CN103379619B - A kind of localization method and system - Google Patents

A kind of localization method and system Download PDF

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Publication number
CN103379619B
CN103379619B CN201210111075.8A CN201210111075A CN103379619B CN 103379619 B CN103379619 B CN 103379619B CN 201210111075 A CN201210111075 A CN 201210111075A CN 103379619 B CN103379619 B CN 103379619B
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CN103379619A (en
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刘兴川
李超
林孝康
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中兴通讯股份有限公司
清华大学深圳研究生院
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02Services making use of location information
    • H04W4/025Services making use of location information using location based information parameters
    • H04W4/027Services making use of location information using location based information parameters using movement velocity, acceleration information

Abstract

The present invention provides a kind of localization method, including:Access point based on WLAN is positioned, and obtains the initial estimated location of user equipment;Obtain the course angle and velocity information of the user equipment;The initial estimated location is modified according to the course angle and velocity information, obtains final position information.The present invention also provides a kind of alignment system.It is of the invention effectively to correct position error and the accumulated error as caused by sensor noise as caused by floating receive information intensity, realize the integrated positioning system of a low-cost and high-precision.

Description

A kind of localization method and system

Technical field

The present invention relates to wireless network positioning field, more particularly to a kind of localization method and system.

Background technology

Alignment system based on WLAN has that cost is low, and precision is high, have a wide range of application (indoor and outdoors) the advantages that, in base As emergency relief, intelligent transportation and indoor positioning navigation etc. achieve very big success in the service of position.But still Following two problem urgent need to resolve so be present:(1) received signal strength (Received caused by the factor such as multi-path jamming Signal Strength, RSS) floating severe exacerbation WLAN positioning precisions;(2) in wireless access node (Access Point, AP) region that is not covered with, because AP missings cause WLAN positioning failures.

In order to solve the above problems, there has been proposed a variety of methods, can be divided into following three class:

1st, the WLAN alignment systems based on time diversity and probability Distribution Model

The basic thought of WLAN alignment systems based on time diversity and probability Distribution Model is:It is fixed in localization region Position obtains multiple samples of received signal strength using time diversity, and it is strong to establish reception signal according to multiple sample informations The probability Distribution Model of degree, the probability Distribution Model of received signal strength is stored into property data base;In positioning stage, move Moving-target obtains multiple samples of received signal strength using time diversity, passes through the reception for asking for sample average to obtain stable Signal intensity is positioned.Because time diversity needs to consume the substantial amounts of time, positioning delay is added, can not be realized in real time Positioning, can not be used in running fix.

2. the WLAN alignment systems based on Kalman filtering

The basic thought of WLAN alignment systems based on Kalman filtering is:Moved first with WLAN location algorithms The location estimation of target, then using the speed of mobile target trajectory continuity or the mobile target of hypothesis within the specific limits, The state equation and observational equation of Kalman filter are constructed, processing is filtered to the location estimation of user.Though this method The positioning precision of WLAN alignment systems is so improved, but due to setting mobile target velocity in advance, therefore can not realize certainly Adaptive filtering, limit application in practice.Can not solve the WLAN positioning failure caused by AP is lacked simultaneously.

3.WLAN/GPS integrated positionings

Because GPS is in outdoor open space, good positioning precision can be obtained, therefore in the region of AP missings, can be with Accurate positional information is obtained using GPS;Simultaneously in built-up urban district, can be positioned using WLAN to make up GPS location Deficiency, it may be said that WLAN/GPS integrated positionings achieve good positioning performance in an outdoor environment, but environment indoors Under, because gps signal is blocked, WLAN indoor position accuracies can not be improved.

The content of the invention

The technical problem to be solved in the present invention is to provide a kind of localization method and system, solves existing alignment system positioning accurate Spend the problem of inadequate.

In order to solve the above problems, the invention provides a kind of localization method, including:

Access point based on WLAN is positioned, and obtains the initial estimated location of user equipment;

Obtain the course angle and velocity information of the user equipment;

The initial estimated location is modified according to the course angle and velocity information, obtains final position information.

Further, the above method can also be had the characteristics that, the access point based on WLAN is positioned Including:

Reference point is chosen, the signal intensity from each access point is measured in each reference point, by the position of the reference point, institute Mark/the position for stating signal intensity and corresponding access point is stored into database;Respectively connect around user equipment measurement to be positioned The signal intensity of access point, searching data storehouse obtain corresponding to refer to point set, with it is described with reference to point set match determine use The initial estimated location of family equipment.

Further, the above method can also have the characteristics that, described to determine the use with reference to point set match The initial estimated location of family equipment includes:

The reference point of the Euclidean distance minimum of m received signal strength of selection, uses the line of the position of the m reference point Property initial estimated location of the weighted sum as the user equipment, the m is more than or equal to 1.

Further, the above method can also have the characteristics that, the course angle and speed for obtaining the user equipment Information includes:

The course angle and velocity information are obtained according to the metrical information of MARG sensors.

Further, the above method can also have the characteristics that, described to obtain institute according to the metrical information of MARG sensors Stating course angle includes:

First course angle φ is obtained according to the metrical information of the magnetometer of the MARG sensorsmag, passed according to the MARG The metrical information of the gyroscope of sensor obtains the second course angleAccording to obtaining first course angle and the second course angle The course angle φ of user equipment:

Wherein, the W is default weighted value, 0≤W≤1.

