CN110208740A - TDOA-IMU data adaptive merges positioning device and method - Google Patents
TDOA-IMU data adaptive merges positioning device and method Download PDFInfo
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- G—PHYSICS
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
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- G01C21/18—Stabilised platforms, e.g. by gyroscope
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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
- G01S5/0257—Hybrid positioning
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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
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Abstract
The invention discloses a kind of TDOA-IMU data adaptive fusion positioning device and methods, and Inertial Measurement Unit is for obtaining component of the measurand relative to the velocity and acceleration of geographic coordinate system；TDOA positioning system is used to carry out Primary Location to measurand to obtain the location information of measurand；Adaptive Kalman filter corrects location information acquired in TDOA positioning system for being filtered according to adaptive Kalman algorithm to TDOA positioning system；Shot and long term Memory Neural Networks unit according to position of the shot and long term Memory Neural Networks model to measurand for predicting；For exact position determining module for the location information that the revised location information of adaptive Kalman filter is predicted with shot and long term Memory Neural Networks unit to be weighted and averaged, which is positioning result.TDOA-IMU data adaptive fusion positioning device and method can be improved positioning accuracy.
Description
Technical field
The present invention relates to wireless communication technology fields, merge and position especially with regard to a kind of TDOA-IMU data adaptive
Device and method.
Background technique
In recent years, with the continuous development of location technology, location based service (Location Based Service,
LBS it) is more and more widely paid close attention to, LBS is that numerous technologies such as mobile terminal, Internet of Things, virtual reality are able to carry out and make
For indispensable basis and support.Such as intelligent storage, wisdom traffic in internet of things field etc.；In field of virtual reality
Somatic sensation television game, human-computer interaction etc.；The acquisition of LBS and perception are all placed on by navigation Service, data statistics of field of mobile terminals etc.
Essential link shows wide commercial promise and huge market value.In modern development in science and technology life, such as
The information query services such as luxurious life by eating, drinking and playing, the unexpected LBS services such as position location services that occur all have very high want to positioning accuracy
It asks, if deviations are excessive, application value can be lost.The ultra wideband location techniques of compatible IEEE 802.15.4-2011 standard
Positioning accuracy, real-time performance, in terms of there is very big advantage, ultra wideband location techniques theoretically may be implemented centimetre
The positioning accuracy of grade, very well satisfies location requirement.Although the UWB (no-load wave communication) based on TDOA (reaching time-difference) is fixed
Position technology has been able to achieve the positioning accuracy of Centimeter Level at present, but for substation, and above-mentioned localization method is difficult to meet multiple
The substation demand of miscellaneous variation, it is therefore desirable to use a variety of localization method alignment by union.Existing intelligent mobile phone terminal is matched
Many sensor modules are had, and its IMU (Inertial Measurement Unit) is cheap, therefore on TDOA location base, utilized
IMU, which carries out joint auxiliary positioning, becomes new trend.
Inventor has found in the implementation of the present invention, in the integrated positioning algorithm of TDOA and IMU in the prior art
Used Kalman filtering and expanded Kalman filtration algorithm assume that the noise of positioning system obeys the Gauss point of 0 mean value
Cloth, but in real life, due to the variation shaken up and down with measuring device noise itself in survey crew's motion process,
So that the noise of research system constantly changes.Therefore it is positioned with existing method, positioning accuracy is not high.
The information disclosed in the background technology section is intended only to increase the understanding to general background of the invention, without answering
When being considered as recognizing or imply that the information constitutes the prior art already known to those of ordinary skill in the art in any form.
Summary of the invention
The purpose of the present invention is to provide a kind of TDOA-IMU data adaptive fusion positioning device and methods, can mention
High position precision.
To achieve the above object, the present invention provides a kind of TDOA-IMU data adaptives to merge positioning device, installation
In measurand, for positioning to the measurand, the TDOA-IMU data adaptive merges positioning device packet
It includes: Inertial Measurement Unit, TDOA positioning system, adaptive Kalman filter, shot and long term Memory Neural Networks unit, accurate position
Set determining module.Inertial Measurement Unit is used to obtain point of the measurand relative to the velocity and acceleration of geographic coordinate system
Amount；TDOA positioning system is used to carry out Primary Location to the measurand to obtain the rough position of the measurand
Information；Adaptive Kalman filter is coupled with the Inertial Measurement Unit and the TDOA positioning system, is used for root
The TDOA positioning system is filtered according to adaptive Kalman algorithm, corrects position acquired in the TDOA positioning system
Information；Shot and long term Memory Neural Networks unit is coupled with the adaptive Kalman filter, for being remembered according to shot and long term
Neural network model predicts the position of the measurand；Exact position determining module and the adaptive Kalman are filtered
Wave device and the shot and long term Memory Neural Networks unit are coupled, for will the adaptive Kalman filter amendment after
The location information predicted with the shot and long term Memory Neural Networks unit of location information be weighted and averaged, the average value quilt
It is determined as the positioning result of the TDOA-IMU data adaptive fusion positioning device.
