CN109539884A - A kind of Vector Target-missing Quantity method for parameter estimation based on GA - Google Patents
A kind of Vector Target-missing Quantity method for parameter estimation based on GA Download PDFInfo
- Publication number
- CN109539884A CN109539884A CN201811389761.5A CN201811389761A CN109539884A CN 109539884 A CN109539884 A CN 109539884A CN 201811389761 A CN201811389761 A CN 201811389761A CN 109539884 A CN109539884 A CN 109539884A
- Authority
- CN
- China
- Prior art keywords
- function
- population
- probability
- channel
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F41—WEAPONS
- F41G—WEAPON SIGHTS; AIMING
- F41G3/00—Aiming or laying means
- F41G3/32—Devices for testing or checking
Abstract
The invention discloses the Vector Target-missing Quantity method for parameter estimation based on GA, objective function is solved including building: according to the top-quality channel of signal quality selection signal as reference channel, the opposite phase difference with this channel in each channel is calculated, objective function is constructed based on phase difference；Construct constraint function；Using genetic algorithm, the objective function of setting is optimized, acquires its optimal solution；Iteration initial value of the optimal solution as non-linear estimations function is obtained, global optimum is obtained.The advantages of present invention incorporates GA algorithm and traditional non-linear estimation algorithms and deficiency, develop a kind of fast and accurately method for parameter estimation, the method overcome the weak disadvantages of the weak and traditional non-linear estimation algorithm global optimizing ability of GA local optimal searching ability, take full advantage of the global optimizing ability of GA and the local optimal searching ability of traditional non-linear estimations, and consider the characteristic of GA itself randomness, using the thought of the synchronous optimizing of multiple GA, the Parameter Estimation Problem of the challenge effectively solved.
Description
Technical field
The present invention relates to a kind of Vector Target-missing Quantity method for parameter estimation more particularly to a kind of Vector Target-missing Quantities based on GA
Method for parameter estimation belongs to miss distance measurement technology field.
Background technique
In modern war, guided missile is military for various novel guidances since it gains great popularity with accurate guidance performance
The development of device is increasingly important.However but have that high cost, difficulty be big, experiment condition is difficult to for the test of guided weapon performance
The problems such as meeting.Therefore weapon performance is detected using Miss distance measurement equipment is installed in target.The performance of guided weapon is general
The embodiment of concentration compared with bullet is obtained with the encounter phase of trajectory of target, therefore in range test, it is met with by measuring guided weapon
Kinematic parameter (including scalar missile parameter (the operating distance L of section0, Missile Motion speed v0, miss distance r0) and vector motion
Parameter (miss point coordinate, the azimuth of Missile Motion track and pitch angle) can determine guided missile close to this process of target
In skyborne motion profile to examine the precision of guided weapon evaluate the superiority and inferiority of its performance.
Vector Target-missing Quantity measurement is actually to measure the motion profile for playing target encounter phase of trajectory guided missile.Assuming that playing target encounter phase of trajectory
Target makees linear uniform motion, and Missile Motion track and the relationship of instrumentation radar dual-mode antenna battle array are as shown in Figure 2.It is fixed in figure
In the systematic survey coordinate of justice, if target is located at coordinate origin.Obviously, the straight-line trajectory of target can be by five parameters only
One determines: the coordinate (x of miss point0,y0,z0)；The angle of ballistic movement track and oxy plane both trajectory tilt angle β；Move rail
Mark deflection both trajectory deflection angle α；The moment t in addition target speed v and target are missed the target0(or initial time target is to miss point
Distance L0), target can be uniquely determined in the entire time history of spatial movement.