Further, the above method can also have the characteristics that methods described also includes, according to the MARG sensors Metrical information obtains the roll angle of the user equipment, the angle of pitch;

It is described according to the course angle and velocity information the initial estimated location is modified including:

The roll angle obtained according to the metrical information of the MARG sensors, the angle of pitch, course angle and the speed are believed The input as Kalman filter is ceased, Kalman filtering is carried out, exports new course angle and velocity information;

The course angle and velocity information and the initial estimated location that the Kalman filter is exported are as particle The input of wave filter, carry out particle filter, output position information, course angle and velocity information, using the positional information of output as The final position information of the user equipment.

Further, the above method can also have the characteristics that, the progress Kalman filtering includes:

The state one-step prediction of Kalman filtering is carried out,

Calculate prediction varivance matrix

Calculate filtering gain matrix

Carry out state estimation

Calculate estimation error variance

Wherein, it is describedRepresent the angular speed of the gyroscope output of the MARG sensors, φk-1Represent root during moment k-1 The course angle obtained according to the MARG sensors, Δ T represent the time of measuring interval of the MARG sensors, and Q and R are represented respectively The covariance matrix of process noise and measurement noise, KkFor Kalman filter gain,And PkRepresent varivance matrix, φPF The course angle of particle filter output, first φ during Kalman filtering when being positioned for the last timePFFor designated value.

Further, the above method can also have the characteristics that, the progress particle filter includes:

, it is necessary to initialize particle when carrying out particle filter first, the probability density of particle is initialized using Gaussian Profile Function;

According to the course angle and velocity information, and the initial estimated location, predict the user equipment in next step Status information:

The weight of each particle and normalization are calculated, it is as follows:

Carry out particle resampling, the particle as particle filter next time;

Wherein, described [xk, yk]TFor the state vector of each particle, TsRepresent the last access based on WLAN The time interval of the positioning and the positioning of this access point based on WLAN of point, φkRepresent the course angle, vkRepresent The velocity information, [ηx, ηy]TAcceleration is represented, is simulated with the Gaussian noise of zero-mean, variance is sensed by the MARG The metrical information estimation of device,To input the state value of the particle filter,Represent state of i-th of particle in moment k Value, σ represent the noise variance of signal strength measurement.

The present invention also provides a kind of alignment system, including:

WLAN locating modules, positioned for the access point based on WLAN, obtain the initial of user equipment and estimate Count position;

Sensor locating module, for obtaining the course angle and velocity information of the user equipment;

Fusion Module, for being modified according to the course angle and velocity information to the initial estimated location, obtain Final position information.

Further, said system can also have the characteristics that, the WLAN locating modules include:

Database, for being stored in the signal intensity from each access point of each reference point measurement, the position of the reference point Put and correspond to mark/position of access point;

RSS measuring units, for measuring the signal intensity of each access point around user equipment measurement to be positioned;

Positioning unit, for the signal intensity measured according to the RSS measuring units, searching data storehouse obtains corresponding join Examination point set, with the initial estimated location for reference to point set match determination user equipment.

Further, said system can also have the characteristics that, the positioning unit with reference to point set match really The initial estimated location of the fixed user equipment includes:

The positioning unit selects the minimum reference point of the Euclidean distance of m received signal strength, uses described m reference The linear weighted function of the position of point and the initial estimated location as the user equipment, the m are more than or equal to 1.

Further, said system can also have the characteristics that, the sensor locating module includes:MARG sensors and Data processing unit, wherein:

The MARG sensors are used for, and the user equipment is measured, and obtain metrical information;

The data processing unit is used for, and obtaining the course angle and speed according to the metrical information of MARG sensors believes Breath.

Further, said system can also have the characteristics that, the data processing unit is according to the surveys of MARG sensors Course angle described in amount acquisition of information includes:

The data processing unit obtains the first course angle according to the metrical information of the magnetometer of the MARG sensors φmag, the second course angle is obtained according to the metrical information of the gyroscope of the MARG sensorsAccording to first course angle The course angle φ of the user equipment is obtained with the second course angle:

Wherein, the W is default weighted value, 0≤W≤1.

Further, said system can also have the characteristics that, the Fusion Module includes:Kalman filter and particle Wave filter, wherein:

The data processing unit is additionally operable to, and the user equipment is obtained according to the metrical information of the MARG sensors Roll angle, the angle of pitch;

The Kalman filter is used for, by it is described according to the metrical information of the MARG sensors obtain roll angle, The angle of pitch, course angle and velocity information input as the state value of Kalman filter, carry out Kalman filtering, export new boat To angle and velocity information;

The particle filter is used for, the course angle and velocity information that the Kalman filter is exported, and described Initial estimated location inputs as state value, carries out particle filter, output position information, course angle and velocity information, will export Final position information of the positional information as the user equipment.

Further, said system can also be had the characteristics that, the Kalman filter, which carries out Kalman filtering, to be included:

The state one-step prediction of Kalman filtering is carried out,

Calculate prediction varivance matrix

Calculate filtering gain matrix

Carry out state estimation

Calculate estimation error variance

Wherein, it is describedRepresent the angular speed of the gyroscope output of the MARG sensors, φk-1Represent root during moment k-1 The course angle obtained according to the MARG sensors, Δ T represent the time of measuring interval of the MARG sensors, and Q and R are represented respectively The covariance matrix of process noise and measurement noise, KkFor Kalman filter gain,And PkRepresent varivance matrix, φPF The course angle of particle filter output, first φ during Kalman filtering when being positioned for the last timePFFor designated value.

Further, said system can also have the characteristics that, the progress particle filter includes:

Particle is initialized, the probability density function of particle is initialized using Gaussian Profile;

According to the course angle and velocity information, and the initial estimated location, predict the user equipment in next step Status information:

The weight of each particle and normalization are calculated, it is as follows:

Carry out particle resampling, the particle as particle filter next time;

Wherein, described [xk, yk]TFor the state vector of each particle, TsRepresent the last access based on WLAN The time interval of the positioning and the positioning of this access point based on WLAN of point, φkRepresent the course angle, vkRepresent The velocity information, [ηx, ηy]TAcceleration is represented, is simulated with the Gaussian noise of zero-mean, variance is sensed by the MARG The metrical information estimation of device,Currently to input the state value of the particle filter,Represent shape of i-th of particle in moment k State value, σ represent the noise variance of signal strength measurement.

The present invention utilizes MARG (Magnetic, Angular Rate, and Gravity, magnetometer, gyroscope and acceleration Degree meter) sensor auxiliary WLAN (Wireless-LAN) alignment system, devise one and be based on particle filter and Kalman filtering Data anastomosing algorithm, the blending algorithm makes full use of WLAN (Wireless-LAN) and the complementary characteristic of MARG location technologies, Position error and the accumulated error as caused by sensor noise as caused by floating receive information intensity are effectively corrected, is realized The WLAN/MARG integrated positioning systems of one low-cost and high-precision.

Brief description of the drawings

Fig. 1 is alignment system block diagram of the embodiment of the present invention;

Fig. 2 is localization method flow chart of the embodiment of the present invention.

Embodiment

For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with accompanying drawing to the present invention Embodiment be described in detail.It should be noted that in the case where not conflicting, in the embodiment and embodiment in the application Feature can mutually be combined.

The embodiment of the present invention provides a kind of localization method, including:

Access point based on WLAN is positioned, and obtains the initial estimated location of user equipment;

Obtain the course angle and velocity information of the user equipment;

The initial estimated location is modified according to the course angle and velocity information, obtains final position information.

Wherein, the access point based on WLAN, which carries out positioning, includes:

Reference point is chosen, the signal intensity from each access point is measured in each reference point, by the position of the reference point, institute Mark/the position for stating signal intensity and corresponding access point is stored into database;Respectively connect around user equipment measurement to be positioned The signal intensity of access point, searching data storehouse obtain corresponding to refer to point set, with it is described with reference to point set match determine use The initial estimated location of family equipment.

Wherein, it is described to determine that the initial estimated location of the user equipment includes with reference to point set match:

The reference point of the Euclidean distance minimum of m received signal strength of selection, uses the line of the position of the m reference point Property initial estimated location of the weighted sum as the user equipment, the m is more than or equal to 1.

Wherein, the course angle for obtaining the user equipment and velocity information include:

The course angle and velocity information are obtained according to the metrical information of MARG sensors.

Wherein, it is described to be included according to the metrical information of the MARG sensors acquisition course angle:

First course angle φ is obtained according to the metrical information of the magnetometer of the MARG sensorsmag, passed according to the MARG The metrical information of the gyroscope of sensor obtains the second course angleAccording to obtaining first course angle and the second course angle The course angle φ of user equipment:

Wherein, the W is default weighted value, 0≤W≤1.

Wherein methods described also includes, and the rolling of the user equipment is obtained according to the metrical information of the MARG sensors Angle, the angle of pitch;

It is described according to the course angle and velocity information the initial estimated location is modified including:

The roll angle obtained according to the metrical information of the MARG sensors, the angle of pitch, course angle and the speed are believed The input as Kalman filter is ceased, Kalman filtering is carried out, exports new course angle and velocity information;

The course angle and velocity information and the initial estimated location that the Kalman filter is exported are as particle The input of wave filter, carry out particle filter, output position information, course angle and velocity information, using the positional information of output as The final position information of the user equipment.

It is of course also possible to without Kalman filtering, directly the course angle and speed that are obtained according to MARG sensors are believed Breath carries out particle filter, obtains final positional information.

In addition to Kalman filtering and particle filter, Bayesian filter, complementary filter, spreading kalman filter also can be used The blending algorithms such as ripple, Federated Kalman Filtering.

Wherein, the progress Kalman filtering includes:

The state one-step prediction of Kalman filtering is carried out,

Calculate prediction varivance matrix

Calculate filtering gain matrix

Carry out state estimation

Calculate estimation error variance

Wherein, it is describedRepresent the angular speed of the gyroscope output of the MARG sensors, φk-1Represent root during moment k-1 The course angle obtained according to MARG sensors, Δ T represent the time of measuring interval of the MARG sensors, and Q and R represent process respectively The covariance matrix of noise and measurement noise, KkFor Kalman filter gain,And PkRepresent varivance matrix, φPFTo be upper The course angle that particle filter exports during one-time positioning, first φ during Kalman filteringPFFor designated value.

