JP6284151B2 - Position estimation apparatus, method, and program - Google Patents

Description

  The present invention relates to a position estimation apparatus, method, and program, and more particularly, to a position estimation apparatus, method, and program for estimating the position of a mobile terminal such as a smartphone.

  As position measurement in a mobile terminal such as a smartphone, position measurement using position measurement means such as GPS (Global Positioning System) or Wi-Fi (registered trademark) Wi-Fi output from a wireless LAN standard Wi-Fi (registered trademark) access point. Position measurement using Fi signals has become common.

  In recent years, as a position measurement using GPS, there is also a method called A-GPS (Assisted GPS) in which a position is measured using base station information of a portable terminal obtained by inquiring a server in addition to a GPS signal from a GPS satellite. Many are used.

  Since GPS cannot be used indoors or underground, a technique for estimating a position using a Wi-Fi signal output from a Wi-Fi access point is useful in indoors or underground.

  In position measurement and fingerprinting using a Wi-Fi signal, the Wi-Fi signal is observed at various training points, and the radio wave intensity information is collected in advance as a fingerprint. The collected radio wave intensity information and the coordinates of the position of the training point are stored in the position information database in association with each other. When it is desired to know the position of the mobile terminal, the radio wave intensity information of the Wi-Fi signal observed by the mobile terminal is compared with the radio wave intensity information of the position information database, and the most similar one is searched. And the position coordinate matched with the most similar radio wave intensity information is made into the coordinate of a portable terminal.

  However, a method that relies on an access point of Wi-Fi, which is an external infrastructure, greatly reduces the estimation accuracy due to the movement or disappearance of the access point and the change in the surrounding environment.

  To deal with such a problem, Non-Patent Document 1 includes a small number of Wi-Fi receivers, a node whose position coordinates are known is installed in the environment, and the radio field intensity of the Wi-Fi signal received by the node. A method for predicting the relationship between information and radio wave intensity information of a Wi-Fi signal received by a user terminal by regression analysis is disclosed.

  Further, Non-Patent Document 2 discloses that Manifold is based on the assumption that the Wi-Fi signal does not change greatly in a short period of time in order to solve the problem that the fingerprint database deteriorates due to the Wi-Fi signal that changes daily. A method of updating a fingerprint database by semi-supervised learning using co-Regularization is disclosed.

Yin, J., Yang, Q. and Ni, L. "Adaptive temporal radio maps for indoor location estimation", Proc. Of Third IEEE International Conference on Pervasive Computing and Communications (PerCom 2005), pp. 85-94 (2005) . Pan, S. J., Kwok, J. T., Yang, Q. and Pan, J. J. "Adaptive localization in a dynamic WiFi environment through multi-view learning", Proc. Of AAAI '07, Vol. 2, pp. 1108.1113 (2007).

  However, the technique described in Non-Patent Document 1 has a problem that a node having a Wi-Fi receiver needs to be installed in the environment, and a new sensor network needs to be introduced.

  Further, the technique described in Non-Patent Document 2 is a technique mainly assuming that the environment gradually changes, and has a problem that it cannot cope with a sudden large environmental change.

  Note that there are the following two types of cases where the Wi-Fi signal suddenly changes.

(1) Disappearance of Wi-Fi signal: The access point moves as the user moves, and the Wi-Fi signal cannot be observed at all from the target environment.

(2) Large change in radio field intensity of the Wi-Fi signal: The radio field intensity of the Wi-Fi signal that can be observed at a specific position as the access point moves in the environment or the furniture moves in the environment. Changes greatly before and after the movement of furniture.

  When such a sudden change occurs, radio wave intensity information that is completely different from the fingerprints collected in advance is obtained, making it difficult to accurately estimate the position.

  The present invention has been made in view of the above circumstances, and provides a position estimation apparatus, method, and program capable of accurately estimating a position even when an environment related to an electromagnetic wave signal output from an access point changes. The purpose is to do.

