CN106793077A - The UWB localization methods and system of dynamic object in a kind of self adaptation room - Google Patents

The UWB localization methods and system of dynamic object in a kind of self adaptation room Download PDF

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CN106793077A
CN106793077A CN201710003063.6A CN201710003063A CN106793077A CN 106793077 A CN106793077 A CN 106793077A CN 201710003063 A CN201710003063 A CN 201710003063A CN 106793077 A CN106793077 A CN 106793077A
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base station
positioning
uwb
represent
dynamic object
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CN106793077B (en
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屈洪春
宋冀生
邱泽良
吕强
伍永波
王平
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Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • 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
    • G01S5/0009—Transmission of position information to remote stations

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a kind of UWB localization methods of dynamic object in self adaptation room, TDOA values are measured first with UWB alignment systems；Secondly the TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, exports the TDOA values of reconstruct, the TDOA values after reconstruct are converted into distance difference and Nonlinear System of Equations is set up, and obtains the optimal solution of equation group；Then using optimal solution as initial value, positioning is tracked to dynamic object using expanded Kalman filtration algorithm, tries to achieve final estimate；Finally export final estimate.The UWB localization methods of dynamic object in the self adaptation room that the present invention is provided, can weaken even eliminate UWB signal communication process in be subject to multipath transmisstion (Multipath) and non line of sight interference (NLOS) influence caused by range error, positioning precision is improved, and can realize being accurately positioned dynamic object under non line of sight indoor environment.Ultra-wideband positioning system demonstrates the validity of localization method and realizes the indoor positioning to positioning dynamic object.

Description

The UWB localization methods and system of dynamic object in a kind of self adaptation room
Technical field
The present invention relates to wireless location technology field, the UWB localization methods of dynamic object in particularly a kind of self adaptation room And system.
Background technology
With the fast development of Novel movable equipment, mobile communication and radio sensing network (WSN) and based on position The surge of aware application, location technology has gradually stepped into the brand-new stage.In wide outdoor, global positioning system (GPS) Precision high can be reached by satellite fix.But traditional global positioning system (GPS) is indoors under environment due to being hindered Gear satellite communication can not meet daily life needs, with the improvement that present people live, the more urgent need of people Ask the application to short distance indoor locating system.
At present, for the location Calculation of indoor objects, have and many emerging realize technology and related algorithm, such as infrared ray Location technology, Bluetooth technology, ZigBee technology, RFID (radio frequency identification) technology, ultra wide band (UWB) technology etc. in Internet of Things, do The every aspect of the life and works such as public room, intelligent building is widely used so that the application based on wireless location has More broad prospect.But, existing indoor positioning technologies but all can not although there is preferable performance under certain circumstances Requirement to location modeling is well adapted under universal computing environment, and such as high precision, adaptable, cost of implementation is low Deng.Compared with traditional indoor positioning technologies, ultra wideband location techniques because with multi-path resolved rate is high, strong penetrating power, low-power consumption, Be easily integrated, the advantage such as positioning performance is high, as a current most widely used wireless communication technology.
The research of ultra wideband location techniques mainly has：R.Jean-Marc Cramer et al. propose a kind of based on CLEAN The TOA methods of estimation of algorithm；Joon-Yong Lee etc. propose a kind of side of the broad sense maximal possibility estimation based on relevant mechanism Method；Pratt and Wheeler et al. propose the ultra wide band location method based on aerial array, have broken former ultra wide band positioning In using single antenna situation, it improves positioning precision of the ultra wideband location techniques under nlos environment. The Ubisense companies of Britain are proposed the product of UWB wireless location technologies, and frequency range is 5.8-7.2GHz, is joined using TDOA/AOA Location technology is closed, in the range of 20 meters to 50 meters, positioning precision can reach 15cm.Time domain companies of the other U.S. push away The scenograph through walls based on UWB technology for going out, the detection that it can carry out to object with permeability number layer metope, product is also obtained It is widely applied.
Effective indoor positioning solution needs to meet following requirement：Precision, coverage, reliability, cost, Power consumption, scalability and response time.The subject matter that current indoor positioning faces has：
First, precision and reliability, the factor of influence positioning precision and stability mainly have：(1) multipath effect；(2) it is non- Line-of-sight propagation (NLOS)；(3) multiple access is accessed.
Second, coverage, existing technology is substantially all dependence location database, and the generation of database is relied on mostly Artificial on-site land survey, the layout and maintenance cost so brought is very high.
3rd, cost and power consumption, it is desirable to which the location technology for being used is low in energy consumption, does not increase extra cost.
