JP2009168792A - Step computing device, walking distance specifying device, position specifying device, computer program, and step computing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a step computing device for computing a walker's step of accurately specifying the position of the walker, a walking distance specifying device, a position specifying device, a computer program and a step computing method. <P>SOLUTION: A correlation parameter computing part 172 computes correlation parameters of the walker's step and a walking speed of each step. The correlation between the step and walking speed is expressed by d=Σwi=α×Σvi+n×β, wherein d is a walking distance between two spots, n is the number of steps of the walker between the two spots, wi(i=1, 2, ... n) is a line of steps for each step between the two spots, vi(i=1, 2, ... n) is a line of the walking speed for each step between the two spots, and α, β are correlation parameters. A step computing part 173 obtains the number of steps and the walking speed of each step from a distance sensor 132 or the like, and computes the step of each step based on the obtained number of steps and walking speed and the correlation parameters α, β. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a position specifying technique, and in particular, a stride calculation device that calculates a pedestrian's stride for accurately specifying the position of the pedestrian when carried by the pedestrian, and a walking distance specifying device including the stride calculation device. The present invention relates to a position specifying device including the walking distance specifying device, a computer program for realizing the stride calculating device, and a stride calculating method.

  Examples of position detection methods widely used in navigation for detecting the position of a moving body such as a vehicle include self-contained navigation, satellite navigation, map matching, and hybrid navigation. Self-contained navigation uses a distance sensor, an azimuth sensor, an angular velocity sensor, etc., for example, based on the azimuth angle of the vehicle traveling with respect to an orthogonal coordinate system based on the longitude-latitude coordinate system and the traveling distance per unit time. Although it is calculated, consistency with the road is not taken into consideration, and there is a problem that errors in the vehicle position accumulate as the travel distance increases.

Satellite navigation uses GPS (Global Positioning System), and the detected position includes an error of about 10 to 20 m. Since GPS is used, an in-vehicle sensor such as a distance sensor, an azimuth sensor, or an angular velocity sensor is unnecessary. However, on roads under elevated roads, roads sandwiched between buildings, mountain roads, roadside trees, etc., radio waves cannot be received from a predetermined number of GPS satellites, and the detection accuracy is greatly degraded. is there. In addition, there is also a problem that the traveling road is wrong in a narrow street with a narrow road interval.

  In addition, the map matching method detects the position of the vehicle in consideration of the consistency (matching) between the travel locus by the self-contained navigation and the road map (see Patent Document 1). That is, the most probable road is selected from a plurality of road candidates considered to be traveling while comparing the trajectory obtained by the self-contained navigation with the road map data. Then, when the number of candidate roads is limited to one, the traveling locus of the vehicle obtained by the self-contained navigation is matched with the road. However, if the limited road is wrong, there is a problem that position detection after that becomes impossible.

  Hybrid navigation is a combination of satellite navigation and map matching, and it rationally estimates the position of the vehicle and identifies the road on which it is traveling, taking into account the errors between autonomous navigation and satellite navigation. (See Patent Document 2). In hybrid navigation, for example, the position of a vehicle is detected using a map matching method in normal times. When the vehicle position cannot be detected by the map matching method, the vehicle position and direction are detected by satellite navigation to estimate the vehicle position, and the vehicle position is detected in consideration of consistency with the road map data. To do. With hybrid navigation, except for special cases, there is almost no possibility of mistaken roads on which vehicles are traveling, and the positional accuracy in the direction of the road is within an error range of about 10 m on average. In the target navigation, the accuracy level is almost no problem in practical use.

  On the other hand, in the pedestrian position detection method, position detection is performed using a portable device such as a mobile phone or a simple navigation device carried by the pedestrian. In such portable devices, for example, a method for detecting the current position of a pedestrian by radio waves from GPS satellites or communication with a base station has been put into practical use.