In order to achieve the above object, the position estimation apparatus of the present invention is based on a received signal received from an electromagnetic wave signal output from an access point randomly determined from among a plurality of access points that output an electromagnetic wave signal. A plurality of weak position estimators for estimating the reception position of the electromagnetic wave signal, the reception positions estimated in the past by each of the plurality of weak position estimators, and obstacle position information representing the positions of obstacles obtained in advance If, on the basis of a weighting calculation units each calculating the weights of the plurality of weak position estimator, and the reception position, which is respectively estimated by the plurality of weak position estimator is calculated by the weighting calculator on the basis of the weight of a plurality of weak position estimator, to the, wherein the position calculating section that calculates the receiving position, the weight calculation unit includes a plurality of Pate representative of the received position of the electromagnetic wave signal A plurality of particle filter position estimators for moving the position of the vehicle according to a predetermined movement model, and the plurality of particle positions and the plurality of weak position estimators. Based on a particle weight calculation unit that calculates the weight of each particle, the reception position estimated by each of the plurality of weak position estimators, and the position of each particle sampled by the particle filter position estimation unit A weak position estimator weight calculation unit that calculates weights of each of the plurality of weak position estimators, wherein the particle filter position estimation unit is configured to each of the plurality of particles calculated by the particle weight calculation unit. Based on the weight of the obstacle and the obstacle position information. Each sampling particles from, on the basis of the particle sampled by the particle weight calculator, to create a plurality of particles, the positions of a plurality of particles the created, moves according to the movement model.

The position estimation apparatus of the present invention estimates a reception position of the electromagnetic wave signal based on a reception signal received from the electromagnetic wave signal output from an access point determined at random from among a plurality of access points that output the electromagnetic wave signal. Based on a plurality of weak position estimators, the reception position estimated in the past by each of the plurality of weak position estimators, and obstacle position information representing a position of an obstacle obtained in advance, the plurality A weight calculation unit for calculating the weights of the weak position estimators, the reception positions estimated by the plurality of weak position estimators, and the plurality of weak positions calculated by the weak position estimator weight calculation unit. the weight of the estimator, based on, wherein the position calculating section that calculates the receiving position, the weighting calculation unit were estimated in the past by each of said plurality of weak position estimator said The weights of the plurality of weak position estimators are calculated based on the received position and the obstacle position information, respectively, and the calculated weights of the plurality of weak position estimators and the weak position estimates calculated in the past are calculated. and the weight of the vessel, the predetermined adjustment factor for adjusting the importance of the weight of the weak position estimator, based on, to update the weights of the plurality of weak position estimator.

Position estimating method of the present invention, a plurality of weak position estimator, weighting calculation unit, and a position estimation method in the position estimation device comprising a position calculating unit, the plurality of weak locations estimator, an electromagnetic wave signal output to based on a plurality of access points the received signal received electromagnetic wave signal outputted from the access point determined at random from, estimating a reception position of the electromagnetic wave signal, the weighting calculation unit, said plurality The weights of the plurality of weak position estimators are respectively calculated based on the reception position estimated in the past by each of the weak position estimators and obstacle position information indicating the position of the obstacle obtained in advance. a step of, position calculation unit, and the receiving position are respectively estimated by the plurality of weak position estimator, and the weight of the weighting calculation plurality of weak position estimator calculated by unit, the And Zui includes a step of calculating the reception position, the step of each to calculate the weights of the plurality of weak locations estimator, the positions of a plurality of particles representing the received position of the electromagnetic wave signal, a predetermined Moving according to a movement model; calculating a weight of each of the plurality of particles based on the positions of the plurality of particles and the reception positions respectively estimated by the plurality of weak position estimators; Each of the reception positions estimated by the weak position estimator, and each of the positions of the particles sampled by the step of moving the positions of the plurality of particles representing the reception positions of the electromagnetic wave signal according to a predetermined movement model; Each of calculating a weight of the plurality of weak position estimators based on The step of moving the positions of the plurality of particles representing the reception position of the electromagnetic wave signal in accordance with a predetermined movement model includes the step of calculating the weight of each of the plurality of particles. Based on the weight and the obstacle position information, each particle is sampled from the plurality of particles, and a plurality of particles are created based on the particles sampled by the step of calculating the weight of each of the plurality of particles. Then, the positions of the created particles are moved according to the movement model.
The position estimation method of the present invention is a position estimation method in a position estimation device including a plurality of weak position estimators, a weight calculation unit, and a position calculation unit, and the plurality of weak position estimators output electromagnetic wave signals. Estimating a reception position of the electromagnetic wave signal based on a reception signal received from an electromagnetic wave signal output from an access point randomly determined from a plurality of access points; and the weight calculation unit includes the plurality of weak signals. Calculating each of the weights of the plurality of weak position estimators based on the reception position estimated in the past by each of the position estimators and obstacle position information indicating the position of the obstacle obtained in advance. And the position calculation unit includes the reception positions respectively estimated by the plurality of weak position estimators and the weights of the plurality of weak position estimators calculated by the weight calculation unit. Calculating the reception position, and calculating each of the weights of the plurality of weak position estimators with the reception position estimated in the past by each of the plurality of weak position estimators. The weights of the plurality of weak position estimators are respectively calculated based on the obstacle position information, and the weights of the plurality of weak position estimators calculated and the weights of the weak position estimators calculated in the past are calculated. And the weights of the plurality of weak position estimators are updated based on a predetermined adjustment coefficient for adjusting the importance of the weights of the weak position estimators .