Accordingly, it would be desirable in a kind of self adaptation room dynamic object UWB localization methods and system.
The content of the invention
An object of the present invention is the UWB localization methods for proposing dynamic object in a kind of self adaptation room；Mesh of the invention Two be the UWB alignment systems for proposing dynamic object in a kind of self adaptation room.
An object of the present invention is achieved through the following technical solutions：
The UWB localization methods of dynamic object, comprise the following steps in a kind of self adaptation room that the present invention is provided：
Step 1：One group of TDOA value with error is measured using UWB alignment systems；
Step 2：The TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, exports the TDOA of reconstruct Value；
Step 3：TDOA values are converted into distance value, are constructed according to hyperbolic model and is contained destination node location coordinate letter The Nonlinear System of Equations of breath, the nonlinear optimization of unconfined condition is carried out using nonlinear optimization method to the equation group for constructing The optimal solution of equation group is obtained, the estimate of destination node is obtained；
Step 4：Using estimate as EKF initial value, and using expanded Kalman filtration algorithm to move Dynamic positioning target is tracked positioning, tries to achieve final estimate；
Step 5：The Rule of judgment that selection carat Metro lower bound CRLB terminates as method, when the mean square error of algorithm is less than Carat Metro lower bound CRLB, return to step 1 remeasures TDOA values and is positioned；When the mean square error of algorithm is beautiful more than carat Sieve lower bound CRLB, then algorithm terminate, export final estimate.
Further, being processed the TDOA values for measuring using wavelet analysis self-adaptive solution method in the step 2, tool Body implementation process is as follows：
201：Selection biorthogonal wavelet race (biorthogonal (biorNr.Nd)) carries out wavelet decomposition to TDOA values；
202：Initialization network weight and neuron biasing：
203：Iterations is initialized；
204：Obtain the output of each hidden layer and output layer forward according to below equation：
Determine hidden layer node number rounds formula：
Wherein, h is hidden layer node number, and m is input layer number, and n is output layer interstitial content；
Using wavelet reconstruction function as object function, the reconstruction signal after noise reduction is obtained as output valve Ok；
205：Obtain the error E of output layer and anticipated outputj：
Wherein, TkIt is the desired output of output unit；OkRepresent the output valve of neutral net；N is output layer interstitial content；k Represent 1 to n number；J represents output unit number.
206：Reverse propagated error, obtains the error of all hidden layers；
207：Regulation weights and neuron biasing：
Adjust weights formula be：wij=wij+Δwij=wij+lOiEj；
Adjustment neuron biasing formula be：θj=θj+Δθj=θj+lEj；
Wherein, l is learning rate, takes the inverse of iterations；I and j represent the numbering of NE；wijRepresent upper unit Network weights of the i to unit j；ΔwijRepresent the parameter of adjustment weight；OiRepresent the output valve of unit i；θjRepresent that unit j's is inclined Put；ΔθjRepresent the parameter of adjustment biasing；
208：Judge that neural metwork training terminates：
For each sample, if final output error or iterations have reached predetermined threshold value, training process Terminate, export the TDOA values of reconstruct；Otherwise, iterations adds 1, then turns to step 204 and is continuing with current sample training.
Further, the non-thread containing destination node MS location coordinate informations is constructed using hyperbolic model in the step 3 Property equation group process is as follows：
M locating base station, (x are set in two dimensional surface at randomi, yi) it is i-th coordinate of base station, i=1 ..., m are to be measured The coordinate of node M S is (x, y), it is assumed that base station 1 is reference location base station；
The distance of node M S to i-th base station to be measured is：
Then have
Wherein,
The distance of node M S to i-th base station to be measured and node M S to be measured to the 1st difference r of the distance of base stationi1For：
Wherein, t is the TDOA measured values under view distance environment；N is the measurement error of system, τNLOSi1Caused by NLOS Additional time delay error is NLOS errors；
Had by (2) formula：
(3) formula is launched：
(1) formula has in i=1：
(4) formula subtracts (5) formula and obtains：
I.e.
In formula, xi1=xi-x1,yi1=yi-y1, i=2 ..., m.
Formula (7) is the Hyperbolic Equation group of construction.
Further, the computing formula of carat Metro lower bound is in the step 5：
CRLB=(GTB-TQ-1B-1G)-1；
Wherein, CRLB represents a carat Metro lower bound；Q is the covariance matrix of NLOS errors；B is represented by positioning target and base station Between distance constitute diagonal matrix, B-TRepresent the transposed matrix of the inverse matrix of B；G represents the coefficient matrix of Hyperbolic Equation group；GT Represent the transposed matrix of G.