  However, when detecting a position by receiving radio waves from a GPS satellite, the position error is about 10 to 20 m when the reception condition of the GPS satellite is good. In such cases, the position cannot be detected, or the position error may be about several hundreds of meters due to the influence of multipath or the like, and an accurate position may not be obtained. In particular, in the case of pedestrians, unlike mobile objects such as vehicles, there is a tendency to walk near buildings along the buildings, so the reception level of radio waves from GPS satellites decreases.

  Therefore, technology development for accurately obtaining the position of a pedestrian has been performed. For example, it detects the walking of each step of the pedestrian, calculates the walking distance based on the detected walking and the registered step length, and calculates the relative position of the pedestrian viewed from a predetermined starting point by self-contained navigation. A portable navigation device that detects the current position of a person is disclosed (see Patent Document 3).

  A mobile phone that detects the number of steps of a pedestrian, detects the current position of the pedestrian on the map while calculating the walking distance from the preset step length and the detected number of steps, and obtaining the position on the map by independent navigation. A navigation device is disclosed (see Patent Document 4).

In a portable device carried by a pedestrian, the distance sensor used to detect the position of the pedestrian is a device that counts the number of steps of the pedestrian or an acceleration sensor, and is a distance used in the case of a vehicle. The detection accuracy is low compared to sensors (for example, vehicle speed sensors, wheel speed sensors, etc.). Even when the number of steps is detected to determine the walking distance, it is necessary to calibrate the distance (step length) per step as in the case of the vehicle speed sensor or the like. For this reason, in Patent Document 4 described above, the position on the map is measured by GPS, the walking distance of the pedestrian between the measured positions is obtained, and the walking distance is divided by the number of steps detected during the walking distance. The stride is calculated and set.
JP-A-63-148115 JP-A-2-275310 JP-A-8-68643 Japanese Patent Laid-Open No. 9-89584

  However, when calculating the walking distance between two points measured by GPS to calculate the stride, if the distance between the two points is relatively short (for example, about 100 m to 500 m), the positioning error by GPS cannot be ignored. An accurate walking distance cannot be calculated. In particular, when a pedestrian walks around a building along the building, the accuracy of GPS positioning is significantly reduced due to the influence of multipath and the like. Furthermore, when the distance between the two points is relatively long (for example, about 1 km), it is difficult to calculate a more accurate walking distance because the pedestrian does not always go straight between the two points. Become.

  On the other hand, when a pedestrian walks, the stride is not always constant. For example, the stride changes depending on the mood of the pedestrian. Therefore, it is conceivable that the walking rhythm and the stride are associated in advance and the stride or walking distance corresponding to the walking rhythm of the pedestrian is obtained. However, when the walking rhythm fluctuates while walking between two points, the stride or walking distance cannot be obtained accurately. Further, even when the walking rhythm (for example, the number of steps per second) is the same, the stride may be different (for example, when walking with a large crotch and when walking normally). For this reason, it has been desired to accurately calculate the pedestrian's stride.

  The present invention has been made in view of such circumstances, and includes a stride calculation device that calculates a pedestrian's stride for accurately specifying the position of the pedestrian, a walking distance specifying device including the stride calculation device, It is an object of the present invention to provide a position specifying device including a walking distance specifying device, a computer program for realizing the step calculating device, and a step calculating method.

  The stride calculation device according to the first invention is a stride calculation device for calculating a stride of a pedestrian, a step acquisition means for acquiring the number of steps of the pedestrian, and a walking distance between two points out of the points where the pedestrian has walked. A walking distance calculation means for calculating, a walking characteristic acquisition means for acquiring a walking characteristic of a pedestrian, a walking distance between the two points, the number of steps of the pedestrian between the two points, and a walking characteristic based on the walking characteristics Correlation specifying means for specifying the correlation with the step, and stride calculation means for calculating a stride based on the correlation specified by the correlation specifying means and the walking characteristics.

