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

Abstract

This position estimation device is configured to: acquire an existence probability, at each of a plurality of representative points, that a positioning target exists; acquire a transition probability indicating a probability that the positioning target moves to each of the plurality of representative points; acquire, on the basis of the existence probability and the transition probability and as a pre-positioning existence probability, an existence probability, at each of the plurality of representative points, that the positioning target exists at the time of positioning for next positioning data of the positioning target; acquire the next positioning data; and acquire a post-positioning existence probability by updating the existence probability, at each of the plurality of representative points, that the positioning target exists at the time of positioning for the next positioning data, on the basis of the next positioning data.

Description

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

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

[0002] Conventionally, positioning methods 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. These positioning methods involve errors in the positioning data due to the influence of noise, etc. Therefore, the technology disclosed in Patent Document 1 corrects the positioning data, which may contain errors, using displacement data calculated based on the detection values ​​of an acceleration sensor or the like provided in the object to be positioned, thereby reducing the positioning error.

[0003] Japanese Patent Application Laid-Open No. 2021-50969

[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, one aspect of the present disclosure aims to reduce errors in the position of a positioning target estimated based on positioning data with a simple configuration, thereby improving the accuracy of position estimation.

[0006] A position estimation device according to one aspect of the present disclosure includes a memory that pre-stores, as a discretization 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, and a processing circuit that performs arithmetic processing based on the discretization map. The processing circuit is configured to: acquire a presence probability of the positioning object being present at each of the plurality of representative points; acquire a transition probability indicating a probability that the positioning object will move to each of the plurality of representative points; acquire, based on the presence probability and the transition probability, a presence probability that the positioning object will be present at each of the plurality of representative points upon positioning of next positioning data of the positioning object, as a pre-positioning presence probability; acquire the next positioning data; and acquire a post-positioning presence probability by updating, based on the next positioning data, the presence probability of the positioning object being present at each of the plurality of representative points upon positioning of the next positioning data.

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

[0008] A location estimation program according to one aspect 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, tangible medium. The storage medium may be built into or external 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 in a computer connected to the storage medium via a network (e.g., the Internet).

[0009] According to one aspect of the present disclosure, a positioning target is assumed to exist at one of a plurality of representative points discretely distributed in a predetermined area, and a presence probability that the positioning target exists at each of the plurality of representative points is obtained, and the obtained 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 location of the positioning target can be reasonably estimated based on the probability. Therefore, the position of the positioning target can be estimated accurately with a simple configuration.

[0010] FIG. 1 is a schematic diagram of a position estimation system according to an embodiment. FIG. 2 is a block diagram of a terminal to be positioned in FIG. 1. FIG. 3 is a block diagram of a server in FIG. 1. FIG. 4 is a schematic diagram of a discretization map stored in the server in FIG. 3. FIG. 5 is a diagram of a transition probability table stored in the server in FIG. 3. FIG. 6 is a flowchart of position estimation by the server in FIG. 3. FIG. 7A is a diagram showing the presence probability that a positioning target exists at each representative point in the discretization map in 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 updated based on the next positioning data (post-positioning presence probability).

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

[0012] 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 shopping 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 positioning target terminal 10 means locating the positioning target 4. In a predetermined area 2, a plurality of wireless access points 3 capable of wireless communication 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 emit radio waves including their own identification information (e.g., MAC addresses).

[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 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 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, etc.

[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 points 3 that are the transmitters 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 the 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 via a wired or wireless connection.

[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 within 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 positioning target terminal 10 calculated based on the positioning 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 discretely distributed in a space in which the positioning target 4 can move within a predetermined area 2. The representative points A to D are connected by an assumed movement route R that the positioning target 4 (see FIG. 1) is assumed to take during its movement. The predetermined area 2 is, for example, partitioned by a wall 2 a (obstacle) and includes a room 2 b and a corridor 2 c. In order for the positioning target 4 to move from representative point A to representative point D, the positioning target 4 must move while avoiding wall 2 a. Therefore, the positioning target 4 cannot move directly from representative point A to representative point D, but must instead move via representative points B and C. Therefore, the assumed movement route R from representative point A to representative point D is longer than the straight-line distance from representative point A to representative point D.

