Method and system for estimating airspeed, angle of attack, and sideslip angle of aircraft

Abstract

Disclosed in the present invention are a method and system for estimating an airspeed, an angle of attack, and a sideslip angle of an aircraft. The method comprises: modeling the full six-degree-of-freedom dynamics, the aerodynamics, a force, and a moment of an aircraft in a nonlinear state-space form; using an inertial measurement unit to measure the body angular rate and body acceleration of the aircraft; using a global navigation satellite system to measure a ground speed; using data of systems of the aircraft other than an air data system to determine air data measurement; and using a plurality of Kalman filters to estimate the airspeed and / or the angle of attack and / or the sideslip angle from all measured data. The method and the system provided by the present invention solve the problem in the prior art of inability to improve the safety and reliability by installing a redundant air data computer / sensor on a size-, weight-, and power-constrained aircraft, and the problem in the prior art of being not independent of a conventional air data system at the measurement principle level.

Description

A method and system for estimating aircraft airspeed, angle of attack and sideslip angle Technical Field

[0001] The present application relates to the field of aircraft technology, and in particular to a method and system for estimating the airspeed, angle of attack, and sideslip angle of an aircraft. Background Art

[0002] Modern aircraft place higher demands on the availability and integrity of atmospheric data. Atmospheric data describes the state of the air mass surrounding the aircraft. Aircraft atmospheric data typically includes total pressure, static pressure, angle of attack, angle of sideslip, total air temperature, and other appropriate atmospheric data. During flight, pilots and onboard systems utilize atmospheric data to measure aircraft performance and provide input for flight control and navigation. Traditional air data systems (ADS) primarily utilize atmospheric data probes (sensors) mounted on the aircraft's exterior or skin, such as pitot tubes, fuselage static pressure ports, wind vane angle of attack sensors, and total air temperature sensors. These traditional sensors can be adversely affected by environmental and other conditions. For example, if a pitot tube becomes clogged with ice or other foreign matter, it can cause errors in total pressure measurements, leading to incorrect airspeed calculations. With the recent rise in the prevalence of fly-by-wire aircraft, the demand for reliable atmospheric data has increased significantly, as atmospheric data is used for full-time control enhancements such as closed-loop sideslip control and envelope protection. A common approach is to utilize physical redundancy in atmospheric data sensors and computers, detecting errors in atmospheric data through cross-comparison and voting. However, the physical redundancy of conventional air data systems does not provide independent measurement principles, and there are still unresolved error conditions, such as when an aircraft encounters multiple pitot tube failures due to regional icing. In this case, the correct total pressure cannot be obtained through cross-comparison or voting.

[0003] The fused air data system (SADS) is an alternative method for determining air data measurements. It does not require the functioning of air data sensors. Its basic concept is to estimate air data by fusing non-air data sensors with an aircraft dynamics model. There are two types of SADS approaches. One is aerodynamic model-based SADS, which combines information from the aircraft's equations of motion and other sensors to estimate air data. Model-based SADS typically fuses inertial measurement unit (IMU) and global navigation satellite system (GNSS) data with aircraft dynamics predictions to derive estimates of airspeed, angle of attack, and sideslip angle. When implemented using recursive linear filtering methods (such as the Kalman filter), model-based SADS can include airspeed as one of the state variables. State equations are established based on the aircraft dynamics model, and measurement equations are established based on non-air data sensor measurements and their relationships with state variables. Airspeed is then estimated using recursive linear filtering schemes such as the Kalman filter (KF), extended Kalman filter (EKF), and unscented Kalman filter (UKF). The implementation of aerodynamic model-based SADS is challenging because it requires comprehensive information about vehicle dynamics, control surface parameters, and other vehicle characteristics to establish a vehicle dynamics model with the required fidelity to ensure accurate estimation of atmospheric data. Another approach is model-free SADS, which does not require an aircraft dynamics model to provide predictions of updated aircraft states. One model-free approach uses a lift model to calculate atmospheric data using the relationship between lift coefficient, angle of attack, and airspeed (or dynamic pressure). For example, angle of attack can be calculated using dynamic pressure, or angle of attack can be used to calculate dynamic pressure, or angle of attack and altitude can be used to calculate airspeed. However, this approach still uses parameters provided by the air data system (ADS) and is therefore not completely independent of ADS.