B=diag { r2,r3,…,rm}；
gi=ri1+(ri-r1)T, i=2 ..., m；
Wherein, (x, y) represents the coordinate of target to be positioned；(xi,yi) represent i-th coordinate of base station；M represents participation base The number stood；I represents base station number；riRepresent positioning target to i-th distance of base station；ri1Represent positioning target to i-th Base station and positioning target to the 1st base station.
The second object of the present invention is achieved through the following technical solutions：
The UWB alignment systems of dynamic object in a kind of self adaptation room that the present invention is provided, including positioning label, fixed base stations And host computer, visualization localization process module is provided with the host computer；
The positioning label is arranged at target to be positioned, and the positioning label sends UWB according to preset interval time to be believed Number, the fixed base stations receive UWB signal；The UWB signal that the fixed base stations will be received uploads to host computer；It is described UWB signal includes arrival time stamp, the identification numbering of label, signal receiving strength；
Visualization localization process module in the host computer is realized positioning the UWB of dynamic object according to following steps, Specifically include following steps：
Step 1：One group of TDOA value with error is measured using UWB alignment systems；
Step 2：The TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, exports the TDOA of reconstruct Value；
Step 3：TDOA values are converted into distance value, are constructed according to hyperbolic model and is contained destination node location coordinate letter The Nonlinear System of Equations of breath, the nonlinear optimization of unconfined condition is carried out using nonlinear optimization method to the equation group for constructing The optimal solution of equation group is obtained, the estimate of destination node is obtained；
Step 4：Using estimate as EKF initial value, and using expanded Kalman filtration algorithm to move Dynamic positioning target is tracked positioning, tries to achieve final estimate；
Step 5：The Rule of judgment that selection carat Metro lower bound CRLB terminates as method, when the mean square error of algorithm is less than Carat Metro lower bound CRLB, return to step 1 remeasures TDOA values and is positioned；When the mean square error of algorithm is beautiful more than carat Sieve lower bound CRLB, then algorithm terminate；Export final estimate.
Further, the visualization localization process module also includes positioning result display unit；The positioning result shows Web Three-dimensional Displays are realized using in browser end.
By adopting the above-described technical solution, the present invention has the advantage that：
The UWB localization methods of dynamic object in the self adaptation room that the present invention is provided, can adaptively weaken and even eliminate UWB Multipath transmisstion (Multipath) and the caused range error of non line of sight interference (NLOS) influence are subject in signal communication process, Positioning precision is improved, and can realize being accurately positioned dynamic object under non line of sight indoor environment.Ultra-wideband positioning system For verifying the validity of localization method and realizing the indoor positioning to positioning dynamic object；Beneficial effect is specific as follows：
(1) localization method analyzes Denoising Algorithm decrease non-market value by adaptive wavelet in the present invention.Conventional small echo Analysis Denoising Algorithm acts on detail coefficients by hard -threshold or soft-threshold, and threshold value does not have a kind of general model, and they have certainly Oneself scope of application, and this method can lose the energy ingredient of original signal.Method of the present invention combination BP neural network, can Obtain acting on the optimal threshold of each detail coefficients with self adaptation.
(2) Kalman filtering is applied in the alignment system of dynamic object, both can accurately track in real time target or The noise in data can be further filtered out and draw relative real value, improve positioning precision.Because target section during actual location Point is constantly mobile, makes full use of the good tracking characteristics of EKF, target can be accurately tracked in real time, and And Kalman filtering can estimate dynamical system in the case of known to measurement variance from a series of data that there is measurement noise The data of Noise can be filtered by the state of system.Missed because the TDOA values that wavelet analysis is treated can still may be present Difference, can further filter out the error percentage in data and draw relative real value using EKF.
(3) method of the present invention prevents historical data the negative shadow that relatively large deviation is produced to Kalman filtered results occur Ring.Because method positions number using the optimization solution of the Nonlinear System of Equations of TDOA values construction as the history of EKF According to, first to being processed by the measurement data of NLOS serious interferences, the positioning precision of raising expanded Kalman filtration algorithm.
Other advantages of the invention, target and feature will be illustrated in the following description to a certain extent, and And to a certain extent, based on being will be apparent to those skilled in the art to investigating hereafter, Huo Zheke To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and Obtain.
Brief description of the drawings
Brief description of the drawings of the invention is as follows.
Fig. 1 is the schematic diagram of UWB alignment systems embodiment of the invention.
Fig. 2 is the structure chart of upper computer software general frame.
