CN114440896B - High-speed aircraft flight pipeline planning method based on dynamic identification of threat scene - Google Patents

High-speed aircraft flight pipeline planning method based on dynamic identification of threat scene Download PDF

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CN114440896B
CN114440896B CN202210367587.4A CN202210367587A CN114440896B CN 114440896 B CN114440896 B CN 114440896B CN 202210367587 A CN202210367587 A CN 202210367587A CN 114440896 B CN114440896 B CN 114440896B
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CN114440896A (en
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王才红
何浩东
高军强
许馨月
宫树香
吴明强
刘庆国
马召
黄蓓
华岳阳
马骏
温永禄
李强
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Abstract

The invention relates to a high-speed aircraft flight pipeline planning method based on threat scene dynamic identification, and belongs to the technical field of flight pipeline planning. The method comprises the following steps: s1, constructing a regional target quantitative threat level and a target threat level judgment function; s2, constructing a flight pipeline optimization function of the high-speed aircraft; s3, calculating the deviation of the approach point of the high-speed aircraft according to the emission initial state error vector and the state deviation; s4, calculating flight pipeline parameters, correcting according to the deviation of the approach point to eliminate transverse deviation, and generating flight pipeline parameters of the flight path after the correction point according to the corrected flight path; restarting the deviation calculation process of the flight pipeline; and S5, optimizing the flight pipeline by using a particle swarm algorithm. The method solves the problem of planning the flight pipeline of the high-speed aircraft under the complex threat situation, and effectively supports the efficient application of the high-speed aircraft under the complex and multi-threat scene.

Description

High-speed aircraft flight pipeline planning method based on dynamic identification of threat scene
Technical Field
The invention belongs to the field of flight tracks, and particularly relates to a high-speed aircraft flight pipeline planning method based on dynamic identification of a threat scene.
Background
With the rapid development of science and technology, the threats faced by high-speed aircrafts are increasing day by day, including threat information such as the safety consideration of the flight of the high-speed aircrafts and the external threat consideration of flight scenes. In order to fully and effectively solve the threat scene of the high-speed aircraft in the flight process, the flight pipeline of the high-speed aircraft needs to be optimized and planned, and the problem of safety threat in the flight process is solved.
Disclosure of Invention
The invention aims to provide a high-speed aircraft flight pipeline planning method based on threat scene dynamic identification aiming at the requirement that a flight pipeline of a high-speed aircraft avoids flight detour under different scene quantitative threats, and the method is used for solving the problems of medium quantitative threat assessment and optimization target function establishment in the flight process of the high-speed aircraft by constructing a flight pipeline optimization function based on region quantitative threat assessment in the flight pipeline planning process of the high-speed aircraft; the method comprises the steps that a flight pipeline model based on multiple constraints of the high-speed aircraft is established, so that the influence on the flight pipeline constraint in the flight process of the high-speed aircraft is analyzed; and finally, establishing a particle swarm optimization-based high-speed aircraft flight pipeline optimization model, realizing optimal flight pipeline planning aiming at the quantized threat area, and realizing effective avoidance and fly-around of the high-speed aircraft to the threat.
In order to achieve the purpose, the invention adopts the following technical scheme:
a high-speed aircraft flight pipeline planning method based on threat scene dynamic identification comprises the following steps:
s1, constructing a regional target quantitative threat level and a target threat level judgment function;
s2, constructing a flight pipeline optimization function of the high-speed aircraft,
the following cost equation is adopted to describe the performance index of the flight pipeline optimization function:
Figure 100002_DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE002
for a high-speed aircraft threat avoidance cost index,
Figure 100002_DEST_PATH_IMAGE003
is the time of flight from the emission point to the target point,
Figure DEST_PATH_IMAGE004
the offset distance is set for the current time instant,
Figure 100002_DEST_PATH_IMAGE005
as the flying height at the present moment,
Figure DEST_PATH_IMAGE006
is the threat indicator at the current time,
Figure 100002_DEST_PATH_IMAGE007
in order to deviate from the distance-controlling coefficient,
Figure DEST_PATH_IMAGE008
in order to control the coefficient of the fly height,
Figure 100002_DEST_PATH_IMAGE009
controlling coefficients for threat metrics;
s3, calculating the deviation of the approach point of the high-speed aircraft according to the emission initial state error vector and the state deviation;
s4, calculating flight pipeline parameters, correcting according to the deviation of the approach point to eliminate transverse deviation, and generating flight pipeline parameters of the flight path after the correction point according to the corrected flight path; restarting the deviation calculation process of the flight pipeline;
s5, optimizing the flight pipeline by using a particle swarm algorithm, and specifically comprising the following steps:
s51, initializing a particle swarm, randomly generating an initialized particle swarm in the coefficient solution space, wherein the position coordinates of the particles in the swarm are potential solutions;
s52, selecting a fitness function of the flight pipeline planning, and calculating the fitness function of each particle;
s53, comparing fitness functions of different particles, and determining the best position searched by each particle and the best position searched by the whole particle swarm currently;
s54, adjusting the speed and the position of the particles according to the best position searched by each particle obtained in S53 and the best position searched by the whole particle swarm currently;
s55, judging whether the fitness function reaches the optimum or meets the condition of terminating iteration, if so, ending the method, otherwise, jumping to S52;
s6, calculating the threat avoidance cost index of the high-speed aircraft
Figure DEST_PATH_IMAGE010
When is coming into contact with
Figure 100002_DEST_PATH_IMAGE011
And obtaining the optimal threat flight pipeline under the comprehensive quantitative threat scene when the minimum value is obtained.