Position estimation program of the present invention, a computer, a program to function as each section of the position estimation device.

  As described above, according to the position estimation device, method, and program of the present invention, there is an effect that the position can be estimated with high accuracy even when the environment related to the electromagnetic wave signal output from the access point changes. can get.

It is a block diagram which shows the functional structural example of the position estimation apparatus which concerns on this Embodiment. It is a block diagram which shows the structural example of an ensemble position estimator. It is a flowchart which shows the position estimation process routine in this Embodiment. It is a figure for demonstrating a particle.

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

  In the present embodiment, as an example, a reception position where a mobile terminal receives a Wi-Fi signal output from a Wi-Fi access point is estimated based on intensity information of the Wi-Fi signal received by the mobile terminal. The case where it does is demonstrated. In addition, although a smart phone, a mobile phone, a tablet terminal etc. are contained in a portable terminal, for example, it is not restricted to these. In other words, any portable terminal having a wireless communication function may be used. For example, a wristwatch-type portable terminal or a glasses-type portable terminal such as Google Glass (registered trademark) may be used. Alternatively, a mobile terminal having a short-range wireless communication function such as Bluetooth (registered trademark) may be used.

<System configuration>

  The position estimation apparatus 10 according to the present embodiment includes an input unit 12, a position estimation unit 14, and an output unit 16. The position estimation apparatus 10 is provided on, for example, a cloud server, but may be provided on the mobile terminal itself.

  The input unit 12 receives input of radio field intensity information indicating the radio field intensity of the Wi-Fi signal transmitted from the mobile terminal that has received the Wi-Fi signal output from the Wi-Fi access point. The input unit 12 stores, for example, a communication device that receives radio field intensity information of a Wi-Fi signal received by the mobile terminal by wireless communication with the mobile terminal, and radio field intensity information of the Wi-Fi signal received from the mobile terminal. This is realized by an input device such as a storage device.

  The position estimation unit 14 estimates the reception position at which the mobile terminal has received the Wi-Fi signal based on the radio field intensity information of the Wi-Fi signal input by the input unit 12.

  The output unit 16 outputs the reception position of the mobile terminal estimated by the position estimation unit 14. The output unit 16 is realized by, for example, an output device such as a communication device that transmits the reception position of the mobile terminal to the mobile terminal by wireless communication.

  The position estimation unit 14 functionally includes an ensemble position estimator 20, a weak position estimator weight calculation unit 22, a particle filter position estimator 24, a particle weight calculation unit 26, a position calculation unit 28, and a storage unit 29. . The position estimation unit 14 includes, for example, a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory) that stores a position estimation processing program for executing a position estimation processing routine described later. It is realized by a computer provided. Note that a nonvolatile memory may be used instead of the ROM.

  As shown in FIG. 2, the ensemble position estimator 20 includes a plurality of weak position estimators 30-1 to 30-N (N ≧ 2). When the weak position estimators 30-1 to 30-N are not distinguished from each other, they may be simply referred to as weak position estimators 30.

  Access points are randomly associated with the weak position estimator 30 from among all the access points. Therefore, the weak position estimators 30-1 to 30-N are associated with access points that are at least partially different. For example, as shown in FIG. 2, access points AP1, AP2, AP7, AP11, AP15... Are associated with the weak position estimator 30-1, and an access point is associated with the weak position estimator 30-2. AP2, AP4, AP7, AP10, AP12... Are associated with each other.

