CN108363622A - A kind of implementation method of the Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation - Google Patents
A kind of implementation method of the Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation Download PDFInfo
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Abstract
The invention discloses a kind of implementation methods of the Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation, belong to passive location field.First, each location information from the collected TOA data of receiving station and each receiving station is transmitted to by main equipment and multi-core DSP by the wireless communication module of each receiving station；Multi-core DSP to receive TOA carry out pairing TDOA is calculated；It plans the particle populations quantity of particle cluster algorithm, distributes each kernel operation task of multi-core DSP；The adaptive value for initializing total optimization particle, determines fitness function；The particle from the non-global optimum in core is replaced with the spare particle in main core；It is obtained after the particle that main core replaces forms new population from core, the general estimated location that iteration is calculated with newly obtaining population；The result finally obtained using particle cluster algorithm in main core is carried out location Calculation as the initial value of Taylor series algorithm and obtains final positioning result.The diversity that particle populations can be increased, to make algorithmic statement faster, the solution of positioning is faster.
Description
Technical field
The invention belongs to passive location fields, and in particular to a kind of Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation
Implementation method.
Background technology
Electronic information equipment, the fight of electromagnetic space is used to grow in intensity with large batch of on modern battlefield.Wherein such as
What accurately and rapidly obtains hostile electromagnetic situation, and fast, essence, accurate which kind of radiation source of understanding where is it are to obtain electromagnetic warfare actively
The premise of power.So being positioned to the quick high accuracy in target emanation source for primary research topic, with processor technology
Increasingly update and location algorithm it is perfect, monokaryon DSP cannot be satisfied the positioning precisely in real time to target gradually, and multinuclear number
Word signal processor (DSP) has performance high, computing capability low in energy consumption, powerful, the odds for effectiveness of multi-core parallel concurrent and efficiently
Floating-point processing capacity, meet requirement of the positioning using TDOA system to operational efficiency, accuracy, real-time, reliability, therefore more
Applying in location technology from now on for core DSP is inexorable trend.
At present the peaks Yang Jun (nuclear electronics and Detection Techniques the 4th phase of volume 33, in April, 2013, based on Chan algorithms and
The step-out times of Taylor series hybrid algorithms positions) one kind for proposing being based on Chan algorithm initial values and chooses and Taylor series expansion
The time difference positioning method of the accurate iteration of method, but in the case of non line of sight transmission and poor channel performance, positioning accuracy declines,
The positioning initial value solved is inaccurate, influences the solution of Taylor Series Algorithms.
Invention content
The purpose of the present invention is to provide can quickly, in real time, accurately positioning, and positioning result can be fed back in real time
To a kind of implementation method for Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation that computer is shown.
The purpose of the present invention is realized by following technical solution：
A kind of implementation method of the Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation, includes the following steps：
Step (1)：It will be each from the collected TOA data of receiving station and each by the wireless communication module of each receiving station
The location information of a receiving station is transmitted to main equipment and multi-core DSP；
Step (2)：Multi-core DSP carries out pairing to receiving TOA and TDOA is calculated, according to the location information of receiving station and
The step-out time of radiation source utilizes Taylor series-positioning of the population synergetic to radiation source,
The TOA that 4 groups of difference receiving stations receive is matched one by one according to corresponding pairing rules in main core, is obtained
The different TOA that four different receiving stations of synchronization receive, subtract each other obtaining TDOA；
Step (3)：Plan the distribution of the particle populations quantity and each kernel operation task of multi-core DSP of particle cluster algorithm,
Multi-core DSP when with PSO Algorithm by the way of executing parallel and main core core0 be responsible for task distribution, scheduling
Work, is responsible for the execution of task from core；
Step (4)：The information of each particle is initialized, the adaptive value of total optimization particle is initialized, determines fitness letter
Number, and calculate the individual and globally optimal solution of particle in more each core；
Step (5)：It globally optimal solution in each core that step 4 obtains is transmitted to main core is compared and obtain entire grain
The globally optimal solution of subgroup is used in combination the spare particle in main core to replace the particle from the non-global optimum in core；
Step (6)：The particle that main core replaces is obtained from core and forms new population, updates position, the speed of new particle populations
Information is spent, calculates the adaptive value of particle, each existing adaptive value of particle obtains newest compared with being preferably adapted to value with history before
Individual optimal value replaces original history optimal value, the newest individual optimal value of more each particle, update from core it is global most
The figure of merit and the main core of biography are compared with whole global optimum before obtains newest whole global optimum；
Step (7)：Judge whether to meet termination criteria, if meeting termination criteria, stop search；Otherwise, step is repeated
(4) (6) are arrived；
Step (8)：The result obtained using particle cluster algorithm in main core is determined as the initial value of Taylor series algorithm
Final positioning result is calculated in position.