[0004] Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and system for estimating the airspeed, angle of attack, and sideslip angle of an aircraft, which solves the following problems:

[0006] 1. Existing methods for estimating aircraft airspeed, angle of attack, and sideslip angle cannot improve safety and reliability by installing redundant air data computers and / or sensors on aircraft with limited size, weight, and power.

[0007] 2. The existing methods for estimating aircraft airspeed, angle of attack and sideslip angle are not independent of traditional atmospheric data systems in terms of measurement principles.

[0008] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: to provide a method for estimating the airspeed, angle of attack and sideslip angle of an aircraft, comprising the following steps:

[0009] Modeling of the vehicle's full six-degree-of-freedom dynamics, aerodynamics, forces, and moments in a nonlinear state-space form;

[0010] Using an inertial measurement unit to measure the angular rate and acceleration of the aircraft;

[0011] Measurement of ground speed using the Global Navigation Satellite System;

[0012] Determine air data measurements using data from aircraft systems other than the air data system;

[0013] Multiple Kalman filters are used to estimate airspeed, angle of attack and / or sideslip angle from all measured data.

[0014] Furthermore: the input of the modeled six-degree-of-freedom model of the aircraft is control input information.

[0015] Furthermore: the aircraft six-degree-of-freedom model uses a linear or nonlinear aircraft six-degree-of-freedom model.

[0016] Furthermore, the multiple Kalman filters may also use the ground speed and attitude generated by the fusion of the global navigation satellite system and / or the inertial navigation system.

[0017] Furthermore, the plurality of Kalman filters may also use total temperature measurements determined from engine sensor data.

[0018] Furthermore, the plurality of Kalman filters may also be fused using pressure measurements determined from a global navigation satellite system and / or an inertial navigation system and / or engine sensor data.

[0019] Furthermore, the multiple Kalman filters may also use a wind field model to obtain wind speed estimation values.

[0020] Furthermore: the multiple Kalman filters use a Kalman filter, an extended Kalman filter, an error-state-based Kalman filter and / or an unscented Kalman filter.

[0021] Further: using a weighted filter or algorithm to fuse the outputs of multiple Kalman filters.

[0022] Furthermore: the airspeed described in this method includes Mach number, calibrated airspeed and / or true airspeed.

[0023] The present invention also provides a system for estimating an aircraft's airspeed, angle of attack, and sideslip angle based on the method for estimating the aircraft's airspeed, angle of attack, and sideslip angle, which includes a control input, an aircraft dynamics model, a global navigation satellite system, an inertial navigation system, a wind field model, an engine sensor, an atmospheric data fusion module, and a Kalman filter module;

[0024] The control inputs transmitted to the aircraft dynamics model include aircraft control surface deflections, throttle lever positions, or thrust;

[0025] The aircraft dynamics model is used to predict the aircraft state;

[0026] The global navigation satellite system and / or inertial navigation system is used to identify the altitude of the aircraft and generate static pressure through the altitude;

[0027] The global navigation satellite system and / or inertial navigation system is used to identify ground speed measurements and attitude measurements and transmit them to the Kalman filter module;

[0028] The wind field model is used to estimate the wind speed and transmit it to the Kalman filter module;

[0029] The engine sensor is used to identify static pressure, total pressure and / or total temperature measurements;

[0030] The atmospheric data fusion module is used to fuse static pressure, total pressure and / or total temperature;

[0031] The Kalman filter module is used to estimate airspeed, angle of attack and / or sideslip angle.

[0032] Furthermore: the aircraft dynamics model uses a linear or nonlinear aircraft dynamics model.

[0033] Furthermore: the system for estimating the aircraft's airspeed, angle of attack, and sideslip angle identifies altitude from systems other than the air data system and generates static pressure from the altitude.

[0034] Furthermore, the system for estimating the airspeed, angle of attack and sideslip angle of an aircraft may further include an inertial measurement unit, wherein the inertial measurement unit is used to measure the angular rate and acceleration of the aircraft body.

