CN107702712A - Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases - Google Patents
Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases Download PDFInfo
- Publication number
- CN107702712A CN107702712A CN201710838171.5A CN201710838171A CN107702712A CN 107702712 A CN107702712 A CN 107702712A CN 201710838171 A CN201710838171 A CN 201710838171A CN 107702712 A CN107702712 A CN 107702712A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msubsup
- mover
- msup
- pedestrian
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
-
- 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
Abstract
The invention belongs to indoor positioning technologies field, more particularly to the indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases improved.The present invention positions for pedestrian's inertial navigation and WLAN bilayers fingerprint base first, is resolved by the accelerometer and gyroscope output data that are carried to pedestrian, obtains three shaft positions, three axle speeds, three shaft angle degree.The storehouse stage is built by establishing indoor three-dimensional coordinate system offline, and grid division chooses observed samples point in the horizontal plane；Sample point wireless signal strength is extracted, obtains the Grad in all directions, and carries out using Grad as characteristic value building storehouse, is mapped one by one with sampling point position, acquisition WLAN gradient fingerprint base signals.The characteristic information of sample point signal is extracted, establishes WLAN fingerprint bases；In the tuning on-line stage, realize the conversion between two fingerprint databases.Using nearest neighbour method matching characteristic value, the WLAN fingerprint locations position of pedestrian is obtained.Accuracy of the present invention is high, applied widely.
Description
Technical field
The invention belongs to indoor positioning technologies field, more particularly to the indoor row based on inertia measurement bilayer WLAN fingerprint bases
People's combined positioning method.
Background technology
The major programme of pedestrian's indoor positioning has based on GPS, such as GPS positioning system, Big Dipper positioning system
System etc. the targeting scheme of global position system, the targeting scheme based on autonomous navigation system and based on images match location technology,
The targeting scheme of earth magnetism location technology.Wherein, GPS navigation signal is decayed under environment larger, pole indoors
Earth effect positioning precision.Therefore, the technology of indoor pedestrian's positioning precision is improved by using other sensor information sources
Study hotspot always in recent years.
Inertial navigation is a kind of full autonomous navigation system, the movable information of carrier is measured in real time by inertia component, through leading
Speed, position and the attitude information of boat continuous output carrier after resolving.Inertial navigation belongs to navigate from main classes, it is not necessary to extra
Holding equipment, thus application scenarios are more rich, but its position error, with time accumulated divergence, this just seriously constrains inertia
The long endurance workability of navigation system, and then influence the positioning precision based on inertial navigation system targeting scheme.Therefore, need
Introduce External Reference information and periodicity re-graduation is carried out to inertial navigation position error, inertial navigation indoor positioning scheme is based on to improve
Positioning precision.Zero-speed correction is a kind of correcting algorithm using velocity error as observed quantity, and it can effectively suppress inertial navigation navigation
Drift error, still, when velocity error is as observed quantity, course error can not be entered with site error by good estimation compensation
One step causes the position error of indoor positioning.
WLAN fingerprint locations are a kind of targeting schemes led to based on short-distance wireless, and it is established by measurement signal intensity
Database simultaneously matching obtains the positional information of pedestrian.The error accumulation problem of inertial navigation is not present in the program, therefore
Inertial navigation system divergence expression position error is corrected using the output of WLAN fingerprint locations, and then reduces the tired of inertial navigation system
Product error.
However, widely using with intelligent radio route, its transmission power will be carried out with the number of accessing user
Dynamic adjusts, and the localization method that simple dependence WLAN absolute intensities establish fingerprint base has very strong limitation, therefore at this
In invention, it is proposed that indoor pedestrian's combined positioning method based on WLAN fingerprints and WLAN gradient fingerprint bilayer fingerprint bases.Root
According to WLAN signal mode, WLAN signal intensity is to be relatively fixed in the changing value i.e. WLAN Grad of adjacent area
, in actual environment, the WLAN Grad apart from wireless routing immediate area has more preferable stability compared with absolute intensity.
But as the increase of localization region and wireless routing signal source distance, WLAN gradient value changes are smaller.Summary situation,
There is more preferable stability using double-deck WLAN fingerprint bases, to make up influence of the inertial navigation cumulative errors to positioning precision.
Jia Zi celebratings et al. have made intensive studies to inertia/WLAN fingerprint locations, it is proposed that one kind is based on spreading kalman
Although the inertia of filtering/WLAN fingerprint positioning methods, this method inhibit influence of the site error to positioning precision,.But not
Have and furtherd investigate for course angle error；Qin Zhanming etc., the characteristics of people is directed to indoor environment complexity, it is proposed that one kind based on plus
Inertia/WLAN propagation model localization methods of blending algorithm are weighed, although this method effectively raises WLAN positioning precisions, but
It is not solve the problems, such as that positioning precision is not high caused by the angle error of course.For this problem, the present invention uses one
Indoor pedestrian localization method of the kind based on inertia measurement/double-deck WLAN fingerprint bases, by using the Bayesian filter of Two-orders
Framework, WLAN fingerprint locations information and building geometry are introduced, and then reduce accumulated error caused by inertial navigation.
