CN110068793A - A kind of positioning and tracing method - Google Patents
A kind of positioning and tracing method Download PDFInfo
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- CN110068793A CN110068793A CN201910321384.XA CN201910321384A CN110068793A CN 110068793 A CN110068793 A CN 110068793A CN 201910321384 A CN201910321384 A CN 201910321384A CN 110068793 A CN110068793 A CN 110068793A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
Abstract
The present invention provides a kind of positioning and tracing method, which comprises calculates the time difference that echo signal reaches each receiving station；The observation model of multiple target is established according to the time difference, and obtains target measurement value；Target measurement value is handled by filtering algorithm, realizes the locating and tracking of multiple target state and track.
Description
Technical field
The present invention relates to a kind of locating and tracking technologies for ensureing Flight Safety；Belong to technical field of aerospace.
Background technique
The prior art can effectively solve the Target Tracking Problem of multipoint positioning.However, when the algorithm is applied to large size
When airport, due near terminal building and the barriers such as aircraft it is more, and Overlapped Spectrum Signals are easy to collide, by more
The influence of the factors such as diameter, intertexture, there are a large amount of outlier in the anchor point near airplane parking area, the track that the prior art is formed is easy to
It is drawn partially by outlier, the track of continuous-stable can not be formed.
The electromagnetic wave that passive location system receives objective emission completely passively is positioned and is tracked, and has concealment
It is good, the strong feature of survival ability, when only using a pair of sensors reaching time-difference (abbreviation TDOA) to target progress passive location
When, the state estimation problem of target is non-linear, and estimation is more pessimistic when distance is remoter, these features, which determine, select
Multisensor carries out locating and tracking to multiple target.
Under complex environment in multiple target tracking scene, tracked target all can often carry out motor-driven.Therefore, these mesh
Target movement cannot be modeled well by single group state equation.Have been found that Kalman filter to Unknown worm and not
The modification for the case where determining target dynamic is very undesirable.
Summary of the invention
The application, which can be realized, realizes aircraft positioning independent of satellite positioning information, can be used as location information accuracy
The method of verification, while Interacting Multiple Model Algorithm is used, enhance the accuracy and reliability of locating and tracking.
Present applicant proposes a kind of positioning and tracing method, method includes:
Calculate the time difference that echo signal reaches each receiving station；
The observation model of multiple target is established according to the time difference, and obtains target measurement value；
Target measurement value is handled by filtering algorithm, realizes the locating and tracking of multiple target state and track.
Optionally, it the time difference for calculating echo signal and reaching each sensor, specifically includes:
The time difference of the arrival receiving station of multiple targets in tracing area is obtained by each receiving station.
Optionally, the observation model that multiple target is established according to the time difference, and target measurement value is obtained, it is specific to wrap
It includes:
Distance r of the acquisition target to four receiving stations0、r1、r2、r3；
According to the r0、r1、r2、r3And the coordinate information of four receiving stations, obtaining coordinates of targets is (X, Y, Z)；
It is the longitude and latitude and altitude information that (X, Y, Z) and conversion formula obtain the target according to coordinates of targets, described turn
Change formula are as follows:
Wherein, a is earth reference ellipsoid major radius, and e is that the earth first is eccentric
Rate.
Optionally, target measurement value is handled by filtering algorithm, realize the positioning of multiple target state and track with
Track specifically includes:
Each track is initialized after detecting three continuous positions of target；
Measurement association door is established around it according to the prediction measured value of target, selects candidate measurement associated with target
Value；
The predicted state of each target is fed to corresponding association door, after demonstrating measurement collection, has started data
Association process；
It is handled by dbjective state and Current observation value of the filtering algorithm to the previous moment, it is final to obtain target
State estimation.
Optionally, it is handled by dbjective state and Current observation value of the filtering algorithm to the previous moment, obtains mesh
Final state estimation is marked, is specifically included:
The dbjective state at previous moment and Current observation value are input in IMM algoritic module, it is final to obtain target
State estimation.
Optionally, the IMM algoritic module is when IMM is filtered, using approximate uniform motion model CV and at the uniform velocity turning mould
Type CT carries out Monte Carlo simulation, obtains IMM filter result.
Optionally, method further include: the conversion between the model is controlled with a Markov chain.
Optionally, the receiving station includes sensor.
In conclusion can be realized and believe independent of satellite positioning by being efficiently modified to existing locating and tracking algorithm
Breath realizes aircraft positioning, the method that can be used as the verification of location information accuracy, while using Interacting Multiple Model Algorithm, increases
The accuracy and reliability of strong fix tracking.
Detailed description of the invention
Fig. 1 positioning using TDOA model schematic in four stations provided in an embodiment of the present invention；
Fig. 2 two association doors provided in an embodiment of the present invention intersect schematic diagram；
Fig. 3 locating and tracking algoritic module functional block diagram provided in an embodiment of the present invention；
Fig. 4 target trajectory schematic diagram provided in an embodiment of the present invention；
Fig. 5 track algorithm filter result schematic diagram provided in an embodiment of the present invention.
Specific embodiment
Under complex environment in multiple target tracking scene, tracked target all can often carry out motor-driven.Therefore, these mesh
Target movement cannot be modeled well by single group state equation.Have been found that Kalman filter to Unknown worm and not
The modification for the case where determining target dynamic is very undesirable.Therefore, superior in order to be realized while Tracking Maneuvering Targets
Performance, estimator must be allowed for target movement in different times interval in described by different state equations, target
Movement should be modeled as random.The important estimation of one of this system is Interacting Multiple Model Algorithm, wherein clearly advising
It sets the goal and moves with probabilistic manner from a motion model automatic " switching " to another motion model, to realize that multimachine moves mesh
Adaptive adjustment needed for marking tracking filter.
Embodiment one
By being efficiently modified to existing locating and tracking algorithm, it can be realized and realize aviation independent of satellite positioning information
Device positioning, the method that can be used as the verification of location information accuracy, while Interacting Multiple Model Algorithm is used, enhance locating and tracking
Accuracy and reliability.
The TOA (arrival time) of most common standard edition MLAT systems measures signal connects from aircraft transmissions and by one group of ground
It is received in the receiving station disposed around overlay area.One 2D needs minimum 3 sensor positions.TOA measurement result is sent to CPS
The position of (central processing subsystem) processing and positioning aircraft or vehicle, detection, decoding and processing received signal are winged to execute
The identification of machine.MLAT is based on inquiry operation, and target retro has encoded signal.FDM (frequency division multiplexing) be used for by earth station with
The inquiry of 1030MHz aircraft is replied in earth station (1090MHz) from aircraft and is separated, and return signal can be Mode A, C
Or S1.
During tracking for exist wrong report or clutter in the case where aircraft of the detection probability less than 1, data correlation mistake
Journey is considered vital.Data correlation is the process for determining to come using which received multiple measurement more new-track.It builds
Vertical tracking gate, and the detection in this can be associated with interested aircraft.The survey except validation region can be ignored
Amount, because they are too wide in the gap with the measurement result of prediction.
To achieve the above object, using following technical scheme:
1 system modelling
We set a target and move in two-dimensional surface, and state X (n) is made of position, speed, i.e.,Assuming that the sampling interval is T, target detection probability PD=1, no false-alarm exists, in flute card
Under your coordinate system target discrete motion model and observation model (it is assumed that in sampling instant k) are as follows:
State equation: X (k+1)=FX (k)+GV (k)
Observational equation: Z (k)=H (k) X (k)+W (k)
Target motion model in two-dimensional surface is as follows:
1) CV: approximate uniform motion model
CV model regards acceleration as random perturbation (state-noise), takes dbjective stateThen state-transition matrix interferes transfer matrix and observing matrix to be respectively as follows:
2) CT: at the uniform velocity Turn Models
Only consider CT model known to angular velocity of satellite motion ω.Then state-transition matrix interferes transfer matrix and observing matrix
It is respectively as follows:
Noise covariance matrix R is measured to be determined by sensor.
2 localization methods
Common sensor passive location method have reaching time-difference positioning mode, lateral register method, arrival time positioning mode,
Doppler frequency shift positioning mode etc..Comprehensively consider from positioning accuracy, locating speed and anti-interference ability, the present invention uses arrival time
Poor positioning mode (timedifferentofarrival, abbreviation TDOA), specifically includes the following steps:
Being located at main website coordinate in local coordinate system is S0(x0,y0,z0), 3 secondary station coordinates are respectively S1(x1,y1,z1)、S2
(x2,y2,z2)、S3(x3,y3,z3), (x, y, z) is position of the target S in space coordinates, r0、r1、r2、r3Respectively target
The distance stood to 4, positional relationship are as shown in Figure 1.
It can be obtained by Fig. 1, the positioning equation of target is as follows.
Abbreviation obtains:
xix+yiy+ziZ=ti-r0Δr
(3)
Wherein
Above formula is indicated using matrix form, it is as follows matrix equation can be obtained:
AX=F
Wherein X=[x y z]T,
If in the same plane and wherein any three station not on the same line, does not make four earth station sites layout
It obtains matrix A and meets rank (A)=3, then the least square solution of X are as follows:
Wherein
It is obtained by positioning equation:
Formula (5) substitution formula (6) is obtained:
pr2- 2qr+s=0
(7)
Wherein
Then equation (7) can obtain, r0Solution are as follows:
Formula (9) substitution formula (1) is solved into (x, y, z).
(x, y, z) is target S using main website as the coordinate of origin having in coordinate system, is sat target by coordinate translation
It is (X, Y, Z) that mark, which transforms to and obtains coordinates of targets in rectangular coordinate system in space,.
It, will by formula (10) by above-mentioned target position because target position information output is using WGS-84 coordinate system
Rectangular coordinate system in space, which is converted to WGS-84 coordinate system, can be obtained longitude and latitude and altitude information.
Wherein a is earth reference ellipsoid major radius, and e is the first eccentricity of the earth.
3 interacting multiple model algorithms
It suppose there is r model:
X (k+1)=FjX(k)+GjVj(k), j=1 ..., r
(11)
Wherein, Wj(k) be mean value be zero, covariance matrix QjWhite noise sequence.It is controlled with a Markov chain
Conversion between these models, the transition probability matrix of Markov chain are as follows:
Measurement model are as follows:
Z (k)=Hj(k)Xj(k)+Wj(k)
(13)
The various calculating involved in a scan period of IMM filtering algorithm can be divided into: input interaction, prediction, more
New and output interaction.Its step can be summarized as follows:
1) input interaction
- 1 period of kth admixture estimated matrixAnd its covariance matrix PojIt is as follows respectively
Wherein j=1 ..., r is model call number, and k is sampling period, μijIt (k-1/k-1) is mixing probability, pijIt is mould
Type i goes to the transition probability of model j, μi(k-1) it is probability that k-1 periodic model is i,It is j's for k-th periodic model
Prediction probability,
2) it predicts
The state-transition matrix that prediction process is based primarily upon each model obtains the predicted state that each target corresponds to each model,
Corresponding to model Mj(k), withPoj(k-1/k-1) and Z (k) is as input progress predictive filtering.
Wherein state space matrices Fj, GjWith process noise covariance matrix QjCorresponding to jth motion model.
3) it updates
Filtering gain
Kj(k)=Pj(k/k-1)HT(k)[H(k)Pj(k/k-1)HT(k)+R(k)]-1 (18)
Updated model j state and covariance matrix are
Pj(k/k)=[I-Kj(k)H(k)]Pj(k/k-1)
(20)
4) model probability updates
Wherein, c is Z (k) combination likelihood function, andAnd ΛjIt (k) is model j for observation Z (k)
Likelihood function,
5) output interaction
Association algorithm decides whether that will observe Z (k) distributes to targetpath according to combination likelihood function c.Formula (24),
(25) for determining the estimation dbjective state updated.The target position of the update is converted for local coordinate.If Z will be observed
(k) targetpath is distributed to, then its statistical information will update therewith.
Then specific locating and tracking algorithm block diagram is held as shown in figure 3, correlated variables such as initializing sensor position first
Row Localization Estimate Algorithm of TDOA obtains target position information shown in formula (10).After detecting three continuous positions of target
Each track is initialized, measurement association door is established around it according to the prediction measured value of target later, with selection and required mesh
Mark associated candidate measured value.The predicted state of each target is fed to it and is associated with door.After demonstrating measurement collection, open
The data correlation process that begun (as shown in Figure 2).The dbjective state at previous moment and target Current observation value will be inputted later
Into IMM algoritic module, the final state estimation of target is obtained.
This alignment by union track algorithm updates targetpath for monitoring, has the feature that
Use four station positioning using TDOA models；
Have two kinds of goal behavior models of management: uniformly, uniform rotation campaign, each goal behavior model uses one
Special filter；
Global state is determined by the corresponding special filter of two models and corresponding likelihood function；
Trajectory Prediction is executed using the global state in system cartesian coordinate, then according to the class of the sensor of processing
Type is converted in sensor reference coordinate；
Second order filter: track state vector is made of position and speed；
Markov chain is for carrying out conversion interaction between two models.
Embodiment two
The present invention provides a kind of multimachine moving-target alignment by union tracking, specific embodiment is as follows:
1) setting target movement starting position coordinates (x, y) is (1000,1000), and initial velocity is (10,10), between sampling
Every T=1s, the angular speed of CT model sportDo at the uniform velocity turning motion clockwise.X and y are independently seen
It surveys, observation standard deviation is 50 meters.Target is CV in 1 ~ 150s motion model, and 151 ~ 270s motion models are CT, 271 ~ 400s
Motion model is CV.Target moves real trace and measurement track as illustrated in figures 4-5.
2) time difference of each sensor is reached according to echo signal to obtain the measurement of target, in interacting multiple model filters
The observation model of system is established under frame, then selects filtering algorithm in Multi-target position tracking system, to realize to more
The locating and tracking of dbjective state and track.
3) when IMM is filtered, 2 Models Sets, i.e. CV, CT are used, it is assumed that it is known that the target of CT model moves
Angular speed w, Markov transfer matrixMonte Carlo simulation is carried out, IMM filter result is obtained.It will
The standard Kalman filter result of this filter result and individual CV, CT model compares, as shown in Figure 4.As seen from the figure, CT model
Filter result and true value have relatively large deviation, the CV model Kalman filtered results deviation true value in turning, and IMM algorithm energy
Preferable tracking target.Finally illustrate, above description is only used to illustrate the technical scheme of the present invention and not to limit it its included model
It encloses, i.e., modification or equivalent replacement of the technical solution of the present invention are made, and without departing from its purpose and range, it should all cover
In scope of the presently claimed invention.
Pair finally illustrate, above description is only used to illustrate the technical scheme of the present invention and not to limit it its scope, i.e.,
Technical solution of the present invention is modified or replaced equivalently, and without departing from its purpose and range, this hair should all be covered by
In bright scope of the claims.
Claims (8)
1. a kind of positioning and tracing method, which is characterized in that the described method includes:
Calculate the time difference that echo signal reaches each receiving station；
The observation model of multiple target is established according to the time difference, and obtains target measurement value；
Target measurement value is handled by filtering algorithm, realizes the locating and tracking of multiple target state and track.
2. according to the method described in claim 1, it is characterized by: the time for calculating echo signal and reaching each sensor
Difference specifically includes:
The time difference of the arrival receiving station of multiple targets in tracing area is obtained by each receiving station.
3. according to the method described in claim 1, it is characterized by: the observation mould for establishing multiple target according to the time difference
Type, and target measurement value is obtained, it specifically includes:
Distance r of the acquisition target to four receiving stations0、r1、r2、r3；
According to the r0、r1、r2、r3And the coordinate information of four receiving stations, obtaining coordinates of targets is (X, Y, Z)；
It is the longitude and latitude and altitude information that (X, Y, Z) and conversion formula obtain the target according to coordinates of targets, the conversion is public
Formula are as follows:
Wherein, a is earth reference ellipsoid major radius, and e is the first eccentricity of the earth.
4. according to the method described in claim 3, it is characterized in that, being handled by filtering algorithm target measurement value, in fact
The locating and tracking of existing multiple target state and track, specifically includes:
Each track is initialized after detecting three continuous positions of target；
Measurement association door is established around it according to the prediction measured value of target, selects candidate measured value associated with target；
The predicted state of each target is fed to corresponding association door, after demonstrating measurement collection, has started data correlation
Process；
It is handled by dbjective state and Current observation value of the filtering algorithm to the previous moment, obtains the final state of target
Estimation.
5. according to the method described in claim 4, it is characterized in that, by filtering algorithm to the dbjective state at previous moment and
Current observation value is handled, and is obtained the final state estimation of target, is specifically included:
The dbjective state at previous moment and Current observation value are input in IMM algoritic module, the final state of target is obtained
Estimation.
6. according to the method described in claim 5, it is characterized in that, the IMM algoritic module uses approximation when IMM is filtered
Uniform motion model CV and at the uniform velocity Turn Models CT carry out Monte Carlo simulation, obtain IMM filter result.
7. according to the method described in claim 6, it is characterized in that, method further include:
The conversion between the model is controlled with a Markov chain.
8. according to the method described in claim 2, it is characterized in that, the receiving station includes sensor.
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