KR20170084896A - Apparatus for estimating indoor position information of user base on paticle filters method and using the same - Google Patents

Abstract

An apparatus and method for estimating a user location information based on a particle filter are disclosed. A particle filter-based user indoor location information estimation apparatus according to the present invention includes: a receiver for receiving at least one of indoor location information of a user and indoor location infrastructure acquisition information to generate reception information; A particle setting unit configured to set at least one of the local particle and the global particle based on the received information; And a position estimator for estimating the optimum indoor position information of the user using at least one of the local particle setting and the global particle setting.

Description

[0001] APPARATUS FOR ESTIMATING INDOOR POSITION INFORMATION OF USER BASE ON PATIENT FILTERS [0002] METHOD AND USING THE SAME [0003]

The present invention relates to a particle filter based position estimation technique for overcoming a local minimum problem that occurs in estimating a user's position in a room.

The location estimation technology using wireless communication infrastructure exists in various ways depending on the type of infrastructure and service range. For example, the Global Navigation Satellite System (GNSS) refers to a system that determines the location of a user by using satellite signals on the earth's orbit. Similar global positioning systems (GPS) in the United States, GLONASS ) And Galileo of Europe are currently in operation or scheduled to operate.

These GNSSs provide high location accuracy and availability within 10m of a flat or suburban area where the direct line of sight of the satellite part and the receiving part is secured. However, in a non-line-of-sight area, The position error reaches 50m due to multi-path error. Especially in indoor area, it is difficult to determine the position because signal sensitivity can not be obtained due to low sensitivity.

Among other wireless communication infrastructures, the cellular based positioning technique is a technique for determining a location of a user using position information of a mobile communication base station and a measurement signal. Specifically, the cellular-based location estimation technique is classified into Cell-ID, Enhanced-Observed Time Difference (E-OTD), and Advanced-Forward Link Trilateration (AFLT) according to the number of base stations receivable at the terminal. It has the merit of being able to locate in indoor as well as indoors due to the characteristics of mobile communication infrastructure with the service area of most of the city and church. However, it is difficult to apply the cellular-based localization technique to the indoor and outdoor navigation service which requires a position accuracy of about several meters since the position estimation accuracy varies depending on the density of the base station and the average positioning accuracy is about 100 to 800 m .

Assited-GNSS is a technique for acquiring auxiliary information from the position estimation server to improve the minimum received signal sensitivity of the GNSS receiver built in the user terminal and to shorten the time to first fix time. Assited-GNSS enables quick positioning using GNSS in a dense signal environment, but the signal is very weak in the indoor area and can not achieve a great effect.

In the room, Wi-Fi based positioning technology has been proposed. Generally, Wi-Fi based location estimation technology can be classified into location DB based and propagation map based DB.

The location DB includes information such as an identifier, a location, a transmission signal strength, and a signal attenuation coefficient of a Wi-Fi AP (base station) existing in a service area. The subject calculating the position receives the position DB and estimates the position by a method such as Cell-ID, triangulation, weighted centroid localization (WCL).

The propagation map DB includes information such as base station information, signal strength, and various statistical information received at a plurality of preset reference positions in a service area. The subject calculating the position compares the currently searched positioning resource with the signal strength recorded in the propagation map DB, and estimates the reference position having the most similar information as the current position.

Due to the nature of the wireless communication infrastructure, the wireless signal includes noise. In estimating the indoor position of the user, since the received signal is used only once, generally statistical characteristics for canceling the noise can not be obtained. Accordingly, a Kalman filter or a particle filter is used to reconfigure the movement path of the user.

Particle filters are one of the simulation-based prediction techniques and are also called continuous Monte Carlo methods. Particle filters are also important in econometrics.

Particle filters are generally used to estimate the Bayes model. This is similar to the Hidden Markov Model (HMM) where latent variables are related to each other by the Markov chain. However, the state space of an unexplained variable is contiguous and not so definite as to be accurately estimated. And in the context of the related model, filtering determines the distribution of potential variables at any particular time, but observations are only given up to that time. The reason for getting a particle filter name is because it uses "particles" that have different weights in each group, "filtering" the approximation.

Particle filters are similar in sequence to the Markov chain Monte Carlo (MCMC) batch process and are often similar to impotency sampling. Making particle filters well is much faster than MCMC. It is sometimes used in place of Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF). The advantage over the EKF or the UKF is that the optimal solution is more accurate than the EKF or UKF because if the particle sample is sufficient, it approaches the best estimate. However, problems may arise if the number of particle samples is not sufficient. The problem that arises when the number of samples is insufficient is the local minimum problem. The local minimum value problem refers to the problem of finding the optimal solution for the global section and erroneously finding the optimal solution for the local section as the optimal solution for the global section.

This problem also occurs when the position of the user is corrected using the particle filter in the indoor location-based service. Particles are not scattered around the location where the user is located, and the particles stay in an area that has been mistakenly collected in the past, so that only the optimal solution in the area is found, resulting in a large positional error.

Korean Patent No. 10-1547025 entitled " Method and apparatus for predicting the position of a mobile device "discloses a method and apparatus for predicting the position of a particle filter-based mobile device. Discloses a method and apparatus for predicting a position of a mobile device by grouping based on a particle having a high weight and assigning a weight within a predetermined range to a new particle based on a particle having a high weight.

Korean Patent No. 10-1547025, however, mentions particle grouping, weight setting, and copying of particles, and it has been known that a local minimum value problem arises due to particle weight or duplication position error due to noise of a signal and malfunction of a mobile device, Is silent on how to calibrate.

SUMMARY OF THE INVENTION An object of the present invention is to provide a method and an apparatus for estimating a position of a user by using a particle filter to prevent a local minimum value problem, The purpose of this paper is to estimate the location of users.

It is also an object of the present invention to create global particles based on received information and to operate with local particles.

It is also an object of the present invention to determine that a local minimum value problem has occurred using information calculated from local and global particles.

In addition, an object of the present invention is to calculate an optimal solution by using a local minimum value problem determination result.

According to an aspect of the present invention, there is provided an apparatus for estimating a user indoor location information based on a particle filter, the apparatus including: a receiver for receiving at least one of indoor location information and indoor location infrastructure acquisition information of a user to generate reception information; A particle setting unit configured to set at least one of the local particle and the global particle based on the received information; And a position estimator for estimating the optimum indoor position information of the user using at least one of the local particle setting and the global particle setting.

At this time, the receiving unit receives the indoor location information of the user from the user's mobile communication device (smart phone, etc.) and sets and replicates the location of the local particle. In addition, after initializing the particles from the indoor positioning infrastructure collection information on the propagation map DB, local and global particles are generated in all or a part of the indoor area.

At this time, based on the generated particles, the receiving information is generated to set the particle.

At this time, the particle setting unit includes a local particle setting unit and a global particle setting unit.

At this time, the degree of similarity between all the local particles and the global particles is evaluated based on the received information, the weight is given, and the sum of the weights of all the local particles is 1 and the sum of the weights of all the global particles is 1.

At this time, the position estimating unit includes a local particle-based position estimating unit, a global particle-based position estimating unit, a local minimum value problem determining unit, and an optimal solution position estimating unit.

In this case, the local particle-based position estimator resamples the local particles, and estimates the local particles set as the highest weight based on the weight of the local particles as the local indoor position information.

In this case, the global particle-based position estimator resamples the global particle, and estimates the global particle set as the highest weight based on the weight of the global particle as the global indoor position information.

At this time, the local minimum value problem determination unit compares the local indoor location information and the global indoor location information to determine a local minimum value problem and outputs the result.

At this time, the optimal solution position estimating unit estimates the optimal solution indoor position information based on the result of the local minimum value problem determination.