The content of the invention
High it is an object of the invention to provide positioning precision, the strong one kind of the stability of a system is based on inertia measurement/bilayer
Indoor pedestrian's combined positioning method of WLAN fingerprint bases.
The object of the present invention is achieved like this：
Based on indoor pedestrian's combined positioning method of inertia measurement bilayer WLAN fingerprint bases, comprise the following steps：
Step (1)：Indoor three-dimensional coordinate system is established, using indoor corner as origin, selection x-axis, y-axis and z-axis, x-axis,
Y-axis is mutually perpendicular in geographical horizontal plane with wall, and vertically upward, three reference axis meet right-hand rule to z-axis；
Step (2)：In xOy planes, along x-axis, every 1 meter of grid division on y-axis direction, grid element center point is observation
Sampled point, collection observed samples point correspond to the wireless signal strength information of different routers；
Step (3)：The wireless signal strength information of different routers is corresponded to by handling sampled point, WLAN gradients is established and refers to
Line storehouse, while WLAN signal is further handled, extract characteristic information, i.e., the average of WLAN signal intensity and side in the sampling time
Difference, establish WLAN fingerprint bases；
Step (4)：3-axis acceleration, the three axis angular rate data of pedestrian's foot sensor are gathered, pass through 3-axis acceleration
Calculate and obtain Eulerian angles R, spin matrix C, quaternary number vector q, three in navigational coordinate system are obtained by using spin matrix C
Axle acceleration；
Step (5)：Zero-speed detection is performed, by calculating acceleration and angular speed modulus value, is relatively sentenced with wireless routing threshold value
Line-break people's state, the state of pedestrian include ambulatory status and halted state；
Step (6)：If pedestrian is ambulatory status, speed and position are obtained by acceleration respectively to the integration of time
Move.Speed is equal with last moment with position if pedestrian is halted state；
Step (7)：Perform and expand Kalman prediction, perform zero-speed detection again, pedestrian's state is motion state
Step (4) is then returned to, otherwise performs step (8)；
Step (8)：Perform and expand Kalman filtering renewal, after new acceleration renewal quaternary number, return to step (4)；
And calculate the horizontal level obtained and calculate Euclidean distance one by one with the particle position in step (9), Euclidean distance is substituted into Gauss
Model obtains particle weights；
Step (9)：Pedestrian's reckoning, including the detection of pedestrian's gait, step-size estimation, pedestrian position calculating.Execution is based on
Pedestrian's reckoning of particle filter can obtain a series of pedestrian by the acceleration and angular speed information of the pedestrian of acquisition
The position of particle；
Step (10)：By the WLAN signal of online acquisition, by comparing collection signal intensity and threshold value, judge to use
WLAN fingerprint bases or WLAN gradient fingerprint bases are positioned, WLAN localization methods corresponding to selection, calculate the pedestrian position of acquisition
Confidence ceases, the Euclidean distance between the position of the particle obtained with pedestrian's reckoning in step (9), substitutes into Gauss model and obtains
Obtain the WLAN fingerprint weights of the particle；
Step (11)：Pedestrian's horizontal two-dimension positional information and the particle position obtained in step (9) are obtained in calculation procedure (8)
Euclidean distance between putting, and then calculate the expansion Kalman filtering weight for obtaining the particle；
Step (12)：For same particle, normalized after the acquisition particle weights in step (8), step (10) are multiplied,
Particle weights are obtained, pedestrian position is obtained eventually through the weighting to particle location information.
Beneficial effects of the present invention are：
The present invention uses zero-speed correction algorithm, and estimation compensation is carried out to inertial navigation speed；Utilize expanded Kalman filtration algorithm
Solves the nonlinear problem between input and output；By using particle filter fusion WLAN location informations as observed quantity, to used
Lead position and carry out estimation compensation；Double-deck WLAN fingerprint bases are devised, improve alignment system stability so that the present invention has
Universality, improve the precision of application and indoor positioning.
Brief description of the drawings
Fig. 1 is indoor pedestrian's combined positioning method flow chart based on inertia measurement bilayer WLAN fingerprint bases；
Fig. 2 is Zero velocity Updating schematic diagram；
Fig. 3 is indoor pedestrian's combined positioning method experimental result comparison diagram based on inertia measurement bilayer WLAN fingerprint bases；
Fig. 4 is double-deck WLAN fingerprint bases schematic diagram.
Embodiment
It is specific below in conjunction with the accompanying drawings to introduce the present invention：
Embodiment 1：
Such as Fig. 1, based on indoor pedestrian's combined positioning method of inertia measurement bilayer WLAN fingerprint bases, following step is specifically included
Suddenly：
Step 1：Indoor three-dimensional coordinate system is established, using indoor corner as origin, chooses x-axis, y-axis and z-axis, x, y-axis
In geographical horizontal plane, it is mutually perpendicular to wall, vertically upward, three reference axis meet right-hand rule to z-axis.
Step 2：In xOy planes, along x, every 1 meter of grid division on y directions, grid element center point is observed samples point,
The wireless signal strength information of different routers corresponding to observed samples point is gathered, the signal for comparing adjacent area sample point is strong
Angle value, then every 2 meters of grid divisions, WLAN characteristic informations are extracted, i.e., the average of WLAN signal intensity, builds in the sampling time
Vertical WLAN fingerprint bases.
WLAN fingerprint bases Q represents as follows：
Q=[Q1,Q2,…,Qi,…Qs]；
Wherein s is sampled point number, Qi(i=1,2 ..., s) represents the fingerprint base information of ith sample point, represents such as
Under：
Qi=[Xi,Yi,APi]；
Wherein XiIt is the abscissa value of the i-th sampled point, YiIt is the ordinate value of the i-th sampled point, APiIt is ith sample point
The signal value of router, represent as follows：
Wherein SSIDk(k=1,2 ..., n) is the service set of k-th of router, for distinguishing different routers；
Represent the system of k-th of access point signals intensity at ith sample point
Count average value.
WLAN gradient fingerprint bases Q ' represents as follows：
Q '=[Q'1,Q'2,…,Q'i,…Q's]；
Wherein s is sampled point number, Q'i(i=1,2 ..., s) represents the gradient fingerprint base information of ith sample point, table
Show as follows：
Q'i=[Xi,Yi,bi,ai,li,ri]
Represent the router AP of ith sample point place collectioniSignal value with its front neighbouring sample point signal
Intensity gradient values,Represent the router AP of ith sample point place collectioniSignal value and its rear neighbouring sample point
Signal intensity gradient value,Represent the router AP of ith sample point place collectioniSignal value with its left side neighbouring sample
The signal intensity gradient value of point,Represent the router AP of ith sample point place collectioniSignal value with its right side it is adjacent
The signal of sampled point
Intensity gradient values, its expression are as follows：
Represent k-th of the access point signals intensity and its rear sample point of the i-th sample point
The absolute value of the difference of assembly average,Represent k-th access point signals intensity of the i-th sample point with
The absolute value of the difference of the assembly average of its left side sample point,Represent k-th of the i-th sample point
The absolute value of the difference of the assembly average of access point signals intensity and its right side sample point.
Step 3：The setting of wireless routing threshold value is determined by the minimum strength difference in the route locating environment, such as collection letter
Number and all around the Grad between neighbouring sample point is more than wireless routing threshold value μ, then perform step 4, otherwise, perform step
Rapid 5.
Step 4：Position (x to be askedi,yi) to obtain fingerprint gradient intensity be [x at placei,yi,b'i,a'i,l'i,r'i], calculate successively
The all around Euclidean of Grad and each data point of WLAN gradient fingerprint bases all around Grad in the fingerprint gradient intensity value
Distance d'i_b,d'i_a,d'i_l,d'i_r：
Choose four coordinates apart from the minimum database point of sum is as the position of unknown point
(xwlan_grad,ywlan_grad) represent WLAN gradient fingerprint base positioning results.
Step 5：In the tuning on-line stage, using the WLAN fingerprint bases and WLAN gradient fingerprint bases of step 2, collection pedestrian carries
WLAN signal reception device data, matched using nearest neighbour method to obtain current time pedestrian position information, obtain WLAN fingerprints
Location information PWLAN, i.e. PWLAN=[xWLAN yWLAN]T；Wherein, xWLANAnd yWLANRespectively WLAN fingerprints coordinate system in step 1
Under horizontal plane position information.
Then by Step1 to Step3, WLAN positioning results (x is obtainedwlan,ywlan)：
Step1:The WLAN signal for gathering ten n routers at current time pedestrian position with the speed of 2 seconds is strong
Degree, it is as follows：
Wherein APj(j=1,2 ..., 10) is the signal for the router that pedestrian current position jth time is gathered；
It is that k-th of the access point jth time sampling in pedestrian current position obtains
WLAN
Signal intensity.
Step2:N access point n average value of ten WLAN signal intensity is obtained, it is as follows：
WhereinRepresent the average value that k-th of pedestrian current position access point gathers ten WLAN signal intensity
It is that k-th of the access point jth time sampling in pedestrian current position obtains
WLAN
Signal intensity.
Step3:Matched using nearest neighbour method with WLAN fingerprint bases in step 2 and WLAN gradient fingerprint bases, to obtain current line
People's positional information；Calculating pedestrian current position receives WLAN signal intensity successively and s sampled point signal in database is strong
The Euclidean distance d of degreei(i=1,2 ..., s), it is as follows：
Wherein,K-th of access in representation database at ith sample point
The assembly average of point signal, diSample corresponding to minimum is set to WLAN positioning results (xwlan,ywlan)。
Step 6：Accelerometer and gyro data are obtained, obtains the acceleration in pedestrian x directionsThe acceleration in y directionsIt is as follows to calculate acquisition inertial sensor Eulerian angles R, spin matrix C and quaternary number q；
Mode by acceleration calculation Eulerian angles R=(θ, φ, ψ) is as follows：
ψ=0.
Quaternary number and spin matrix can be calculated to obtain by Eulerian angles RRepresent to sit from navigational coordinate system to carrier
Mark system direction cosine matrix：
Transformational relation is obtained between following formula expression quaternary number and spin matrix,
Step 7：Carrier coordinate system acceleration a is realized using the spin matrix C that acquisition is calculated in step 6bodySat to navigation
Mark system acceleration anavConversion, further pass through acceleration anavPedestrian position is obtained to the multiple integral and double integral of time
Put and speed；
The mode for calculating pedestrian's speed and position is as follows：
Wherein, vx(k) it is the velocity component on x-axis direction, vy(k) it is the velocity component on y-axis direction, vz(k) it is z-axis
Velocity component on direction, xkFor the location components on x-axis direction, ykFor the location components on y-axis direction, zkFor z-axis direction
On location components.
Step 8：Zero-speed detection, it is that 3-axis acceleration information calculates 3-axis acceleration mould using the navigation obtained in step 8
Value, and compared with given wireless routing threshold value, wireless routing threshold value can be obtained by empirical model；Calculate 3-axis acceleration mould
Value is as follows：
Step 9：Judge now whether pedestrian's foot contacts to earth according to step 8, if contacting to earth, carry out step 7；If not contacting to earth, enter
Row step 9；
Step 10：Kalman filtering is expanded, inertial navigation system velocity information is led as observed quantity based on inertia using in step 8
Boat models coupling EKF is estimated each location information of inertial navigation；
Indoor locating system is used as EKF observed quantity Z, alignment system state using inertial navigation system location information
Equation and measurement equation concrete form are as follows：
Wherein, X=[px py pz vx vy vz φ θ h]TRepresent that inertial navigation system resolves item, px、py、pzRepresent indoor
Three shaft position under coordinate system, vx、vx、vxRepresent to represent rolling, pitching, boat along geographical east orientation, north orientation, sky orientation speed, φ, θ, h
To；A (t) expression state-transition matrixes, form are as follows：
Wherein, StFor skew symmetric matrix, I is unit battle array, and Δ t is sampling time interval；
H (t) expression state-transition matrixes, form are as follows：
Hk=[03×3 I3×3 03×3]；
Pedestrian's two-dimensional position x can be obtained by expanding Kalman filteringekf,yekf, particle filter is based on for second-order
Pedestrian's reckoning in, while obtain new Eulerian anglesθ, h, available for quaternary number qtRenewal, renewal process is as follows
Shown in formula：
Ω'tFor skew symmetry intersectionproduct operator matrix, ωx、ωy、ωzRespectively three axis angular rates.| ω | for three shaft angles speed
The mould of degree.
Step 11：It is 3-axis acceleration information using the navigation obtained in step 7, particle is obtained by pedestrian's reckoning
Pedestrian position, the formula of pedestrian's reckoning are；
For moment t i-th of particle step-length,For moment t-1 i-th of particle course angle,For moment t
I-th particle x, y-coordinate.Step-lengthCalculation formula be：
az_max, az_minFor in two zero-speed detection intervals, the maximum and minimum value of acceleration on z-axis direction.KstrideFor
Conversion of measurement unit constant.
Step 12：The final position of pedestrian is obtained by particle position, the calculation of final position is：
In the present invention,By inertial navigation, WlAN fingerprint locations together decide on.
The position x obtained is expanded after Kalman filteringekf(t),yekf(t) influence of the position to weight be：
Weight w after the influence of WLAN fingerprintsx_bs(t) calculation is：
Particle weightsIt can be obtained by following formula：
Particle weights after normalization are:
Following table shows contrast table for the positioning of algorithms of different：
Algorithm one：Based on the inertial navigation algorithm for expanding Kalman filtering；Algorithm two：Pedestrian's reckoning algorithm；Algorithm
Three：The algorithm proposed in the present invention.
Generally speaking, the invention mainly comprises two parts, Part I is pedestrian's inertial navigation and WLAN bilayer fingerprints
Storehouse is positioned, and pedestrian's inertial navigation is resolved by the accelerometer and gyroscope output data carried to pedestrian, obtains pedestrian
Three shaft positions, three axle speeds, and three shaft angle degree.Double-deck WLAN fingerprint bases positioning includes two stages, i.e., builds storehouse rank offline
Section and tuning on-line stage.The storehouse stage is built by establishing indoor three-dimensional coordinate system offline, and grid division selects in the horizontal plane
Take observed samples point；Extract sample point wireless signal strength, by with the sampled point is adjacent on four direction all around adopts
The intensity of sampling point is compared, and obtains the Grad in all directions, and carries out using Grad as characteristic value building storehouse, with sampling
Point position maps one by one, and then obtains WLAN gradient fingerprint base signals.The characteristic information of sample point signal is extracted simultaneously, is built
Vertical WLAN fingerprint bases；In the tuning on-line stage, by sampling the WLAN signal intensity level obtained compared with threshold value, realize two fingers
Conversion between line database.Finally, using nearest neighbour method matching characteristic value, the WLAN fingerprint locations position of pedestrian is obtained.