The measuring principle of Vector Target-missing Quantity is to play the different receiving antenna of target intersection process spatial location by measurement to connect
The target echo phase difference of receipts changes over time curve, by the phase difference optimal fitting with motion trajectory model, estimates arrow
Miss-distance parameters are measured, Computing Principle is as follows:
Assuming that the coordinate of miss point is (x0,y0,z0), the coordinate of M receiving antenna is (xi,yi,zi), wherein i=1,2,
3....M, the trajectory deflection angle of guided missile and trajectory tilt angle are respectively α and β, the coordinate of the track of t moment guided missile be (x (t), y (t),
z(t)).Since guided missile is different from the oblique distance of each receiving antenna, different receiving antennas is returned in the target that synchronization receives
There are phase difference, the phase differences of the i-th doppler echo received with jth road antenna for wave are as follows:
Wherein ri(t) and rj(t) be respectively i-th and jth road t moment guided missile to receiving antenna instantaneous distance:
Wherein,
The rule and the coordinate (x of miss point that the received target echo phase difference of different receiving antennas changes over time0,
y0,z0), trajectory deflection angle α, trajectory tilt angle β and { v, L0,r0Related, Missile Motion speed is v, Missile Motion track and target
Distance, that is, the scalar missile for marking closest approach to target is r0, and at the time reference t=0 moment, guided missile is at a distance from miss point
L0.Therefore, as long as measuring the M-1 phase difference actual change curve at any time of intersection enabling objective echo, acquisition v,
L0,r0Measurement result after, following criterion function can be constructed
In formulaWithRespectively in tiThe notional phase difference and actual estimated phase of moment target reflection echo
Difference.Vector Target-missing Quantity the parameter { (x for being achieved with target trajectory by solving following constrained nonlinear systems problem0,y0,
z0), α, β } estimation:
subject to
By above-mentioned mathematical model it is found that Vector Target-missing Quantity parameter Estimation is a complicated mathematical problem, it is directed to more
Nonlinearity in parameters constraint condition, the target complex cost function of the phase difference construction of multi-channel data different moments, target letter
Parameter and function output valve is complicated non-linear relation in number, and phase difference can also exist to be pasted by modulus of periodicity of 2 π, because
This, which is a multi peak value, in entire parameter space, it may appear that multiple local optimum peak values.Using
Conventional non-linear estimation algorithm is easily trapped near the part time figure of merit near initial value, cannot get global optimum, from
And cause the actual parameter error of finally obtained parameter and target larger, or even carry on the back completely with true target component completely
From.Furthermore, it is contemplated that the needs of experiment, the requirement of real-time which needs to have certain are generally obtaining echo
Need to provide the calculated result of relevant parameter within a few minutes of data, therefore there is an urgent need to a kind of fast and accurately parameters to estimate
Meter method solves the problems, such as this, and the present invention exactly arises under such demand.
Summary of the invention
The technical problem to be solved by the present invention is to overcome the deficiencies of existing technologies, one kind is provided and calculates accurate parameter fastly
Estimation method solves the Solve problems of complex nonlinear objective function global optimum encountered.
In order to solve the above technical problems, the invention adopts the following technical scheme:
Based on the Vector Target-missing Quantity method for parameter estimation of GA (Genetic Algorithm, genetic algorithm), including it is following
Step:
1) building solves objective function: according to the top-quality channel of signal quality selection signal as reference channel, meter
The opposite phase difference with this channel in each channel is calculated, objective function is constructed based on phase difference；
2) constraint function is constructed；
3) genetic algorithm is utilized, the objective function of setting is optimized, its optimal solution is acquired；
4) step 3) is obtained into optimal solution as the iteration initial value of non-linear estimations function, obtains global optimum.
Further, building objective function is as follows:
In formulaWithRespectively in tiThe notional phase difference and actual estimated of moment target reflection echo
Phase difference, 1i are imaginary unit, and i_ck is the subscript of reference channel, and j is channel subscript, and i is time index, and N is valid data
The number of point,For (α, β, x0,y0,z0) function, physical relationship is as follows
Wherein ri(t) and rj(t) be respectively i-th and jth road t moment guided missile to receiving antenna instantaneous distance:
Wherein,
{v,L0,r0It is scalar missile parameter.
Further, building nonlinear restriction function is as follows
Wherein the coordinate of miss point is (x0,y0,z0), the coordinate of M receiving antenna is (xi,yi,zi), i=1,2,
3....M, the trajectory deflection angle of guided missile and trajectory tilt angle are respectively α and β.