Wherein, the progress particle filter includes:

, it is necessary to initialize particle when carrying out particle filter first, the probability density of particle is initialized using Gaussian Profile Function;

According to the course angle and velocity information, and the initial estimated location, predict the user equipment in next step Status information:

The weight of each particle and normalization are calculated, it is as follows:

Carry out particle resampling, the particle as particle filter next time;

Wherein, described [xk, yk]TFor the state vector of each particle, TsRepresent the last access based on WLAN The time interval of the positioning and the positioning of this access point based on WLAN of point, φkRepresent the course angle, vkRepresent The velocity information, [ηx, ηy]TAcceleration is represented, is simulated with the Gaussian noise of zero-mean, variance is sensed by the MARG The metrical information estimation of device,Currently to input the state value of the particle filter,Represent shape of i-th of particle in moment k State value, σ represent the noise variance of signal strength measurement.

Alignment system provided in an embodiment of the present invention realized shown in block diagram as accompanying drawing 1, including:WLAN locating modules, sensing Device locating module and Fusion Module, wherein:

WLAN locating modules, positioned for the access point based on WLAN, obtain the initial of user equipment and estimate Count position;

Sensor locating module, for obtaining the course angle and velocity information of the user equipment;

Fusion Module, for being modified according to the course angle and velocity information to the initial estimated location, obtain Final position information.

The WLAN locating modules include:

Database, for being stored in the signal intensity from each access point of each reference point measurement, the position of the reference point Put and correspond to mark/position of access point;

RSS measuring units, for measuring the signal intensity of each access point around user equipment measurement to be positioned;

Positioning unit, for the signal intensity measured according to the RSS measuring units, searching data storehouse obtains corresponding join Examination point set, with the initial estimated location for reference to point set match determination user equipment.

The positioning unit determines that the initial estimated location of the user equipment includes with reference to point set match:

The positioning unit selects the minimum reference point of the Euclidean distance of m received signal strength, uses described m reference The linear weighted function of the position of point and the initial estimated location as the user equipment, the m are more than or equal to 1.

The sensor locating module includes:MARG sensors and data processing unit, wherein:

The MARG sensors are used for, and the user equipment is measured, and obtain metrical information;

The data processing unit is used for, and the course angle and speed are obtained according to the metrical information of the MARG sensors Information.

The data processing unit obtains the course angle according to the metrical information of MARG sensors to be included:

The data processing unit obtains the first course angle according to the metrical information of the magnetometer of the MARG sensors φmag, the second course angle is obtained according to the metrical information of the gyroscope of the MARG sensorsAccording to first course angle The course angle φ of the user equipment is obtained with the second course angle:

Wherein, the W is default weighted value, 0≤W≤1.

The Fusion Module includes:Kalman filter and particle filter, wherein:

The data processing unit is additionally operable to, and the user equipment is obtained according to the metrical information of the MARG sensors Roll angle, the angle of pitch;

The Kalman filter is used for, by it is described according to the metrical information of the MARG sensors obtain roll angle, The angle of pitch, course angle and velocity information input as the state value of Kalman filter, carry out Kalman filtering, export new boat To angle and velocity information;

The particle filter is used for, the course angle and velocity information that the Kalman filter is exported, and described Initial estimated location inputs as state value, carries out particle filter, output position information, course angle and velocity information, will export Final position information of the positional information as the user equipment.

Particle filter and the specific method of Kalman filtering are referring to embodiment of the method.

The present invention is further illustrated below by concrete application example.

1) WLAN is positioned

Fingerprinting (fingerprint characteristic) position fixing process based on WLAN is broadly divided into training and two stages of positioning As shown in Figure 2.

(1) training stage

It, which is aimed at, establishes a location fingerprint identification database.Groundwork is respectively referred in collection area-of-interest The fingerprint feature information of point (Reference Point, RP) position --- RSS (Received Signal Strength, is received Signal intensity).As shown in figure 1, measurement comes from different AP to mobile subscriber (Mobile User, MU) in each reference point successively RSS values, and by corresponding MAC Address and latitude and longitude coordinates information storage into database, until traversal area-of-interest in institute Some reference points, this process complete RSS measurement and the foundation of RP databases.

Specifically:

2-3 rice, outdoor selection 8-11 rice are chosen in localization region selection reference point (RP), the spacing room of reference;

The signal intensity samples of WLAN access points, AP addresses or mark are measured in reference point by space diversity technology The positional information of (such as MAC Address) and corresponding reference point (can be represented) with latitude and longitude coordinates;

Filtering is weighted by signal intensity samples, obtains reference point RP finger print information, finger print information number is arrived in storage According in storehouse.

(2) positioning stage

(1) mobile subscriber measures RSS (Received Signal Strength, the reception signal of surrounding WLAN access points Intensity), and data are filtered with processing;

(2) set of fingerprint information corresponding to being searched according to MAC Address in fingerprint database;

(3) matching primitives are carried out with the reference point in set, to determine the position of mobile subscriber.Matching primitives principle is to connect The Euclidean distance of signal intensity is received, the minimum reference point of m Euclidean distance is selected, with the linear weighted function of the coordinate of these reference points With the position for representing mobile subscriber, referred to as initial estimated location.

