CN107339989A - A kind of pedestrian's indoor orientation method based on particle filter - Google Patents
A kind of pedestrian's indoor orientation method based on particle filter Download PDFInfo
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- CN107339989A CN107339989A CN201710493018.3A CN201710493018A CN107339989A CN 107339989 A CN107339989 A CN 107339989A CN 201710493018 A CN201710493018 A CN 201710493018A CN 107339989 A CN107339989 A CN 107339989A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- 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
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Abstract
The invention discloses a kind of pedestrian's indoor orientation method based on particle filter, this method is a kind of collection RSS measurements, the fusion frame model that mems accelerometer and cartographic information mutually merge with particle filter, a mems accelerometer and cartographic information are added in alignment system, walking distance is estimated based on the motion model of zero crossing algorithm, it is used to integrate the nonlinear transformations from mems accelerometer and building map using particle filter, avoid the accumulated error as caused by sensor noise, the blending algorithm is compared with Kalman filtering, it is significantly improved on the average value and standard deviation of evaluated error, estimation result relative to wrong step-length is in robustness.The result of emulation experiment and actual test shows that this method is significantly improved compared with Kalman filtering on the average value and standard deviation of evaluated error.
Description
Technical field
The present invention relates to a kind of pedestrian's indoor orientation method based on particle filter, belong to pedestrian's Camera calibration technology
Field.
Background technology
Positioning and navigation system are in personal security, assets and personnel tracking, intelligently guiding, location aware multimedia service etc.
Huge success is achieved in extensive use based on location-based service (LBS).Generally, these systems can be divided into three groups：Satellite
Alignment system, communications localization system and sensor alignment system.
For example famous GPS of global position system, the Big Dipper, Galileo system, it is primarily adapted for use in outdoor positioning.However, work as
When being positioned indoors under environment, satellite system is subject to the decay of building and wall, reflection and refraction.
Communications localization system utilizes existing communication network infrastructure, such as WLAN (WLAN), ultra wide band (UWB)
Or DECT network, use received signal strength (RSS), arrival time (TOA), reaching time-difference (TDOA) and angle of arrival (AOA)
It is that can dispose that outdoor can also be deployed in indoors the advantages of communications localization system to calculate the position of user.It is in addition, logical
The communication network that letter alignment system uses need not additionally complete the construction of hardware, and cost of installation and maintenance is than relatively low.It is but logical
Letter alignment system is vulnerable to the puzzlement of the noise characteristic of wireless channel and multipath distortion, causes the reduction of positioning precision.
Sensor alignment system senses the relevant informations such as absolute position, such as magnetic transducing using various sensor specials
Device, laser sensor, ultrasonic wave, infrared sensor, inertial sensor etc., wherein inertial sensor are mainly used to sensing the feedback of position phase
Close the change of information.Because inertial sensor can only provide relative information, they usually require to be combined with other alignment systems
Complete positioning.For example, GPS/INS solutions correct the accumulated error of inertial sensor using GPS as manager, it is another
Aspect, inertial navigation system (INS) can also improve GPS performance, particularly such as in building or other cause gps signal
In the case of temporary block, traditional inertial navigation system is huge and costly, and which has limited they and indoor locating system
It is integrated.
However, emerging MEMS technology causes the inertial sensor of low cost and small size to become a reality.One example is
Mems accelerometer, its price can be successfully integrated into mobile device less than current 1 dollar.
The content of the invention
The defects of the present invention seeks to exist for prior art, provides fixed in a kind of pedestrian room based on particle filter
Position method, this method provide a kind of fusion frame model based on particle filter, one is added in alignment system
Mems accelerometer and cartographic information, it is used to integrating using particle filter non-from mems accelerometer and building map
Linear information, avoid the accumulated error as caused by sensor noise.
