Position estimation device, position estimation method, and position estimation program

Abstract

To increase position estimation precision of a positioned object estimated based on positioning data by a simple configuration.SOLUTION: A position estimation device is configured so as to acquire a presence probability that a positioned object exists in each of multiple representative points, acquire a transition probability showing a probability that the positioned object travels to each of the multiple representative points, acquire a presence probability that the positioned object exists in each of the multiple representative points when positioning next positioning data of the positioned object as a presence probability before positioning on the basis of the presence probability and the transition probability, acquire the next positioning data, and acquire a presence probability after positioning by updating the presence probability that the positioned object exists in each of the multiple representative points when positioning the next positioning data on the basis of the next positioning data.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to an apparatus, a method, and a program for estimating the position of a positioning object using positioning data. [Background technology]

[0002] Conventionally, positioning based on the reception strength of radio waves output from multiple base stations and positioning using a satellite positioning system such as GPS are known. In these positioning methods, errors are included in the positioning data due to the influence of noise, etc. Therefore, in the technology disclosed in Patent Document 1, positioning data that may include errors is corrected using displacement data calculated based on the detection value of an acceleration sensor or the like provided on the target to be positioned, thereby reducing the positioning error. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-50969 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there are cases where it is desired to reduce the influence of errors occurring in positioning data using a simpler configuration different from the configuration of Patent Document 1.

[0005] Therefore, an object of one aspect of the present disclosure is to reduce an error in the position of a positioning object estimated based on positioning data and improve the position estimation accuracy with a simple configuration. [Means for solving the problem]

[0006] A position estimation device according to an aspect of the present disclosure includes a memory that stores, as a discretization map, coordinates of a plurality of representative points that are discretely distributed in a space in which a positioning target can move in a predetermined area, and a processing circuit that performs arithmetic processing based on the discretization map. The processing circuit is configured to: acquire a presence probability that the positioning target exists at each of the plurality of representative points; acquire a transition probability indicating a probability that the positioning target moves to each of the plurality of representative points; acquire a presence probability that the positioning target exists at each of the plurality of representative points at the time of positioning of next positioning data of the positioning target based on the presence probability and the transition probability as a pre-positioning presence probability; acquire the next positioning data; and acquire a post-positioning presence probability by updating the presence probability that the positioning target exists at each of the plurality of representative points at the time of positioning of the next positioning data based on the next positioning data.

[0007] A position estimation method according to one aspect of the present disclosure includes acquiring a presence probability that the positioning object is present at each of a plurality of representative points that are discretely distributed in a space in which the positioning object can move within a predetermined area; acquiring a transition probability indicating a probability that the positioning object will move to each of the plurality of representative points; acquiring a presence probability that the positioning object will be present at each of the plurality of representative points when next positioning data of the positioning object is positioned based on the presence probability and the transition probability as a pre-positioning presence probability; acquiring the next positioning data; and acquiring a post-positioning presence probability by updating the presence probability that the positioning object is present at each of the plurality of representative points when next positioning data is positioned based on the next positioning data.

[0008] A position estimation program according to an embodiment of the present disclosure causes at least one processor to execute the method. The program may be stored in a computer-readable storage medium. The storage medium is a non-transitory and tangible medium. The storage medium may be built into or externally attached to a computer (e.g., a mobile information terminal, a personal computer, a server, etc.). The storage medium may include RAM, ROM, EEPROM, storage, etc., and may be, for example, a hard disk, a flash memory, an optical disk, etc. The program stored in the storage medium may be executed in a computer to which the storage medium is directly connected, or may be executed in a computer connected to the storage medium via a network (e.g., the Internet). Effect of the Invention