  Each of the weak position estimators 30-1 to 30N is calculated by the weak position estimator weight calculation unit 22 and the radio field intensity information of the Wi-Fi signal received by the mobile terminal at the time t from the associated access point. Based on its own weight, the reception position of the mobile terminal at time t is estimated. The reception position estimated by the weak position estimator 30 is output to the weak position estimator weight calculator 22 and the particle weight calculator 26.

  The position calculation unit 28 estimates the weights of the weak position estimators 30-1 to 30-N calculated by the weak position estimator weight calculation unit 22 and the weak position estimators 30-1 to 30-N. Based on the received position of the mobile terminal at time t, the reception position of the mobile terminal at time t is calculated. The reception position calculated by the position calculation unit 28 is output by the output unit 16.

  The weak position estimator weight calculator 22 receives the reception position of the mobile terminal at time t estimated by each of the weak position estimators 30-1 to 30 -N and the time t resampled by the particle filter position estimator 24. The weights of the weak position estimators 30-1 to 30-N are calculated based on the positions of the plurality of particles. The calculated weights of the weak position estimators 30-1 to 30-N are output to the position calculation unit 28.

  The particle filter position estimator 24 sets the position of the mobile terminal to be tracked as the position of the particle, and sets the position of the mobile terminal at time t−1 calculated based on the radio field intensity information of the Wi-Fi signal received in the past. Responding to a plurality of particles whose weights are respectively calculated, the particles are resampled based on the weight of each particle and obstacle position information indicating the position of the obstacle obtained in advance. A plurality of particles are newly generated from the particles, and each of the generated particles is moved according to a predetermined movement model, and the position of the mobile terminal at the current time t is estimated. The resampled particle position information is output to the weak position estimator weight calculator 22. The estimated particle position information is output to the particle weight calculator 26.

  The particle weight calculation unit 26 receives the reception position of the mobile terminal at time t estimated by the weak position estimators 30-1 to 30 -N, and the position information of each particle at time t estimated by the particle filter position estimator 24. Based on, the weight of each particle is calculated. The calculated weight of each particle is output to the particle filter position estimator 24.

  The storage unit 29 stores the radio wave intensity information of the Wi-Fi signal received from the mobile terminal and the radio wave intensity information received from the mobile terminal in the past.

<Operation of position estimation device>

  Next, with reference to FIG. 3, the position estimation processing routine executed in the position estimation apparatus 10 according to the present embodiment will be described. This routine is executed every time the radio field intensity information of the Wi-Fi signal is received from the mobile terminal.

  In step S100, the input unit 12 inputs the radio field intensity information of the Wi-Fi signal transmitted from the mobile terminal and received by the mobile terminal from the Wi-Fi access point at time t. Note that a plurality of access points are installed, and the mobile terminal transmits the radio field intensity information of the Wi-Fi signal received from the plurality of access points to the position estimation device 10. Accordingly, in step S100, the input unit 12 inputs the radio wave intensity information for each access point.

  In the processes from step S102 to step S108, each of the weak position estimators 30-1 to 30-N executes the same process.

First, in step S102, the weak position estimator 30 includes one access point i among all the access points associated with the weak position estimator 30 in the radio wave intensity information of the Wi-Fi signal received in step S100. Distribution probability f _i (x _i , μ _{i, n} , σ _{i, n} ) of the radio field intensity information x _i for the n-th training point is calculated based on the radio field intensity information x _i of the Wi-Fi signal from. Training points are preset in a plurality of places.

The distribution probability f _i (x _i , μ _{i, n} , σ _{i, n} ) of the radio wave intensity information x _i is expressed by a probability density function of a normal distribution as shown in the following equation (1).

... (1)

Here, μ _{i, n} and σ ² _{i, n} are the average and variance of the distribution of the radio wave intensity information of the Wi-Fi signal from the access point i at the n-th training point n, and were obtained at the training stage. Value. The training stage refers to receiving the Wi-Fi signal from each access point at a plurality of training points whose coordinates are known, collecting radio wave intensity information, and collecting the radio wave intensity information for each access point based on the collected radio wave intensity information. The stage of calculating the mean and variance of the distribution. This is executed for each training point. The average and variance of the radio wave intensity information for each training point and each access point calculated in the training stage are stored in the storage unit 29 in advance.

  In step S104, the weak position estimator 30 determines whether or not the distribution probability of the radio wave intensity information has been calculated for all the associated access points. If there is an uncalculated access point, the process proceeds to step S102. Returning, the distribution probability of the radio wave intensity information is calculated for an access point for which the distribution probability of the radio wave intensity information has not been calculated.

