CN108387233A - A kind of pedestrian movement patterns' judgment method based on fuzzy logic - Google Patents
A kind of pedestrian movement patterns' judgment method based on fuzzy logic Download PDFInfo
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- CN108387233A CN108387233A CN201810088587.4A CN201810088587A CN108387233A CN 108387233 A CN108387233 A CN 108387233A CN 201810088587 A CN201810088587 A CN 201810088587A CN 108387233 A CN108387233 A CN 108387233A
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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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Abstract
The present invention provides a kind of pedestrian movement patterns' judgment method based on fuzzy logic, includes the following steps：Fitting a straight line is carried out to it, constructs fuzzy membership functions based on this, determines degree of membership of the walking path to straight line using continuous several steps as unit of account according to the location information that the pedestrian that inertial navigation system calculates often walks；Using the time interval of adjacent two step as fuzzy variable, the motion state of pedestrian is characterized by membership function, determines the degree of membership of motion state；Walking path is carried out fuzzy comprehensive evoluation to it, judge whether motor pattern is normal by evaluation result to the degree of membership of straight line and the degree of membership of motion state as the factor for judging pedestrian movement patterns；If pedestrian movement patterns are normal, by further judge walking path whether along main building to obtaining the specific motor pattern of pedestrian.This method can judge the different motor pattern of pedestrian, ensure rationally to correct course using HDE on this basis, improve positioning accuracy.
Description
Technical field
The invention belongs to indoor pedestrian's field of locating technology, and in particular to a kind of pedestrian movement patterns based on fuzzy logic
Judgment method.
Background technology
Demand with people to location-based service gradually increases, and indoor positioning becomes research hotspot in recent years.Wherein, base
Be widely used in pedestrian's dead reckoning (PDR) method of inertial sensor, this method on the basis of resolving pedestrian movement's information,
Error is estimated and is corrected by Zero velocity Updating (ZUPT) and Extended Kalman filter (EKF) there is very strong independence
And anti-interference.It however, since course error is unobservable, can constantly accumulate, therefore need to be used such as with the increase of travel distance
Heuristic heading effect is eliminated the methods of (HDE) and is corrected to it.Wall and corridor of the HDE based on most of building interiors are by straight
The fact that line and right angle are constituted, only with 4-8 main building to, such as 0 °, 90 °, 180 ° of conducts with reference to direction, pedestrian can be effectively reduced
Along main building to walking when course error, but to path be non-main building to the case where, if still use the method will lead to course
By error correction.In order to solve this problem, before correcting course, it should first judge the motor pattern of pedestrian.
Invention content
In view of this, the present invention provides a kind of pedestrian movement patterns' judgment method based on fuzzy logic, this method energy
Enough judge the different motor pattern of pedestrian, ensures rationally to correct course using HDE on this basis, improve positioning accuracy.
Realize that technical scheme is as follows：
A kind of pedestrian movement patterns' judgment method based on fuzzy logic, includes the following steps：
Step 1, the location information often walked according to the pedestrian that inertial navigation system calculates, it is right using continuous several steps as unit of account
It carries out fitting a straight line, constructs fuzzy membership functions based on this, determines degree of membership of the walking path to straight line；
Step 2, it is contemplated that the motor pattern of pedestrian is not only related with walking path, and motion state will also result in influence, with
The time interval of adjacent two step is fuzzy variable, and the motion state of pedestrian is characterized by membership function, determines the person in servitude of motion state
Category degree；
Step 3, using walking path to the degree of membership of the degree of membership of straight line and motion state as judging pedestrian movement patterns
Factor, fuzzy comprehensive evoluation is carried out to it, judges whether motor pattern normal by evaluation result；
Step 4, if pedestrian movement patterns are normal, by further judge walking path whether along main building to being gone
The specific motor pattern of people.
Further, fuzzy membership functions is constructed described in step 1 of the present invention is：Each step is calculated relative to the straight line
Distance and take maximum value D therein, using D as fuzzy variable, shown in construction fuzziness membership function such as formula (4),
Wherein, k1And c1It is the relevant parameter of membership function, μ1(D) degree of membership of the walking path to straight line is indicated.