[0009] According to one aspect of the present disclosure, the location target is assumed to exist at any one of a plurality of representative points that are distributed discretely in a predetermined area, and a presence probability that the location target exists at each of the plurality of representative points is obtained, and the obtained presence probability is updated based on the location data of the location target. Therefore, even if the accuracy of the positioning data is low, the location where the location target exists can be reasonably estimated based on the probability. Therefore, the location of the estimated location target can be estimated with high accuracy with a simple configuration. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram of a position estimation system according to an embodiment. [Diagram 2] FIG. 2 is a block diagram of the positioning target terminal of FIG. [Diagram 3] FIG. 3 is a block diagram of the server of FIG. [Figure 4] FIG. 4 is a schematic diagram of a discretized map stored in the server of FIG. [Diagram 5] FIG. 5 is a diagram of a transition probability table stored in the server of FIG. [Figure 6]FIG. 6 is a flow chart of location estimation by the server of FIG. [Figure 7] Fig. 7A is a diagram showing the presence probability that a positioning target exists at each representative point in the discretized map of Fig. 4. Fig. 7B is a diagram showing a one-period-ahead prediction (pre-positioning presence probability) of the presence probability that a positioning target exists at each representative point. Fig. 7C is a diagram showing the presence probability (post-positioning presence probability) updated based on the next positioning data. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment will be described with reference to the drawings.

[0012] Fig. 1 is a schematic diagram of a position estimation system 1 according to an embodiment. As shown in Fig. 1, a positioning target 4 exists within a predetermined area 2, and the positioning target 4 may move within the predetermined area 2. The positioning target 4 is, for example, a human being. The predetermined area 2 is, for example, an indoor facility. The indoor facility is not particularly limited, but may be, for example, a hospital, a factory, a logistics warehouse, an underground mall, a station facility, an airport facility, a commercial facility, a government office, an amusement park, an art gallery, a museum, an exhibition center, a government office, an office, a hotel, a restaurant, a movie theater, a theater, a school, a sports gym, a nursing home, or an area such as a castle.

[0013] A positioning target terminal 10 is associated with a positioning target 4. In other words, locating the position of the positioning target terminal 10 means locating the position of the positioning target 4. In a predetermined area 2, a plurality of wireless access points 3 capable of wirelessly communicating with the positioning target terminal 10 are installed in a dispersed manner. In other words, a wireless LAN is constructed in the predetermined area 2. The wireless access points 3 transmit radio waves including their own identification information (e.g., MAC address).

[0014] The position estimation system 1 includes a positioning target terminal 10 connectable to the Internet 5, and a server 11 connectable to the Internet 5. The positioning target terminal 10 is carried by a positioning target 4. The positioning target terminal 10 is a terminal capable of receiving radio waves from a wireless access point 3 and communicating with the server 11. The positioning target terminal 10 may be, for example, a smartphone, a tablet terminal, a personal computer, or a communication device. The positioning target terminal 10 may be connected to the Internet 5 via a mobile phone line, or may be connected to the Internet 5 via the wireless access point 3.

[0015] Fig. 2 is a block diagram of the positioning target terminal 10 of Fig. 1. As shown in Fig. 2, the positioning target terminal 10 includes a processing circuit 20, a display 24, an operation interface 25, a positioning radio wave receiver 26, and a communication interface 27. The processing circuit 20 includes a processor 21, a system memory 22, and a storage memory 23.

[0016] The processor 21 may include a CPU (Central Processing Unit). The system memory 22 may include a RAM. The storage memory 23 is an example of a computer-readable medium, and is a non-transitory, tangible medium. The storage memory 23 may include a ROM. The storage memory 23 may include a hard disk, a flash memory, or a combination thereof. The storage memory 23 stores a terminal program P1. A configuration in which the processor 21 executes the terminal program P1 read into the system memory 22 is an example of a processing circuit 20.

[0017] The display 24 may be, for example, a liquid crystal display or an organic EL display. The operation interface 25 is a user interface operated by a person who is the positioning target 4. The operation interface 25 includes, for example, at least one selected from a touch panel, a keyboard, a mouse, and the like.

[0018] The positioning radio wave receiver 26 receives radio waves for wireless communication transmitted from multiple wireless access points 3. The positioning radio wave receiver 26 receives strong radio waves from nearby wireless access points 3 and weak radio waves from far away wireless access points 3. The radio waves received by the positioning radio wave receiver 26 contain identification information of the wireless access point 3 that is the source of the radio waves.

[0019] The communication interface 27 is for connecting to the Internet 5, and may be, for example, a communication device that connects to a mobile phone line. The communication interface 27 may also connect to the Internet 5 via a wireless access point 3. In other words, the communication interface 27 may also function as the positioning radio wave receiver 26.

[0020] Fig. 3 is a block diagram of the server 11 in Fig. 1. As shown in Fig. 3, the server 11 includes a processing circuit 30 and a communication interface 34. The processing circuit 30 includes a processor 31, a system memory 32, and a storage memory 33. The communication interface 34 is a communication interface that connects to the Internet 5 in a wired or wireless manner.

