CN107105404B - Pedestrian indoor positioning method based on step length matching - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The invention discloses a pedestrian indoor positioning method based on step length matching, and relates to the technical field of indoor positioning. The invention mainly aims at the problem that the traditional step size model cannot sufficiently reflect individual differences in a Pedestrian Dead Reckoning (PDR) algorithm in indoor positioning research, and provides a method for establishing an individual step size characteristic database to replace the step size model on the basis of satellite ranging; the method comprises the steps of utilizing a smart phone as a carrier, applying satellite ranging to step length measurement and calculation outdoors, and establishing corresponding relations between different speed characteristics and step lengths; in the room, a K-nearest neighbor (KNN) algorithm is improved, real-time step length matching is carried out on the strides, and the current position of the pedestrian is calculated by combining direction information. Aiming at individual difference, the single step size model is not adopted any more, and the indoor positioning precision is greatly improved compared with the traditional step size model.
Description
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a pedestrian indoor positioning method based on step length matching.
Background
With the development of economic society and the high popularization of smart phones, more and more attention has been paid to location-based services, and people have urgent needs for location services particularly in large and complex indoor environments, such as railway stations, airports, supermarkets, hospitals and other areas. The passive positioning method in indoor pedestrian positioning does not need to arrange signal nodes, one is to introduce an inertial navigation mechanism into mobile equipment, and the other is a Pedestrian Dead Reckoning (PDR) algorithm.
The PDR algorithm calculates the walking step number, step length and direction of the pedestrian according to the data of the inertial sensor to obtain the walking distance and direction, and the key point of the PDR algorithm is to accurately estimate the step length of the pedestrian. The existing step size model comprises a step size estimation model based on a linear relation between the step frequency and the step size; a non-linear empirical model relating to maximum and minimum acceleration over a stride period; the walking mode of the person is approximated to an inverted simple pendulum, and the step length is calculated through a triangular relation. Because the step length difference of different people has a certain relation with the height, habit, mood and the like of the individual, the linear and nonlinear models can hardly reflect the step length difference of different individuals.
Disclosure of Invention
In order to overcome the defect that the accuracy of the step size model cannot be guaranteed when the individual difference is faced, the invention provides a pedestrian indoor positioning method based on step size matching.
The invention adopts the following technical scheme:
a pedestrian indoor positioning method based on step matching is applied to portable equipment with a built-in accelerometer, a magnetic orientation meter and other MEMS sensors, and comprises the following steps:
s1, determining the initial longitude and latitude information of the pedestrian outdoors based on the satellite positioning system;
s2, calculating the average step length of the pedestrian outdoors based on satellite ranging;
s3, recording the step characteristic value of each step based on the accelerometer while satellite ranging, wherein the step characteristic value comprises the maximum acceleration A in a step periodmaxMinimum acceleration AminAnd a period duration T;
s4, establishing an individual step length feature database of the pedestrian according to the corresponding relation between the step feature value and the average step length;
if there are m pedestrian speed states, recording D ═ D1,d2,···,dm) Represents a set of steps in m different speed states, C ═ amax,AminT) represents a set of stride characteristic values, expressed as (C, d)j) Storing data in the form of key value pairs, establishing an individual step length characteristic database of the pedestrian, wherein dj∈D；
S5, after the pedestrian enters the room, determining the initial longitude and latitude information of the pedestrian in the room based on a satellite positioning system;
s6, acquiring new step characteristic value based on accelerometer, and recording
S7, obtaining the new step characteristic value C obtained in the step S6 through a KNN algorithm*Matching with the individual step length feature database established in the step S4 to obtain the average step length corresponding to the new step length feature value;
s8, determining the walking direction of the pedestrian based on the magnetic heading meter;
and S9, calculating the current position of the pedestrian based on the PDR algorithm according to the pedestrian average step length information acquired in the step S7 and the pedestrian walking direction information acquired in the step S8.
Preferably, the specific step of step S2 includes:
s21, measuring the longitude and latitude of the positions of the two points A, B of the pedestrian based on the satellite positioning system, and calculating the linear distance S between the positions of the two points by using the formula (1).
Wherein (Lng1, Lat1) represents longitude and latitude of a point, and (Lng2, Lat2) represents longitude and latitude of B point, a-Lng 1-Lng2 represents the difference between the longitudes of two points, B-Lat 1-Lat2 represents the difference between the latitudes of two points, 6378137 represents the radius of the earth, and the unit is meter.
S22, when the pedestrian walks in an approximately straight line from point a to point B, and maintains one speed state as much as possible, the average step size of the speed state is calculated by equation (2).
Wherein S is the straight-line distance between the points A and B calculated by the formula (1), and N is the total number of steps detected in the process.