The step (1) is specially：
(1.1) main equipment and multi-core DSP are to the message for sending out start-up operation from receiving station；
(1.2) it after the broadcast message for receiving multi-core DSP from receiving station, is acquired since the acquisition system of receiving station
Its collected TOA is simultaneously put into FIFO by TOA；
(1.3) after FIFO full scale will the TOA in FIFO is read from the DSP of equipment；
(1.4) TOA that module is read by radio communication and oneself location information are transmitted to multi-core DSP.
The step (4) is specially：
(4.1) A is initialized0—A7In all particles position and speed information, and initialize the adaptation of total optimization particle
Value f=0；3 are (x from station coordinates is receivedi,yi,zi) (i=1,2,3) and main receiving station (x0,y0,z0) in same plane, target
The position of radiation source is (x, y, z), and it is R that the target emanation source of positioning using TDOA, which is arrived from the distance of equipment,i(i=1,2,3), target spoke
The distance for penetrating source to main equipment is R0, can be obtained according to relationship between target emanation source and the position of master-slave equipment
In formula, Δ ti0For the measured value of the reaching time-difference in target emanation source to master-slave equipment, c is the light velocity, wherein ni0It is
Obedience is independently distributed, be 0 mean variance be σ2White Gaussian noise；
(4.2) position coordinates of receiving station are brought into can obtain it is as follows
Assuming that Δ
R=[Δ R10,ΔR20,ΔR30]T, n=[n10,n20,n30]TFormula (1) can indicate as follows
Because of Δ Ri0Obedience mean value is (Ri-R0), variance σ2Gaussian Profile, so likelihood can be released according to formula (2)
Function is：
Wherein object function is：
(x, y, z)=arg { min [(Δ R-Ri+R0)T(ΔR-Ri+R0)] (i=1,2,3) (4)
Therefore, it can be deduced that the fitness function of the particle populations is
(4.3) the adaptive value f of each particle from core is calculated according to the fitness function f drawnij(i=1,2...10,
J=1,2...7), wherein i is expressed as the population from core, and j is expressed as from check figure, more each f from coreijIt obtains from core
The best particle b of middle adaptive valuei(i=1,2...7).
The step (5) is specially：
By way of message queue it is each from core by respective best particle biIt is transmitted to main core, main core distributes certain sky
Between for storing these particles biAnd compare the globally optimal solution g for obtaining integral particles groupbest；Divide from spare particle in main core
2 particles Wei not selected at random from core each and feed back to each position and speed information for replacing the worst particle from core from core,
The particle group A from core1—A7Update becomes new particle populations after obtaining new particle.
The step (6) is specially：
(6.1) assume that the speed of i-th of particle is vi=[vix,viy,viz] (i=1,2...10), position is
si=[six,siy,siz] (i=1,2...10),
Each position and speed information that new particle population is updated from core, is updated according to formula (6),
In formula：J=x, y, z；W is inertia weight；c1, c2For Studying factors or acceleration constant；r1、r2Indicate that 0 arrives respectively
Random number between 1；pi,jFor the desired positions of the particle found so far；pg,jOwn so far from core to be each
The desired positions that particle is found；si,j(t), vi,j(t), si,j(t+1), vi,j(t+1) it is respectively i-th of particle at t the and t+1 moment
Position and speed；
(6.2) the adaptive value f of particle in new population is found out according to formula (5)ij(t+1) (i=1,2...10, j=1,
2...7), wherein i is expressed as the population from core, and j is expressed as from check figure；Compare the present adaptive value f of each particleij(t+1)
It is preferably adapted to value fb with history beforeij, show that the newest individual of the particle is preferably adapted to value and replaces original fbij, and by the grain
The adaptive value and location information of son are put into pbest, are then compared each from the newest fb of all particles in pbest in coreij,
Find out the best particle b of each adaptive value newest from corei(t+1), t+1 is the t+1 times update of particle；
(6.3) by bi(t+1) main core and integral particles group's globally optimal solution g before main core are transmitted tobestIt is compared, more
New gbestPositionVelocity information is simultaneously stored into memory space.
