Flight state estimation method and system for low-cost small propeller aircraft

Abstract

The invention discloses a flight state estimation method and system for a low-cost small propeller aircraft, and belongs to the technical field of navigation, guidance and control, and the method mainly comprises the steps: building parameterized models of aerodynamic coefficients and propeller thrust coefficients, the difference between the air speed and the ground speed and the derivative deviation of the normal force coefficient to the attack angle are used as system state quantities to be estimated, and the axial apparent acceleration measured by an accelerometer is used as a quantity for measurement; calculating a theoretical value of the quantity measurement by using a kinetic model; and estimating the flight state of the aircraft based on extended Kalman filtering recursion. According to the method, an existing navigation system is utilized, and a simple, practical and effective flight state estimation means is provided for the propeller propulsion aircraft on the premise that no hardware needs to be added.

Description

Technical Field

[0001] The present invention relates to the field of navigation, guidance and control technology, and in particular to a flight state estimation method and system for a low-cost small propeller aircraft. Background Art

[0002] With the rapid development of swarm technology, low-cost small propeller aircraft are becoming increasingly popular. Due to their low flight speed, such aircraft are greatly affected by wind. In order to ensure good flight control quality, it is urgent to use a low-cost, simple and effective means to estimate the flight state, especially the airspeed and dynamic pressure. The cost, weight and volume of low-cost small propeller aircraft are strictly limited, so it is not desirable to add new hardware, but to use existing conditions to achieve flight state estimation that meets performance requirements through algorithms.

[0003] The article "Atmospheric Parameter Estimation Method Aided by Aerodynamic Model and Navigation Information" in the 3rd issue of "Control and Decision" in 2018 used the existing navigation system data, took the two wind speed components in the horizontal plane as the system state, and took the six quantities of apparent acceleration components and angular acceleration components (obtained by differentiating the angular velocity components) measured or solved by the navigation system as the quantity measurement. The theoretical value of the quantity measurement was calculated by considering the aerodynamic model, power model and configuration model, and the wind speed was estimated by EKF (Extended Kalman Filter), and the simulation results were given. This method does not consider the aerodynamic model deviation, and its algorithm robustness cannot be evaluated. In addition, its angular acceleration is obtained by differentiating the angular velocity components, which will amplify the influence of angular velocity measurement noise, and may not be able to obtain ideal results in engineering.

[0004] The article "Wind Vector Estimation Based on Fixed-wing UAV Platform" in the 4th issue of "Firepower and Command and Control" in 2021 uses dynamic pressure sensors to calculate wind speed. The estimated state is the two wind speed components in the horizontal plane and the correction coefficient of the dynamic pressure sensor. The measured quantity is the dynamic pressure measurement value and the lateral acceleration value measured by the table. The aerodynamic model and configuration model are used to calculate the theoretical value of the measured quantity, and the wind speed is estimated through EKF. This method is relatively simple and can converge to a more accurate wind speed value, but the convergence time is long, and a new hardware dynamic pressure sensor is added. In addition, although the deviation of the three aerodynamic coefficients is considered, the Monte-Carlo simulation test of the entire estimation object is not carried out, and the robustness of the algorithm cannot be evaluated. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem that low-cost propeller aircraft urgently need a simple and effective means of flight state estimation, and to provide a method and system for estimating the flight state of a low-cost small propeller aircraft.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] In a first aspect, a method for estimating a flight state of a low-cost small propeller aircraft is provided, comprising:

[0008] A parameterized model of aerodynamic coefficients and propeller thrust coefficients is established, wherein the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient with respect to the angle of attack are used as the system state quantities to be estimated, and the axial apparent acceleration measured by the accelerometer is used as the quantity to be measured;

[0009] The theoretical value of the quantity measurement is calculated using a dynamic model; and the flight state of the aircraft is estimated recursively based on an extended Kalman filter.

