CN114236543B - Method for designing bistatic forward-looking SAR (synthetic aperture radar) track of motorized platform - Google Patents
Method for designing bistatic forward-looking SAR (synthetic aperture radar) track of motorized platform Download PDFInfo
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Abstract
The invention discloses a method for designing a bistatic foresight SAR track of a maneuvering platform, which utilizes GEO-SAR as an irradiation source and a high-speed maneuvering platform as a receiving station to realize foresight imaging, comprehensively considers the flight performance and the imaging performance of the platform, models the actual situation, establishes a proper optimization function and a constraint condition, models the track design problem into a multi-constraint binocular optimization problem, discretizes state variables and control variables, and solves the optimization problem by adopting a multi-objective differential evolution algorithm, thereby realizing the bistatic foresight SAR track design of the high-speed maneuvering platform, calculating a flight path which meets the imaging indexes and can accurately reach a target landing point, and realizing the foresight imaging performance optimization of the high-speed maneuvering platform.
Description
Technical Field
The invention belongs to the technical field of radars, and relates to a method for designing a bistatic forward-looking SAR track of a maneuvering platform based on GEO-SAR satellite irradiation.
Background
Synthetic Aperture Radar (SAR) is a full-time and all-weather high-resolution imaging system, and is characterized in that a large-time wide and large-bandwidth Linear Frequency Modulation (LFM) signal is transmitted, the distance direction of an echo signal is subjected to matched filtering to obtain a pulse compression signal, so that the distance direction high resolution is obtained, the azimuth direction high resolution is realized by utilizing a synthetic aperture technology, the imaging quality is not influenced by weather conditions (cloud layers, illumination) and the like, and the SAR has the characteristics of detecting and positioning a remote target. Typical application fields of SAR include disaster monitoring, resource exploration, geological mapping, and the like.
Compared with the single-base SAR, the double-base SAR has the following advantages: 1. the concealment is good, the bistatic SAR receiving and transmitting are separately arranged, and the receiving platform does not transmit signals, so that the bistatic SAR receiving and transmitting are not easy to detect; 2. the target can be detected from different angles, and the traditional single-base radar can only acquire the backscattering characteristic of the target. And the target can be detected in multiple angles such as front view, side view, rear view and the like by using the double-base structure. Especially, the detection capability of the stealth target is greatly enhanced; 3. the system is flexible, and the receiving and transmitting platforms can be configured at will according to the needs and are independent.
The geosynchronous orbit synthetic aperture radar (GEO-SAR) is a geosynchronous orbit synthetic aperture radar satellite which runs on a geosynchronous orbit with a certain inclination angle, and the running period is the same as the autorotation period of the earth. The method has larger mapping bandwidth and shorter revisit period, so that the method can be widely applied to disaster monitoring and imaging of the earth structure. The bistatic GEO-SAR can also conveniently and efficiently improve the imaging performance by adjusting the flight parameters of the receiver.
Recent studies on bistatic SAR are increasing in temperature, with good progress being made in imaging algorithms, synchronization methods and experiments. Documents Meng Zijiang, li Yachao, wang Zongfu, wu Chunfeng, xing Mengdao, ensuring the shining.A missile-borne double-base forward-looking SAR downward-diving segment trajectory design method [ J ]. Systematic engineering and electronic technology, 2015,37 (04): 768-774' proposes a missile-borne double-base forward-looking SAR motion trajectory design method, however, the method only considers ballistic design on a transmitter, and simultaneously, the analysis of the imaging performance by the design method mainly considers the distance direction resolution, and other indexes such as signal-to-noise ratio and included angle of resolution are not analyzed; the literature "Resolution calculation and Resolution in stationary SAR with spatial Resolution illuminator," IEEE geosci. Removal sens.lett., vol.10, no.1, pp.194-198, jan 2013 "analyzes spatial Resolution in consideration of ellipsoid surface and large equivalent angle, but the analysis method of spatial Resolution and the characteristics of spatial Resolution cannot be directly applied to GEO bistatic SAR with non-zero tilt angle.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for designing a bistatic forward-looking SAR track of a motorized platform.