  According to a second aspect of the present invention, in the first aspect of the invention, the stride calculation device includes walking speed acquisition means for acquiring a walking speed for each step as the walking characteristic, and the correlation specifying means includes the walking distance between the two points, the walking distance, It is configured to specify the correlation between the stride and the walking speed based on the number of steps of the pedestrian between the two points and the walking speed for each step between the two points, and the step calculating means includes the step The step is calculated based on the correlation specified by the correlation specifying means and the walking speed.

  According to a third aspect of the present invention, there is provided a stride calculation device according to the first aspect, further comprising walking time acquisition means for acquiring a walking time for each step as the walking characteristic, wherein the correlation specifying means includes the walking distance between the two points, It is configured to identify the correlation between the stride and the walking time based on the number of steps of the pedestrian between the two points and the walking time for each step between the two points, and the step calculating means includes the step The step is calculated based on the correlation specified by the correlation specifying means and the walking time.

  According to a fourth aspect of the present invention, in the first aspect of the invention, the stride calculation device includes impact acquisition means for acquiring an impact at the time of landing for each step as the walking characteristic, and the correlation specifying means includes the walking distance between the two points, The step is configured to identify the correlation between the stride and the impact at the time of landing based on the number of steps of the pedestrian between the two points and the impact at the time of landing at each step between the two points. The calculating means is configured to calculate the stride based on the correlation specified by the correlation specifying means and the impact at the time of landing.

  According to a fifth aspect of the present invention, in any one of the first to fourth aspects, the stride calculation device includes inclination information acquisition means for acquiring inclination information relating to the inclination between the two points, and the correlation specifying means includes: It is configured to specify the correlation between the stride and the walking characteristic according to the inclination information acquired by the inclination information acquisition means.

  According to a sixth aspect of the present invention, in any one of the first to fifth aspects, the stride calculation device includes a storage unit that stores map information, a first specifying unit that specifies two points where a pedestrian has walked, First distance calculating means for calculating a straight distance between two points specified by the first specifying means, and second distance calculating means for calculating a distance along the walking locus of the two points specified by the first specifying means by self-contained navigation A second specifying means for specifying two points on the map by a map matching method using map information stored in the storage means and the two points specified by the first specifying means; and Third distance calculating means for calculating a straight line distance between the two specified points, wherein the walking distance calculating means is based on the distances calculated by the first distance calculating means, the second distance calculating means, and the third distance calculating means. The two points where the pedestrian walked Characterized in that is arranged to calculate the row distance.

  According to a seventh aspect of the present invention, in the fifth aspect of the invention, the correlation specifying means specifies a correlation parameter indicating a correlation between the stride and the walking characteristic according to the inclination information acquired by the inclination information acquiring means. The second correlation specification for specifying the second correlation between the correlation parameter and the inclination information based on the correlation parameter according to the inclination information specified by the correlation specifying means and the inclination information. And the correlation specifying means is further configured to specify a correlation parameter for arbitrary slope information based on the second correlation specified by the second correlation specifying means. And

  According to an eighth aspect of the present invention, in any one of the first through seventh aspects, the stride calculation device includes an error calculation unit that calculates an error of the stride calculated by the step calculation unit.

  A walking distance specifying device according to a ninth invention specifies a walking distance based on the stride calculation device according to any one of the first to eighth inventions, the number of steps of the pedestrian and the stride calculated by the stride calculation device. And a walking distance specifying means.

  A position specifying device according to a tenth aspect of the invention is the walking distance specifying device according to the ninth aspect, positioning means for positioning the position of the pedestrian, the positioning position determined by the positioning means, and the walking specified by the walking distance specifying device. And a position specifying means for specifying the position of the pedestrian based on the distance.

  A computer program according to an eleventh aspect of the present invention is a computer program for causing a computer to acquire the number of steps and walking characteristics of a pedestrian and to function as a means for specifying a correlation between a pedestrian's step length and walking characteristics, The computer calculates the walking distance calculation means for calculating the walking distance between two points among the points where the pedestrian has walked, the calculated walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking characteristics. It is made to function as a correlation specifying means for specifying the correlation between the stride and the walking characteristic based on the above.