According to another aspect of the present invention, there is provided a method for estimating a user indoor location information based on a particle filter, the method comprising: receiving at least one of indoor location information and indoor location infrastructure acquisition information of a user to generate reception information; Setting at least one of the local particle and the global particle based on the received information; And estimating a position based on the optimal indoor location information using at least one of the local particle setting and the global particle setting.

At this time, the receiving step receives the indoor location information of the user from the user's mobile communication device (smart phone or the like), sets and replicates the location of the local particle. In addition, after initializing the particles from the indoor positioning infrastructure collection information on the propagation map DB, local and global particles are generated in all or a part of the indoor area.

At this time, reception information is generated based on the generated particle and transmitted to the step of setting the particle.

At this time, the step of setting the particle includes the step of setting the local particle and the step of setting the global particle.

In this case, the step of setting the local / global particles may be performed by evaluating the similarity between all the local particles and the global particles based on the received information, and then assigning weights. When the sum of the weights of all the local particles is 1, Lt; RTI ID = 0.0 > 1 < / RTI >

At this time, estimating the position includes estimating a local particle-based position, estimating a global particle-based position, determining a local minimum value problem, and estimating an optimal solution position.

In this case, the step of estimating the local particle-based position estimates the local particles set to the highest weight based on the weight of the local particles after the local particles are resampled.

In this case, the step of estimating the global particle-based position estimates the global particle set as the global indoor position information, which is set to the highest weight based on the weight of the global particle after resampling the global particle.

At this time, in the step of determining the local minimum value problem, the local minimum position problem is determined by comparing the local indoor position information and the global indoor position information, and the result is outputted.

At this time, the step of estimating the optimal solution position estimates the optimal solution indoor position information based on the result of determination of the local minimum value problem, and outputs the result.

According to the present invention, in a method and apparatus for estimating user indoor location information based on a particle filter, optimal location information can be calculated using global particles.

In addition, the present invention can estimate the global particle-based position information by setting the weight of the global particle.

Further, the present invention can determine the local minimum value problem and output the result.

Further, the present invention can calculate the optimum solution and overcome the local minimum value problem using the result of determining the local minimum value problem.

In addition, the present invention can appropriately utilize the global particle to construct a particle filter that is stronger against the noise of the smartphone reception information and the positioning infrastructure collection information.

In addition, the present invention can overcome the local minimum value problem and calculate the optimal solution for the sensor error in the smartphone and the noise in the wireless communication infrastructure scan information.

1 is a block diagram illustrating a particle filter based user indoor position information estimation apparatus according to an embodiment of the present invention.
2 is a block diagram illustrating a particle setting unit according to an embodiment of the present invention.
3 is a block diagram illustrating a position estimator according to an embodiment of the present invention.
FIG. 4 is a flowchart illustrating an operation of estimating user room location information based on a particle filter according to an exemplary embodiment of the present invention.
5 is a flowchart illustrating an operation of generating reception information according to an embodiment of the present invention.
6 is a flowchart illustrating a process of setting a particle according to an embodiment of the present invention.
7 is an operation flowchart illustrating a step of estimating position information according to an embodiment of the present invention.
8 to 10 are views illustrating an example of a process of estimating a local particle-based position information according to an embodiment of the present invention.
11 to 13 are diagrams illustrating an example in which a local minimum value problem occurs according to an embodiment of the present invention to generate a position information error.
FIG. 14 is an exemplary view illustrating generation of global particles based on the indoor positioning infrastructure collection information according to an embodiment of the present invention. FIG.
15 to 16 are exemplary diagrams for solving the local minimum value problem and estimating an optimal solution according to an embodiment of the present invention.

The present invention will now be described in detail with reference to the accompanying drawings. Hereinafter, a repeated description, a known function that may obscure the gist of the present invention, and a detailed description of the configuration will be omitted. Embodiments of the present invention are provided to more fully describe the present invention to those skilled in the art. Accordingly, the shapes and sizes of the elements in the drawings and the like can be exaggerated for clarity.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating a particle filter based user indoor position information estimation apparatus according to an embodiment of the present invention.

