CN116203600A - Method for tracking motion trail with power after communication signal of unmanned aerial vehicle is lost - Google Patents
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Abstract
The invention provides a method for tracking a motion track with power after communication signals of an unmanned aerial vehicle are lost, which comprises the following steps: the unmanned aerial vehicle landing method comprises the steps of obtaining an unmanned aerial vehicle target, identifying and confirming the model and characteristic parameters of the unmanned aerial vehicle, utilizing an unmanned aerial vehicle model database, carrying out characteristic matching, judging whether the unmanned aerial vehicle is out of control or not due to emergency reaction and gesture after being interfered, obtaining real-time characteristics of the unmanned aerial vehicle target if the unmanned aerial vehicle is out of control, calculating the landing position of the unmanned aerial vehicle if the unmanned aerial vehicle is out of control, correcting the landing position of the unmanned aerial vehicle through a Kalman filtering algorithm, continuously tracking the state of the unmanned aerial vehicle target in real time before the unmanned aerial vehicle lands, repeating the steps until the unmanned aerial vehicle lands, demarcating a landing range according to an error coefficient after the unmanned aerial vehicle lands, and outputting a landing track point range. The method for tracking the motion trail of the unmanned aerial vehicle with power after the communication signal of the unmanned aerial vehicle is lost can realize the tracking of the motion trail of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method for tracking a motion track with power after communication signals of the unmanned aerial vehicle are lost.
Background
With the development of technology, unmanned aerial vehicle's application is more and more extensive, but current anti-unmanned aerial vehicle technology is generally to unmanned aerial vehicle radio signal's detection and discernment, and offset unmanned aerial vehicle based on various electromagnetism countermeasure technique, the common technique in market has radar, radio, optics, agreement are cracked etc. generally speaking, do not consider unmanned aerial vehicle forced landing's secondary disaster, unmanned aerial vehicle is interfered the back, can drift along with the wind and descend, unmanned aerial vehicle rotor can cause serious secondary disaster to ground target this moment, consequently, need to take different interference modes to different scenes, consequently, it is very necessary to design a unmanned aerial vehicle communication signal and lose the method of back power motion track tracking.
Disclosure of Invention
The invention aims to provide a method for tracking a motion trail of an unmanned aerial vehicle with power after communication signals of the unmanned aerial vehicle are lost, so that the tracking of the motion trail of the unmanned aerial vehicle can be realized, and the searching difficulty of the falling unmanned aerial vehicle is reduced.
In order to achieve the above object, the present invention provides the following solutions:
a method for tracking a motion track with power after communication signals of an unmanned aerial vehicle are lost comprises the following steps:
step 1: acquiring an unmanned aerial vehicle target, performing AI analysis on the unmanned aerial vehicle target, and identifying and confirming the model of the unmanned aerial vehicle;
step 2: performing feature matching according to the model of the unmanned aerial vehicle by using an unmanned aerial vehicle model database to obtain feature parameters of the unmanned aerial vehicle;
step 3: judging whether the emergency response, the gesture and the runaway of the unmanned aerial vehicle are caused or not through an artificial intelligent algorithm;
step 4: if the unmanned aerial vehicle is not out of control, acquiring the target real-time characteristics of the unmanned aerial vehicle, if the unmanned aerial vehicle is out of control, acquiring the target real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration of the unmanned aerial vehicle, and calculating the landing point position of the unmanned aerial vehicle according to the target real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration;
step 5: correcting the position of the landing point of the unmanned aerial vehicle through a Kalman filtering algorithm, and calculating the landing track of the unmanned aerial vehicle, wherein the target state of the unmanned aerial vehicle is continuously tracked in real time before the unmanned aerial vehicle lands, and the steps 2-5 are repeated until the unmanned aerial vehicle lands;
step 6: after the unmanned aerial vehicle lands, a landing range is defined according to the error coefficient, and a landing track point range is output.
Optionally, in step 1, an unmanned aerial vehicle target is obtained, AI analysis is performed on the unmanned aerial vehicle target, and the model of the unmanned aerial vehicle is identified and confirmed, specifically:
and capturing the unmanned aerial vehicle target through radio and photoelectric equipment, carrying out AI analysis on the unmanned aerial vehicle target, and identifying and confirming the model of the unmanned aerial vehicle.
