed and direction is a prerequisite for long-term airborne operations and improved flight control quality.

[0041] When an anemometer is installed on an aircraft, the wind speed measured is the relative wind speed, that is, the speed of the atmosphere relative to the aircraft. It is usually more customary to describe the relative wind speed by the speed of the aircraft relative to the atmosphere, which is called airspeed. There is the following relationship between airspeed (or relative wind speed) and true wind speed.

[0042]

[0043] in, are true wind speed, ground speed and airspeed vector respectively.

[0044] Ground speed The relative wind speed (airspeed) can be accurately measured by the GPS (Global Positioning System) / INS (inertial navigation system) combined navigation system equipped on the aircraft, so the relative wind speed (airspeed) can be obtained. , which means that the true wind speed is obtained For atmospheric aircraft, the significance of accurately measuring airspeed is not limited to obtaining true wind speed. The airspeed / sideslip angle itself is an important reference quantity in flight control.

[0045] At high altitudes, the atmosphere is very thin and the speed of low-speed aircraft is low, resulting in very small dynamic pressure, making it very difficult to accurately measure airspeed information.

[0046] In view of this, an embodiment of the present invention provides a method for calculating aircraft anemometer data, comprising: collecting the pressure difference between the two ends of a rigid rotating rod to obtain a kth pressure difference signal, the rigid rotating rod is installed on the rotating shaft of a motor, the aircraft anemometer is driven by the motor, k is an integer greater than or equal to 2, and the kth pressure difference signal is the pressure difference signal at the kth moment; inputting the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor rotation angle signal into a linear regression model, and outputting the kth wind speed related vector at the kth moment, the k-1th wind speed vector to be estimated is based on The linear regression model is estimated, and the regression parameters of the linear regression model are determined by the pressure difference signal model. The regression parameters include wind speed parameters and angle parameters corresponding to the k-th wind speed related vector. The pressure difference signal model is established based on the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information. The k-th motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the k-th moment; the k-th wind speed related vector and the k-th motor angle signal are solved to obtain the k-th anemometer data at the k-th moment, and the k-th anemometer data is used to determine the position and heading of the aircraft.

[0047] Figure 1 The figure shows the overall structure of an aircraft anemometer according to an embodiment of the present invention.

[0048] like Figure 1 As shown, the aircraft anemometer mainly includes a rigid spin dryer, a motor and a pressure sensor. The rigid spin dryer may include a main rigid spin rod (AB) and a spare rigid spin rod (CD), and the pressure sensor may include a main pressure sensor and a spare pressure sensor.

[0049] The main rigid rotating rod (AB) and the spare rigid rotating rod (CD) are installed on the rotating shaft of the motor, and the aircraft anemometer is driven by the motor. A rotary encoder can be installed on the motor. The rotary encoder can collect the motor position of the aircraft anemometer. The motor type can be a brushless motor.

[0050] The two ends of the main rigid rotating rod are end A and end B, respectively, and end A and end B are connected to end a and end b of the main pressure sensor through a pitot tube. The main pressure sensor can collect the pressure difference between the two ends A and B of the main rigid rotating rod. The two ends of the main rigid rotating rod can be perpendicular to the main rod of the main rigid rotating rod, and the distance from the motor rotating shaft is equal, both r.

[0051] The two ends of the spare rigid rotating rod are end C and end D, respectively, and end C and end D are connected to end C and end D of the main pressure sensor respectively. The spare pressure sensor can collect the pressure difference between the two ends C and end D of the spare rigid rotating rod. The two ends of the spare rigid rotating rod can be perpendicular to the spare rod of the spare rigid rotating rod. The main pressure sensor and the spare pressure sensor can both be bipolar differential pressure sensors.

[0052] When working, the motor drives the main rigid rotating rod (AB) and the backup rigid rotating rod (CD) to rotate at a constant angular rate ω. For example, the brushless motor is equipped with an absolute rotary encoder, which is used for both motor speed control and output of the rotation angle of the rotating rod at each moment.