[0035] Furthermore: the system for estimating the aircraft's airspeed, angle of attack, and sideslip angle uses data related to any one of static pressure, total pressure, and total temperature identified by systems other than the atmospheric data system.

[0036] Furthermore: the Kalman filter module uses a Kalman filter, an extended Kalman filter, an error-state-based Kalman filter and / or an unscented Kalman filter.

[0037] Furthermore: the output of the Kalman filter module is fused using a weighted filter or algorithm.

[0038] Furthermore: the airspeed described in this system includes Mach number, calibrated airspeed and / or true airspeed.

[0039] The beneficial effects of the present invention are:

[0040] 1. The method and system provided by the present invention estimate an aircraft's airspeed, angle of attack, and sideslip angle based on data provided by other aircraft systems. This method and system are completely independent of the traditional air data system (ADS) and can serve as a backup in the event of a traditional ADS failure, thereby improving aircraft safety.

[0041] 2. The method and system provided by the present invention utilize sensor measurement data from the aircraft's existing systems, eliminating the need to install additional sensors on the aircraft unless additional hardware is specifically required. The algorithms involved can share hardware with the aircraft's existing systems, significantly saving costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a block diagram of a method for estimating an aircraft's airspeed, angle of attack, and sideslip angle.

[0043] Figure 2 is a diagram of the system architecture for estimating the aircraft's airspeed, angle of attack, and sideslip angle.

[0044] Figure 3 shows the algorithm flow for estimating the aircraft's airspeed, angle of attack, and sideslip angle. DETAILED DESCRIPTION

[0045] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0046] As shown in FIG1 , in one embodiment of the present invention, a method for estimating an aircraft's airspeed, angle of attack, and sideslip angle is provided, comprising the following steps:

[0047] Modeling of the vehicle's full six-degree-of-freedom dynamics, aerodynamics, forces, and moments in a nonlinear state-space form;

[0048] Using an inertial measurement unit to measure the angular rate and acceleration of the aircraft;

[0049] Measurement of ground speed using the Global Navigation Satellite System;

[0050] Determine air data measurements using data from aircraft systems other than the air data system;

[0051] Multiple Kalman filters are used to estimate airspeed, angle of attack and / or sideslip angle from all measured data.

[0052] In one embodiment of the present invention, the input of the modeled six-degree-of-freedom aircraft model is control input information. In this embodiment, the control input information includes aircraft control surface deflections and throttle lever position or thrust. The aircraft control surface deflections include elevator deflections, aileron deflections, and rudder deflections, and the expression is: u(t) = [δe δa δr δT] T

[0053] where u(t) represents the input information, and δe, δa, δr, and δT are the elevator deflection, aileron deflection, rudder deflection, and throttle lever position or thrust, respectively.

[0054] In one embodiment of the present invention, the aircraft six-degree-of-freedom model uses a linear or nonlinear aircraft six-degree-of-freedom model;

[0055] In this embodiment, the formula for modeling the full six-degree-of-freedom dynamics, aerodynamics, forces, and moments of the aircraft in the form of nonlinear spatial states is:

[0056] in, is the vehicle state vector, Represents the real number field, superscript n x represents the dimension of the vehicle state vector x, is the derivative of x with respect to time t, is the control input, with superscript n u represents the dimension of the control input u, is the random object process disturbance, superscript n w represents the dimension of the random object process disturbance w, is the measurement vector, with superscript n z represents the dimension of the measurement vector z, is random measurement noise, superscript n v represents the dimension of random measurement noise v, is the random object process disturbance distribution matrix, assuming that w and v are wide-sense stationary zero-mean noises unrelated to the state vector x, and their covariance matrices are Q and R respectively. The vector field f(·) and To model the nonlinear dynamics and measurements, their Jacobian matrices with respect to the state vector x are needed in the Kalman filter and are:

[0057] in, is an estimate of x.

