CN113391307A - Method and device for quickly estimating missile terminal motion parameters in incomplete signals - Google Patents

Method and device for quickly estimating missile terminal motion parameters in incomplete signals Download PDF

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CN113391307A
CN113391307A CN202010184315.1A CN202010184315A CN113391307A CN 113391307 A CN113391307 A CN 113391307A CN 202010184315 A CN202010184315 A CN 202010184315A CN 113391307 A CN113391307 A CN 113391307A
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杨维忠
尹光
朱成林
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Abstract

The invention discloses a method and a device for quickly estimating the terminal motion parameters of a missile in an incomplete signal, wherein a constructed first objective function is solved through an improved genetic algorithm to obtain the optimal solution of the motion speed of the missile, the acting distance of measuring equipment and the nearest distance between the missile and the measuring equipment; and solving the constructed second objective function through another improved genetic algorithm according to the missile movement speed, the acting distance of the measuring equipment, the optimal solution of the nearest distance between the missile and the measuring equipment and the constructed nonlinear constraint function to obtain the optimal solution of the azimuth angle, the pitch angle and the coordinate position of the nearest point of the measuring equipment of the missile. Parameters in incomplete data in the guided weapon experiment are accurately estimated.

Description

Method and device for quickly estimating missile terminal motion parameters in incomplete signals
Technical Field
The invention relates to the technical field of radar data processing, in particular to a method and a device for quickly estimating missile terminal motion parameters in an incomplete signal.
Background
After the world war II is finished, local wars around the world are uninterrupted, and the development of modern weaponry is still an important task in the current stage of China no matter whether the world is maintained peacefully or the influence of China on the world is improved. The weapon technology is rapidly developed along with the development of society, the improvement of technological level and the requirement of modern war, and especially the rapid development of modern electronic technology greatly improves the equipment level of modern weapons. In modern weaponry, precise guidance of weapons plays an important role. For example, in the iraq war, the admiralty has a clear breakthrough in the ability to strike precisely on the army as compared to the gulf war due to the extensive use of precisely guided weapons. The accurate guided weapon has gradually become the leading role of the present war, so the development of the accurate guided weapon has important significance for improving national defense strength in China. In recent years, the research and development work of the accurate guided weapons in China is greatly advanced, and various weapons suitable for different combat occasions are continuously promoted, so that the research and development tasks of the high-level measurement technology for rapidly, accurately and comprehensively identifying the performance of the accurate guided weapon system are more and more urgent.
The performance of the guided munition is generally reflected in a shot and target encountering section in a centralized manner, so that in a target range test, the motion parameters of the guided munition in the encountering section, namely the motion parameters at the tail end of the guided munition, are measured as important parameters for measuring the efficiency of the guided munition, are one of the most important tactical indexes of the guided munition, and have important significance for checking the precision of the guided munition, evaluating the performance of the guided munition, perfecting the design and improving the performance. The missile terminal motion parameter measurement plays the following roles in the development of guided weapons:
1) the method is a technical means for checking and accepting the guided weapon. Acquiring actual hit precision of the guided weapon through a target practice test, and evaluating the performance of the guided weapon;
2) is the basis and reference for checking, adjusting and improving the guided munition system. On the basis of obtaining the hit precision of the guided weapon, the problems in the aspects of software and hardware influencing the precision and the performance are found out, and corresponding adjustment and improvement measures are taken;
3) and a precision basis is provided for evaluating the mission undertaking capacity of the guided weapon and eliminating and updating the weapon system. Under the overall test planning, the test results of the previous times are accumulated, comprehensive analysis is carried out, and the precision and the bearing capacity of the guided weapon are evaluated.
Therefore, the measurement of the motion parameters of the tail end of the missile plays a key role in the development of guided weapons, the development of the measurement technology of the motion parameters of the tail end of the missile is a requirement of national defense modernization construction, and the method has important significance in improving the level of the weapon system of the army.
The missile terminal motion parameters mainly comprise the motion speed of the missile, the closest distance (namely miss distance) of the missile to a target when the missile targets are intersected, the coordinate position of the closest point of the missile to the target, the azimuth angle of a trajectory and the pitch angle (the parameters are vector miss distance parameters). Through the parameters, the whole movement process of the missile in the missile target convergence process can be described, and the missile target convergence process is shown in the left diagram in fig. 1. In the motion parameter solving process, in order to obtain a good motion result, the missile is generally required to pass through complete data near the target to obtain an accurate parameter estimation value, and a doppler value extracted from the complete experimental data is shown in a right graph in fig. 1.
In a simulation experiment, signal doppler characteristics extracted from complete test echo signal data are given in the right diagram in fig. 1, and the nearest distance (i.e., miss distance) from a target to a missile when the missiles meet the target, the action distance of a measuring device and the movement speed of the missiles can be obtained by establishing parameter fitting on the signal doppler characteristics. And calculating phase difference information among the receiving channels according to signals obtained by the plurality of receiving channels, and establishing a parameter fitting model to obtain the coordinate position of the closest point, the azimuth angle and the pitch angle of the trajectory.