  On the other hand, when the distribution probability of the radio wave intensity information is calculated for all the associated access points, the process proceeds to step S106.

  In step S106, the weak position estimator 30 calculates the sum f (x, n) of the distribution probability of the signal strength for all access points by the following equation (2).

... (2)

  In step S108, it is determined whether the weak position estimator 30 has calculated the sum of the distribution probability of the above-mentioned radio wave intensity information for all n training points. If there is a training point that has not been calculated, step S108 is performed. Returning to S102, the sum of the distribution probability of the radio wave intensity information is calculated for the training points for which the sum of the distribution probability of the radio wave intensity information has not been calculated.

  On the other hand, when the sum of the distribution probability of the radio wave intensity information is calculated for all n training points, the process proceeds to step S110.

  In step S110, the top-k training point of the sum of the distribution probability of the radio field intensity information calculated by the weak position estimator 30 for the n training points calculated in step S108, that is, the training point whose sum is within the top k ranks. The reception position of the mobile terminal is estimated based on the coordinates corresponding to, and the sum of the distribution probability of the radio wave intensity information calculated for the training points within the top k ranks. Specifically, the coordinates obtained by weighting and averaging the coordinates of the training points whose sum total is within the top k ranks with the sum of the distribution probability of the radio wave intensity information calculated for each training point are estimated as the reception position of the mobile terminal.

  Each of the weak position estimators 30-1 to 30-N executes the processing of steps S102 to S110, thereby obtaining N position estimation results. Since each of the weak position estimators 30-1 to 30-N has an access point set at random, even if a change in radio wave intensity occurs for a specific access point, the weak point estimators 30-1 to 30-N Weak position estimators that do not use radio waves are not affected. Therefore, the reception position of the mobile terminal can be accurately estimated even when the environment related to radio waves changes.

  The estimation of the reception position of the mobile terminal by the weak position estimator 30 is not limited to the above method, and various known methods can be used. For example, the methods described in Non-Patent Documents 1 and 2 are used. May be.

  In steps S112 to S118, the weak position estimator 30 is evaluated based on the reception position of the mobile terminal estimated by the weak position estimator 30 in the past. Specifically, based on the obstacle position information and the reception position of the mobile terminal estimated by the weak position estimator 30 in the past, the movement trajectory of the particle (mobile terminal) is estimated (tracked) using the particle filter. The weak position estimator 30 is evaluated based on how close the estimated position of the particle at a certain time t is to the reception position of the mobile terminal at the time t of the weak position estimator 30.

  The particle filter is used to predict the state of the tracking target (mobile terminal) that performs nonlinear state transition, and the algorithm includes three steps of sampling processing, weight calculation processing, and resampling processing (for example, the following) Non-Patent Document 3).

  [Non-Patent Document 3] Doucet, A., and AM Johansen. "A Tutorial on Particle filtering and smoothing: Fifteen years later", In Oxford Handbook of Nonlinear Filtering, D. Crisan and B. Rozovsky (eds.). Oxford University Press (2011).

  First, in step S112, the particle filter position estimator 24 uses a plurality of particles resampled in step S120 when this routine was executed last time as a sampling process, and represents a plurality of mobile terminals representing tracking targets. Create new particles and move them.

Specifically, based on the movement model expressed by the following equation (3), to create a new particle p ^{j t} _{+ 1} at time t + 1 from the particle p _t at time t was last resampled. In the initial state, for example, a state where particles are widely distributed is set. Further, the created particles represent the state (position) of the mobile terminal that is the tracking target.

p ^j _{t + 1} = Ap _t + w (3)

Here, A is a transition matrix, and in the present embodiment, a transition matrix assuming constant velocity linear motion is used. That is, the particle p _t time t, the transition matrix for moving the particles using the speed obtained from the particle p _t-1 of the past time t-1 is used. w is a normal distribution with an average of 0, and thus movement including Gaussian noise occurs. Note that the transition matrix to be used is not limited to the transition matrix assuming constant-velocity linear motion, and the transition matrix may be appropriately set according to the movement characteristics of the tracking target.