The technical scheme of the invention is as follows: a method for designing a bistatic forward-looking SAR track of a motorized platform specifically comprises the following steps:
step S1, modeling a flight track,
in order to more conveniently plan the flight path of the high-speed motion platform, the flight path is divided into N sections according to time, the time of each section is t, and the acceleration of the high-speed motion platform is recorded in a ground coordinate system
The acceleration of the high-speed motion platform along the three directions is constant in the same flight path, and the total flight path can be obtained by designing the acceleration of each flight path and integrating the acceleration.
S2, establishing an optimization function,
and according to the task target, comprehensively selecting four track performance indexes of control energy, flight time, resolution unit area and non-imaging time to establish an optimization function.
(1) Controlling the energy: due to the volume limit of the flight platform, the fuel carried by the flight platform is limited, and therefore, the control energy of the flight trajectory is an important index for describing the trajectory performance.
The optimization function for the control energy is established as:
where i represents the segment number of the flight trajectory, for example: and when i =1, the flight path of the 1 st segment is represented. The control energy is the sum of the squares of the accelerations in three directions in each flight path, and the optimization aims to minimize the control energy of the flight path.
(2) Time of flight: since the longer the flight time, the longer the platform is exposed, increasing the platform risk, the optimization function that controls the flight time is established as:
f 2 ＝kNt (2)
and k is a weight coefficient and is used for ensuring that the flight time and the optimization function of the control energy are in the same order, and the optimization aim is to minimize the flight time of the platform.
(3) Area of a resolution unit: the high-speed motion platform is provided with a synthetic aperture radar receiver, passively receives signals of the GEO-SAR satellite, and requires bistatic SAR imaging on a target area according to task requirements. And continuously equally dividing each section of the flying track which is equally divided according to time into M sections according to time, and calculating the imaging resolution performance of each subsection of the flying track.
The optimization function of the area of the resolution cell is noted as:
wherein,
the smaller the area of the resolution unit of each subsection is, the better the radar imaging performance is, so that the target rho can be accurately identified gr For distance resolution, p az For azimuthal resolution, α is the resolving directional angle.
Wherein the distance resolution ρ gr Comprises the following steps:
where c is the speed of light, B r Is the signal bandwidth, H ⊥ The ground projection matrix can be expressed as:
wherein I is the identity matrix, P G Is the imaging area coordinateThe normal unit vector of the system is,
is P G The transposing of (1).
u TA (t 0 ) Is at t 0 Unit vector of time of day target to transmitting station, u RA (t 0 ) Is at t 0 A unit vector from a time target to a receiving station;
azimuthal resolution ρ az Expressed as:
wherein λ is the carrier wavelength, T a For synthesizing the aperture time, omega TA (t) is the angular velocity of the transmitting station, ω RA (t) is the angular velocity of the receiving station.
The resolving direction angle α can be expressed as:
α＝cos -1 (Ξ·Θ) (7)
wherein Θ denotes a unit vector in the distance resolution direction, and xi denotes a unit vector in the azimuth resolution direction.
(4) Imaging failure time: the imaging performance of the tail flight section is rapidly deteriorated due to the fact that the tail section of the flight track of the high-speed platform needs to aim at the target direction to fly, imaging cannot be performed, and the time when the flight track cannot be imaged is set as t un_image Therefore, the optimization function that controls the time of inability to image is written as:
f 4 ＝t un_image (10)
it is desirable to minimize the end-segment non-imaging time to optimize full-segment imaging performance.
To sum up, four objective functions of control energy, flight time, resolution unit area and non-imaging time are considered, modeling is a dual-objective optimization function, aiming at the kinetic energy and energy limitation of a flight platform, the minimum control energy is considered, and the shortest flight time is used for establishing an optimization function I:
aiming at the imaging performance of the whole flight path of the flight platform, an optimization function II is established by considering the minimum area of a resolution unit and the shortest imaging incapable time:
F 2 ＝f 3 +f 4 (12)
and comprehensively considering the two optimization functions to design the flight path.
Step S3, determining a constraint condition,
after the optimization function is established, constraint conditions are determined according to task requirements. The main considerations are: the terminal position of the flight platform, the maneuverability of the flight platform and the sight angle.
(1) And (3) position constraint of a flight platform terminal: according to the task requirement, after flying with a certain track, the flying platform is ensured to land in a target area, and in order to make the flying platform fall at a specified position, a constraint condition is established:
||[R dx (Nt)-R dx ,R dy (Nt)-R dy ,R dz (Nt)-R dz ]||＝0 (13)
wherein a position vector [ R ] of the flight platform at time t is specified dx (t),R dy (t),R dz (t)]，[R dx (Nt),R dy (Nt),R dz (Nt)]The vector represents the position vector of the flight platform at the landing time Nt, and | | represents 2 norm calculation, namely the vector length. The target position vector is [ R ] tx ,R ty ,R tz ]Then finally may be represented by | | [ R | ] dx (Nt)-R dx ,R dy (Nt)-R dy ,R dz (Nt)-R dz ]I judges whether the aircraft meets the terminal constraint before landing, namely: when aboutWhen the beam condition (13) is established, the landing position of the flight platform coincides with the target position.
(2) The maneuverability of the flight platform is restrained: given the strength of the flying platform, the shape of the fuel carried thereby, and the air resistance of the flying platform during flight, the acceleration that the flying platform can provide is limited, and excessive acceleration can affect the stability of the flying platform, and can severely cause stalling or disassembly.
For acceleration in three directions of x, y and z (a) x ,a y ,a z ) Constraint is performed as shown in equation (14).
Wherein,
a x_max ,a y_max ,a z_max the maximum values of the acceleration of the flight platform along the x direction, the y direction and the z direction are respectively.
(3) And (3) line-of-sight angle constraint: because the flight platform needs to image the target area to find the task indication point to land in the flight process, when the visual angle of the aircraft exceeds the field of view, the target point cannot be observed by the flight platform, and the designated position of the target point of the flight platform is influenced.
The line-of-sight angle constraint is set as:
|σ(t)|≤σ max (1)
wherein σ (t) represents the line-of-sight angle of the flight platform at time t, σ max The maximum beam pointing angle of the flying platform radar antenna.
In summary, the established optimization function and constraint conditions are shown as follows:
and S4, searching the optimal path of the flight platform by using a multi-constraint differential evolution algorithm (DE).
The concrete description is as follows:
the differential evolution algorithm firstly generates a random initial population, generates a new individual by summing the vector difference of any two individuals in the population and a third individual, then compares the new individual with the corresponding individual in the current population, replaces the old individual with the new individual in the next generation if the fitness of the new individual is better than that of the current individual, and otherwise still stores the old individual. Through continuous evolution, excellent individuals are reserved, inferior individuals are eliminated, and search is guided to approach to the optimal solution.
The main control parameters of the DE algorithm include: population size (NP), scaling factor (F) and crossover probability (CR). NP mainly reflects the size of population information, cross probability (CR) represents the degree of information exchange between each generation of variants, and a scaling factor (F) is a factor which has the greatest influence on the performance of the algorithm and mainly influences the global optimizing capability of the algorithm.
The differential evolution algorithm comprises the following specific steps:
and S41, selecting a proper population size (NP), a maximum iteration number (GM), a cross probability (CR) and a scaling factor (F) according to different scenes.
Step S42, setting X i,G ＝(x 1,i,G ,x 2,i,G ,x 3,i,G ,…,x D,i,G ) Representing the ith individual in the G generation, D being the dimension of the optimization problem, x 1,i,G ,x 2,i,G ,x 3,i,G ,…,x D,i,G A value, x, representing each search variable of the individual j,i,0 To be at a specified maximum value x j,max And the minimum value x j,min Is randomly generated within a range of (a), wherein,
as shown in formulas (17-18)
Randomly generating an initial population:
x j,i,0 ＝x j,min +rand ij [0,1]×(x j,max -x j,min ) (18)
wherein, rand ij [0,1]Representing uniformly distributed random numbers between 0 and 1.
S43, carrying out mutation by the formula (19) to form a new intermediate individual,
where F is a scaling factor for the scaling,
is three mutually different vectors randomly selected from a population.
S44, after the intermediate individual vector is generated, a track vector u is generated through crossing i,G ＝[u 1,i,G ,u 2,i,G ,…,u D,i,G ]The crossover operation increases the diversity of individuals, and in the most commonly used binomial crossover operators, it will interact with the current individual x as long as the randomly generated number between 0 and 1 is less than or equal to the value Cr i,G Exchange, as shown in equation (20):
wherein j is rand ∈[1,2,…,D]Is an index randomly selected to ensure that at least u i,G Is a component from v i,G Is selected from (1).
And S45, finally, calculating an optimization function and a constraint condition of the offspring population obtained after variation and intersection, and selecting each group of parent individuals and offspring individuals according to a feasibility criterion:
1) If the parent individuals and the child individuals are both non-feasible solutions, selecting the individuals with smaller constraint violation amount;
2) If one of the parent individual and the child individual is a feasible solution and the other is an infeasible solution, selecting the feasible solution
3) If parent individuals and childrenIf all generations are feasible solutions, selecting summation F of objective function 1 +F 2 And selecting each group of parent individuals and descendant individuals as next generation individuals to obtain a next generation population, terminating the evolution when a termination condition is met or the evolution algebra reaches the maximum, and outputting the obtained optimal individual as an optimal solution to obtain an optimal path.
The invention has the beneficial effects that: according to the method, the GEO-SAR is used as an irradiation source, the high-speed maneuvering platform is used as a receiving station to realize forward-looking imaging, the flight performance and the imaging performance of the platform are comprehensively considered, modeling is carried out on the actual situation, a proper optimization function and constraint conditions are determined, a track design problem is modeled into a multi-constraint dual-target optimization problem, then state variables and control variables are discretized, and the optimization problem is solved by adopting a multi-target differential evolution algorithm, so that the dual-base forward-looking SAR track design of the high-speed maneuvering platform is realized, a flight path which meets imaging indexes and can accurately reach a target landing point can be calculated, and forward-looking imaging performance optimization of the high-speed maneuvering platform is realized.
Drawings
FIG. 1 is a schematic geometric diagram of an embodiment of the present invention.
Fig. 2 is a flow chart diagram of a method provided by an embodiment of the invention.
FIG. 3 is a schematic diagram of a double-base forward-looking SAR three-dimensional flight trajectory generated by the embodiment of the invention.
Fig. 4 is a schematic flight trajectory diagram of a bistatic forward-looking SAR XY plane generated by an embodiment of the invention.
Fig. 5 is a schematic flight path diagram of a bistatic forward-looking SAR ZY plane of a high-speed maneuvering platform generated by the embodiment of the invention.
Fig. 6 is a schematic flight path diagram of a double-base forward-looking SAR ZX plane of a high-speed maneuvering platform generated by the embodiment of the invention.
FIG. 7 is a distance resolution diagram of the optimal flight trajectory at the end of the high-speed aircraft according to the embodiment of the invention.
FIG. 8 is a schematic view of the azimuth resolution of the optimal flight trajectory of the end segment of the high-speed aircraft according to the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments.
The geometric structure diagram of the embodiment of the invention is shown in fig. 1, the GEO-SAR system parameters adopted by the specific implementation mode are shown in table 1, a synchronous orbit satellite 3 ten thousand kilometers away from the earth surface is selected as the satellite, the satellite is considered to be stationary relative to a target in the embodiment, and the satellite incidence angle is the included angle between a star-eye line and a Z axis)
TABLE 1
The specific process is shown in fig. 2, and comprises the following steps:
step S1: the flight path is divided into N sections according to time, as shown in FIG. 2, the time of each section is t, and the acceleration of the high-speed motion platform is marked as [ a ] in the ground coordinate system x ,a y ,a z ]And the acceleration in three directions of an x axis, a y axis and a z axis is represented respectively.
Step S2: and according to the task target, comprehensively selecting four factors of minimum control energy, shortest flight time, smallest area of a resolution unit and shortest non-imaging time to establish an optimization function.
And step S3: after the optimization function is established, constraint conditions are determined according to task requirements. The main consideration is as follows: the terminal position of the flight platform, the maneuverability of the flight platform and the sight angle.
And step S4: and (3) finding the optimal path of the flight platform by using a differential evolution algorithm (DE). The method comprises the following specific steps:
and S41, setting a proper population size (NP), a scaling factor (F) and a cross probability (CR).
Step S42, setting X i,G ＝(x 1,i,G ,x 2,i,G ,x 3,i,G ,…,x D,i,G ) Representing the ith individual in the G generation, D being the dimension of the optimization problem, x j,i,0 To be at a specified maximum value x j,max And the minimum value x j,min Is randomly generated within a range of (1), wherein
As shown in formulas (23-24)
Randomly generating an initial population:
x j,i,0 ＝x j,min +rand ij [0,1]×(x j,max -x j,min ) (24)
wherein, rand ij [0,1]Representing uniformly distributed random numbers between 0 and 1.
Step S43: carrying out mutation by the formula (25) to form a new intermediate,
wherein F is a scaling factor for the scaling,
is three mutually different vectors randomly selected from a population.
Step S44. Generating a track vector u by crossing i,G ＝[u 1,i,G ,u 2,i,G ,…,u D,i,G ]If the randomly generated number between 0 and 1 is less than or equal to the value Cr, x is compared with the current individual i,G Exchange, as shown in equation (26):
wherein j is rand ∈[1,2,…,D]Is an index randomly selected to ensure that at least u i,G Is a component from v i,G Is selected from (1).
S45, selecting each group of parent and child individuals according to feasibility criteria by using the optimization functions and constraint conditions of the child populations obtained after mutation and intersection:
1) If the parent and the child individuals are both non-feasible solutions, selecting an individual with a smaller constraint violation amount;
2) If one of the parent and the child is a feasible solution and the other is an infeasible solution, selecting the feasible solution;
3) If the parent and the child are both feasible solutions, selecting the summation F of the objective function 1 +F 2 And selecting each group of parent individuals and child individuals to obtain a next generation population.
And when the termination condition is reached or the evolution algebra reaches the maximum, terminating the evolution, and outputting the obtained optimal individual as the optimal solution to obtain the optimal path.
The optimal path is shown in FIG. 3, and in order to further show the details of the optimal path, cross-sectional views of the X-Y, Y-Z, X-Z plane of the optimal path are respectively drawn, as shown in FIGS. 4, 5 and 6; fig. 7 and 8 are distance-direction resolution curves and azimuth-direction resolution curves under the optimal path, respectively, and it can be seen that the imaging performance under the path meets the task index.
According to the specific implementation mode of the invention, the planning of the double-base foresight SAR flight trajectory of the high-speed maneuvering platform can be realized, the optimization function and the constraint condition are established by modeling the flight trajectory, the problem is modeled into a multi-constraint double-target optimization problem, the state variable and the control variable are discretized by using a discretization method, and finally, the optimal solution is obtained by using a differential evolution algorithm. The method solves the problem of planning the flight path of the bistatic forward-looking SAR of the high-speed maneuvering platform, can be used for designing the flight path of the high-speed maneuvering platform, and can be used in the fields of earth remote sensing, autonomous landing, autonomous navigation and the like.
Claims (2)
1. A method for designing a bistatic forward-looking SAR track of a motorized platform specifically comprises the following steps:
step S1, modeling a flight track,
the flight path is divided into N sections according to time, the time of each section is t, and the acceleration of the high-speed motion platform is recorded in a ground coordinate system
Respectively representing the acceleration of the x axis, the y axis and the z axis;
s2, establishing an optimization function,
according to a task target, selecting four track performance indexes of control energy, flight time, distinguishing unit area and non-imaging time to establish an optimization function:
(1) Controlling the energy: the optimization function for the control energy is established as:
wherein i represents the sequence number of the flight path, and the control energy is the sum of the squares of the accelerations in three directions in each flight path;
(2) Time of flight: the optimization function that controls the time of flight is established as:
f 2 ＝kNt
wherein k is a weight coefficient used for ensuring that the flight time and the optimization function of the control energy are in the same order of magnitude;
(3) Area of a resolution unit: continuously equally dividing each section of the flying track equally divided according to time into M sections according to time, calculating the imaging resolution performance of the flying track of each subsection,
the optimization function of the area of the resolution cell is noted as:
wherein，
For the area of the resolution cell, p, of each sub-section gr For distance resolution, ρ az Alpha is a resolution direction included angle;
distance resolution ρ gr Comprises the following steps:
where c is the speed of light, B r Is the signal bandwidth, H ⊥ It is the ground projection matrix that can be expressed as:
wherein I is the identity matrix, P G Is the normal unit vector of the imaging area coordinate system,
is P G Transposing;
u TA (t 0 ) Is at t 0 Unit vector of time of day target to transmitting station, u RA (t 0 ) Is at t 0 A unit vector from a time target to a receiving station;
azimuthal resolution ρ az Expressed as:
wherein λ is the carrier wavelength, T a For synthesizing the aperture time, omega TA (t) is the angular velocity of the transmitting station, ω RA (t) is the angular velocity of the receiving station;
the resolving direction angle α is expressed as:
α＝cos -1 (Ξ·Θ)
wherein Θ denotes a unit vector in the distance resolution direction, and xi denotes a unit vector in the azimuth resolution direction,
(4) Imaging failure time: setting the time when the flight path cannot be imaged as t un_image The optimization function for controlling the time of the non-imaging is recorded as:
f 4 ＝t un_image
four objective functions of control energy, flight time, area of a resolution unit and non-imaging time are considered, modeling is carried out to obtain a dual-objective optimization function, the kinetic energy and the energy of the flight platform are limited, meanwhile, the control energy is minimum, and the flight time is shortest to establish an optimization function I:
aiming at the imaging performance of the whole flight path of the flight platform, an optimization function II is established by considering the minimum area of a resolution unit and the shortest imaging incapable time:
F 2 ＝f 3 +f 4
s3, determining a constraint condition,
determining three constraint conditions of a terminal position, maneuverability and a line-of-sight angle of a flight platform;
(1) And (3) flight platform terminal position constraint: in order to bring the flight platform to a specified position, the constraint conditions are established:
||[R dx (Nt)-R dx ,R dy (Nt)-R dy ,R dz (Nt)-R dz ]||＝0
wherein the position vector of the specified flight platform at the time t is [ R ] dx (t),R dy (t),R dz (t)]，[R dx (Nt),R dy (Nt),R dz (Nt)]Representing the position vector of the flight platform at the landing time Nt, | | | | represents 2 norm operation, namely the length of the vector is taken, and the target position vector is [ R |) tx ,R ty ,R tz ]From | | [ R | ] dx (Nt)-R dx ,R dy (Nt)-R dy ,R dz (Nt)-R dz ]I, judging whether the aircraft meets terminal constraint before landing;
(2) The maneuverability of the flight platform is restrained: for acceleration in three directions of x, y and z (a) x ,a y ,a z ) And (4) carrying out constraint:
wherein,
a x_max ,a y_max ,a z_max the maximum values of the acceleration of the flight platform along the x direction, the y direction and the z direction are respectively;
(3) And (3) visual angle constraint: the line-of-sight angle constraint is set as: the [ sigma (t) | is less than or equal to the [ sigma ] max Where σ (t) represents the line of sight angle of the flight platform at time t, σ max The maximum beam pointing angle of the flying platform radar antenna;
the established optimization function and constraint conditions are shown as follows:
s.t.
||[R dx (Nt)-R dx ,R dy (Nt)-R dy ,R dz (Nt)-R dz ]||＝0
|σ(t)|≤σ max
and S4, searching the optimal path of the flight platform by using a multi-constraint differential evolution algorithm.
2. The method for designing the bistatic forward-looking SAR trajectory of the motorized platform according to claim 1, wherein the specific steps of the step S4 are as follows:
s41, selecting a population size (NP), a maximum iteration number (GM), a cross probability (CR) and a scaling factor (F) according to different scenes;
step S42, setting X i,G ＝(x 1,i,G ,x 2,i,G ,x 3,i,G ,…,x D,i,G ) Representing the ith individual in the G generation, D being the dimension of the optimization problem, x 1,i,G ,x 2,i,G ,x 3,i,G ,…,x D,i,G A value, x, representing each search variable of the individual j,i,0 Is to be at a prescribed maximum value x j,max And the minimum value x j,min Is randomly generated within a range of (a), wherein,
randomly generating an initial population:
x j,i,0 ＝x j,min +rand ij [0,1]×(x j,max -x j,min )
wherein, rand ij [0,1]Random numbers representing a uniform distribution between 0 and 1;
step S43. Passing
Performing mutation to form new intermediate individuals, wherein F is a scaling factor for scaling,
three mutually different vectors are randomly selected from the population;
step S44. Generating a track vector u by crossing i,G ＝[u 1,i,G ,u 2,i,G ,…,u D,i,G ]As long as the randomly generated number between 0 and 1 is less than or equal to the value Cr, it will be compared with the current oneBody x i,G Exchanging:
wherein j is rand ∈[1,2,…,D]Is an index randomly selected to ensure that at least u i,G Is a component from v i,G Is selected from;
s45, respectively calculating an optimization function and a constraint condition of the filial generation population obtained after variation and intersection, and selecting each group of parent generation and filial generation individuals according to a feasibility criterion:
if the parent and the child are both non-feasible solutions, selecting an individual with a smaller constraint violation amount;
if one of the parent and the child is a feasible solution and the other is an infeasible solution, selecting the feasible solution;
if the parent and the child are both feasible solutions, selecting the summation F of the objective function 1 +F 2 And selecting each group of parent individuals and offspring individuals as next generation individuals to obtain a next generation population, terminating evolution when a termination condition is met or an evolution algebra reaches the maximum, and outputting the obtained best individual as an optimal solution to obtain an optimal path.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7589662B1 (en) * | 2007-06-20 | 2009-09-15 | The United States Of America As Represented By The Secretary Of The Navy | Synthetic aperture radar systems and methods |
| CN102830390A (en) * | 2012-08-24 | 2012-12-19 | 电子科技大学 | Bistatic SAR (Synthetic Aperture Radar) echo simulation method |
| CN103869318A (en) * | 2014-04-08 | 2014-06-18 | 电子科技大学 | Double-base foresight synthetic aperture radar moving-target speed error function construction method |
| CN104865573A (en) * | 2015-06-12 | 2015-08-26 | 西安电子科技大学 | Method for optimizing SAR (Specific Absorption Rate) extended scene imaging on double-base forward-looking high-mobility platform |
| CN108562902A (en) * | 2018-06-15 | 2018-09-21 | 西安电子科技大学 | Height rail double-base SAR configuration designing method based on simulated annealing |
| CN109358328A (en) * | 2018-11-06 | 2019-02-19 | 电子科技大学 | The polar coordinates format image-forming method of the bistatic Forward-looking SAR of motor platform |
| CN110187345A (en) * | 2019-06-14 | 2019-08-30 | 西安电子科技大学 | Biradical Forward-looking SAR transmitter flight path design method based on gradient theory |
| CN111398960A (en) * | 2020-04-16 | 2020-07-10 | 北京理工大学重庆创新中心 | GEO satellite-borne SAR bistatic configuration design method based on moving target detection |
| CN113671492A (en) * | 2021-07-29 | 2021-11-19 | 西安电子科技大学 | SAMP reconstruction method for forward-looking imaging of maneuvering platform |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7701380B2 (en) * | 2007-03-07 | 2010-04-20 | Chirp Corporation | Beam phase modulation for improved synthetic aperture detection and estimation |
-
2021
- 2021-12-16 CN CN202111541173.0A patent/CN114236543B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7589662B1 (en) * | 2007-06-20 | 2009-09-15 | The United States Of America As Represented By The Secretary Of The Navy | Synthetic aperture radar systems and methods |
| CN102830390A (en) * | 2012-08-24 | 2012-12-19 | 电子科技大学 | Bistatic SAR (Synthetic Aperture Radar) echo simulation method |
| CN103869318A (en) * | 2014-04-08 | 2014-06-18 | 电子科技大学 | Double-base foresight synthetic aperture radar moving-target speed error function construction method |
| CN104865573A (en) * | 2015-06-12 | 2015-08-26 | 西安电子科技大学 | Method for optimizing SAR (Specific Absorption Rate) extended scene imaging on double-base forward-looking high-mobility platform |
| CN108562902A (en) * | 2018-06-15 | 2018-09-21 | 西安电子科技大学 | Height rail double-base SAR configuration designing method based on simulated annealing |
| CN109358328A (en) * | 2018-11-06 | 2019-02-19 | 电子科技大学 | The polar coordinates format image-forming method of the bistatic Forward-looking SAR of motor platform |
| CN110187345A (en) * | 2019-06-14 | 2019-08-30 | 西安电子科技大学 | Biradical Forward-looking SAR transmitter flight path design method based on gradient theory |
| CN111398960A (en) * | 2020-04-16 | 2020-07-10 | 北京理工大学重庆创新中心 | GEO satellite-borne SAR bistatic configuration design method based on moving target detection |
| CN113671492A (en) * | 2021-07-29 | 2021-11-19 | 西安电子科技大学 | SAMP reconstruction method for forward-looking imaging of maneuvering platform |
Non-Patent Citations (2)
| Title |
|---|
| Wang J 等.Resolution calculation and analysis in bistatic SAR with geostationary illuminator.2013,第第10卷卷(第第10卷期),第194-198页. * |
| 孟自强 等.弹载双基前视SAR俯冲段弹道设计方法.2015,第第37卷卷(第第37卷期),第768-774页. * |
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