As can be seen that the present invention has incomparable advantage in terms of measurement accuracy and stability.
The present embodiment does not provide constraints to the present invention.
The present invention uses zero-speed correction algorithm, and estimation compensation is carried out to inertial navigation speed；Utilize expanded Kalman filtration algorithm
Solves the nonlinear problem between input and output；By using particle filter fusion WLAN location informations as observed quantity, to used
Lead position and carry out estimation compensation；Double-deck WLAN fingerprint bases are devised, improve alignment system stability so that the present invention has
Universality, improve the precision of application and indoor positioning.
Here it must be noted that other unaccounted structures that the present invention provides are because be all the known knot of this area
Structure, according to title of the present invention or function, those skilled in the art can just find the document of related record, therefore not
It is described further.Technological means disclosed in the present invention program is not limited only to the technological means disclosed in above-mentioned embodiment,
Also include being combined formed technical scheme by above technical characteristic.
Claims (5)
1. based on indoor pedestrian's combined positioning method of inertia measurement bilayer WLAN fingerprint bases, comprise the following steps：
Step (1)：Indoor three-dimensional coordinate system is established, using indoor corner as origin, chooses x-axis, y-axis and z-axis, x-axis, y-axis
In geographical horizontal plane, it is mutually perpendicular to wall, vertically upward, three reference axis meet right-hand rule to z-axis；
Step (2)：In xOy planes, along x-axis, every 1 meter of grid division on y-axis direction, grid element center point is observed samples
Point, collection observed samples point correspond to the wireless signal strength information of different routers；
Step (3)：The wireless signal strength information of different routers is corresponded to by handling sampled point, establishes WLAN gradient fingerprints
Storehouse, while WLAN signal is further handled, extraction characteristic information, i.e., the average and variance of WLAN signal intensity in the sampling time,
Establish WLAN fingerprint bases；
Step (4)：3-axis acceleration, the three axis angular rate data of pedestrian's foot sensor are gathered, are calculated by 3-axis acceleration
Eulerian angles R, spin matrix C, quaternary number q are obtained, the 3-axis acceleration in navigational coordinate system is obtained by using spin matrix；
Step (5)：Zero-speed detection is performed, by calculating acceleration and angular speed modulus value, with wireless routing threshold value multilevel iudge row
People's state, the state of pedestrian include ambulatory status and halted state；
Step (6)：If pedestrian is ambulatory status, speed and displacement are obtained by acceleration respectively to the integration of time, such as
Fruit pedestrian is that then speed is equal with last moment with position for halted state；
Step (7)：Perform and expand Kalman prediction, perform zero-speed detection again, pedestrian's state is that motion state is then returned
To step (4), step (8) is otherwise performed；
Step (8)：Perform and expand Kalman filtering renewal, after new acceleration renewal quaternary number, return to step (4)；And count
Calculate the horizontal level obtained and calculate Euclidean distance one by one with the particle position in step (9), Euclidean distance is substituted into Gauss model
Obtain particle weights；
Step (9)：Pedestrian's reckoning, including the detection of pedestrian's gait, step-size estimation, pedestrian position calculating, execution are based on particle
Pedestrian's reckoning of filtering can obtain a series of pedestrian's particle by the acceleration and angular speed information of the pedestrian of acquisition
Position；
Step (10)：By the WLAN signal of online acquisition, by comparing collection signal intensity and threshold value, judge to refer to using WLAN
Line storehouse or WLAN gradient fingerprint bases are positioned, WLAN localization methods corresponding to selection, calculate the pedestrian position information of acquisition,
Euclidean distance between the position of the particle obtained with pedestrian's reckoning in step (9), substitute into Gauss model and obtain the particle
WLAN fingerprint weights；
Step (11)：Obtain in calculation procedure (8) pedestrian's horizontal two-dimension positional information and the particle position that is obtained in step (9) it
Between Euclidean distance, and then calculate obtain the particle expansion Kalman filtering weight；
Step (12)：For same particle, normalize, obtain after the acquisition particle weights in step (8), step (10) are multiplied
Particle weights, pedestrian position is obtained eventually through the weighting to particle location information.
A kind of 2. indoor pedestrian integrated positioning side based on inertia measurement/double-deck WLAN fingerprint bases according to claim 1
Method, it is characterised in that：WLAN fingerprint bases Q in described step (3)：
Q=[Q1,Q2,…,Qi,…Qs]；
Qi=[Xi,Yi,APi]；
<mrow>
<msub>
<mi>AP</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mo>&lsqb;</mo>
<msup>
<mi>SSID</mi>
<mn>1</mn>
</msup>
<mo>:</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mn>2</mn>
</msup>
<mo>:</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>k</mi>
</msup>
<mo>:</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>n</mi>
</msup>
<mo>:</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>&rsqb;</mo>
<mo>;</mo>
</mrow>
S is sampled point number, Qi(i=1,2 ..., s) represents the fingerprint base information of ith sample point, XiIt is the horizontal stroke of the i-th sampled point
Coordinate value, YiIt is the ordinate value of the i-th sampled point, APiIt is the signal value of the router of ith sample point；
SSIDk(k=1,2 ..., n) is the service set of k-th of router, for distinguishing different routers；
The statistics for representing k-th of access point signals intensity at ith sample point is put down
Average.
3. indoor pedestrian's combined positioning method according to claim 1 based on inertia measurement bilayer WLAN fingerprint bases, its
It is characterised by：WLAN gradient fingerprint bases Q ' in described step (3)：
Q '=[Q1',Q'2,…,Qi',…Qs']；
Qi'=[Xi,Yi,bi,ai,li,ri]；
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>b</mi>
<mrow>
<msub>
<mi>AP</mi>
<mi>i</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<mo>&lsqb;</mo>
<msup>
<mi>SSID</mi>
<mn>1</mn>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>k</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>n</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>a</mi>
<mrow>
<msub>
<mi>AP</mi>
<mi>i</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<mo>&lsqb;</mo>
<msup>
<mi>SSID</mi>
<mn>1</mn>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>k</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msup>
<mi>SSID</mi>
<mi>n</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>l</mi>
<mrow>
<msub>
<mi>AP</mi>
<mi>i</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<mo>&lsqb;</mo>
<msup>
<mi>SSID</mi>
<mn>1</mn>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mi>m</mi>
</mrow>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>k</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mi>m</mi>
</mrow>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msup>
<mi>SSID</mi>
<mi>n</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mi>m</mi>
</mrow>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>r</mi>
<mrow>
<msub>
<mi>AP</mi>
<mi>i</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<mo>&lsqb;</mo>
<msup>
<mi>SSID</mi>
<mn>1</mn>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mi>m</mi>
</mrow>
<mn>1</mn>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msup>
<mi>SSID</mi>
<mi>k</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mi>m</mi>
</mrow>
<mi>k</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msup>
<mi>SSID</mi>
<mi>n</mi>
</msup>
<mo>:</mo>
<mrow>
<mo>|</mo>
<mrow>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mover>
<mrow>
<msubsup>
<mi>RSSI</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mi>m</mi>
</mrow>
<mi>n</mi>
</msubsup>
</mrow>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mo>|</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>;</mo>
</mrow>
Represent the router AP of ith sample point place collectioniSignal value with its front neighbouring sample point signal intensity
Grad,Represent the router AP of ith sample point place collectioniSignal value and its rear neighbouring sample point signal
Intensity gradient values,Represent the router AP of ith sample point place collectioniSignal value with its left side neighbouring sample point letter
Number intensity gradient values,Represent the router AP of ith sample point place collectioniSignal value and its right side neighbouring sample point
Signal intensity gradient value, Qi' (i=1,2 ..., s) represent the gradient fingerprint base information of ith sample point；
The absolute value of the difference of k-th of access point signals intensity of the i-th sample point and the assembly average of its front sample point is represented,
The statistics for representing k-th of the access point signals intensity and its rear sample point of the i-th sample point is put down
The absolute value of the difference of average,K-th of the access point signals intensity and its left side for representing the i-th sample point are adopted
The absolute value of the difference of assembly average at sampling point,Represent k-th of access point signals of the i-th sample point
The absolute value of the difference of the assembly average of intensity and its right side sample point.
4. indoor pedestrian's combined positioning method according to claim 1 based on inertia measurement bilayer WLAN fingerprint bases, its
It is characterised by：Eulerian angles R in described step (4)：
R=(θ, φ, ψ),
<mrow>
<mi>&theta;</mi>
<mo>=</mo>
<mo>-</mo>
<mi>arcsin</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mi>b</mi>
<mi>o</mi>
<mi>d</mi>
<mi>y</mi>
</mrow>
</msubsup>
<mo>/</mo>
<mi>g</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
<mrow>
<mi>&phi;</mi>
<mo>=</mo>
<mi>arcsin</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>a</mi>
<mi>y</mi>
<mrow>
<mi>b</mi>
<mi>o</mi>
<mi>d</mi>
<mi>y</mi>
</mrow>
</msubsup>
<mo>/</mo>
<mo>(</mo>
<mrow>
<mi>g</mi>
<mo>&CenterDot;</mo>
<mi>c</mi>
<mi>o</mi>
<mi>s</mi>
<mi>&theta;</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
ψ=0；
It is the acceleration in pedestrian x directions,It is the acceleration in y directions.
5. indoor pedestrian's combined positioning method according to claim 1 based on inertia measurement bilayer WLAN fingerprint bases, its
It is characterised by：Quaternary number q in described step (4)：
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710838171.5A CN107702712A (en) | 2017-09-18 | 2017-09-18 | Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710838171.5A CN107702712A (en) | 2017-09-18 | 2017-09-18 | Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107702712A true CN107702712A (en) | 2018-02-16 |
Family
ID=61172740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710838171.5A Pending CN107702712A (en) | 2017-09-18 | 2017-09-18 | Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107702712A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108566677A (en) * | 2018-03-20 | 2018-09-21 | 北京邮电大学 | A kind of fingerprint positioning method and device |
| CN108810802A (en) * | 2018-06-08 | 2018-11-13 | 杭州电子科技大学 | A kind of parking garage vehicle positioning method based on iBeacon |
| CN109341682A (en) * | 2018-11-12 | 2019-02-15 | 浙江工业大学 | A method of improving earth's magnetic field positioning accuracy |
| CN109447128A (en) * | 2018-09-29 | 2019-03-08 | 中国科学院自动化研究所 | Walking based on micro- inertial technology and the classification of motions method and system that remains where one is |
| CN110174106A (en) * | 2019-04-01 | 2019-08-27 | 香港理工大学深圳研究院 | A kind of healthy walking paths planning method and terminal device based on PM2.5 |
| CN110470294A (en) * | 2019-08-09 | 2019-11-19 | 西安电子科技大学 | A kind of attitude of carrier estimation method that virtual measurement is merged with Kalman filtering |
| CN112511972A (en) * | 2020-11-27 | 2021-03-16 | 国网天津市电力公司 | Transformer substation inspection robot positioning method and device based on 5G |
| CN114185003A (en) * | 2022-02-15 | 2022-03-15 | 武汉华康世纪医疗股份有限公司 | Indoor positioning method and system based on multi-source signal fusion |
| WO2023082797A1 (en) * | 2021-11-09 | 2023-05-19 | Oppo广东移动通信有限公司 | Positioning method, positioning apparatus, storage medium, and electronic device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110057840A1 (en) * | 2008-09-09 | 2011-03-10 | National Pingtung University Of Science And Technology | Method of Positioning RFID Tags |
| CN102419180A (en) * | 2011-09-02 | 2012-04-18 | 无锡智感星际科技有限公司 | Indoor positioning method based on inertial navigation system and WIFI (wireless fidelity) |
| CN104197938A (en) * | 2014-09-03 | 2014-12-10 | 厦门大学 | Indoor localization method based on foot inertia sensor |
| CN104482933A (en) * | 2014-12-03 | 2015-04-01 | 北京航空航天大学 | Method for dead reckoning and WLAN (wireless local area network) integrated positioning based on particle filter |
| CN104819716A (en) * | 2015-04-21 | 2015-08-05 | 北京工业大学 | Indoor and outdoor personal navigation algorithm based on INS/GPS (inertial navigation system/global position system) integration of MEMS (micro-electromechanical system) |
| CN105519140A (en) * | 2014-08-13 | 2016-04-20 | 华为技术有限公司 | Method and device for locating AP |
| CN106412839A (en) * | 2016-09-12 | 2017-02-15 | 南京邮电大学 | Indoor positioning and tracking method based on secondary partition and gradient fingerprint match |
| CN107063251A (en) * | 2016-11-15 | 2017-08-18 | 华南理工大学 | A kind of navigation cart system and localization method based on WiFi indoor positionings |
-
2017
- 2017-09-18 CN CN201710838171.5A patent/CN107702712A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110057840A1 (en) * | 2008-09-09 | 2011-03-10 | National Pingtung University Of Science And Technology | Method of Positioning RFID Tags |
| CN102419180A (en) * | 2011-09-02 | 2012-04-18 | 无锡智感星际科技有限公司 | Indoor positioning method based on inertial navigation system and WIFI (wireless fidelity) |
| CN105519140A (en) * | 2014-08-13 | 2016-04-20 | 华为技术有限公司 | Method and device for locating AP |
| CN104197938A (en) * | 2014-09-03 | 2014-12-10 | 厦门大学 | Indoor localization method based on foot inertia sensor |
| CN104482933A (en) * | 2014-12-03 | 2015-04-01 | 北京航空航天大学 | Method for dead reckoning and WLAN (wireless local area network) integrated positioning based on particle filter |
| CN104819716A (en) * | 2015-04-21 | 2015-08-05 | 北京工业大学 | Indoor and outdoor personal navigation algorithm based on INS/GPS (inertial navigation system/global position system) integration of MEMS (micro-electromechanical system) |
| CN106412839A (en) * | 2016-09-12 | 2017-02-15 | 南京邮电大学 | Indoor positioning and tracking method based on secondary partition and gradient fingerprint match |
| CN107063251A (en) * | 2016-11-15 | 2017-08-18 | 华南理工大学 | A kind of navigation cart system and localization method based on WiFi indoor positionings |
Non-Patent Citations (3)
| Title |
|---|
| GRADIENT-BASED FINGERPRINTING FOR INDOOR LOCALIZATION AND TRACKI: "Yuanchao Shu等", 《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》 * |