Referring to FIG. 1, the particle filter based user indoor location information estimation apparatus may include a receiving unit 100, a particle setting unit 110, and a position estimating unit 120.

The receiving unit 100 may receive the indoor location information of the user and the indoor positioning infrastructure collection information obtained from the smartphone in the indoor location-based service. It is assumed that the indoor positioning infrastructure collection information can be received from the radio wave map DB and the radio wave map DB is constructed in advance in the service area. The propagation map DB can be classified into a method of collecting directly from a reference point (RP) or a method of predicting a propagation map using a propagation model or the like.

In addition, the receiver 100 initializes the particles of the particle filter, and when the user's indoor location information is moved based on the indoor location information of the user and the indoor location positioning infrastructure collection information, A particle created by moving a particle by a stride or preset value is called a local particle. Particles generated randomly in the entire area or a part of the service area based on the indoor positioning infrastructure collection information are called global particles

The reception unit 100 may generate reception information based on the indoor location information of the user, the indoor positioning infrastructure collection information, the local and global particles, and transmit the generated reception information to the particle setting unit 110.

In addition, the receiving unit 100 may utilize the estimated optimal indoor location estimation information from the location estimating unit 120 as the indoor location information of the user.

The particle setting unit 110 includes a local particle setting unit 200 for generating setting information of a local particle based on the received information and a global particle setting unit 210 for generating global particle setting information based on the received information ).

The position estimating unit 120 may include a local particle-based position estimating unit 300 for estimating local indoor position information based on the local particle setting information, a global particle based estimating unit 300 for estimating global indoor position information based on the global particle setting information, A local minimum value problem determination unit 320 for comparing the local indoor positional information with the global indoor positional information to determine a local minimum value problem, and a local minimum value problem determination unit 320 for estimating an optimal indoor location information based on the local minimum value problem determination And an optimal solution position estimating unit 330 for estimating the optimum solution position.

2 is a block diagram showing a particle setting unit 110 according to an embodiment of the present invention.

2, the particle setting unit 110 may include a local particle setting unit 200 and a global particle setting unit 210.

The local particle setting unit 200 can evaluate the similarity of all the local particles based on the indoor location information of the user and the indoor positioning infrastructure collection information.

In addition, the local particle setting unit 200 may set weights on all the local particles based on the evaluated similarity.

In addition, the local particle setting unit 200 may normalize the weights of all the local particles so that the sum of the set weights is 1.

The global particle setting unit 210 may evaluate the similarity of all the global particles based on the indoor location information of the user and the indoor positioning infrastructure collection information.

In addition, the global particle setting unit 210 may set weights on all global particles based on the evaluated similarity.

The global particle setting unit 210 may normalize the weights of all the global particles so that the sum of the set weights is 1. [

3 is a block diagram illustrating a position estimator 120 according to an embodiment of the present invention. The position estimating unit 120 may include a local particle based position estimating unit 300, a global particle based position estimating unit 310, a local minimum value determining unit 320, and an optimal solution position estimating unit 330.

Referring to FIG. 3, the local particle-based position estimating unit 300 can estimate the local particles having the highest weight among all the normalized local particles as the local indoor position information.

The global particle-based position estimating unit 310 can estimate the global particle having the highest weight among all the normalized local particles as the global indoor position information.

The local minimum value problem determination unit 320 compares the local indoor position information and the global indoor position information with the indoor position information of the user to determine that the local minimum value problem has occurred when the error is greater than a predetermined threshold value, Can be output.

Based on the result of the local minimum value problem determination unit 320, the optimal solution location estimation unit 330 can estimate the similarity of the local indoor location information and the global indoor location information and estimate the optimal indoor location information.