Except above-mentioned matching primitives principle, also the matching based on nerual network technique, based on histogram probabilistic synchronization algorithm Matching principle, matching principle based on SVMs etc..

One example is as follows:

Mobile subscriber measures surrounding AP RSS, and it is carried out into matching meter with the RSS vectors being stored in advance in database Calculate, matching principle is the Euclidean distance of received signal strength, as shown in formula (1):

Wherein DjRepresent the Euclidean distance or similarity of signal intensity between reference point j and mobile subscriber, DjIt is smaller to show two Apart nearer between person;Rss=(rssi, rss2..., rssn) vector representation mobile subscriber currently measure to n AP's RSS;RSS=(RSSj1, RSSj2..., RSSjn) vector representation in j-th reference point storage to the RSS in database, represents ginseng Examination point j finger print information.

Minimum reference point (the x of m Euclidean distance of selection1, x2..., xm), with these with reference to point coordinates linear weighted function and Represent the current position coordinates x of mobile subscriber0=(x0, y0), calculation formula is as follows:

Wherein wkExpression reference point k weight, calculation formula are as follows:

Above-mentioned WLAN localization methods are merely illustrative, and other WLAN localization methods are also applicable in invention.

2) MARG sensing datas are handled

The processing of MARG sensing datas is the information such as the acceleration and angular speed provided using sensor, to obtain carrier The information such as attitude angle and relative position.The intelligence of three-dimensional gyroscope, three-dimensional accelerometer and three-dimensional magnetometer will be integrated with first For energy terminal definitions to an x-y-z coordinate system, commonly referred to as carrier coordinate system, the center of gravity for taking carrier is carrier coordinate system origin, Three axles coincide with the longitudinal axis, transverse axis and vertical pivot of carrier respectively.Corresponding absolute coordinate system is commonly referred to as X-Y-Z navigation East, north, day are pointed in coordinate system, X, Y, Z axis distribution, it then follows the right-hand rule.

Such as:Roll anglePitching angle theta and course angle φ represent what carrier coordinate system rotated around x-axis, y-axis and z-axis respectively Corner, for representing orientation of the carrier coordinate system relative to navigational coordinate system, the also referred to as attitude angle of carrier.Roll angle and pitching The calculation formula at angle is as follows:

Wherein WithRepresent accelerometer under carrier coordinate system along the output valve of x, y and z axes.The output of course angle It can be obtained by the output of magnetometer or the output of gyroscope.With magnetometer m=[mx, my, mz]TWhen asking for course angle, Need to make the z-axis of carrier coordinate system and navigational coordinate system Z axis align by spin matrix, then ask for navigating by water angle, formula is as follows:

M '=R ' m (7)

Wherein m 'xWith m 'yRepresent earth magnetic field intensity component along the x ' axles and y ' axis components after alignment.

Accelerometer measures obtain carrier in x, y, the acceleration in z-axis, by acceleration obtain carrier roll angle and The angle of pitch, and the velocity information of carrier.

Magnetometer survey obtains magnetic field of the earth in x, y, the magnetic field strength component in z-axis, obtains the course angle of carrier, is referred to as First course angle.

Gyroscope measures carrier angular velocity information, calculates roll angle, the angle of pitch and the second course angle for obtaining carrier.

Navigated by complementary filter to merge the second course angle that gyroscope calculates and first that magnetometer calculates To angle, course angle is obtained.It is of course also possible to without complementary filter, the first course angle or the second course angle are directly used.When So, course angle can also be obtained using GPS technology.

The course angle calculated using gyroscope corrects the output of magnetometer, and formula is as follows:

WhereinRepresent the course angle calculated by gyroscope, ωkAngular speed of the gyroscope in the k periods is represented, W is institute The complementary weight of the complementary filter of design, 0≤W≤1.

3) the particle filter design based on WLAN and MARG

Particle filter step:

(1) course angle of Kalman filter output and velocity information are inputted as the state value of particle filter;

(2) particle is initialized:The probability density function of particle is initialized using Gaussian Profile, average is target initial shape State, the original state include course angle and velocity information;

(3) predict:Using course angle and velocity information, and WLAN positioning results, particle filter prediction target is next The status information of step;

(4) granular Weights Computing and normalization:The power of each particle is asked for by measurement model and present measured value Weight, when particle position estimated state current closer to target, the weight that particle obtains is bigger;

(5) resampling:New particle is produced according to posterior probability density function, solves the problems, such as sample degeneracy.

Particle filter output position information, course angle information, velocity information.Position of the positional information as mobile subscriber Estimation.

Specific filtering method is as follows:

Particle filter be particle collection that is being randomly selected using one group from probability density function and attaching related weights come Approach posterior probability density function:

Wherein xkRepresent target in moment k state vector, z0:kThe measurement value sequence before moment k+1 is represented,Table Show i-th of particle or sample point,For its weight, N is population.The particle filter used in the system is divided into following four Step:

1) initialize

According to initial probability density function Pr (x0) produce N number of particleWherein Pr (x0) use Gauss Distribution, average is target original state, and the value can be set as needed.Carrying out particle filter first needs to be initialized, The particle obtained before follow-up use after a filtering resampling.