[0053] The aircraft anemometer may also include a microprocessor, which may be used to input the pressure difference signal and the motor angle signal collected by the main pressure sensor and / or the backup pressure sensor into a pressure difference signal model to obtain a wind speed related vector and a motor angle vector with random noise removed, wherein the motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the current moment, and the pressure difference signal model is established based on the pressure difference between the two ends of the main rigid rotating rod and / or the backup rigid rotating rod, the motor position and random noise information; the wind speed related vector and the motor angle vector are input into a linear regression model to output a predicted wind speed related vector and a predicted motor angle vector at the next moment; the predicted wind speed related vector and the predicted motor angle vector are solved to obtain anemometer data at the next moment, and the anemometer data is used to determine the position and heading of the aircraft.

[0054] The aircraft anemometer may also include an atmospheric pressure sensor and an atmospheric temperature sensor. The atmospheric pressure sensor is used to collect the atmospheric pressure during the flight of the aircraft. The atmospheric temperature sensor is used to collect the atmospheric temperature during the flight of the aircraft. The microprocessor is used to calculate the atmospheric density based on the atmospheric pressure and atmospheric temperature.

[0055] If the aircraft already has an atmospheric data sensor, the atmospheric density information is directly provided by the atmospheric data sensor.

[0056] Figure 2 A flow chart of a method for calculating aircraft anemometer data according to an embodiment of the present invention is shown.

[0057] like Figure 2 As shown, the method for calculating aircraft anemometer data in this embodiment includes operations S210 to S230.

[0058] In operation S210, the pressure difference between two ends of the rigid rotating rod is collected to obtain a kth pressure difference signal.

[0059] According to an embodiment of the present invention, the rigid rotating rod is installed on the rotating shaft of the motor, the aircraft anemometer is driven by the motor, k is an integer greater than or equal to 2, and the kth pressure difference signal is the pressure difference signal at the kth moment.

[0060] According to an embodiment of the present invention, the two ends of the rigid rotating rod can be as follows: Figure 1 The A and B ends of the main rigid swivel rod are shown.

[0061] According to an embodiment of the present invention, Figure 1 As shown, the rigid spin dryer can be installed on the rotating shaft of the motor, and the aircraft anemometer is driven by the motor, which drives the rigid rotating rod to rotate at a constant angular velocity ω.

[0062] In operation S220, the k-1th wind speed vector to be estimated at the k-1th moment, the k-th pressure difference signal, and the k-th motor angle signal are input into a linear regression model, and the k-th wind speed related vector at the k-1th moment is output.

[0063] According to an embodiment of the present invention, the k-1th wind speed vector to be estimated is estimated according to a linear regression model, and the regression parameters of the linear regression model are determined by a pressure difference signal model, and the regression parameters include wind speed parameters and rotation angle parameters corresponding to the kth wind speed related vector.

[0064] According to an embodiment of the present invention, the pressure difference signal model is established based on the pressure difference between the two ends of the rigid rotating rod, the motor position and the random noise information.

[0065] According to an embodiment of the present invention, the kth motor rotation angle signal is obtained by collecting the motor position of the aircraft anemometer at the kth moment. The motor position may be the angle information of the motor rotation.

[0066] According to an embodiment of the present invention, the random noise may be noise generated by various disturbance factors such as aerodynamic interference caused by high-frequency vibration of the rotating motor, drift of the differential pressure sensor itself, and dynamic measurement noise.

[0067] In operation S230, the kth wind speed related vector and the kth motor rotation angle signal are solved to obtain the kth anemometer data at the kth moment, and the kth anemometer data is used to determine the position and heading of the aircraft.

[0068] According to an embodiment of the present invention, the kth pressure difference signal is Fourier transformed to obtain the Fourier coefficients. The kth wind speed related vector and the kth motor rotation angle signal are solved according to the Fourier coefficients to obtain the kth anemometer data at the kth moment.

[0069] According to an embodiment of the present invention, the kth wind speed related vector may be a vectorized process of the kth pressure difference signal. For example, the kth wind speed related vector composed of noise information and amplitude information is obtained by vectorizing the noise information and amplitude information related to the kth pressure difference signal.