J is an identifier of a particle. That is, by using the movement model shown in the expression (3), a plurality of particles p ^j _{t + 1} are sampled, that is, created from one particle p _t according to Gaussian noise. As a result, as shown in FIG. 4, one particle P is split to create a plurality of particles. FIG. 4 is a plan view of a building 42 having a plurality of rooms 40 and shows the trajectory of particles P that move in the hallway 44.

In this embodiment, for example, one particle p _t is divided into five (j = 1 to 5) particles p ^j _{t + 1} . Note that the number of particles to be split is not limited to five, and may be set as appropriate in consideration of processing load, estimation accuracy, and the like.

In step S114, the particle weight calculator 26 calculates the weight of each particle based on the reception position of the mobile terminal estimated by each weak position estimator 30 in step S110. Hereinafter, calculation of the weight of each particle when the current time is t + 1, that is, at time t + 1 will be described. Specifically, for each of the particles, the closest reception position m _{t + 1} among the N reception positions m _{t + 1} at time t + 1 of the mobile terminal estimated by each of the weak position estimators 30-1 to 30-N. Calculate the weight based on it.

Particle close to the weak position estimator 30 receives position m _{t + 1} estimated by, since it is highly possible that there is actually a mobile terminal to the position of the particle, the greater the weight the more particles near the receiving position m _{t + 1} give. The weights w ^j _{t + 1} of the particles p ^j _{t +} 1 are N reception positions m at time t + 1 of the mobile terminal estimated by each of the weak position estimators 30-1 to 30-N, as shown in the following equation (4). is calculated by the probability density function ^w _{j t + 1} of the normal distribution of the received position _{m t + 1} closest of _{t + 1} and the average.

w ^j _{t + 1} = N (p ^j _{t + 1} | m _{t + 1} ) (4)

  In step S116, the particle filter position estimator 24 determines whether or not the weights of all particles have been calculated. If the weights of all particles have been calculated, the process proceeds to step S118 to calculate the weights of all particles. If not, the process returns to step S114 to calculate the particle weight in the same manner as described above.

  In step S118, the particle filter position estimator 24 uses the plurality of particles obtained in step S112 and the weight of each particle calculated in step S114 as a resampling process. Exclude. Specifically, of the weights of all particles calculated in step S114, particles having a predetermined ratio (for example, 4/5) are deleted in ascending order of the weights.

  Further, particles that collide with the obstacle due to the movement are excluded based on the obstacle position information indicating the position of the obstacle and the position of the particle. An obstacle is an object that a user with a mobile terminal cannot physically pass through, such as a building wall 46 as shown in FIG. 4, but is not limited thereto. Absent. The obstacle position information is stored in advance in the storage unit 29, for example. Thus, the locus of the position of the mobile terminal can be estimated by using the particle filter.

  In step S120, the weak position estimator weight calculator 22 calculates the weight of the weak position estimator 30. In the initial state, the weight of the weak position estimator 30 is set to the same predetermined value. Specifically, the weak position estimator weight calculator 22 receives the mobile terminal reception position estimated by the weak position estimators 30-1 to 30-N in step S110 and the particle filter position estimator 24 in step S118. The weight of the weak position estimator 30 is calculated based on the position of each particle resampled by.

The weight of the weak position estimator 30 is calculated by the same method as the calculation of the particle weight in step S114. Specifically, for example, at time t, the k-th (1 <k <N) weak position estimator 30-k executes the process of step S110 to estimate the position of the mobile terminal based on the Wi-Fi signal. To do. At this time, among each particle estimated by the particle filter position estimator 24, a weak position is obtained by a probability density function using a normal distribution centered on the position of the particle closest to the estimation result of the weak position estimator 30-k. The weight w ′ ^k _t of the estimator 30-k is calculated.

When calculating the weight of the weak position estimator 30-k, the weight w ^k _t-1 of the weak position estimator 30-k at the time t−1 may be reflected.

Specifically, by using the weight w ^'k _t of the weak position estimator 30-k calculated as described above, the weight ^w _{k t-1} weak position estimator 30-k at time t-1, the The weight w ^kt of the weak position estimator 30-k at time _t is calculated and updated by the following equation (6).

w ^k _t = λw ^k _t−1 + (1−λ) w ′ ^k _t (6)

  Here, λ (0 <λ <1) is an adjustment coefficient for adjusting the importance of the weight at time t. For example, the user sets an arbitrary value. The user can set a large value for λ when he wants to increase the importance of the weight at time t, and can set a small value for λ when he wants to increase the importance of the weight at time t−1 in the past. Thus, since the importance of the weight can be changed, the position of the mobile terminal can be estimated with high accuracy according to, for example, the movement characteristics of the user.