2) predict

With reference to MARG sensing data results, particle filter predicts the status information (x of target next stepk+1, yk+1), Formula is as follows:

Wherein [xk, yk]TFor the state vector of each particle, TsRepresent that -1 WLAN positioning of kth positions with kth time WLAN Time interval, φkRepresent target of the MARG sensing datas by Kalman filtering acquisition around the anglec of rotation (the i.e. course of z-axis Angle), vkRepresent the target velocity obtained after the processing of MARG sensing datas, [ηx, ηy]TThe acceleration of target is represented, with zero The Gaussian noise simulation of average, variance can be estimated by MARG sensing datas.

3) weight calculation and normalization

The weight of particle is asked for by measurement model and Current observation value:

Wherein zkThe RSS that target currently measures is represented,The current status information (position, course angle) of target is represented,Table Show status information of i-th of particle in moment k, σ represents measurement noise variance, is selected according to the variance of the floatings of RSS in practice. Formula (13) represents that when particle position is closer to target current estimated location the weight that particle obtains is bigger, accurate so as to obtain Posterior probability distribution.

4) resampling

Resampling is the key of particle filter, according to probability density function Pr (xk|zk) produce N number of new particleTo solve the problems, such as sample degeneracy, a kind of method for resampling is as follows:

4) the Kalman filtering design based on WLAN and MARG

The fine or not heavy dependence course angle φ of the particle filter degree of accuracy, tire out to further eliminate because gyroscope is present Product error and magnetometer are easily disturbed by local magnetic field around, cause the course angle φ errors obtained after MARG data processings, this In embodiment, a Kalman filter is may also provide, (including particle filter is defeated for the attitude information obtained using particle filter The positional information gone out, bearer rate information, course angle information etc.) course angle φ is corrected, so as to obtaining reliable and stable course Angle information.

The step of Kalman filtering:

(1) roll angle, the angle of pitch and the course angle that MARG sensors obtain, and bearer rate information, as Kalman The input of filter state value.

(2) one-step prediction in the state of progress Kalman filtering;

(3) prediction varivance matrix is calculated;

(4) filtering gain matrix are calculated;

(5) state estimation;

(6) estimation error variance calculates.

Kalman filtering algorithm such as following formula:

WhereinRepresent the angular speed of gyroscope output, φk-1The course angle predicted during moment k-1 is represented, Δ T represents MARG The time of measuring interval of sensor, Q and R represent process noise and the covariance matrix of measurement noise, K respectivelykFiltered for Kalman Ripple device gain,And PkRepresent varivance matrix, φPFFor the course angle of particle filter estimation.

The WLAN/MARG integrated positioning systems based on data fusion that the embodiment of the present invention proposes, the system utilize MARG Sensor obtains the information such as the speed of mobile target, posture, is melted by data such as complementary filter, Kalman filtering and particle filters Hop algorithm improves WLAN positioning precisions, realizes a low-cost and high-precision WLAN/MARG integrated positioning system.

One of ordinary skill in the art will appreciate that all or part of step in the above method can be instructed by program Related hardware is completed, and described program can be stored in computer-readable recording medium, such as read-only storage, disk or CD Deng.Alternatively, all or part of step of above-described embodiment can also be realized using one or more integrated circuits.Accordingly Ground, each module/unit in above-described embodiment can be realized in the form of hardware, can also use the shape of software function module Formula is realized.The present invention is not restricted to the combination of the hardware and software of any particular form.

Claims (16)