Fig. 3 is the localization method flow chart of dynamic object in self adaptation room.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
Embodiment 1
The UWB localization methods of dynamic object, implement process as follows in the self adaptation room that the present embodiment is provided：
Step 1：One group of TDOA value with error is measured using UWB alignment systems；
Step 2：The TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, exports the TDOA of reconstruct Value.
Because the absolute value of useful signal decomposition coefficient is big, it is stored in each layer approximation coefficient, noise signal decomposition coefficient Absolute value it is small, be stored in each layer detail coefficients, and influence of each layer detail coefficients to useful signal after decomposing is different, from Adapt to denoising to process detail coefficients, obtain the output valve under optimal factor of influence (weight i.e. in network)；Specifically such as Under：
The detail coefficients after wavelet decomposition are trained using BP neural network, network passes through output valve and anticipated output Between error transfer factor network weight and neuron biasing.Network takes each layer detail coefficients value conduct after TDOA signals are decomposed The input value of neuron, it is contemplated that output takes the TDOA values not being decomposed, the output valve of neuron takes treated each layer coefficients weight The TDOA values of structure.
Process is implemented the following is adaptive wavelet：
201：Selection biorthogonal wavelet race biorthogonal (biorNr.Nd) carries out wavelet decomposition to TDOA values；
Because biorthogonal small echos have, symmetry, self-similarity, vanishing moment exponent number be big, Accurate Reconstruction, with this TDOA signals after small echo treatment keep self similarity, phase shift, energy concentration, reconstruct will not be produced accurate with original signal, meet this Requirement of the method to reconstruction signal.
202：Initialization network weight and neuron biasing：
Network weight is initialized to a random number for very little, and scope is in (- 1,1)；Neuron biasing is initialized as one Individual random number.
203：Iterations t is initialized：T=1；
204：The output of each hidden layer and output layer is obtained forward：
Determine hidden layer node number rounds formula：Wherein h is hidden layer node number, and m is input Node layer number, n is output layer interstitial content.
Using wavelet reconstruction function as object function, the reconstruction signal after noise reduction is obtained as output valve Ok。
205：Obtain the error E of output layer and anticipated outputj；
Wherein, TkIt is the desired output of output unit；OkRepresent the output valve of neutral net；N is output layer interstitial content；k Represent 1 to n number；J represents that output unit is numbered；
206：Reverse propagated error, obtains the error of all hidden layers；
207：Regulation weights and neuron biasing：
Adjust weights formula be：wij=wij+Δwij=wij+lOiEj；
Adjustment neuron biasing formula be：θj=θj+Δθj=θj+lEj；
Wherein, l is learning rate, takes the inverse of iterations；I and j represent the numbering of NE；wijRepresent upper unit Network weights of the i to unit j；ΔwijRepresent the parameter of adjustment weight；OiRepresent the output valve of unit i；θjRepresent that unit j's is inclined Put；ΔθjRepresent the parameter of adjustment biasing；
208：Judge that neural metwork training terminates：
For each sample, if final output error or iterations have reached predetermined threshold value, training process Terminate, export the TDOA values of reconstruct；Otherwise, iterations adds 1, then turns to step 204 and is continuing with current sample training.
Step 3：The reconstruct TDOA values that step 2 is obtained are converted into distance value, are constructed according to hyperbolic model and contain mesh The Nonlinear System of Equations of node location coordinate information is marked, the equation group for constructing is carried out without constraint using nonlinear optimization method The nonlinear optimization of condition obtains the optimal solution of equation group, obtains the estimate of destination node.
Step 4：The estimate that selecting step 3 is obtained as EKF initial value, and using extending karr Graceful filtering algorithm is tracked positioning to running fix target, tries to achieve final estimate.
Step 5：What selection carat Metro lower bound (Cramer-Rao Lower Bound, abbreviation CRLB) terminated as method Rule of judgment, when the mean square error of algorithm is more than CRLB, algorithm terminates；Otherwise, return to step 1, remeasuring TDOA values is carried out Positioning.
Wherein, mean square error positions under nlos environment with CRLB and positive correlation should be presented.When m base station participates in positioning When, the computing formula of carat Metro lower bound is：CRLB=(GTB-TQ-1B-1G)-1；
Wherein, CRLB represents a carat Metro lower bound；Q is the covariance matrix of NLOS errors；B is represented by positioning target and base station Between distance constitute diagonal matrix, B-TRepresent the transposed matrix of the inverse matrix of B；G represents the coefficient matrix of Hyperbolic Equation group；GT Represent the transposed matrix of G.
B=diag { r2,r3,…,rm}；
gi=ri1+(ri-r1)T, i=2 ..., m；
Wherein, (x, y) represents the coordinate of target to be positioned；(xi,yi) represent i-th coordinate of base station；M represents participation base The number stood；I represents base station number；riRepresent positioning target to i-th distance of base station；ri1Represent positioning target to i-th Base station and positioning target to the 1st base station.
Further, using hyperbolic model nonlinear equation of the construction containing destination node MS position coordinateses x, y in step 3 Group process is as follows：
M locating base station, (x are set in two dimensional surface at randomi, yi) it is i-th coordinate of base station, i=1 ..., m are to be measured The coordinate of node M S is (x, y), it is assumed that base station 1 is reference location base station.
MS to i-th base station apart from riFor：
Then have
Wherein
MS to i-th distance of base station and MS to the 1st difference r of the distance of base stationi1For：
Wherein t is the TDOA measured values under view distance environment；N is the measurement error of system, τNLOSi1Be by NLOS cause it is attached Plus time delay error is NLOS errors.
Had by (2) formula：
(3) formula is launched：
(1) formula has in i=1：
(4) formula subtracts (5) formula and obtains：
I.e.
In formula：xi1=xi-x1, yi1=yi-y1, i=2 ..., m.
Formula (7) is the Hyperbolic Equation group of construction.
UWB indoor locating systems in the present embodiment are mainly set by hardware such as positioning label, fixed base stations (being also anchor point) Upper computer software composition needed for standby and visualization positioning, it is specific as follows：
The personnel or object being positioned need to wear label, by positioning the UWB signal that label interval time sends, base station Receive the signal and form arrival time stamp, and information interaction passage via between base station is aggregated into host computer.Base station receives UWB signal formed information data in cover up to timestamp, the identification numbering of label, signal receiving strength and other from The information of definition.Upper computer software is main to be made up of algorithm and visualization locating interface.Locating base station and host computer service end Information carries out transmitted in both directions according to certain communications protocol, including TDOA information and base station itself configuration information, transmission communication Mode is UDP.
The label of hardware platform is UWB signal transmitting terminal, and corresponding working frequency, peak frequency are set with reference to being actually needed Up to 100Hz.Labeling apparatus are powered by rechargeable battery, are periodically into resting state.Additionally, there is one kind in whole alignment system More special base station (commonly referred to as AP base stations), its in addition to possessing conventional UWB signal receive capabilities, also as other bases The concentrator or router stood are used, and the information data of other base stations is uploaded to position display terminal by the base station, base station Configuration is basic consistent with traditional configuration of routers mode.
Upper computer software is the software platform of whole UWB alignment systems, including data access, data processing, algorithm output, The modules such as effect displaying.What whole visualization was positioned realizes that flow is as follows：
(1) data receiver and treatment：Base station uploads information data to server by way of UDP, it is desirable to which server is held It is continuous to monitor certain Single port.Except conventional measurement Value Data, base station interval sends heartbeat packet, can be understood by heartbeat bag data The current working condition in base station.Base station independence uplink time stamp data, in the case where labeling task frequency is slightly larger, with once fixed The timestamp information that position produces may be received at different time period being serviced ends, it is therefore desirable to using data number to Information Number According to carrying out packet transaction.When the number of a certain group of data reaches lowest positioned requirement, just meeting cup is transferred into next place to data Reason link；
(2) location estimation：Treated one group of data are introduced to algorithm processing module, and TDOA algorithms are surveyed using these The configuration data of value and current localizing environment calculates the current position coordinates estimate of label to be positioned, then by UDP Mode send visualization display module to；
(3) positioning result shows：Real time position display mode includes two kinds：One kind is by calling Matlab interfaces, directly It is connected on realization two dimension display in Matlab；Another kind is to realize Web Three-dimensional Displays in browser end；The refresh rate of the two differs Cause, former data refreshing frequency is high, the latter is based on performance requirement, refreshing frequency is relatively low.
Embodiment 2
As shown in figure 1, the UWB alignment systems that the present embodiment is provided include the positioning label that the personnel by being positioned carry, The positioning label sends UWB signal according to the frequency interval of setting.There are 5 base stations to participate in positioning, wherein there is a special AP WiFi Router (are also) in base station, and it is responsible for the uploading information data of other base stations to position display management terminal.