  A stride calculation method according to a twelfth aspect of the present invention is a stride calculation method in which a stride calculation device calculates a stride of a pedestrian, wherein the stride calculation device acquires the number of steps of the pedestrian and 2 out of points where the pedestrian walks. The walking distance between the points is calculated, the walking characteristics of the pedestrian are obtained, and the correlation between the stride and the walking characteristics based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking characteristics. , And the stride is calculated based on the specified correlation and walking characteristics.

  In the first invention, the eleventh invention and the twelfth invention, the two points where the pedestrian walks are specified, the walking distance between the two points, the number of steps of the pedestrian between the two points, and between the two points The correlation between the stride and the walking characteristics is specified based on the walking characteristics of the pedestrians. The two points where the pedestrian walks are determined by positioning using positioning data such as GPS, base station communication, distance sensor, direction sensor, or communication using optical beacon, radio beacon, etc. It can be specified by obtaining a point corresponding to the positioning position by map matching. By using map matching together, for example, it is possible to specify two points with higher accuracy than in the case of positioning using only GPS, and to determine the walking distance between the two points.

  The walking characteristics of the pedestrian include, for example, the walking speed for each step of the pedestrian between the two points, the walking time for each step (walking pace), and the walking strength (strength of walking) as an impact at the time of landing for each step. And so on. For example, when the walking speed is used as the walking characteristic, the walking distance between two points is d, the number of pedestrians between two points is n, and the step length sequence between two points is wi (i = 1, 2). ,... N) If the sequence of walking speeds for each step between two points is vi (i = 1, 2,... N), the correlation between the stride and the walking speed is, for example, d = Σwi = α · Σvi + n • Can be represented by β. Here, α and β are correlation parameters. By collecting these data and calculating the correlation parameters α and β as solutions of the linear regression line, the correlation between the stride and the walking characteristics (for example, the walking speed for each step) can be specified.

  When calculating the step length, for example, the walking speed for each step is acquired as the number of steps and the walking characteristics by an acceleration sensor or the like, and the step length for each step is obtained based on the acquired number of steps, the walking speed for each step, and the correlation parameter. it can. Thereby, even when the walking characteristic (for example, walking speed) of a pedestrian fluctuates between two points, the stride can be calculated with high accuracy.

  In the second invention, the walking speed for each step is acquired as the walking characteristic. The two points where the pedestrian walks are identified, and the correlation between the stride and the walking speed is determined based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking speed for each step between the two points. Identify. The walking distance between two points is d, the number of pedestrians between two points is n, the step length sequence between two points is wi (i = 1, 2,... N), and the step distance between two points is If the walking speed column is vi (i = 1, 2,..., N), the correlation between the stride and the walking speed can be expressed by, for example, d = Σwi = α · Σvi + n · β. Here, α and β are correlation parameters. By collecting these data and calculating the correlation parameters α and β as solutions of the linear regression line, the correlation between the stride and the walking speed can be specified.

  When calculating the step length, for example, the number of steps and the walking speed for each step can be acquired by an acceleration sensor or the like, and the step length for each step can be obtained from the acquired number of steps and the walking speed and the correlation parameter. Thereby, even when the walking speed of a pedestrian fluctuates between two points, the stride can be calculated with high accuracy.

  In the third invention, the walking time (walking pace) for each step is acquired as the walking characteristic. Based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking time (walking pace) for each step between the two points, Identify the correlation. The walking distance between two points is d, the number of pedestrians between two points is n, the step length sequence between two points is wi (i = 1, 2,... N), and the step distance between two points is If the walking time column is tri (i = 1, 2,... N), the correlation between the stride and the walking time can be expressed by, for example, d = Σwi = α ′ · Σtri + n · β ′. Here, α ′ and β ′ are correlation parameters. By collecting these data and calculating the correlation parameters α ′ and β ′ as solutions of the linear regression line, the correlation between the stride and the walking time can be specified.