[0010] In some embodiments, the calculating the theoretical value of the quantity measurement using a kinetic model comprises:

[0011] The airspeed and dynamic pressure are obtained by correcting the ground speed with the estimated state value;

[0012] After determining the angle of attack based on the normal force coefficient, the axial force coefficient is calculated using the aerodynamic model;

[0013] Calculating a propeller advance ratio from the airspeed and the propeller rotation speed, calculating a propeller thrust coefficient from the propeller advance ratio, and calculating the propeller thrust from the propeller thrust coefficient;

[0014] The theoretical value of the axial apparent acceleration is calculated based on the axial force coefficient and the propeller thrust.

[0015] Preferably, the step of correcting the ground speed with the estimated state value to obtain the airspeed and the dynamic pressure comprises:

[0016] The airspeed is corrected by the estimated state value:

[0017] V W =V+ΔV

[0018] Where V W is the airspeed, V is the ground speed output by the navigation system; the dynamic pressure is calculated as

[0019]

[0020] Where ρ is the atmospheric density.

[0021] Preferably, determining the angle of attack according to the normal force coefficient comprises:

[0022] The derivative of the normal force coefficient with respect to the angle of attack (i.e. the first-order coefficient) is corrected by the estimated state to be

[0023] k n1 =k n1_n +Δk n1

[0024] Where k n1_nis the nominal value of the derivative of the normal force coefficient with respect to the angle of attack;

[0025] The aerodynamic model of the normal force coefficient is:

[0026] C n =k n0 +k n1 α

[0027] Where α is the angle of attack, k n0 and k n1 are the zeroth and first order coefficients of the normal force coefficient with respect to the angle of attack.

[0028] Preferably, the calculating the axial force coefficient using the aerodynamic model comprises:

[0029] The aerodynamic model of the axial force coefficient is:

[0030] C a =k a0 +k a1 α+k a2 α 2 +k az δ z

[0031] Where k a0 , k a1 and k a2 are the zero-order, first-order and second-order coefficients of the axial force coefficient with respect to the angle of attack, k az It is the derivative of the axial force coefficient with respect to the absolute value of the rudder deflection of the pitch channel.

[0032] Preferably, the propeller advance ratio is

[0033]

[0034] Where n s is the propeller speed, D is the propeller diameter;

[0035] The propeller thrust coefficient is expressed as a quadratic polynomial of the advance ratio:

[0036] C T =a0+a1λ+a2λ 2

[0037] Where a0, a1 and a2 are the coefficients of the zeroth, first and second order terms of the quadratic polynomial respectively;

[0038] The propeller thrust is

[0039]

[0040] The theoretical value of the axial apparent acceleration is

[0041]

[0042] In some embodiments, the recursive estimation of the flight state of the aircraft based on the extended Kalman filter includes:

[0043] The observation matrix is ​​calculated according to the measurement equation; then the system covariance matrix, the observation noise covariance matrix and the error covariance matrix are determined.

[0044] Preferably, the partial derivative of the observation matrix is

[0045]

[0046] The observation matrix elements are expanded into

[0047]

[0048] Preferably, the system covariance matrix is

[0049]

[0050] The observation noise covariance matrix is

[0051]

[0052] The system covariance matrix is

[0053]

[0054] The observation noise covariance matrix is

[0055]

[0056] Where σ acc is the standard deviation of the accelerometer measurement white noise;

[0057] The initial value of the error covariance matrix is

[0058]

[0059] Where σ1 is the standard deviation of the probability distribution of the initial value of the speed difference between airspeed and ground speed, and σ2 is the standard deviation of the probability distribution of the initial value of the derivative deviation of the normal force coefficient with respect to the angle of attack.

[0060] In a second aspect, a flight state estimation system for a low-cost small propeller aircraft is provided, comprising:

[0061] A parameterized model building module is used to establish a parameterized model of aerodynamic coefficients and propeller thrust coefficients, wherein the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient with respect to the angle of attack are used as the system state quantities to be estimated, and the axial apparent acceleration measured by the accelerometer is used as the measurement quantity;

[0062] A quantity measurement calculation module, used to calculate the theoretical value of the quantity measurement using a kinetic model;

[0063] The flight state estimation module is used to estimate the flight state of the aircraft based on the extended Kalman filter recursion.

[0064] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] (1) The present invention utilizes the existing navigation system, takes the speed difference between airspeed and groundspeed and the derivative deviation of the normal force coefficient with respect to the angle of attack as the system state quantities to be estimated, takes the axial apparent acceleration measured by the accelerometer as the quantity measurement, and establishes a parameterized model of the aerodynamic coefficient and the propeller thrust coefficient. Since there are only two system states and one quantity measurement, the required amount of calculation is small, and it is easy to meet the real-time calculation requirements; the existing navigation system is utilized, and from the perspective of the overall system, no hardware is added, and no cost, weight and volume are paid; at the same time, based on the extended Kalman filter, the airspeed, dynamic pressure, angle of attack, thrust, axial force coefficient and normal force coefficient are recursively estimated, providing a simple, practical and effective flight state estimation method for small propeller-propelled aircraft.

[0067] (2) The algorithm of the present invention has good robustness after testing and has strong practicality in engineering. It greatly increases the ability of the aircraft to perceive the flight status without increasing the cost, weight and volume, which is very beneficial to improving the flight control quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a method for estimating the flight state of a low-cost small propeller aircraft shown in an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of an axial force coefficient curve in a balancing state using a quadratic curve to fit an embodiment of the present invention;

[0070] Figure 3 A schematic diagram of a normal force coefficient curve in a balancing state using a quadratic curve to fit an embodiment of the present invention;

[0071] Figure 4 A schematic diagram of a thrust coefficient curve fitted by a quadratic polynomial according to an embodiment of the present invention;

[0072] Figure 5 A comparison diagram of ground speed, air speed and estimated air speed shown in an embodiment of the present invention;

[0073] Figure 6A comparison diagram of dynamic pressure, calculated dynamic pressure and estimated dynamic pressure shown in an embodiment of the present invention;

[0074] Figure 7 A comparison diagram of the angle of attack, calculated angle of attack and estimated angle of attack shown in an embodiment of the present invention;

[0075] Figure 8 A comparison diagram of thrust, calculated thrust and estimated thrust shown in an embodiment of the present invention;

[0076] Fig. 9 A comparison diagram of the actual value and the estimated value of the axial force coefficient shown in an embodiment of the present invention;

[0077] Fig.10 This is a comparison diagram of the actual value, calculated value and estimated value of the normal force coefficient shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0079] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present application for the above-mentioned problems below should all be the contributions made by the inventor to the present application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0080] In view of the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows:

[0081] Reference Figure 1 In an exemplary embodiment, a method for estimating a flight state of a low-cost small propeller aircraft is provided, comprising:

[0082] A parameterized model of aerodynamic coefficients and propeller thrust coefficients is established, wherein the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient with respect to the angle of attack are used as the system state quantities to be estimated, and the axial apparent acceleration measured by the accelerometer is used as the quantity to be measured;

[0083] The theoretical values ​​of the measured quantities are calculated using a dynamic model; and the flight state of the aircraft is recursively estimated based on an extended Kalman filter, including estimated airspeed, dynamic pressure, angle of attack, thrust, axial force coefficient and normal force coefficient, with the estimated error being within a satisfactory range.

[0084] It specifically includes the following parts:

[0085] 1) The speed difference between airspeed and groundspeed and the derivative deviation of normal force coefficient with respect to angle of attack are used as the system state quantities to be estimated.

[0086] Low-cost aircraft will not be equipped with too many sensors, and the only thing they can rely on is the navigation system. The navigation system generally includes a satellite navigation system, which can provide more accurate ground speed information; at the same time, the aerodynamic model is generally obtained only through engineering calculations or CFD methods, which has considerable deviations. In the case of limited measurable information and large model deviations, the estimated state should be minimized to ensure observability. After testing, it is appropriate to use the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient to the angle of attack as the system state quantity. The speed difference plays a great role in controlling flight quality; the derivative deviation of the normal force coefficient to the angle of attack reflects the deviation of the aerodynamic model.

[0087]

[0088] Where ΔV is the speed difference between airspeed and ground speed, Δk n1 is the derivative deviation of the normal force coefficient with respect to the angle of attack.

[0089] 2) The axial apparent acceleration measured by the accelerometer is used as the measurement quantity.

[0090] Propeller-driven aircraft mainly fly in a straight line along the route, and the flight quality is also mainly determined by the longitudinal flight state. The axial and normal apparent accelerometers measured by the accelerometer are the measurement values ​​that can reflect the longitudinal flight state. Among them, the normal apparent acceleration will be used to determine the angle of attack, so the axial apparent acceleration will be measured as a quantity.

[0091] Z=a x1

[0092] Where a x1 is the apparent axial acceleration.

[0093] 3) The theoretical value of the axial apparent acceleration is calculated using the estimated state value combined with the aerodynamic model, configuration model and thrust model.

[0094] The estimated state value is used to correct the ground speed to obtain the airspeed, and then the dynamic pressure. After the angle of attack is determined according to the normal force coefficient, the aerodynamic model can be used to calculate the axial force coefficient. The mass of the aircraft can be determined in advance. The forward ratio is calculated from the airspeed and propeller speed, and then the thrust coefficient is calculated, and finally the thrust is obtained. The dynamic pressure can be obtained from the airspeed. After the thrust and axial aerodynamic force are determined, the theoretical value of the axial apparent acceleration can be calculated.

[0095] The airspeed is corrected from the estimated state to

[0096] V W =V+ΔV

[0097] Where V is the ground speed output by the navigation system.

[0098] The derivative of the normal force coefficient with respect to the angle of attack (i.e. the first-order coefficient) is corrected by the estimated state to be

[0099] k n1 =k n1_n +Δk n1

[0100] Where k n1_n is the nominal value of the derivative of the normal force coefficient with respect to the angle of attack.

[0101] The aerodynamic model of the normal force coefficient is:

[0102] C n =k n0 +k n1 α

[0103] Where α is the angle of attack, k n0 and k n1 are the zeroth and first order coefficients of the normal force coefficient with respect to the angle of attack. The angle of attack can be calculated from this formula.

[0104] Calculate the dynamic pressure as

[0105]

[0106] Where ρ is the atmospheric density, which can be calculated by the standard atmospheric model.

[0107] Calculate the angle of attack as

[0108]

[0109] Where m is the mass of the aircraft, a y1 is the axial accelerometer measurement, and S is the aerodynamic model reference area.

[0110] The aerodynamic model of the axial force coefficient is:

[0111] C a =k a0 +k a1 α+k a2 α 2 +k az δ z

[0112] Where k a0 , k a1 and k a2 are the zero-order, first-order and second-order coefficients of the axial force coefficient with respect to the angle of attack, k az It is the derivative of the axial force coefficient with respect to the absolute value of the rudder deflection of the pitch channel.

[0113] The propeller advance ratio is

[0114]

[0115] Where n s is the propeller speed and D is the propeller diameter.

[0116] The propeller thrust coefficient is expressed as a quadratic polynomial of the advance ratio:

[0117] C T =a0+a1λ+a2λ 2

[0118] a0, a1 and a2 are the coefficients of the zeroth, first and second degree terms of the quadratic polynomial respectively.

[0119] The propeller thrust is

[0120]

[0121] The theoretical value of the axial apparent acceleration is

[0122]

[0123] 4) Use the extended Kalman filter to recursively estimate the system state.

[0124] The derivative of the speed difference is white noise, and the derivative deviation of the normal force coefficient with respect to the angle of attack is a constant, so the system state transfer equation is a unit matrix; the observation matrix is ​​calculated according to the measurement equation; then the system covariance matrix, the observation noise covariance matrix and the error covariance matrix are determined, and the extended Kalman filter can be used to recursively estimate the system state.

[0125] The system equation is

[0126]

[0127] Where w V The standard deviation is σ V Gaussian white noise.

[0128] The state transfer equation is

[0129] Φ k,k-1 =I

[0130] The partial derivative of the observation matrix is ​​calculated as

[0131]

[0132] The observation matrix elements are expanded into

[0133]

[0134] The relevant partial derivatives in the above formula are calculated as follows

[0135]

[0136] The system covariance matrix is

[0137]

[0138] The observation noise covariance matrix is

[0139]

[0140] Where σ acc is the standard deviation of the accelerometer measurement white noise.

[0141] The initial value of the error covariance matrix is

[0142]

[0143] Where σ1 is the standard deviation of the probability distribution of the initial value of the speed difference between airspeed and ground speed, and σ2 is the standard deviation of the probability distribution of the initial value of the derivative deviation of the normal force coefficient with respect to the angle of attack.

[0144] The calculation is recursively calculated by the usual extended Kalman filter (EKF) as follows:

[0145]

[0146] P k =(IK k H k ) kk-1 (IK k H k ) Τ +K k R k K k T

[0147] 5) Airspeed and dynamic pressure filter output.

[0148] The estimated airspeed and dynamic pressure are then calculated using a time constant T f The first-order filter output.

[0149]

[0150] Where T c It is the flight control calculation cycle.

[0151] In an example, a specific instance is given, and the main steps include:

[0152] 1) Establish a parameterized model of aerodynamic coefficients and thrust coefficients;

[0153] 2) Determine the accelerometer measurement noise level and system noise level;

[0154] 3) Determine the filtering time constant;

[0155] 4) Calculate according to EKF recursion.

[0156] Step 1): In order to simplify the algorithm, the aerodynamic coefficient and thrust coefficient need to generate a parameterized model. For example, the aerodynamic coefficient and thrust coefficient are usually given in a table. Figure 2 , Figure 3 and Figure 4 As shown, a parameterized model was established using quadratic curve fitting.

[0157] Step 2): Determine the measurement noise level based on the accelerometer used. For example, if a MEMS accelerometer is used, the typical measurement noise standard deviation is 0.1 m / s. 2 The system noise level is determined by the turning capability of the aircraft. If the turning capability is strong, the noise level is high, otherwise it is low. It can generally be set to 0.5m / s~2m / s.

[0158] Step 3): The airspeed and dynamic pressure change rates generally change slowly, and the filter time constant can be set to 1s to 3s.

[0159] Step 4): Calculate by EKF recursion.

[0160] Figures 5 to 10 This is the flight state estimation result of a certain aircraft flying along a rectangular route in a 5m / s wind field. It can be seen that the estimated results of airspeed, dynamic pressure and thrust are significantly better than the direct calculation results. The estimated angle of attack and the calculated angle of attack have their own advantages in accuracy at different stages. The axial force coefficient estimation error is about 0.01, and the normal force estimation error is less than 0.05. In short, the present invention greatly increases the ability of the aircraft to perceive the flight state without increasing the cost, weight and volume, which is very beneficial to improving the flight control quality.

[0161] In another exemplary embodiment, based on the same inventive concept as the above method, a flight state estimation system for a low-cost small propeller aircraft is provided, comprising:

[0162] A parameterized model building module is used to establish a parameterized model of aerodynamic coefficients and propeller thrust coefficients, wherein the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient with respect to the angle of attack are used as the system state quantities to be estimated, and the axial apparent acceleration measured by the accelerometer is used as the measurement quantity;

[0163] A quantity measurement calculation module, used to calculate the theoretical value of the quantity measurement using a kinetic model;

[0164] The flight state estimation module is used to estimate the flight state of the aircraft based on the extended Kalman filter recursion.

[0165] It should be noted that each module in the system implements the same functions as each step in the method, which will not be described in detail here.

[0166] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for estimating the flight state of a low-cost small propeller aircraft, characterized in that: include: A parameterized model of aerodynamic coefficients and propeller thrust coefficients is established, wherein the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient with respect to the angle of attack are used as the system state quantities to be estimated, and the axial apparent acceleration measured by the accelerometer is used as the quantity to be measured; The theoretical value of the quantity measurement is calculated using a dynamic model; and the flight state of the aircraft is estimated recursively based on an extended Kalman filter.

