CN108387233B - Pedestrian motion mode judgment method based on fuzzy logic - Google Patents
Pedestrian motion mode judgment method based on fuzzy logic Download PDFInfo
- Publication number
- CN108387233B CN108387233B CN201810088587.4A CN201810088587A CN108387233B CN 108387233 B CN108387233 B CN 108387233B CN 201810088587 A CN201810088587 A CN 201810088587A CN 108387233 B CN108387233 B CN 108387233B
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- membership
- fuzzy
- straight line
- taking
- 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.)
- Active
Links
Images
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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
Abstract
The invention provides a pedestrian motion mode judging method based on fuzzy logic, which comprises the following steps: according to the position information of each step of the pedestrian solved by the inertial navigation system, taking continuous steps as a calculation unit, performing straight line fitting on the position information, taking the calculation unit as a basis to construct a fuzzy membership function, and determining the membership degree of a walking path to a straight line; the time interval of two adjacent steps is taken as a fuzzy variable, the motion state of the pedestrian is represented through a membership function, and the membership degree of the motion state is determined; taking the membership degree of the walking path to the straight line and the membership degree of the motion state as factors for judging the motion mode of the pedestrian, carrying out fuzzy comprehensive judgment on the factors, and judging whether the motion mode is normal or not according to the judgment result; if the pedestrian movement mode is normal, the specific movement mode of the pedestrian is obtained by further judging whether the walking path is along the main floor direction. The method can judge different movement modes of the travelers, ensure that the course is corrected by reasonably adopting HDE on the basis, and improve the positioning accuracy.
Description
Technical Field
The invention belongs to the technical field of indoor pedestrian positioning, and particularly relates to a pedestrian motion mode judgment method based on fuzzy logic.
Background
As the demand for location services has increased, indoor positioning has become a research focus in recent years. The Pedestrian Dead Reckoning (PDR) method based on the inertial sensor is widely applied, and on the basis of resolving pedestrian motion information, errors are estimated and corrected through zero-speed updating (ZUPT) and Extended Kalman Filtering (EKF), so that the method has strong autonomy and anti-interference performance. However, since the heading error is not observable and is accumulated with the increase of the walking distance, it needs to be corrected by using methods such as heuristic Heading Drift Elimination (HDE). The HDE is based on the fact that walls and corridors inside most buildings are formed by straight lines and right angles, only 4-8 main directions, such as 0 degrees, 90 degrees, 180 degrees and the like, are taken as reference directions, so that the course error of pedestrians walking along the main directions can be effectively reduced, but in the case that the path is not in the main directions, the course is corrected wrongly if the method is still adopted. To solve this problem, the moving pattern of the pedestrian should be determined before the heading is corrected.
Disclosure of Invention
In view of the above, the invention provides a pedestrian movement mode judgment method based on fuzzy logic, which can judge different movement modes of a traveling person, ensure that the course is corrected by reasonably adopting HDE on the basis, and improve the positioning accuracy.
The technical scheme for realizing the invention is as follows:
a pedestrian motion mode judging method based on fuzzy logic comprises the following steps:
step 1, according to the position information of each step of the pedestrian calculated by an inertial navigation system, taking continuous steps as a calculation unit, performing straight line fitting on the pedestrian, taking the continuous steps as a basis to construct a fuzzy membership function, and determining the membership degree of a walking path to a straight line;
step 2, considering that the motion mode of the pedestrian is not only related to the walking path, but also the motion state can cause influence, the time interval of two adjacent steps is taken as a fuzzy variable, the motion state of the pedestrian is represented through a membership function, and the membership degree of the motion state is determined;
step 3, taking the membership degree of the walking path to the straight line and the membership degree of the motion state as factors for judging the motion mode of the pedestrian, carrying out fuzzy comprehensive judgment on the factors, and judging whether the motion mode is normal or not according to the judgment result;
and 4, if the pedestrian motion mode is normal, further judging whether the walking path is along the main floor direction to obtain the specific motion mode of the pedestrian.