  Moreover, when the position estimation apparatus 10 is provided on the cloud server, the weight of the weak position estimator 30 calculated by the above processing is shared with other users. At this time, if the weight of the weak position estimator 30 is updated each time Wi-Fi signal radio wave intensity information is transmitted from the mobile terminal of each user, the load on the cloud server increases. Therefore, for example, the weight of the weak position estimator 30 may be updated after a certain period of time has elapsed.

  In step S122, the position calculation unit 28 totals the reception positions of the mobile terminals estimated in step S110 by each of the weak position estimators 30-1 to 30-N, and each weak position estimation calculated in step S120 above. The final position of the mobile terminal is calculated using the weight of the device 30. Specifically, the reception position of the mobile terminal estimated by each of the weak position estimators 30-1 to 30-N in step S110, that is, the coordinates, are used as the weak position estimators 30-1 to 30-1 calculated in step S120. The final coordinates of the mobile terminal are calculated by obtaining the weighted average coordinates with a weight of 30-N.

  In step S124, the output unit 16 outputs the final reception position of the mobile terminal calculated by the estimation result totaling unit 122 to the mobile terminal, for example, and notifies the position of the mobile terminal.

  As described above, in this embodiment, the weight of each weak position estimator 30 is obtained based on the position of the particles estimated by the particle filter position estimator 24, while the mobile terminal received by the weak position estimator 30 is received. The weight of each particle is obtained based on the position. This process is repeated each time the radio field intensity information of the Wi-Fi signal is obtained from the mobile terminal.

  Since each of the weak position estimators 30-1 to 30-N has an access point set at random, even if a change in radio wave intensity occurs for a specific access point, the access point The weak position estimator 30 that does not use the radio wave from is not affected. Therefore, the reception position of the mobile terminal can be accurately estimated even when the radio wave environment of the Wi-Fi signal changes.

  Note that the present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present invention.

  For example, in the present embodiment, the case of estimating the reception position of a mobile terminal that receives a Wi-Fi signal has been described. However, the present invention is not limited to a radio signal such as a Wi-Fi signal, The present invention can also be applied when estimating the receiving position of an apparatus that receives an electromagnetic wave signal such as an ultrasonic signal.

  Moreover, although the above-mentioned position estimation apparatus 10 has a computer system inside, if a "computer system" is using the WWW system, it will also include a homepage provision environment (or display environment). .

  Further, in the present specification, the embodiment has been described in which the program is installed in advance. However, the program can be provided by being stored in a computer-readable recording medium such as a CD-ROM or a memory card. It is.

DESCRIPTION OF SYMBOLS 10 Position estimation apparatus 12 Input part 14 Position estimation part 16 Output part 20 Ensemble position estimator 22 Weak position estimator weight calculation part 24 Particle filter position estimator 26 Particle weight calculation part 26
28 Position Calculation Unit 29 Storage Units 30-1 to 30-N Weak Position Estimator

Claims (5)