| TAO JIANG等: "Performance Enhancement of Indoor Pedestrian Positioning with Two-Order Bayesian Estimation Based on EKF and PF", 《SYMMETRY-BASEL》 * |
| 刘晓文等: "基于WI-FI的井下定位算法研究", 《传感技术学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108566677A (en) * | 2018-03-20 | 2018-09-21 | 北京邮电大学 | A kind of fingerprint positioning method and device |
| CN108810802A (en) * | 2018-06-08 | 2018-11-13 | 杭州电子科技大学 | A kind of parking garage vehicle positioning method based on iBeacon |
| CN109447128A (en) * | 2018-09-29 | 2019-03-08 | 中国科学院自动化研究所 | Walking based on micro- inertial technology and the classification of motions method and system that remains where one is |
| CN109447128B (en) * | 2018-09-29 | 2021-10-01 | 中国科学院自动化研究所 | Micro-inertia technology-based walking and stepping in-place movement classification method and system |
| CN109341682A (en) * | 2018-11-12 | 2019-02-15 | 浙江工业大学 | A method of improving earth's magnetic field positioning accuracy |
| CN110174106A (en) * | 2019-04-01 | 2019-08-27 | 香港理工大学深圳研究院 | A kind of healthy walking paths planning method and terminal device based on PM2.5 |
| CN110470294A (en) * | 2019-08-09 | 2019-11-19 | 西安电子科技大学 | A kind of attitude of carrier estimation method that virtual measurement is merged with Kalman filtering |
| CN110470294B (en) * | 2019-08-09 | 2020-12-18 | 西安电子科技大学 | Carrier attitude estimation method integrating virtual measurement and Kalman filtering |
| CN112511972A (en) * | 2020-11-27 | 2021-03-16 | 国网天津市电力公司 | Transformer substation inspection robot positioning method and device based on 5G |
| WO2023082797A1 (en) * | 2021-11-09 | 2023-05-19 | Oppo广东移动通信有限公司 | Positioning method, positioning apparatus, storage medium, and electronic device |
| CN114185003A (en) * | 2022-02-15 | 2022-03-15 | 武汉华康世纪医疗股份有限公司 | Indoor positioning method and system based on multi-source signal fusion |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107702712A (en) | Indoor pedestrian's combined positioning method based on inertia measurement bilayer WLAN fingerprint bases | |
| CN107289933B (en) | Double card Kalman Filtering navigation device and method based on MEMS sensor and VLC positioning fusion | |
| CN102538781B (en) | Machine vision and inertial navigation fusion-based mobile robot motion attitude estimation method | |
| CN105509739B (en) | Using fixed interval CRTS smooth INS/UWB tight integrations navigation system and method | |
| CN105241445B (en) | A kind of indoor navigation data capture method and system based on intelligent mobile terminal | |
| CN104713554B (en) | A kind of indoor orientation method merged based on MEMS inertia devices with Android smartphone | |
| CN103968827B (en) | A kind of autonomic positioning method of wearable body gait detection | |
| CN105698822B (en) | Initial Alignment Method between autonomous type inertial navigation based on reversed Attitude Tracking is advanced | |
| CN110095116A (en) | A kind of localization method of vision positioning and inertial navigation combination based on LIFT | |
| CN105589064A (en) | Rapid establishing and dynamic updating system and method for WLAN position fingerprint database | |
| WO2019136714A1 (en) | 3d laser-based map building method and system | |
| CN106681320A (en) | Mobile robot navigation control method based on laser data | |
| CN106840211A (en) | A kind of SINS Initial Alignment of Large Azimuth Misalignment On methods based on KF and STUPF combined filters | |
| CN111912416A (en) | Method, device and equipment for positioning equipment | |
| CN110455301A (en) | A kind of dynamic scene SLAM method based on Inertial Measurement Unit | |
| CN106197406A (en) | A kind of based on inertial navigation with the fusion method of RSSI wireless location | |
| CN106705959A (en) | Method and device for detecting course of mobile terminal | |
| CN107289932A (en) | Single deck tape-recorder Kalman Filtering guider and method based on MEMS sensor and VLC positioning fusions | |
| CN104613966B (en) | A kind of cadastration off-line data processing method | |
| CN107270898A (en) | Double particle filter guiders and method based on MEMS sensor and VLC positioning fusions | |
| CN107063245A (en) | A kind of SINS/DVL integrated navigation filtering methods based on 5 rank SSRCKF | |
| CN105004351A (en) | SINS large-azimuth misalignment angle initial alignment method based on self-adaptation UPF | |
| Jin et al. | Fast and accurate initialization for monocular vision/INS/GNSS integrated system on land vehicle | |
| CN105737850B (en) | Mutative scale one direction gravity sample vector matching locating method based on particle filter | |
| CN107246872A (en) | Single-particle filtering guider and method based on MEMS sensor and VLC positioning fusions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180216 |
|
| WD01 | Invention patent application deemed withdrawn after publication |