Further, in step 3), the Optimization Solution process of genetic algorithm are as follows:
(1) parameter setting is solved: subscript, the algebra of maximum heredity, the size of population, individual including reference channel
Length, the probability of generation gap, the probability of intersection, the probability of variation, genetic iteration number；
(2) initialization kind；Group uses binary coding method, and generation population is generated every using the method for generating random number
The population of initialization is converted into metric number by coding function by the value of one chromosome
(3) fitness calculates: calculating function for objective function as fitness, the smaller then fitness of value is better, passes through
Ranking functions are ranked up the fitness size of a population；
(4) it selects: using roulette selection algorithm, the selected probability of individual is calculated by following formula:
Wherein FjFor the fitness of individual j, N is the number of population at individual；
(5) intersect: setting the probability P of intersectionx, each individual in population, the corresponding random number for generating one [0,1], if
Random number is less than crossover probability Px, then intersected；
(6) it makes a variation: setting the probability P of variationm, each individual in population, the corresponding random number for generating one [0,1],
If random number is less than mutation probability Pm, then make a variation；
(7) new population is generated by (5) (6), is then proceeded to circulation and is generated next-generation population；
(8) the smallest solution of selection target functional value, as output.
Advantageous effects of the invention: the method for the present invention establishes objective function according to problem is practical, it is contemplated that phase
It is pasted in the presence of by modulus of periodicity of 2 Π, therefore phase difference is converted toMode avoids fuzzy influence；The present invention combines
The advantages of GA algorithm and traditional non-linear estimation algorithm and deficiency, develop a kind of fast and accurately method for parameter estimation, should
Method overcomes the weak disadvantage of the weak and traditional non-linear estimation algorithm global optimizing ability of GA local optimal searching ability, makes full use of
The local optimal searching ability of the global optimizing ability of GA and traditional non-linear estimations, and consider the characteristic of GA itself randomness, it adopts
With the thought of the synchronous optimizing of multiple GA, the Parameter Estimation Problem of the challenge effectively solved.
Detailed description of the invention
Fig. 1 is inventive method flow diagram；
Fig. 2 is the relationship of Missile Motion track Yu instrumentation radar dual-mode antenna battle array.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating this hair
Bright technical solution, and not intended to limit the protection scope of the present invention.
Fig. 1 is inventive method flow diagram；Fig. 1 is shown: the Vector Target-missing Quantity method for parameter estimation based on GA, packet
It includes:
(1) building solves objective function
In specific embodiment, during being crossed according to the bullet target that miss distance data collection terminal measures when eight channel differences
The phase data for carving target echo, calculates the phase difference of corresponding channel.In order to reach the real-time of calculating, constructed in the present invention
Phase difference be M-1, wherein M be receiving channel.When constructing objective function because in observation process exist block, clutter
Deng influence, different channel receiving signal quality can be had differences, and the top-quality receiving channel of selection signal refers to the most, structure
It is as follows to build objective function:
In formulaWithRespectively in tiThe notional phase difference and actual estimated of moment target reflection echo
Phase difference.1i is imaginary unit, and i_ck is the subscript of reference channel, and j is channel subscript, and i is time index, N significant figure strong point
Number,For (α, β, x0,y0,z0) function, physical relationship is as follows
Wherein ri(t) and rj(t) be respectively i-th and jth road t moment guided missile to receiving antenna instantaneous distance:
Wherein,
{v,L0,r0It is scalar missile parameter.
(2) constraint function is constructed
It is as follows to construct nonlinear restriction function
Parameter is defined as described above in formula.
(3) parameter setting is solved
Parameter required for solving is set, mainly includes following parameter: the hereditary algebra of subscript, the maximum of reference channel,
The size of population, the length of individual, the probability of generation gap, the probability of intersection, the probability of variation, genetic iteration number.
(4) initialization population
Genetic algorithm is iterated search in a given initialization population.In the present invention, using binary coding
Method generates population using the method for generating random number and generates the value of each chromosome, by coding function by initialization
Population is converted into metric number.
(5) fitness calculates
It writes fitness and calculates function.Valuation functions are determined according to the optimization aim of problem.In the present invention, due to
The problem of solution is the corresponding parameter of objective function minimum value, therefore calculates function for objective function as fitness, and value is got over
Small then fitness is better, is ranked up by fitness size of the ranking functions to a population.
(6) it selects, intersect, variation
Selection operation forms new population from old group with the excellent individual of given generation gap probability selection, with numerous
It grows to obtain next-generation individual, the selected probability of individual is obtained by fitness, and fitness is bigger, and selected probability is also bigger.