[0021] The processor 31 may include a CPU (Central Processing Unit). The system memory 32 may include a RAM. The storage memory 33 may include a hard disk, a flash memory, or a combination thereof. The storage memory 33 stores a position estimation program P2. A configuration in which the processor 31 executes the position estimation program P2 read from the storage memory 33 to the system memory 32 is an example of a processing circuit 30. The server 11 is an example of a position estimation device.

[0022] The storage memory 33 includes an AP location information storage unit 33a, a discretization map storage unit 33b, a probability storage unit 33c, and a target location information storage unit 33d. The AP location information storage unit 33a stores in advance an AP location information list indicating the correspondence between the identification information of each wireless access point 3 in the predetermined area 2 and the location information of each wireless access point 3 in the predetermined area 2. The discretization map storage unit 33b stores in advance a discretization map 40 (see FIG. 4). The probability storage unit 33c stores an existence probability table and a transition probability table 50 (see FIG. 5). The location information storage unit 33d stores the existence probability table, the transition probability table 50, and location information of the target terminal 10 to be located obtained based on the location data.

[0023] FIG. 4 is a schematic diagram of a discretization map 40 stored in the server 11 of FIG. 3. As shown in FIG. 4, the discretization map 40 specifies the coordinates of a plurality of representative points A to D that are distributed discretely in a space in which the positioning target 4 can move in the predetermined area 2. The representative points A to D are connected by an assumed moving route R that the positioning target 4 (see FIG. 1) is assumed to take when moving. The predetermined area 2 is, for example, a space divided by a wall 2a (obstacle), and includes a room 2b and a corridor 2c. In order for the positioning target 4 to move from the representative point A to the representative point D, it is necessary to move while avoiding the wall 2a, so that the positioning target 4 cannot move directly from the representative point A to the representative point D, but must pass through the representative points B and C. Therefore, the assumed moving route R from the representative point A to the representative point D is longer than the straight-line distance from the representative point A to the representative point D.

[0024] The discretized map 40 may have attribute information indicating the attributes of the locations of the representative points A to D. The attribute information may be the type or name of the location. The type of the location may be, for example, a room, a corridor, a lobby, an elevator, or a category name indicating the purpose of the location. The name of the location may be a specific name of a room, a store name, an event space name, or the like. In this embodiment, the representative points A and D are linked to the attribute information indicating that the attribute of the location is a room. The representative points B and C are linked to the attribute information indicating that the attribute of the location is a corridor. Note that the attribute information indicating the attribute of the location of the representative points may define various information depending on the type of the predetermined area 2. For example, when the purpose is to understand the purpose of a visit of shoppers to a shopping mall, it is more useful to use the attribute information as a category (food, clothing, etc.) rather than a store name. On the other hand, when the purpose is a traffic volume survey, it is not necessary to define the attribute information.

[0025] Fig. 5 is a diagram of a transition probability table 50 stored in the server 11 of Fig. 3. As shown in Fig. 5, the transition probability table 50 indicates the probability that a positioning target will exist at each of the representative points A to D (i.e., the movement destination) when the next positioning data is acquired, when a positioning target exists at any one of the representative points A to D (i.e., the movement origin).

[0026] For example, when the representative point A is set as the specific representative point where the positioning target 4 exists, the straight-line distance from the representative point A (specific representative point) to the representative point C (first representative point) is longer than the straight-line distance from the representative point A to the representative point D (second representative point), but the distance from the representative point A to the representative point C on the assumed moving route R is shorter than the distance from the representative point A to the representative point D on the assumed moving route R (see FIG. 4). Therefore, when the movement origin is the representative point A, the presence probability that the positioning target 4 exists at the representative point C at the time of positioning of the next positioning data is set to be higher than the presence probability that the positioning target 4 exists at the representative point D. This increases the accuracy of the presence probability that the positioning target exists at each of the representative points A to D even when there is an obstacle such as a wall 2a in the predetermined area 2.