  When calculating the step length, for example, the number of steps and the walking time for each step can be acquired by an acceleration sensor or the like, and the step length for each step can be obtained from the acquired number of steps and the walking time and the correlation parameter. Thereby, even when the walking time (walking pace) of a pedestrian fluctuates between two points, the stride can be calculated with high accuracy.

  In the fourth invention, the walking strength (strength of walking) as an impact at the time of landing for each step is acquired as a walking characteristic. The two points where the pedestrian walks are identified, the step length and one step based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking strength (walking strength) for each step between the two points. The correlation with each walking intensity is specified. The walking distance between two points is d, the number of pedestrians between two points is n, the step length sequence between two points is wi (i = 1, 2,... N), and the step distance between two points is Assuming that the walking intensity column is st i (i = 1, 2,..., N), the correlation between the stride and the walking intensity for each step can be expressed by, for example, d = Σwi = α ″ · Σst i + n · β ″. . Here, α ″ and β ″ are correlation parameters. By collecting these data and calculating the correlation parameters α ″ and β ″ as solutions of the linear regression line, the correlation between the stride and the walking intensity for each step can be specified.

  When calculating the step length, for example, the number of steps and the walking strength for each step can be acquired by an acceleration sensor or the like, and the step length for each step can be obtained from the acquired number of steps and the walking strength and the correlation parameter. Thereby, even when the walking strength (walking strength) of the pedestrian fluctuates between the two points, the stride can be calculated with high accuracy.

  In the fifth aspect of the invention, inclination information relating to the inclination between the two points is acquired. The inclination information is, for example, information such as an altitude at two points, an altitude difference between two points, or an inclination angle at two points obtained by dividing the altitude difference between two points by a distance. The configuration may be such that the tilt information is previously included in the map information and stored, may be detected by an altitude sensor, or may be configured to receive tilt information from the outside. For example, an inclination angle between two points is φ, positive is an uphill, and negative is a downhill. For example, when the walking speed is used as the walking characteristic, the walking distance between two points is d, the number of pedestrians between two points is n, and the step length sequence between two points is wi (i = 1, 2). ,... N) When the sequence of walking speeds for each step between two points is vi (i = 1, 2,... N), the correlation between the stride and the walking speed in consideration of the inclination angle φ at the two points is For example, d = Σwi = α (φ) · Σvi + n · β (φ). Here, α (φ) and β (φ) are correlation parameters. By collecting these data and calculating the correlation parameters α (φ) and β (φ) as solutions of the linear regression line, it is possible not only for a flat road but also for a case where there is a slope between two points. And the walking speed can be identified. The same applies when using walking time (walking pace) and walking strength (walking strength) as walking characteristics.

  In the sixth invention, two points where the pedestrian walks are specified. Two points (for example, A1 and B1) where a pedestrian walks are measured by using positioning data such as GPS, base station communication, distance sensor, direction sensor, or communication by optical beacon, radio beacon, etc. can do. The straight line distance L1 between the two specified points (A1, B1) is calculated, and the distance D1 along the walking locus of the two specified points (A1, B1) is calculated by the sum of the distances obtained for each step of the autonomous navigation. calculate. Next, two points (A2, B2) on the map corresponding to the two specified points (A1, B1) are specified. In order to specify the position (A2, B2) on the map corresponding to the positioning position (A1, B1), for example, map matching can be used. The actual walking distance between the two specified points is calculated by, for example, L2 · D1 / L1 using the linear distance L2 between the two points (A2, B2) on the map.

  As a result, the measured position can be accurately identified by map matching, and the pedestrian does not go straight by considering the ratio of the distance along the walking trajectory obtained by the self-contained navigation and the linear distance. Even when walking, the walking distance between the two specified points can be obtained with high accuracy.