Further, in step 1 of the present invention, the constructing of fuzzy membership functions is: calculating the distance of each step relative to the straight line and taking the maximum value D, taking D as a fuzzy variable, constructing a fuzzy degree membership function as shown in a formula (4),
wherein k is1And c1Is a related parameter of a membership function, mu1(D) And representing the degree of membership of the walking path to the straight line.
Further, in step 2 of the present invention, a membership function for characterizing the motion state of the pedestrian is represented by formula (5):
wherein k is2P and c2Is a related parameter of a membership function, mu2(Δ T) represents the degree of membership of the motion state.
Further, the specific process of step 3 of the present invention is: setting the weight w of two membership degrees as w1w2]Calculating E ═ w1μ1(D)+w2μ2(ΔT)；
If en is equal to 0, the apparent pedestrian is in the abnormal motion mode, otherwise, the apparent pedestrian is in the normal motion mode.
Has the advantages that:
(1) the invention makes up the defect that the traditional HDE always takes the main floor direction as reference information to correct the course, and prohibits adopting the HDE to correct the course aiming at abnormal conditions by judging the motion mode of the pedestrian, thereby ensuring the correct estimation of the walking track.
(2) The invention takes the moving state of the pedestrian as another factor for judging the moving mode while considering the walking path, thereby avoiding that the heading is not corrected by mistake accidentally because the walking path adopts HDE linearly and ignores the abnormity of stillness and the like of the pedestrian in the process of advancing.
Drawings
Fig. 1 is a block diagram of a pedestrian motion mode determination method based on fuzzy logic.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides a pedestrian movement mode judging method based on fuzzy logic, which is used for judging different movement modes of pedestrians and providing a basis for subsequent course correction before adopting HDE, and comprises the following specific steps:
step 1, firstly, taking continuous steps as a calculation unit, and performing straight line fitting on the positions of the current step and the previous steps solved by the inertial navigation system, wherein the fitted straight line is as follows:
y＝a+bx (1)
wherein, a and b are respectively straight line fitting coefficients obtained by a linear regression method, and the specific calculation process is as follows:
wherein W is the selected number of steps of the calculation unit, k represents the current step of the pedestrian, and xiAnd yiRespectively, the position information of each step which is obtained by the inertial navigation system
The mean value thereof is shown.
Further, the distance of each step relative to the straight line is calculated for the fitted straight line, and the maximum value is taken:
D＝max(di) (3)
on the basis of obtaining D, according to a fuzzy mathematical theory, D is used as a fuzzy variable, a membership function can be used for describing a walking path, and the smaller D is, the more likely the path is a straight line. For this description, assignment is used to take the Z-shaped membership functions:
k1and c1Is a related parameter of a membership function, mu1(D) The range is between (0,1), representing degrees of membership that the travel path is a straight line for different D.
Step 2, in order to prevent the course from being corrected by errors due to motion abnormality such as stillness and the like, besides the walking path, considering the influence of the motion state on judging the motion mode of the pedestrian, taking the time interval delta T of two adjacent steps of the pedestrian as the characteristic for measuring the motion state of the pedestrian and as a fuzzy variable, and describing by adopting a bell-shaped membership function:
wherein k is2P and c2Is a related parameter of a membership function, mu2(Δ T) represents the degree of membership normal for different Δ T motion states by μ2(Δ T) the moving state of the pedestrian can be further judged.
And 3, judging the pedestrian motion mode through fuzzy comprehensive judgment by combining the walking path and the motion state of the pedestrian. Given set of factors U ═ U1,u2}，u1Finger path, u2In motion state, according to the stepsStep 1 and step 2, the corresponding evaluation vector is R ═ mu1(D) μ2(ΔT)]TAnd integrating the effects of the two factors on judging the pedestrian motion mode, comprehensively judging by adopting an M (, +) model, and weighing w ═ w1 w2]Obtaining:
E＝w·M＝w1μ1(D)+w2μ2(ΔT) (6)
according to the comprehensive evaluation result, introducing an enabling variable en:
wherein γ represents a set criterion.