In addition, when the local minimum value problem does not occur, the optimal solution location estimation unit 330 can estimate the local indoor location information as the optimal indoor location information, and if the local minimum value problem occurs, . The estimated optimal indoor location information can be transmitted to the receiver 100 as the indoor location information of the user, and can be used to initialize the particles and estimate the new optimal indoor location information.

FIG. 4 is a flowchart illustrating an operation of estimating a user location information based on a particle filter according to an exemplary embodiment of the present invention.

Referring to FIG. 4, the operation flow diagram may include generating reception information (S400), setting particles (S410), and estimating position information (S420).

The step S400 may include receiving the indoor location information of the user obtained from the smartphone in the indoor location-based service (S401) and receiving the indoor positioning infrastructure collection information (S402). It is assumed that the indoor positioning infrastructure collection information is received from the radio wave map DB and the radio wave map DB is constructed in advance in the service area. The propagation map DB can be classified into a method of collecting directly from a reference point (RP) or a method of predicting a propagation map using a propagation model or the like.

In step S400, after the particles are initialized, when the indoor location information of the user is moved based on the indoor location information of the user and the indoor location positioning infrastructure collection information, A step (S404) of arbitrarily generating local particles in all the regions where the user can locate by moving the particles by the set values, and a step (S404) of generating local particles in the entire area or a part of the service area based on the user's indoor location information and indoor location positioning infrastructure collection information And optionally creating a global particle (S405).

In addition, step S400 may include generating reception information based on the received information (S406).

In addition, the step S400 can utilize the result estimated from the step S420 as the indoor location information of the user.

In operation S410, the similarity of all the local particles is evaluated based on the received information in operation S411. In operation S412, a weight is set for all the local particles. In operation S412, A step S415 of evaluating the degree of similarity of all the global particles, a step S415 of setting a weight to all the global particles S415, and a step S416 of normalizing the weights such that the sum of all the global particle weights is 1, . ≪ / RTI >

In operation S420, a step S421 of resampling the particle S421, a step S422 of estimating the indoor position information of the user based on the local particle weight, a step S442 of estimating the indoor position information of the user based on the global particle weight, A step S426 of outputting a local minimum value problem occurrence result, a step S427 of outputting a local minimum value problem occurrence occurrence step S427, (Step S429), a step S430 of estimating the optimal indoor room position information of the user, and a step S431 of outputting the estimation result can do.

5 is a flowchart illustrating an operation of generating reception information according to an embodiment of the present invention.

Referring to FIG. 5, the operational flow of step S400 includes receiving the indoor location information of the user (S401), receiving the indoor positioning infrastructure collection information (S402), initializing the particles (S403) Step S404 of generating particles, step S405 of generating global particles, and step S406 of generating reception information.

Step S401 may receive the location information of the user's mobile communication terminal device (smartphone).

In addition, the step S401 may receive the optimal indoor location information from the step S431 and utilize it as the user indoor location information.

In step S402, a reference position can be estimated by receiving a signal through a wireless communication infrastructure such as Wi-Fi based on a propagation map DB. For example, if pattern matching is performed using only the indoor positioning infrastructure collection information stored in the propagation map DB without using a filter, the location of the user can be estimated as one of the reference locations.

Step S403 may initialize the particle to create new regional particles and new global particles based on the updated user indoor location information and the indoor positioning infrastructure collection information.

In step S404, when the indoor position information of the user is moved based on the indoor position information of the user, the local particles may be generated by moving the particles by the stride of the moving direction or the preset value.

In step S405, the global particle can arbitrarily generate global particles and fix the position of the global particles based on the indoor positioning infrastructure acquisition information.

Step S406 may generate reception information based on the user's indoor location information, indoor positioning infrastructure area particle creation information, global particle creation information, user indoor location information, and indoor positioning infrastructure collection information.

6 is a flowchart illustrating a process of setting a particle according to an embodiment of the present invention.