[0027] FIG. 6 is a flowchart of position estimation by the server 11 of FIG. 3. Hereinafter, the flow of FIG. 6 will be described with reference to FIGS. 1 to 5 and 7 as appropriate. The processing of the positioning target terminal 10 is executed by the processing circuit 20, and the processing of the server 11 is executed by the processing circuit 30. The server 11 refers to the existence probability table of the probability storage unit 33c, and acquires the existence probability that the positioning target 4 exists at each of the multiple representative points A to D of the discretization map 40 (step S1). As an example, the server 11 acquires a numerical value as shown in FIG. 7A as the pre-positioning existence probability at the representative points A to D. Note that, when step S1 is the start of positioning of the positioning target 4, a predetermined initial value is set in the existence probability table. In addition, acquiring the existence probability may be receiving the existence probability, or may be calculating the existence probability.

[0028] Next, the server 11 performs one-period-ahead prediction of the sequential Bayes filter (step S2). The one-period-ahead prediction calculates the presence probability that the positioning target 4 will be present at each of the representative points A to D at the time of positioning of the next positioning data by the following formula (1). The presence probability calculated in step S2 is a value before taking into account the positioning data acquired in step S3, and is called the pre-positioning presence probability (or predicted presence probability).

[0029]

number

[0030] In formula (1), time t means the time when the positioning data is measured, and is incremented every time the positioning data is sampled. FIG. 7A shows the state at time t-1, and FIG. 7B shows the state at time t when the next positioning data is measured. That is, FIG. 7B shows a one-period-ahead prediction of the presence probability that the positioning target 4 is present at each of the representative points A to D. In formula (1), x t is the probability of the target 4 being located at each of the representative points A to D, and y t is the positioning data of the positioning target 4, that is, the positioning target terminal 10.

[0031] p(x t-1 |y0:t-1 ) is the probability of the location target 4 being present at each of the representative points A to D at time t-1, taking into consideration all the location data from the location start time 0 to time t-1 of the location target terminal 10. t |y 0:t-1 ) is the predicted presence probability of the positioning target 4 at each of the representative points A to D at the time t when the next positioning data is measured, and is the pre-positioning presence probability (that is, the predicted presence probability).

[0032] p(x t |x t-1 ) is a transition probability defined based on at least one of physical laws, empirical rules, etc., and corresponds to the value of the transition probability table 50 in FIG. 5. The value of the transition probability table 50 may be fixed, or may vary depending on at least one of the state of the positioning target 4, environmental factors, etc. For example, p(x t |x t-1 ) is a transition probability determined based on the assumed moving route R, attribute information of the representative points A to D, the assumed moving speed and sampling frequency of the object 4 to be positioned, etc., when transitioning from time t-1 to time t.

[0033] For example, the transition probability p(x t |x t-1 ) may be set so as to increase the presence probability of a representative point (e.g., representative points B to D) that is closer to a specific representative point (e.g., representative point A) at which the positioning target 4 exists at time t-1, as the distance from the specific representative point along the assumed moving route R to the other representative points. This allows the presence probability to be set in consideration of the assumed moving route R, thereby increasing the accuracy of the presence probability.

[0034] In addition, the transition probability p(x t |x t-1 ) may be set based on the attribute information of the specific representative point A. Specifically, when the attribute information of the specific representative point A indicates that the positioning target 4 has a strong tendency to stay at the current representative point, for example, when the type of location is the room 2b, the transition probability p(x t |x t-1) may be set to increase the probability of the positioning target 4 being at the specific representative point A, which is the current location. On the other hand, if the attribute information of the specific representative point A indicates that the positioning target 4 has a strong tendency to move from the current location, for example, if the type of location indicates that the corridor 2c, the transition probability p(x t |x t-1 ) may be set to increase the probability of the positioning target 4 existing at representative points A, C, and D other than the specific representative point B, which is the current location. This allows the movement tendency of the positioning target 4 depending on the type of location of the specific representative point to be reflected in the probability of existence. Therefore, the accuracy of the probability of the positioning target 4 existing at each representative point is improved.

[0035] In addition, the transition probability p(x t |x t-1 ) may be set based on the expected moving speed (e.g., walking speed) of the target 4 and the sampling frequency of the positioning data. Specifically, the interval time from time t-1 to time t is known from the sampling frequency, and the possible moving distance of the target 4 during the interval time is known from the expected moving speed. Therefore, the transition probability p(x t |x t-1 ) may be set to correct the presence probability of a representative point (e.g., representative point D) that is outside the range of the movable distance from a specific representative point (e.g., representative point A) to a lower probability compared to representative points (e.g., representative points B and C) that are within the range of the movable distance. This allows the presence probability of the positioning target 4 being present at each of representative points A to D to be set taking into account the movable distance of the positioning target 4, thereby improving the accuracy of the presence probability.

[0036] In addition, the transition probability p(x t |x t-1 ) may vary depending on the traveling direction, purpose of action, etc. of the target 4 to be located, or may vary depending on the time of day, etc. For example, if the predetermined area 2 is a school, each value of the transition probability table 50 may be set to lower the probability that the target 4 to be located moves during class hours and to increase the probability that the target 4 to be located moves during break hours.

[0037] Next, the server 11 acquires the next positioning data, which is the positioning data at time t (step S3). Specifically, the server 11 receives the strength of each radio wave received by the positioning target terminal 10 from each wireless access point 3, and the identification information of the wireless access point 3 that is the source of each radio wave. The server 11 refers to the AP position information list in the AP position information storage unit 33a, and identifies the position of the wireless access point 3 corresponding to each radio wave. The server 11 calculates the distance from each wireless access point 3 to the positioning target terminal 10 based on the strength of each radio wave, thereby calculating the position of the positioning target terminal 10 as positioning data. Note that the server 11 may acquire the positioning data by receiving it itself from outside.

[0038] Next, the server 11 performs a sequential Bayes filter (step S4). That is, the server 11 uses the next positioning data, which is the positioning data at time t, to calculate the pre-positioning existence probability p(x t |y 0:t-1 ) is filtered. This filtering is performed using the following formula (2).

[0039]

number

[0040] The probability of existence before positioning on the right side of equation (2) p(x t |y 0:t-1 ) is calculated using formula (1). p(x t |y 0:t ) is the presence probability of the target 4 at each of the representative points A to D at time t, taking into account all the positioning data from the positioning start time 0 to time t of the target terminal 10, and is called the post-positioning presence probability (or the positioning-corrected presence probability). t |x t ) is the point where the enthroned object is a specific representative point x t When the positioning data is present in t In this embodiment, x trefers to any of the representative points A to D. In general, p(y t |x t ) is the positioning data y t The position coordinates and representative point x t There is a negative correlation between the linear distance between the position coordinates of the

[0041] The server 11 calculates the pre-location presence probability p(x t |y 0:t-1 ) by the positioning data y t By updating based on t |y 0:t ) is calculated. FIG. 7C shows the updated presence probability after positioning p(x t |y 0:t ) is shown.

[0042] The server 11 updates the pre-location presence probability based on the positioning data, and stores the post-location presence probability in the probability storage unit 33c in association with information indicating the time t (step S5). The server 11 determines the representative point (representative point B in FIG. 7C) corresponding to the highest presence probability among the presence probabilities updated in step S4 as the position of the target 4 at the time t (step S6). In this way, the server 11 estimates the position of the target 4 and obtains a maximum likelihood estimate. The server 11 stores the estimated position information indicating the position of the target 4 in the position information storage unit 33d in association with information indicating the time t (step S7).

[0043] After that, the process returns to step S1, and the set of steps S1 to S7 is cyclically repeated. That is, the server 11 acquires the presence probability stored in the probability storage unit 33c in the previous step S5, that is, the presence probability updated by the filter process in the previous step S4 (step S1). Then, in formulas (1) and (2), the time t is updated to time t+1, and the time t-1 is updated to time t. That is, p(x t-1 |y 0:t-1) is substituted with the presence probability acquired in this step S1, that is, the presence probability updated by the previous filter process in step S4. In the repetition of the set of steps S1 to S7, the process may return from step S5 to step S1 in parallel with the transition from step S5 to steps S6 and S7.

[0044] The server 11 may transmit the position information of the target 4 stored in the position information storage unit 33d in step S7 and the time information associated therewith to the target terminal 10 via the Internet 5. The target terminal 10 may transmit the received position information and time information to another server, or may display it on the display 24. The server 11 may transmit the position information of the target 4 stored in the position information storage unit 33d in step S7 and the time information associated therewith to another server via the Internet 5 without going through the target terminal 10.