However, in the live-action experiment process, because the missile usually has a certain killing radius, the on-missile fuze can detonate the warhead according to the distance between the target and the missile body, and therefore, the measuring equipment cannot obtain a complete attack section signal. Considering the position of the installation of the measuring equipment and the possibility of multi-angle attack of the missile, only half of the data or even less than one fourth of the data can be obtained sometimes. As shown in some experimental data in fig. 2, only less than half of the available information can be extracted from the echo signal due to the early detonation of the projectile. Limited observation data can cause the extraction precision of the missile terminal motion parameters to be reduced sharply, and even completely infeasible results can be obtained, especially in the process of extracting the ballistic azimuth angle and the ballistic pitch angle, due to the complexity of a parameter model and the limitation of data, the parameter estimation precision can be unreliable, and even the parameter extraction process can not be converged. Therefore, how to accurately and quickly extract the missile terminal motion characteristic parameters from limited observation data becomes a technical problem of guided weapon performance evaluation in a targeting test.
The motion parameter estimation of the end-guided weapon is a complex mathematical problem, wherein the nonlinear constraint condition of multiple parameters and a complex objective function constructed by phase differences of multi-channel data at different moments are involved, parameters and function output values in the objective function are complex nonlinear relations, and the phase differences can also have ambiguity with a period of 2 pi, so that the objective function is a multi-peak function, a plurality of local optimal peaks can appear in the whole parameter space, and the difficulty of accurately estimating the motion parameters is aggravated by combining the limitation of experimental data. By adopting a conventional nonlinear estimation algorithm, the method is easy to fall into the vicinity of a local suboptimal value near an initial value, and a global optimal value cannot be obtained, so that the finally obtained parameter has a larger error with a real parameter of a target, and even completely deviates from the real parameter of the target. In addition, considering the needs of experiments, the calculation method needs to have certain real-time requirements, and generally, the calculation result of the relevant parameters needs to be given within several minutes of obtaining the echo data of the experiments, so that a fast and accurate parameter estimation method is urgently needed to solve the problem.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a device for quickly estimating the motion parameters of the tail end of a missile in an incomplete signal, and solves the problem of inaccurate parameter estimation in incomplete data in a guided weapon experiment.
In order to achieve the above purpose, the invention adopts the following technical scheme: a method for quickly estimating the motion parameters of the tail end of a missile in an incomplete signal is characterized by comprising the following steps:
solving the constructed first objective function through an improved genetic algorithm to obtain the optimal solution of the movement speed of the missile, the acting distance of the measuring equipment and the closest distance between the missile and the measuring equipment;
and solving the constructed second objective function through another improved genetic algorithm according to the missile movement speed, the acting distance of the measuring equipment, the optimal solution of the nearest distance between the missile and the measuring equipment and the constructed nonlinear constraint function to obtain the optimal solution of the azimuth angle, the pitch angle and the coordinate position of the nearest point of the measuring equipment of the missile.
Further, the first objective function is:
Figure BSA0000204090220000031
wherein, tnObserving the processing time of the nth frame of the missile echo signal for the measuring equipment,
Figure BSA0000204090220000032
the Doppler frequency of the target echo signal measured in the nth frame processing time is obtained; r is0，L0V is the nearest distance between the missile to be solved and the measuring equipment, the acting distance of the measuring equipment and the movement speed of the missile, lambda is the working wavelength of the measuring equipment, N is the total frame number of echo signals of the measuring equipment, N is 1, 2, … and N,
Figure BSA0000204090220000033
respectively obtaining the optimal solution of the shortest distance between the missile and the measuring equipment, the movement speed of the missile and the acting distance of the measuring equipment.
Further, the second objective function is:
Figure BSA0000204090220000034
in the formula
Figure BSA0000204090220000035
And
Figure BSA0000204090220000036
are each at tiTheoretical phase difference and actual estimated phase difference of the target reflection echo at the moment; 1i is an imaginary number, i _ ck is a subscript of a device reference receiving channel, j is a subscript of a receiving channel, i is a time index, alpha, beta, x0，y0，z0Respectively representing the positions of the missile attack azimuth angle to be solved, the pitch angle and the nearest point coordinate of the distance measuring equipment on three coordinate axes,
Figure BSA0000204090220000037
is (alpha, beta, x)0，y0，z0) By replacing m and n, respectively
Figure BSA0000204090220000038
The index j and index i _ ck in (1) are replaced by t to indicate the frame processing time tiTo obtain a function
Figure BSA0000204090220000039
Is defined as follows:
Figure BSA00002040902200000310
where mod is a modulo operation, rm(t) and rn(t) are the instantaneous distances of the m-th signal receiving channel and the n-th signal receiving channel from the receiving antenna at the moment t respectively:
Figure BSA0000204090220000041
Figure BSA0000204090220000042
wherein the content of the first and second substances,
Figure BSA0000204090220000043
the nonlinear constraint function is as follows:
Figure BSA0000204090220000044
further, the solution is carried out through an improved genetic algorithm to obtain the optimal solution of the movement speed of the missile, the action distance of the measuring equipment and the nearest distance between the missile and the measuring equipment, and the steps comprise:
(1) setting parameters required by solving a first objective function;
(2) initializing a population;
when generating a population, generating an initialization value of each chromosome by adopting a method of generating a random 0 or 1 array, converting the initialized chromosomes into decimal numbers to form an individual in the population, and forming the initialized population by a plurality of individuals;
(3) calculating the fitness of each particle in the population and sequencing;
taking the first objective function as a fitness calculation function, sequentially substituting each value in the population into the first objective function, and sequencing the fitness of various population individuals;
(4) selecting, crossing and mutating the initialized population to obtain a new population, recalculating the fitness of the new population, inserting the new population into the old population according to the fitness, and updating the optimal chromosome; if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (3), otherwise, taking the generated new population as the initial value of the next inheritance until the given genetic algebra is met;
(5) and outputting parameters corresponding to the optimal fitness given by the last genetic calculation as parameter estimation values:
Figure BSA0000204090220000045
further, the selection operation uses a roulette selection algorithm, and the probability p that the individual is selectedlCalculated from the following formula:
Figure BSA0000204090220000051
wherein FlThe fitness of an individual l in the population, M is the number of the population individuals, and l is the serial number of the population individuals.