Selection operation uses roulette selection algorithm in the present invention, and the selected probability of individual is calculated by following formula
Wherein FjIt for the fitness of individual j, is calculated by previous step, N is the number of population at individual.
Crossover operation is two individuals of random selection from population, and whether each chromosome intersects general by given intersection
Rate determines.Its process is: for each chromosome, the random number between 0~1 is generated, if the value is general less than specified intersection
Rate, then selected chromosome is intersected, and otherwise chromosome is not involved in intersection, is copied directly in new population, crossover operation
As follows
Every two individual is intersected by crossover probability, into excessively respective portion gene exchange, generates two new sons
Generation.It is randomly generated an effective mating position when specific operation, chromosome exchange be located at behind mating position so base
Cause.
Mutation operation be intersect after new population in chromosome each gene, which is determined according to mutation probability
Whether make a variation.Its process is: the random number between 0~1 is generated, if the value is less than specified mutation probability, selected base
Because making a variation, new chromosome is generated, mutation operation is as follows:
After completing the above operation, the fitness of the new population of generation is recalculated, by the new population of generation by fitness
Size is inserted into old population, and updates optimal chromosome.
Fitness individual in newly-generated population is calculated, is recombinated new individual with old population according to fitness,
Obtain new population.
If genetic algebra is less than maximum genetic algebra, go to step (5), the new population conduct of generation is next time hereditary
Initial value, until meeting given genetic algebra.
If genetic computation number is less than given number, step (4) are transferred to, new optimal population is calculated, until meeting
Specified calculation times.It is transferred to step (7).
(7) optimal output is selected
Optimal selection is carried out to the optimal result that multiple genetic computation provides.Heredity output optimum individual is corresponding every time
Solve objective function optimal value corresponding with the solution.The objective function optimal value of each heredity output is selected, selection target
The smallest solution of functional value gives constraint non-linear estimations as iteration initial value as output.
(8) non-linear estimations are constrained
By optimal solution obtained in (7) as the iteration initial value for non-linear estimations function fmincon, sought with this
The optimal value near the figure of merit of part time is looked for, obtains globe optimum, as the optimal value of parameter Estimation, i.e., we to be estimated
Vector Target-missing Quantity relevant parameter.
It explains below to specific embodiment.
The related experiment parameter used in this experiment is as follows:
Missile Motion speed: 300m/s
The operating distance of Miss distance measurement equipment: 75m, Detailed Experimental parameter are shown in Table 1.
1 experiment parameter table of table
After collecting echo-signal, the number of phases of the different moments of each receiving channel is obtained by correlation technique
According to estimated value, range estimation value, the estimated value of miss distance size with Missile Motion speed, using method of the invention
Vector Target-missing Quantity parametric solution is carried out, process is as follows:
(1) reference channel is selected, according to the top-quality channel of signal quality selection signal as reference channel, is calculated
The opposite phase difference with this channel in each channelIt is as follows to construct objective function
(2) building constraint function is as follows:
Wherein α, β, x0,y0,z0For parameter to be solved.
(3) setting solves parameter
In test, the solution parameter that the present invention uses is as follows:
Population Size NIND=200；Genetic algebra MAXGEN=80；Individual lengths PRECI=40；Generation gap
Probability GGAP=0.95；Crossover probability px=0.7；Mutation probability pm=0.01；Passing number of repetition is 3 times；
(4) initialization population,
According to 4 given Population Sizes and individual lengths, population is created, real number is converted by corresponding coding function.
(5) fitness function
In this experiment, since the target of parameter Estimation is parameter value corresponding when acquiring objective function minimum value,
Therefore fitness function is taken as objective function, finds out its corresponding target function value according to different individual values, according to sequence letter
Several pairs of population's fitness are ranked up；
(6) it selects, intersect, variation
Individual in population is selected according to algorithm above-mentioned, is intersected, mutation operation, is calculated in newly-generated population
New individual is recombinated with old population according to fitness, obtains new population by the fitness of individual.
If genetic algebra reaches specified maximum genetic algebra, then the optimal solution of this genetic computation and its optimal is exported
Solve the value of corresponding objective function.