Further, shown in the membership function such as formula (5) that the motion state of pedestrian is characterized in step 2 of the present invention：
Wherein, k2, p and c2It is the relevant parameter of membership function, μ2(Δ T) indicates the degree of membership of motion state.
Further, the detailed process of step 3 of the present invention is：Set the weight w=[w of two degrees of membership1w2], meter
Calculate E=w1μ1(D)+w2μ2(ΔT)；
If en is equal to 0, it is under abnormity motion style depending on pedestrian, otherwise, proper motion pattern is in depending on pedestrian.
Advantageous effect：
(1) present invention compensates for traditional HDE always with main building to the defect being modified to course for reference information, passes through
The motor pattern of pedestrian is judged, forbids correcting course using HDE for abnormal conditions, to ensure run trace just
Really estimation.
(2) present invention is while considering walking path, using the motion state of pedestrian as judging the another of its motor pattern
One factor, avoid simply because walking path is straight line ignored using HDE pedestrian advance occur in the process it is stationary etc.
It is abnormal so that course will not be by unexpectedly error correction.
Description of the drawings
Fig. 1 is pedestrian movement patterns' judgment method block diagram based on fuzzy logic.
Specific implementation mode
The present invention will now be described in detail with reference to the accompanying drawings and examples.
A kind of pedestrian movement patterns' judgment method based on fuzzy logic provided by the invention is used before using HDE
In judging the different motor pattern of pedestrian and provide foundation for subsequent navigational calibration, specific steps include：
Step 1, it first using continuous several steps as unit of account, current takes a step and former steps to what inertial navigation system calculated
Position carries out fitting a straight line, and the straight line fitted is：
Y=a+bx (1)
Wherein, a, b are the fitting a straight line coefficient obtained with linear regression method respectively, and specific calculating process is as follows：
Wherein, W is the unit of account step number chosen, and k indicates that pedestrian currently takes a step, xiAnd yiBy inertial navigation system in being respectively
The location information often walked that system calculates, andIndicate its mean value.
Further, distance of each step with respect to the straight line is asked to the straight line fitted and takes maximum value therein：
D=max (di) (3)
On the basis of obtaining D, according to fuzzy mathematics theory, using D as fuzzy variable, it can be walked with membership function lines of description
Diameter, D is smaller, and path more may be straight line.This corresponding description, Z-shaped membership function is taken with assignment technique：
k1And c1It is the relevant parameter of membership function, μ1(D) range indicates between (0,1) for different D, walking path
For the degree of membership of straight line.
Step 2, course in addition to walking path, is examined since the dyskinesias such as stationary are by error correction in order to prevent
Motion state is considered to judging the influence of pedestrian movement patterns, and using the time interval Δ T of adjacent two step of pedestrian as measurement, its is moved
The feature of state and as fuzzy variable, is described using bell membership function：
Wherein, k2, p and c2It is the relevant parameter of membership function, μ2(Δ T) indicates normal for different Δ T motion states
Degree of membership passes through μ2(Δ T) can further judge the motion state of pedestrian.
Step 3, in conjunction with the walking path of pedestrian and motion state, pedestrian movement patterns are carried out by fuzzy comprehensive evoluation
Judge.Given set of factors U={ u1,u2, u1Refer to walking path, u2It is commented accordingly according to step 1 and step 2 for motion state
It is R=[μ to sentence vector1(D) μ2(ΔT)]T, comprehensive two kinds of factors are for judging the effect of pedestrian movement patterns, using M (,+)
Model carries out Comprehensive Evaluation, to weight w=[w1 w2]：
E=wM=w1μ1(D)+w2μ2(ΔT) (6)
According to Comprehensive Evaluation variable en is enabled as a result, introducing：
Wherein, γ indicates the judge benchmark of setting.
If en is equal to 0, it is under abnormity motion style depending on pedestrian, HDE will not be used to carry out subsequent navigational calibration, it is no
Specific motor pattern is further then judged by step 4.
