CN110345939B - Indoor positioning method integrating fuzzy logic judgment and map information - Google Patents

Indoor positioning method integrating fuzzy logic judgment and map information Download PDF

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CN110345939B
CN110345939B CN201910587333.1A CN201910587333A CN110345939B CN 110345939 B CN110345939 B CN 110345939B CN 201910587333 A CN201910587333 A CN 201910587333A CN 110345939 B CN110345939 B CN 110345939B
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赵建立
朱晨迪
孙国强
石敬诗
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Shandong University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract

The invention discloses an indoor positioning method integrating fuzzy logic judgment and map information, and relates to the field of indoor positioning. The indoor positioning method integrating fuzzy logic judgment and map information comprises the following steps: calculating the indoor pedestrian track; step two: firstly, judging whether to start the fusion algorithm or simply utilize the direction angle data of a certain optimal state; if the fusion algorithm is judged to be started, the weight is adaptively adjusted according to the actually acquired data of the gyroscope and the magnetometer to select the fusion algorithm suitable for the current state, the correlation between the gyroscope and the magnetometer is judged by giving a correlation threshold value of the gyroscope and the magnetometer, then the walking state is judged by utilizing the direction angle of the magnetometer, whether the direction angle of the previous moment is added or not is determined, and finally fusion is carried out according to the fixed weight; step three: and adding a landmark identified by a WiFi peak point based on the particle filtering of map matching, and correcting the position of the particle.

Description

Indoor positioning method integrating fuzzy logic judgment and map information
Technical Field
The invention relates to the field of indoor positioning, in particular to an indoor positioning method integrating fuzzy logic judgment and map information.
Background
With the advent of the digital information age, Location Based Service (LBS) has become increasingly important, and has a wide application prospect in fire rescue, medical services, parking Location, and the like. The Global Positioning System (GPS) can basically meet the requirement of positioning pedestrians outdoors, but because the GPS cannot be applied indoors due to the blockage of the GPS signal by the indoor complex environment, indoor positioning becomes a new research hotspot. Currently, typical indoor positioning technologies mainly include fingerprint positioning based on a wireless network, a Pedestrian Dead Reckoning (PDR) method based on an Inertial Navigation System (INS), a trilateral triangulation method, infrared positioning based on infrared, and Radio Frequency (RF) positioning based on Radio Frequency (RF). With the perfection of sensor allocation of the smart phone, it is possible to solve the indoor positioning problem by using the INS. However, the PDR system using the INS positioning may cause accumulation of final positioning errors because the sensor may contain a lot of noise in the process of acquiring data and the PDR positioning is calculated based on the positioning result at the last time. How to suppress the accumulation of sensor data errors is a problem which needs to be solved urgently at present.
Common calibration methods include calibrating a direction angle obtained by fusing gyroscope and magnetometer data to improve subsequent positioning accuracy, fusing a PDR and a WiFi positioning result, or correcting a positioning result of the PDR in real time by using map data. However, the existing PDR calibration method has the following problems:
the current direction angle fusion method only uses a gyroscope and a magnetometer for fusion, and does not consider whether different direction angle fusion algorithms need to be started under different conditions to adapt to the current state.
The PDR positioning result is corrected by simply adding landmarks, the accuracy of the positioning result depends on the number of landmarks, a large amount of manpower and material resources are consumed for more landmark acquisition, and the final positioning accuracy is not ideal due to the fact that the smaller number of landmarks can cause larger position difference between the calibrated position and the previous position.
Although the WiFi positioning result has no accumulated error, WiFi signals are easy to be interfered and fluctuated, so that the positioning result is poor, and the integral fusion positioning precision is reduced.
Disclosure of Invention
The invention aims to provide an indoor positioning method integrating multi-sensor fusion, fusion fuzzy logic judgment of map information and map information.
The invention specifically adopts the following technical scheme:
an indoor positioning method integrating fuzzy logic judgment and map information comprises the following steps:
the method comprises the following steps: calculating the indoor pedestrian track by using a two-dimensional vector [ x, y]TRepresenting the position of the pedestrian and updating the position of the pedestrian according to equation (1):
Figure GDA0002746319210000021
wherein, [ x ]i-1,yi-1]TRepresenting the position of the pedestrian at step i-1, thetaiAnd siRespectively representing the walking direction and the step length of the ith step;
step two: firstly, judging whether to start the fusion algorithm or simply utilize the direction angle data of a certain optimal state; if the fusion algorithm is judged to be started, the weight is adaptively adjusted to select the fusion algorithm suitable for the current state according to the actual collected data conditions of the gyroscope and the magnetometer, the correlation between the gyroscope and the magnetometer is judged by giving a correlation threshold value of the gyroscope and the magnetometer, then the walking state is judged by using the direction angle of the magnetometer, whether the direction angle of the previous moment is added or not is determined, and finally fusion is carried out according to the adaptive weight;
step three: and adding a landmark identified by a WiFi peak point based on the particle filtering of map matching, and correcting the position of the particle.
Preferably, in the second step, the first step,
the offset states of the gyroscope and the magnetometer are divided into four types, corresponding to four intervals, and then the data required to be input is normalized by the following formula (2):
Figure GDA0002746319210000022
wherein st represents the normalized value of the output, valuetMin (value), max (value) represent the minimum and maximum values in the current data, respectively, for the current input value;
defining five output values which respectively correspond to respective fusion states;
giving an If-Then judgment rule of fuzzy logic;
selecting a corresponding fusion algorithm according to a given range output value;
whether the pedestrian is in a straight line stage is detected through the formula (3):
||θmi|-|θmi-1||＞Th1∪||θmi|-|θmi+1||＜Th2 (3)
wherein, thetamIndicating the measured values of the direction angle, respectively by the direction angle theta at the current momentmiAngle of direction theta from the previous momentmi-1The difference is calculated so that the difference is greater than a predetermined threshold Th1 and θ at time imiThe angle theta from the moment i +1 is requiredmi+1Is smaller than the threshold Th2, is defined by the above two conditions, and thereby the pedestrian walking state is determined.
Preferably, in the determination of the pedestrian moving state using equation (3), the fusion direction angle is calculated using equation (4) in the case of turning:
anglet＝ωog(ωoriangleori t-1+ωorianglegyor t-1) (4)
the direction fusion angle in the straight-ahead state is calculated by adopting the formula (5):
anglet＝ωpog(ωpreanglet-1+ωorianglet ori+ωgyoanglet gyor) (5)
wherein the content of the first and second substances,
Figure GDA0002746319210000031
Figure GDA0002746319210000032
anglet、anglet-1respectively the direction angle after the fusion and the direction angle at the last moment, omegapre、ωori、ωgyoRespectively, a time azimuth weight, a magnetometer weight and a gyroscope weight, wherein when the magnetometer is severely deviated and the gyroscope is in an acceptable range, the ratio of the last time weight is omegapre：ωori：ωgyo2:1:2, and vice versa 2:2: 1.
Preferably, in the third step, the formula (8) is adopted to perform the particle filtering based on map matching,
Figure GDA0002746319210000033
wherein, p (x)0) The state x of the system at time k is the prior probability distribution of a always dynamic systemkHas a posterior probability distribution of p (x)0:k|z1:k)，NsRepresenting the number of particles, x0:kRepresenting the state of the system from 0 to k, z1:kRepresents the observed value of the system, represents the distance from the PDR result after adding the landmark in application, and the weight of the particle satisfies the normalization condition shown in the formula (9)
Figure GDA0002746319210000034
The weight update is as shown in equation (10),
Figure GDA0002746319210000035
the weight updating of the particles is constrained by the map information, and the weight of the through-wall particles is updated to 0 when the weight of the particles is updated, wherein
Figure GDA0002746319210000036
The posterior probability of the pedestrian position is expressed as the formula (11)
Figure GDA0002746319210000037
Updating the particle output position according to equation (11) when the pedestrian is in a straight-going state, and updating the data stored in the database to the particle output position at that time when the system detects an anchor point or detects a pedestrian turning state, where equation (12) represents the distance of the ith particle from the landmark at time k, and then for the next update of the particle position:
Figure GDA0002746319210000041
the invention has the following beneficial effects:
the positioning method adopts a fuzzy logic reasoning system, the specific situation of the direction angle is judged by using fuzzy logic, and then the weight of the direction angle fusion algorithm used at the moment is judged according to the output membership degree, and whether the fusion algorithm is triggered or not is judged;
the method for carrying out landmark detection on WiFi signal peaks is improved by combining particle filtering, on the basis of the method for detecting the landmarks on the WiFi signal peaks, the particle resampling process is output to landmark points, the pedestrian track is predicted, jumping and fluctuation among positioning points are reduced, and the final positioning precision is improved.
The positioning method does not need to consume manpower and material resources to acquire a large amount of map information; fuzzy logic judges that various conditions of the direction angle are considered, and the weight is self-adaptive and is more suitable for actual conditions; the combination of the landmark point and the particle filter increases the stability of the positioning result, and solves the problem of positioning point jump after calibration, which rarely occurs in landmark point acquisition; a conventional method for positioning and fusing PDR and WiFi is not utilized, and fusion positioning errors caused by large WiFi signal fluctuation are avoided.
Drawings
FIG. 1 is a diagram of a PDR system architecture;
FIG. 2 is a block diagram of a fuzzy logic decision process flow;
fig. 3 is a flow chart of a landmark based particle filtering method.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings:
with the combination of the attached drawings, the indoor positioning method integrating fuzzy logic judgment and map information comprises the following steps:
the method comprises the following steps: calculating the indoor pedestrian track by using a two-dimensional vector [ x, y]TRepresenting the position of the pedestrian and updating the position of the pedestrian according to equation (1):
Figure GDA0002746319210000042
wherein, [ x ]i-1,yi-1]TRepresenting the position of the pedestrian at step i-1, thetaiAnd siRespectively representing the walking direction and the step length of the ith step; the Pedestrian Dead Reckoning subsystem mainly comprises three parts of walking detection, walking direction and step length estimation, and the detailed architecture diagram of a Pedestrian Dead Reckoning (PDR) algorithm is shown in fig. 1 below.
Step two: firstly, judging whether to start the fusion algorithm or simply utilize the direction angle data of a certain optimal state; if the fusion algorithm is judged to be started, the weight is adaptively adjusted to select the fusion algorithm suitable for the current state according to the actual collected data conditions of the gyroscope and the magnetometer, the correlation between the gyroscope and the magnetometer is judged by giving a correlation threshold value of the gyroscope and the magnetometer, then the walking state is judged by using the direction angle of the magnetometer, whether the direction angle of the previous moment is added or not is determined, and finally fusion is carried out according to the adaptive weight;
in the second step, the first step is carried out,
the offset states of the gyroscope and the magnetometer are divided into four states, namely { verylow, low, med and high }, the four corresponding intervals are [ 00.05 ], [ 0.050.1 ], [ 0.10.3 ] and [ 0.31 ], and then required input data is normalized by an equation (2):
Figure GDA0002746319210000051
wherein st represents the normalized value of the output, valuetMin (value), max (value) represent the minimum and maximum values in the current data, respectively, for the current input value;
five output values are defined, respectively, beter, mean, ooffset, goffset, pre. The five output values respectively correspond to the following fusion states:
1) beter simply utilizes magnetometer data.
2) mean-the offset of the magnetometer at this time is relatively low. At this time, the average value of the direction angles from the magnetometer t-4 to the time t is taken as the direction data at this time
3) And ooffset, namely, when the offset of the magnetometer is serious and the gyroscope is in an acceptable range, switching to another fusion technology, namely, fusing according to the weight, wherein the weight of the magnetometer is lower than that of the gyroscope.
4) And goffset, namely, when the offset of the gyroscope is serious and the offset of the magnetometer is in an acceptable range, switching to another fusion technology, namely, fusing according to the weight, wherein the weight of the magnetometer is higher than that of the gyroscope.
5) pre if the gyroscope and magnetometer are both biased severely, then the previous time heading angle is used.
Giving an If-Then judgment rule of fuzzy logic;
wherein, m-ori and m-tuo represent the variation values of the data of the magnetometer and the gyroscope at the current moment and the previous moment collected by the mobile phone sensor respectively, and range represents the corresponding output range.
If m-tuo belongs to the low state or the med state and m-ori belongs to the high state then the output region is ooffset;
if m-ori belongs to the low state or the med state and m-tuo belongs to the high state then the output region is goffset;
the output region is better if m-ori belongs to the high state;
if m-ori belongs to the med state then the output region is mean;
the output region is pre if m-ori belongs to the low state and m-tuo belongs to the low state;
and selecting a corresponding fusion algorithm according to the given range output value. The fuzzy logic decision process is detailed in figure 2 below.
Whether the pedestrian is in a straight line stage is detected through the formula (3):
||θmi|-|θmi-1||＞Th1∪||θmi|-|θmi+1||＜Th2 (3)
wherein, thetamIndicating the measured values of the direction angle, respectively by the direction angle theta at the current momentmiAngle of direction theta from the previous momentmi-1Difference and greater than a given threshold Th1, and theta at time imiThe angle theta from the moment i +1 is requiredmi+1Is smaller than the threshold Th2, is defined by the above two conditions, and thereby the pedestrian walking state is determined.
In the process of determining the pedestrian motion state by using the equation (3), in the case of turning, the fusion direction angle is calculated by using the equation (4):
anglet＝ωog(ωoriangleori t-1+ωorianglegyor t-1) (4)
the direction fusion angle in the straight-ahead state is calculated by adopting the formula (5):
anglet＝ωpog(ωpreanglet-1+ωorianglet ori+ωgyoanglet gyor) (5)
wherein the content of the first and second substances,
Figure GDA0002746319210000061
Figure GDA0002746319210000062
anglet、anglet-1respectively the direction angle after the fusion and the direction angle at the last moment, omegapre、ωori、ωgyoRespectively, a time azimuth weight, a magnetometer weight and a gyroscope weight, wherein when the magnetometer is severely deviated and the gyroscope is in an acceptable range, the ratio of the last time weight is omegapre：ωori：ωgyo2:1:2, and vice versa 2:2: 1.
Step three: and adding a landmark identified by a WiFi peak point based on the particle filtering of map matching, and correcting the position of the particle. The specific modification flowchart is shown in fig. 3.
In the third step, the particle filtering based on the map matching is carried out by adopting the formula (8),
Figure GDA0002746319210000063
wherein, p (x)0) The state x of the system at time k is the prior probability distribution of a always dynamic systemkHas a posterior probability distribution of p (x)0:k|z1:k)，NsRepresenting the number of particles, x0:kRepresenting the state of the system from 0 to k, z1:kRepresents the observed value of the system, represents the distance from the PDR result after adding the landmark in application, and the weight of the particle satisfies the normalization condition shown in the formula (9)
Figure GDA0002746319210000064
The weight update is as shown in equation (10),
Figure GDA0002746319210000071
the weight updating of the particles is constrained by the map information, and the weight of the through-wall particles is updated to 0 when the weight of the particles is updated, wherein
Figure GDA0002746319210000072
The posterior probability of the pedestrian position is expressed as the formula (11)
Figure GDA0002746319210000073
Updating the particle output position according to equation (11) when the pedestrian is in a straight-going state, and updating the data stored in the database to the particle output position at that time when the system detects an anchor point or detects a pedestrian turning state, where equation (12) represents the distance of the ith particle from the landmark at time k, and then for the next update of the particle position:
Figure GDA0002746319210000074
it is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.

Claims (3)

1. An indoor positioning method integrating fuzzy logic judgment and map information is characterized by comprising the following steps:
the method comprises the following steps: calculating the indoor pedestrian track by using a two-dimensional vector [ x, y]TIndicating the position of a pedestrianAnd updating the pedestrian position according to equation (1):
Figure FDA0002746319200000011
wherein, [ x ]i-1,yi-1]TRepresenting the position of the pedestrian at step i-1, thetaiAnd siRespectively representing the walking direction and the step length of the ith step;
step two: firstly, judging whether to start the fusion algorithm or simply utilize the direction angle data of a certain optimal state; if the fusion algorithm is judged to be started, the weight is adaptively adjusted to select the fusion algorithm suitable for the current state according to the actual collected data conditions of the gyroscope and the magnetometer, the correlation between the gyroscope and the magnetometer is judged by giving a correlation threshold value of the gyroscope and the magnetometer, then the walking state is judged by using the direction angle of the magnetometer, whether the direction angle of the previous moment is added or not is determined, and finally fusion is carried out according to the adaptive weight;
in the second step, the offset states of the gyroscope and the magnetometer are divided into four states, namely { verylow, low, med, and high }, and the four corresponding intervals are [ 00.05 ], [ 0.050.1 ], [ 0.10.3 ], [ 0.31 ], and then the data required to be input is normalized by the following equation (2):
Figure FDA0002746319200000012
wherein st represents the normalized value of the output, valuetMin (value), max (value) represent the minimum and maximum values in the current data, respectively, for the current input value;
defining five output values which are respectively beta, mean, ooffset, goffset and pre, wherein the five output values respectively correspond to respective fusion states:
1) better, simply using magnetometer data;
2) mean, the offset of the magnetometer is lower at this moment, and the average value of the direction angles from t-4 to t moment of the magnetometer is taken as the direction data at this moment;
3) ooffset, namely switching to fusion according to the weight when the offset of the magnetometer is serious and the gyroscope is in an acceptable range, wherein the weight of the magnetometer is lower than that of the gyroscope;
4) goffset, namely switching to fusion according to the weight when the offset of the gyroscope is serious and the offset of the magnetometer is within an acceptable range, wherein the weight of the magnetometer is higher than that of the gyroscope;
5) pre, if the deviations of the gyroscope and the magnetometer are serious, utilizing the direction angle at the last moment;
giving an If-Then judgment rule of fuzzy logic;
selecting a corresponding fusion algorithm according to a given range output value;
whether the pedestrian is in a straight line stage is detected through the formula (3):
||θmi|-|θmi-1||＞Th1∪||θmi|-|θmi+1||＜Th2 (3)
wherein, thetamIndicating the measured values of the direction angle, respectively by the direction angle theta at the current momentmiAngle of direction theta from the previous momentmi-1The difference is calculated so that the difference is greater than a predetermined threshold Th1 and θ at time imiThe angle theta from the moment i +1 is requiredmi+1Is less than a threshold Th2, defined by the above two conditions, thereby determining the pedestrian walking state;
step three: and adding a landmark identified by a WiFi peak point based on the particle filtering of map matching, and correcting the position of the particle.
2. The method of claim 1, wherein the fusion direction angle is calculated by formula (4) in case of turning during the determination of the pedestrian motion state by formula (3):
anglet＝ωog(ωoriangleori t-1+ωorianglegyor t-1) (4)
the direction fusion angle in the straight-ahead state is calculated by adopting the formula (5):
anglet＝ωpog(ωpreanglet-1+ωorianglet ori+ωgyoanglet gyor) (5)
wherein the content of the first and second substances,
Figure FDA0002746319200000021
Figure FDA0002746319200000022
anglet、anglet-1respectively the direction angle after the fusion and the direction angle at the last moment, omegapre、ωori、ωgyoRespectively, a time azimuth weight, a magnetometer weight and a gyroscope weight, wherein when the magnetometer is severely deviated and the gyroscope is in an acceptable range, the ratio of the last time weight is omegapre：ωori：ωgyo2:1:2, and vice versa 2:2: 1.
3. The method as claimed in claim 1, wherein the step three is a particle filtering based on map matching using equation (8),
Figure FDA0002746319200000023
wherein, p (x)0) The state x of the system at time k is the prior probability distribution of a always dynamic systemkHas a posterior probability distribution of p (x)0:k|z1:k)，NsRepresenting the number of particles, x0:kRepresenting the state of the system from 0 to k, z1:kRepresents the observed value of the system, represents the distance from the PDR result after adding the landmark in application, and the weight of the particle satisfies the normalization condition shown in the formula (9)
Figure FDA0002746319200000031
The weight update is as shown in equation (10),
Figure FDA0002746319200000032
the weight updating of the particles is constrained by the map information, and the weight of the through-wall particles is updated to 0 when the weight of the particles is updated, wherein
Figure FDA0002746319200000033
The posterior probability of the pedestrian position is expressed as the formula (11)
Figure FDA0002746319200000034
Updating the particle output position according to equation (11) when the pedestrian is in a straight-going state, and updating the data stored in the database to the particle output position at that time when the system detects an anchor point or detects a pedestrian turning state, where equation (12) represents the distance of the ith particle from the landmark at time k, and then for the next update of the particle position:
Figure FDA0002746319200000035
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