2. The flight state estimation method according to claim 1, characterized in that: The method of calculating the theoretical value of the quantity measurement by using a kinetic model comprises: The airspeed and dynamic pressure are obtained by correcting the ground speed with the estimated state value; After determining the angle of attack based on the normal force coefficient, the axial force coefficient is calculated using the aerodynamic model; Calculating a propeller advance ratio from the airspeed and the propeller rotation speed, calculating a propeller thrust coefficient from the propeller advance ratio, and calculating the propeller thrust from the propeller thrust coefficient; The theoretical value of the axial apparent acceleration is calculated based on the axial force coefficient and the propeller thrust.

3. The flight state estimation method according to claim 2, characterized in that: The method of correcting the ground speed with the estimated state value to obtain the airspeed and dynamic pressure includes: The airspeed is corrected by the estimated state value: V W =V+ΔV Where V W is the airspeed, V is the ground speed output by the navigation system; the dynamic pressure is calculated as Where ρ is the atmospheric density.

4. The flight state estimation method according to claim 2, characterized in that: Determining the angle of attack according to the normal force coefficient comprises: The derivative of the normal force coefficient with respect to the angle of attack (i.e. the first-order coefficient) is corrected by the estimated state to be k n1 =k n1_n +Δk n1 Where k n1_n is the nominal value of the derivative of the normal force coefficient with respect to the angle of attack; The aerodynamic model of the normal force coefficient is: C n =k n0 +k n1 a Where α is the angle of attack, k n0 and k n1 are the zeroth and first order coefficients of the normal force coefficient with respect to the angle of attack.

5. The flight state estimation method according to claim 4, characterized in that: The method of calculating the axial force coefficient using the aerodynamic model includes: The aerodynamic model of the axial force coefficient is: C a =k a0 +k a1 a+k a2 a 2 +k az d z Where k a0 , k a1 and k a2 are the zero-order, first-order and second-order coefficients of the axial force coefficient with respect to the angle of attack, k az It is the derivative of the axial force coefficient with respect to the absolute value of the rudder deflection of the pitch channel.

6. The flight state estimation method according to claim 5, characterized in that: The propeller advance ratio is Where n s is the propeller speed, D is the propeller diameter; The propeller thrust coefficient is expressed as a quadratic polynomial of the advance ratio: C T =a0+a1λ+a2λ 2 Where a0, a1 and a2 are the coefficients of the zeroth, first and second order terms of the quadratic polynomial respectively; The propeller thrust is The theoretical value of the axial apparent acceleration is 7. The flight state estimation method according to claim 1, characterized in that: The recursive estimation of the flight state of the aircraft based on the extended Kalman filter includes: The observation matrix is ​​calculated according to the measurement equation; then the system covariance matrix, the observation noise covariance matrix and the error covariance matrix are determined.

8. The flight state estimation method according to claim 7, characterized in that: The partial derivative of the observation matrix is The observation matrix elements are expanded into 9. The flight state estimation method according to claim 7, characterized in that: The system covariance matrix is The observation noise covariance matrix is The system covariance matrix is The observation noise covariance matrix is Where σ acc is the standard deviation of the accelerometer measurement white noise; The initial value of the error covariance matrix is Where σ1 is the standard deviation of the probability distribution of the initial value of the speed difference between airspeed and ground speed, and σ2 is the standard deviation of the probability distribution of the initial value of the derivative deviation of the normal force coefficient with respect to the angle of attack.

10. A low-cost small propeller aircraft flight state estimation system, characterized in that: include: A parameterized model building module is used to establish a parameterized model of aerodynamic coefficients and propeller thrust coefficients, wherein the speed difference between airspeed and ground speed and the derivative deviation of the normal force coefficient with respect to the angle of attack are used as the system state quantities to be estimated, and the axial apparent acceleration measured by the accelerometer is used as the measurement quantity; A quantity measurement calculation module, used to calculate the theoretical value of the quantity measurement using a kinetic model; The flight state estimation module is used to estimate the flight state of the aircraft based on the extended Kalman filter recursion.

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