  In the seventh invention, the correlation parameter indicating the correlation between the stride and the walking characteristic is associated with the inclination information. For example, a walking speed is considered as walking characteristics, and a tilt angle is considered as tilt information. If the correlation parameters indicating the correlation between the stride and the walking characteristics are α (φ) and β (φ), the change of the correlation parameters α (φ) and β (φ) when the inclination angle φ changes is minute. Therefore, the association between the correlation parameters α (φ), β (φ) and the inclination information (inclination angle φ) is, for example, α (φ) = α1 · φ + α2, and β (φ) = β1 · φ + β2. Thus, the correlation parameters α1, α2, β1, and β2 can be expressed as new second correlations. When correlation parameters α (φ) and β (φ) corresponding to several tilt angles φ are known in advance, correlation parameters can be obtained for an arbitrary tilt angle by using the above-described association. it can. The same applies when using walking time (walking pace) and walking strength (walking strength) as walking characteristics.

  In the eighth invention, the error of the calculated stride is calculated. The error of the stride can be obtained by the variance around the regression line when specifying the correlation between the stride and the walking characteristics (for example, walking speed, walking time, walking strength, etc.). Thereby, when calculating the walking distance based on the stride, it is possible to grasp the degree of error of the calculated walking distance, and if the error exceeds the allowable range, the erroneous walking distance is identified, Or specification of the position of an incorrect pedestrian can be prevented.

  In the ninth invention, the walking distance is specified based on the number of steps of the pedestrian and the calculated step length. Thereby, the walking distance can be obtained with high accuracy.

  In the tenth invention, the position of the pedestrian is measured, and the position of the pedestrian is specified based on the measured positioning position and the specified walking distance. Thereby, the position of a pedestrian can be calculated | required accurately.

  In the present invention, even in a situation where the pedestrian's stride varies, the pedestrian's stride can be calculated with higher accuracy than in the past.

Embodiment 1
Hereinafter, the present invention will be described with reference to the drawings illustrating embodiments. FIG. 1 is a block diagram showing an example of the configuration of a position detection apparatus 10 which is a position specifying apparatus according to the present invention. The position detection device 10 calculates the pedestrian's stride, specifies the walking distance of the pedestrian using the calculated stride, and specifies (detects) the position of the pedestrian.

  The position detection device 10 is portable by a pedestrian, and has a control unit 11, a communication unit 12, a positioning unit 13, a map database 14, a storage unit 15, an operation unit 16, a position detection processing unit 17, which controls the entire device, A display unit 18, an audio output unit 19, and the like are provided. The positioning unit 13 includes a GPS 131, a distance sensor 132, an orientation sensor 133, and the like. Further, the position detection processing unit 17 includes a distance calculation unit 171, a correlation parameter calculation unit 172, a stride calculation unit 173, an error calculation unit 174, a walking distance specification unit 175, a position specification unit 176, and the like.

  The communication unit 12 includes an optical beacon, a radio beacon, an RFID or a DSRC, a narrow area communication function for communicating with a road device, a mid-range communication function such as a wireless LAN such as a UHF band or a VHF band, or a mobile phone, A wide-area communication function such as PHS, multiple FM broadcasting, or Internet communication is provided. The communication unit 12 acquires, for example, map information communicated by road-to-road communication between road devices, road-to-vehicle communication between road devices and vehicles, or vehicle-to-vehicle communication. The road device may be, for example, a traffic information collection device such as an ultrasonic sensor, an optical beacon, or an image sensor, an information board device that provides traffic information in characters or figures, a signal control device, or the like. Moreover, the communication part 12 can also acquire the map information around a pedestrian, etc. from center apparatuses, such as an information processing center or a traffic control center, using wide communication, such as a mobile telephone.