If en is equal to 0, subsequent course correction is not performed by adopting HDE according to the fact that the pedestrian is in the abnormal motion mode, otherwise, the specific motion mode is further judged by the step 4.
Step 4, if the enable variable en in the step 3 is set to 1, which indicates that the pedestrian walks along the straight line and the motion state is normal, further judging whether the walking path obtains the specific motion mode of the pedestrian along the main floor direction, wherein the judging method comprises the following steps:
|ψd-ψ|＜thψ (8)
ψdrepresenting the main direction of the building, psi is the current heading th solved by the inertial navigation systemψIs a set threshold. If the above formula is satisfied, the psi is near the main floor direction, and the pedestrian walks along the main floor direction; if the above formula is not satisfied, it indicates that the pedestrian walks along the non-main-building direction.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present 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 (1)
1. A pedestrian motion mode judging method based on fuzzy logic is characterized by comprising the following steps:
step 1, according to the position information of each step of the pedestrian calculated by an inertial navigation system, taking continuous steps as a calculation unit, performing straight line fitting on the pedestrian, taking the continuous steps as a basis to construct a fuzzy membership function, and determining the membership degree of a walking path to a straight line;
the fuzzy membership function is constructed by: calculating the distance of each step relative to the straight line and taking the maximum value D, taking D as a fuzzy variable, constructing a fuzzy degree membership function as shown in a formula (4),
wherein k is1And c1Is a related parameter of a membership function, mu1(D) Representing the membership degree of the walking path to the straight line;
step 2, representing the motion state of the pedestrian by a membership function by taking the time interval of two adjacent steps as a fuzzy variable, and determining the membership degree of the motion state;
the membership function representing the motion state of the pedestrian is represented by the formula (5):
wherein k is2P and c2Is a related parameter of a membership function, mu2(Δ T) represents a degree of membership of the motion state, Δ T being a time interval of two adjacent steps of the pedestrian;
step 3, taking the membership degree of the walking path to the straight line and the membership degree of the motion state as factors for judging the motion mode of the pedestrian, carrying out fuzzy comprehensive judgment on the factors, and judging whether the motion mode is normal or not according to the judgment result;
the specific process is as follows: setting the weight w of two membership degrees as w1 w2]Calculating E ═ w1μ1(D)+w2μ2(ΔT)；
Wherein, mu1(D) Representing degree of membership, mu, of the walking path to the straight line2(Δ T) represents a membership degree of the motion state, and γ represents a set criterion;
if en is equal to 0, the apparent pedestrian is in an abnormal motion mode, otherwise, the apparent pedestrian is in a normal motion mode;
and 4, if the pedestrian motion mode is normal, further judging whether the walking path is along the main floor direction to obtain the specific motion mode of the pedestrian.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810088587.4A CN108387233B (en) | 2018-01-30 | 2018-01-30 | Pedestrian motion mode judgment method based on fuzzy logic |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810088587.4A CN108387233B (en) | 2018-01-30 | 2018-01-30 | Pedestrian motion mode judgment method based on fuzzy logic |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108387233A CN108387233A (en) | 2018-08-10 |
| CN108387233B true CN108387233B (en) | 2021-04-23 |
Family
ID=63074091
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810088587.4A Active CN108387233B (en) | 2018-01-30 | 2018-01-30 | Pedestrian motion mode judgment method based on fuzzy logic |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108387233B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110823222B (en) * | 2018-08-14 | 2023-04-07 | 北京自动化控制设备研究所 | Multi-information fusion data post-processing method based on design line shape |