[0058] The aircraft state prediction value can be obtained according to the modeling results. The error covariance matrix of the aircraft state prediction value is The expression is:

[0059] Where E{.} is the expectation operator, and the superscript T represents the transpose of the matrix;

[0060] The error covariance matrix of the aircraft state prediction value is updated over time as follows:

[0061] in, is the derivative of P with respect to time t; F(t) is the abbreviation of F(x(t), u(t), t), W is the random object process disturbance distribution matrix; Q is the covariance matrix of the wide-sense stationary zero-mean noise w that is unrelated to the state vector x;

[0062] In this embodiment, the body angular rate ω includes the roll rate p, the pitch rate q, and the yaw rate r, as shown in the following formula: ω = [pqr] T

[0063] In this embodiment, the body acceleration A can be expressed in the body coordinate system as: A = [A x A y A z ] T

[0064] Among them, A x is the acceleration along the longitudinal axis of the body, A y is the acceleration along the horizontal axis of the body, A z is the acceleration along the vertical axis of the machine body.

[0065] In one embodiment of the present invention, the multiple Kalman filters may further use ground speed and attitude generated by fusion of a global navigation satellite system and / or an inertial navigation system.

[0066] In one embodiment of the present invention, the plurality of Kalman filters may also utilize total temperature measurements determined from engine sensor data.

[0067] In one embodiment of the invention, the plurality of Kalman filters may also use pressure measurements determined from a global navigation satellite system and an inertial navigation system and / or engine sensor data.

[0068] In one embodiment of the present invention, the multiple Kalman filters can also use the ground speed and attitude generated by the fusion of the global navigation satellite system and the inertial navigation system. In this embodiment, the ground speed In the north-east coordinate system, it can be expressed as:

[0069] Wherein, the superscript N represents the north-east coordinate system; V Nis the north velocity, V E is the eastward velocity, V D is the ground velocity.

[0070] In one embodiment of the present invention, the ground speed Airspeed V B and wind speed W N The vector relationship between them is as follows:

[0071] Among them, the superscript B represents the body coordinate system; is the direction cosine matrix.

[0072] In one embodiment of the present invention, the multiple Kalman filters may also use a wind field model to obtain wind speed estimates; the wind field in the wind field model is a three-dimensional vector having components in the northeast coordinate system: W N =[W N W E W D ] T

[0073] Among them, W N Indicates the wind speed in the northeast coordinate system, W N 、W E and W D They represent the components of the N, E, and D directions in the northeastern coordinate system respectively;

[0074] When obtaining the wind speed estimate, the wind field W N Modeled as an average wind field Plus the change δW N , the expression is:

[0075] In one embodiment of the present invention, the multiple Kalman filters use Kalman filters, extended Kalman filters, error-state-based Kalman filters and / or unscented Kalman filters; these Kalman filters estimate random aircraft state vectors by processing random measurement values ​​of different measurement sets, and the estimated aircraft state vectors are discrete.

[0076] The Kalman filter consists of a time update algorithm and a measurement update algorithm. The time update algorithm is used to propagate the aircraft state vector and error covariance matrix, while the measurement update algorithm updates the aircraft state vector and error covariance matrix based on the measurement vector. In other words, the Kalman filter uses the state vector at the previous moment to predict the state vector at the current moment, and then updates the predicted state vector with the current measurement vector, resulting in an estimated state vector, which is used as the state vector input for the next cycle.

[0077] The time update of the Kalman filter is as follows:

[0078] Where subscript k represents discrete time, the discrete time interval is Δt, k = 1, 2, ..., n; the parameter with subscript k+1|k represents the prediction of the parameter at time k for time k+1, that is, the prior value; the parameter with subscript k|k represents the updated value of the parameter at time k, that is, the posterior value; Φ and G are vector fields The Jacobian matrices for x and u are as follows:

[0079] The Kalman filter measurement update is as follows: P k+1 =P k+1|k -K k+1 H k+1 P k+1|k

[0080] Where K is the Kalman gain, z is the measurement vector, R is the covariance matrix of the measurement noise, H is the vector field The Jacobian matrix is ​​as follows:

[0081] True airspeed is one of the aircraft state vectors and can be directly obtained from the estimated state vector. In the aircraft coordinate system, the true airspeed component is: V B =[uvw] T

[0082] Where u is V B The component along the longitudinal axis of the body, v is V B The component along the longitudinal axis of the body, w is V B Component along the longitudinal axis of the body.

[0083] Since airspeed is one of the aircraft state vectors, it can be directly obtained from the posterior state vector. The airspeed estimate is:

[0084] According to the definitions of angle of attack and sideslip angle, the angle of attack α and sideslip angle β can be calculated from the true airspeed as follows:

[0085] In one embodiment of the present invention, a weighted filter or algorithm is used to fuse the outputs of multiple Kalman filters.

[0086] In one embodiment of the present invention, the airspeed estimation value in the method includes Mach number and / or calibrated airspeed and / or true airspeed. In this embodiment, the total temperature measurement value T is determined from the engine sensor data. t , using static pressure measurements P determined from GNSS and inertial navigation systems and / or engine sensor data s and total pressure measurement value P t These atmospheric data measurements can be used to calculate the dynamic pressure q c , Mach number M, calibrated airspeed V CAS , true airspeed V TAS The value of q is: c =P t -P s V CAS =c n ·MV TAS =c·M

[0087] Among them, q c represents dynamic pressure, c n represents the standard speed of sound at sea level; c represents the speed of sound, and R is the special gas constant.

[0088] As shown in FIG2 , in one embodiment of the present invention, a system for estimating an aircraft's airspeed, angle of attack, and sideslip angle is provided, including a control input, an aircraft dynamics model, a global navigation satellite system, an inertial navigation system, a wind field model, an engine sensor, an atmospheric data fusion module, and a Kalman filter module.

[0089] The control inputs transmitted to the aircraft dynamics model include aircraft control surface deflections, throttle lever positions, or thrust;

[0090] The aircraft dynamics model is used to predict the aircraft state;

[0091] The global navigation satellite system and / or inertial navigation system is used to identify the altitude of the aircraft and generate static pressure through the altitude;

[0092] The global navigation satellite system and / or inertial navigation system is used to identify ground speed measurements and attitude measurements and transmit them to the Kalman filter module;

[0093] The wind field model is used to estimate the wind speed and transmit it to the Kalman filter module;

[0094] The engine sensor is used to identify static pressure, total pressure and / or total temperature measurements;

[0095] The atmospheric data fusion module is used to fuse static pressure, total pressure and / or total temperature;

[0096] The Kalman filter module is used to estimate airspeed, angle of attack and / or sideslip angle.

[0097] In one embodiment of the present invention, the aircraft dynamics model uses a linear or nonlinear aircraft dynamics model.

[0098] In one embodiment of the present invention, the system for estimating aircraft airspeed, angle of attack, and angle of sideslip identifies altitude from a system other than the air data system and generates static pressure from the altitude.

[0099] In one embodiment of the present invention, the system for estimating the airspeed, angle of attack, and sideslip angle of an aircraft may further include an inertial measurement unit, and the inertial measurement unit is used to measure the angular rate and acceleration of the aircraft body.

[0100] In one embodiment of the present invention, the system for estimating the aircraft airspeed, angle of attack, and sideslip angle uses data related to any one of static pressure, total pressure, and total temperature identified by a system other than the air data system.

[0101] In one embodiment of the present invention, the Kalman filtering module includes multiple Kalman filters, and the Kalman filtering module uses a Kalman filter, an extended Kalman filter, an error-state-based Kalman filter and / or an unscented Kalman filter.

[0102] In one embodiment of the present invention, the output of the Kalman filter module is fused using a filter or an algorithm.

[0103] In one embodiment of the present invention, the airspeed described in the system includes Mach number, calibrated airspeed and / or true airspeed.

[0104] As shown in FIG3 , in one embodiment of the present invention, in a cycle of estimating airspeed, angle of attack, and sideslip angle, the following steps are included:

[0105] Initialize the system;

[0106] Determining measurements from an inertial navigation unit and a global navigation satellite system or vice versa;

[0107] Determine air data measurements from systems other than the ADS;

[0108] Identify control inputs for the aircraft;

[0109] Using control input information, the aircraft state is predicted through the aircraft six-degree-of-freedom model;

[0110] Based on the measured values ​​and state predictions, the airspeed, angle of attack, and sideslip angle are estimated through multiple Kalman filters.