Further, the solution is performed through another improved genetic algorithm to obtain an optimal solution of the missile azimuth angle, the pitch angle and the coordinate position of the closest point of the distance measuring equipment, and the method comprises the following steps:
(1) setting parameters required for solving the second objective function;
(2) initializing a population;
when generating a population, generating an initialization value of each chromosome by adopting a method of generating a random 0 or 1 array, converting the initialized chromosomes into decimal numbers to form an individual in the population, and forming the initialized population by a plurality of individuals;
(3) calculating the fitness;
taking the second objective function as a fitness calculation function, sequentially substituting each value in the population into the first objective function, and sequencing the fitness of various population individuals;
(4) selecting, crossing and mutating the initialized population to obtain a new population, recalculating the fitness of the new population, inserting the new population into the old population according to the fitness, and updating the optimal chromosome; if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (3), otherwise, taking the generated new population as the initial value of the next inheritance until the given genetic algebra is met;
(5) constrained non-linear estimation
If the genetic algebra is integral multiple of 10 in the current genetic iterative computation, carrying out one-time nonlinear estimation;
if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (3), and taking the generated new population as an initial value of next inheritance until the given genetic algebra is met;
(6) and selecting the individual with the highest fitness of the last iteration as an optimal value of parameter estimation, and taking the optimal value as the azimuth angle and the pitch angle of the trajectory to be estimated and the coordinate position of the closest point of the missile and the measuring equipment.
A device for rapidly estimating the motion parameters of the tail end of a missile in an incomplete signal comprises:
the first optimal solution estimation module is used for solving a first objective function of the constructed guided missile movement speed, the action distance of the measuring equipment and the closest distance between the guided missile and the measuring equipment through an improved genetic algorithm to obtain an optimal solution of the guided missile movement speed, the action distance of the measuring equipment and the closest distance between the guided missile and the measuring equipment;
and the second optimal solution estimation module is used for constructing a second objective function related to the azimuth angle and the pitch angle of the missile and the coordinate position of the closest point of the distance measuring equipment through a nonlinear constraint function constructed by the optimal solution of the movement speed of the missile, the action distance of the measuring equipment and the closest distance between the missile and the measuring equipment, solving the second objective function through another improved genetic algorithm, and obtaining the optimal solution of the azimuth angle and the pitch angle of the missile and the coordinate position of the closest point of the distance measuring equipment.
Further, the first objective function is:
Figure BSA0000204090220000061
wherein, tnObserving the processing time of the nth frame of the missile echo signal for the measuring equipment,
Figure BSA0000204090220000062
the Doppler frequency of the target echo signal measured in the nth frame processing time is obtained; r is0，L0V is the nearest distance between the missile to be solved and the measuring equipment, the acting distance of the measuring equipment and the movement speed of the missile, lambda is the working wavelength of the measuring equipment, N is the total frame number of echo signals of the measuring equipment, N is 1, 2, … and N,
Figure BSA0000204090220000063
respectively obtaining the optimal solution of the shortest distance between the missile and the measuring equipment, the movement speed of the missile and the acting distance of the measuring equipment.
Further, the second objective function is:
Figure BSA0000204090220000064
in the formula
Figure BSA0000204090220000065
And
Figure BSA0000204090220000066
are each at tiTheoretical phase difference and actual estimated phase difference of the target reflection echo at the moment; 1i is an imaginary number, i _ ck is a subscript of a device reference receiving channel, j is a subscript of a receiving channel, i is a time index, alpha, beta, x0，y0，z0Respectively representing the positions of the missile attack azimuth angle to be solved, the pitch angle and the nearest point coordinate of the distance measuring equipment on three coordinate axes,
Figure BSA0000204090220000067
is (alpha, beta, x)0，y0，z0) By replacing m and n, respectively
Figure BSA0000204090220000068
The index j and index i _ ck in (1) are replaced by t to indicate the frame processing time tiTo obtain a function
Figure BSA0000204090220000069
Is defined as follows:
Figure BSA00002040902200000610
where mod is a modulo operation, rm(t) and rn(t) are respectively the mth signal receiving channel and the nth signal receiving channelThe instantaneous distance from the missile to the receiving antenna at the moment t of the signal receiving channel is as follows:
Figure BSA00002040902200000611
Figure BSA0000204090220000071
wherein the content of the first and second substances,
Figure BSA0000204090220000072
the nonlinear constraint function is as follows:
Figure BSA0000204090220000073
the invention achieves the following beneficial effects:
according to the parameter estimation problem in incomplete data in a guided weapon experiment, an improved parameter estimation method based on a genetic algorithm is provided to quickly and accurately estimate the guided weapon motion parameters from limited experimental data, the method adopts a nonlinear solving algorithm to accelerate the convergence of the genetic algorithm and improve the defect that the local convergence capacity of the genetic algorithm is insufficient, the optimal value estimation value of the missile motion parameters under an incomplete signal is quickly and accurately solved, and the parameter estimation method can also be expanded and used for solving other complex nonlinear problems.
The invention combines the advantages and the disadvantages of a genetic algorithm and a traditional nonlinear estimation algorithm, develops a rapid and accurate parameter estimation method, adopts a nonlinear solving algorithm to accelerate the convergence of the genetic algorithm, improves the defect of insufficient local convergence capability of the genetic algorithm, and rapidly and accurately solves to obtain the optimal value estimation value of the missile motion parameter under an incomplete signal. The algorithm is suitable for parallel computation, multiple heredity can be synchronously computed under the condition met by a hardware platform, a computation result with higher precision can be obtained in a shorter time, and the requirements of a user on high precision and real-time performance are met.
The method is used for parameter calculation of large complex models, has good real-time performance and high precision, and can effectively solve the existing problems. Simulation results and experimental results show that by adopting the estimation algorithm, the solving time is greatly shortened, the instability of the estimation result is improved, and the optimal estimation value can be obtained by each estimation.
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FIG. 1 is a Doppler value extracted from the projectile convergence process and the complete experimental data;
FIG. 2 shows the Doppler values extracted from incomplete experimental data (the right image is corrected data);
FIG. 3 is a flow chart of an estimation method in accordance with an embodiment of the present invention;
FIG. 4 is the principle of population crossing in genetic computing;
FIG. 5 is an implementation principle of population variation in genetic calculation;
FIG. 6 shows Doppler values (v-300 m/s, L) extracted from incomplete experimental data in the experiments performed in the examples of the present invention0＝100m，r0＝10m)；
FIG. 7 shows the first estimate of the residual error (v 300m/s, L) from the incomplete experimental data (optimized solution to objective function 1) from the experiments performed in the example of the present invention0＝100m，r0＝10m)；
FIG. 8 is a curve of the estimation error with genetic algebra after the second estimation (solving for the optimal value of objective function 2) in the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1:
as shown in fig. 3, a method for rapidly estimating the missile terminal motion parameters in an incomplete signal includes the steps of:
(1) constructing an objective function 1 for solving the movement speed of the missile, the action distance of the measuring equipment and the closest distance between the missile and the measuring equipment
In the process of solving the objective function 1, the objective function mainly solves the moving speed of the missile, the acting distance of the measuring equipment and the nearest distance between the missile and the measuring equipment through Doppler data of limited echo data. According to the Doppler relation between the solving parameters and the echo, the solving objective function 1 is established as follows:
Figure BSA0000204090220000081
wherein, tnObserving the processing time of the nth frame of missile echo signal for the measuring equipment (when the echo signal of the measuring equipment is processed, the observed echo signal in a period of time is taken as one frame of data to be processed),
Figure BSA0000204090220000082
the Doppler frequency of the target echo signal measured in the nth frame processing time is used. r is0，L0V is the nearest distance between the missile to be solved and the measuring equipment, the acting distance of the measuring equipment and the movement speed of the missile, lambda is the working wavelength of the measuring equipment, N is the total frame number of echo signals of the measuring equipment, N is 1, 2, … and N,
Figure BSA0000204090220000083
respectively solving the optimal solution of the shortest distance between the missile and the measuring equipment, the missile movement speed and the acting distance of the measuring equipment, wherein the mathematical meaning of argmin indicates that r is the minimum value of the following formula0，v，L0Is the corresponding optimal solution.
(2) Setting parameters required by the solution of the objective function 1
Parameters required by the objective function 1 for solving are set, and the parameters mainly comprise the following parameters: the generation number of the maximum inheritance, the size of the population, the length of the chromosome, the probability of the generation groove, the probability of the cross, the probability of the mutation and the number of genetic iterations. Because the parameters required to be solved in the objective function 1 are the missile movement speed, the action distance of the measuring equipment and the nearest distance between the missile and the measuring equipment, the parameters are relatively few, the size of a population and the length of an individual can be properly reduced during parameter setting, and the calculation time is reduced.
(4) Initializing a population
Genetic algorithms perform iterative searches within a given initialization population. In the invention, a binary coding method is adopted to increase the randomness of the initialized population (binary coding is to complete the conversion between decimal numbers and binary numbers), when the population is generated, a method of generating random 0 or 1 arrays is adopted (namely, only 0 or 1 values exist in the generated arrays, and the probability of 0 or 1 occurrence is equal) to generate the initialized value of each chromosome, namely, each 0 or 1 represents a chromosome, the initialized chromosomes are converted into decimal numbers through coding conversion, namely, one individual in the population, and a plurality of individuals form the initialized population.
(5) Calculating the fitness of each particle in the population and sequencing;
and writing a fitness calculation function. The fitness computation function is determined according to the optimization objective of the problem. In the invention, because the solved problem is the parameter corresponding to the minimum value of the objective function, the objective function is used as a fitness calculation function, each value in the population is sequentially brought into the objective function 1, the smaller the function value is, the better the fitness is, and the fitness of various population individuals is ranked through the ranking function.
(6) Selecting, crossing and mutating the population to obtain a new population, referring to the crossing and mutating principles of the population as shown in fig. 4 and fig. 5 or referring to related reference contents, recalculating the fitness of the new population, inserting the new population into the old population according to the fitness, and updating the optimal chromosome; if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (5), and taking the generated new population as an initial value of next inheritance until the given genetic algebra is met;
and selecting excellent individuals from the old population with a given gully probability to form a new population so as to breed to obtain next generation individuals, wherein the selected probability of the individuals is obtained by the fitness, and the higher the fitness is, the higher the selected probability is. The inventionThe selection operation adopts a roulette selection algorithm, and the probability p that the individual is selectedlCalculated from the following formula
Figure BSA0000204090220000091
Wherein FlAnd (4) calculating the fitness of the individual l in the population according to the step (5), wherein M is the number of the population individuals, and l is the serial number of the population individuals.
The crossover operation is a random selection of two individuals from the population, with each chromosome crossing or not being determined by a given crossover probability. The process is as follows: for each chromosome, generating a random number between 0 and 1, if the value is less than a specified cross probability, crossing the selected chromosome, otherwise, directly copying the chromosome into a new population without participating in crossing, and the crossing operation is shown in FIG. 4:
every two individuals are crossed according to the cross probability, and two new filial generations are generated after respective partial gene exchange. The specific operation is to randomly generate an effective mating position, and chromosome exchange is carried out on all genes positioned after the mating position.
And the mutation operation is to determine whether each gene of the chromosomes in the crossed new population is mutated according to the mutation probability. The process is as follows: generating random numbers between 0 and 1, if the value is less than the designated mutation probability, mutating the selected gene to generate new chromosomes, and mutating as shown in FIG. 5:
after the operations are completed, the fitness of the generated new population is recalculated, the generated new population is inserted into the old population according to the fitness, and the optimal chromosome is updated.
If the genetic algebra is smaller than the maximum genetic algebra, turning to the step (5), and taking the generated new population as an initial value of next inheritance until the given genetic algebra is met;
(7) outputting the parameters corresponding to the optimal fitness given by the last genetic calculation as parameter estimation values
Optimal adaptation given by last genetic calculationThe corresponding parameter is output as a parameter estimation value, i.e.
Figure BSA0000204090220000101
(8) Solving objective function 2 for constructing coordinate positions of closest points of missile azimuth angle, pitch angle and distance measurement equipment
And calculating the phase difference of the corresponding channels according to the phase data of the target echoes of the eight channels at different moments in the bullet target intersection process measured by the measuring equipment. In order to achieve the real-time performance of the calculation, the phase difference constructed in the invention is M-1, wherein M is the total number of receiving channels. When the objective function 2 is constructed, because of the influences of occlusion, clutter and the like in the observation process, the quality of the received signals of different channels can have differences, the receiving channel with the best signal quality is selected as a reference, and the constructed objective function 2 is as follows:
Figure BSA0000204090220000102
in the formula
Figure BSA0000204090220000103
And
Figure BSA0000204090220000104
are each at tiTheoretical phase difference and actual estimated phase difference of target reflected echo at the moment (when echo signals received by the measuring equipment are processed, observation echo signals within a period of time are used as a frame of data to be processed, and the time of each frame is tiTo (c); 1i is an imaginary number, i _ ck is a subscript of a device reference receiving channel, j is a subscript of a receiving channel, i is a time index, alpha, beta, x0，y0，z0Respectively representing the positions of the missile attack azimuth angle to be solved, the pitch angle and the nearest point coordinate of the distance measuring equipment on three coordinate axes,
Figure BSA0000204090220000105
is (alpha, beta, x)0，y0，z0) Function of (2), described below
Figure BSA0000204090220000106
Definition of function, in order to simplify the representation and make the definition of the function more concise, m and n are respectively used to replace
Figure BSA0000204090220000111
The index j and index i _ ck in (1) are replaced by t to indicate the frame processing time tiTo obtain a function
Figure BSA0000204090220000112
Is defined as follows:
Figure BSA0000204090220000113
where mod is a modulo operation, rm(t) and rn(t) are the instantaneous distances of the m-th signal receiving channel and the n-th signal receiving channel from the receiving antenna at the moment t respectively:
Figure BSA0000204090220000114
Figure BSA0000204090220000115
wherein the content of the first and second substances,
Figure BSA0000204090220000116
Figure BSA0000204090220000117
the specific definition of the optimal solution output in step (7) is described in step (1).
(9) Constructing a non-linear constraint function of the objective function 2
The nonlinear constraint function of the objective function 2 is as follows:
Figure BSA0000204090220000118
the parameters in the formula are defined as described above.
(10) Setting parameters required for solving the objective function 2
Parameters required by the solution are set, and the parameters mainly comprise the following parameters: subscript of reference channel, algebra of maximum inheritance, size of population, length of individual, probability of gully, probability of cross, probability of mutation, and number of genetic iterations. The above parameters have the same meaning as the parameters in (2), but different values need to be set for different solving objective functions. Considering that the parameters of the calculation include the coordinate position of the nearest point of the missile and the measuring equipment, the azimuth angle of the trajectory and the pitch angle, wherein the calculation of the angle has multivalue property and is easy to fall into local optimization, therefore, the population number is usually 2 to 3 times of that in the first estimation when the population is set.
(11) Initializing a population
The principle is the same as that in (4).
(12) Fitness calculation
The principle is the same as that in (5).
(13) Selection, crossing, mutation
The principle is the same as that in (6).
(14) Constrained non-linear estimation (using the non-linear constraint function in (9) as constraint condition for estimation)
If the genetic algebra in the genetic iterative computation is integral multiple of 10, carrying out one-time nonlinear estimation (in the prior art) to accelerate the convergence of the genetic algorithm, otherwise, continuing to execute the next-generation genetic algorithm.
When the nonlinear estimation is executed, the optimal individual obtained in the genetic algorithm population at this time is used as an iteration initial value of a nonlinear estimation function fmincon (the function is a nonlinear estimation function, and the nonlinear constraint function obtained in (9) is used as a limiting condition input of the fmincon function, and is the prior art), so as to find an optimal value near the genetic algorithm estimated next-to-optimal value (optimal individual), replace the optimal population individual in the genetic population at this time with the optimal value, and continue the related operation in the subsequent genetic algorithm.
(14) Selecting an optimal output
After parameter estimation is finished, selecting an individual with the highest last iteration fitness as an optimal value of parameter estimation, namely missile motion related parameters to be estimated, namely azimuth angles and pitch angles of trajectories and coordinate positions of closest points of missiles and measuring equipment.
Example 2:
a device for rapidly estimating the motion parameters of the tail end of a missile in an incomplete signal comprises:
the first optimal solution estimation module is used for solving a first objective function of the constructed guided missile movement speed, the action distance of the measuring equipment and the closest distance between the guided missile and the measuring equipment through an improved genetic algorithm to obtain an optimal solution of the guided missile movement speed, the action distance of the measuring equipment and the closest distance between the guided missile and the measuring equipment;
and the second optimal solution estimation module is used for constructing a second objective function related to the azimuth angle and the pitch angle of the missile and the coordinate position of the closest point of the distance measuring equipment through a nonlinear constraint function constructed by the optimal solution of the movement speed of the missile, the action distance of the measuring equipment and the closest distance between the missile and the measuring equipment, solving the second objective function through another improved genetic algorithm, and obtaining the optimal solution of the azimuth angle and the pitch angle of the missile and the coordinate position of the closest point of the distance measuring equipment.
Further, the second objective function is:
Figure BSA0000204090220000131
in the formula
Figure BSA0000204090220000132
And
Figure BSA0000204090220000133
are each at tiTheoretical phase difference and actual estimated phase difference of the target reflection echo at the moment; 1i is an imaginary number, i _ ck is a subscript of a device reference receiving channel, j is a subscript of a receiving channel, i is a time index, alpha, beta, x0，y0，z0Respectively representing the positions of the missile attack azimuth angle to be solved, the pitch angle and the nearest point coordinate of the distance measuring equipment on three coordinate axes,
Figure BSA0000204090220000134
is (alpha, beta, x)0，y0，z0) By replacing m and n, respectively
Figure BSA0000204090220000135
The index j and index i _ ck in (1) are replaced by t to indicate the frame processing time tiTo obtain a function
Figure BSA0000204090220000136
Is defined as follows:
Figure BSA0000204090220000137
where mod is a modulo operation, rm(t) and rn(t) are the instantaneous distances of the m-th signal receiving channel and the n-th signal receiving channel from the receiving antenna at the moment t respectively:
Figure BSA0000204090220000138
Figure BSA0000204090220000139
wherein the content of the first and second substances,
Figure BSA00002040902200001310
the nonlinear constraint function is as follows:
Figure BSA00002040902200001311
the specific solving method is the same as that of the embodiment 1 or the invention content.
Example 3:
the relevant experimental parameters used in this experiment are shown in the following table: the results of the experiment are shown in FIGS. 6 to 8.
TABLE 1, parameters table of target practice simulation test
Figure BSA0000204090220000141
In the test process, the devices to be tested are arranged according to the parameter statistics. After the echo signals are acquired, the parameters are estimated according to the method of the invention.
In the estimation process of the objective function 1 for solving the missile movement speed, the measuring equipment action distance and the nearest distance between the missile and the measuring equipment, solving parameters are set as follows:
the population size NIND is 100; genetic algebra MAXGEN is 80; individual length PRECI 40; the ditch replacing probability GGAP is 0.95; the crossover probability px is 0.7; the variation probability pm is 0.01;
in the estimation process of the solving objective function 2 of the missile azimuth angle, the pitch angle and the nearest point coordinate position of the distance measurement equipment, solving parameters are set as follows:
the population size NIND is 300; genetic algebra MAXGEN is 100; individual length PRECI 100; the ditch replacing probability GGAP is 0.95; the crossover probability px is 0.7; the variation probability pm is 0.01;
for the first set of test parameters given in table 1: the movement speed of the missile: 300 m/s; measuring the action distance of the equipment: 100 m; distance between missile and closest point of measuring equipment: 10 m; the closest point coordinate position is (10, 0, 0), the azimuth angle is 90 degrees, and the pitch angle is 60 degrees.
The method of the invention is adopted to carry out parameter estimation, and the result of the first estimation (the optimal solution of the objective function 1) is as follows:
Figure BSA0000204090220000151
the curve of the estimated error versus the genetic algebra after the second estimation (optimal solution to the objective function 2) for the first set of trial parameters is shown in fig. 8:
second parameter estimation (optimal solution to objective function 2) results:
Figure BSA0000204090220000152
the calculation time is as follows: 49.284496 seconds
Corresponding to the set multiple test parameters, the method of the invention is adopted to calculate the parameters, and the corresponding simulation results and the calculation time are shown in the table 2:
table 2: simulation test parameter estimation results set for table 1
Figure BSA0000204090220000153
Figure BSA0000204090220000161
The experimental parameter calculation employs a computer configuration:
a processor: intel (r) Xeon (R) CPU E3-1270V 2@3.50GHz
Install memory (RAM): 16.0GB
The system type is as follows: 64-bit operating system
The simulation result and the experimental result show that the estimation method of the invention does not have the defects of low convergence speed, long calculation time, easy convergence to a local optimal value and incapability of obtaining global optimum by independently using a nonlinear estimation algorithm in the prior art, has very high calculation precision, has short calculation time, can obtain an accurate result within dozens of seconds, effectively solves the problem of rapidly and accurately extracting the missile motion parameter from limited experimental data, and provides a feasible data solution for the parameter estimation work of a target practice.
The method can be used for expanding the parameter calculation problem of the large complex model, has good real-time performance and higher precision, and can effectively solve the existing problems. Simulation results and experimental results show that by adopting the estimation algorithm, the solving time is greatly shortened, the instability of the estimation result is improved, and the optimal estimation value can be obtained by each estimation.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (9)

1. A method for quickly estimating the motion parameters of the tail end of a missile in an incomplete signal is characterized by comprising the following steps:
solving the constructed first objective function through an improved genetic algorithm to obtain the optimal solution of the movement speed of the missile, the acting distance of the measuring equipment and the closest distance between the missile and the measuring equipment;
and solving the constructed second objective function through another improved genetic algorithm according to the missile movement speed, the acting distance of the measuring equipment, the optimal solution of the nearest distance between the missile and the measuring equipment and the constructed nonlinear constraint function to obtain the optimal solution of the azimuth angle, the pitch angle and the coordinate position of the nearest point of the measuring equipment of the missile.
2. The method for rapidly estimating the missile end motion parameters in the incomplete signals as claimed in claim 1, wherein the method comprises the following steps: the first objective function is:
Figure FSA0000204090210000011
wherein, tnObserving the processing time of the nth frame of the missile echo signal for the measuring equipment,
Figure FSA0000204090210000012
the Doppler frequency of the target echo signal measured in the nth frame processing time is obtained; r is0，L0V is the nearest distance between the missile to be solved and the measuring equipment, the acting distance of the measuring equipment and the movement speed of the missile, lambda is the working wavelength of the measuring equipment, N is the total frame number of echo signals of the measuring equipment, N is 1, 2, … and N,
Figure FSA0000204090210000013
respectively obtaining the optimal solution of the shortest distance between the missile and the measuring equipment, the movement speed of the missile and the acting distance of the measuring equipment.
3. The method for rapidly estimating the missile end motion parameters in the incomplete signals as claimed in claim 2, wherein the method comprises the following steps: the second objective function is:
Figure FSA0000204090210000014
in the formula
Figure FSA0000204090210000015
And
Figure FSA0000204090210000016
are each at tiTheoretical phase difference and actual estimated phase difference of the target reflection echo at the moment; 1i is an imaginary number, i _ ck is a subscript of a device reference receiving channel, j is a subscript of a receiving channel, i is a time index, alpha, beta, x0，y0，z0Respectively representing the positions of the missile attack azimuth angle to be solved, the pitch angle and the nearest point coordinate of the distance measuring equipment on three coordinate axes,
Figure FSA0000204090210000017
is (alpha, beta, x)0，y0，z0) By replacing m and n, respectively
Figure FSA0000204090210000018
The index j and index i _ ck in (1) are replaced by t to indicate the frame processing time tiTo obtain a function
Figure FSA0000204090210000019
Is defined as follows:
Figure FSA0000204090210000021
where mod is a modulo operation, rm(t) and rn(t) are the instantaneous distances of the m-th signal receiving channel and the n-th signal receiving channel from the receiving antenna at the moment t respectively:
Figure FSA0000204090210000022
Figure FSA0000204090210000023
wherein the content of the first and second substances,
Figure FSA0000204090210000024
the nonlinear constraint function is as follows:
Figure FSA0000204090210000025
4. the method for rapidly estimating the missile end motion parameters in the incomplete signals as claimed in claim 1, wherein the method comprises the following steps: the method comprises the following steps of solving through an improved genetic algorithm to obtain the optimal solution of the movement speed of the missile, the action distance of the measuring equipment and the nearest distance between the missile and the measuring equipment, and comprises the following steps:
(1) setting parameters required by solving a first objective function;
(2) initializing a population;
when generating a population, generating an initialization value of each chromosome by adopting a method of generating a random 0 or 1 array, converting the initialized chromosomes into decimal numbers to form an individual in the population, and forming the initialized population by a plurality of individuals;
(3) calculating the fitness of each particle in the population and sequencing;
taking the first objective function as a fitness calculation function, sequentially substituting each value in the population into the first objective function, and sequencing the fitness of various population individuals;
(4) selecting, crossing and mutating the initialized population to obtain a new population, recalculating the fitness of the new population, inserting the new population into the old population according to the fitness, and updating the optimal chromosome; if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (3), otherwise, taking the generated new population as the initial value of the next inheritance until the given genetic algebra is met;
(5) and outputting parameters corresponding to the optimal fitness given by the last genetic calculation as parameter estimation values:
Figure FSA0000204090210000031
5. the method for rapidly estimating the missile end motion parameters in the incomplete signal as claimed in claim 4, wherein the method comprises the following steps: the selection operation adopts a roulette selection algorithm, and the probability p that the individual is selectedlCalculated from the following formula:
Figure FSA0000204090210000032
wherein FlThe fitness of an individual l in the population, M is the number of the population individuals, and l is the serial number of the population individuals.