A plurality of weak position estimators for estimating a reception position of the electromagnetic wave signal based on a reception signal received from the electromagnetic wave signal output from an access point randomly determined from a plurality of access points that output the electromagnetic wave signal;
Based on the reception position estimated in the past by each of the plurality of weak position estimators and obstacle position information indicating the position of the obstacle obtained in advance, the weights of the plurality of weak position estimators are calculated. and the weighting calculation unit each to calculate,
And the receiving position are respectively estimated by the plurality of weak position estimator, and the weight of the plurality of weak position estimator calculated by the weighting calculator, on the basis of a position calculation unit for calculating the reception position When,
It includes,
The weight calculation unit
A particle filter position estimation unit that moves the positions of a plurality of particles representing the reception position of the electromagnetic wave signal according to a predetermined movement model;
A particle weight calculation unit for calculating a weight of each of the plurality of particles based on the positions of the plurality of particles and the reception positions estimated by the plurality of weak position estimators;
The weights of the plurality of weak position estimators are respectively calculated based on the reception positions estimated by the plurality of weak position estimators and the positions of the particles sampled by the particle filter position estimation unit. A weak position estimator weight calculation unit,
Including
The particle filter position estimation unit samples each particle from the plurality of particles based on the weight of each of the plurality of particles calculated by the particle weight calculation unit and the obstacle position information, and the particle A position estimation device that creates a plurality of particles based on particles sampled by a weight calculation unit and moves the positions of the created particles according to the movement model .   A plurality of weak position estimators for estimating a reception position of the electromagnetic wave signal based on a reception signal received from the electromagnetic wave signal output from an access point randomly determined from a plurality of access points that output the electromagnetic wave signal;
  Based on the reception position estimated in the past by each of the plurality of weak position estimators and obstacle position information indicating the position of the obstacle obtained in advance, the weights of the plurality of weak position estimators are calculated. A weight calculator for calculating each;
  A position calculation unit that calculates the reception position based on the reception positions estimated by the plurality of weak position estimators and the weights of the plurality of weak position estimators calculated by the weight calculation unit; ,
  Including
  The weight calculation unit calculates weights of the plurality of weak position estimators based on the reception position estimated in the past by each of the plurality of weak position estimators and the obstacle position information, The calculated weights of the weak position estimators, the weights of the weak position estimators calculated in the past, and predetermined adjustment coefficients for adjusting the importance of the weights of the weak position estimators. Update the weights of the plurality of weak position estimators based on
  Position estimation device. Multiple weak position estimator, a position estimation method in the position estimation device comprising weighting calculation unit, and a position calculating unit,
The plurality of weak position estimators estimate the reception position of the electromagnetic wave signal based on the reception signal received from the electromagnetic wave signal output from the access point randomly determined from the plurality of access points that output the electromagnetic wave signal And steps to
The weighting calculation unit, and the receiving position, which is estimated in the past by each of said plurality of weak position estimator, the obstacle position information indicating the position of the previously obtained obstacles, on the basis of the plurality of Calculating the weight of each weak position estimator;
Position calculating unit, and the receiving position are respectively estimated by the plurality of weak position estimator, and the weight of the weighting said plurality of calculated by the calculation unit weak position estimator, based on, the reception position A calculating step;
It includes,
Calculating each of the weights of the plurality of weak position estimators,
Moving the position of a plurality of particles representing the reception position of the electromagnetic wave signal according to a predetermined movement model;
Calculating the weight of each of the plurality of particles based on the positions of the plurality of particles and the received positions estimated by the plurality of weak position estimators, respectively.
Each of the particles sampled by the step of moving the reception position estimated by the plurality of weak position estimators and the position of the plurality of particles representing the reception position of the electromagnetic wave signal according to a predetermined movement model. Calculating a weight of each of the plurality of weak position estimators based on the position; and
Including
The step of moving the positions of a plurality of particles representing the reception position of the electromagnetic wave signal in accordance with a predetermined movement model includes calculating each weight of the plurality of particles. Based on the weight and the obstacle position information, each particle is sampled from the plurality of particles, and a plurality of particles are created based on the particles sampled by the step of calculating the weight of each of the plurality of particles. And a position estimation method for moving the positions of the plurality of created particles according to the movement model .   A position estimation method in a position estimation device including a plurality of weak position estimators, a weight calculation unit, and a position calculation unit,
  The plurality of weak position estimators estimate the reception position of the electromagnetic wave signal based on the reception signal received from the electromagnetic wave signal output from the access point randomly determined from the plurality of access points that output the electromagnetic wave signal And steps to
  The weight calculation unit is configured to determine the plurality of weak positions based on the reception position estimated in the past by each of the plurality of weak position estimators and obstacle position information indicating a position of an obstacle obtained in advance. Calculating each weight of the position estimator;
  A position calculation unit calculates the reception position based on the reception positions respectively estimated by the plurality of weak position estimators and the weights of the plurality of weak position estimators calculated by the weight calculation unit. And steps to
  Including
  The step of calculating the weights of the plurality of weak position estimators is based on the reception position estimated in the past by each of the plurality of weak position estimators and the obstacle position information. Each of the weights of the position estimator is calculated, and the weights of the calculated weak position estimators, the weights of the weak position estimators calculated in the past, and the weights of the weak position estimators are adjusted. Updating the weights of the plurality of weak position estimators based on a predetermined adjustment factor for
  Position estimation method. The position estimation program for functioning a computer as each part of the position estimation apparatus of Claim 1 or Claim 2 .