[0111] In the description of the present invention, it should be understood that the terms "center", "thickness", "upper", "lower", "horizontal", "top", "bottom", "inner", "outer", "radial", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying the relative importance or the number of technical features implicitly specified. Therefore, the features defined by "first", "second", and "third" may explicitly or implicitly include one or more of such features.

Claims

1. A method for estimating the airspeed, angle of attack and sideslip angle of an aircraft, characterized in that: The following steps are involved: Modeling of the full six-DOF dynamics, aerodynamics, forces, and moments of the vehicle in a nonlinear state-space form; The inertial measurement unit is used to measure the body angular rate and body acceleration of the aircraft; Measurement of ground speed using the Global Navigation Satellite System; Determine air data measurements using data from aircraft systems other than the air data system; Multiple Kalman filters are used to estimate airspeed, angle of attack and / or sideslip angle from all measured data.

2. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The input of the modeled aircraft six-degree-of-freedom model is control input information.

3. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 2, characterized in that: The aircraft six-degree-of-freedom model uses a linear or nonlinear aircraft six-degree-of-freedom model.

4. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The multiple Kalman filters may also use ground speed and attitude generated by fusion of a global navigation satellite system and / or an inertial navigation system.

5. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The plurality of Kalman filters may also use total temperature measurements determined from engine sensor data.

6. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The plurality of Kalman filters may also use pressure measurements determined from a global navigation satellite system, an inertial navigation system, and / or engine sensor data.

7. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The plurality of Kalman filters may also use a wind field model to obtain wind speed estimates.

8. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The plurality of Kalman filters use a Kalman filter, an extended Kalman filter, an error state based Kalman filter and / or an unscented Kalman filter.

9. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 8, characterized in that: Use a weighted filter or algorithm to fuse the outputs of multiple Kalman filters.

10. The method for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 1, characterized in that: The airspeed described in this method includes Mach number, calibrated airspeed and / or true airspeed.

11. A system for estimating an aircraft's airspeed, angle of attack and sideslip angle based on the method for estimating an aircraft's airspeed, angle of attack and sideslip angle according to any one of claims 1 to 10, characterized in that: Includes control input, aircraft dynamics model, global navigation satellite system, inertial navigation system, wind field model, engine sensor, atmospheric data fusion module and Kalman filter module; The control inputs transmitted to the aircraft dynamics model include aircraft control surface deflections, throttle lever positions or thrust; The aircraft dynamics model is used to predict the aircraft state; The global navigation satellite system and / or the inertial navigation system are used to identify the altitude of the aircraft and generate static pressure through the altitude; The GNSS and / or the inertial navigation system are used to identify ground speed measurements and attitude measurements and transmit them to the Kalman filter module; The wind field model is used to estimate the wind speed and transmit it to the Kalman filter module; The engine sensor is used to identify static pressure, total pressure and / or total temperature measurements; The atmospheric data fusion module is used to fuse static pressure, total pressure and / or total temperature; The Kalman filter module is used to estimate airspeed, angle of attack and / or sideslip angle.

12. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 11, characterized in that: The aircraft dynamics model uses a linear or nonlinear aircraft dynamics model.

13. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 11, characterized in that: The system that estimates the aircraft's airspeed, angle of attack, and angle of sideslip identifies altitude from systems other than the air data system and generates static pressure from altitude.

14. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 11, characterized in that: The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft may further include an inertial measurement unit, wherein the inertial measurement unit is used to measure the angular velocity and acceleration of the aircraft body.

15. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 11, characterized in that: The system for estimating the airspeed, angle of attack and sideslip angle of the aircraft uses data related to any one of static pressure, total pressure and total temperature identified by a system other than the air data system.

16. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 11, characterized in that: The Kalman filter module uses a Kalman filter, an extended Kalman filter, an error-state based Kalman filter and / or an unscented Kalman filter.

17. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 16, characterized in that: The output of the Kalman filter module is fused using a weighted filter or algorithm.

18. The system for estimating the airspeed, angle of attack and sideslip angle of an aircraft according to claim 11, characterized in that: The airspeed referred to in this system includes Mach number, calibrated airspeed and / or true airspeed.

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