The step (8) is specially：
(8.1) initial position in the target emanation source obtained according to main coreFormula (1) is existedPlace carries out
Taylor series expansion, removes high order component, and positioning equation is represented by
e≈h-Gδ (7)
In formula：
Wherein h is difference of the target emanation source to receiving station's distance measure and actual value；
In formula (8)：For selected target initial positionThe distance between each receiving station, adopts
Can solve position deviation with least square (WLS) method is：
In formula (9), Q is the covariance matrix of range difference measurement value；
(8.2) it is iterated calculating successively, when δ meets preset threshold value ε, i.e., | Δ x |+| Δ y |+| Δ z | ＜
ε, so that it may obtain the position in target emanation source.
The beneficial effects of the present invention are：
The solution wherein obtained using particle cluster algorithm will improve the accuracy of positioning as the initial value of Newton iterative.
And the efficiency of Newton iterative can be improved.
The advantage of multi-core DSP is utilized, particle cluster algorithm improves operational efficiency by the way of executing parallel, shortens
Time of operation, to realize positioning in real time.
Spare particle replaces each particle worst from core and keeps its composition new from main core in particle cluster algorithm execution
Population can increase the diversity of particle populations, and to make algorithmic statement faster, the solution of positioning is faster.
Description of the drawings
Fig. 1 is the implementation method schematic diagram of positioning using TDOA of the invention based on multi-core DSP operation.
Fig. 2 is the mode invented main core and executed parallel from core.
Fig. 3 is inventing main core and being executed respectively from core for task.
Specific implementation mode
The specific implementation mode of the present invention is described further below in conjunction with the accompanying drawings：
In conjunction with Fig. 1, a kind of implementation method of the Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation, steps are as follows：
Step 1：It will be each from the collected TOA data of receiving station and each by the wireless communication module of each receiving station
The location information of a receiving station is transmitted to main equipment and multi-core DSP.
First, then main equipment and multi-core DSP receive more to the message for sending out start-up operation from receiving station from receiving station
After the broadcast message of core DSP, TOA is acquired since the acquisition system of receiving station and its collected TOA is put into FIFO,
Read the TOA in FIFO from the DSP of equipment after FIFO full scale will, TOA that then module is read by radio communication with
Oneself location information is transmitted to multi-core DSP.
Step 2：Multi-core DSP carries out pairing to receiving TOA and TDOA is calculated, according to the location information of receiving station and
The step-out time of radiation source utilizes Taylor series-positioning of the population synergetic to radiation source.
The TOA that 4 groups of difference receiving stations receive is matched one by one according to corresponding pairing rules in main core, finally
The different TOA that four different receiving stations of synchronization receive are obtained, is subtracted each other and has just obtained TDOA.
Step 3：Plan the distribution of the particle populations quantity and each kernel operation task of multi-core DSP of particle cluster algorithm.
Multi-core DSP when with PSO Algorithm by the way of executing parallel and main core core0 be responsible for task point
Match, traffic control, is responsible for the execution of task from core.
Since the quantity of population will influence whether the length of the accuracy and operation time of result, particle
Quantity is particularly important, we use the eight core TMS320C6678 of high speed digital signal processor of TI companies here, which is
The reachable maximum operating frequency of eight core floating type DSP, each core are 1.25GHz, have very strong calculation processing power, so I
Each to distribute 10 particles from core, and distribute 100 particles as spare particle in main core to replace from the non-complete of core
Office's optimal particle.Particle is namely divided into 8 groups of A0—A7, from 7 groups of A of core1—A7It is handled respectively by respective processor, and
This group of A of main core0Spare particle is replaced the most, and wherein intercore communication is by the way of message queue MessageQ.
Step 4：The information of each particle is initialized, the adaptive value of total optimization particle is initialized, determines fitness letter
Number, and calculate the individual and globally optimal solution of particle in more each core.
A is first initialized before being calculated0—A7In all particles position and speed information, and initialize total optimization
The adaptive value f=0 of particle.Assuming that 4 are (x from station coordinates is receivedi,yi,zi) (i=1,2,3) and main receiving station (x0,y0,z0)
In same plane, the position in target emanation source is (x, y, z), and it is R that the target emanation source of positioning using TDOA, which is arrived from the distance of equipment,i(i
=1,2,3), the distance of target emanation source to main equipment is R0.According to relationship between target emanation source and the position of master-slave equipment
It can obtain
In formula, Δ ti0For the measured value of the reaching time-difference in target emanation source to master-slave equipment, c is the light velocity.Wherein ni0It is
Obedience is independently distributed, be 0 mean variance be σ2White Gaussian noise, the position coordinates of receiving station are brought into can obtain it is as follows
Assuming that Δ
R=[Δ R10,ΔR20,ΔR30]T, n=[n10,n20,n30]TFormula (1) can indicate as follows
Because of Δ Ri0Obedience mean value is (Ri-R0), variance σ2Gaussian Profile, so likelihood can be released according to formula (2)
Function is：
Wherein object function is：
(x, y, z)=arg { min [(Δ R-Ri+R0)T(ΔR-Ri+R0)] (i=1,2,3) (4)
Therefore, it can be deduced that the fitness function of the particle populations is
The adaptive value f of each particle from core is calculated according to the fitness function f drawnij(i=1,2...10, j=1,
2...7), wherein i is expressed as the population from core, and j is expressed as from check figure.More each f from coreijIt obtains and is adapted to from core
It is worth best particle bi(i=1,2...7).
Step 5：It globally optimal solution in each core that step 4 obtains is transmitted to main core is compared and obtain entire particle
The globally optimal solution of group is used in combination the spare particle in main core to replace the particle from the non-global optimum in core.
By way of message queue it is each from core by respective best particle biIt is transmitted to main core, main core distributes certain sky
Between for storing these particles biAnd compare the globally optimal solution g for obtaining integral particles groupbest.Divide from spare particle in main core
2 particles Wei not selected at random from core each and feed back to each position and speed information for replacing the worst particle from core from core,
The particle group A from core1—A7Update becomes new particle populations after obtaining new particle.
Step 6：The particle that main core replaces is obtained from core and forms new population, updates position, the speed of new particle populations
Information, calculates the adaptive value of particle, and each existing adaptive value of particle obtains newest compared with being preferably adapted to value with history before
Body optimal value replaces original history optimal value, the newest individual optimal value of more each particle to update the global optimum from core
It is worth and passes main core and be compared with whole global optimum before and obtains newest entirety global optimum..
Because being to scan in three dimensions, it is assumed that the speed of i-th of particle is vi=[vix,viy,viz] (i=1,
2...10), position si=[six,siy,siz] (i=1,2...10)
Each position and speed information that new particle population is updated from core, is updated according to formula (6),
In formula：J=x, y, z；W is inertia weight；c1, c2For Studying factors or acceleration constant；r1、r2Indicate that 0 arrives respectively
Random number between 1；pi,jFor the desired positions of the particle found so far；pg,jOwn so far from core to be each
The desired positions that particle is found.si,j(t), vi,j(t), si,j(t+1), vi,j(t+1) it is respectively i-th of particle at t the and t+1 moment
Position and speed.The adaptive value f of particle in new population is found out according to formula (5)ij(t+1) (i=1,2...10, j=1,
2...7), wherein i is expressed as the population from core, and j is expressed as from check figure.Compare the present adaptive value f of each particleij(t+1)
It is preferably adapted to value fb with history beforeij, show that the newest individual of the particle is preferably adapted to value and replaces original fbij, and by the grain
The adaptive value and location information of son are put into pbest, are then compared each from the newest fb of all particles in pbest in coreij,
Find out the best particle b of each adaptive value newest from corei(t+1), t+1 is the t+1 times update of particle.By bi(t+1) it is transmitted to
Main core and integral particles group's globally optimal solution g before main corebestIt is compared, updates gbestPositionVelocity information
And it is stored into memory space.