In one embodiment of the present invention, in the adaptive Kalman algorithm, observation vector is Z=[Z1,Z2]T,
Wherein, the observation vector of the TDOA system positioning output is Z1=[x, y]T, the x expression measurand is in the geographical seat
The value of the x-axis of system is marked, y indicates the measurand in the value of the y-axis of the geographic coordinate system；The IMU system passes through data
Pretreated observation vector is Z2=[vx,vy,ax,ay]T, vxIndicate the measurand in the x-axis of the physical coordinates system
Movement velocity, vyIndicate the measurand in the movement velocity of the y-axis of the physical coordinates system, axIndicate described tested pair
As the acceleration of motion of the x-axis in the physical coordinates system, ayIndicate the measurand in the y-axis of the physical coordinates system
Acceleration of motion；
State vector is Xk=[xk,yk,vx,k,vy,k,ax,k,ay,k], wherein xkIndicate measurand described in the k moment in institute
State the value of the x-axis of physical coordinates system, ykIndicate value of the measurand in the y-axis of the physical coordinates system described in the k moment, vx,kTable
Show measurand described in the k moment in the movement velocity of the x-axis of the physical coordinates system, vy,kIndicate that measurand described in the k moment exists
The movement velocity of the y-axis of the physical coordinates system, ax,kIndicate measurand described in the k moment in the x-axis of the physical coordinates system
Acceleration of motion, ay,kIndicate measurand described in the k moment in the acceleration of motion of the y-axis of the physical coordinates system；
The state-transition matrix at k-1 moment to k moment is Ak,k-1
Wherein, Δ t is the measurand single step run duration；
The variance Q of state-noise matrix is
Wherein, σvx,k-1Indicate measurand described in from the k-1 moment to the k moment in the speed of the x-axis of the geographic coordinate system
Noise variance, σvy,k-1Indicate measurand described in from the k-1 moment to the k moment in the velocity noise of the y-axis of the geographic coordinate system
Variance, σax,k-1Indicate measurand described in from the k-1 moment to the k moment in the acceleration noise side of the x-axis of the geographic coordinate system
Difference, σay,k-1Indicate measurand described in from the k-1 moment to the k moment in the acceleration noise side of the y-axis of the geographic coordinate system
Difference；
Observing matrix H=I, R are to measure noise covariance matrix, and after Q and R is carried out initial value setting, substitution is described certainly
It adapts to constantly be updated in Kalman filtering algorithm.
In one embodiment of the present invention, the input gate in the shot and long term memory network model isWherein xtIndicate the input data of t moment network, ht-1Indicate t-1 moment hidden layer mind
Activation value through member, σ and tanh respectively indicate sigmoid activation primitive and tanh activation primitive, WiAnd WCIndicate input gate weight
Matrix, UiAnd UaIndicate the weight matrix between input gate input layer and hidden layer, biAnd baIndicate the biasing of input gate.Forget
Door is ft=σ (Wf·ht-1+Ufxt+bf), wherein WfAnd bfIt respectively indicates and forgets door weight matrix and biasing, UfIndicate that forgetting door is defeated
Enter the weight matrix between layer and hidden layer.The input gate and the forgetting door all act on cell state Ct=ft⊙Ct-1+
it⊙at, wherein ct-1Indicate a memory unit at t-1 moment, the Hadamard product of ⊙ representing matrix.Out gate is updated laterWherein WoAnd boRespectively indicate out gate weight matrix and biasing, UoIndicate out gate input
Weight matrix between layer and hidden layer determines the cell state for entering next stage, and exports measurand under described
The predicted value Z of one stage positiont=σ (Vht+ c), wherein V indicates output sample weights matrix.
The present invention also provides a kind of TDOA-IMU data adaptive fusion and positioning method, it is used to carry out measurand
Positioning, which is characterized in that the TDOA-IMU data adaptive fusion and positioning method includes: to obtain institute by Inertial Measurement Unit
State component of the measurand relative to the velocity and acceleration of geographic coordinate system；By TDOA positioning system to the measurand
Primary Location is carried out to obtain the rough location information of the measurand；According to adaptive Kalman algorithm to described
TDOA positioning system is filtered, and corrects location information acquired in the TDOA positioning system；Nerve is remembered according to shot and long term
Network model predicts the position of the measurand；By the revised location information of the adaptive Kalman filter
The location information predicted with the shot and long term Memory Neural Networks unit is weighted and averaged, which is confirmed as described
The positioning result of TDOA-IMU data adaptive fusion positioning device.
In one embodiment of the present invention, in the adaptive Kalman algorithm, observation vector is Z=[Z1,Z2]T,
Wherein, the observation vector of the TDOA system positioning output is Z1=[x, y]T, the x expression measurand is in the geographical seat
The value of the x-axis of system is marked, y indicates the measurand in the value of the y-axis of the geographic coordinate system；The IMU system passes through data
Pretreated observation vector is Z2=[vx,vy,ax,ay]T, vxIndicate the measurand in the x-axis of the physical coordinates system
Movement velocity, vyIndicate the measurand in the movement velocity of the y-axis of the physical coordinates system, axIndicate described tested pair
As the acceleration of motion of the x-axis in the physical coordinates system, ayIndicate the measurand in the y-axis of the physical coordinates system
Acceleration of motion；
State vector is Xk=[xk,yk,vx,k,vy,k,ax,k,ay,k], wherein xkIndicate measurand described in the k moment in institute
State the value of the x-axis of physical coordinates system, ykIndicate value of the measurand in the y-axis of the physical coordinates system described in the k moment, vx,kTable
Show measurand described in the k moment in the movement velocity of the x-axis of the physical coordinates system, vy,kIndicate that measurand described in the k moment exists
The movement velocity of the y-axis of the physical coordinates system, ax,kIndicate measurand described in the k moment in the x-axis of the physical coordinates system
Acceleration of motion, ay,kIndicate measurand described in the k moment in the acceleration of motion of the y-axis of the physical coordinates system；
The state-transition matrix at k-1 moment to k moment is Ak,k-1
Wherein, Δ t is the measurand single step run duration；
The variance Q of state-noise matrix is
Wherein, σvx,k-1Indicate measurand described in from the k-1 moment to the k moment in the speed of the x-axis of the geographic coordinate system
Noise variance, σvy,k-1Indicate measurand described in from the k-1 moment to the k moment in the velocity noise of the y-axis of the geographic coordinate system
Variance, σax,k-1Indicate measurand described in from the k-1 moment to the k moment in the acceleration noise side of the x-axis of the geographic coordinate system
Difference, σay,k-1Indicate measurand described in from the k-1 moment to the k moment in the acceleration noise side of the y-axis of the geographic coordinate system
Difference；
Observing matrix H=I, R are to measure noise covariance matrix, and after Q and R is carried out initial value setting, substitution is described certainly
It adapts to constantly be updated in Kalman filtering algorithm.
In one embodiment of the present invention, the input gate in the shot and long term memory network model isWherein xtIndicate the input data of t moment network, ht-1Indicate t-1 moment hidden layer
The activation value of neuron, σ and tanh respectively indicate sigmoid activation primitive and tanh activation primitive, WiAnd WCIndicate input gate power
Weight matrix, UiAnd UaIndicate the weight matrix between input gate input layer and hidden layer, biAnd baIndicate the biasing of input gate.It loses
Forgetting door is ft=σ (Wf·ht-1+Ufxt+bf), wherein WfAnd bfIt respectively indicates and forgets door weight matrix and biasing, UfIt indicates to forget door
Weight matrix between input layer and hidden layer.The input gate and the forgetting door all act on cell state Ct=ft⊙Ct-1
+it⊙at, wherein ct-1Indicate a memory unit at t-1 moment, the Hadamard product of ⊙ representing matrix.Out gate is updated laterWherein WoAnd boRespectively indicate out gate weight matrix and biasing, UoIndicate out gate input
Weight matrix between layer and hidden layer determines the cell state for entering next stage, and exports measurand under described
The predicted value Z of one stage positiont=σ (Vht+ c), wherein V indicates output sample weights matrix.
Compared with prior art, TDOA-IMU data adaptive fusion positioning device and method according to the present invention, pass through
Inertial Measurement Unit obtains velocity and acceleration of the measurand under geographic coordinate system, passes through adaptive Kalman filter algorithm
Position, state-noise matrix Q and the measurement noise R of adaptive online updating measurand, promote positioning accuracy, and use LSTM
Model is smoothed the data under geographical coordinate, reduces the burr or abnormal point of TDOA location data.Worked as using distance
Preceding closer time locus forgets the output of door to update, and need to update input gate for study recent history track movement tendency, lose
The result for forgetting door and input gate acts on cell state, then updates out gate to determine the cell state of next stage, finally
It indexes to obtain the predicted value of carrier next stage position by renewal sequence, measurand is efficiently solved by this method and is existed
System mode noise and the measurement continually changing problem of noise, are greatly improved positioning accuracy in motion process.
Detailed description of the invention
Fig. 1 is that the structure composition of TDOA-IMU data adaptive fusion positioning device according to an embodiment of the present invention is shown
It is intended to；
Fig. 2 is the coordinate system of measurand according to an embodiment of the present invention and the relationship of geographic coordinate system；
Fig. 3 is shot and long term memory network model according to an embodiment of the present invention；
Fig. 4 is the cell structure of shot and long term memory network model according to an embodiment of the present invention；
Fig. 5 is each step group of TDOA-IMU data adaptive fusion and positioning method according to an embodiment of the present invention
At.
Specific embodiment
With reference to the accompanying drawing, specific embodiments of the present invention will be described in detail, it is to be understood that guarantor of the invention
Shield range is not limited by the specific implementation.
Unless otherwise explicitly stated, otherwise in entire disclosure and claims, term " includes " or its change
Changing such as "comprising" or " including " etc. will be understood to comprise stated element or component, and not exclude other members
Part or other component parts.
The present invention provides a kind of TDOA-IMU data adaptives to merge positioning device, is mounted in measurand, uses
It is positioned in measurand, as shown in Figure 1, in one embodiment, TDOA-IMU data adaptive fusion positioning dress
Set includes: Inertial Measurement Unit 10, TDOA positioning system 11, adaptive Kalman filter 12, shot and long term Memory Neural Networks
Unit 13, exact position determining module 14.
Inertial Measurement Unit 10 is for obtaining component of the measurand relative to the velocity and acceleration of geographic coordinate system.Tool
Body, it include accelerometer, gyroscope and magnetometer in Inertial Measurement Unit 10.Since Inertial Measurement Unit 10 can be
Different location in research object is installed, and the direction where three-axis gyroscope is not known when it intervenes work, therefore
Rotation angle must be calculated using gyro instrument meter, and accelerometer data is corrected, realizes the fusion of above- mentioned information.Research pair
As during the motion, the relationship of measurand coordinate system and geographic coordinate system is as shown in Figure 2.Tested pair is determined by magnetometer
As coordinate system direct north after, as seen from Figure 2, xg、ygAnd zgFor the expression of geographic coordinate system, x0、yoAnd zoIt is tested
The expression of object coordinates system, the speed of measurand movement and the angle of direct north are ρ, measurand coordinate system and geographical seat
The angle of mark system is α, voFor movement velocity of the measurand under measurand coordinate system.In the present embodiment, object is in plane
It is moved under scene, it is assumed that step-length is directly proportional to the acceleration change of traveling process, and measurand step-length isWherein AmaxWith AminThe maximum and the smallest measurement of accelerometer respectively in the measurand step
Value, δ are the zoom factor for handling different motion behavior and height, and the speed of measurand is continued by the single step of measurand later
Time calculates to obtain v=d (t2-t1)。
Rotation angle is calculated using accelerometer, gyroscope rotation angle is corrected, at this time the direction of instantaneous velocity
For voWith the x of measurand coordinate systemoThe angle of axis, since the instantaneous angular velocity of gyroscope measurement is discrete, current wink
The direction of Shi Sudu be accumulation from initial time to current time andKnown angle [alpha] and ρ can be asked at this time
Measurand, which is obtained, relative to the speed of geographic coordinate system isAccording to accelerometer
The acceleration of measurement can acquire acceleration of the measurand relative to geographic coordinate systemTherefore, by pair
The data prediction of Inertial Measurement Unit, velocity and acceleration component of the available measurand relative to geographic coordinate system,
And then the measurement result for correcting TDoA positioning system 11 carries out fusion positioning.
