CN108717176A - Time difference locating technology method based on artificial bee colony algorithm - Google Patents
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- G—PHYSICS
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Abstract
The invention discloses the time difference locating technology methods based on artificial bee colony algorithm, belong to multistation passive TDOA location technical field.It is mainly used in the positioning to radiant source target under the conditions of multistation.Artificial bee colony algorithm is combined by the present invention with conventional time difference locating technology, effectively realizes being accurately positioned for radiation source.The present invention is directed to the characteristics of Newton iteration method and artificial bee colony algorithm, the two is combined and is had complementary advantages, first conventional time difference locating technology solving equations problem is converted to using maximal possibility estimation and finds a function Constrained and Unconstrained Optimization, artificial bee colony algorithm is used to obtain coordinate according to a preliminary estimate again, it is finally used the coordinate as the iteration initial value of Newton iterative, and then realizes and be quickly accurately positioned.The present invention effectively solves the problems, such as that Newton iterative needs access to the initial value of optimal solution and could realize that good result and artificial bee colony algorithm later stage are slow-footed, and can effectively improve positioning accuracy.
Description
Technical field
The invention belongs to multistation passive TDOA location technical fields, and in particular to the multistation time difference based on artificial bee colony algorithm
Localization method.
Background technology
Multistation passive TDOA location technology is a kind of reaching time-difference structure measuring emitter Signals using different observation stations
Hyperbola is built, solves a plurality of hyperbola intersection point to realize the method positioned to radiation source.Multistation passive TDOA location can
It is equivalent to solve nonlinear multivariable equation group, positioning purpose is realized by solving equations.Method for solving such as Taylor series Method, Chan
Algorithm, least square method, Newton iteration method etc., wherein Newton iteration method calculating speed is fast, process is simple and positioning result is accurate,
But it needs access to the iteration initial value competence exertion of optimal solution its performance, and it is mostly locally optimal solution to acquire.Artificial bee colony
Algorithm can solve function value optimization etc. and ask as a kind of biological intelligence optimization algorithm of the intelligent search behavior of simulation bee colony
Topic, does not depend on initial coordinate values, and control parameter is few, easy to operate, and robustness is high and to possess stronger global optimizing ability etc. excellent
Point, but local search ability is weaker, and algorithm late convergence is slower.
Newton iterative is combined with artificial bee colony algorithm, the two has complementary advantages.It will be solved using maximal possibility estimation
Equation group problem is converted to solved function Constrained and Unconstrained Optimization, then artificial bee colony algorithm is used to obtain coordinate according to a preliminary estimate, then should
Coordinate is used as the iteration initial value of Newton iterative, and then is realized and be quickly accurately positioned.Newton iteration is effectively solved to calculate
The initial value that method needs access to optimal solution could realize good result and artificial bee colony algorithm later stage slow-footed problem.
Invention content
It is asked the purpose of the present invention is to provide existing when solving to carry out time difference locating technology using Newton iterative
When topic, i.e. Newton iterative will realize quick high accuracy positioning, what is needed has iteration initial value and to be desorbed close to optimal
Near field could realize its Fast Convergent effect, play its maximum performance, and acquired solution is mostly that locally optimal solution is asked
The time difference locating technology method based on artificial bee colony algorithm of topic and the slow problem of artificial bee colony algorithm late convergence.
The purpose of the present invention is realized by following technical solution：
Multistation time difference passive location mathematical model is initially set up, TDOA (time difference) value is calculated according to mathematical model and establishes pass
In the Nonlinear System of Equations of coordinates of targets Gaussian data, approximation clothes are can be considered since white Gaussian noise is added in TDOA measured values
From normal distribution, distance between sites difference can be obtained by measured TDOA values, and it is to meet the stochastic variable being just distributed very much, is used
Maximal possibility estimation is converted to solved function minimum problems by nonlinear multivariable equation group problem is solved.Then people can be used
Worker bee group's algorithmic preliminaries solve radiation source positions coordinate, and detailed process is first to initialize control parameter, such as bee colony scale, nectar source number
Mesh, maximum cycle etc.；Calculate each initial solution xijFitness value, gathering honey bee according to formula carry out field search generate it is new
Solve vij, and the corresponding fitness value of new explanation is calculated, greedy selection is carried out, even new explanation fitness value is better than the fitness of old solution
Value then replaces old solution with new explanation, and it is constant otherwise to retain old solution；The select probability P with decorrelation is calculated further according to formulai, observation
Bee is further according to roulette wheel selection with probability PiFood source is selected, carrying out field search further according to formula generates new explanation vij, and count
The corresponding fitness value of new explanation is calculated, greedy selection is carried out.Then the solution to be abandoned is judged whether there is, if it is present according to public affairs
Formula generates a new explanation and replaces the solution to be abandoned, and records the best solution of effect so far, finally judges whether to meet termination
Condition just exports optimal result if meeting, and is unsatisfactory for continuing to repeat to generate the old solution of new explanation replacement and later step according to formula.