Referring to FIG. 6, the operational flowchart of step S410 includes steps S411, S412, S412, S412, S412, S412, S412, (S414) of evaluating the similarity of all the global particles (S414), setting a weight for all the global particles (S415), and all the global And normalizing the weight so that the sum of the weights of the particles is 1 (S416).

In step S411, it is possible to evaluate the degree of similarity using the generation information of the local particles and the indoor positioning infrastructure collection information from the received information. In the various algorithms for evaluating the similarity, the NN (Nearest Neighbor), kNN (k-Nearest Neighbors) and w-kNN (weighted k-Nearest Neighbors) algorithms are widely used for indoor location estimation.

Step S412 may set weights for all local particles in proportion to the evaluated similarity.

Step S413 may normalize the sum of the set weights to be 1.

In step S414, the degree of similarity can be evaluated using the generation information of the global particle and the collection information of the indoor positioning infrastructure from the reception information. In the various algorithms for evaluating the similarity, the NN (Nearest Neighbor), kNN (k-Nearest Neighbors) and w-kNN (weighted k-Nearest Neighbors) algorithms are widely used for indoor location estimation.

Step S415 may set weights on all the global particles in proportion to the evaluated similarity.

Step S416 may normalize the sum of the set weights to be 1.

7 is an operation flowchart illustrating a step of estimating position information according to an embodiment of the present invention.

Referring to FIG. 7, the operation flow of step S420 includes a step S421 of resampling the particles, a step S422 of estimating the indoor position information of the user based on the local particle weight, the step S422 of calculating the indoor position information of the user based on the global particle weight, A step S423 of estimating a local minimum value error, a step S424 of estimating an error between positional information, a step S425 of determining a value equal to or larger than a predetermined threshold value, a step of outputting a local minimum value problem occurrence step S426, (Step S427), a step S428 of calling the user's global indoor location information, a step S429 of calling the user's local indoor location information, a step S430 of estimating the user's optimal indoor location information, (S431). ≪ / RTI >

In step S421, based on the local particle weight setting and global particle setting, the particles having a small weight are regarded as irrelevant to the position of the current user, and the particles having a high weight are closely related to the indoor position information of the current user . The resampling step may be repeated again each time the receiving information and the weight setting are newly calculated.

Step S422 may generate the local indoor location information by estimating the local particle having the highest weighted value through resampling as indoor location information of the user.

In step S423, global particle position information may be generated by estimating the global particle having the highest weighted weight through resampling as indoor position information of the user.

In step S424, the local indoor position information and the global indoor position information are compared with each other, and the error can be estimated based on the difference value.

In step S425, it is possible to determine whether the local minimum value problem has occurred by comparing the local indoor position information and the global indoor position information to the user's indoor position information with a preset threshold value.

Step S426 may indicate that a local minimum value problem has occurred if the predetermined threshold value or more is exceeded.

Step S427 may output that the local minimum value problem has not occurred when the predetermined threshold value is less than the preset threshold value.

Step S428 may invoke global indoor location information when a local minimum value problem occurs.

In step S429, the local indoor location information may be retrieved when the local minimum value problem does not occur.

In step S430, the global indoor location information retrieved when the local minimum value problem occurs can be estimated as the optimal indoor location information based on the occurrence of the local minimum value problem. If the local minimum value problem does not occur, It is possible to estimate the information as the optimal indoor location information.

Step S431 may output the estimated optimal indoor location information and may transmit it to step S401.

8 to 10 are views illustrating an example of a process of estimating a local particle-based position information according to an embodiment of the present invention.

Referring to FIG. 8, an area 1010 indicates an indoor area in which an indoor positioning infrastructure location information based service is performed.

In addition, the path 1020 shows an example in which the user has moved a specific path.