  The communication unit 12 has a communication function for communicating with a base station, receives radio waves from a plurality of base stations, and outputs reception results to the positioning unit 13. In addition, the communication unit 12 outputs the position information of the communication point obtained by the narrow area communication with the road device to the positioning unit 13.

  The positioning unit 13 measures the position of the pedestrian from time to time (for example, every time 0.5 seconds, 1 second, etc., every 1 m, 2 m, etc.) (determines the positioning position) The moving distance and moving direction (positioning direction) are stored in the storage unit 15 together with the time as a walking trajectory of the pedestrian.

  The GPS 131 receives radio waves from a plurality of GPS satellites and measures the position of the pedestrian. In addition to the GPS 131, a DGPS (differential GPS) can be mounted. DGPS can receive FM broadcasts or medium waves transmitted from a reference station whose position is known in advance, and can correct the displacement of the positioning position obtained by GPS 131, thereby improving the accuracy of the position of the pedestrian. .

  The distance sensor 132 includes a step number sensor that detects the number of steps of the pedestrian or an acceleration sensor that detects the number of steps by acquiring a periodic peak of impact (vibration) accompanying the landing of the pedestrian during walking. The acceleration sensor may also detect walking speed (walking pace) for each step of the pedestrian, walking strength (walking strength) as an impact when the pedestrian's feet land on the ground. it can.

  The azimuth sensor 133 includes an angular velocity sensor, an angular acceleration sensor (relative azimuth sensor), a geomagnetic sensor (absolute azimuth sensor), or the like. Thereby, in the self-contained navigation, the moving direction of the pedestrian can be detected for each short time and short distance walking.

  The positioning unit 13 is based on the measured positioning data, the reception result of the radio wave from the base station obtained via the communication unit 12, or the position information of the communication point obtained by the narrow area communication with the road device. The positioning position and the positioning position error are calculated. Hereinafter, a positioning position and a calculation method of the error will be described.

FIG. 2 is an explanatory diagram showing an example of the error range of the positioning position. In the orthogonal coordinate system (x direction and y direction), as an example, a position error range detected by narrow-range communication with GPS, a base station, or a road device is a rectangular area (x direction length is 4a, y direction). Is set as 4b). That is, the positioning position is the center position of the rectangular area, and the error range extends from the center position by a range of ± 2a in the x direction and by a range of ± 2b in the y direction. For example, when 2a is set to 2 sigma, the variance in the x direction is a ² and the standard deviation can be set to a. When 2b is set to 2 sigma, the variance in the y direction is b ² and the standard deviation can be set to b.

  When GPS is used to measure the position of a pedestrian, the error range is an environmental condition, more specifically, the GPS reception level, the number of captured satellites, 2D or 3D positioning, or CEP ( It changes with time due to circular error probability. In the case of base station communication, the error range varies with time depending on the communication level with the base station, the communication range of the base station, and the like. A predetermined constant in which the error range is set to be large in advance, a constant determined in advance according to the place or time, or the like may be used. The shape of the error range is not limited to a rectangular shape, and may be an arbitrary shape such as a circle or an ellipse. For example, when positioning is performed using only GPS, when the environmental conditions are favorable, an error range of about 10 to 20 m can be set.

  Hereinafter, a method for calculating the positioning position of the pedestrian will be described. The positioning position is expressed by a two-dimensional vector in an orthogonal coordinate system, but in three dimensions, only altitude information is added and can be easily expanded. Further, in the following description, it is formulated by time, but in actual processing, processing for each unit walking distance may be performed instead of processing for each unit time. In the following, capital letters are assumed to be vectors or matrices.