Step 7：Judge whether to meet termination criteria, if meeting termination criteria, stop search；Otherwise, step 4 is repeated
To six.
Step 8：The result obtained using particle cluster algorithm in main core is positioned as the initial value of Taylor series algorithm
Final positioning result is calculated.
According to the initial position in the target emanation source that main core obtainsFormula (1) is existedPlace carries out Taylor
Series expansion removes high order component, then positioning equation is represented by
e≈h-Gδ (7)
In formula：
Wherein h is difference of the target emanation source to receiving station's distance measure and actual value.
In formula (8)：For selected target initial positionThe distance between each receiving station, adopts
Can solve position deviation with least square (WLS) method is：
In formula (9), Q is the covariance matrix of range difference measurement value.
It is iterated calculating successively, when δ meets preset threshold value ε, i.e., | Δ x |+| Δ y |+| Δ z | ＜ ε, so that it may
Obtain the position in target emanation source.
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 (6)
1. a kind of implementation method of the Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation, which is characterized in that including following step
Suddenly：
Step (1)：It from the collected TOA data of receiving station and each is connect each by the wireless communication module of each receiving station
The location information for receiving station is transmitted to main equipment and multi-core DSP；
Step (2)：Multi-core DSP to receive TOA carry out pairing TDOA is calculated, according to the location information of receiving station and radiation
The step-out time in source utilizes Taylor series-positioning of the population synergetic to radiation source,
The TOA that 4 groups of difference receiving stations receive is matched one by one according to corresponding pairing rules in main core, is obtained same
The different TOA that four different receiving stations of moment receive, subtract each other obtaining TDOA；
Step (3)：Plan the distribution of the particle populations quantity and each kernel operation task of multi-core DSP of particle cluster algorithm, multinuclear
DSP when with PSO Algorithm by the way of executing parallel and main core core0 is responsible for the distribution of task, traffic control,
It is responsible for the execution of task from core；
Step (4)：The information of each particle is initialized, the adaptive value of total optimization particle is initialized, determines fitness function, and
Calculate the individual and globally optimal solution of particle in more each core；
Step (5)：It globally optimal solution in each core that step 4 obtains is transmitted to main core is compared and obtain entire population
Globally optimal solution, be used in combination spare particle in main core to replace the particle from the non-global optimum in core；
Step (6)：The particle that main core replaces is obtained from core and forms new population, updates position, the speed letter of new particle populations
Breath, calculates the adaptive value of particle, each existing adaptive value of particle obtains newest individual compared with being preferably adapted to value with history before
Optimal value replaces original history optimal value, the newest individual optimal value of more each particle to update the global optimum from core
And the main core of biography is compared with whole global optimum before and obtains newest whole global optimum；
Step (7)：Judge whether to meet termination criteria, if meeting termination criteria, stop search；Otherwise, step (4) is repeated to arrive
(6)；
Step (8)：The result obtained using particle cluster algorithm in main core carries out positioning meter as the initial value of Taylor series algorithm
Calculation obtains final positioning result.
2. a kind of implementation method of Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation according to claim 1, special
Sign is that the step (1) is specially：
(1.1) main equipment and multi-core DSP are to the message for sending out start-up operation from receiving station；
(1.2) after the broadcast message for receiving multi-core DSP from receiving station, TOA is acquired simultaneously since the acquisition system of receiving station
Its collected TOA is put into FIFO；
(1.3) after FIFO full scale will the TOA in FIFO is read from the DSP of equipment；
(1.4) TOA that module is read by radio communication and oneself location information are transmitted to multi-core DSP.