Optionally, in step 2, feature matching is performed according to the model of the unmanned aerial vehicle by using an unmanned aerial vehicle model database, so as to obtain feature parameters of the unmanned aerial vehicle, which specifically are:
the unmanned aerial vehicle model database is established, feature matching is carried out in the unmanned aerial vehicle model database according to the model of the unmanned aerial vehicle, and parameters of the unmanned aerial vehicle corresponding to the model are obtained, wherein the feature parameters of the unmanned aerial vehicle comprise weight parameters, explosion-proof capability parameters, unmanned aerial vehicle response time parameters, unmanned aerial vehicle descending speed parameters and return speed parameters.
Optionally, in step 3, the emergency response, the gesture and whether the out of control can be caused after the unmanned aerial vehicle is interfered are judged through an artificial intelligence algorithm, which specifically comprises:
according to the characteristic parameters of the unmanned aerial vehicle, the unmanned aerial vehicle judges whether the unmanned aerial vehicle is out of control or not through the BP network according to the emergency response and the gesture after being interfered, wherein if the unmanned aerial vehicle is not provided with a self-protection mechanism, the unmanned aerial vehicle is out of control after being interfered, and if the unmanned aerial vehicle is provided with the self-protection mechanism, the unmanned aerial vehicle is not out of control after being interfered.
Optionally, in step 4, if the unmanned aerial vehicle is not out of control, acquiring the real-time characteristics of the unmanned aerial vehicle target, specifically:
if the unmanned aerial vehicle is not out of control, acquiring real-time characteristics of the unmanned aerial vehicle target, including real-time position information of the unmanned aerial vehicle and real-time height information of the unmanned aerial vehicle.
Optionally, in step 4, if the unmanned aerial vehicle is out of control, acquiring the real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravitational acceleration of the unmanned aerial vehicle target, and calculating the landing point position of the unmanned aerial vehicle according to the real-time characteristics, the real-time geographic environment characteristics and the gravitational acceleration, specifically:
if the unmanned aerial vehicle is out of control, acquiring real-time characteristics, real-time meteorological characteristics, real-time geographic environment characteristics and gravitational acceleration of the unmanned aerial vehicle, wherein the real-time characteristics of the unmanned aerial vehicle include real-time position information of the unmanned aerial vehicle and real-time height information of the unmanned aerial vehicle, the real-time meteorological characteristics include wind power information, wind direction information and air density, the real-time geographic environment characteristics are altitude, geographic coordinates of the unmanned aerial vehicle are acquired, gravitational acceleration g of the unmanned aerial vehicle at the moment is calculated according to the geographic coordinates, and air density ρ is calculated w The method comprises the following steps:
ρ w =1.293× (actual pressure/standard physical atmospheric pressure) × (273.15/actual absolute temperature)
Absolute temperature = celsius +273.15
Calculating wind speed V w The method comprises the following steps:
in the formula, h 0 、h w The calculated height and the reference height are respectively given in m and V w To calculate the height h w Wind speed at m/s, V 0 With reference height h 0 Alpha is a ground roughness index corresponding to different landforms;
calculating air resistance coefficient C w ；
Calculating the air force F w The method comprises the following steps:
wherein ρ is w Is air density, S w For the windward volume of the unmanned aerial vehicle, V w Is the wind speed;
calculating the acceleration g generated by the wind power of the unmanned aerial vehicle w The method comprises the following steps:
the time t to maximum speed is calculated as:
and finally, calculating to obtain the landing time and the horizontal displacement.