  1. A kind of 1. localization method, it is characterised in that including:
    Access point based on WLAN is positioned, and obtains the initial estimated location of user equipment;
    Obtain the course angle and velocity information of the user equipment;
    The initial estimated location is modified according to the course angle and velocity information, obtains final position information;
    It is described according to the course angle and velocity information the initial estimated location is modified including:
    Kalman filtering is carried out to the course angle and velocity information, exports new course angle and velocity information;
    Particle filter is carried out to the new course angle and velocity information and the initial estimated location, by the position of output Information is as final position information.
  2. 2. the method as described in claim 1, it is characterised in that the access point based on WLAN carries out positioning bag Include:
    Reference point is chosen, the signal intensity from each access point is measured in each reference point, by the position of the reference point, the letter Mark/position of number intensity and corresponding access point is stored into database;Each access point around user equipment measurement to be positioned Signal intensity, searching data storehouse obtain corresponding to refer to point set, match determination user with the reference point set sets Standby initial estimated location.
  3. 3. method as claimed in claim 2, it is characterised in that described to determine that the user sets with reference to point set match Standby initial estimated location includes:
    The reference point of the Euclidean distance minimum of m received signal strength of selection, added using the linear of position of the m reference point Power and the initial estimated location as the user equipment, the m are more than or equal to 1.
  4. 4. the method as described in claim 1, it is characterised in that the course angle and velocity information for obtaining the user equipment Including:
    The course angle and velocity information are obtained according to the metrical information of MARG sensors.
  5. 5. method as claimed in claim 4, it is characterised in that described that the boat is obtained according to the metrical information of MARG sensors Include to angle:
    First course angle φ is obtained according to the metrical information of the magnetometer of the MARG sensorsmag, according to the MARG sensors Gyroscope metrical information obtain the second course angleThe use is obtained according to first course angle and the second course angle The course angle φ of family equipment:
    <mrow> <mi>&amp;phi;</mi> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>W</mi> <mo>)</mo> </mrow> <msubsup> <mi>&amp;phi;</mi> <mi>k</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> </mrow> </msubsup> <mo>+</mo> <msub> <mi>W&amp;phi;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>g</mi> </mrow> </msub> </mrow>
    Wherein, the W is default weighted value, 0≤W≤1.
  6. 6. the method as described in claim 4 or 5, it is characterised in that
    Methods described also includes, and roll angle, the pitching of the user equipment are obtained according to the metrical information of the MARG sensors Angle;
    It is described according to the course angle and velocity information the initial estimated location is modified including:
    The roll angle obtained according to the metrical information of the MARG sensors, the angle of pitch, course angle and the velocity information are made For the input of Kalman filter, Kalman filtering is carried out, exports new course angle and velocity information;
    The course angle and velocity information and the initial estimated location that the Kalman filter is exported are as particle filter The input of device, carry out particle filter, output position information, course angle and velocity information, using the positional information of output as described in The final position information of user equipment.
  7. 7. method as claimed in claim 6, it is characterised in that the progress Kalman filtering includes:
    The state one-step prediction of Kalman filtering is carried out,
    Calculate prediction varivance matrix
    Calculate filtering gain matrix
    Carry out state estimation
    Calculate estimation error variance
    Wherein, it is describedRepresent the angular speed of the gyroscope output of the MARG sensors, φk-1According to institute during expression moment k-1 The course angle of MARG sensors acquisition is stated, Δ T represents the time of measuring interval of the MARG sensors, and Q and R represent process respectively The covariance matrix of noise and measurement noise, KkFor Kalman filter gain,And PkRepresent varivance matrix, φPFFor The course angle of particle filter output, first φ during Kalman filtering during last time positioningPFFor designated value.
  8. 8. method as claimed in claim 6, it is characterised in that the progress particle filter includes:
    , it is necessary to initialize particle when carrying out particle filter first, the probability density function of particle is initialized using Gaussian Profile;
    According to the course angle and velocity information, and the initial estimated location, the shape of the user equipment next step is predicted State information:
    <mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> <mo>&amp;CenterDot;</mo> <mi>cos</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;phi;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> <mo>&amp;CenterDot;</mo> <mi>sin</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;phi;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>k</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mi>k</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>z</mi> <mi>k</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mfrac> <msubsup> <mi>T</mi> <mi>s</mi> <mn>2</mn> </msubsup> <mn>2</mn> </mfrac> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mfrac> <msubsup> <mi>T</mi> <mi>s</mi> <mn>2</mn> </msubsup> <mn>2</mn> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&amp;eta;</mi> <mi>x</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;eta;</mi> <mi>y</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    The weight of each particle and normalization are calculated, it is as follows:
    <mrow> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msqrt> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> </mrow> </msqrt> <mi>&amp;sigma;</mi> </mrow> </mfrac> <mi>exp</mi> <mo>&amp;lsqb;</mo> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <mo>|</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mi>z</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mi>i</mi> </msubsup> <mo>|</mo> <mo>|</mo> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>&amp;rsqb;</mo> </mrow>
    <mrow> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mfrac> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> </mrow> </mfrac> </mrow>
    Carry out particle resampling, the particle as particle filter next time;
    Wherein, described [xk,yk]TFor the state vector of each particle, TsRepresent the last access point based on WLAN The time interval of positioning and the positioning of this access point based on WLAN, φkRepresent the course angle, vkDescribed in expression Velocity information, [ηxy]TAcceleration is represented, is simulated with the Gaussian noise of zero-mean, variance is by the MARG sensors Metrical information estimation,To input the state value of the particle filter,Represent state value of i-th of particle in moment k, σ Represent the noise variance of signal strength measurement.
  9. A kind of 9. alignment system, it is characterised in that including:
    WLAN locating modules, positioned for the access point based on WLAN, obtain the initial estimation position of user equipment Put;
    Sensor locating module, for obtaining the course angle and velocity information of the user equipment;