[0045] In addition, as shown in Table 1, the server 11 may calculate the stay time and stay time ratio of the positioning target 4 for each attribute of the location where the positioning target 4 was present (e.g., type of location) as statistical data based on the presence probability accumulated and stored in the probability memory unit 33c by cyclically repeating steps S1 to S7.

[0046] [Table 1]

[0047] The total measurement time from the start of positioning of the target terminal 10 to the end of positioning is T total Let P be the integrated value of the existence probability of representative points A and D having attribute X (for example, room 2b). i_1 Let P be the integrated value of the existence probability of representative points B and C having attribute Y (e.g., corridor 2c). i_2 Then, the time spent at representative points A and D of attribute X is T S_1 is calculated by the following formula (3), and the staying time at the representative points B and C of attribute Y is T S_2 is calculated by the following formula (4): This makes it possible to statistically grasp the tendency of the types of places where the positioning target 4 stays.

[0048]

number

[0049]

number

[0050] According to the configuration described above, in order to improve the accuracy of estimating the position of the target 4 to be positioned at the next positioning, a pre-positioning existence probability (i.e., predicted existence probability) is used that takes into account physical constraints such as the presence of obstacles and moving speed, and human behavioral characteristics, movement tendencies based on customs, and the like, and the pre-positioning existence probability is corrected (updated) with the positioning data to obtain the post-positioning existence probability. Therefore, even if the accuracy of the positioning data is low, the position where the target 4 to be positioned exists can be estimated probabilistically with high accuracy. Thus, the accuracy of the estimated position of the target 4 to be positioned can be improved with a simple configuration. In addition, by using representative points that discretize the position, physical constraints, movement tendencies, and the like can be easily described in a mathematically calculable format called the pre-positioning existence probability (i.e., predicted existence probability), and the amount of calculation can be reduced.

[0051] The technology of the present disclosure is not limited to the above-mentioned embodiment. For example, the predetermined area 2 may be an outdoor area where a wireless LAN is constructed. The outdoor area may be, for example, an outdoor event venue such as a concert, a park, or an amusement park. The positioning target 4 may not be a human being, and may be, for example, an unmanned mobile body, a manned mobile body, a mobile robot, an article to be transported, an animal, etc. The positioning data may be satellite positioning data such as GPS data. The positioning target terminal 10 may receive sound waves, light, or magnetism from multiple locators installed in the predetermined area 2 for positioning, instead of receiving radio waves from multiple wireless access points 3 for positioning.

[0052] In the embodiment described above, the representative point (representative point B in FIG. 7C) corresponding to the highest presence probability among the presence probabilities updated in step S4 is determined as the location where the positioning target 4 exists at time t, but it is not necessary to determine a single representative point where the positioning target 4 exists. For example, the presence probabilities of the positioning target 4 at each of representative points A to D accumulated and stored in step S5 may be output as a heat map.

[0053] The position estimation program P2 may be distributed by a plurality of computers. In that case, the position estimation device is composed of the plurality of computers. For example, the position estimation program P2 may be distributed by the positioning target terminal 10 and the server 11, or may be distributed by a plurality of servers. The entire position estimation program P2 may be executed by the positioning target terminal 10. In that case, the positioning target terminal 10 may be the position estimation device. The two blocks shown in order in the flowchart of FIG. 6 may be executed simultaneously or in reverse order depending on the case. The server 11 may receive and synthesize intermediate data to make final data, or the positioning target terminal 10 may receive the final data.

[0054] As described above, the above embodiment has been described as an example of the technology disclosed in this application. However, the technology in this disclosure is not limited to this, and can be applied to embodiments in which modifications, replacements, additions, omissions, etc. have been made as appropriate. In addition, it is also possible to combine the components described in the above embodiment to create a new embodiment. For example, some configurations in an embodiment can be separated from other configurations in the embodiment and arbitrarily extracted. In addition, the components described in the attached drawings and detailed description include not only components essential for solving the problem, but also components that are not essential for solving the problem in order to exemplify the technology.

[0055] The functions of the elements disclosed herein can be performed using circuits or processing circuits, including general purpose processors, special purpose processors, integrated circuits, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), conventional circuits, and / or combinations thereof, configured or programmed to perform the disclosed functions. Processors are considered processing circuits or circuits because they include transistors and other circuits. In this disclosure, a circuit, unit, or means is hardware that performs the recited functions or hardware that is programmed to perform the recited functions. The hardware may be hardware disclosed herein or other known hardware that is programmed or configured to perform the recited functions. In the case where the hardware is a processor, which is considered a type of circuit, the circuit, means, or unit is a combination of hardware and software, and the software is used to configure the hardware and / or the processor.