TDOA positioning system 11 is used to carry out Primary Location to measurand to obtain the rough position of measurand
Information.
Adaptive Kalman filter 12 is coupled with Inertial Measurement Unit 10 and TDOA positioning system 11, is used for root
TDOA positioning system 11 is filtered according to adaptive Kalman algorithm, corrects the letter of position acquired in TDOA positioning system 11
Breath.
Specifically, Kalman filtering algorithm is a kind of using linear system state equation, is observed by system input and output
Data carry out the algorithm of optimal estimation to system mode.Its main process is expressed as X by state equation and observational equationk=
Ak,k-1Xk-1+wk wk~N (0, Q) and Zk=HXk+vk vk~N (0, R), wherein X is state vector, Ak,k-1It indicates by the k-1 moment
Transfer matrix of the state to k moment state, wkFor state-noise matrix, obey mean value be 0, the Gaussian Profile that variance is Q.Z is
Observation vector, mapping relations of the H between k moment state vector and observation vector, vkIndicate the observing matrix at k moment.It obeys
The normal distribution that mean value is 0, variance is R.Kalman filtering is the process of a recursive resolve, in the amount for having measured the k moment
In the case where measured value, state value update is broadly divided into two stages, respectively forecast period and more new stage, in forecast period,
Demand obtains prediction probability distributionMean valueWith varianceWherein Δ XtFor the variable quantity of carrier state in single step.The more new stage first
Calculate intermediate variable kalman gainThe mean value of update probabilityThe variance of update probabilityWherein update probability volume mean valueAfter updating
State Xt。
In adaptive Kalman algorithm, observation vector is Z=[Z1,Z2]T, wherein the observation of TDOA system positioning output
Vector is Z1=[x, y]T, x expression measurand is in the value of the x-axis of geographic coordinate system, and y expression measurand is in geographic coordinate system
Y-axis value；Observation vector of the IMU system after data prediction is Z2=[vx,vy,ax,ay]T, vxIndicate measurand
In the movement velocity of the x-axis of physical coordinates system, vyIndicate movement velocity of the measurand in the y-axis of physical coordinates system, axIndicate quilt
Survey acceleration of motion of the object in the x-axis of physical coordinates system, ayIndicate that measurand accelerates in the movement of the y-axis of physical coordinates system
Degree；State vector is Xk=[xk,yk,vx,k,vy,k,ax,k,ay,k], wherein xkIndicate k moment measurand in physical coordinates system
X-axis value, ykIndicate value of the k moment measurand in the y-axis of physical coordinates system, vx,kIndicate k moment measurand in physics
The movement velocity of the x-axis of coordinate system, vy,kIndicate movement velocity of the k moment measurand in the y-axis of physical coordinates system, ax,kIt indicates
Acceleration of motion of the k moment measurand in the x-axis of physical coordinates system, ay,kIndicate k moment measurand in physical coordinates system
The acceleration of motion of y-axis；
The state-transition matrix at k-1 moment to k moment is Ak,k-1
Wherein, Δ t is measurand single step run duration；
The variance Q of state-noise matrix is
Wherein, σvx,k-1It indicates from k-1 moment to k moment measurand in the velocity noise side of the x-axis of geographic coordinate system
Difference, σvy,k-1Indicate the velocity noise variance from k-1 moment to k moment measurand in the y-axis of geographic coordinate system, σax,k-1It indicates
Acceleration noise variance from k-1 moment to k moment measurand in the x-axis of geographic coordinate system, σay,k-1It indicates from the k-1 moment
To k moment measurand the y-axis of geographic coordinate system acceleration noise variance；Observing matrix H=I, R are measurement noise association side
Poor matrix after Q and R is carried out initial value setting, is substituted into adaptive Kalman filter algorithm and is constantly updated.
Shot and long term Memory Neural Networks unit 13 is coupled with adaptive Kalman filter 12, for being remembered according to shot and long term
Recall neural network model to predict the position of measurand.
Specifically, LSTM shot and long term memory network is a kind of time recurrent neural network, is one to Recognition with Recurrent Neural Network
Kind enhancing, is suitable for processing and predicted time sequence.Present embodiment carries out measurand track using LSTM network model and repairs
Just.Difference between Recognition with Recurrent Neural Network and the structure of standard neuron is that it has recursive structure, can be by the last one
The information of state is transmitted to current state, as shown in figure 3, the monolayer neural networks on each node on behalf single time point.From
The weight of input layer to hidden layer is labeled as U, and W, the weight of hidden layer to output layer are labeled as from hidden layer to its own weight
Labeled as V.These weights will be reused in each sequence.But for traditional Recognition with Recurrent Neural Network, partial information will
It is lost in each feedback procedure.When the time reaching certain point, initial information will degrade, and gradient disappears, therefore recycle
Neural network loses the ability of memory for a long time.
Difference between shot and long term memory network LSTM and standard cycle neural network is hiding for Recognition with Recurrent Neural Network
The structure of unit is replaced by memory module, and the gradient of regular circulation neural network is avoided to disappear.It is a Cell in memory unit
Structure, a Cell have been placed three fan doors, respectively input gate, forgetting door and out gate in the middle.One information passes through input
Door enters in LSTM network, can be according to rule to determine whether useful.The information for only meeting algorithm certification can be left simultaneously
The information for exporting, and not being inconsistent through out gate is passed into silence by forgeing door, it can be described as Fig. 4.In cell factory (cell) more
In new process, the output of door is forgotten by updating first, determines the part that the historical movement track of measurand passes into silence, with
Current time be associated at a distance of the prediction of longer time section and current path it is smaller, and in the period being closer with current time
Measurand motion profile and following time measurand track correlation it is larger, therefore the partial information retains.It loses
Forgetting an output expression formula is ft=σ (Wf·ht-1+Ufxt+bf), the output of input gate, part decision systems study are updated later
Information, when inputting the new Grid Track point of measurand, Cell analyzes current track, learns recent history track
Movement tendency, to be corrected later to measurand track value.The expression formula of input gate isThe result for forgeing door and input gate can all act on cell state Ct=ft⊙Ct-1+it
⊙at, wherein the Hadamard of ⊙ representing matrix is long-pending.Next out gate is updated, determines the Cell information for entering next stage
StateThe index of final updated current sequence, and measurand is exported in next stage position
The predicted value Z sett=σ (Vht+c)。
Exact position determining module 14 and adaptive Kalman filter 12 and shot and long term Memory Neural Networks unit 13
It is coupled, is used for the revised location information of adaptive Kalman filter 12 and shot and long term Memory Neural Networks unit 13
The location information predicted is weighted and averaged, which is confirmed as TDOA-IMU data adaptive fusion positioning device
Positioning result.
Based on same inventive concept, the present invention also provides a kind of TDOA-IMU data adaptive fusion and positioning method,
As shown in figure 5, in one embodiment, which includes: step S1~step S5.
Velocity and acceleration of the measurand relative to geographic coordinate system is obtained by Inertial Measurement Unit in step sl
Component.It specifically, include accelerometer, gyroscope and magnetometer in Inertial Measurement Unit.Due to Inertial Measurement Unit
It can be installed in the different location in research object, the side where three-axis gyroscope not known when it intervenes work
To, it is therefore necessary to rotation angle is calculated using gyro instrument meter, accelerometer data is corrected, realizes melting for above- mentioned information
It closes.During the motion, the relationship of measurand coordinate system and geographic coordinate system is as shown in Figure 2 for research object.By magnetometer
After the direct north for determining measurand coordinate system, as seen from Figure 2, xg、ygAnd zgFor the expression of geographic coordinate system, x0、yo
And zoFor the expression of measurand coordinate system, the speed of measurand movement and the angle of direct north are ρ, measurand coordinate
System and the angle of geographic coordinate system are α, voFor movement velocity of the measurand under measurand coordinate system.In the present embodiment,
Object moves under plane scene, it is assumed that step-length is directly proportional to the acceleration change of traveling process, and measurand step-length isWherein AmaxWith AminThe maximum and the smallest measurement of accelerometer respectively in the measurand step
Value, δ are the zoom factor for handling different motion behavior and height, and the speed of measurand is continued by the single step of measurand later
Time calculates to obtain v=d (t2-t1)。
Rotation angle is calculated using accelerometer, gyroscope rotation angle is corrected, at this time the direction of instantaneous velocity
For voWith the x of measurand coordinate systemoThe angle of axis, since the instantaneous angular velocity of gyroscope measurement is discrete, current wink
The direction of Shi Sudu be accumulation from initial time to current time andKnown angle [alpha] and ρ can be asked at this time
Measurand, which is obtained, relative to the speed of geographic coordinate system isAccording to accelerometer
The acceleration of measurement can acquire acceleration of the measurand relative to geographic coordinate systemTherefore, by pair
The data prediction of Inertial Measurement Unit, velocity and acceleration component of the available measurand relative to geographic coordinate system,
And then it corrects TDoA positioning system measurement result and carries out fusion positioning.
In step s 2, Primary Location is carried out to measurand to obtain the thick of measurand by TDOA positioning system
Location information slightly.
In step s3, TDOA positioning system is filtered according to adaptive Kalman algorithm, amendment TDOA positioning system
The acquired location information of system.