| CN111257592B (en) * | 2020-03-05 | 2022-04-12 | 广东零偏科技有限公司 | Static discrimination method for detection device |
| CN113483753B (en) * | 2021-06-30 | 2022-11-01 | 北京航空航天大学 | Inertial course error elimination method based on environmental constraint |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2827101A1 (en) * | 2013-07-18 | 2015-01-21 | Astrium GmbH | Method for determing a position of a tracking device |
| CN104977006A (en) * | 2015-08-11 | 2015-10-14 | 北京纳尔信通科技有限公司 | Indoor positioning method based on fuzzy theory and multi-sensor fusion |
| CN106996780A (en) * | 2017-04-24 | 2017-08-01 | 湖南格纳微信息科技有限公司 | A kind of course error modification method and device and magnetic field detection method and device |
-
2018
- 2018-01-30 CN CN201810088587.4A patent/CN108387233B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2827101A1 (en) * | 2013-07-18 | 2015-01-21 | Astrium GmbH | Method for determing a position of a tracking device |
| CN104977006A (en) * | 2015-08-11 | 2015-10-14 | 北京纳尔信通科技有限公司 | Indoor positioning method based on fuzzy theory and multi-sensor fusion |
| CN106996780A (en) * | 2017-04-24 | 2017-08-01 | 湖南格纳微信息科技有限公司 | A kind of course error modification method and device and magnetic field detection method and device |
Non-Patent Citations (4)
| Title |
|---|
| Advanced Heuristic Drift Elimination for indoor pedestrian navigation;Ho Jin Ju等;《2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)》;20150928;第729-732页 * |
| Improved Heuristic Drift Elimination (iHDE) for pedestrian navigation in complex buildings;A.R.Jiménez等;《2011 International Conference on Indoor Positioning and Indoor Navigation》;20111110;第1-8页 * |
| 基于主方向的行人自主定位航向修正算法;赵辉等;《电子技术应用》;20161130;第42卷(第11期);第108-111页 * |
| 基于手机陀螺仪航向修正算法;郭英等;《中国惯性技术学报》;20171231;第25卷(第6期);第719-724页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108387233A (en) | 2018-08-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110286674B (en) | Angle correction method of mobile robot in working area and mobile robot | |
| US9961508B1 (en) | Systems and methods for determining indoor location and floor of a mobile device | |
| JP6783751B2 (en) | Methods and equipment to use portable navigation with improved quality of map information assistance | |
| US10006772B2 (en) | Map production method, mobile robot, and map production system | |
| JP6462692B2 (en) | Autonomous mobile device | |
| KR101976241B1 (en) | Map building system and its method based on multi-robot localization | |
| JP5661079B2 (en) | Method, apparatus and system with error correction for inertial navigation systems | |
| CN108387233B (en) | Pedestrian motion mode judgment method based on fuzzy logic | |
| US20220113366A1 (en) | Method and system for tracking a mobile device | |
| KR20090121092A (en) | Apparatus for localizing moving robot and method the same | |
| CN111307147B (en) | AGV high-precision positioning method integrating positioning reflector and laser characteristics | |
| CN107741745A (en) | It is a kind of to realize mobile robot autonomous positioning and the method for map structuring | |
| CN110546459A (en) | Robot tracking navigation with data fusion | |
| WO2019113611A2 (en) | Method and system for fingerprinting survey | |
| JP2016024598A (en) | Control method of autonomous mobile apparatus | |
| CN106017486A (en) | Trajectory inflection point filter-based map location method for unmanned vehicle navigation | |
| CN109211233A (en) | Elevator motion detection and abnormal position parking judgement based on acceleration transducer | |
| KR101167627B1 (en) | Apparatus and Method for Double-Updating in simultaneous localization and mapping for a mobile robot | |
| JP7275553B2 (en) | MOBILE BODY, MOBILE BODY CONTROL METHOD AND PROGRAM | |
| Boche et al. | Visual-inertial slam with tightly-coupled dropout-tolerant gps fusion | |
| CN112539747B (en) | Pedestrian dead reckoning method and system based on inertial sensor and radar | |
| He et al. | Lanematch: A practical real-time localization method via lane-matching | |
| CN112697153A (en) | Positioning method of autonomous mobile device, electronic device and storage medium | |
| Emter et al. | Stochastic cloning for robust fusion of multiple relative and absolute measurements | |
| CN111007518A (en) | Underwater robot underwater positioning and path planning method based on sonar image processing |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |