CN105912019A - Powered parafoil system's air-drop wind field identification method - Google Patents

Abstract

The present invention proposes an identification method for the air-dropped wind field of the powered parafoil system, which can dynamically identify the wind speed and wind direction of the wind field where the powered parafoil system is in the flight process, and provides trajectory control and sparrow landing operation for the powered parafoil system. Provide the necessary reference information. The wind field identification process is divided into three steps. The first step is to obtain the position information of the powered parafoil system through the GPS positioning module, and calculate the flight speed and direction of the powered parafoil system according to the position change of the powered parafoil system. In the second step, the Kalman filter algorithm is used to filter the flight speed of the parafoil system to obtain accurate speed information. The third step is to introduce the recursive least squares method to update the wind field identification results online.

Description

Identification method of airdrop wind field for powered parafoil system

technical field

The invention belongs to the field of unmanned aircraft control, and relates to a method for identifying wind field information of the flight environment of a powered parafoil system.

Background technique

The parafoil is a special parachute evolved from the traditional round parachute. It has a history of more than 50 years since its creation. The parafoil is characterized by controllability, gliding, safety and large load ratio. It can realize the turning action by manipulating the parachute and control the flight direction of the parafoil. With the development of science and technology and the transformation of military strategic thinking, precise airdrop and "fixed-point non-destructive" landing are more and more widely used in military, aerospace and other fields. In modern warfare, ground combat troops can replenish weapons, ammunition, and supplies in a timely manner through airdrops, which is convenient and efficient; in the event of major natural disasters, the relief materials and equipment can be transported to the disaster center as soon as possible by airdrops, completing Emergency rescue missions; in the aerospace industry, the safe recovery of spacecraft, satellites, missiles and other equipment can save resources and reduce space junk; in addition, in the civilian field, parafoils are also used for sports, cruises, sightseeing, etc. The parafoil makes up for the lack of accuracy and safety of traditional airdrop technology. It can achieve precise airdrop and stable landing through manipulation control and sparrow drop technology, ensuring that the load lands at a lower speed and reduces shock and vibration.

The powered parafoil system adds thrust equipment to the load of the ordinary parafoil system, which not only inherits all the characteristics of the parafoil, but also can make up for the airdrop error caused by the lack of airdrop height, expanding the application range of the parafoil. For example, in cruising applications, the powered parafoil system has the advantages of low speed, high cost performance, high safety, long battery life and strong anti-interference ability compared with fixed-wing UAVs and quadrotors, so it has a wider range of applications. Application prospect.

Due to the portability of the material of the parafoil itself, the parafoil will be strongly disturbed by the wind field during the flight process after inflation. When the powered parafoil system is performing cruising and other tasks for trajectory tracking, a certain compensation strategy for the known wind speed and direction can improve the tracking accuracy of the system and increase the stability of the system. In the sparrow drop technique, the first thing to do is to align the parafoil system against the wind at the target point, and pull down the trailing edge flap of the parafoil to the maximum deflection in a short time, so as to instantly increase the lift-drag coefficient of the parafoil , quickly reduce the speed of the parafoil system, and protect the load for a smooth landing. Based on the above reasons, the research on wind field identification has practical significance for parafoil airdrop system and powered parafoil system.

At present, the methods for obtaining wind speed and direction in wind farms are mainly divided into two types: equipment measurement and model prediction. The latter is mainly used for weather forecasting and research on the average wind speed and direction of wind farms. For example, modeling and analyzing the historical meteorological data of wind farms can predict the overall wind direction and wind conditions for a period of time. However, the average wind speed and direction obtained by this method cannot meet the requirements of real-time wind field information for homing and trajectory tracking control of powered parafoil systems. In contrast, equipment measurement has better real-time performance. Pitot tubes are commonly used speed measuring devices for high-speed aircraft such as airplanes. They can measure wind speed, but they are not suitable for powered parafoil systems with low speeds, and wind direction information cannot be obtained. A method proposed by foreign countries using GPS and gyroscope to measure the wind speed and direction of the environment where the parafoil system is located. This method uses the dynamic characteristics of the parafoil system and the ground flight speed and yaw angle of the parafoil system to identify the wind speed of the wind field and wind direction. However, the load of the parafoil system and the parafoil itself are a kind of flexible connection, with relative motion, the yaw angle measured by the gyroscope has a certain deviation from the flight direction of the parafoil system, so the measurement results have a large deviation, and the measurement accuracy poor.

Contents of the invention

The purpose of the present invention is to provide a method for online wind field identification based on GPS positioning data and the dynamic characteristics of the powered parafoil system, so as to realize the dynamic identification of the wind speed and direction of the wind field during the flight of the powered parafoil system, so as to achieve high precision Trajectory tracking and sparrow landing.

To achieve the above object, the present invention adopts the following technical solutions:

An identification method for a powered parafoil airdrop wind field, the method comprising the following steps:

Step 1. Calculate the flight speed of the current powered parafoil system according to the GPS positioning data.

The real-time flight speed of the powered parafoil system is calculated through GPS positioning data. During the flight of the powered parafoil, the position positioning data collected by the GPS positioning module are recorded in real time, and the longitude and latitude of the GPS positioning data for two consecutive times are P ₁ and P ₂ , then the horizontal flight speed of the powered parafoil system at this time is

V=(P ₂ -P ₁ )·f (1)

In the formula, V represents the horizontal flight speed of the powered parafoil system, including the speed components in the meridian and latitudinal directions. f represents the sampling frequency of the GPS positioning module.

Step 2, filtering the horizontal flight speed of the powered parafoil system.

The GPS positioning module will cause certain positioning errors due to noise and other reasons during operation, so the horizontal flight speed of the powered parafoil system calculated in the first step also contains error information, which is a set of measurement sequences containing noise. In order to filter out the noise signal and obtain a more realistic smooth speed signal, it is necessary to filter the obtained horizontal flight speed of the powered parafoil system. In the filtering process, the horizontal flight velocity V of the powered parafoil system is divided into the meridional velocity V _x and the latitudinal velocity V _y , respectively, and the Kalman filter is used for filtering.

Step 3, wind field identification

The wind field is identified according to the relative speed between the powered parafoil system and the air, the relative speed between the powered parafoil system and the ground, and the vector relationship between the wind speed. The identification results are the wind direction of the wind field and the velocity components of the wind speed in the meridional and latitudinal directions. In order to realize the real-time and stability of wind field identification, the least squares method with forgetting factor is introduced into the wind field identification process; the identification results of wind field are updated online through recursive least squares identification. Specific steps include:

1) Calculate the filtered horizontal velocity of the powered parafoil system according to the filtering results of the horizontal velocity of the powered parafoil system in the meridional and latitudinal velocity components;

2) Identify the wind speed and wind direction through the horizontal speed of the powered parafoil system and its speed components in the longitudinal and latitudinal directions;

3) Update the wind field identification results.

The airdrop wind field identification method of powered parafoil system based on GPS positioning data does not need to add additional equipment to the original system, but only needs to use GPS positioning data as the basis, combined with the dynamic characteristics of powered parafoil system in the wind field, adopts the recursive minimum The quadratic method identifies the components of the wind speed on each coordinate axis in the horizontal coordinate system, and conducts real-time online identification of the wind field, which has good economy and practicability.

Theoretical derivation process of the inventive method:

1. Principle of wind field identification for powered parafoil system

Under the action of the wind field, the motion speed of the powered parafoil system relative to the ground will change. According to the motion characteristics of the powered parafoil system, the flying speed of the powered parafoil relative to the ground is decomposed into wind speed and the relative air speed of the powered parafoil system The vector sum of . As shown in Figure 1, the flight speed of the powered parafoil system relative to the ground is the vector sum of the wind speed and the relative air speed of the powered parafoil system.

In the figure, Ψ represents the Euler yaw angle, β represents the sideslip angle, V ₀ represents the speed of the powered parafoil system relative to the air, that is, the airspeed, V _W represents the wind speed, and V represents the speed of the powered parafoil system relative to the ground, That is ground speed. Ground speed is the actual speed that can be measured by the GPS positioning module. It can be seen from Figure 1 that V ₀ , V _W and V form a velocity vector triangle. χ ₀ , Ψ _W and χ are the angles between V ₀ , V _W and V and the true north direction, respectively.

According to the motion characteristics of the powered parafoil, it can be assumed that the wind speed and the airspeed of the parafoil system remain constant, and the vector relationship in the figure can be obtained:

In the formula, x and y represent the two coordinate axes of the horizontal Cartesian coordinate system, pointing to the due east and the due north respectively, and V _x and V _y represent the components of the parafoil ground speed V in the x-axis and y-axis directions respectively. V _Wx and V _Wy represent the components of the wind speed in the x-axis and y-axis directions, respectively.

The two parts in formula (2) are squared and summed to obtain the equation:

Three points are selected in the parafoil trajectory, and the calculated ground speeds of the parafoil system are V ₁ , V ₂ and V ₃ . According to formula (3), a set of equations is obtained:

V _xi and V _yi in formula (4) represent the components of velocity V _i on x-axis and y-axis respectively (i=1, 2, 3).

It can be seen from the previous assumptions that V ₀ and V _W in the formula remain unchanged, and the speeds V, V _x and V _y can be calculated from GPS data. Therefore, the three equations in formula (4) can be subtracted in turn to obtain:

So far, the problem of wind field identification has become an ordinary problem of solving linear equations in two variables. The solution to the system of equations is:

It should be pointed out that when the parafoil sails along a straight line, the two sets of equations in the equation group (4) are the same, and the wind field cannot be identified, so the data points must be selected in the parafoil turning trajectory, that is, it is necessary to ensure that the equation (4 The directions and magnitudes of the ground speeds V ₁ , V ₂ and V ₃ of the parafoil system selected in ) are different.

2. Real-time update of wind field identification results

The single calculation result is limited by the GPS positioning accuracy, the result is accidental, and there are certain errors. In order to improve the accuracy and stability of the wind field identification result, the present invention introduces the recursive least squares method to iteratively update the identification result online.

Assume

y(k)=(V(k) ² -V(k-1) ² )/2 (7)

θ(k)＝[V _Wx (k) V _Wy (k)] ^T (9)

In formulas (7)-(9), (k) represents the value of the variable at time k, and (k-1) represents the value of the previous sampling time at time k. Then the equations in equation group (5) can be rewritten as:

The recursive formula of wind field prediction with forgetting factor is shown in formula (11):

In the formula, λ represents the forgetting factor, θ(k) represents the wind field identification result at time k, and K(k) and P(k) represent the intermediate variables of the least squares method at time k.

3. Filter processing of flight speed of powered parafoil system

Since positioning by the GPS positioning module is a complicated process, and the interference factors are complex and cannot be completely avoided, the ground speed of the powered parafoil system calculated from the GPS positioning data needs to be filtered before being used for wind field identification to improve wind field accuracy. recognition accuracy.

Kalman filtering algorithm is the most widely used filtering algorithm in the field of navigation. Therefore, the present invention selects the Kalman filter algorithm as the preprocessing method for the flight speed information of the powered parafoil system. In the Kalman filter algorithm, the system state equation and measurement equation are:

X _k ＝Φ _k|k-1 X _k-1 +Γ _k-1 W _k-1 (12)

Z _k ＝H _k X _k +V _k (13)

where X _k is the state matrix of the powered parafoil system, Φ _k|k-1 is the state transition matrix from k-1 time to k time, Γ _k-1 is the noise transition matrix at k-1 time, W _k-1 is the system noise, Z _k is the system measurement vector, H _k is the system measurement matrix, and V _k is the system noise. The noise characteristics are described as E(W _k )=q, E(V _k )=r, E(W _k W _i ^T )=Qδ _i , E(V _k V _i ^T )=Rδ _i , where q and r are respectively The means of W _k-1 and V _k , R and Q are the covariances of W _k-1 and V _k , respectively, and δ _i is the Kronecker function.

The Kalman filter algorithm is described as:

X _k|k-1 ＝Φ _k|k-1 X _k-1 +Γ _k-1 q (14)

ε _k ＝Z _k -H _k X _k|k-1 -r (15)

X _k ＝X _k|k-1 +K _k ε _k (18)

P _k ＝[IK _k H _k ]P _k|k-1 [IK _k H _k ] ^T (19)

Advantage and positive effect of the present invention:

1. The method of the present invention does not need to add additional equipment to the original system. It only needs to use the GPS positioning data of the powered parafoil system as the basis and combine the motion characteristics of the powered parafoil system in the wind to realize real-time monitoring of the airdropped wind field. Accurate identification, good economy and practicability.

2. The present invention filters the flight speed of the powered parafoil system through the Kalman filter algorithm, which reduces the influence of GPS positioning noise on the wind field identification results and improves the wind field identification accuracy.

3. The present invention updates the wind field identification results online in real time through the recursive least squares method with a forgetting factor, which avoids accidental errors in the identification results independently calculated at each time point, and increases the accuracy and effectiveness of the wind field identification results. sex.

4. The identification results of the present invention on the airdrop wind field of the powered parafoil system can be used for track optimization, track tracking control and sparrow landing control of the powered parafoil system, improve the track optimization effect, and increase the power of the powered parafoil system in the wind field. The stability of the flight, ensuring the headwind alignment and smooth landing of the sparrow landing control, has important engineering application value for the development and application of the powered parafoil system.

Description of drawings

Fig. 1 Schematic diagram of wind field identification for powered parafoil system.

Figure 2 Flow chart of wind field identification

Fig. 3 The horizontal trajectory of the powered parafoil system in a stable wind field.

Figure 4. The ground speed of the powered parafoil system in a stable wind field.

Figure 5. The wind field identification results of the powered parafoil system in a stable wind field.

Fig. 6 The horizontal trajectory of the powered parafoil system in a gust environment.

Figure 7 The identification results of the powered parafoil system in a gust of wind.

Figure 8 The horizontal trajectory of the parafoil system in a changing wind field.

Figure 9 The identification results of the powered parafoil system to the changing wind field.

Figure 10 Horizontal trajectory of the powered parafoil system when following a circular reference trajectory.

Figure 11 The wind field identification results when the powered parafoil system tracks the circular reference trajectory.

Figure 12 The horizontal trajectory of the powered parafoil system during landing.

Figure 13 The identification results of the landing wind field of the powered parafoil system.

detailed description:

The sampling frequency of the GPS module simulated in the simulation is 4Hz. The speed of the parafoil system obtained according to the GPS signal is the component of the ground speed on the x-axis and the y-axis, so the second-order extended state observation filter is selected for the ground speed on the x-axis and the y-axis. The components on the y-axis are filtered separately. The identification process of the airdropped wind field of the powered parafoil system is shown in Figure 2.

Embodiment 1: Single downward bias control (simulation 1)

(1) Wind field identification of powered parafoil system under stable wind field

The single downward deflection control of the powered parafoil system is in a circular motion in a windless state, and the radius of the circle is related to the downward deflection. When the influence of the wind field is added, the trajectory of the powered parafoil will shift along the direction of the wind field under the action of the wind field.

Fig. 3 shows the horizontal trajectory of the powered parafoil system under the control action of single downward deflection of 40% in the wind field of V _W =(12)m/s. The parameters of the powered parafoil system are shown in Table 1.

Table 1 Power parafoil system parameters

It can be seen from Fig. 3 and Fig. 4 that the powered parafoil system advances helically along the wind direction, and its ground speed varies periodically with the difference between its flight direction and the included angle of the wind direction.

1) Calculate the flight speed of the powered parafoil system

In order to be closer to the actual situation, the simulation process simulates the sampling frequency and positioning accuracy of GPS for information collection. The sampling frequency of the GPS positioning module is 4Hz, and the root mean square error of positioning is 1.2m. Let (x(k-1), y(k-1)) and (x(k), y(k)) denote the coordinates of the projected points on the horizontal plane on the flight trajectory of the powered parafoil system respectively, and the sampling frequency is 4Hz, The velocity components V _x (k) and V _y (k) of the powered parafoil system on the x-axis and y-axis at time k are:

V _x (k)=4(x(k)-x(k-1)) (22)

V _y (k)=4(y(k)-y(k-1)) (23)

Thereby, the flight speed V(k) of the powered parafoil system can be obtained as:

Fig. 4 shows the movement speed of the powered parafoil system under the influence of the wind field under the single downward deflection state.

2) Filter processing of the horizontal velocity of the powered parafoil system

According to the accuracy of the GPS positioning module, the noise statistical information of the Kalman filter is set, and the flight speed of the powered parafoil system is filtered according to formulas (12)-(19), and the filtered flying speed of the powered parafoil system is obtained as The velocity components corresponding to the x-axis and y-axis are and

3) Wind field identification

The velocity components of the filtered powered parafoil system flight velocity on the x-axis and y-axis and Substituting equations (7)-(10), using the recursive least squares method with forgetting factor to iteratively update the wind field identification results.

The dynamic identification results of the powered parafoil system in a stable wind field with one-sided downward deviation are shown in Fig. 5.

It can be seen from Figure 5 that since the initial condition of the forgetting factor of the least squares method in the identification algorithm is set to 0, the identification results in the initial stage are in a state of oscillation, which is invalid data. The result of wind direction identification before 30s deviates far from the real wind direction. After 30s, the identification result reaches a stable state, and only fluctuates around the real wind direction angle under the action of noise. The maximum deviation of wind direction identification results is 3.5°, and the average absolute error reaches 0.93°. Figure 5 also shows that the wind speed identification results stabilize after a certain period of time. Compared with the wind direction identification results, the wind speed identification results have a slightly longer stabilization time and only reach stability after 40s. At 40s, the wind speed identification error is 0.14m /s. After the wind speed identification results are stable, the error is in a small range, the maximum error reaches 0.13m/s at 182s, and the average absolute error of wind speed is 0.04m/s.

The results in Fig. 5 show that this method makes the identification results of the powered parafoil system under the action of a single downward bias and a stable wind field more ideal, and has higher identification accuracy in both wind speed and wind direction.

(2) Wind field identification of powered parafoil system in gust

In addition to the stable component, the wind field in the actual environment also includes turbulence and gusts. When evaluating the proposed wind field identification method, the influence of gusts on the identification results needs to be considered. The initial value of the wind field in the simulation environment is set to V _W = (24)m/s. Add a gust of V _W = (0 -2) m/s at the 75th second, and the movement of the parafoil system when the stable wind is superimposed on the gust The trajectory is given in Fig. 6.

Due to the change of the initial wind field setting value, the speed of the horizontal motion track of the parafoil system moving along the wind direction increases, and the overlapping part of the motion track of each circle decreases. At this time, the wind direction and wind speed identification results are shown in Fig. 7.

It can be seen from Figure 7 that the wind gust has little influence on the wind direction identification results. The wind direction prediction decreased by 5.1° from the accurate identification after the gust was added, and then the identified wind direction increased after the gust effect disappeared in the 90s, and remained 3° higher than the actual wind direction until the 129th second, and then the wind direction was obtained again Accurate identification of corners.

Gusts have a great influence on the identification results of wind speed, and the trend of the identification results is the same as that of the wind direction. When the gusts join, the identified wind speed values begin to drop, with a maximum drop of 0.16m/s. After the gusts disappear, the identified wind speed values rapidly Ascent, the maximum error reached 0.32m/s, and the impact of the gust on the wind speed identification results lasted until the 125th second.

(3) Influence of changing wind field on identification results

In the wind field identification method proposed in the present invention, the least square method with forgetting factor is used to identify the wind field in real time. When the wind field changes during the identification process, the identification result can still produce a better identification effect on the changed wind field .

The initial value of the wind field in the simulation environment is set to V _W =(1 2)m/s, and the wind field is changed to V _W =(13)m/s at the 100s moment. The identification method is 50% free flight on one side of the parafoil system.

Fig. 8 is the horizontal motion trajectory of the parafoil system in a changing wind field.

It can be seen from Figure 8 that the movement speed of the parafoil system along the wind direction in the initial wind field is small, and the wind field changes in the third circle, and the component of the wind speed on the y-axis changes from 2m/s to 3m/s , the speed of the parafoil system moving with the wind field increases, and the drift distance in the y-axis direction within the third rotation time is greater than the change amount during the second rotation time.

The identification results of wind speed and wind direction are shown in Figure 8.

Figure 8 shows that the parafoil system achieves high-precision and stable identification of the wind speed and wind direction of the unchanged wind field after 40s. After the wind field changed in the 100s, the identification result of the wind direction reached stability within 20s, and there was no large fluctuation. Since the new identification results are obtained by continuous calculation on the basis of the original results, the error of the wind direction identification results changes slightly during the wind field change process, and the maximum error value appears at 107s, with an error value of 7.2°.

As for the wind speed identification results, it can be seen from Figure 8 that the time required for the parafoil system to identify the changed wind field is shorter than the time required to identify the wind field from the initial state, and a better result is achieved 20s after the wind field changes. However, the identification error of about 0.3m/s between the actual value and the actual value was not eliminated until 155s. After 155s, the wind speed identification result of the parafoil system fluctuated around the real wind speed in a small range, reaching an ideal state.

The simulation analysis shows that the parafoil system can accurately identify the wind speed and direction of the wind field in a single downward free flight, and the time required for identification is less than the time required for the parafoil system to move for one week. It can be seen from the simulation results that the wind direction identification effect of the parafoil system is better than that of the wind speed. It takes a short time to obtain a stable wind direction through identification, and it can achieve fast tracking with a small error when the wind direction changes. .

Embodiment 2: Wind field identification during trajectory tracking control (simulation 2)

The wind field identification accuracy of the parafoil system is ideal in the single downward deflection state, but in some cases the movement area of the parafoil system is limited, and it is not allowed to fly with the wind in the single downward deflection state. Wind field identification in control state.

The previous chapters introduced the control method for the parafoil system to track the reference trajectory. The parafoil system can track the circular trajectory under the control of the linear active disturbance rejection controller. By controlling the parafoil system to track the circular trajectory, the movement of the parafoil system The area is limited within a certain range. Fig. 10 is the horizontal projected trajectory of the parafoil system V _W =(1 2)m/s in the wind field when tracking the height-fixed circular trajectory to collect wind direction identification data, and the radius of the reference trajectory is 150m.

Figures 10 and 11 show the identification results of the parafoil system tracking a circular trajectory in the wind field.

In the results shown in Fig. 11, since the motion state of the parafoil system is affected by the thrust change in the altitude control, the fluctuation of the wind field identification result after stabilization is larger than that in the case of single downward bias control. The maximum value of the wind direction identification result is 5°, and the average absolute error is 2.3°.

The wind speed identification results in Figure 11 also show a large error, and the wind speed identification results after 80s are always lower than the true wind speed value. The maximum error of the wind speed identification result after stabilization is 0.15m/s, and the average absolute value of the error increases more than that of single downward deviation, reaching 0.08m/s.

The simulation results show that the parafoil system can realize high-precision identification of the wind field when tracking the circular trajectory.

Embodiment 3: Wind field identification (simulation 3) of powered parafoil system landing stage

The powered parafoil system can accurately identify the wind speed and direction of the wind field where it is located by means of single downward deflection or tracking the circular trajectory. The wind field information plays an important role in the homing control, trajectory tracking and sparrow landing operation of the powered parafoil. . In this embodiment, the landing control of the powered parafoil system is taken as an example. Firstly, the wind field is identified by using the powered parafoil to track a circular trajectory, and then the headwind alignment and landing are performed.

Figure 12 and Figure 13 show the horizontal trajectory and wind field identification results of the landing process. The stable wind field vector added to the system is V _w =(0 3)m/s.

It can be seen from Figure 12 that the reference trajectory radius of the powered parafoil system is 150m. Under the action of the wind field, the maximum distance between the horizontal trajectory of the powered parafoil system and the circular reference trajectory is 8.1m. It has a better trajectory tracking effect.

Excluding the headwind alignment and landing time of the powered parafoil system, the effective time for wind direction identification is 130s. It can be seen from Fig. 13 that the identification result of wind speed is close to the real value at the 20th second, and the identification error of the wind direction identification result at the 20th second is 8.4°, reaching an ideal state. It takes 69 seconds for the powered parafoil system to rotate one circle from the initial position. It can be seen that the accurate identification of the wind direction can be realized within the period of one circle of the powered parafoil system around the target. The error is 0.004m/s, and the identification accuracy is high. As shown in Figure 12, after obtaining the wind direction identification result, the powered parafoil system continues to track the reference trajectory to the turning point to realize the turn. Since the powered parafoil system cannot realize a right-angle turn, the position of the turning point is appropriately advanced. In the simulation, the position 75m away from the y-axis on the reference trajectory is selected as the trajectory switching point. After the trajectory was switched to align against the wind, the powered parafoil system gradually lowered the altitude and landed near the target point, which was 7.1m away from the target point.

Claims (4)

1. an identification method of a powered parafoil system airdrop wind field, is characterized in that, comprises the following steps: (1) Calculate the current flight speed of the powered parafoil system according to the GPS positioning data: The real-time flight speed of the powered parafoil system is obtained by calculating the GPS positioning data; during the flying process of the powered parafoil, the positioning data collected by the GPS positioning module is recorded in real time, and the longitude and latitude of two consecutive GPS positioning data are set as P ₁ and P ₂ , then the horizontal flight speed of the powered parafoil system is V=(P ₂ −P ₁ )·fk (1) In the formula, V represents the horizontal flight speed of the powered parafoil system, which can be decomposed into the velocity components in the longitude and latitude directions; f represents the sampling frequency of the GPS positioning module; The radius is calculated; (2) filter the horizontal flight speed of the powered parafoil system: The GPS positioning module will produce a certain positioning error due to its own noise during operation, so the horizontal flight speed of the powered parafoil system calculated in step (1) also has errors, which is a set of measurement sequences containing noise; Noise signal, to obtain a more realistic smooth speed signal, it is necessary to filter the obtained horizontal flight speed information; (3) Wind field identification: According to the relative speed between the parafoil system and the atmosphere, the relative speed between the parafoil system and the ground, and the vector relationship between the wind speed, the wind field is identified; the identification results are the wind direction of the wind field and the wind speed in the longitude and latitude velocity component; (4) Repeat steps (1) to (3) to update the wind field identification results online in real time. 2. the identification method of airdrop wind field of powered parafoil system as claimed in claim 1, it is characterized in that, in described step (2), the filter processing method to the horizontal velocity of powered parafoil system is: in step (1) The calculated horizontal velocity of the powered parafoil system is decomposed into the meridional velocity V _x and the latitudinal velocity V _y , which are filtered by Kalman filter respectively. 3. the identification method of power parafoil system airdrop wind field as claimed in claim 1, is characterized in that, described step (3) carries out identification to wind field and comprises the following specific steps: 1) Calculate the filtered horizontal velocity of the powered parafoil system according to the aluminum foil results of the horizontal velocity of the powered parafoil system in the meridional and latitudinal velocity components; 2) Identify the wind speed and wind direction through the horizontal speed of the powered parafoil system and its speed components in the longitudinal and latitudinal directions; 3) Update the wind field identification results. 4. the identification method of airdrop wind field of power parafoil system as claimed in claim 3, it is characterized in that, the method for wind field identification in described step (3) is: according to the principle of wind field identification, in order to realize wind field identification The real-time and stability of the results, the recursive least squares method with forgetting factor is introduced into the wind field identification process; the identification results of the wind field are updated online through the recursive least squares method identification.