[0024] The discretized map 40 may have attribute information indicating the attributes of each location of each representative point A to D. The attribute information may be the type or name of the location. The type of location may be, for example, a room, a hallway, a lobby, an elevator, or a category name indicating the purpose of the location. The name of the location may be a specific room name, a store name, an event space name, or the like. In this embodiment, representative points A and D are linked to the attribute information indicating that the attribute of the location is a room. Representative points B and C are linked to the attribute information indicating that the attribute of the location is a hallway. Note that the attribute information indicating the attributes of the locations of the representative points may be defined in various ways depending on the type of the specified area 2. For example, if the purpose is to understand the purpose of shoppers' visits to a shopping mall, it is more useful to use the attribute information as a category (e.g., groceries, clothing, etc.) rather than a store name. On the other hand, if 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 be present 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 is present at any one of the representative points A to D (i.e., the movement origin).

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

[0027] FIG. 6 is a flowchart of position estimation by the server 11 of FIG. 3. The following description will be given following the flow of FIG. 6, 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 references the presence probability table in the probability storage unit 33c and acquires the presence 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 values ​​such as those shown in FIG. 7A as the pre-positioning presence probabilities 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 presence probability table. Furthermore, acquiring the presence probability may mean receiving the presence probability or calculating the presence probability.

[0028] Next, the server 11 performs one-stage-ahead prediction using a sequential Bayes filter (step S2). The one-stage-ahead prediction calculates the presence probability that the target 4 will be present at each of the representative points A to D at the time of positioning of the next positioning data using 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 referred to as the pre-positioning presence probability (or predicted presence probability).

[0029]

[0030] In formula (1), time t means the time of positioning data measurement, and is incremented each 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 of the positioning target 4 being 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) on the right side of Equation (1) ｔ－１ |y ０：ｔ－１) is the probability of the location target 4 existing at each of the representative points A to D at time t-1, taking into consideration all the positioning data from the positioning 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 object 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 (i.e., predicted presence probability).

[0032] p(x) on the right side of Equation (1) ｔ |x ｔ－１ ) is a transition probability defined based on at least one of physical laws, empirical rules, etc., and corresponds to the values ​​in the transition probability table 50 in FIG. 5. The values ​​in the transition probability table 50 may be fixed or may vary depending on at least one of the state of the object 4 to be positioned, environmental factors, etc. For example, p(x ｔ |x ｔ－１ ) is a transition probability determined based on the expected movement route R, attribute information of representative points A to D, the expected movement 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 that the presence probability is increased for representative points (e.g., representative points B to D) other than the specific representative point (e.g., representative point A) where the positioning target 4 is located at time t-1, as the distance from the specific representative point along the assumed movement route R becomes shorter. As a result, the presence probability is set taking the assumed movement route R into consideration, and the accuracy of the presence probability is improved.

[0034] Also, 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 target 4 being present 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 target 4 is likely to move from the current location, for example, if the type of location is the corridor 2c, the transition probability p(x ｔ |x ｔ－１ ) may be set to increase the probability of the target 4 being present 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 target 4 being present depending on the type of location of the specific representative point to be reflected in the probability of presence. This increases the accuracy of the probability of the target 4 being present at each representative point.

[0035] Also, the transition probability p(x ｔ |x ｔ－１ ) may be set based on the assumed moving speed (for example, walking speed) of the object 4 to be located 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 object 4 to be located in the interval time is known from the assumed moving speed. Therefore, the transition probability p(x ｔ |x ｔ－１ ) can be set to correct the probability of presence of a representative point (e.g., representative point D) outside the range of the movable distance from a specific representative point (e.g., representative point A) to be reduced compared to representative points (e.g., representative points B and C) within the range of the movable distance. As a result, the probability of presence of the positioning target 4 at each of representative points A to D is set taking into account the movable distance of the positioning target 4, thereby increasing the accuracy of the presence probability.

[0036] Also, the transition probability p(x ｔ |x ｔ－１ ) may vary depending on the direction of travel of the object 4 to be located, the purpose of its activity, the time of day, etc. For example, if the predetermined area 2 is a school, the values ​​in the transition probability table 50 may be set to lower the probability that the object to be located moves during class hours and to increase the probability that the object 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 itself by receiving it from an external source.

[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 by the following equation (2).

[0039]

[0040] The pre-positioning existence probability p(x ｔ |y ０：ｔ－１ ) is calculated by the formula (1). ｔ |y ０：ｔ ) is the probability of the location 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 positioning-corrected presence probability). ｔ |x ｔ ) is the point where the enthronement object is a specific representative point x ｔ When the positioning data exists in y ｔ In this embodiment, x ｔ indicates one of the representative points A to D. Generally, p(y ｔ |x ｔ ) is the positioning data y ｔ The position coordinates and representative point xｔ There is a negative correlation between the linear distance and the position coordinates of

[0041] The server 11 calculates the pre-location existence probability p(x ｔ |y ０：ｔ－１ ) as 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 to obtain a post-location presence probability, 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 location of the positioning target 4 at the time t (step S6). In this way, the server 11 estimates the location of the positioning target 4 and obtains a maximum likelihood estimate. The server 11 stores the estimated location information indicating the location of the positioning target 4 in the location information storage unit 33d in association with information indicating the time t (step S7).

[0043] After this, the process returns to step S1, and the set of steps S1 to S7 is cyclically repeated. That is, the server 11 acquires the existence probability stored in the probability storage unit 33c in the previous step S5, that is, the existence probability updated by the filter processing 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 existence probability acquired in this step S1, i.e., the existence probability updated by the previous filter processing 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 positioning target 4 and the time information linked thereto, which are stored in the position information storage unit 33d in step S7, to the positioning target terminal 10 via the Internet 5. The positioning target terminal 10 may transmit the received position information and time information to another server, or may display them on the display 24. The server 11 may transmit the position information of the positioning target 4 and the time information linked thereto, which are stored in the position information storage unit 33d in step S7, to another server via the Internet 5 without going through the positioning target terminal 10.

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

[0046]

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

[0048]

[0049]

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

[0051] The technology of the present disclosure is not limited to the above-described embodiment. For example, the predetermined area 2 may be an outdoor area in which a wireless LAN is established. 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 does not have to be a human, but may be, for example, an unmanned mobile object, a manned mobile object, a mobile robot, a transported item, an animal, etc. The positioning data may be satellite positioning data such as GPS data. Instead of receiving radio waves from multiple wireless access points 3 for positioning, the positioning target terminal 10 may receive sound waves, light, or magnetism from multiple locators installed in the predetermined area 2 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 of the positioning target 4 at time t, but it is not necessary to determine a single representative point where the positioning target 4 is located. 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 processed in a distributed manner by multiple computers. In this case, the position estimation device is composed of the multiple computers. For example, the position estimation program P2 may be processed in a distributed manner by the positioning target terminal 10 and the server 11, or may be processed in a distributed manner by multiple servers. The entire position estimation program P2 may be executed by the positioning target terminal 10. In this case, the positioning target terminal 10 can serve as the position estimation device. The two blocks shown in order in the flowchart of FIG. 6 may be executed simultaneously or in the reverse order, depending on the situation. The server 11 may receive and combine intermediate data to generate final data, or the positioning target terminal 10 may receive the final data.

[0054] As described above, the above-described embodiments have been described as examples of the technology disclosed in this application. However, the technology of the present disclosure is not limited to these embodiments and can be applied to embodiments in which modifications, substitutions, additions, omissions, etc. are made as appropriate. Furthermore, the components described in the above-described embodiments can be combined to create new embodiments. For example, some configurations in the embodiments can be separated and arbitrarily extracted from other configurations in the embodiment. Furthermore, the components described in the accompanying drawings and detailed description include not only components essential for solving the problem, but also components that are not essential for solving the problem, and are used to illustrate 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 circuitry. 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. 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 processor.

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

[0057] [Item 1] A position estimation device comprising: a memory that pre-stores, as a discretization 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; and a processing circuit that performs arithmetic processing based on the discretization map, wherein the processing circuit is configured to: acquire a presence probability that the positioning object will be present at each of the plurality of representative points; acquire a transition probability that indicates a probability that the positioning object will move to each of the plurality of representative points; acquire, based on the presence probability and the transition probability, a presence probability that the positioning object will be present at each of the plurality of representative points upon positioning of next positioning data of the positioning object, 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 object will be present at each of the plurality of representative points upon positioning of the next positioning data, based on the next positioning data.

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

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

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

[0061] [Item 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 estimated movement route is taken into consideration when estimating the probability that the object to be positioned exists at each representative point, thereby improving the accuracy of the position estimation of the object to be positioned.

[0063] [Item 4] The position estimation device according to Item 3, wherein 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, wherein 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, wherein a distance from the specific representative point to the first representative point on the assumed movement path is shorter than a distance from the specific representative point to the second representative point on the assumed movement path, and 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 when there are obstacles in the predetermined area, the accuracy of the position estimation of the positioning object can be improved. That is, even when it is not possible to move directly from one representative point to another without going around the obstacle, by setting the transition probability taking the presence of the obstacle into consideration, it is possible to rationally calculate the probability that the positioning object exists at each representative point, thereby improving the accuracy of the position estimation of the positioning object.

[0065] [Item 5] The position estimation device according to any one of items 1 to 4, 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 probability that the object to be positioned is present at each representative point, thereby improving the accuracy of the position estimation of the object to be positioned.

[0067] [Item 6] The position estimation device according to any one of Items 1 to 5, wherein the discretization map has attribute information indicating attributes of locations of the plurality of representative points, and the transition probabilities are set based on the attribute information.

[0068] According to this configuration, the movement tendency of the object to be positioned, which depends on the location 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 positioned.

[0069] [Item 7] The position estimation device according to any one of Items 1 to 6, wherein the discretization map has attribute information indicating location types of the plurality of representative points, and the processing circuit is configured to calculate a stay time or a stay time ratio of the positioning target for each location type 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] A position estimation method including: acquiring a presence probability that the object to be positioned exists at each of a plurality of representative points that are discretely distributed in a space in which the object to be positioned can move in a predetermined area; acquiring a transition probability indicating a probability that the object to be positioned will move to each of the plurality of representative points; acquiring, based on the presence probability and the transition probability, a presence probability that the object to be positioned will exist at each of the plurality of representative points when next positioning data of the object to be positioned is measured, as a pre-positioning presence probability; acquiring the next positioning data; and acquiring a post-positioning presence probability by updating, based on the next positioning data, the presence probability that the object to be positioned will exist at each of the plurality of representative points when next positioning data is measured.

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

[0073] REFERENCE SIGNS LIST 1 Position estimation system 2 Predetermined area 3 Wireless access point 4 Positioning target 10 Positioning target terminal 11 Server 20 Processing circuit 30 Processing circuit 31 Processor 40 Discretization map 50 Transition probability table A to D Representative points P1 Terminal program P2 Position estimation program R Assumed movement route

Claims

1. A position estimation device comprising: a memory in which coordinates of a plurality of representative points discretely distributed in a space in which a positioning object can move within a specified area are stored in advance as a discretization map; and a processing circuit that performs calculations based on the discretization map, wherein the processing circuit is configured to: acquire a presence probability that the positioning object is present at each of the plurality of representative points; acquire a transition probability indicating the probability that the positioning object will move to each of the plurality of representative points; acquire a pre-positioning presence probability, based on the presence probability and the transition probability, which is 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 measured; acquire the next positioning data; and acquire a post-positioning presence probability by updating the pre-positioning presence probability that the positioning object will be present at each of the plurality of representative points when the next positioning data is measured based on the next positioning data.

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 location where the positioning target will be located when the next positioning data is located.

3. The position estimation device according to claim 1 or 2, wherein the transition probability is set based on an expected movement route connecting the plurality of representative points.

4. The position estimation device of claim 3, wherein the multiple 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 movement route is shorter than a distance from the specific representative point to the second representative point on the assumed movement route, and the transition probability is set such 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. A position estimation device according to any one of claims 1 to 4, 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. A position estimation device according to any one of claims 1 to 5, wherein the discretized map has attribute information indicating attributes of the locations of the plurality of representative points, and the transition probabilities are set based on the attribute information.

7. A position estimation device as described in any one of claims 1 to 6, wherein the discretized map has attribute information indicating attributes of the locations of the multiple representative points, and the processing circuit is configured to calculate the stay time or stay time ratio of the positioning target for each attribute of the location based on the post-positioning presence probability.

8. A position estimation method comprising: acquiring a presence probability that the positioning object exists at each of a plurality of representative points that are discretely distributed in a space in which the positioning object can move within a specified area; acquiring a transition probability indicating the probability that the positioning object will move to each of the plurality of representative points; acquiring a presence probability that the positioning object will exist at each of the plurality of representative points when next positioning data of the positioning object is measured 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 will exist at each of the plurality of representative points when next positioning data is measured based on the next positioning data.

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

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