As shown in Fig. 2 Fig. 2 is the structure chart of upper computer software general frame, upper computer software includes data receiver, data The modules such as treatment, algorithm output, effect displaying, all of module is all based on Java language realization, each module described in detail below Functional realiey process：
(1) data access module：Its major function is to carry out parsing pretreatment to the initial data that anchor point is uploaded, generally place The packet of reason is mainly the heartbeat packet and passive location data bag of interval time, heartbeat packet can select to process after receiving into And show some status informations of base station, it is also possible to directly abandon.Whole data access module is based on Apache MINA frameworks Realize, MINA is one can help the web application framework of User Exploitation high-performance and high scalability, and it is in Java TCP/IP and UDP/IP agreements are encapsulated in nio technical foundation, is then provided abstract, event driven, asynchronous API.Location data bag enters a packet transaction link after resolved, and packet is realized based on two-dimensional array, and timing Unavailable data are cleared up, this is derived from a certain numbering base station data missing causes the quantity of the numbering data can not to reach forever most The number of low positioning requirements, and then as unavailable data.
(2) algorithm processing module：Its core is TDOA location algorithms, and the input value of TDOA algorithms includes incoming measured value The configuration data of data and environmental correclation.Environment configurations refer to coordinate position of the current base station in the middle of self-defined coordinate system, are led to The unique identifier and the base station number in measurement Value Data for crossing base station match, and algorithm processing module determines whole alignment system Performance.
(3) display module during fructufy：The estimate of the label current location to be positioned exported by algoritic module uses UDP Mode be sent to display module.The display of two dimension can be realized by the drawing interface that Matlab is directly invoked in Java, Its realization principle is the APMB package by the way that the SQL in Matlab to be output as Java, is then introduced into current Java In project, with using common Java function fashions, unanimously, directly invoking customized function interface can just reach Matlab The purpose of display.This mode is used frequently as the test of heuristics of UWB positioning prototype test systems, can intuitively show current Position error scope, to assess TDOA algorithms performance play critical effect.At the same time, based on Java Web technologies Web Three-dimensional Displays scheme the locating effect for more visualizing can be provided, its core technology is WebGL.WebGL is browser The basis of 3D figures and animation is drawn at end, and this drawing technique standard allows Javascript to be combined with OpenGL ES 2.0 Together, by increasing the binding of Javascript, WebGL can accelerate to render for the GPU that HTML5Canvas provides hardware. WebGL ideally solves two problems of existing Web interactive three-dimensionals animation：Realize that Web is handed in itself by html script The making of mutual formula three-dimensional animation, supports without any browser plug-in；It is carried out using the graphic hardware GPU acceleration function of bottom Figure is rendered, and is realized by unified, standard, cross-platform OpenGL interfaces.The implementation of this paper is to be based on The third party library Three.js of WebGL is completed, and it supports most 3-D graphic form, can provided by conversion work Tool, is converted to json forms, and then directly model can be imported into current scene by common threedimensional model form.
(4) configuration and log pattern：, it is necessary to record all of pilot process information data during assignment test, side Just the stability and positioning precision of analytical error producing cause and assessment algorithm.Whole logger module is based on Apache Log4j storehouses complete, and it can easily control the way of output of log information：Console, file, GUI component etc., log recording The txt file of module final output compressed format.The configuration of base station is based on configuration client and realizes that whole base station configures client Completed based on communications protocol mentioned above, support most configuration management, including base station operation pattern, labeling task mould Formula, locating periodically etc..
The present embodiment verifies UWB localization methods, is known by checking, and the localization method of dynamic object is in meter in self adaptation room The aspects such as calculation amount, positioning precision are all advantageous.
As shown in figure 3, Fig. 3 is the flow chart of the localization method of dynamic object in self adaptation room, detailed process is as follows：
301 measure one group of TDOA value with NLOS errors using UWB alignment systems；
302 selected small echos carry out wavelet decomposition to TDOA values：
303 initialization network weights and neuron biasing：
Network weight is initialized to a random number for very little, and scope is in (- 1,1)；Neuron biasing is initialized as one Individual random number.
304 iterationses are initialized；
305 outputs for obtaining each hidden layer and output layer forward：
Sample provides the input value of neuron：Each layer detail coefficients value after TDOA value wavelet decompositions；Anticipated output：With wrong The TDOA values that difference is not decomposed.Using wavelet reconstruction function as object function, the reconstruction signal after noise reduction is obtained as output Value Ok.