[0056] Each of the following sections is a disclosure of a preferred embodiment.

[0057] [Item 1] A memory that stores in advance, as a discretized map, coordinates of a plurality of representative points that are discretely distributed in a space in which a positioning object can move within a predetermined area; A processing circuit that performs a calculation process based on the discretization map, The processing circuitry includes: acquiring a probability that the positioning target exists at each of the plurality of representative points; obtaining a transition probability indicating a probability that the positioning object moves to each of the plurality of representative points; acquiring, as a pre-location presence probability, a presence probability that the positioning object will be present at each of the plurality of representative points when positioning of next positioning data of the positioning object based on the presence probability and the transition probability; obtaining the next positioning data; updating the presence probability that the positioning target is present at each of the plurality of representative points at the time of positioning of the next positioning data based on the next positioning data, thereby acquiring a presence probability after positioning; A position estimation device configured to:

[0058] According to this configuration, the positioning target is considered to exist at one of a plurality of representative points distributed discretely in a predetermined area, and the presence probability of the positioning target at each of the plurality of representative points is acquired, and the acquired presence probability is updated based on the positioning data of the positioning target. Therefore, even if the accuracy of the positioning data is low, the position where the positioning target exists can be reasonably estimated based on the probability. Therefore, the position of the estimated positioning target can be estimated with high accuracy with a simple configuration.

[0059] [Item 2] 2. The position estimation device according to claim 1, wherein the processing circuit is configured to determine the representative point corresponding to the highest presence probability among the updated presence probabilities as the position where the positioning target is located when the next positioning data is measured.

[0060] According to this configuration, the location of the object to be located can be estimated probabilistically.

[0061] [Item 3] 3. The position estimation device according to item 1 or 2, wherein the transition probability is set based on an assumed movement route connecting the plurality of representative points.

[0062] According to this configuration, the expected movement route is taken into consideration when estimating the presence probability of the object to be positioned at each representative point, so that the accuracy of the position estimation of the object to be positioned can be improved.

[0063] [Item 4] the plurality of representative points include a specific representative point, a first representative point different from the specific representative point, and a second representative point different from the specific representative point; a straight-line distance from the specific representative point to the first representative point is longer than a straight-line distance from the specific representative point to the second representative point; a distance from the specific representative point to the first representative point on the assumed moving route is shorter than a distance from the specific representative point to the second representative point on the assumed moving route; 4. The position estimation device according to item 3, wherein the transition probability is set so that a probability that the positioning object moves from the specific representative point to the first representative point when the next positioning data is measured is higher than a probability that the positioning object moves from the specific representative point to the second representative point when the next positioning data is measured.

[0064] According to this configuration, even if there is an obstacle in the specified area, the accuracy of the position estimation of the position target can be improved. That is, even if it is not possible to move directly from one representative point to another one without going around the obstacle, by setting the transition probability considering the existence of the obstacle, the probability that the position target exists at each representative point can be calculated rationally, and the accuracy of the position estimation of the position target can be improved.

[0065] [Item 5] 5. The position estimation device according to claim 1, wherein the transition probability is set based on an assumed moving speed of the object to be positioned and a sampling frequency of the positioning data.

[0066] According to this configuration, the movable distance of the object to be positioned can be taken into consideration when estimating the presence probability of the object to be positioned at each representative point, thereby improving the accuracy of the position estimation of the object to be positioned.

[0067] [Item 6] the discretized map has attribute information indicating attributes of locations of the plurality of representative points; 6. The position estimation device according to any one of items 1 to 5, wherein the transition probability is set based on the attribute information.

[0068] According to this configuration, the movement tendency of the object to be located, which depends on the attribute of the representative point, can be reflected in the transition probability, thereby improving the accuracy of the position estimation of the object to be located.

[0069] [Item 7] the discretized map has attribute information indicating types of locations of the plurality of representative points; 7. The position estimation device according to any one of claims 1 to 6, wherein the processing circuit is configured to calculate a stay time or a stay time ratio of the positioning target for each type of location based on the post-positioning presence probability.