Optionally, in step 5, correction is performed on the landing position of the unmanned aerial vehicle by using a kalman filter algorithm, which specifically includes:
and removing noise data through a Kalman filtering algorithm according to real-time height information, speed, horizontal displacement, wind power information and wind direction information of the unmanned aerial vehicle, and correcting the position of a landing point of the unmanned aerial vehicle.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the method comprises the steps of acquiring an unmanned aerial vehicle target, carrying out AI analysis on the unmanned aerial vehicle target, identifying and confirming the model and characteristic parameters of the unmanned aerial vehicle, carrying out characteristic matching according to the model and characteristic parameters of the unmanned aerial vehicle by utilizing an unmanned aerial vehicle model database, judging emergency response and gesture after the unmanned aerial vehicle is interfered and whether the unmanned aerial vehicle is out of control by an artificial intelligent algorithm, acquiring real-time characteristics of the unmanned aerial vehicle target if the unmanned aerial vehicle is not out of control, acquiring the real-time characteristics, real-time meteorological characteristics, real-time geographic environment characteristics and gravity acceleration of the unmanned aerial vehicle if the unmanned aerial vehicle is out of control, calculating the landing point position of the unmanned aerial vehicle according to the unmanned aerial vehicle, correcting the landing point position of the unmanned aerial vehicle by a Kalman filtering algorithm, and calculating the landing track of the unmanned aerial vehicle, wherein the unmanned aerial vehicle target state is continuously tracked in real time before the unmanned aerial vehicle lands, and the steps are repeated until the unmanned aerial vehicle lands, and a landing track point range is defined according to an error coefficient and a landing track point range is output; the method can reduce the search range of the unmanned aerial vehicle, and is helpful for accurately searching the unmanned aerial vehicle with the communication signal lost and fallen down.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for tracking a motion track with power after a communication signal of an unmanned aerial vehicle is lost in an embodiment of the invention;
FIG. 2 is a schematic diagram of a first case of calculating a landing time;
fig. 3 is a schematic diagram of a second case of calculating the landing time.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method for tracking a motion track with power after communication signals of an unmanned aerial vehicle are lost, which can reduce the training period of a network and accelerate the convergence of the network.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
After the unmanned aerial vehicle interference equipment sends interference signals to the unmanned aerial vehicle, especially after the unmanned aerial vehicle navigation signals are interfered, unmanned aerial vehicle can lose positioning ability, in the process of landing, can be because the aerial wind-force is great and move along with wind (because there is still certain power, can produce the displacement relative to ground, but not free fall motion), the direction and the speed of removal can be according to wind-force, wind direction size change.
As shown in fig. 1, the method for tracking the motion track with power after the communication signal of the unmanned aerial vehicle is lost, provided by the embodiment of the invention, comprises the following steps:
step 1: acquiring an unmanned aerial vehicle target, performing AI analysis on the unmanned aerial vehicle target, and identifying and confirming the model of the unmanned aerial vehicle;
step 2: performing feature matching according to the model of the unmanned aerial vehicle by using an unmanned aerial vehicle model database to obtain feature parameters of the unmanned aerial vehicle;
step 3: judging whether the emergency response, the gesture and the runaway of the unmanned aerial vehicle are caused or not through an artificial intelligent algorithm;
step 4: if the unmanned aerial vehicle is not out of control, acquiring the target real-time characteristics of the unmanned aerial vehicle, if the unmanned aerial vehicle is out of control, acquiring the target real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration of the unmanned aerial vehicle, and calculating the landing point position of the unmanned aerial vehicle according to the target real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration;
step 5: correcting the position of the landing point of the unmanned aerial vehicle through a Kalman filtering algorithm, and calculating the landing track of the unmanned aerial vehicle, wherein the target state of the unmanned aerial vehicle is continuously tracked in real time before the unmanned aerial vehicle lands, and the steps 2-5 are repeated until the unmanned aerial vehicle lands;
step 6: after the unmanned aerial vehicle lands, a landing range is defined according to the error coefficient, and a landing track point range is output.
In step 1, an unmanned aerial vehicle target is obtained, AI analysis is carried out on the unmanned aerial vehicle target, and the model of the unmanned aerial vehicle is identified and confirmed, specifically:
and capturing the unmanned aerial vehicle target through radio and photoelectric equipment, carrying out AI analysis on the unmanned aerial vehicle target, and identifying and confirming the model of the unmanned aerial vehicle.
In step 2, feature matching is performed by using an unmanned aerial vehicle model database according to the model of the unmanned aerial vehicle, so as to obtain feature parameters of the unmanned aerial vehicle, which are specifically as follows:
the unmanned aerial vehicle model database is established, feature matching is carried out in the unmanned aerial vehicle model database according to the model of the unmanned aerial vehicle, and parameters of the unmanned aerial vehicle corresponding to the model are obtained, wherein the feature parameters of the unmanned aerial vehicle comprise weight parameters, explosion-proof capability parameters, unmanned aerial vehicle response time parameters, unmanned aerial vehicle descending speed parameters and return speed parameters.
For example: the model query of the unmanned aerial vehicle model data is as follows:
weight m=1380 g, maximum descent speed v d =4m/s, flight speed v e =20m/s, calculating the interference onset time as t Δ =5 s, calculate the frontal area: micro unmanned plane (RCS is more than or equal to 0.01 m) 2 ) Therefore, assume an area S w ＝0.01m 2 。
In step 3, the emergency response, the gesture and whether the unmanned aerial vehicle can cause out of control after being interfered are judged through an artificial intelligence algorithm, specifically:
according to the characteristic parameters of the unmanned aerial vehicle, the unmanned aerial vehicle judges whether the unmanned aerial vehicle is out of control or not through the BP network according to the emergency response and the gesture after being interfered, wherein if the unmanned aerial vehicle is not provided with a self-protection mechanism, the unmanned aerial vehicle is out of control after being interfered, and if the unmanned aerial vehicle is provided with the self-protection mechanism, the unmanned aerial vehicle is not out of control after being interfered.
The flight control technology of each unmanned aerial vehicle is different, for example, after some unmanned aerial vehicles are interfered, the unmanned aerial vehicles can directly lose power and fall from the air, some unmanned aerial vehicles have a height fixing function and cannot lose power, but can lose control and positioning, and the unmanned aerial vehicles can slowly fall at a certain height in the air along with the movement of wind, so that emergency response and gesture refer to judging whether the unmanned aerial vehicles of the model can cause the out-of-control condition or not through parameters of the unmanned aerial vehicles.
In step 4, if the unmanned aerial vehicle is not out of control, acquiring target real-time characteristics of the unmanned aerial vehicle, specifically:
if the unmanned aerial vehicle is not out of control, acquiring real-time characteristics of the unmanned aerial vehicle target, including real-time position information of the unmanned aerial vehicle and real-time height information of the unmanned aerial vehicle.
In step 4, if the unmanned aerial vehicle is out of control, acquiring the target real-time characteristic, the real-time meteorological characteristic, the real-time geographic environment characteristic and the gravity acceleration of the unmanned aerial vehicle, and calculating the landing point position of the unmanned aerial vehicle according to the target real-time characteristic, the real-time meteorological characteristic, the real-time geographic environment characteristic and the gravity acceleration, wherein the landing point position of the unmanned aerial vehicle is specifically as follows:
if the unmanned aerial vehicle is out of control, acquiring real-time characteristics, real-time meteorological characteristics, real-time geographic environment characteristics and gravitational acceleration of the unmanned aerial vehicle, wherein the real-time characteristics of the unmanned aerial vehicle include real-time position information of the unmanned aerial vehicle and real-time height information of the unmanned aerial vehicle, the real-time meteorological characteristics include wind power information, wind direction information and air density, the real-time geographic environment characteristics are altitude, geographic coordinates of the unmanned aerial vehicle are acquired, gravitational acceleration g of the unmanned aerial vehicle at the moment is calculated according to the geographic coordinates, and air density ρ is calculated w The method comprises the following steps:
ρ w =1.293× (actual pressure/standard physical atmospheric pressure) × (273.15/actual absolute temperature)
Absolute temperature = celsius +273.15
In the air boundary layer, the wind speed is smaller as approaching to the ground, and only the height of 300-500 m or more can be used for freely flowing without being influenced by the roughness of the ground, so that the gradient wind is formed. The wind speed recorded by the meteorological station generally refers to the wind speed measured at 10 m-15 m above ground. The relation between wind speed and altitude can be determined according to an empirical formula, and the wind speed V is calculated w The method comprises the following steps:
in the formula, h 0 、h w The calculated height and the reference height are respectively given in m and V w To calculate the height h w Wind speed at m/s, V 0 With reference height h 0 Alpha is a ground roughness index corresponding to different landforms, and is mainly related to the ground roughness and the temperature vertical gradient of a measured place, and the value range is 0.1-0.5;
calculating air resistance coefficient C w Wherein C is a sphere w A value of 0.45, C of the automobile w The value is even only 0.15, C of raindrops w Resistance coefficient C of 0.05 for general civil aircraft w A value of 0.08, similar to the unmanned aerial vehicle of the present invention, where C w =0.08, which can be fine-tuned as a variation value;
considering that wind speed acts on an object to generate acting force, the momentum theorem can know that:
F w ＝m k V w
wherein F is w Represents the force generated by wind, m k To generate the mass of the forced air, V w When wind blows on the unmanned aerial vehicle body, an acting force is generated, and the acting force forms a stress acting surface S on the unmanned aerial vehicle body w Thus, for a certain period of time, when wind acts on the unmanned aerial vehicle body, the air mass generating the wind disturbance force can be expressed as:
m k ＝ρ w ×S w ×V w ×t
calculating the air force F w The method comprises the following steps:
wherein ρ is w Is air density, S w For the windward volume of the unmanned aerial vehicle, V w Is the wind speed; the magnitude of the disturbance acting force generated by wind speed on the four-rotor unmanned aerial vehicle is only related to 3 factors of air density, acting area and wind speed. When the four-rotor unmanned aerial vehicle actually executes the task to fly, the space span change can not be generated greatly in a certain time, and the air density can not be changed, so that the air density can be regarded as a constant value when the wind speed generates disturbance acting force to the four-rotor unmanned aerial vehicle. Meanwhile, the unique engine body structure of the four-rotor unmanned aerial vehicle is characterized in that when task wind is executed, the engine body cannot be easily changed and is regarded as a complete rigid body, so that when the wind speed generates disturbance acting force on the four-rotor unmanned aerial vehicle, a stress acting surface formed on the engine body of the four-rotor unmanned aerial vehicle cannot be changed, and the stress acting surface is regarded as a fixed value. In summary, the magnitude of the wind force applied to the quadrotor unmanned aerial vehicle is only related to the magnitude of the wind speed, so that:
wherein C is w Is the air resistance coefficient ρ w Is air density, S w The wind-facing volume of the unmanned aerial vehicle is V, and the relative movement speed of the object and the air is V. From the above, the air resistance is proportional to the air resistance coefficient and the windward area and proportional to the square of the speed under normal conditions;
the unmanned aerial vehicle hovers in the air in a static manner, the horizontal speed is not generated at the beginning, the moving acceleration of the unmanned aerial vehicle is formed by the generated acting force when the wind moves relative to the unmanned aerial vehicle, the acceleration in the horizontal direction is generated, and the acceleration g generated by the wind power of the unmanned aerial vehicle is calculated w The method comprises the following steps:
the time t to maximum speed is calculated as:
as the unmanned aerial vehicle receives the acting force of wind, the unmanned aerial vehicle moves towards the direction of the wind, and under the acting force of the wind, the unmanned aerial vehicle can reach the maximum speed v x Initial v of unmanned aerial vehicle 0 =0, the time to maximum speed was calculated as:
as shown in fig. 2 and 3, the initial height H of the unmanned aerial vehicle is assumed to be 120m, and the maximum speed of the vertical method is assumed to be V dmax Two operation modes exist for vertical landing, the first is the vertical velocity V of the unmanned aerial vehicle d =0m/s, at acceleration g 1 Accelerating to V dmax The method comprises the steps of carrying out a first treatment on the surface of the Maintain V dmax At a constant speed, and is close to the landing place V dmax Decelerating to V d =0m/s. The second type is that if the initial height H of the unmanned aerial vehicle is smaller, V d May not reach V dmax Deceleration to 0m/s is started, and calculation is separately performed:
wherein, the first case:
H＝0.5g 1 t 1 +v dmax *t 2 +0.5g 1 t 3 2
t 1 ＝t 3 ＝v dmax /g 1
t 2 ＝(H-g 1 t 1 2 )/v dmax
T 1 ＝t 1 +t 2 +t 3
in the formula, v dmax 、g 1 And H are all known amounts, t 1 、t 2 、t 3 The time of segmentation is T, and the total landing time is T;
second case:
H＝0.5g 1 t 4 2 +0.5g 1 t 5 2
t 4 ＝t 5
t 4 ＝sqrt(H/g 1 )
T 2 ＝t 4 +t 5
in the formula, v dmax 、g 1 And H are all known amounts, t 4 、t 5 The time of segmentation is T, and the total landing time is T;
calculating horizontal displacement of the unmanned aerial vehicle as follows:
two conditions of the landing time T, T and T are calculated, and the horizontal displacement is S:
first case (T.ltoreq.t)
S 1 ＝0.5*g w *T 2
Second case (T > T)
S 2 ＝0.5*g w *t 2 +v x *(T-t)
In step 5, correcting the position of the landing point of the unmanned aerial vehicle by a Kalman filtering algorithm, specifically:
removing noise point data through a Kalman filtering algorithm according to real-time height information, speed, horizontal displacement, wind power information and wind direction information of the unmanned aerial vehicle, and correcting the position of a landing point of the unmanned aerial vehicle;
according to the formula, the relation between displacement and time is obtained:
wherein T is 1 T and T 2 Corresponding to the first mode and the second mode respectively:
wherein t is 0 ＝v dmax /g 1 ；
After the unmanned aerial vehicle falls to the ground, a landing range is defined according to an error coefficient, and a landing track point range is output, specifically:
calculating longitude and latitude coordinates of a landing track point according to the original longitude and latitude coordinates of the unmanned aerial vehicle and the horizontal displacement, and calculating a tracking matrix of the dynamic motion track after the unmanned aerial vehicle communication signal is interrupted according to discrete boundary data of Kalman filtering and the interval range;
let Δh be the displacement deviation amount, obtain:
wherein t is 0 ＝v dmax /g 1 ；
The relationship between calculated displacement and time is:
remaining deviation DeltaS 1 And DeltaS 2 Expressed as:
and obtaining a deviation area delta S expression area, namely an irregular area which can fall.
The method comprises the steps of acquiring an unmanned aerial vehicle target, carrying out AI analysis on the unmanned aerial vehicle target, identifying and confirming the model and characteristic parameters of the unmanned aerial vehicle, carrying out characteristic matching according to the model and characteristic parameters of the unmanned aerial vehicle by utilizing an unmanned aerial vehicle model database, judging emergency response and gesture after the unmanned aerial vehicle is interfered and whether the unmanned aerial vehicle is out of control by an artificial intelligent algorithm, acquiring real-time characteristics of the unmanned aerial vehicle target if the unmanned aerial vehicle is not out of control, acquiring the real-time characteristics, real-time meteorological characteristics, real-time geographic environment characteristics and gravity acceleration of the unmanned aerial vehicle if the unmanned aerial vehicle is out of control, calculating the landing point position of the unmanned aerial vehicle according to the unmanned aerial vehicle, correcting the landing point position of the unmanned aerial vehicle by a Kalman filtering algorithm, and calculating the landing track of the unmanned aerial vehicle, wherein the unmanned aerial vehicle target state is continuously tracked in real time before the unmanned aerial vehicle lands, and the steps are repeated until the unmanned aerial vehicle lands, and a landing track point range is defined according to an error coefficient and a landing track point range is output; the method can reduce the search range of the unmanned aerial vehicle, and is helpful for accurately searching the unmanned aerial vehicle with the communication signal lost and fallen down.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (7)
1. The method for tracking the motion track with power after the communication signal of the unmanned aerial vehicle is lost is characterized by comprising the following steps:
step 1: acquiring an unmanned aerial vehicle target, performing AI analysis on the unmanned aerial vehicle target, and identifying and confirming the model of the unmanned aerial vehicle;
step 2: performing feature matching according to the model of the unmanned aerial vehicle by using an unmanned aerial vehicle model database to obtain feature parameters of the unmanned aerial vehicle;
step 3: judging whether the emergency response, the gesture and the runaway of the unmanned aerial vehicle are caused or not through an artificial intelligent algorithm;
step 4: if the unmanned aerial vehicle is not out of control, acquiring the target real-time characteristics of the unmanned aerial vehicle, if the unmanned aerial vehicle is out of control, acquiring the target real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration of the unmanned aerial vehicle, and calculating the landing point position of the unmanned aerial vehicle according to the target real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration;
step 5: correcting the position of the landing point of the unmanned aerial vehicle through a Kalman filtering algorithm, and calculating the landing track of the unmanned aerial vehicle, wherein the target state of the unmanned aerial vehicle is continuously tracked in real time before the unmanned aerial vehicle lands, and the steps 2-5 are repeated until the unmanned aerial vehicle lands;
step 6: after the unmanned aerial vehicle lands, a landing range is defined according to the error coefficient, and a landing track point range is output.
2. The method for tracking the motion trail with power after the communication signal of the unmanned aerial vehicle is lost according to claim 1, wherein in the step 1, the unmanned aerial vehicle target is obtained, AI analysis is carried out on the unmanned aerial vehicle target, and the model of the unmanned aerial vehicle is identified and confirmed, specifically:
and capturing the unmanned aerial vehicle target through radio and photoelectric equipment, carrying out AI analysis on the unmanned aerial vehicle target, and identifying and confirming the model of the unmanned aerial vehicle.