306 errors for obtaining output valve and anticipated output；
307 reverse propagated errors, obtain the error of all hidden layers；
308 regulation weights and neuron biasing：
Adjust weights formula be：wij=wij+Δwij=wij+lOiE；
Regulation neuron biasing formula be：θj=θj+Δθj=θj+lE；
Wherein, l is learning rate, takes the inverse of iterations；I and j represent the numbering of NE；E represents each hidden layer With the error of output layer, wijRepresent the network weight of upper unit i to unit j；ΔwijRepresent the parameter of adjustment weight；OiRepresent The output valve of unit i；θjRepresent the biasing of unit j；ΔθjRepresent the parameter of adjustment biasing；
309 end for judging neural network training process：
For each sample, if final output error or iterations have reached default threshold value, trained Journey terminates；Otherwise, iterations adds 1, then turns to step 305 and is continuing with current sample training.
The 310 reconstruct TDOA values for obtaining step 309 are converted into distance value, are constructed according to hyperbolic model and contain target The Nonlinear System of Equations of node location coordinate information, is carried out without constraint bar using nonlinear optimization method to the equation group for constructing The nonlinear optimization of part obtains the optimal solution of equation group, obtains the estimate of destination node.
The estimate that 311 selecting steps 310 are obtained is filtered as the initial value of EKF using spreading kalman Ripple algorithm is tracked positioning to running fix target, tries to achieve final estimate.
312 selections carat Metro lower bound (Cramer-Rao Lower Bound, abbreviation CRLB) terminate as determination methods Condition, when the mean square error of algorithm is more than CRLB, algorithm terminates, the estimate of output positioning target, otherwise, return to step 301, remeasure TDOA values and positioned.
The localization method of dynamic object can be by programming realization in self adaptation room of the invention, and the flow chart of the method is as schemed Shown in 3, the position coordinates of positioning target can be quickly and accurately calculated by the location algorithm, realized to the accurate of positioning target Positioning.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with Good embodiment has been described in detail to the present invention, it will be understood by those within the art that, can be to skill of the invention Art scheme is modified or equivalent, and without deviating from the objective and scope of the technical program, it all should cover in the present invention Protection domain in the middle of.

Claims (6)

1. in a kind of self adaptation room dynamic object UWB localization methods, it is characterised in that：Comprise the following steps：
Step 1：One group of TDOA value with error is measured using UWB alignment systems；
Step 2：The TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, exports the TDOA values of reconstruct；
Step 3：TDOA values are converted into distance value, are constructed according to hyperbolic model and is contained destination node location coordinate information Nonlinear System of Equations, is obtained using nonlinear optimization method to the nonlinear optimization that the equation group for constructing carries out unconfined condition The optimal solution of equation group, obtains the estimate of destination node；
Step 4：Using estimate as EKF initial value, and using expanded Kalman filtration algorithm to mobile fixed Position target is tracked positioning, tries to achieve final estimate；
Step 5：The Rule of judgment that selection carat Metro lower bound CRLB terminates as method, when the mean square error of algorithm is less than carat Metro lower bound CRLB, return to step 1 remeasures TDOA values and is positioned；Under the mean square error of algorithm is more than carat Metro Boundary CRLB, then algorithm terminate, export final estimate.
2. in self adaptation room as claimed in claim 1 dynamic object UWB localization methods, it is characterised in that：In the step 2 The TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, implement process as follows：
201：Selection biorthogonal wavelet race carries out wavelet decomposition to TDOA values；
202：Initialization network weight and neuron biasing：
203：Iterations is initialized；
204：Obtain the output of each hidden layer and output layer forward according to below equation：
Determine hidden layer node number rounds formula：
Wherein, h is hidden layer node number, and m is input layer number, and n is output layer interstitial content；
Using wavelet reconstruction function as object function, the reconstruction signal after noise reduction is obtained as output valve Ok；
205：Obtain the error E of output layer and anticipated outputj：
Wherein, TkIt is the desired output of output unit；OkRepresent the output valve of NE k；N is output layer interstitial content；K tables Show 1 to n number；J represents that output unit is numbered；
206：Reverse propagated error, obtains the error of all hidden layers；
207：Regulation weights and neuron biasing：
Adjust weights formula be：wij=wij+Δwij=wij+lOiEj；
Adjustment neuron biasing formula be：θj=θj+Δθj=θj+lEj；
Wherein, l is learning rate, takes the inverse of iterations；I and j represent the numbering of NE；wijRepresent that upper unit i is arrived The network weight of unit j；ΔwijRepresent the parameter of adjustment weight；OiRepresent the output valve of unit i；θjRepresent the biasing of unit j； ΔθjRepresent the parameter of adjustment biasing；
208：Judge that neural metwork training terminates：
For each sample, if final output error or iterations have reached predetermined threshold value, training process terminates, Export the TDOA values of reconstruct；Otherwise, iterations adds 1, then turns to step 204 and is continuing with current sample training.