[0070] According to an embodiment of the present invention, a linear regression model can be used to predict wind speed related vectors and motor rotation angle vectors. For example, the linear regression model can be a least squares model, a binary linear regression model, a multivariate regression model, and the like.

[0071] According to an embodiment of the present invention, the position and heading of the aircraft may be an altitude position, a route direction, a fuselage direction of the aircraft, and the like.

[0072] When the wind speed is V w , when the rigid rotating rod rotates at a constant angular velocity ω, the atmospheric density is ρ, and the rotation radius of the rigid rotating rod is r. is the wind direction angle, θ is the motor rotation angle, p A represents the pressure at the A end of the main rigid rotating rod, p B Indicates the pressure at the B end of the main rigid rotating rod. Under ideal conditions where various interferences are ignored, the output of the main pressure sensor is .

[0073]

[0074] p C Indicates the pressure at the C end of the rigid rotating rod, p D Indicates the pressure at the D end of the rigid rotating rod, φ Indicates the installation angle of the rotating rod. The output of the standby pressure sensor is .

[0075]

[0076] When the two sets of rigid rotating rods are orthogonal, that is, φ=90°

[0077]

[0078] Figure 3 FIG. 4 shows a pressure difference signal image without interference according to an embodiment of the present invention.

[0079] Ideally, the differential pressure signal output by the main differential pressure sensor and the backup differential pressure sensor is a periodic sine (cosine) curve, such as Figure 3 shown.

[0080] Figure 4 FIG. 1 shows a pressure difference signal image under interference according to an embodiment of the present invention.

[0081] like Figure 4 As shown in the figure, due to the influence of various disturbance factors such as aerodynamic interference caused by high-frequency vibration of the electromechanical system after rotation, drift of the differential pressure sensor itself, and dynamic measurement noise, the differential pressure measurement signal is greatly distorted from the theoretical cosine signal, and the burr of the interference signal is large. Differential pressure 1 is the actual output of the main differential pressure sensor, and differential pressure 2 is the actual output of the backup differential pressure sensor.

[0082] In the case of large interference, the anemometer data calculated based on the pressure difference signal is inaccurate, making it difficult to accurately determine the position and heading of the aircraft.

[0083] According to an embodiment of the present invention, the kth pressure difference signal is obtained by collecting the pressure difference between the two ends of the rigid rotating rod; the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal are input into the linear regression model, and the kth wind speed related vector at the kth moment is output, the k-1th wind speed vector to be estimated is obtained according to the linear regression model, and the regression parameters of the linear regression model are determined by the pressure difference signal model, and the regression parameters include the wind speed parameters and the angle parameters corresponding to the kth wind speed related vector, and the pressure difference signal model is established based on the pressure difference between the two ends of the rigid rotating rod, the motor position and the random noise information. Since the kth wind speed related vector is not obtained by sensor acquisition, it not only reduces the influence of various disturbance factors on the kth wind speed related vector, but also realizes the real-time acquisition of the wind speed related vector, thereby improving the accuracy of the kth wind speed related vector.

[0084] By solving the kth wind speed related vector and the kth motor angle signal, the kth anemometer data at the kth moment during the flight of the aircraft can be accurately solved, thereby improving the accuracy of determining the position and heading of the aircraft based on the kth anemometer data.

[0085] According to an embodiment of the present invention, the kth wind speed related vector includes a kth DC noise element and at least one kth denoised wind-induced pressure difference component element, the kth DC noise element represents the DC noise information in the kth pressure difference signal, and the kth denoised wind-induced pressure difference component element represents the component information of the kth pressure difference signal after denoising.

[0086] According to an embodiment of the present invention, the kth rotation angle related vector corresponding to the rotation angle parameter includes at least one kth motor rotation angle component element, and the kth motor rotation angle component element represents component information of the kth motor rotation angle.

[0087] According to an embodiment of the present invention, the k-th motor angle vector may be a vectorized process of the k-th motor angle signal. For example, the k-th motor angle signal is processed in the x-axis and y-axis directions to obtain the x-axis motor angle component and the y-axis motor angle component. The x-axis motor angle component and the y-axis motor angle component are combined into the k-th motor angle vector.

[0088] According to an embodiment of the present invention, the formula of the pressure difference signal model is as follows:

[0089]

[0090] ε represents a random noise signal, θ Indicates the motor position, It represents the pressure difference between the two ends of the rigid rotating rod; , , b 1 is the pressure difference signal model parameter corresponding to the wind speed parameter;

[0091] The k-th wind speed correlation vector corresponding to the wind speed parameter is expressed as , represents the kth DC noise element, , b 1( k ) represent the kth denoised wind-induced pressure difference component element; the kth rotation angle correlation vector corresponding to the rotation angle parameter is expressed as , , All represent the k-th motor angle component element. According to an embodiment of the present invention, This includes DC noise from slow-changing disturbances such as differential pressure sensor drift. , b 1( k ) contains the actual wind speed and / or wind direction information.

[0092] According to an embodiment of the present invention, the kth pressure difference signal can be written as formula (6).

[0093]

[0094] According to an embodiment of the present invention, the kth pressure difference signal includes a first pressure difference sub-signal and a second pressure difference sub-signal, and the rigid rotating rod includes a main rigid rotating rod and a spare rigid rotating rod; the pressure difference between the two ends of the rigid rotating rod is collected to obtain the kth pressure difference signal, including: collecting the pressure difference between the two ends of the main rigid rotating rod to obtain the first pressure difference sub-signal; collecting the pressure difference between the two ends of the spare rigid rotating rod to obtain the second pressure difference sub-signal, and the main rigid rotating rod and the spare rigid rotating rod are symmetrically installed on the rotating shaft of the motor.

[0095] According to an embodiment of the present invention, the aircraft anemometer is started, and after the motor speed is stabilized, the pressure difference information and the motor position are collected to obtain the first pressure difference sub-signal. , the second pressure difference sub-signal and a motor angle signal θ These three signals constitute the original signals of the measurement channels of two independent anemometer data, which are recorded as and .

[0096] The microprocessor performs initialization processing on each measurement signal separately. The initialization algorithm of the two measurement channels is the same.

[0097] According to an embodiment of the present invention, the k-1th wind speed vector to be estimated, the k-1th pressure difference signal and the k-th motor angle signal at the k-1th moment are input into a linear regression model, and the k-th wind speed related vector at the k-1th moment is output, and the method also includes: determining the k-1th covariance matrix according to the estimation accuracy of the k-1th wind speed vector to be estimated; inputting the k-1th wind speed vector to be estimated, the k-1th pressure difference signal, the k-1th motor angle signal and the k-1th covariance matrix at the k-1th moment into a recursive least squares model, and outputting the k-th denoised wind-induced pressure difference component element in the k-th wind speed related vector.

[0098] According to an embodiment of the present invention, the estimation accuracy of the k-1th wind speed correlation vector can characterize the fitting effect of the recursive least squares model. For example, the recursive least squares model can be trained first to obtain fitting parameters. The fitting parameters are determined as the estimation accuracy of the k-1th wind speed correlation vector.

[0099] For example, the microprocessor performs initialization processing on each measurement signal separately. The initialization algorithm of the two measurement channels is the same. Represents the i-th sampling moment or .

[0100]

[0101] The initial covariance matrix of the recursive least squares model can be shown as formula (11).

[0102]

[0103] Among them, m represents the number of samples for initialization processing (i.e., the mth moment). The mth moment is smaller than the kth moment, and the mth moment can also be the k-1th moment. m -1 It means taking the reciprocal of m. For example, the number of initial processing samples may be the first 200 pressure difference signals and motor rotation angles obtained.

[0104]

[0105] symbol Indicates rounding. f s is the sampling frequency, f m is the motor speed per second. For example, the sampling frequency f s =200Hz, for motor speed 600rpm ( f m =10Hz), then m=400, corresponding to 2s of data.

[0106] The microprocessor performs recursive calculation on each measurement signal.

[0107] According to an embodiment of the present invention, the recursive least squares model may have a forgetting factor, and the recursive identification parameter may be the kth denoised wind-induced pressure difference component element, that is, The forgetting factor can be The formula of the established recursive least squares model is shown in formula (13). The k-1th wind speed correlation vector corresponding to the wind speed parameter is expressed as The covariance matrix at the k-1th moment, The motor rotation angle signal at the kth moment can be represented.

[0108]

[0109] According to an embodiment of the present invention, the kth wind speed related vector and the kth motor angle signal are solved to obtain the kth anemometer data at the kth moment, which also includes: calculating the atmospheric density according to the atmospheric pressure and the atmospheric temperature, the atmospheric pressure and the atmospheric temperature are respectively obtained by collecting environmental information during the flight of the aircraft through an atmospheric pressure sensor and an atmospheric temperature sensor; the kth denoised wind-induced pressure difference component element, the kth motor angle information and the atmospheric density are solved to obtain at least one of the kth wind speed and the kth wind direction angle, and the kth anemometer data includes at least one of the kth wind speed and the kth wind direction angle during the flight of the aircraft.

[0110] According to an embodiment of the present invention, based on the atmospheric pressure P and the atmospheric temperature T, the atmospheric density can be calculated by formula (14): ρ , R=287.

[0111]

[0112] Wind speed at time k Wind direction The solution formula is shown in formula (15). is the average motor speed.

[0113]

[0114] According to an embodiment of the present invention, the wind speed at the kth moment The components in the x-axis and y-axis directions can be solved to obtain the x-axis component and the y-axis component .

[0115]

[0116] According to an embodiment of the present invention, the kth wind speed related vector and the kth motor angle signal are solved to obtain the kth anemometer data at the kth moment, which also includes: solving the kth wind speed and the kth wind direction angle of the main rigid rotating rod according to the first wind speed related sub-vector and the kth motor angle signal; solving the kth wind speed and the kth wind direction angle of the spare rigid rotating rod according to the second wind speed related sub-vector and the kth motor angle signal; solving the kth wind speed during the flight of the aircraft according to the kth wind speed of the main rigid rotating rod and the kth wind speed of the spare rigid rotating rod; solving the kth wind direction angle during the flight of the aircraft according to the kth wind direction angle of the main rigid rotating rod and the kth wind direction angle of the spare rigid rotating rod.

[0117] According to an embodiment of the present invention, the kth wind speed related vector includes a first wind speed related sub-vector corresponding to the main rigid rotating rod and a second wind speed related sub-vector corresponding to the standby rigid rotating rod.

[0118] According to an embodiment of the present invention, the kth wind speed and the kth wind direction angle of the main rigid rotating rod or the spare rigid rotating rod may be used as output data of the anemometer.

[0119] According to an embodiment of the present invention, the kth wind speed of the main rigid rotating rod and the kth wind speed of the standby rigid rotating rod may be averaged to obtain the kth wind speed during the flight of the aircraft.

[0120] According to an embodiment of the present invention, the kth wind direction angle of the main rigid rotating rod and the kth wind direction angle of the standby rigid rotating rod may be averaged to obtain the kth wind direction angle during the flight of the aircraft.

[0121] Figure 5The pressure difference signal image obtained by the aircraft anemometer data solution method according to an embodiment of the present invention is shown.

[0122] like Figure 5 As shown in the figure, differential pressure 1 is the actual output of the main differential pressure sensor, and differential pressure 2 is the actual output of the backup differential pressure sensor. Figure 3 The pressure difference signal image shown in the figure is the same as that in the case of no interference, showing a periodic and smooth sine (cosine) curve.

[0123] According to an embodiment of the present invention, the wind speed and wind direction adopt the results of the main measurement unit, the wind speed and wind direction adopt the results of the redundant measurement unit, and the two measurement units are merged and outputted.

[0124] According to an embodiment of the present invention, a system for calculating aircraft anemometer data includes:

[0125] The pressure sensor is used to collect the pressure difference between the two ends of the rigid rotating rod to obtain the kth pressure difference signal, the rigid rotating rod is installed on the rotating shaft of the motor, the aircraft anemometer is driven by the motor, k is an integer greater than or equal to 2, and the k-1th pressure difference signal is the pressure difference signal at the kth moment;

[0126] A rotary encoder is used to collect the motor position of the aircraft anemometer to obtain a k-th motor angle signal, wherein the k-th motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the k-th moment;

[0127] The microprocessor is used for inputting the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal into a linear regression model, and outputting the kth wind speed related vector at the kth moment, wherein the k-1th wind speed vector to be estimated is obtained according to the linear regression model, and the regression parameters of the linear regression model are determined by the pressure difference signal model, and the regression parameters include wind speed parameters and angle parameters, and the pressure difference signal model is established according to the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information; the kth wind speed related vector and the kth motor angle signal are solved to obtain the kth anemometer data at the kth moment, and the kth anemometer data is used to determine the position and heading of the aircraft.

[0128] According to an embodiment of the present invention, the system further includes:

[0129] Atmospheric pressure sensor, used to collect atmospheric pressure during the flight of the aircraft;

[0130] Atmospheric temperature sensor, used to collect the atmospheric temperature during the flight of the aircraft;

[0131] A microprocessor, used for calculating the atmospheric density according to the atmospheric pressure and the atmospheric temperature;

[0132] The microprocessor is also used to solve the k-th wind speed related vector and the k-th motor rotation angle signal, and obtain the k-th anemometer data at the k-th moment including:

[0133] The kth anemometer data at the kth moment is calculated according to the kth wind speed related vector, the kth motor rotation angle signal and the atmospheric density. The kth anemometer data includes at least one of the kth wind speed and the kth wind direction angle during the flight of the aircraft.

[0134] Figure 6 A schematic diagram of signal processing of a system for solving aircraft anemometer data according to an embodiment of the present invention is shown.

[0135] like Figure 6 As shown, the pressure sensor includes a main pressure sensor and a backup pressure sensor. The main pressure sensor is used to measure the pressure P corresponding to the end A and the end B of the main rigid rotating rod. A and P B , to obtain the pressure difference △p1 between the two ends of the main rigid rotating rod. That is, the measuring unit 1 obtains the pressure difference △p1 between the two ends of the main rigid rotating rod.

[0136] The pressure sensor is used to measure the pressure P corresponding to the end C and end D of the rigid rotating rod. C and P D , to obtain the pressure difference △p2 between the two ends of the spare rigid rotating rod. That is, the measuring unit 2 obtains the pressure difference △p2 between the two ends of the main rigid rotating rod.

[0137] The rotary encoder is used to collect the motor position of the aircraft anemometer, that is, the motor angle.

[0138] The atmospheric pressure sensor is used to collect the atmospheric pressure P during the flight of the aircraft.

[0139] The atmospheric temperature sensor is used to collect the atmospheric temperature T during the flight of the aircraft.

[0140] The microprocessor is used to solve the kth wind speed related vector and the kth motor angle signal to obtain the kth anemometer data at the kth moment, including: solving the kth anemometer data at the kth moment according to the kth wind speed related vector, the kth motor angle signal and the atmospheric density, and the kth anemometer data includes at least one of the kth wind speed and the kth wind direction angle during the flight of the aircraft.

[0141] Figure 7 The structure block diagram of the device for calculating aircraft anemometer data according to an embodiment of the present invention is shown.

[0142] like Figure 7As shown, the aircraft anemometer data solving device 700 of this embodiment includes a collection module 710 , a first input module 720 , a second input module 730 and a solving module 740 .

[0143] The acquisition module 710 is used to acquire the pressure difference between the two ends of the rigid rotating rod to obtain the kth pressure difference signal. In one embodiment, the acquisition module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0144] The first input module 720 is used to input the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal into the linear regression model, and output the kth wind speed related vector at the kth moment. In one embodiment, the first input module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0145] The solution module 730 is used to solve the k-th wind speed related vector and the k-th motor angle signal to obtain the k-th anemometer data at the k-th moment, and the k-th anemometer data is used to determine the position and heading of the aircraft. In one embodiment, the solution module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0146] It should be noted that the aircraft anemometer data solving device part in the embodiment of the present invention corresponds to the aircraft anemometer data solving method part in the embodiment of the present invention. The description of the aircraft anemometer data solving device part specifically refers to the aircraft anemometer data solving method part, which will not be repeated here.

[0147] According to an embodiment of the present invention, any multiple modules among the acquisition module 710, the first input module 720, and the solution module 730 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the acquisition module 710, the first input module 720, and the solution module 730 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 710, the first input module 720, and the solution module 730 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0148] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All of these combinations and / or combinations fall within the scope of the present invention.

[0151] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination. The scope of the present invention is defined by the attached claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for calculating aircraft anemometer data, characterized in that: The method comprises: The pressure difference between the two ends of the rigid rotating rod is collected to obtain a kth pressure difference signal, wherein the rigid rotating rod is installed on the rotating shaft of the motor, and the aircraft anemometer is driven by the motor, k is an integer greater than or equal to 2, and the kth pressure difference signal is the pressure difference signal at the kth moment; The k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal are input into a linear regression model, and the kth wind speed related vector at the kth moment is output, the k-1th wind speed vector to be estimated is estimated according to the linear regression model, the regression parameters of the linear regression model are determined by the pressure difference signal model, the regression parameters include wind speed parameters and angle parameters corresponding to the kth wind speed related vector, the kth angle related vector corresponding to the angle parameter includes at least one kth motor angle component element, the kth motor angle component element represents component information of the kth motor angle, the pressure difference signal model is established according to the pressure difference between the two ends of the rigid rotating rod, the motor position and random noise information, and the kth motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the kth moment; Solving the k-th wind speed related vector and the k-th motor rotation angle signal to obtain the k-th anemometer data at the k-th moment, wherein the k-th anemometer data is used to determine the position and heading of the aircraft; The formula of the pressure difference signal model is as follows: ； ε represents the random noise signal, θ represents the motor position, represents the pressure difference between the two ends of the rigid rotating rod; , , b1 is the pressure difference signal model parameter corresponding to the wind speed parameter; the k-th wind speed related vector corresponding to the wind speed parameter is expressed as , represents the kth DC noise element, , b1(k) both represent the kth denoised wind-induced pressure difference component element; the kth rotation angle related vector corresponding to the rotation angle parameter is expressed as , , All represent the k-th motor angle component element; The formula of the kth pressure difference signal is as follows: 。 2. The method according to claim 1, characterized in that The kth wind speed related vector includes a kth DC noise element and at least one kth denoised wind-induced pressure difference component element, the kth DC noise element represents the DC noise information in the kth pressure difference signal, and the kth denoised wind-induced pressure difference component element represents the component information of the denoised kth pressure difference signal.

3. The method according to claim 1, characterized in that The step of inputting the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor rotation angle signal into a linear regression model and outputting the kth wind speed related vector at the kth moment also includes: Determine the k-1th covariance matrix according to the estimation accuracy of the k-1th wind speed vector to be estimated; The k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal, the kth motor angle signal and the k-1th covariance matrix are input into a recursive least squares model, and the kth denoised wind-induced pressure difference component element in the kth wind speed related vector is output.

4. The method according to claim 1, characterized in that: The step of solving the k-th wind speed related vector and the k-th motor rotation angle signal to obtain the k-th anemometer data at the k-th moment also includes: The atmospheric density is calculated according to the atmospheric pressure and the atmospheric temperature, wherein the atmospheric pressure and the atmospheric temperature are respectively obtained by collecting environmental information of the aircraft during flight through an atmospheric pressure sensor and an atmospheric temperature sensor; The kth denoised wind-induced pressure difference component element, the kth motor angle information and the atmospheric density are solved to obtain at least one of the kth wind speed and the kth wind direction angle, and the kth anemometer data includes at least one of the kth wind speed and the kth wind direction angle during the flight of the aircraft.

5. The method according to claim 1, characterized in that The kth pressure difference signal includes a first pressure difference sub-signal and a second pressure difference sub-signal, and the rigid rotary rod includes a main rigid rotary rod and a spare rigid rotary rod; The step of collecting the pressure difference between the two ends of the rigid rotating rod to obtain the kth pressure difference signal comprises: collecting the pressure difference between the two ends of the main rigid rotating rod to obtain the first pressure difference sub-signal; The pressure difference between the two ends of the spare rigid rotary rod is collected to obtain the second pressure difference sub-signal. The main rigid rotary rod and the spare rigid rotary rod are symmetrically mounted on the rotating shaft of the motor.