6. The method for rapidly estimating the missile end motion parameters in the incomplete signals as claimed in claim 1, wherein the method comprises the following steps: the optimal solution of the missile azimuth angle, the pitch angle and the coordinate position of the closest point of the distance measuring equipment is obtained by solving through another improved genetic algorithm, and the method comprises the following steps:
(1) setting parameters required for solving the second objective function;
(2) initializing a population;
when generating a population, generating an initialization value of each chromosome by adopting a method of generating a random 0 or 1 array, converting the initialized chromosomes into decimal numbers to form an individual in the population, and forming the initialized population by a plurality of individuals;
(3) calculating the fitness;
taking the second objective function as a fitness calculation function, sequentially substituting each value in the population into the first objective function, and sequencing the fitness of various population individuals;
(4) selecting, crossing and mutating the initialized population to obtain a new population, recalculating the fitness of the new population, inserting the new population into the old population according to the fitness, and updating the optimal chromosome; if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (3), otherwise, taking the generated new population as the initial value of the next inheritance until the given genetic algebra is met;
(5) constrained non-linear estimation
If the genetic algebra is integral multiple of 10 in the current genetic iterative computation, carrying out one-time nonlinear estimation;
if the current genetic algebra is smaller than the maximum genetic algebra, turning to the step (3), and taking the generated new population as an initial value of next inheritance until the given genetic algebra is met;
(6) and selecting the individual with the highest fitness of the last iteration as an optimal value of parameter estimation, and taking the optimal value as the azimuth angle and the pitch angle of the trajectory to be estimated and the coordinate position of the closest point of the missile and the measuring equipment.
7. A missile terminal motion parameter fast estimation device in incomplete signals is characterized in that: the method comprises the following steps:
the first optimal solution estimation module is used for solving a first objective function of the constructed guided missile movement speed, the action distance of the measuring equipment and the closest distance between the guided missile and the measuring equipment through an improved genetic algorithm to obtain an optimal solution of the guided missile movement speed, the action distance of the measuring equipment and the closest distance between the guided missile and the measuring equipment;
and the second optimal solution estimation module is used for constructing a second objective function related to the azimuth angle and the pitch angle of the missile and the coordinate position of the closest point of the distance measuring equipment through a nonlinear constraint function constructed by the optimal solution of the movement speed of the missile, the action distance of the measuring equipment and the closest distance between the missile and the measuring equipment, solving the second objective function through another improved genetic algorithm, and obtaining the optimal solution of the azimuth angle and the pitch angle of the missile and the coordinate position of the closest point of the distance measuring equipment.
8. The device for rapidly estimating the motion parameters of the tail end of the missile in the incomplete signal as claimed in claim 7, wherein: the first objective function is:
Figure FSA0000204090210000041
wherein, tnObserving the processing time of the nth frame of the missile echo signal for the measuring equipment,
Figure FSA0000204090210000042
the Doppler frequency of the target echo signal measured in the nth frame processing time is obtained; r is0，L0V is the closest distance between the missile to be solved and the measuring equipment and the measurementThe device action distance and the missile movement speed are measured, lambda is the working wavelength of the measuring device, N is the total frame number of echo signals of the measuring device, N is 1, 2, …, N,
Figure FSA0000204090210000043
respectively obtaining the optimal solution of the shortest distance between the missile and the measuring equipment, the movement speed of the missile and the acting distance of the measuring equipment.
9. The device for rapidly estimating the motion parameters of the tail end of the missile in the incomplete signal as claimed in claim 7, wherein: the second objective function is:
Figure FSA0000204090210000044
in the formula
Figure FSA0000204090210000045
And
Figure FSA0000204090210000046
are each at tiTheoretical phase difference and actual estimated phase difference of the target reflection echo at the moment; 1i is an imaginary number, i _ ck is a subscript of a device reference receiving channel, j is a subscript of a receiving channel, i is a time index, alpha, beta, x0，y0，z0Respectively representing the positions of the missile attack azimuth angle to be solved, the pitch angle and the nearest point coordinate of the distance measuring equipment on three coordinate axes,
Figure FSA0000204090210000051
is (alpha, beta, x)0，y0，z0) By replacing m and n, respectively
Figure FSA0000204090210000052
The index j and index i _ ck in (1) are replaced by t to indicate the frame processing time tiTo obtain a function
Figure FSA0000204090210000053
Is defined as follows:
Figure FSA0000204090210000054
where mod is a modulo operation, rm(t) and rn(t) are the instantaneous distances of the m-th signal receiving channel and the n-th signal receiving channel from the receiving antenna at the moment t respectively:
Figure FSA0000204090210000055
Figure FSA0000204090210000056
wherein the content of the first and second substances,
Figure FSA0000204090210000057
the nonlinear constraint function is as follows:
Figure FSA0000204090210000058
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