The operation for repeating (4)~(6), the optimal solution and its corresponding by multiple genetic computation, after obtaining heredity every time
Target function value.When calculation times meet sets requirement, it is transferred to step (7)；
Table 2 is output data (the corresponding experiment parameter are as follows: missile velocity 300m/s is corresponded to that a specific embodiment obtains
Miss point coordinate is (15,0,0), and corresponding trajectory azimuth is 90 degree, and pitch angle is 60 degree)
The output data that 2 specific embodiment of table obtains
| 13.5991 | -4.6694 | 13.6371 | 13.6371 | 1.1746 |
| 4.5841 | 12.7193 | -1.3679 | 0.7668 | 1.8107 |
| 14.9279 | -0.1487 | 0.2706 | 1.0472 | 1.5724 |
Last two values are the pitch angle and azimuth that radian indicates
The optimal value of corresponding objective function is
822.5582 669.9185 14.5790
(7) optimal value of the objective function obtained according to multiple genetic computation, wherein optimal one group of selection, at this
In experiment, i.e. the corresponding one group of solution of target function value.
Corresponding to the data in (6), selected optimal solution is shown in Table 3,
The optimal solution being selected in 3 specific embodiment of table
| 14.9279 | -0.1487 | 0.2706 | 1.0472 | 1.5724 |
Corresponding target function value is 14.5790.
(8) optimal solution obtained in (7) is input in non-linear estimations function fmincon, is brought into as initial value
Constraint function, the optimal estimation parameter value that Vector Target-missing Quantity is obtained after calculating are shown in Table 4,
4 Vector Target-missing Quantity parameter estimation result of table
| x0 | y0 | z0 | β | α |
| 14.9296 | 0.00762977 | 0.0728745 | 59.9668 | 89.5455 |
Corresponding to the experiment parameter of above-mentioned specific embodiment setting, parameter calculating is carried out using the method for the present invention, obtains phase
The simulation result answered and calculating time such as table 5:
The simulation result of 5 specific embodiment of table and calculating time
Experiment parameter calculates the computer used and is configured to
Processor: Intel (R) Xeon (R) CPU E3-1270 V2@3.50GHz 3.50GHz
Memory (RAM): 16.0GB is installed
System type: 64 bit manipulation systems
Simulation result and experimental result all show that computational accuracy is higher using algorithm for estimating of the invention, and calculate
Time is shorter, can obtain accurately in tens seconds as a result, the computational accuracy of effective solution requires and the requirement of real-time.
The method of the present invention combines the advantages of GA algorithm and traditional non-linear estimation algorithm and deficiency, for estimating complexity
Non-linear Vector Target-missing Quantity parameters calculation, have good real-time and higher precision, can effectively solve the problem that at present
There are the problem of.Simulation result and experimental result all show using algorithm for estimating of the invention, not only big on solving the time
It is big to shorten, and the unstability of estimated result is improved, so that estimation can obtain optimal estimation value every time
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improve and become
Shape also should be regarded as protection scope of the present invention.
Claims (6)
1. the Vector Target-missing Quantity method for parameter estimation based on GA, characterized in that include the following steps:
1) building solves objective function: according to the top-quality channel of signal quality selection signal as reference channel, calculating each
The opposite phase difference with this channel in a channel, constructs objective function based on phase difference；
2) constraint function is constructed；
3) genetic algorithm is utilized, the objective function of setting is optimized, its optimal solution is acquired；
4) step 3) is obtained into optimal solution as the iteration initial value of non-linear estimations function, obtains global optimum.
2. Vector Target-missing Quantity method for parameter estimation according to claim 1, characterized in that building objective function is as follows:
In formulaWithRespectively in tiThe notional phase difference and actual estimated phase of moment target reflection echo
Difference, 1i are imaginary unit, and i_ck is the subscript of reference channel, and j is channel subscript, and i is time index, and N is significant figure strong point
Number,For (α, β, x0,y0,z0) function, physical relationship is as follows
Wherein ri(t) and rj(t) be respectively i-th and jth road t moment guided missile to receiving antenna instantaneous distance:
Wherein,
{v,L0,r0It is scalar missile parameter.
3. Vector Target-missing Quantity method for parameter estimation according to claim 1, characterized in that building nonlinear restriction function is such as
Under
Wherein the coordinate of miss point is (x0,y0,z0), the coordinate of M receiving antenna is (xi,yi,zi), i=1,2,3....M, it leads
The trajectory deflection angle and trajectory tilt angle of bullet are respectively α and β.