[0070] According to this configuration, it is possible to grasp the tendency of the types of places where the positioning target stays.

[0071] [Item 8] acquiring a probability of presence of the positioning object at each of a plurality of representative points that are discretely distributed in a space in which the positioning object can move within a predetermined area; obtaining a transition probability indicating a probability that the positioning object moves to each of the plurality of representative points; acquiring, as a pre-location presence probability, a presence probability that the positioning object will be present at each of the plurality of representative points when positioning of next positioning data of the positioning object based on the presence probability and the transition probability; obtaining the next positioning data; updating the presence probability that the positioning target is present at each of the plurality of representative points at the time of positioning of the next positioning data based on the next positioning data, thereby acquiring a presence probability after positioning; A location estimation method comprising:

[0072] [Item 9] A location estimation program that causes at least one processor to execute the method described in item 8. [Explanation of symbols]

[0073] 1. Location Estimation System 2. Designated area 3. Wireless Access Points 4 Positioning target 10 Positioning target terminal 11 Server 20 Processing circuit 30 Processing circuit 31 Processors 40 Discretized Map 50 Transition Probability Table A~D Representative points P1 terminal program P2 Position Estimation Program R Expected movement route

Claims

1. A memory that stores in advance, as a discretized map, coordinates of a plurality of representative points that are discretely distributed in a space in which a positioning object can move within a predetermined area; A processing circuit that performs a calculation process based on the discretization map, The processing circuitry includes: acquiring a probability that the positioning target exists at each of the plurality of representative points; obtaining a transition probability indicating a probability that the positioning object moves to each of the plurality of representative points; acquiring, as a pre-location presence probability, a presence probability that the positioning object will be present at each of the plurality of representative points when positioning of next positioning data of the positioning object is performed based on the presence probability and the transition probability; obtaining the next positioning data; updating the pre-positioning presence probability that the positioning target is present at each of the plurality of representative points at the time of positioning of the next positioning data based on the next positioning data, thereby acquiring a post-positioning presence probability; A position estimation device configured to:

2. 2. The position estimation device according to claim 1, wherein the processing circuit is configured to determine the representative point corresponding to the highest presence probability among the post-positioning presence probabilities as the position where the positioning target exists when the next positioning data is positioned.

3. The position estimation device according to claim 1 , wherein the transition probability is set based on an expected movement route connecting the plurality of representative points.

4. the plurality of representative points include a specific representative point, a first representative point different from the specific representative point, and a second representative point different from the specific representative point; a straight-line distance from the specific representative point to the first representative point is longer than a straight-line distance from the specific representative point to the second representative point; a distance from the specific representative point to the first representative point on the assumed moving route is shorter than a distance from the specific representative point to the second representative point on the assumed moving route; 4. The position estimation device according to claim 3, wherein the transition probability is set so that a probability that the positioning object moves from the specific representative point to the first representative point when the next positioning data is measured is higher than a probability that the positioning object moves from the specific representative point to the second representative point when the next positioning data is measured.

5. The position estimation device according to claim 1 , wherein the transition probability is set based on an assumed moving speed of the object to be positioned and a sampling frequency of the positioning data.

6. the discretized map has attribute information indicating attributes of locations of the plurality of representative points; The position estimation device according to claim 1 , wherein the transition probability is set based on the attribute information.

7. the discretized map has attribute information indicating attributes of locations of the plurality of representative points; The position estimation device according to claim 1 or 2, wherein the processing circuitry is configured to calculate a stay time or a stay time ratio of the positioning target for each attribute of the location based on the post-positioning presence probability.

8. acquiring a probability of presence of the positioning object at each of a plurality of representative points that are discretely distributed in a space in which the positioning object can move within a predetermined area; obtaining a transition probability indicating a probability that the positioning object moves to each of the plurality of representative points; acquiring, as a pre-location presence probability, a presence probability that the positioning object will be present at each of the plurality of representative points when positioning of next positioning data of the positioning object is performed based on the presence probability and the transition probability; obtaining the next positioning data; updating the presence probability that the positioning target is present at each of the plurality of representative points at the time of positioning of the next positioning data based on the next positioning data, thereby acquiring a presence probability after positioning; A location estimation method comprising:

9. A location estimation program causing at least one processor to carry out the method of claim 8.

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