Step 4, if in step 3 enable variable en set 1, show pedestrian along straight line moving and motion state it is normal, then into one
Step judge walking path whether along main building be to the specific motor pattern of pedestrian, judgment method is obtained：
|ψd- ψ | ＜ thψ (8)
ψdMain building is represented to ψ is the current course that inertial navigation system calculates, thψIt is the threshold value of setting.If above formula is set up, table
Show ψ in main building near, depending on pedestrian along main building to walking；If above formula is invalid, show pedestrian along non-main building to walking.
In conclusion the above is merely preferred embodiments of the present invention, being not intended to limit the scope of the present invention.
All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in the present invention's
Within protection domain.
Claims (4)
1. a kind of pedestrian movement patterns' judgment method based on fuzzy logic, which is characterized in that include the following steps：
Step 1, the location information often walked according to the pedestrian that inertial navigation system calculates, using continuous several steps as unit of account, to its into
Row fitting a straight line, constructs fuzzy membership functions based on this, determines degree of membership of the walking path to straight line；
Step 2, using the time interval of adjacent two step as fuzzy variable, the motion state of pedestrian is characterized by membership function, is determined
The degree of membership of motion state；
Step 3, using walking path to the degree of membership of the degree of membership of straight line and motion state as judge pedestrian movement patterns because
Element carries out fuzzy comprehensive evoluation to it, judges whether motor pattern is normal by evaluation result；
Step 4, if pedestrian movement patterns are normal, by further judge walking path whether along main building to, obtain pedestrian tool
The motor pattern of body.
2. pedestrian movement patterns' judgment method based on fuzzy logic according to claim 1, which is characterized in that the step
In 1：Each step is calculated relative to the distance of the straight line and takes maximum value D therein, using D as fuzzy variable, construction fuzziness is subordinate to
Shown in function such as formula (4),
Wherein, k1And c1It is the relevant parameter of membership function, μ1(D) degree of membership of the walking path to straight line is indicated.
3. pedestrian movement patterns' judgment method based on fuzzy logic according to claim 1, which is characterized in that the step
Shown in the membership function such as formula (5) for characterizing the motion state of pedestrian in 2：
Wherein, k2, p and c2It is the relevant parameter of membership function, μ2(Δ T) indicates the degree of membership of motion state.
4. pedestrian movement patterns' judgment method based on fuzzy logic according to claim 1, which is characterized in that the step
3 detailed process is：Set the weight w=[w of two degrees of membership1 w2], calculate E=w1μ1(D)+w2μ2(ΔT)；
Wherein, μ1(D) indicate walking path to the degree of membership of straight line, μ2(Δ T) indicates the degree of membership of motion state.
If en is equal to 0, it is under abnormity motion style depending on pedestrian, otherwise, proper motion pattern is in depending on pedestrian.
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Cited By (3)
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| CN110823222A (en) * | 2018-08-14 | 2020-02-21 | 北京自动化控制设备研究所 | Multi-information fusion data post-processing method based on design line shape |
| CN111257592A (en) * | 2020-03-05 | 2020-06-09 | 广东零偏科技有限公司 | Static discrimination method for detection device |
| CN113483753A (en) * | 2021-06-30 | 2021-10-08 | 北京航空航天大学 | Inertial heading error elimination method based on environmental constraint |
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| CN110823222A (en) * | 2018-08-14 | 2020-02-21 | 北京自动化控制设备研究所 | Multi-information fusion data post-processing method based on design line shape |
| CN111257592A (en) * | 2020-03-05 | 2020-06-09 | 广东零偏科技有限公司 | Static discrimination method for detection device |
| CN111257592B (en) * | 2020-03-05 | 2022-04-12 | 广东零偏科技有限公司 | Static discrimination method for detection device |
| CN113483753A (en) * | 2021-06-30 | 2021-10-08 | 北京航空航天大学 | Inertial heading error elimination method based on environmental constraint |
| 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 |
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