4. Vector Target-missing Quantity method for parameter estimation according to claim 1, characterized in that in step 3), genetic algorithm
Optimization Solution process are as follows:
(1) solve parameter setting: subscript including reference channel, the algebra of maximum heredity, the size of population, the length of individual,
The probability of generation gap, the probability of intersection, the probability of variation, genetic iteration number；
(2) initialization kind；Group uses binary coding method, generates population using the method for generating random number and generates each dye
The population of initialization is converted into metric number by coding function by the value of colour solid
(3) fitness calculates: calculating function for objective function as fitness, the smaller then fitness of value is better, passes through sequence
Function is ranked up the fitness size of a population；
(4) it selects: using roulette selection algorithm, the selected probability of individual is calculated by following formula:
Wherein FjFor the fitness of individual j, N is the number of population at individual；
(5) intersect: setting the probability P of intersectionx, each individual in population, the corresponding random number for generating one [0,1], if random number
Less than crossover probability Px, then intersected；
(6) it makes a variation: setting the probability P of variationm, each individual in population, the corresponding random number for generating one [0,1], if at random
Number is less than mutation probability Pm, then make a variation；
(7) new population is generated by (5) (6), is then proceeded to circulation and is generated next-generation population；
(8) the smallest solution of selection target functional value, as output.
5. Vector Target-missing Quantity method for parameter estimation according to claim 4, characterized in that the size of population is 200；It is maximum
The algebra of heredity is 80；The length of individual is 40；The probability of generation gap is 0.95；The probability of intersection is 0.7；The probability of variation is
0.01；The number of genetic iteration was 3 generations.
6. Vector Target-missing Quantity method for parameter estimation according to claim 1, characterized in that non-linear estimations function is
Fmincon function, the fmincon function are the matlab functions for solving linear multivariate function minimum value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811389761.5A CN109539884A (en) | 2018-11-21 | 2018-11-21 | A kind of Vector Target-missing Quantity method for parameter estimation based on GA |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811389761.5A CN109539884A (en) | 2018-11-21 | 2018-11-21 | A kind of Vector Target-missing Quantity method for parameter estimation based on GA |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109539884A true CN109539884A (en) | 2019-03-29 |
Family
ID=65848661
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811389761.5A Pending CN109539884A (en) | 2018-11-21 | 2018-11-21 | A kind of Vector Target-missing Quantity method for parameter estimation based on GA |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109539884A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110108162A (en) * | 2019-06-18 | 2019-08-09 | 北京电子工程总体研究所 | A kind of drop point that motion platform long distance is thrown automatically amendment Guidance and control method |
| CN111060871A (en) * | 2019-12-26 | 2020-04-24 | 南京长峰航天电子科技有限公司 | Five-element array positioning method and device based on improved genetic algorithm |
| CN112364528A (en) * | 2020-12-09 | 2021-02-12 | 南京长峰航天电子科技有限公司 | Microwave darkroom multipath effect evaluation algorithm based on genetic algorithm |
| CN113391307A (en) * | 2020-03-12 | 2021-09-14 | 中国人民解放军火箭军研究院系统工程研究所 | Method and device for quickly estimating missile terminal motion parameters in incomplete signals |
| CN114065630A (en) * | 2021-11-17 | 2022-02-18 | 西北工业大学 | Uncertain parameter focus matching field sound source power estimation method based on genetic algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4057708A (en) * | 1975-04-07 | 1977-11-08 | Motorola Inc. | Minimum miss distance vector measuring system |
| CN106643297A (en) * | 2016-12-23 | 2017-05-10 | 南京长峰航天电子科技有限公司 | Estimation and correction method for vector miss distance parameters of motion platform |
| CN107392420A (en) * | 2017-06-12 | 2017-11-24 | 上海电力学院 | A kind of household energy management system intelligent control method based on demand response |
| CN108416103A (en) * | 2018-02-05 | 2018-08-17 | 武汉大学 | A kind of method for diagnosing faults of electric automobile of series hybrid powder AC/DC convertor |