The point 1031 to the point 1035, the point 1041 to the point 1044 and the point 1051 to the point 1053 are points at which the reference point exists in the radio wave map DB among the paths that the user traveled. For example, if pattern matching is performed using only the indoor positioning infrastructure collection information stored in the propagation map DB without using a filter, the position of the user is changed from the point 1031 to the point 1035, the point 1041 to the point 1044, And the reference position 12 indicated in the points 1051 to 1053.

Referring to FIG. 9, point 1032 is the point at which the user is located.

Also, it can be seen that the local particle 1210 is normally created around the point 1032 based on the user indoor location information.

Referring to Fig. 10, point 1034 is the point at which the user is moving from point 1032.

Also, it can be seen that the local particles 1310 are normally created around the point 1032 based on the user's indoor location information.

11 to 13 are diagrams illustrating an example in which a local minimum value problem occurs according to an embodiment of the present invention and a positional information error occurs.

Referring to FIG. 11, point 1041 is a point at which a user moves from point 1034 to point 1035, and switches direction to move to point 1041.

Also, it can be seen that the local particle 1410 is generated around the point 1035 based on the user indoor location information. The user is located at point 1041, but the local particle is misidentified at point 1035. [

This phenomenon is caused by the azimuth error of the smartphone and the wireless communication signal noise error.

In this case, the local particle may incorrectly generate the local particle 1410 around the point 1035 by determining the point 1035 as the true position.

In addition, even if the local particle judges that the point 1041 is a true position and the local particle is generated around the point 1041, the degree of similarity between the point 1035 and the point 1041 is evaluated, If the particle is high, the local particles 1410 are erroneously generated around the point 1035. The local minimum problem of particles is caused by the above two situations.

Referring to Fig. 12, point 1043 is the point at which the user is moved from point 1041. Fig.

Also, the local particle 1510 can be seen to be generated at point 1035, even though the user has moved.

The above phenomenon is a local minimum value problem caused by at least one of the above two situations. This local minimum value problem generates the user room location information estimation error.

Further, it can be seen that the estimation error of the point 1043 becomes larger than when the user is at the point 1041. [

Referring to Fig. 13, point 1051 is the point at which the user is moving from point 1043.

Also, the local particle 1610 can be seen to be created at point 1035, even though the user has moved.

The above phenomenon is a local minimum value problem caused by at least one of the above two situations.

Further, it can be seen that the estimation error of the point 1051 becomes larger than when the user is at the point 1041 or 1043.

FIG. 14 is an exemplary view illustrating generation of global particles based on the indoor positioning infrastructure collection information according to an embodiment of the present invention. FIG.

14, in the example of FIG. 11, it can be seen that the global particle 1710 is further operated around the reference position on the path 1020.

Running the local and global particles together allows the global particle 1720 around the point 1041 to be included in the similarity assessment.

FIGS. 15 to 16 are diagrams for explaining an embodiment of the present invention to overcome the local minimum value problem and to estimate an optimal solution.

Referring to FIG. 15, point 1041 is the point at which the user is moved from point 1035.

It can be seen that the local particle 1810 is normally generated at the point 1041 in consideration of the global particle at the time of the similarity evaluation.

It can also be seen that the local minimum value problem has been solved.

Referring to FIG. 16, point 1043 is the point at which the user is moved from point 1041.

It can be seen that the local particle 1910 is normally generated at the point 1043 in consideration of global particles in the similarity evaluation.

It can also be seen that the local minimum value problem has been solved.

100:
110: Particle setting unit
120:
200: Regional particle setting unit
210: Global particle setting unit
300: local particle-based location estimating unit
310: global particle-based position estimating unit
320: local minimum value problem determination section
330:

Claims (1)

A reception unit for receiving at least one of indoor location information of a user and indoor location infrastructure acquisition information to generate reception information;
A particle setting unit configured to set at least one of the local particle and the global particle based on the received information; And
A position estimator for estimating the optimal indoor position information of the user using at least one of the local particle and the global particle;
And estimating a user's indoor location based on the particle filter.

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