The S1 specifically includes:
s11, determining a feature set required for judging the target threat level;
s12, establishing a grade judgment function of any characteristic parameter in the characteristic set aiming at different types of target threats;
s13, determining weighting factors of different types of targets in threat level judgment, and forming a target threat level weighting vector;
and S14, determining final threat levels of different targets according to the level judgment function of the target threats and the target threat level weighting vector.
The characteristic set comprises characteristic parameters for judging the level of the target threat; and the grade judging function is used for forming a target threat grade judging matrix.
The weighting factors for the different types of target threat levels are determined by an analytic hierarchy process.
The S3 specifically includes: s31, acquiring a high-speed aircraft launching initial state error vector according to the nominal launching coordinate system and the actual launching coordinate system; s32, calculating the state deviation of the high-speed aircraft launching initial state error at a shutdown point; s33, calculating the deviation of the approach point of the high-speed aircraft; and calculating to obtain the deviation of the approach point according to the state quantity and the state deviation of the emission coordinate system of the shutdown point.
The S32 specifically includes: and multiplying the sum of the rotation item deviation propagation matrix, the initial value item deviation propagation matrix and the translation item deviation propagation matrix caused by the emission initial state error vector obtained in the step S31 to obtain the state deviation of the emission initial state error at the shutdown point.
In S33, the route point deviation includes a longitudinal deviation and a lateral deviation of the route point.
In S4, the pipeline parameter calculation method is as follows:
Figure DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE013
、
Figure DEST_PATH_IMAGE014
and
Figure 100002_DEST_PATH_IMAGE015
are respectively a unit vector
Figure DEST_PATH_IMAGE016
The three components of (a) and (b),
Figure 100002_DEST_PATH_IMAGE017
、
Figure DEST_PATH_IMAGE018
and
Figure 100002_DEST_PATH_IMAGE019
are respectively unit vectors
Figure DEST_PATH_IMAGE020
The three components of (a) and (b),
Figure 100002_DEST_PATH_IMAGE021
and
Figure DEST_PATH_IMAGE022
on a circular plane corresponding to the unit normal vector n, n being the number of 2 adjacent course points
Figure 100002_DEST_PATH_IMAGE023
And
Figure DEST_PATH_IMAGE024
subtracting the three-dimensional rectangular coordinates to obtain a vector
Figure 100002_DEST_PATH_IMAGE025
After unitization, the unit normal vector of each track point on the theoretical track trend is approximately used;
Figure DEST_PATH_IMAGE026
is the unit coordinate of the assumed track point C;
Figure 100002_DEST_PATH_IMAGE027
is the radius of the circle, is also the lateral deviation of the passing point,
Figure DEST_PATH_IMAGE028
is the polar angle of the circle, with vector n as the normal vector.
In S52, a voyage is selected as a fitness function.
Further, considering the requirement of avoiding the threat, when the track point falls into the threat area, the fitness function is set to be infinite as a punishment; considering the minimum turn radius constraint, if the minimum turn radius in the maneuverability of the aircraft is greater than the radius of curvature of the track curve, the fitness function is set to infinity.