Specifically, Kalman filtering algorithm is a kind of using linear system state equation, is observed by system input and output
Data carry out the algorithm of optimal estimation to system mode.Its main process is expressed as X by state equation and observational equationk=
Ak,k-1Xk-1+wk wk~N (0, Q) and Zk=HXk+vk vk~N (0, R), wherein X is state vector, Ak,k-1It indicates by the k-1 moment
Transfer matrix of the state to k moment state, wkFor state-noise matrix, obey mean value be 0, the Gaussian Profile that variance is Q.Z is
Observation vector, mapping relations of the H between k moment state vector and observation vector, vkIndicate the observing matrix at k moment.It obeys
The normal distribution that mean value is 0, variance is R.Kalman filtering is the process of a recursive resolve, in the amount for having measured the k moment
In the case where measured value, state value update is broadly divided into two stages, respectively forecast period and more new stage, in forecast period,
Demand obtains prediction probability distributionMean valueWith varianceWherein Δ XtFor the variable quantity of carrier state in single step.The more new stage first
Calculate intermediate variable kalman gainThe mean value of update probabilityThe variance of update probabilityWherein update probability volume mean valueAfter updating
State Xt。
In adaptive Kalman algorithm, observation vector is Z=[Z1,Z2]T, wherein the observation of TDOA system positioning output
Vector is Z1=[x, y]T, x expression measurand is in the value of the x-axis of geographic coordinate system, and y expression measurand is in geographic coordinate system
Y-axis value；Observation vector of the IMU system after data prediction is Z2=[vx,vy,ax,ay]T, vxIndicate measurand
In the movement velocity of the x-axis of physical coordinates system, vyIndicate movement velocity of the measurand in the y-axis of physical coordinates system, axIndicate quilt
Survey acceleration of motion of the object in the x-axis of physical coordinates system, ayIndicate that measurand accelerates in the movement of the y-axis of physical coordinates system
Degree；State vector is Xk=[xk,yk,vx,k,vy,k,ax,k,ay,k], wherein xkIndicate k moment measurand in physical coordinates system
X-axis value, ykIndicate value of the k moment measurand in the y-axis of physical coordinates system, vx,kIndicate k moment measurand in physics
The movement velocity of the x-axis of coordinate system, vy,kIndicate movement velocity of the k moment measurand in the y-axis of physical coordinates system, ax,kIt indicates
Acceleration of motion of the k moment measurand in the x-axis of physical coordinates system, ay,kIndicate k moment measurand in physical coordinates system
The acceleration of motion of y-axis；
The state-transition matrix at k-1 moment to k moment is Ak,k-1
Wherein, Δ t is measurand single step run duration；
The variance Q of state-noise matrix is
Wherein, σvx,k-1It indicates from k-1 moment to k moment measurand in the velocity noise side of the x-axis of geographic coordinate system
Difference, σvy,k-1Indicate the velocity noise variance from k-1 moment to k moment measurand in the y-axis of geographic coordinate system, σax,k-1It indicates
Acceleration noise variance from k-1 moment to k moment measurand in the x-axis of geographic coordinate system, σay,k-1It indicates from the k-1 moment
To k moment measurand the y-axis of geographic coordinate system acceleration noise variance；Observing matrix H=I (unit matrix), R are to survey
Noise covariance matrix is measured, after Q and R is carried out initial value setting, substitutes into adaptive Kalman filter algorithm and constantly carries out more
Newly.
In step s 4, it is predicted according to position of the shot and long term Memory Neural Networks model to measurand.
Specifically, LSTM shot and long term memory network is a kind of time recurrent neural network, is one to Recognition with Recurrent Neural Network
Kind enhancing, is suitable for processing and predicted time sequence.Present embodiment carries out measurand track using LSTM network model and repairs
Just.Difference between Recognition with Recurrent Neural Network and the structure of standard neuron is that it has recursive structure, can be by the last one
The information of state is transmitted to current state, as shown in figure 3, the monolayer neural networks on each node on behalf single time point.From
The weight of input layer to hidden layer is labeled as U, and W, the weight of hidden layer to output layer are labeled as from hidden layer to its own weight
Labeled as V.These weights will be reused in each sequence.But for traditional Recognition with Recurrent Neural Network, partial information will
It is lost in each feedback procedure.When the time reaching certain point, initial information will degrade, and gradient disappears, therefore recycle
Neural network loses the ability of memory for a long time.
Difference between shot and long term memory network LSTM and standard cycle neural network is hiding for Recognition with Recurrent Neural Network
The structure of unit is replaced by memory module, and the gradient of regular circulation neural network is avoided to disappear.It is a Cell in memory unit
Structure, a Cell have been placed three fan doors, respectively input gate, forgetting door and out gate in the middle.One information passes through input
Door enters in LSTM network, can be according to rule to determine whether useful.The information for only meeting algorithm certification can be left simultaneously
The information for exporting, and not being inconsistent through out gate is passed into silence by forgeing door, it can be described as Fig. 4.In cell factory (cell) more
In new process, the output of door is forgotten by updating first, determines the part that the historical movement track of measurand passes into silence, with
Current time be associated at a distance of the prediction of longer time section and current path it is smaller, and in the period being closer with current time
Measurand motion profile and following time measurand track correlation it is larger, therefore the partial information retains.It loses
Forgetting an output expression formula is ft=σ (Wf·ht-1+Ufxt+bf), the output of input gate, part decision systems study are updated later
Information, when inputting the new Grid Track point of measurand, Cell analyzes current track, learns recent history track
Movement tendency, to be corrected later to measurand track value.The expression formula of input gate isThe result for forgeing door and input gate can all act on cell state Ct=ft⊙Ct-1+it
⊙at, wherein the Hadamard of ⊙ representing matrix is long-pending.Next out gate is updated, determines the Cell information for entering next stage
StateThe index of final updated current sequence, and measurand is exported in next stage position
The predicted value Z sett=σ (Vht+c)。
In step s 5, by the revised location information of adaptive Kalman filter and shot and long term Memory Neural Networks list
The location information that member is predicted is weighted and averaged, which is confirmed as TDOA-IMU data adaptive fusion positioning device
Positioning result.
To sum up, the TDOA-IMU data adaptive fusion positioning device of present embodiment and method pass through Inertial Measurement Unit
Obtain velocity and acceleration of the measurand under geographic coordinate system, by adaptive Kalman filter algorithm it is adaptive it is online more
Position, state-noise matrix Q and the measurement noise R of new measurand are promoted positioning accuracy, and are sat using LSTM model to geography
Data under mark are smoothed, and reduce the burr or abnormal point of TDOA location data.Utilize the distance current closer time
Track forgets the output of door to update, and need to update input gate for study recent history track movement tendency, forget door and input gate
Result act on cell state, then update out gate to determine the cell state of next stage, eventually by renewal sequence
Index obtains the predicted value of carrier next stage position, and efficiently solving measurand during the motion by this method is
System state-noise and the measurement continually changing problem of noise, are greatly improved positioning accuracy.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The aforementioned description to specific exemplary embodiment of the invention is in order to illustrate and illustration purpose.These descriptions
It is not wishing to limit the invention to disclosed precise forms, and it will be apparent that according to the above instruction, can much be changed
And variation.The purpose of selecting and describing the exemplary embodiment is that explaining specific principle of the invention and its actually answering
With so that those skilled in the art can be realized and utilize a variety of different exemplary implementation schemes of the invention and
Various chooses and changes.The scope of the present invention is intended to be limited by claims and its equivalents.
Claims (10)
1. a kind of TDOA-IMU data adaptive merges positioning device, it is mounted in measurand, for described tested pair
As being positioned, which is characterized in that the TDOA-IMU data adaptive merges positioning device and includes:
Inertial Measurement Unit, for obtaining component of the measurand relative to the velocity and acceleration of geographic coordinate system；
TDOA positioning system gets the location information of the measurand for carrying out Primary Location to the measurand；
Adaptive Kalman filter is coupled with the Inertial Measurement Unit and the TDOA positioning system, is used for root
The component information of the velocity and acceleration obtained according to adaptive Kalman algorithm in conjunction with the Inertial Measurement Unit is to described
TDOA positioning system is filtered, and corrects location information acquired in the TDOA positioning system, wherein the adaptive karr
State-noise and measurement noise real-time update in graceful algorithm；
Neural network unit is coupled with the adaptive Kalman filter, for according to shot and long term Memory Neural Networks mould
Type predicts the position of the measurand；
Exact position determining module, it is equal with the adaptive Kalman filter and the shot and long term Memory Neural Networks unit
It is coupled, is used for the revised location information of the adaptive Kalman filter and the shot and long term Memory Neural Networks list
The location information that member is predicted is weighted and averaged, which is confirmed as the TDOA-IMU data adaptive fusion positioning
The positioning result of device.
2. TDOA-IMU data adaptive as described in claim 1 merges positioning device, which is characterized in that the state-noise
Covariance matrix Q be
Wherein, Δ t is the measurand single step run duration, wherein
σvx,k-1Indicate measurand described in from the k-1 moment to the k moment the x-axis of the geographic coordinate system velocity noise variance,
σvy,k-1Indicate measurand described in from the k-1 moment to the k moment the y-axis of the geographic coordinate system velocity noise variance,
σax,k-1Indicate measurand described in from the k-1 moment to the k moment the x-axis of the geographic coordinate system acceleration noise variance,
σay,k-1Indicate measurand described in from the k-1 moment to the k moment in the acceleration noise variance of the y-axis of the geographic coordinate system.
3. TDOA-IMU data adaptive as described in claim 1 merges positioning device, which is characterized in that the adaptive card
It is Z=[Z that Germania, which filters the observation vector in algorithm,1,Z2]T, wherein the observation vector of the TDOA system positioning output is Z1=
[x,y]T, x indicates the measurand in the value of the x-axis of the geographic coordinate system, and y indicates the measurand in the geography
The value of the y-axis of coordinate system；Observation vector of the IMU system after data prediction is Z2=[vx,vy,ax,ay]T, vxTable
Show the measurand in the movement velocity of the x-axis of the physical coordinates system, vyIndicate that the measurand is sat in the physics
Mark the movement velocity of the y-axis of system, axIndicate the measurand in the acceleration of motion of the x-axis of the physical coordinates system, ayIt indicates
Acceleration of motion of the measurand in the y-axis of the physical coordinates system.
4. TDOA-IMU data adaptive as described in claim 1 merges positioning device, which is characterized in that the adaptive card
It is X that Germania, which filters the state vector in algorithm,k=[xk,yk,vx,k,vy,k,ax,k,ay,k], wherein xkTested pair was indicated described in the k moment
As the value of the x-axis in the physical coordinates system, ykIndicate the k moment described in measurand the y-axis of the physical coordinates system value,
vx,kIndicate movement velocity of the measurand in the x-axis of the physical coordinates system described in the k moment, vy,kIt indicates to be tested described in the k moment
Movement velocity of the object in the y-axis of the physical coordinates system, ax,kIndicate measurand described in the k moment in the physical coordinates system
X-axis acceleration of motion, ay,kIndicate measurand described in the k moment in the acceleration of motion of the y-axis of the physical coordinates system.
5. TDOA-IMU data adaptive as described in claim 1 merges positioning device, which is characterized in that the shot and long term note
The input gate recalled in network model isWherein xtIndicate the input data of t moment network,
ht-1Indicate that the activation value of t-1 moment hidden layer neuron, σ and tanh respectively indicate sigmoid activation primitive and tanh activation letter
Number, WiAnd WCIndicate input gate weight matrix, UiAnd UaIndicate the weight matrix between input gate input layer and hidden layer, biAnd ba
Indicate the biasing of input gate, forgetting door is ft=σ (Wf·ht-1+Ufxt+bf), wherein WfAnd bfIt respectively indicates and forgets door weight square
Battle array and biasing, UfIt indicates to forget the weight matrix between door input layer and hidden layer, the input gate and the forgetting Men Douzuo
For cell state Ct=ft⊙Ct-1+it⊙at, wherein ct-1Indicate a memory unit at t-1 moment, ⊙ representing matrix
Hadamard product, updates out gate laterWherein WoAnd boRespectively indicate out gate weight
Matrix and biasing, UoIt indicates the weight matrix between out gate input layer and hidden layer, determines the cell for entering next stage
State, and measurand is exported in the predicted value Z of the next stage positiont=σ (Vht+ c), wherein V indicates output sample power
Value matrix.
6. a kind of TDOA-IMU data adaptive fusion and positioning method, is used to position measurand, which is characterized in that
The TDOA-IMU data adaptive fusion and positioning method includes:
Component of the measurand relative to the velocity and acceleration of geographic coordinate system is obtained by Inertial Measurement Unit；
Primary Location is carried out to obtain the rough position of the measurand to the measurand by TDOA positioning system
Confidence breath；
According to the component information for the velocity and acceleration that adaptive Kalman algorithm is obtained in conjunction with the Inertial Measurement Unit
The TDOA positioning system is filtered, wherein the state-noise and measurement noise in the adaptive Kalman algorithm are real
Shi Gengxin；
It is predicted according to position of the shot and long term Memory Neural Networks model to the measurand；
The revised location information of the adaptive Kalman filter and shot and long term Memory Neural Networks unit institute is pre-
The location information of survey is weighted and averaged, which is confirmed as the TDOA-IMU data adaptive fusion positioning device
Positioning result.
7. TDOA-IMU data adaptive fusion and positioning method as claimed in claim 6, which is characterized in that the state-noise
Covariance matrix Q be
Wherein, Δ t is the measurand single step run duration, wherein
σvx,k-1Indicate measurand described in from the k-1 moment to the k moment the x-axis of the geographic coordinate system velocity noise variance,
σvy,k-1Indicate measurand described in from the k-1 moment to the k moment the y-axis of the geographic coordinate system velocity noise variance,
σax,k-1Indicate measurand described in from the k-1 moment to the k moment the x-axis of the geographic coordinate system acceleration noise variance,
σay,k-1Indicate measurand described in from the k-1 moment to the k moment in the acceleration noise variance of the y-axis of the geographic coordinate system.
8. TDOA-IMU data adaptive fusion and positioning method as claimed in claim 6, which is characterized in that the adaptive card
It is Z=[Z that Germania, which filters the observation vector in algorithm,1,Z2]T, wherein the observation vector of the TDOA system positioning output is Z1=
[x,y]T, x indicates the measurand in the value of the x-axis of the geographic coordinate system, and y indicates the measurand in the geography
The value of the y-axis of coordinate system；Observation vector of the IMU system after data prediction is Z2=[vx,vy,ax,ay]T, vxTable
Show the measurand in the movement velocity of the x-axis of the physical coordinates system, vyIndicate that the measurand is sat in the physics
Mark the movement velocity of the y-axis of system, axIndicate the measurand in the acceleration of motion of the x-axis of the physical coordinates system, ayIt indicates
Acceleration of motion of the measurand in the y-axis of the physical coordinates system.
9. TDOA-IMU data adaptive fusion and positioning method as claimed in claim 6, which is characterized in that the adaptive card
It is X that Germania, which filters the state vector in algorithm,k=[xk,yk,vx,k,vy,k,ax,k,ay,k], wherein xkTested pair was indicated described in the k moment
As the value of the x-axis in the physical coordinates system, ykIndicate the k moment described in measurand the y-axis of the physical coordinates system value,
vx,kIndicate movement velocity of the measurand in the x-axis of the physical coordinates system described in the k moment, vy,kIt indicates to be tested described in the k moment
Movement velocity of the object in the y-axis of the physical coordinates system, ax,kIndicate measurand described in the k moment in the physical coordinates system
X-axis acceleration of motion, ay,kIndicate measurand described in the k moment in the acceleration of motion of the y-axis of the physical coordinates system.
10. TDOA-IMU data adaptive fusion and positioning method as claimed in claim 6, which is characterized in that the shot and long term
Input gate in memory network model isWherein xtIndicate the input number of t moment network
According to ht-1Indicate the activation value of t-1 moment hidden layer neuron, σ and tanh respectively indicate sigmoid activation primitive and tanh swashs
Function living, WiAnd WCIndicate input gate weight matrix, UiAnd UaIndicate the weight matrix between input gate input layer and hidden layer, bi
And baIndicate the biasing of input gate, forgetting door is ft=σ (Wf·ht-1+Ufxt+bf), wherein WfAnd bfIt respectively indicates and forgets door weight
Matrix and biasing, UfIt indicates to forget the weight matrix between door input layer and hidden layer, the input gate and the forgetting door are all
Act on cell state Ct=ft⊙Ct-1+it⊙at, wherein ct-1Indicate a memory unit at t-1 moment, ⊙ representing matrix
Hadamard product, updates out gate laterWherein WoAnd boRespectively indicate out gate weight
Matrix and biasing, UoIt indicates the weight matrix between out gate input layer and hidden layer, determines the cell for entering next stage
State, and measurand is exported in the predicted value Z of the next stage positiont=σ (Vht+ c), wherein V indicates output sample power
Value matrix.
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