The present invention to achieve the above object, adopts the following technical scheme that：A kind of pedestrian's indoor positioning based on particle filter
Method, this method use following steps：A kind of pedestrian's indoor orientation method based on particle filter, it is characterised in that this method
Comprise the following steps that：
Step 1：Increase a mems accelerometer and cartographic information in typical WLAN pedestrian's alignment system；
Step 2：It is pattern match or K- arest neighbors (KNN) algorithm using location algorithm in based on RSS system, draws basic
Location model, the algorithm mainly include following two steps：
(1) in off-line case, the receiving power vector of multiple access points (AP) at calibration point is measured, and is recorded
For the fingerprint of calibration point,
(2) in on-line case, the fingerprint of the power vector of reception and calibration point is carried out using the distance metric in equation (1)
Compare, finally selection with the average value of K calibration point of the immediate distance of receiving power vector as final predicted value,
Wherein,For access point q measurement power, Pq(x) it is q-th of element of the fingerprint at calibration point x, Q is AP number
Amount；
Step 3：Pedestrian's step number and step number distance are determined to obtain walking distance, such as based on mems accelerometer and motion model
Shown in formula (2)：
D=Step_Size × Num_Steps (2)
Wherein, step number is counted using zero crossing algorithm, because each step of vertical acceleration signal all will extend over zero line twice, institute
With when the counting for the quantity that complete zero cross point, and by itself divided by 2, obtain the current walking step number of pedestrian；
Using formula (3) material calculation：
Wherein, AmaxFor each step peak acceleration of pedestrian, AminFor each step minimum acceleration of pedestrian, C is steady state value, the value from
Obtained in the ambulation training data of different pedestrians；
Step 4：Nonlinear transformations from mems accelerometer and building map are integrated using particle filter；
A, the indoor positioning algorithms based on elementary particle filtering algorithm realize that step is as follows：
Particle filter uses formula (4) direct estimation Z (k) state value and x (k) posterior probability density function pdf, its
In, xi(k) be posterior probability ith sample point or particle, wi(k) be particle weights；
The particle filter comprises the following steps：
Initialization：Initialize pdf functions：P (x (0)) is to N number of particle { xi(0), i=1...N } sampled,
Prediction samples：For each particle xi(k), from pdf state transition equations p (x (k+1) | xi(k) new particle is obtained in)
xi(k+1),
Importance sampling：For each new particle xi(k+1) w, is calculatedi(k+1)=p (z (k+1) | xi(k+1)), normalization and again
Sampling：By weight normalization and final resampling, in resampling steps, the particle of low weight is deleted, and repeat that there is height
The particle of weight so that each particle has identical weight；
B, it is only used for the particle filter of RSS measurements：
For original each particleNew particle x can be obtained from formula (5)i(k+
1)：
Assuming that the motion of pedestrian is dominated by the inertia being superimposed by Gauss acceleration noise, i.e., a is sampled from normal distributionx(k)
And ay(k) weight of new particle, is calculated using equation (6), it is herein assumed that the position estimated by the pattern match based on RSS is to enclose
Around the Gaussian Profile of actual position；
C, RSS measurements and mems accelerometer and particle filter merge：
, it is necessary to which invocation step three obtains the walking distance d (k) between two RSS samples when using mems accelerometer, because
Sigmoidal function is needed for walking distance d (k), so using formula (7) prediction samples equation：
Wherein di(k) from normal distributionIn sampled, be walking distance d (k) and standard deviationBe averaged
Value, due to θi(k) unknown, we can complete sampling from being uniformly distributed in (0~2p), and complete particle using formula (6) and weigh
Re-computation, based on formula (6) and (7), merge mems accelerometer using particle filter and RSS is measured；
D, RSS measurements, mems accelerometer and cartographic information merge with particle filter：
Using particle filter fusion RSS, mems accelerometer and cartographic information, (such as worn by deleting impossible particle
Cross the particle of wall) carry out improved estimator, by weighting, equation (6) can be improved to equation (8), and equation (8) is proposed grain
Subfilter Optimized model.
The beneficial effect that the present invention reaches is：The present invention propose a kind of collection RSS measurements, mems accelerometer and cartographic information with
The fusion frame model that particle filter mutually merges, a mems accelerometer and map letter are added in alignment system
Breath, estimates walking distance based on the motion model of zero crossing algorithm, is used to integrate from MEMS acceleration using particle filter
The nonlinear transformations of degree meter and building map, avoid the accumulated error as caused by sensor noise, the blending algorithm and card
Kalman Filtering is compared, and is significantly improved on the average value and standard deviation of evaluated error, relative to the estimation knot of wrong step-length
Fruit is in robustness.
Brief description of the drawings
Fig. 1 is the indoor tracking frame model figure of the present invention；
Fig. 2 is test trails figure of the present invention under eight kinds of test modes；
Fig. 3 is present invention comparison figure of standard deviation and simulation average value under simulation status；
Fig. 4 is that the present invention tests the result figure using different step-lengths in simulation status Imitating；
Fig. 5 is emulation experiment of the present invention and authentic testing comparative result figure.
Embodiment
The embodiment of the present invention is described further below in conjunction with the accompanying drawings.
As Figure 1-5, the technical solution adopted in the present invention is：A kind of pedestrian indoor positioning side based on particle filter
Method, this method use following steps：A kind of pedestrian's indoor orientation method based on particle filter, it is characterised in that this method has
Body step is as follows：
Step 1：Increase a mems accelerometer and cartographic information in typical WLAN pedestrian's alignment system；
Step 2：It is pattern match or K- arest neighbors (KNN) algorithm using location algorithm in based on RSS system, draws basic
Location model, the algorithm mainly include following two steps：
(1) in off-line case, the receiving power vector of multiple access points (AP) at calibration point is measured, and is recorded
For the fingerprint of calibration point,
(2) in on-line case, the fingerprint of the power vector of reception and calibration point is carried out using the distance metric in equation (1)
Compare, finally selection with the average value of K calibration point of the immediate distance of receiving power vector as final predicted value,
Wherein,For access point q measurement power, Pq(x) it is q-th of element of the fingerprint at calibration point x, Q is AP number
Amount；
Step 3：Pedestrian's step number and step number distance are determined to obtain walking distance, such as based on mems accelerometer and motion model
Shown in formula (2)：
D=Step_Size × Num_Steps (2)
Wherein, step number is counted using zero crossing algorithm, because each step of vertical acceleration signal all will extend over zero line twice, institute
With when the counting for the quantity that complete zero cross point, and by itself divided by 2, obtain the current walking step number of pedestrian；
Using formula (3) material calculation：
Wherein, AmaxFor each step peak acceleration of pedestrian, AminFor each step minimum acceleration of pedestrian, C is steady state value, the value from
Obtained in the ambulation training data of different pedestrians.
Comparative maturity, miniaturization, cheap mems accelerometer are widely used current MEMS technology, MEMS acceleration
Meter is the device for measuring the acceleration of mobile object.In theory, a kind of method is by being integrated to acceleration signal
Calculate and obtain translational speed and distance, but for indoor walking, because acceleration is small, institute in this way the defects of be can hardly
By translational speed, distance and sensor noise, offset drifts and tilt variation data are separated.Another method is detection walking
Distance, when people walk, its locomotory mechanism, normal acceleration periodically fluctuates, and this cyclical signal represents people's step
Row distance, therefore, we obtain walking distance using pedestrian's step number and step number distance is determined, such as formula (2) and formula (3) institute
Show.
Step 4：Nonlinear transformations from mems accelerometer and building map are integrated using particle filter；
Indoors in alignment system, when pedestrian is by consecutive tracking, due to its mobility, the fluctuation of RSS measured values can be caused, this
The reduction of location data precision will be caused, smooth motion trajectories can be helped by particle filter optimization and estimation can be reduced
Error, frequency of use is higher for Kalman filter in target following, but be its operation the shortcomings that Kalman filter according to
Rely in linear model, and what linear model was substantially not present in practical application；Extended Kalman filter (EKF) and nothing
Limit Kalman filter (UKF) solves nonlinear estimation problem by the way that nonlinear model is converted into linear model, but EKF
Or UKF is for solving the problems, such as that part nonlinear system is also relatively difficult；Currently, because particle filter is special based on covering
Caro samples, and has and handles the non-linear and characteristic of non-gaussian estimation problem, so can be with such as walking using particle filter
All kinds of nonlinear transformations such as row distance and cartographic information are integrated.
A, the indoor positioning algorithms based on elementary particle filtering algorithm realize that step is as follows：
Particle filter uses formula (4) direct estimation Z (k) state value and x (k) posterior probability density function pdf, its
In, xi(k) be posterior probability ith sample point or particle, wi(k) be particle weights；
The particle filter comprises the following steps：
Initialization：Initialize pdf functions：P (x (0)) is to N number of particle { xi(0), i=1...N } sampled, prediction samples：It is right
In each particle xi(k), from pdf state transition equations p (x (k+1) | xi(k) new particle x is obtained in)i(k+1),
Importance sampling：For each new particle xi(k+1) w, is calculatedi(k+1)=p (z (k+1) | xi(k+1)), normalization and again
Sampling：By weight normalization and final resampling, in resampling steps, the particle of low weight is deleted, and repeat that there is height
The particle of weight so that each particle has identical weight；
View of the above, it will be seen that for particle filter, pdf state transition equations p (x (k+1) | xi(k)) and pdf is defeated
Go out density function p (z (k+1) | xi(k+1)) should possess the function of being predicted sampling and weight calculation, pdf functions are not herein
Necessarily need to have Gaussian characteristics.
B, it is only used for the particle filter of RSS measurements：
For original each particleNew particle x can be obtained from formula (5)i(k+
1)：
Assuming that the motion of pedestrian is dominated by the inertia being superimposed by Gauss acceleration noise, i.e., a is sampled from normal distributionx(k)
And ay(k) weight of new particle, is calculated using equation (6), it is herein assumed that the position estimated by the pattern match based on RSS is to enclose
Around the Gaussian Profile of actual position；
C, RSS measurements and mems accelerometer and particle filter merge：
, it is necessary to which invocation step three obtains the walking distance d (k) between two RSS samples when using mems accelerometer, because
Sigmoidal function is needed for walking distance d (k), so using formula (7) prediction samples equation：
Wherein di(k) from normal distributionIn sampled, be walking distance d (k) and standard deviationBe averaged
Value, due to θi(k) unknown, we can complete sampling from being uniformly distributed in (0~2p), and complete particle using formula (6) and weigh
Re-computation, based on formula (6) and (7), merge mems accelerometer using particle filter and RSS is measured；
D, RSS measurements, mems accelerometer and cartographic information merge with particle filter：
Using particle filter fusion RSS, mems accelerometer and cartographic information, (such as worn by deleting impossible particle
Cross the particle of wall) carry out improved estimator, by weighting, equation (6) can be improved to equation (8), and equation (8) is proposed grain
Subfilter Optimized model.
Building map is another highly useful information source, because substantial amounts of position can be extracted from fabric structure information
Put related data, such as the distance between floor, wall, the position of door or elevator, for tracking problem, the information helps to subtract
The uncertainty of few foot path.Using particle filter, RSS, accelerometer and cartographic information can be merged, by deleting not
Possible particle carrys out improved estimator (such as through the particle of wall), passes through weighting, it is proposed that particle filter Optimized model.
The experimental result of particle filter Optimized model and analysis：In order to preferably assess different filtering technique and particle
The performance of filter optimization model, we are tested in simulated environment and actual WLAN environment respectively.
1st, simulation test
The transmission of wireless signals model of multiple wall obstacles is have selected in emulation experiment environment, based on the modeling office
RSS in environment is distributed, and 5 AP are contained in the transmission of wireless signals model, and AP position is marked with asterisk in Fig. 4, ginseng
Examination point is used to carry out initial estimation, then using different filtering with 1 meter of resolution ratio uniform design first by KNN algorithms
Device carrys out smooth track and reduces site error.
In order to improve the coverage rate of test and accuracy, eight different foot paths are have selected in testing, such as Fig. 2 institutes
Show the roundabout walking of straight line (test 1), kept straight on (test 2) with variable velocity, walk (test 3) is turned to 90 °, turned to and gone with 180 °
(test 4) is walked, walk (test 5) is turned to 45 °, is walked in an annular (test 6 and 7) and random walk (test 8), simulation
The average value and standard deviation of middle evaluated error are as shown in figure 3, algorithm parameter for details see attached table 1.
Indoor pedestrian's walk test result of the actual WLAN environment of table 1
| Algorithm types | Mean error (m) | Standard deviation (m) |
| KNN | 6.44 | 6.84 |
| Kalman Filter(KF) | 5.81 | 4.07 |
| Particle Filter(PF) | 5.57 | 3.9 |
| PF+Accelerometer | 4.54 | 3.52 |
| PF+Accelerometer+Map | 4.30 | 2.80 |
It was found from being analyzed from simulation result as shown in Figure 3, when only considering RSS measurements, particle filter and Kalman filtering
Device performance is suitable, and after walking distance information is combined, experimental result, which has, to be significantly improved；When only cartographic information is added to
During RSS information, also there is certain improvement；Particle filter, accelerometer, cartographic information, walking distance etc. are added to same
Simulation result in model averagely lifts more than 40% than KNN result, and 30% is averagely lifted than Kalman filtered results.
In emulation experiment, we are performed in test 8 with different step-lengths, intend the step-size estimation of simulation mistake, and Fig. 4 is provided
Mean error when using different step-lengths in test 8, test result indicates that, 10-20% step error will not cause
And the deviation of best orientation precision is excessive.
2nd, indoor pedestrian's walk test of actual WLAN environment
Using all algorithms in real RSS and acceleration analysis checking text, test environment is identical with described in simulation, step
Row track is identical with the track simulated in test 8, acceleration information by motion model algorithm process, and identified walking away from
From as shown in Figure 4.
Indoor pedestrian's walk test result of the WLAN environment of reality is compared with simulation test result, as shown in figure 5,
Compared using filtering algorithm-Kalman filter or particle filter-arest neighbors positioning based on RSS, positioning precision can be with
Improve about 10%.When particulate filter obtains extraneous information from acceleration transducer and building map, about 25% can be obtained
Further optimization lifting.
The present invention proposes a kind of collection RSS measurements, mems accelerometer and cartographic information and mutually merged with particle filter
Fusion frame model, a mems accelerometer and cartographic information are added in alignment system, based on zero crossing algorithm
Motion model estimate walking distance, be used to integrating from mems accelerometer and building map using particle filter
Nonlinear transformations, the accumulated error as caused by sensor noise is avoided, the blending algorithm is being estimated compared with Kalman filtering
It is significantly improved on the average value and standard deviation of error, the estimation result relative to wrong step-length is in robustness.
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all the present invention spirit and
Within principle, any modification, equivalent substitution and improvements made etc., it should be included in the scope of the protection.
Claims (1)
1. a kind of pedestrian's indoor orientation method based on particle filter, it is characterised in that this method comprises the following steps that：
Step 1：Increase a mems accelerometer and cartographic information in typical WLAN pedestrian's alignment system；
Step 2：It is pattern match or K- arest neighbors (KNN) algorithm using location algorithm in based on RSS system, draws basic
Location model, the algorithm mainly include following two steps：
(1) in off-line case, the receiving power vector of multiple access points (AP) at calibration point is measured, and is recorded
For the fingerprint of calibration point,
(2) in on-line case, the fingerprint of the power vector of reception and calibration point is carried out using the distance metric in equation (1)
Compare, finally selection with the average value of K calibration point of the immediate distance of receiving power vector as final predicted value,
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Wherein,For access point q measurement power, Pq(x) it is q-th of element of the fingerprint at calibration point x, Q is AP quantity；
Step 3：Pedestrian's step number and step number distance are determined to obtain walking distance, such as based on mems accelerometer and motion model
Shown in formula (2)：
D=Step_Size × Num_Steps (2)
Wherein, step number is counted using zero crossing algorithm, because each step of vertical acceleration signal all will extend over zero line twice, institute
With when the counting for the quantity that complete zero cross point, and by itself divided by 2, obtain the current walking step number of pedestrian；
Using formula (3) material calculation：
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Wherein, AmaxFor each step peak acceleration of pedestrian, AminFor each step minimum acceleration of pedestrian, C is steady state value, the value from
Obtained in the ambulation training data of different pedestrians；
Step 4：Nonlinear transformations from mems accelerometer and building map are integrated using particle filter；
A, the indoor positioning algorithms based on elementary particle filtering algorithm realize that step is as follows：
Particle filter uses formula (4) direct estimation Z (k) state value and x (k) posterior probability density function pdf, its
In, xi(k) be posterior probability ith sample point or particle, wi(k) be particle weights；
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The particle filter comprises the following steps：
Initialization：Initialize pdf functions：P (x (0)) is to N number of particle { xi(0), i=1...N } sampled,
Prediction samples：For each particle xi(k), from pdf state transition equations p (x (k+1) | xi(k) new particle is obtained in)
xi(k+1), importance sampling：For each new particle xi(k+1) w, is calculatedi(k+1)=p (z (k+1) | xi(k+1)),
Normalization and resampling：By weight normalization and final resampling, in resampling steps, the particle of low weight is deleted,
And repeat the particle with high weight so that each particle has identical weight；
B, it is only used for the particle filter of RSS measurements：
For original each particleNew particle x can be obtained from formula (5)i(k+
1)：
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Assuming that the motion of pedestrian is dominated by the inertia being superimposed by Gauss acceleration noise, i.e., a is sampled from normal distributionx(k) and
ay(k) weight of new particle, is calculated using equation (6), it is herein assumed that the position estimated by the pattern match based on RSS is to surround
The Gaussian Profile of actual position；
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C, RSS measurements and mems accelerometer and particle filter merge
, it is necessary to which invocation step three obtains the walking distance d (k) between two RSS samples when using mems accelerometer, because
Sigmoidal function is needed for walking distance d (k), so using formula (7) prediction samples equation：
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Wherein di(k) from normal distributionIn sampled, be walking distance d (k) and standard deviationBe averaged
Value, due to θi(k) unknown, we can complete sampling from being uniformly distributed in (0~2 π), and complete particle using formula (6) and weigh
Re-computation, based on formula (6) and (7), merge mems accelerometer using particle filter and RSS is measured；
D, RSS measurements, mems accelerometer and cartographic information merge with particle filter：
Using particle filter fusion RSS, mems accelerometer and cartographic information, (such as worn by deleting impossible particle
Cross the particle of wall) carry out improved estimator, by weighting, equation (6) can be improved to equation (8), and equation (8) is proposed grain
Subfilter Optimized model
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2
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Cited By (10)
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| CN107990900A (en) * | 2017-11-24 | 2018-05-04 | 江苏信息职业技术学院 | A kind of particle filter design methods of pedestrian's indoor positioning data |
| CN108089180A (en) * | 2017-12-18 | 2018-05-29 | 江苏添仂智能科技有限公司 | Based on UWB sensors as back indicator to the localization method of GPS and inertial navigation system the suspension type rail vehicle corrected |
| CN108322888A (en) * | 2018-01-30 | 2018-07-24 | 重庆大学 | A kind of indoor orientation method of mobile terminal |
| CN109298389A (en) * | 2018-08-29 | 2019-02-01 | 东南大学 | Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method |
| CN109451425A (en) * | 2018-12-06 | 2019-03-08 | 奇点新源国际技术开发(北京)有限公司 | A kind of localization method and device based on single anchor point |
| CN109870716A (en) * | 2017-12-01 | 2019-06-11 | 北京京东尚科信息技术有限公司 | Localization method and positioning device and computer readable storage medium |
| CN110333479A (en) * | 2019-07-09 | 2019-10-15 | 东华大学 | It is a kind of based on the wireless location method for improving particle filter under complex indoor environment |
| CN111623765A (en) * | 2020-05-18 | 2020-09-04 | 清华大学 | Indoor positioning method and system based on multi-mode data |
| CN112781591A (en) * | 2019-11-06 | 2021-05-11 | 深圳市优必选科技股份有限公司 | Robot positioning method and device, computer readable storage medium and robot |
| CN113155131A (en) * | 2021-04-14 | 2021-07-23 | 浙江工业大学 | Particle filter-based iBeacon and PDR fusion indoor positioning method |
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| CN107990900A (en) * | 2017-11-24 | 2018-05-04 | 江苏信息职业技术学院 | A kind of particle filter design methods of pedestrian's indoor positioning data |
| CN109870716A (en) * | 2017-12-01 | 2019-06-11 | 北京京东尚科信息技术有限公司 | Localization method and positioning device and computer readable storage medium |
| CN108089180A (en) * | 2017-12-18 | 2018-05-29 | 江苏添仂智能科技有限公司 | Based on UWB sensors as back indicator to the localization method of GPS and inertial navigation system the suspension type rail vehicle corrected |
| CN108322888A (en) * | 2018-01-30 | 2018-07-24 | 重庆大学 | A kind of indoor orientation method of mobile terminal |
| CN108322888B (en) * | 2018-01-30 | 2020-05-12 | 重庆大学 | Indoor positioning method of mobile terminal |
| CN109298389B (en) * | 2018-08-29 | 2022-09-23 | 东南大学 | Indoor pedestrian combination pose estimation method based on multi-particle swarm optimization |
| CN109298389A (en) * | 2018-08-29 | 2019-02-01 | 东南大学 | Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method |
| CN109451425A (en) * | 2018-12-06 | 2019-03-08 | 奇点新源国际技术开发(北京)有限公司 | A kind of localization method and device based on single anchor point |
| CN110333479A (en) * | 2019-07-09 | 2019-10-15 | 东华大学 | It is a kind of based on the wireless location method for improving particle filter under complex indoor environment |
| CN112781591A (en) * | 2019-11-06 | 2021-05-11 | 深圳市优必选科技股份有限公司 | Robot positioning method and device, computer readable storage medium and robot |
| CN111623765A (en) * | 2020-05-18 | 2020-09-04 | 清华大学 | Indoor positioning method and system based on multi-mode data |
| CN113155131A (en) * | 2021-04-14 | 2021-07-23 | 浙江工业大学 | Particle filter-based iBeacon and PDR fusion indoor positioning method |
| CN113155131B (en) * | 2021-04-14 | 2022-06-17 | 浙江工业大学 | Particle filter-based iBeacon and PDR fusion indoor positioning method |
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Application publication date: 20171110 |