The iterations of Newton iterative are set again and terminate the parameters such as thresholding, the coordinates of targets that artificial bee colony algorithm is tentatively obtained
Value is built as initial value needed for Newton iterative using TDOA values and the obtained initial alignment coordinate of artificial bee colony algorithm
Jacobian matrix carries out Newton iterative calculation according to newton iteration formula, until defeated when less than threshold value or more than iterations
Go out as a result, to obtain better positioning accuracy, realizes the positioning to radiation source.
Time difference locating technology method based on artificial bee colony algorithm, includes the following steps：
(1) multistation time difference passive location mathematical model is established, using N number of fixed observer station, coordinate is (xi,yi,zi), it is right
Radiation source M (x, y, z) carries out three-dimensional localization, calculates TDOA values according to mathematical model and establishes the non-linear side about coordinates of targets
Journey group；
(2) it uses maximal possibility estimation that solution nonlinear multivariable equation group problem is converted to solved function minimum value to ask
Topic, obtains function g (x, y, z)；
(3) object function by g (x, y, z) as artificial bee colony algorithm is solved using artificial bee colony algorithmic preliminaries and is radiated
Source position coordinate initializes control parameter, generates SN initial solution, and nectar source number is SN/2, obtains maximum cycle；
(4) each initial solution x is calculatedijFitness value, gathering honey bee according to search equation carry out neighborhood search generate new explanation
vij, and the corresponding fitness value of new explanation is calculated, and greedy selection is carried out, if new explanation fitness value is better than the fitness value of old solution,
Old solution is replaced with new explanation, it is constant otherwise to retain old solution；
(5) the select probability P with decorrelation is calculated according to following equationi, observation bee is further according to roulette wheel selection with general
Rate PiFood source is selected, carrying out field search further according to search equation generates new explanation vij, and the corresponding fitness value of new explanation is calculated,
Carry out greedy selection：
Wherein fitiTo solve vijFitness value, fitnFor the fitness value of solution；
(6) solution to be abandoned is judged whether there is, if it is present generating a new explanation replaces the solution to be abandoned, and is recorded
The best solution of effect so far finally judges whether to meet end condition, just exports optimal result if meeting, be unsatisfactory for returning
Return step (4)；
(7) iterations of setting Newton iterative and termination thresholding ε parameters, the radiation that artificial bee colony algorithm is estimated
Source coordinate value is as the radiation source initial value needed for Newton iterative iterative process；
(8) initial value coordinate is assigned toTo calculate new distance between sites difference and new observation station to target away from
From valueTo x, y, z derivations are to build Jacobian matrix G：
(9) coordinate value replacement constantly will be newly obtained according to Newton iterativeNewton iterative calculation is carried out, is obtained
More accurate coordinates value, until output using least square method as a result, obtain error when less than threshold value or more than iterations
Value σ：
Wherein h is the difference of measurement distance difference and estimated distance difference, and Δ x, Δ y, Δ z is respectively x, y, z coordinate
Deviation；
(10) it enablesCompared with setting threshold value ε, above step is repeated, until
When σ is sufficiently small, that is, meet | Δ x |+| Δ y |+| Δ z | when < ε, output iterative calculation is as a result, think the position of radiation source this moment
Estimated coordinates are justTo complete to be accurately positioned.
The formula of Nonlinear System of Equations of the foundation that the step (1) uses about coordinates of targets for：
Wherein r1For main website between target at a distance from, riFor i-th of extension station range-to-go, τi,1It is surveyed for TDOA (time difference)
Magnitude, Δ ri,1It is the target containing measurement error to extension station distance and target to the difference of main website distance, c is Electromagnetic Wave Propagation
Speed, cni,1To measure the white Gaussian noise introduced when TDOA.
Solved function minimum problems are specially in the step (2)：
Distance between sites difference can be obtained by measured TDOA values, and to meet the stochastic variable being just distributed very much, due to ri-r1
The value being to determine, ni,1Variance be δ2, ni,1~N (0, δ2) normal distribution, so corresponding maximum likelihood function is：
Maximum likelihood function is asked to be equivalent to solve following functional minimum value problem：
It is equivalent to function g (x, y, z)：
In above formula, Δ r=[Δ r2,1,Δr3,1,…,ΔrN,1]T, r=[r2,r3,…,rN]T, r1=[r1,r1,…,r1
]T。
Each initial solution x is calculated in the step (4)ijThe formula of fitness value be：
Wherein fn(xij) it is solution xijObject function, fitn(xij) it is solution xijFitness value；
It carries out neighborhood search and generates new explanation vijFormula be：
vij=xbest,j+φij(xij-xkj)
Wherein xbest,jFor last iteration optimal solution, k is the nectar source different from i, and j is randomly selected subscript, φijFor
Random number between [- 1,1].
The beneficial effects of the present invention are：
Radiation source initial value need not be artificially arranged in artificial bee colony algorithm in solution procedure, using artificial bee colony algorithm with
The method that Newton iterative is combined solves traditional Newton iterative and needs good initial value in position fixing process
The problem of could realizing quick high accuracy locating effect.Global search can be carried out simultaneously, obtained globally optimal solution, avoided newton
The problem of result that iterative algorithm may obtain is locally optimal solution.
Use of the Newton iterative in the multistation passive TDOA location later stage, and effectively overcome artificial bee colony algorithm part
Search capability is weak and the slow disadvantage of late convergence.For Newton iterative is used alone, it can obtain higher
Positioning accuracy.
This swarm intelligence optimization algorithm of artificial bee colony is combined with multistation passive TDOA location, by the non-linear of script
Equation solution problem is converted to function value optimization problem, and new solution is provided for multistation passive TDOA location problem.
Description of the drawings
Fig. 1 is process chart
Fig. 2 is the simulation result diagram for handling multistation passive TDOA location problem
Specific implementation mode
The specific implementation mode of the present invention is described further below in conjunction with the accompanying drawings：
In conjunction with attached drawing 1, the present invention is described further：
Step 1：Multistation time difference passive location mathematical model is established, using N number of fixed observer station, coordinate is (xi,yi,
zi), three-dimensional localization is carried out to radiation source M (x, y, z), TDOA values are calculated according to mathematical model and are established about the non-of coordinates of targets
System of linear equations, formula are as follows：
Wherein r1For main website between target at a distance from, riFor i-th of extension station range-to-go, τi,1It is surveyed for TDOA (time difference)
Magnitude, Δ ri,1It is the target containing measurement error to extension station distance and target to the difference of main website distance, c is Electromagnetic Wave Propagation
Speed, cni,1To measure the white Gaussian noise introduced when TDOA.
Step 2：Solved function minimum value is converted to by nonlinear multivariable equation group problem is solved using maximal possibility estimation
Problem.Gaussian data, approximate Normal Distribution, by measured are can be considered since white Gaussian noise being added in TDOA measured values
Distance between sites difference can be obtained in TDOA values, and to meet the stochastic variable being just distributed very much, due to ri-r1The value being to determine, ni,1's
Variance is δ2, ni,1~N (0, δ2) normal distribution, so corresponding maximum likelihood function is：
Maximum likelihood function is asked to be equivalent to solve following functional minimum value problem：
Formula (4) is equivalent to function g (x, y, z)：
In above formula, Δ r=[Δ r2,1,Δr3,1,…,ΔrN,1]T, r=[r2,r3,…,rN]T, r1=[r1,r1,…,r1
]T。
Step 3：Object function by formula (5) as artificial bee colony algorithm solves spoke using artificial bee colony algorithmic preliminaries
Source position coordinate is penetrated, control parameter is initialized, that is, initializes bee colony, generates SN initial solution, nectar source number is SN/2, and maximum is followed
Ring number etc..
Step 4：Each initial solution x is calculated according to formula (6)ijFitness value, gathering honey bee is according to search equation, i.e., public
Formula (7) carries out neighborhood search and generates new explanation vij, and the corresponding fitness value of new explanation is calculated, greedy selection is carried out, even new explanation is suitable
It answers angle value to be better than the fitness value of old solution, then replaces old solution with new explanation, it is constant otherwise to retain old solution.
Wherein fn(xij) it is solution xijObject function, fitn(xij) it is solution xijFitness value.
vij=xbest,j+φij(xij-xkj) (7)
Wherein xbest,jFor last iteration optimal solution, k is the nectar source different from i, and j is randomly selected subscript, φijFor
Random number between [- 1,1].
Step 5：The select probability P with decorrelation is calculated according to formula (8)i, observation bee further according to roulette wheel selection with
Probability PiFood source is selected, carrying out field search further according to search equation generates new explanation vij, and calculate the corresponding fitness of new explanation
Value carries out greedy selection.
Wherein fitiTo solve vijFitness value, fitnFor the fitness value of solution.
Step 6：The solution to be abandoned is judged whether there is, replaces wanting if it is present generating a new explanation according to formula (7)
The solution abandoned, and record the best solution of effect so far, finally judges whether to meet end condition, if meet just export it is optimal
As a result, being unsatisfactory for then return to step four.
Step 7：The iterations of Newton iterative are set and terminate the parameters such as thresholding ε, artificial bee colony algorithm is estimated
Radiation source coordinate value as the radiation source initial value needed for Newton iterative iterative process.
Step 8：Initial value coordinate is assigned toTo calculate new distance between sites difference and new observation station to mesh
Subject distance valueTo x, y, z derivations are to build Jacobian matrix G.
Step 9：Coordinate value replacement constantly will be newly obtained according to Newton iterativeNewton iterative calculation is carried out,
More accurate coordinates value is obtained, until output is as a result, to obtain preferably positioning when less than threshold value or more than iterations
Precision realizes the positioning to radiation source.Error amount σ is obtained using least square method：
Wherein h is the difference of measurement distance difference and estimated distance difference, and Δ x, Δ y, Δ z is respectively x, y, z coordinate
Deviation.
Step 10：It enablesCompared with setting threshold value ε, above step is repeated,
Until when σ is sufficiently small, that is, meet | Δ x |+| Δ y |+| Δ z | when < ε, output iterative calculation is as a result, think radiation source this moment
Location estimation coordinate is justTo complete to be accurately positioned.
Fig. 2 is to choose the matlab simulation result diagrams positioned to single moving emitter under the conditions of four observation stations.
As seen from the figure as the increase of distance between observation station and radiation source, position error are consequently increased, positioning accuracy is more satisfactory.It is imitative
True result effectively demonstrates effectiveness of the invention, is efficiently solved using artificial bee colony algorithm and carries out multistation using Newton iteration
It needs suitable initial value to obtain problem when positioning using TDOA, while artificial bee colony algorithm and Newton iterative being had complementary advantages, realize
Quick pinpoint purpose shows that the present invention has good practicability.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, any made by repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (4)
1. the time difference locating technology method based on artificial bee colony algorithm, which is characterized in that include the following steps：
(1) multistation time difference passive location mathematical model is established, using N number of fixed observer station, coordinate is (xi,yi,zi), to radiation
Source M (x, y, z) carries out three-dimensional localization, calculates TDOA values according to mathematical model and establishes the nonlinear equation about coordinates of targets
Group；
(2) it uses maximal possibility estimation to be converted to solved function minimum problems by nonlinear multivariable equation group problem is solved, obtains
To function g (x, y, z)；
(3) object function by g (x, y, z) as artificial bee colony algorithm solves radiation source position using artificial bee colony algorithmic preliminaries
Coordinate is set, control parameter is initialized, generates SN initial solution, nectar source number is SN2, obtains maximum cycle；
(4) each initial solution x is calculatedijFitness value, gathering honey bee carries out neighborhood search according to search equation and generates new explanation vij, and
The corresponding fitness value of new explanation is calculated, greedy selection is carried out and uses new explanation if new explanation fitness value is better than the fitness value of old solution