6. The method according to claim 5, characterized in that The k-th wind speed related vector includes a first wind speed related sub-vector corresponding to the main rigid rotating rod and a second wind speed related sub-vector corresponding to the standby rigid rotating rod; The step of solving the k-th wind speed related vector and the k-th motor rotation angle signal to obtain the k-th anemometer data at the k-th moment also includes: According to the first wind speed related sub-vector and the kth motor rotation angle signal, the kth wind speed and the kth wind direction angle of the main rigid rotating rod are obtained by solving the above-mentioned problem; According to the second wind speed related sub-vector and the kth motor rotation angle signal, the kth wind speed and the kth wind direction angle of the standby rigid rotating rod are calculated; The kth wind speed of the aircraft during flight is calculated according to the kth wind speed of the main rigid rotary rod and the kth wind speed of the standby rigid rotary rod; The kth wind direction angle during the flight of the aircraft is calculated according to the kth wind direction angle of the main rigid rotary rod and the kth wind direction angle of the standby rigid rotary rod.

7. A system for calculating aircraft anemometer data, characterized in that: The system comprises: A pressure sensor is used to collect the pressure difference between the two ends of the rigid rotating rod to obtain a kth pressure difference signal, wherein the rigid rotating rod is installed on the rotating shaft of the motor, and the aircraft anemometer is driven by the motor, k is an integer greater than or equal to 2, and the kth pressure difference signal is the pressure difference signal at the kth moment; A rotary encoder, used for collecting the motor position of the aircraft anemometer to obtain a kth motor angle signal, wherein the kth motor angle signal is obtained by collecting the motor position of the aircraft anemometer at the kth moment; A microprocessor, for inputting the k-1th wind speed vector to be estimated at the k-1th moment, the kth pressure difference signal and the kth motor angle signal into a linear regression model, and outputting the kth wind speed related vector at the kth moment, wherein the k-1th wind speed vector to be estimated is estimated according to the linear regression model, and the regression parameters of the linear regression model are determined by the pressure difference signal model, and the regression parameters include wind speed parameters and angle parameters, and the kth angle related vector corresponding to the angle parameters includes at least one kth motor angle component element, and the kth motor angle component element represents the component information of the kth motor angle, and the pressure difference signal model is established according to the pressure difference between the two ends of the rigid rotating rod, the motor position and the random noise information; the kth wind speed related vector and the kth motor angle signal are solved to obtain the kth anemometer data at the kth moment, and the kth anemometer data is used to determine the position and heading of the aircraft; The formula of the pressure difference signal model is as follows: ； ε represents the random noise signal, θ represents the motor position, represents the pressure difference between the two ends of the rigid rotating rod; , , b1 is the pressure difference signal model parameter corresponding to the wind speed parameter; the k-th wind speed related vector corresponding to the wind speed parameter is expressed as , represents the kth DC noise element, , b1(k) both represent the kth denoised wind-induced pressure difference component element; the kth rotation angle related vector corresponding to the rotation angle parameter is expressed as , , All represent the k-th motor angle component element; The formula of the kth pressure difference signal is as follows: 。 8. The aircraft anemometer data solution system according to claim 7, characterized in that: The system further comprises: An atmospheric pressure sensor, used to collect the atmospheric pressure during the flight of the aircraft; An atmospheric temperature sensor, used to collect the atmospheric temperature during the flight of the aircraft; The microprocessor is used to calculate the atmospheric density according to the atmospheric pressure and the atmospheric temperature; The microprocessor is also used to solve the k-th wind speed related vector and the k-th motor rotation angle signal, and the k-th anemometer data at the k-th moment is obtained, including: The kth anemometer data at the kth moment is calculated according to the kth wind speed related vector, the kth motor rotation angle signal and the atmospheric density, and the kth anemometer data includes at least one of the kth wind speed and the kth wind direction angle during the flight of the aircraft.

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