    Fusion Module, for being modified according to the course angle and velocity information to the initial estimated location, obtain final Positional information;
    The Fusion Module includes:Kalman filter and particle filter, wherein:
    The Kalman filter is used for, and the course angle and velocity information to the acquisition carry out Kalman filtering, export new Course angle and velocity information;
    The particle filter is used for, and grain is carried out to the new course angle and velocity information, and the initial estimated location Son filtering, using the positional information of output as final position information.
  10. 10. system as claimed in claim 9, it is characterised in that the WLAN locating modules include:
    Database, for being stored in the signal intensity from each access point of each reference point measurement, the position of the reference point and Mark/position of corresponding access point;
    RSS measuring units, for measuring the signal intensity of each access point around user equipment measurement to be positioned;
    Positioning unit, for the signal intensity measured according to the RSS measuring units, reference point corresponding to the acquisition of searching data storehouse Set, with the initial estimated location for reference to point set match determination user equipment.
  11. 11. system as claimed in claim 10, it is characterised in that the positioning unit with reference to point set with carrying out matching determination The initial estimated location of the user equipment includes:
    The positioning unit selects the minimum reference point of the Euclidean distance of m received signal strength, uses the m reference point The linear weighted function of position and the initial estimated location as the user equipment, the m are more than or equal to 1.
  12. 12. system as claimed in claim 9, it is characterised in that the sensor locating module includes:MARG sensor sums According to processing unit, wherein:
    The MARG sensors are used for, and the user equipment is measured, and obtain metrical information;
    The data processing unit is used for, and the course angle and velocity information are obtained according to the metrical information of MARG sensors.
  13. 13. system as claimed in claim 12, it is characterised in that the data processing unit is according to the measurements of MARG sensors Course angle includes described in acquisition of information:
    The data processing unit obtains the first course angle φ according to the metrical information of the magnetometer of the MARG sensorsmag, root The second course angle is obtained according to the metrical information of the gyroscope of the MARG sensorsAccording to first course angle and second Course angle obtains the course angle φ of the user equipment:
    <mrow> <mi>&amp;phi;</mi> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>W</mi> <mo>)</mo> </mrow> <msubsup> <mi>&amp;phi;</mi> <mi>k</mi> <mrow> <mi>g</mi> <mi>y</mi> <mi>r</mi> </mrow> </msubsup> <mo>+</mo> <msub> <mi>W&amp;phi;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>g</mi> </mrow> </msub> </mrow>
    Wherein, the W is default weighted value, 0≤W≤1.
  14. 14. the system as described in claim 12 or 13, it is characterised in that
    The data processing unit is additionally operable to, and the rolling of the user equipment is obtained according to the metrical information of the MARG sensors Angle, the angle of pitch;
    The Kalman filter is used for, by the roll angle obtained according to the metrical information of the MARG sensors, pitching Angle, course angle and velocity information input as the state value of Kalman filter, carry out Kalman filtering, export new course angle And velocity information;
    The particle filter is used for, the course angle and velocity information that the Kalman filter is exported, and described initial Estimated location inputs as state value, carries out particle filter, output position information, course angle and velocity information, by the position of output Confidence ceases the final position information as the user equipment.
  15. 15. system as claimed in claim 14, it is characterised in that the Kalman filter, which carries out Kalman filtering, to be included:
    The state one-step prediction of Kalman filtering is carried out,
    Calculate prediction varivance matrix
    Calculate filtering gain matrix
    Carry out state estimation
    Calculate estimation error variance
    Wherein, it is describedRepresent the angular speed of the gyroscope output of the MARG sensors, φk-1According to institute during expression moment k-1 The course angle of MARG sensors acquisition is stated, Δ T represents the time of measuring interval of the MARG sensors, and Q and R represent process respectively The covariance matrix of noise and measurement noise, KkFor Kalman filter gain,And PkRepresent varivance matrix, φPFFor The course angle of particle filter output, first φ during Kalman filtering during last time positioningPFFor designated value.
  16. 16. system as claimed in claim 14, it is characterised in that the progress particle filter includes:
    Particle is initialized, the probability density function of particle is initialized using Gaussian Profile;
    According to the course angle and velocity information, and the initial estimated location, the shape of the user equipment next step is predicted State information:
    <mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> <mo>&amp;CenterDot;</mo> <mi>cos</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;phi;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>s</mi> </msub> <mo>&amp;CenterDot;</mo> <mi>sin</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;phi;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>k</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>y</mi> <mi>k</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>z</mi> <mi>k</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mfrac> <msubsup> <mi>T</mi> <mi>s</mi> <mn>2</mn> </msubsup> <mn>2</mn> </mfrac> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mfrac> <msubsup> <mi>T</mi> <mi>s</mi> <mn>2</mn> </msubsup> <mn>2</mn> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&amp;eta;</mi> <mi>x</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;eta;</mi> <mi>y</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
    The weight of each particle and normalization are calculated, it is as follows:
    <mrow> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msqrt> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> </mrow> </msqrt> <mi>&amp;sigma;</mi> </mrow> </mfrac> <mi>exp</mi> <mo>&amp;lsqb;</mo> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <mo>|</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mi>z</mi> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mi>k</mi> <mi>i</mi> </msubsup> <mo>|</mo> <mo>|</mo> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>&amp;rsqb;</mo> </mrow>
    <mrow> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mfrac> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msubsup> <mi>&amp;omega;</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>j</mi> </msubsup> </mrow> </mfrac> </mrow>
    Carry out particle resampling, the particle as particle filter next time;
    Wherein, described [xk,yk]TFor the state vector of each particle, TsRepresent the last access point based on WLAN The time interval of positioning and the positioning of this access point based on WLAN, φkRepresent the course angle, vkDescribed in expression Velocity information, [ηxy]TAcceleration is represented, is simulated with the Gaussian noise of zero-mean, variance is by the MARG sensors Metrical information estimation,Currently to input the state value of the particle filter,Represent state of i-th of particle in moment k Value, σ represent the noise variance of signal strength measurement.
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