3. in self adaptation room as claimed in claim 1 dynamic object UWB localization methods, it is characterised in that：In the step 3 It is as follows using hyperbolic model Nonlinear System of Equations process of the construction containing destination node MS location coordinate informations：
M locating base station, (x are set in two dimensional surface at randomi, yi) it is i-th coordinate of base station, i=1 ..., m, node to be measured The coordinate of MS is (x, y), it is assumed that base station 1 is reference location base station；
The distance of node M S to i-th base station to be measured is：
r i = ( x - x i ) 2 + ( y - y i ) 2 , i = 2 , ... , m ;
Then there is ri 2=(x-xi)2+(y-yi)2=Ki-2xix-2yiy+x2+y2 (1)
Wherein,
The distance of node M S to i-th base station to be measured and node M S to be measured to the 1st difference r of the distance of base stationi1For：
r i 1 = c ( t + n + τ N L O S i 1 ) = r i - r 1 = ( x - x i ) 2 + ( y - y i ) 2 - ( x - x 1 ) 2 + ( y - y 1 ) 2 - - - ( 2 )
Wherein, t is the TDOA measured values under view distance environment；N is the measurement error of system, τNLOSi1It is by adding that NLOS causes Time delay error is NLOS errors；
Had by (2) formula：ri 2=(ri1+r1)2, i=2 ..., m (3)
(3) formula is launched：
(1) formula has in i=1：
(4) formula subtracts (5) formula and obtains：
I.e.
In formula, xi1=xi-x1,yi1=yi-y1, i=2 ..., m；
Formula (7) is the Hyperbolic Equation group of construction.
4. in self adaptation room as claimed in claim 1 dynamic object UWB localization methods, it is characterised in that：The carat is beautiful The computing formula of sieve lower bound is：
CRLB=(GTB-TQ-1B-1G)-1；
Wherein, CRLB represents a carat Metro lower bound；Q is the covariance matrix of NLOS errors；B is represented by between positioning target and base station The diagonal matrix that distance is constituted, B-TRepresent the transposed matrix of the inverse matrix of B；G is represented by the Hyperbolic Equation group of range difference structure Coefficient matrix, GTRepresent the transposed matrix of G；
B=diag { r2,r3,…,rm}；
r i = ( x - x i ) 2 + ( y - y i ) 2 ;
G = [ g 2 T , g 3 T , ... , g m T ] T ;
gi=ri1+(ri-r1)T, i=2 ..., m；
Wherein, (x, y) represents the coordinate of target to be positioned；(xi,yi) represent i-th coordinate of base station；M is represented and is participated in base station Number；I represents base station number；riRepresent positioning target to i-th distance of base station；ri1Represent positioning target to i-th base station With positioning target to the 1st base station.
5. in a kind of self adaptation room dynamic object UWB alignment systems, it is characterised in that：Including positioning label, fixed base stations and Host computer, is provided with visualization localization process module in the host computer；
The positioning label is arranged at target to be positioned, and the positioning label sends UWB signal according to preset interval time, institute State fixed base stations and receive UWB signal；The UWB signal that the fixed base stations will be received uploads to host computer；The UWB signal Including arrival time stamp, the identification numbering of label, signal receiving strength；
Visualization localization process module in the host computer is realized positioning the UWB of dynamic object according to following steps, specifically Comprise the following steps：
Step 1：One group of TDOA value with error is measured using UWB alignment systems；
Step 2：The TDOA values for measuring are processed using wavelet analysis self-adaptive solution method, exports the TDOA values of reconstruct；
Step 3：TDOA values are converted into distance value, are constructed according to hyperbolic model and is contained destination node location coordinate information Nonlinear System of Equations, is obtained using nonlinear optimization method to the nonlinear optimization that the equation group for constructing carries out unconfined condition The optimal solution of equation group, obtains the estimate of destination node；
Step 4：Using estimate as EKF initial value, and using expanded Kalman filtration algorithm to mobile fixed Position target is tracked positioning, tries to achieve final estimate；
Step 5：The Rule of judgment that selection carat Metro lower bound CRLB terminates as method, when the mean square error of algorithm is less than carat Metro lower bound CRLB, return to step 1 remeasures TDOA values and is positioned；Under the mean square error of algorithm is more than carat Metro Boundary CRLB, then algorithm terminate；Export final estimate.
6. in self adaptation room as claimed in claim 5 dynamic object UWB alignment systems, it is characterised in that：The visualization Localization process module also includes positioning result display unit；The positioning result display realizes that Web is three-dimensional using in browser end Display.
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