Old solution is replaced, it is constant otherwise to retain old solution；
(5) the select probability P with decorrelation is calculated according to following equationi, observation bee is further according to roulette wheel selection with probability PiChoosing
Food source is selected, carrying out field search further according to search equation generates new explanation vij, and the corresponding fitness value of new explanation is calculated, coveted
Greedy selection：
Wherein fitiTo solve vijFitness value, fitnFor the fitness value of solution；
(6) solution to be abandoned is judged whether there is, if it is present generating a new explanation replaces the solution to be abandoned, and is recorded so far
Until the best solution of effect, finally judge whether to meet end condition, just export optimal result if meeting, be unsatisfactory for returning to step
Suddenly (4)；
(7) iterations of setting Newton iterative and termination thresholding ε parameters, the radiation source of artificial bee colony algorithm estimation is sat
Scale value is as the radiation source initial value needed for Newton iterative iterative process；
(8) initial value coordinate is assigned toTo calculate new distance between sites difference and new observation station to target range valueTo x, y, z derivations are to build Jacobian matrix G：
(9) coordinate value replacement constantly will be newly obtained according to Newton iterativeNewton iterative calculation is carried out, is obtained more
Accurate coordinates value, until output using least square method as a result, obtain error amount σ when less than threshold value or more than iterations：
Wherein h is the difference of measurement distance difference and estimated distance difference, and Δ x, Δ y, Δ z is respectively x, y, the deviation of z coordinate
Value；
(10) it enablesCompared with setting threshold value ε, above step is repeated, until σ foots
Enough hours, that is, meet | Δ x |+| Δ y |+| Δ z | when < ε, output iterative calculation is as a result, think the location estimation of radiation source this moment
Coordinate is justTo complete to be accurately positioned.
2. the time difference locating technology method according to claim 1 based on artificial bee colony algorithm, which is characterized in that described
The foundation that step (1) uses about the Nonlinear System of Equations of coordinates of targets formula for：
Wherein r1For main website between target at a distance from, riFor i-th of extension station range-to-go, τi,1It is measured for TDOA (time difference)
Value, Δ ri,1It is the difference of the target containing measurement error to extension station distance and target to main website distance, c is that Electromagnetic Wave Propagation is fast
Degree, cni,1To measure the white Gaussian noise introduced when TDOA.
3. the time difference locating technology method according to claim 1 based on artificial bee colony algorithm, which is characterized in that described
Solved function minimum problems are specially in step (2)：
Distance between sites difference can be obtained by measured TDOA values, and to meet the stochastic variable being just distributed very much, due to ri-r1It is true
Fixed value, ni,1Variance be δ2, ni,1~N (0, δ2) normal distribution, so corresponding maximum likelihood function is：
Maximum likelihood function is asked to be equivalent to solve following functional minimum value problem：
It is equivalent to function g (x, y, z)：
In above formula, Δ r=[Δ r2,1,Δr3,1,…,ΔrN,1]T, r=[r2,r3,…,rN]T, r1=[r1,r1,…,r1]T。
4. the time difference locating technology method according to claim 1 based on artificial bee colony algorithm, which is characterized in that described
Each initial solution x is calculated in step (4)ijThe formula of fitness value be：
Wherein fn(xij) it is solution xijObject function, fitn(xij) it is solution xijFitness value；
It carries out neighborhood search and generates new explanation vijFormula be：
vij=xbest,j+φij(xij-xkj)
Wherein xbest,jFor last iteration optimal solution, k is the nectar source different from i, and j is randomly selected subscript, φijFor [- 1,
1] random number between.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109581291A (en) * | 2018-12-11 | 2019-04-05 | 哈尔滨工程大学 | A kind of direct localization method based on artificial bee colony |
| CN111381209A (en) * | 2018-12-29 | 2020-07-07 | 深圳市优必选科技有限公司 | Distance measurement positioning method and device |
| CN109871612A (en) * | 2019-02-19 | 2019-06-11 | 华东理工大学 | In conjunction with the heterogeneous catalysis surface coverage acquisition methods of ODE integral and Newton iterative method |
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| CN109884582A (en) * | 2019-03-26 | 2019-06-14 | 电子科技大学 | The method of target three-dimensional coordinate is quickly determined using one-dimensional direction finding |
| CN111880145A (en) * | 2020-08-03 | 2020-11-03 | 中国电子科技集团公司第三十六研究所 | Radiation source time difference positioning method and device and electronic equipment |
| CN111880145B (en) * | 2020-08-03 | 2023-07-07 | 中国电子科技集团公司第三十六研究所 | Radiation source time difference positioning method and device and electronic equipment |
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