And (3) optimizing a flight pipeline based on regional quantitative threat evaluation, evaluating the threat degree of the target according to the state parameters and the identity identification parameters of each threat target in the flight environment, and quantitatively giving the threat capability of enemy troops so as to estimate the target performance and the risk degree of effectively restraining the flight of the high-speed aircraft. The optimization process of the large-range flight pipeline of the high-speed aircraft is considered, a description method of the threat avoidance performance optimization index and a calculation method of the threat index are discussed, and a flight pipeline optimization objective function based on the regional quantitative threat is established.
Based on a flight pipeline model under multiple constraints of the high-speed aircraft, constraint conditions such as flight initial state errors, theoretical flight tracks, correction points and the like are comprehensively considered, intersection boundary values of flight pipelines of the high-speed aircraft are generated, flight pipeline parameters are calculated, and flight pipelines of tracks after correction points are generated according to the corrected flight tracks.
And planning the flight pipeline of the high-speed aircraft according to the flight pipeline characteristics and the quantitative threat result of the high-speed aircraft based on the particle swarm optimization. According to the minimum turning radius of the high-speed aircraft, a quantitative threat model is established in combination, the particle swarm optimization is utilized to obtain the flight pipeline for effectively avoiding the threat, the generated flight pipeline can effectively avoid the threat, and the high-speed aircraft can avoid the quantitative threat and fly around.
Advantageous effects
Compared with the conventional flight pipeline planning method, the high-speed aircraft flight pipeline planning method based on threat scene dynamic identification has the following beneficial effects:
1. the method realizes quantitative evaluation of threats to different target areas and establishment of flight pipeline optimization functions; forming a flight pipeline model under multiple constraints; the optimized planning of the flight pipeline of the high-speed aircraft is realized through a particle swarm optimization method, the problem of planning of the flight pipeline of the high-speed aircraft in a complex threat situation is solved, and the efficient application of the high-speed aircraft in a complex and multi-threat scene is effectively supported;
2. the method is oriented to the target area threat of the high-speed aircraft, provides a flight pipeline optimization function method based on area quantitative threat evaluation, solves the problems of target area threat quantification, flight pipeline optimization and the like, realizes the quantification of complex threats and the process of optimizing and analyzing the influence on the flight pipeline, and forms the threat degree of quantitative index analysis on the flight pipeline;
3. the method considers the flight performance of the high-speed aircraft, provides a flight pipeline model based on multiple constraints of the high-speed aircraft, forms the flight pipeline calculation capacity of the high-speed aircraft, and realizes the flight pipeline planning of a high-speed aircraft cluster;
4. according to the method, threat avoiding and fly-around requirements of a high-speed aircraft on threats are synchronously quantized, threat results and flight performance of the high-speed aircraft are synchronously quantized, a particle swarm optimization-based high-speed aircraft flight pipeline optimization model is provided, and a flight pipeline for threat avoiding and fly-around in a complex threat environment is generated through optimization analysis under multiple constraint conditions; through simulation analysis comparison, the fly-around analysis of 500 flight pipelines is carried out under the same threat scenario, and the average distance of the fly pipelines after fly-around passing through the threat zone is reduced by 38.16% compared with the average distance of the fly-around passing through the threat zone.
Drawings
FIG. 1 is a schematic diagram of an initial state error of transmission in a transmission coordinate system in a high-speed aircraft flight pipeline planning method based on dynamic identification of a threat scene;
FIG. 2 is a schematic diagram of a high-speed aircraft flight path planning method based on dynamic identification of threat scenarios.
Detailed Description
The method for planning the flight pipeline of the high-speed aircraft based on the dynamic identification of the threat scene is described in detail below with reference to the accompanying drawings and embodiments.
The implementation provides a high-speed aircraft flight pipeline planning method based on threat scene dynamic identification aiming at the requirement that a flight pipeline of a high-speed aircraft avoids flight detour under different scene quantitative threats, and the high-speed aircraft effectively avoids the threat detour by a regional quantitative threat evaluation-based method, a flight pipeline model based on multiple constraints of the high-speed aircraft, a particle swarm optimization-based high-speed aircraft flight pipeline optimization model and other technical approaches.