-
2018
- 2018-11-21 CN CN201811389761.5A patent/CN109539884A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4057708A (en) * | 1975-04-07 | 1977-11-08 | Motorola Inc. | Minimum miss distance vector measuring system |
| CN106643297A (en) * | 2016-12-23 | 2017-05-10 | 南京长峰航天电子科技有限公司 | Estimation and correction method for vector miss distance parameters of motion platform |
| CN107392420A (en) * | 2017-06-12 | 2017-11-24 | 上海电力学院 | A kind of household energy management system intelligent control method based on demand response |
| CN108416103A (en) * | 2018-02-05 | 2018-08-17 | 武汉大学 | A kind of method for diagnosing faults of electric automobile of series hybrid powder AC/DC convertor |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110108162A (en) * | 2019-06-18 | 2019-08-09 | 北京电子工程总体研究所 | A kind of drop point that motion platform long distance is thrown automatically amendment Guidance and control method |
| CN111060871A (en) * | 2019-12-26 | 2020-04-24 | 南京长峰航天电子科技有限公司 | Five-element array positioning method and device based on improved genetic algorithm |
| CN113391307A (en) * | 2020-03-12 | 2021-09-14 | 中国人民解放军火箭军研究院系统工程研究所 | Method and device for quickly estimating missile terminal motion parameters in incomplete signals |
| CN112364528A (en) * | 2020-12-09 | 2021-02-12 | 南京长峰航天电子科技有限公司 | Microwave darkroom multipath effect evaluation algorithm based on genetic algorithm |
| CN112364528B (en) * | 2020-12-09 | 2024-02-13 | 南京长峰航天电子科技有限公司 | Genetic algorithm-based microwave darkroom multipath effect influence assessment method |
| CN114065630A (en) * | 2021-11-17 | 2022-02-18 | 西北工业大学 | Uncertain parameter focus matching field sound source power estimation method based on genetic algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109539884A (en) | A kind of Vector Target-missing Quantity method for parameter estimation based on GA | |
| CN103345577B (en) | Variation Bayesian probability assumed density multi-object tracking method | |
| CN106102163B (en) | WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm | |
| CN108089148B (en) | A kind of passive track-corelation direction cross positioning method based on time difference information | |
| CN109246612B (en) | RFID indoor positioning algorithm based on double-label array phase difference | |
| CN105137418B (en) | Multiple target tracking and data interconnection method based on complete adjacent fuzzy clustering | |
| CN101980044B (en) | Method for tracking multiple targets under unknown measurement noise distribution | |
| CN107085205B (en) | Self-adapting detecting method based on clutter covariance matrix structural information | |
| CN101614817A (en) | A kind of multi-object tracking method based on ground moving target indication radar system | |
| CN104021519B (en) | Multiple-moving target tracking method under the conditions of dense clutter based on GPU architecture | |
| CN109752710B (en) | Rapid target angle estimation method based on sparse Bayesian learning | |
| CN110673090B (en) | Passive multi-station multi-target positioning method based on DBSCAN | |
| CN106021697B (en) | A kind of rapid phase-control battle array radar Time-energy resource joint management method | |
| CN108802683A (en) | A kind of source localization method based on management loading | |
| CN106501765B (en) | A kind of Maximum Likelihood DOA Estimation based on quadratic sum and Semidefinite Programming | |
| Fang et al. | Accurate indoor location estimation by incorporating the importance of access points in wireless local area networks | |
| CN107255814A (en) | A kind of radar target detection method based on LFMSK waveforms | |
| CN108680911A (en) | A kind of radar target direction-finding method based on neural network | |
| Fonseka et al. | Indoor localization for IoT applications using fingerprinting | |
| Du et al. | A new scatterer trajectory association method for ISAR image sequence utilizing multiple hypothesis tracking algorithm | |
| CN111539476B (en) | Observation point deployment method for positioning personal information propagation diffusion source based on naive Bayes | |
| CN109783960A (en) | A kind of Wave arrival direction estimating method based on meshing refinement | |
| CN110488276B (en) | Heterogeneous radar network optimal resource on-demand distribution method oriented to multi-target tracking task | |
| CN116437290A (en) | Model fusion method based on CSI fingerprint positioning | |
| CN109188420B (en) | Narrow-band radar target tracking method based on deep long-short term memory network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190329 |
|
| RJ01 | Rejection of invention patent application after publication |