Preferably, the matching method in step S7 is:
s71, calculating C (i) and C in the speed state in m by using the formula (3)*The Euclidean distance between;
wherein i is 1,2
S72, pairing dist (C (i), C) in ascending order*) Sorting, and finding out C (i) corresponding to the minimum distance of the top k;
s73, according to the stored (C, d)j) The key-value pair looks up the k groups C (i) corresponding to the step length d;
s74, taking the D value with the most occurrence times in the D set as the current C*The corresponding step size.
Preferably, in step S71, in the m speed states, the feature data in N sets of speed states of the middle segment are retained for the matching calculation, where N is m × N/3.
Preferably, the following steps are further included between the step S1 and the step S2:
p, detecting whether the strides are effective strides, if so, recording the longitude and latitude, calculating the average step length, and adding 1 to the total strides; and if the judgment result is no, returning to detect the step again until the judgment result is yes.
Preferably, the step is detected by using a peak method in step P, the step with the peak value exceeding the threshold value of 0.4 is an effective peak value, and the peak value not in the reasonable time interval is masked while the peak value is detected.
Preferably, the specific step of step S8 is:
s81, manually correcting the magnetic navigator;
s82, detecting whether the step is an effective step, if so, matching the step length in real time, and acquiring the walking direction of the current pedestrian; and if the judgment result is no, returning to detect the step again until the judgment result is yes.
Due to the adoption of the technical scheme, the invention has the following beneficial effects: compared with the prior art, the dead reckoning method has the advantages that the individual step length feature database belonging to the dead reckoning method is actually established for individuals with differences, and dead reckoning accuracy is greatly improved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is an overall flow chart of the present invention;
fig. 2 is a graph comparing the test results with the existing step size model.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The carrier of the invention is a personal smart phone, a built-in accelerometer, a magnetic orientation meter and other MEMS sensors.
The step is detected by using a peak value method outdoors, in order to improve the accuracy of detection, only the peak value which exceeds a threshold value of 0.4 is judged as an effective peak value, the step frequency of natural walking of a person is between 1 and 3Hz, and the peak value which is not in a reasonable time interval is shielded while the peak value is detected.
At present, the satellite positioning precision of the smart phone can reach the meter level, so that the position information given by the mobile phone is directly used to introduce the satellite ranging into the pedestrian step length measurement and calculation, and further an individual step length feature database is established. And measuring the longitude and latitude of the positions of the two points A and B, and calculating the linear distance S between the positions of the two points by using the formula (1).
Wherein (Lng1, Lat1) represents longitude and latitude of a point, and (Lng2, Lat2) represents longitude and latitude of B point, a-Lng 1-Lng2 represents the difference between the longitudes of two points, B-Lat 1-Lat2 represents the difference between the latitudes of two points, 6378137 represents the radius of the earth, and the unit is meter.
When the pedestrian walks from the point A to the point B in an approximately straight line manner and maintains a speed state as much as possible, the step is detected while the satellite is in range finding, the characteristic value of each step is recorded, and the average step length of the speed state is calculated by using the formula (2).
Wherein S is the straight-line distance between the points A and B calculated by the formula (1), and N is the total number of steps detected in the process.
Assuming m states of velocity, the step characteristics include the maximum acceleration A within one step periodmaxMinimum acceleration AminAnd a period duration T. The process of outdoor satellite ranging is also the process of establishing a step length characteristic database, which has m multiplied by N groups of step characteristic value sets, and is also the process of generating the corresponding relation between the step characteristic values and the step length in m speed states. Let D ═ D1,d2,···,dm) Represents a set of steps in m different speed states, C ═ amax,AminT) represents a set of stride characteristic values, expressed as (C, d)j) Storing data in the form of key-value pairs, wherein dj∈D。
After entering the room, the initial position is given by the satellite positioning information. Note the book
For the new set of detected stride feature values, m velocity state generationsTable m existing classes, the essence of the matching process is for C*The process of making the classification.
The step length characteristic data of the initial and final stages of pedestrian walking is unstable and has large fluctuation. In order to reduce the calculation amount of the KNN algorithm, the individual step size feature database is simplified in the matching process. Experiments show that only the group characteristic data of the interval, namely the middle section, in each speed state is kept, so that the matching accuracy can be completely ensured and recorded. The matching algorithm is as follows:
1. calculating C (i) and C*inter-Euclidean distance dist (C (i), C)*),i＝1,2,...,n；
2. Dist (C (i), C) in ascending order*) Sorting, and finding out C (i) corresponding to the minimum distance of the top k;
3. according to stored (C, d)j) The key-value pair looks up the k groups C (i) corresponding to the step length d;
4. the D value with the most occurrence times in the D set is taken as the current C*The corresponding step size.
Wherein C and C*Is of Euclidean distance dist (C, C)*) Is composed of
The magnetic direction-finding device is arranged in the smart phone, an included angle between the current top of the smart phone and the north direction can be indicated in real time according to induction of a geomagnetic field, when the smart phone rotates around a Z axis, the value of the included angle changes, and the direction can be used as the walking direction of pedestrians.
In the experiment aiming at the method, the outdoor test site is selected on a sidewalk outside a building of the institute of fertilizer-gathering intelligent machinery of Chinese academy of sciences with a wide environment, the speed states are selected to be three, namely a slower speed, a normal speed and a faster speed, the height of a tester is 178cm, the actual steps in the three speed states are controlled to be 100 steps, and the obtained step length calculation results are respectively 0.60m, 0.74m and 0.88 m.
The indoor test site is selected in a corridor in an intelligent station building, the distance is tested to be 50m, the distances under four groups of modes of slow speed, normal speed, fast speed and variable speed walking are respectively calculated, and each group is tested for 5 times. The results of the comparison with the linear step size estimation model calculated by equation (4) and the non-linear empirical model calculated by equation (5) are shown in fig. 2.
d＝a×f+b (4)
Where a, b are coefficients and f is frequency.
Wherein H is a coefficient, Amax，AminRespectively the maximum and minimum acceleration within one stride period.
The method of the invention can be well adapted to individual differences, and the indoor positioning accuracy of pedestrians is greatly improved.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A pedestrian indoor positioning method based on step length matching is applied to portable equipment with various MEMS sensors such as an accelerometer and a magnetic direction finder, and is characterized by comprising the following steps of:
s1, determining the initial longitude and latitude information of the pedestrian outdoors based on the satellite positioning system;
s2, measuring the longitude and latitude of the positions of the two points A, B of the pedestrian based on the satellite positioning system, calculating the linear distance S between the positions of the two points by using the formula (1),
wherein (Lng1, Lat1) represents longitude and latitude of a point, (Lng2, Lat2) represents longitude and latitude of B point, a is Lng1-Lng2 which is the difference between the longitudes of two points, B is Lat1-Lat2 which is the difference between the latitudes of two points, 6378137 is the earth radius, and the unit is meter;
s3, when the pedestrian walks from the point A to the point B in an approximately straight line way and keeps a speed state as much as possible, the average step length d of the speed state is calculated by the formula (2),
wherein S is the linear distance between the points A and B calculated by the formula (1), and N is the total number of steps detected in the process;
s4, recording the step characteristic value of each step based on the accelerometer while satellite ranging, wherein the step characteristic value comprises the maximum acceleration A in a step periodmaxMinimum acceleration AminAnd a period duration T;
s5, establishing an individual step length feature database of the pedestrian according to the corresponding relation between the step feature value and the average step length;
if there are m pedestrian speed states, recording D ═ D1,d2,…,dm) Represents a set of steps in m different speed states, C ═ amax,AminT) represents a set of stride characteristic values, expressed as (C, d)j) Storing data in the form of key value pairs, establishing an individual step length characteristic database of the pedestrian, wherein dj∈D；
S6, after the pedestrian enters the room, determining the initial longitude and latitude information of the pedestrian in the room based on a satellite positioning system;
s7, acquiring new step characteristic value based on accelerometer, and recording
S8, calculating C (i) and C in m speed states by using formula (3)*The Euclidean distance between;
wherein i is 1, 2.. times.n;
dist (C (i), C) in ascending order*) Sorting, and finding out C (i) corresponding to the minimum distance of the top k; according to stored (C, d)j) The key-value pair looks up the k groups C (i) corresponding to the step length d; the D value with the most occurrence times in the D set is taken as the current C*A corresponding step length;
s9, determining the walking direction of the pedestrian based on the magnetic heading meter;
and S10, calculating the current position of the pedestrian based on the PDR algorithm according to the pedestrian average step length information acquired in the step S8 and the pedestrian walking direction information acquired in the step S9.
2. The pedestrian indoor positioning method based on the step length matching as claimed in claim 1, wherein in the step S8, in m speed states, N sets of characteristic data of the middle segment in the speed states are reserved for the matching calculation, wherein N is m × N/3.
3. The pedestrian indoor positioning method based on step size matching according to claim 1 or 2, wherein the step S1 and the step S2 further comprise the following steps:
p, detecting whether the strides are effective strides, if so, recording the longitude and latitude, calculating the average step length, and adding 1 to the total strides; and if the judgment result is no, returning to detect the step again until the judgment result is yes.
4. The pedestrian indoor positioning method based on step size matching as claimed in claim 3, wherein the step is detected in step P by a peak method, the step with the peak size exceeding a threshold value of 0.4 is an effective peak, and the peak not in a reasonable time interval is masked while the peak is detected.
5. The pedestrian indoor positioning method based on step size matching according to claim 4, wherein the specific steps of the step S9 are as follows:
s91, manually correcting the magnetic navigator;
s92, detecting whether the step is an effective step, if so, matching the step length in real time, and acquiring the walking direction of the current pedestrian; and if the judgment result is no, returning to detect the step again until the judgment result is yes.
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