3. a kind of implementation method of Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation according to claim 1, special
Sign is that the step (4) is specially：
(4.1) A is initialized0—A7In all particles position and speed information, and initialize the adaptive value f of total optimization particle
=0；3 are (x from station coordinates is receivedi,yi,zi) (i=1,2,3) and main receiving station (x0,y0,z0) in same plane, target spoke
The position for penetrating source is (x, y, z), and it is R that the target emanation source of positioning using TDOA, which is arrived from the distance of equipment,i(i=1,2,3), target emanation
The distance of source to main equipment is R0, can be obtained according to relationship between target emanation source and the position of master-slave equipment
In formula, Δ ti0For the measured value of the reaching time-difference in target emanation source to master-slave equipment, c is the light velocity, wherein ni0It is to obey
Be independently distributed, be 0 mean variance be σ2White Gaussian noise；
(4.2) position coordinates of receiving station are brought into can obtain it is as follows
It is false
If Δ R=[Δ R10,ΔR20,ΔR30]T, n=[n10,n20,n30]TFormula (1) can indicate as follows
Because of Δ Ri0Obedience mean value is (Ri-R0), variance σ2Gaussian Profile, so likelihood function can be released according to formula (2)
For：
Wherein object function is：
(x, y, z)=arg { min [(Δ R-Ri+R0)T(ΔR-Ri+R0)] (i=1,2,3) (4)
Therefore, it can be deduced that the fitness function of the particle populations is
(4.3) the adaptive value f of each particle from core is calculated according to the fitness function f drawnij(i=1,2...10, j=
1,2...7), wherein i is expressed as the population from core, and j is expressed as from check figure, more each f from coreijIt obtains and is fitted from core
Best particle b should be worthi(i=1,2...7).
4. a kind of implementation method of Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation according to claim 1, special
Sign is that the step (5) is specially：
By way of message queue it is each from core by respective best particle biIt is transmitted to main core, main core distributes certain space and uses
In storage these particles biAnd compare the globally optimal solution g for obtaining integral particles groupbest；It is respectively from spare particle in main core
It each selects 2 particles at random from core and feeds back to each position and speed information for replacing the worst particle from core from core, from core
Middle particle group A1—A7Update becomes new particle populations after obtaining new particle.
5. a kind of implementation method of Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation according to claim 1, special
Sign is that the step (6) is specially：
(6.1) assume that the speed of i-th of particle is vi=[vix,viy,viz] (i=1,2...10), position is
si=[six,siy,siz] (i=1,2...10),
Each position and speed information that new particle population is updated from core, is updated according to formula (6),
In formula：J=x, y, z；W is inertia weight；c1, c2For Studying factors or acceleration constant；r1、r2It indicates between 0 to 1 respectively
Random number；pi,jFor the desired positions of the particle found so far；pg,jFor each all particles so far from core
It was found that desired positions；si,j(t), vi,j(t), si,j(t+1), vi,j(t+1) it is respectively i-th of particle in the position at t and t+1 moment
It sets and speed；
(6.2) the adaptive value f of particle in new population is found out according to formula (5)ij(t+1) (i=1,2...10, j=1,2...7),
Wherein, i is expressed as the population from core, and j is expressed as from check figure；Compare the present adaptive value f of each particleij(t+1) and before
History is preferably adapted to value fbij, show that the newest individual of the particle is preferably adapted to value and replaces original fbij, and fitting the particle
It should be worth and be put into pbest with location information, then be compared each from the newest fb of all particles in pbest in coreij, find out every
The best particle b of a adaptive value newest from corei(t+1), t+1 is the t+1 times update of particle；
(6.3) by bi(t+1) main core and integral particles group's globally optimal solution g before main core are transmitted tobestIt is compared, updates
gbestPositionVelocity information is simultaneously stored into memory space.
6. a kind of implementation method of Passive Localization Estimate Algorithm of TDOA based on multi-core DSP operation according to claim 1, special
Sign is that the step (8) is specially：
(8.1) initial position in the target emanation source obtained according to main coreFormula (1) is existedPlace carries out Taylor
Series expansion, removes high order component, and positioning equation is represented by
e≈h-Gδ (7)
In formula：
Wherein h is difference of the target emanation source to receiving station's distance measure and actual value；
In formula (8)：For selected target initial positionThe distance between each receiving station, using most
Small two, which multiply (WLS) method, can solve position deviation and be：
In formula (9), Q is the covariance matrix of range difference measurement value；
(8.2) it is iterated calculating successively, when δ meets preset threshold value ε, i.e., | Δ x |+| Δ y |+| Δ z | ＜ ε, just
It can obtain the position in target emanation source.
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