In the specific implementation of this embodiment, with reference to fig. 2, the following steps are included:
and optimizing the flight pipeline based on regional quantitative threat assessment, evaluating the threat degree of the target according to the state parameters and the identity identification parameters of each threat target in the flight environment, and quantitatively giving the threat capability of enemy troops so as to estimate the target performance and the risk degree of effectively restraining the flight of the high-speed aircraft. Considering the optimization process of the large-range flight pipeline of the high-speed aircraft, discussing a description method of the threat avoidance performance optimization index and a calculation method of the threat index, and establishing a flight pipeline optimization objective function based on regional quantitative threats;
s1, constructing a regional target quantitative threat level and a target threat level judgment function;
the establishment process of the regional target quantitative threat level evaluation function is as follows:
assume a target threat domain of
Figure 100002_DEST_PATH_IMAGE029
，
Figure DEST_PATH_IMAGE030
Representing the number of target threats, any target threat
Figure 100002_DEST_PATH_IMAGE031
Is characterized by
Figure DEST_PATH_IMAGE032
Wherein, in the step (A),
Figure 100002_DEST_PATH_IMAGE033
with following
Figure DEST_PATH_IMAGE034
The target threat level may be judged according to the following steps:
s11, determining the purpose of proceedingFeature set for threat level assessment
Figure 100002_DEST_PATH_IMAGE035
Wherein, in the step (A),
Figure DEST_PATH_IMAGE036
，
Figure 100002_DEST_PATH_IMAGE037
the number of the characteristic parameters selected for judging the target threat level;
s12, establishing any characteristic parameter
Figure DEST_PATH_IMAGE038
For different types of objects
Figure DEST_PATH_IMAGE039
Is judged function of threat level
Figure DEST_PATH_IMAGE040
；
S13, determining weighting factors of different types of targets in threat level judgment
Figure DEST_PATH_IMAGE041
And forming a weight vector therefrom
Figure DEST_PATH_IMAGE042
；
In particular, an analytic hierarchy process is used to determine weighting factors for different types of targets
Figure DEST_PATH_IMAGE043
；
S14, finally, determining final threat levels of different targets by using the target threat level judgment matrix and the target threat level weighting vector:
Figure DEST_PATH_IMAGE044
（1）
for high-speed flightCharacteristic parameters of the target area threat of the aircraft only take into account the distance of the aircraft from the target, i.e.
Figure DEST_PATH_IMAGE045
(ii) a The target threat level and the target distance are in a decreasing function relationship, namely the larger the target distance is, the smaller the target threat level is; as the target distance decreases, the target threat level will gradually increase:
Figure DEST_PATH_IMAGE046
（2）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE047
is as followsiA threat level coefficient for each target threat,
Figure DEST_PATH_IMAGE048
is as followsiDistance to high speed aircraft under maximum threat for individual targets.
Consider thatiThe individual target threatens to
Figure DEST_PATH_IMAGE049
The high-speed aircraft is not influenced any more under the distance, and the influence degree is
Figure DEST_PATH_IMAGE050
Beyond that, i.e. with an influence probability of 0.3%,
Figure DEST_PATH_IMAGE051
the value of (A) is as shown in formula (3):
Figure DEST_PATH_IMAGE052
（3）
the independence and the maximum value normalization of the quantitative threat assessment function are considered, and objective differences existing between the assessment of the target threat level are caused while the target type or the target characteristic quantity is expanded; the method for compensating the differences is to provide weighting factors of different target threat levels, and the factors represent the differences of the different target threat levels;
s2, constructing a flight pipeline optimization function of the high-speed aircraft,
the flight pipeline optimization takes a reference flight track as a reference, continuously modifies the reference flight pipeline according to the local situation of target threat avoidance and the dynamic threats of a plurality of targets, dynamically calculates the flight track, and tracks the flight track to complete a flight task, thereby realizing the effective avoidance of the threat;
before determining the threat avoidance optimization problem of the high-speed aircraft, determining the performance index of the optimization problem; the following cost equation is adopted to describe the performance index of the flight pipeline optimization function:
Figure DEST_PATH_IMAGE053
（4）
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE054
for a high-speed aircraft threat avoidance cost index,
Figure DEST_PATH_IMAGE055
is the time of flight from the emission point to the target point,
Figure DEST_PATH_IMAGE056
the offset distance is set for the current time instant,
Figure DEST_PATH_IMAGE057
as the flying height at the present moment,
Figure DEST_PATH_IMAGE058
is the threat indicator at the current time,
Figure DEST_PATH_IMAGE059
in order to deviate from the distance-controlling coefficient,
Figure DEST_PATH_IMAGE060
in order to control the coefficient of the fly height,
Figure DEST_PATH_IMAGE061
controlling coefficients for threat indicators;
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE062
，
Figure DEST_PATH_IMAGE063
is the distance from the current time
Figure DEST_PATH_IMAGE064
A threat level of the individual target threat; the first deviated flight pipeline avoids too large distance between a connecting line of the starting point and the target point, so that the high-speed aircraft cannot deviate too far from a specific route point, and the flight energy consumption and the flight time of the high-speed aircraft are reduced; altitude with a second constant height
Figure DEST_PATH_IMAGE065
Too small, this will drive the optimization algorithm to look for higher altitude flight ducts, increasing the distance between the high speed aircraft and the target; the third item is a flight pipeline which is too close to a target threat point, and the index integrates all possible threat information at the position, so that the high-speed aircraft can effectively avoid the threat; ratio of
Figure DEST_PATH_IMAGE066
And
Figure DEST_PATH_IMAGE067
the aircraft is controlled to select whether to fly over a target threat or a flight conduit that bypasses the target threat. The cost equation improves the survival rate of the task by searching for the high-altitude flying target threat and trying to avoid the known threat at the same time;
s3, calculating the deviation of the approach point of the high-speed aircraft according to the emission initial state error vector and the state deviation;
the flight pipeline model based on the high-speed aircraft under multiple constraints comprises the steps of transmitting initial state deviation influence analysis and establishing a flight pipeline generation model;
s31, acquiring a high-speed aircraft launching initial state error vector according to the nominal launching coordinate system and the actual launching coordinate system; the analysis process of the influence of the emission initial state deviation is as follows:
assuming a nominal emission coordinate system
Figure DEST_PATH_IMAGE068
The actual emission coordinate system
Figure DEST_PATH_IMAGE069
As shown in fig. 1; the difference between the two coordinate systems reflects initial positioning errors (geodetic longitude and latitude deviation and elevation deviation of a transmitting point) and initial orientation errors (vertical deviation and transmitting azimuth deviation);
s32, calculating the state deviation of the high-speed aircraft launching initial state error at a shutdown point;
the state deviation expression of the high-speed aircraft launching initial state error at the shutdown point is as follows:
Figure DEST_PATH_IMAGE070
（5）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE071
transmitting a rotation item deviation propagation matrix caused by initial state errors,
Figure DEST_PATH_IMAGE072
the deviation propagation matrix of the initial value terms,
Figure DEST_PATH_IMAGE073
in order to shift the term-deviation propagation matrix,
Figure DEST_PATH_IMAGE074
transmitting an initial state error vector;
s33, calculating the deviation of the approach point of the high-speed aircraft; and calculating to obtain the deviation of the approach point according to the state quantity and the state deviation of the emission coordinate system of the shutdown point.
Assuming that the high-speed aircraft is exhausted and shut down, the shutdown point is K, and the state quantity of the shutdown point in the launching inertia system is K
Figure DEST_PATH_IMAGE075
The amount of state deviation is
Figure DEST_PATH_IMAGE076
The longitudinal and transverse deviations of the passing point are
Figure DEST_PATH_IMAGE077
、
Figure DEST_PATH_IMAGE078
And
Figure DEST_PATH_IMAGE079
the relationship of (1) is:
Figure DEST_PATH_IMAGE080
（6）
therefore, the relationship between the deviation of the high-speed aircraft at the passing point and the emission initial state error parameter is as follows:
Figure DEST_PATH_IMAGE081
（7）
s4, calculating flight pipeline parameters, correcting according to the deviation of the approach point to eliminate transverse deviation, and generating flight pipeline parameters of the flight path after the correction point according to the corrected flight path; and the deviation calculation process of the flight tube is restarted.
Based on a flight pipeline model under multiple constraints of the high-speed aircraft, constraint conditions such as flight initial state errors, theoretical flight tracks, correction points and the like are comprehensively considered, intersection boundary values of flight pipelines of the high-speed aircraft are generated, flight pipeline parameters are calculated, and flight pipelines of tracks after correction points are generated according to the corrected flight tracks.
The flight pipeline generative model is established as follows:
2 adjacent track points
Figure DEST_PATH_IMAGE082
And
Figure DEST_PATH_IMAGE083
subtracting the three-dimensional rectangular coordinates to obtain a vector
Figure DEST_PATH_IMAGE084
After unitization, the unit normal vector n of each track point on the theoretical track trend is approximately used, and 2 unit vectors on the circular plane corresponding to n are obtained by utilizing the principle of vector cross multiplication
Figure DEST_PATH_IMAGE085
And
Figure DEST_PATH_IMAGE086
(ii) a Assume course point C has unit coordinates of
Figure DEST_PATH_IMAGE087
，
Figure DEST_PATH_IMAGE088
Is the radius of the circle, and the radius of the circle,
Figure DEST_PATH_IMAGE089
is the polar angle of the circle, with vector n as the normal vector.
The pipeline parameter equation of the track point in the three-dimensional space is as follows:
Figure DEST_PATH_IMAGE090
（8）
after correction, the transverse deviation of the track point is eliminated, and the deviation calculation process of the flying pipeline is restarted along with the flying process;
and planning the flight pipeline of the high-speed aircraft according to the flight pipeline characteristics and the quantitative threat result of the high-speed aircraft based on the particle swarm optimization. According to the minimum turning radius of the high-speed aircraft, a quantitative threat model is established in combination, the particle swarm optimization is utilized to obtain the flight pipeline for effectively avoiding the threat, the generated flight pipeline can effectively avoid the threat, and the high-speed aircraft can avoid the quantitative threat and fly around;
and S5, optimizing the flight pipeline by using a particle swarm algorithm.
The establishing process of the high-speed aircraft flight pipeline optimization model based on particle swarm optimization is as follows:
a suitable fitness function is selected. For example, the voyage may be taken as a fitness function, wherein the voyage expression is as follows:
Figure DEST_PATH_IMAGE091
（9）
the track of a high-speed aircraft is constrained by threats, flight ducts, and minimum turning radii. The constraints are handled as follows. The projection of the threat in the plane is an ellipse, and the equation of the ellipse obtained by projection can be known from the relation of projection and coordinate transformation as follows:
Figure DEST_PATH_IMAGE092
（10）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE093
is the radius of action of the targeted threat,
Figure DEST_PATH_IMAGE094
is the coordinate of the center of the ellipse,
Figure DEST_PATH_IMAGE095
as the latitude and longitude of the threat center,
Figure DEST_PATH_IMAGE096
is less than
Figure DEST_PATH_IMAGE097
Is to stand for receivingiThe individual target threat factor is the largest,
Figure DEST_PATH_IMAGE098
is greater than
Figure DEST_PATH_IMAGE099
When indicates the firstiIndividual target threats no longer affect the high speed aircraft;
to meet the threat avoidance requirements, the waypoints cannot fall within the range of the threat zone. Let the coordinates of the track point be
Figure DEST_PATH_IMAGE100
Substituting the path points into the left side of the formula, if the result is more than or equal to 1, then the track points avoid the threat, otherwise, the track points fall into the threat area to be used as punishment, and at the moment, the fitness function is changed into infinity;
if the flight path point cannot avoid the threat area, the flight altitude of the high-speed aircraft is controlled to be higher than the threat area, and if the flight altitude of the high-speed aircraft is lower than the height of the threat area, the fitness function is changed into infinity; the constraint of minimum turning radius is considered. Radius of curvature of track curve
Figure DEST_PATH_IMAGE101
Can be represented by the following formula:
Figure DEST_PATH_IMAGE102
（11）
according to the requirements of the maneuvering characteristics of the aircraft,
Figure DEST_PATH_IMAGE103
to satisfy
Figure DEST_PATH_IMAGE104
And if not, as a penalty, changing the fitness function to infinity.
The method comprises the following steps of optimizing a flight pipeline by using a particle swarm algorithm;
s51, randomly generating an initialized particle population in the coefficient solution space determined in the previous step, and assuming that the size of the initialized particle population is equal to
Figure DEST_PATH_IMAGE105
. The particles in the population are recorded as
Figure DEST_PATH_IMAGE106
Is onen-a vector of dimension 1, expressed as
Figure DEST_PATH_IMAGE107
In which
Figure DEST_PATH_IMAGE108
The above vector is the position coordinate of each particle v in the solution space, referred to as the potential solution; the corresponding initial random definition for each particle of the velocity of its flight
Figure DEST_PATH_IMAGE109
Is also onen-a vector of 1 dimension;
s52, introducing a fitness function according to flight pipeline planning in order to evaluate the quality of the positions of the particles, and calculating the fitness function of each particle;
s53, comparing the size of the fitness function, and according to the fitness function of each particle, dividing the particle into individual particles
Figure DEST_PATH_IMAGE110
The best position currently searched is recorded as
Figure DEST_PATH_IMAGE111
(personal best), the best position currently searched by the whole particle swarm is recorded as
Figure DEST_PATH_IMAGE112
(global best)；
S54, adjusting the velocity and position of the particles according to the following two equations:
Figure DEST_PATH_IMAGE113
（12）
Figure DEST_PATH_IMAGE114
（13）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE115
，
Figure DEST_PATH_IMAGE116
in order to be able to perform the number of iterations,
Figure DEST_PATH_IMAGE117
and
Figure DEST_PATH_IMAGE118
a constant that is not negative, called the acceleration constant,
Figure DEST_PATH_IMAGE119
and
Figure DEST_PATH_IMAGE120
is [0,1 ]]A random number in between;
Figure DEST_PATH_IMAGE121
is an inertial weight, reflects the compromise of the algorithm between global search and local search, is large
Figure DEST_PATH_IMAGE122
Prone to global search, small
Figure DEST_PATH_IMAGE123
A local search tends to be performed.
And S55, repeating the process from S52 to S54 until the fitness function reaches the optimal or meets the terminated iteration algebraic condition.
S6, considering threat indexes of multiple threat targets in the whole flight process and combined action of fitness function, and obtaining high-speed aircraft threat avoidance cost index
Figure DEST_PATH_IMAGE124
And the minimum value is obtained, and the optimal threat flight pipeline under the comprehensive quantitative threat scene can be obtained.
While the foregoing is directed to the preferred embodiment of the present invention, it is not intended that the invention be limited to the embodiment and the drawings disclosed herein. Equivalents and modifications may be made without departing from the spirit of the disclosure, which is to be considered as within the scope of the invention.

Claims (9)

1. A high-speed aircraft flight pipeline planning method based on threat scene dynamic identification is characterized by comprising the following steps:
s1, constructing a regional target quantitative threat level and a target threat level judgment function;
assume a target threat domain of
Figure DEST_PATH_IMAGE001
，
Figure 58132DEST_PATH_IMAGE002
Representing the number of target threats, any target threat
Figure DEST_PATH_IMAGE003
Is characterized by
Figure 585060DEST_PATH_IMAGE004
Wherein, in the step (A),jwith followingiThe target threat level may be judged according to the following steps:
s11, determining the feature set required for judging the target threat level
Figure DEST_PATH_IMAGE005
Wherein, in the step (A),
Figure 974584DEST_PATH_IMAGE006
，mthe number of the characteristic parameters selected for judging the target threat level;
s12, establishing any characteristic parameter
Figure DEST_PATH_IMAGE007
For different types of objects
Figure 245159DEST_PATH_IMAGE008
Is judged function of threat level
Figure DEST_PATH_IMAGE009
；
S13, determining weighting factors of different types of targets in threat level judgmentm i And forming therefrom a target threat level weighting vector
Figure 669319DEST_PATH_IMAGE010
；
S14, determining final threat levels of different targets by using the target threat level evaluation matrix and the target threat level weighting vector:
Figure DEST_PATH_IMAGE011
characteristic parameters of the target area threat for high-speed aircraft only take into account the distance of the aircraft from the target, i.e.
Figure 367147DEST_PATH_IMAGE012
(ii) a The target threat level and the target distance are in a decreasing function relationship, namely the larger the target distance is, the smaller the target threat level is; as the target distance decreases, the target threat level will gradually increase;
s2, constructing a flight pipeline optimization function of the high-speed aircraft,
the following cost equation is adopted to describe the performance index of the flight pipeline optimization function:
J
Figure DEST_PATH_IMAGE013
wherein the content of the first and second substances,Jfor a high-speed aircraft threat avoidance cost index,
Figure 243968DEST_PATH_IMAGE014
is the time of flight from the emission point to the target point,
Figure DEST_PATH_IMAGE015
the offset distance is set for the current time instant,has the flying height at the present moment,
Figure 52655DEST_PATH_IMAGE016
is the threat indicator at the current moment of time,
Figure DEST_PATH_IMAGE017
in order to deviate from the distance-controlling coefficient,
Figure 850760DEST_PATH_IMAGE018
in order to control the coefficient of the fly height,
Figure DEST_PATH_IMAGE019
controlling coefficients for threat indicators;
s3, calculating the deviation of the approach point of the high-speed aircraft according to the emission initial state error vector and the state deviation;
s4, calculating flight pipeline parameters, correcting according to the deviation of the approach point to eliminate transverse deviation, and generating flight pipeline parameters of the flight path after the correction point according to the corrected flight path; restarting the deviation calculation process of the flight pipeline;
the pipeline parameter calculation mode is as follows:
Figure 453911DEST_PATH_IMAGE020
Figure DEST_PATH_IMAGE021
Figure 83607DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
、
Figure 695985DEST_PATH_IMAGE024
and
Figure DEST_PATH_IMAGE025
are respectively unit vectors
Figure 360315DEST_PATH_IMAGE026
The three components of (a) and (b),
Figure DEST_PATH_IMAGE027
、
Figure 665526DEST_PATH_IMAGE028
and
Figure DEST_PATH_IMAGE029
are respectively unit vectors
Figure 516938DEST_PATH_IMAGE030
The three components of (a) and (b),
Figure 854379DEST_PATH_IMAGE026
and
Figure DEST_PATH_IMAGE031
on a circular plane corresponding to the unit normal vector n, n being the number of 2 adjacent course points
Figure 373216DEST_PATH_IMAGE032
And
Figure DEST_PATH_IMAGE033
subtracting the three-dimensional rectangular coordinates to obtain a vector
Figure 849328DEST_PATH_IMAGE034
After unitization, the unit normal vector of each track point on the theoretical track trend is approximately used;
Figure DEST_PATH_IMAGE035
is the unit coordinate of the assumed track point C;
Figure 453615DEST_PATH_IMAGE036
is the radius of the circle, is also the lateral deviation of the passing point,
Figure DEST_PATH_IMAGE037
is the polar coordinate angle of the circle, and takes the vector n as the normal vector;
s5, optimizing the flight pipeline by using a particle swarm algorithm, and specifically comprising the following steps:
s51, initializing a particle swarm, randomly generating an initialized particle swarm in the coefficient solution space, wherein the position coordinates of the particles in the swarm are potential solutions;
s52, selecting a fitness function of the flight pipeline plan, and calculating the fitness function of each particle;
s53, comparing fitness functions of different particles, and determining the best position searched by each particle and the best position searched by the whole particle swarm currently;
s54, adjusting the speed and the position of the particles according to the best position searched by each particle obtained in S53 and the best position searched by the whole particle swarm currently;
s55, judging whether the fitness function reaches the optimum or meets the condition of terminating iteration, if so, ending the method, otherwise, jumping to S52;
s6, calculating the threat avoidance cost index of the high-speed aircraftJWhen is coming into contact withJAnd obtaining the optimal flight pipeline under the comprehensive quantitative threat scene when the minimum value is obtained.
2. The method for planning a flight duct of a high-speed aircraft according to claim 1, wherein S13 specifically utilizes hierarchical analysisMethod for determining weighting factors for different types of objectsm i 。
3. The high-speed aircraft flight pipeline planning method according to claim 2, wherein the feature set comprises feature parameters for judging the threat level of a target; and the grade judging function is used for forming a target threat grade judging matrix.
4. A high speed aircraft flight duct planning method according to claim 2, wherein the weighting factors for the different types of target threat levels are determined by an analytic hierarchy process.
5. The method for planning a flight duct of a high-speed aircraft according to claim 1, wherein S3 specifically is:
s31, acquiring a high-speed aircraft launching initial state error vector according to the nominal launching coordinate system and the actual launching coordinate system;
s32, calculating the state deviation of the high-speed aircraft launching initial state error at a shutdown point;
s33, calculating the deviation of the approach point of the high-speed aircraft; and calculating to obtain the deviation of the approach point according to the state quantity and the state deviation of the emission coordinate system of the shutdown point.
6. The method for planning the flight pipeline of the high-speed aircraft according to claim 5, wherein S32 specifically comprises: and multiplying the sum of the rotation item deviation propagation matrix, the initial value item deviation propagation matrix and the translation item deviation propagation matrix caused by the emission initial state error vector obtained in the step S31 to obtain the state deviation of the emission initial state error at the shutdown point.
7. The method for planning a flight pipeline of a high-speed aircraft according to claim 6, wherein in S33, the path point deviation comprises a path point longitudinal deviation and a path point lateral deviation.
8. The method for planning a flight path of a high-speed aircraft according to claim 1, wherein in S52, the voyage is selected as a fitness function.
9. The method for planning a flight duct of a high-speed aircraft according to claim 1, characterized in that, considering the requirement of avoiding the threat, when the track point falls within the threat area, as a penalty, the fitness function is set to infinity; considering the minimum turn radius constraint, if the minimum turn radius in the maneuverability of the aircraft is greater than the radius of curvature of the track curve, the fitness function is set to infinity.
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