3. The method for tracking the motion trail with power after the communication signal of the unmanned aerial vehicle is lost according to claim 2, wherein in the step 2, the characteristic matching is performed according to the model of the unmanned aerial vehicle by utilizing an unmanned aerial vehicle model database, and the characteristic parameters of the unmanned aerial vehicle are obtained specifically as follows:
the unmanned aerial vehicle model database is established, feature matching is carried out in the unmanned aerial vehicle model database according to the model of the unmanned aerial vehicle, and parameters of the unmanned aerial vehicle corresponding to the model are obtained, wherein the feature parameters of the unmanned aerial vehicle comprise weight parameters, explosion-proof capability parameters, unmanned aerial vehicle response time parameters, unmanned aerial vehicle descending speed parameters and return speed parameters.
4. The method for tracking a motion track with power after a communication signal of an unmanned aerial vehicle is lost according to claim 3, wherein in step 3, an artificial intelligence algorithm is used for judging whether an emergency response, an attitude and a loss of control of the unmanned aerial vehicle are caused or not, specifically:
according to the characteristic parameters of the unmanned aerial vehicle, the unmanned aerial vehicle judges whether the unmanned aerial vehicle is out of control or not through the BP network according to the emergency response and the gesture after being interfered, wherein if the unmanned aerial vehicle is not provided with a self-protection mechanism, the unmanned aerial vehicle is out of control after being interfered, and if the unmanned aerial vehicle is provided with the self-protection mechanism, the unmanned aerial vehicle is not out of control after being interfered.
5. The method for tracking a motion track with power after a communication signal of an unmanned aerial vehicle is lost according to claim 3, wherein in step 4, if the unmanned aerial vehicle is not out of control, the real-time characteristics of the target of the unmanned aerial vehicle are obtained, specifically:
if the unmanned aerial vehicle is not out of control, acquiring real-time characteristics of the unmanned aerial vehicle target, including real-time position information of the unmanned aerial vehicle and real-time height information of the unmanned aerial vehicle.
6. The method for tracking the motion track with power after the communication signal of the unmanned aerial vehicle is lost according to claim 3, wherein in the step 4, if the unmanned aerial vehicle is out of control, the real-time characteristics, the real-time meteorological characteristics, the real-time geographic environment characteristics and the gravity acceleration of the unmanned aerial vehicle target are obtained, and the position of the landing point of the unmanned aerial vehicle is calculated according to the real-time characteristics, the real-time geographic environment characteristics and the gravity acceleration, specifically:
if the unmanned aerial vehicle is out of control, acquiring real-time characteristics, real-time meteorological characteristics, real-time geographic environment characteristics and gravitational acceleration of the unmanned aerial vehicle, wherein the real-time characteristics of the unmanned aerial vehicle include real-time position information of the unmanned aerial vehicle and real-time height information of the unmanned aerial vehicle, the real-time meteorological characteristics include wind power information, wind direction information and air density, the real-time geographic environment characteristics are altitude, geographic coordinates of the unmanned aerial vehicle are acquired, gravitational acceleration g of the unmanned aerial vehicle at the moment is calculated according to the geographic coordinates, and air density ρ is calculated w The method comprises the following steps:
ρ w =1.293× (actual pressure/standard physical atmospheric pressure) × (273.15/actual absolute temperature)
Absolute temperature = celsius +273.15
Calculating wind speed V w The method comprises the following steps:
in the formula, h 0 、h w The calculated height and the reference height are respectively given in m and V w To calculate the height h w Wind speed at m/s, V 0 With reference height h 0 Alpha is a ground roughness index corresponding to different landforms;
calculating air resistance coefficient C w ；
Calculating the air force F w The method comprises the following steps:
wherein ρ is w Is air density, S w For the windward volume of the unmanned aerial vehicle, V w Is the wind speed;
calculating the acceleration g generated by the wind power of the unmanned aerial vehicle w The method comprises the following steps:
the time t to maximum speed is calculated as:
and finally, calculating to obtain the landing time and the horizontal displacement.
7. The method for tracking the motion track with power after the communication signal of the unmanned aerial vehicle is lost according to claim 3, wherein in the step 5, the position of the landing point of the unmanned aerial vehicle is corrected by a Kalman filtering algorithm, specifically:
and removing noise data through a Kalman filtering algorithm according to real-time height information, speed, horizontal displacement, wind power information and wind direction information of the unmanned aerial vehicle, and correcting the position of a landing point of the unmanned aerial vehicle.
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