  Assuming that the position P (t) of the pedestrian at time t is Equation (1), walking at time t + 1 (the interval between times t and t + 1 is a predetermined time, for example, 1 second, 0.5 seconds, etc.) The person's position P (t + 1) can be expressed by Expression (2). Alternatively, the time at which a pedestrian walks a predetermined walking distance (for example, 1 m, 2 m, etc.) from time t can be set as time t + 1. Note that “T” added to the vector means transposition. Moreover, Formula (2) shows a pedestrian's dynamic characteristic. Note that the position P (t) of the pedestrian at time t is the true position (actual position) of the pedestrian, and is an unobservable position due to the presence of an unknown error. That is, the positioning position of the pedestrian is to obtain an optimum estimated position with respect to the true position P (t).

  Here, D (t) is expressed by Equation (3), d (t) is the distance that the pedestrian has moved (walked) from time t to time t + 1, and θ (t) is relative to the orthogonal coordinate system. This is the azimuth angle of pedestrian movement (walking). E (t) is expressed by equation (4), and e (t) is an error of the moving distance d (t). Further, the variance Q (t) of the error E (t) is expressed by the equation (5), and q is the error variance in the unit distance movement and can be a constant value.

  In addition, the position S (t) detected by GPS, base station communication, or communication with a road device at time t can be expressed by Equation (6). Here, G (t) is an error of the position S (t), and the covariance matrix R (t) of the error G (t) can be expressed by Expression (7). In Expression (7), a and b are each one-fourth of the length in the x direction and the y direction of the rectangular region that is the error range shown in FIG. That is, the covariance matrix R (t) is composed of variances in the x and y directions when 2a and 2b are 2 sigma. Even if the average value of E (t) and G (t) is 0, generality is not lost.

  The optimum estimated position H (t) of the pedestrian position P (t) at time t is expressed by a recurrence formula as shown in Expression (8) by the Kalman filter.

Here, Γ (t) is a variance of the estimation error of the estimated position H (t), and can be expressed by a recurrence formula like Formula (9). Further, “−1” attached to a matrix means an inverse matrix of the matrix. Further, the estimated position H (0) at the initial time 0 and the variance Γ (0) of the estimation error can be expressed by Expression (10) and Expression (11), respectively. Here, M is a priori information on the initial position of the pedestrian, and Σ is its error variance. If there is no a priori information, M = 0 and Σ ⁻¹ = 0, and the estimated position H (0) at initial time 0 and the variance Γ (0) of the estimated error are respectively expressed by equations (12) and (13). ).

Equation (6) is obtained when a position is detected by GPS, base station communication, or communication with a road device, and therefore, while GPS, base station communication, or communication with a road device is not performed, equation (6) is obtained. Assuming that the errors a and b in (7) are sufficiently large, in Equation (8), if R ⁻¹ (t) = 0, the estimated position is repeatedly calculated using Equation (8) as it is. Can do. That is, in this case, it is equivalent to measuring the position only by the self-contained navigation. In the above mathematical formula, it is formulated as two-dimensional position detection, but it may be formulated in three dimensions by adding a height dimension.

  The map database 14 stores a wide range of map information. In addition, according to the position of a pedestrian, the map information of the vicinity can also be acquired by communication from the outside, such as a center apparatus or a road device, and memorize | stored.

  FIG. 3 is a schematic diagram showing an example of map information. When detecting the position of a pedestrian, it becomes more complicated and difficult than when detecting the position of a vehicle. In other words, in the case of a vehicle, the position of the vehicle can be detected by map matching between the estimated position and the roadway on the map, whereas in the case of a pedestrian, walking other than the pedestrian sidewalk is possible. There are various areas where a person can walk. Moreover, the necessity of detecting the position of a pedestrian is high not only outdoors but indoors. Therefore, when detecting the position of a pedestrian, detailed map matching such as separation of a sidewalk and a roadway is required, and therefore detailed data is also required as map information. However, it is not necessary to store a wide range of map information in the storage unit 15 of the position detection device 10, and may be acquired by communication from the outside such as an information center device or a road device as appropriate according to the position of the pedestrian. .

  There are various areas on the map, such as pedestrian roads (sidewalks), roadways, pedestrian crossings, buildings, retail stores, parks, ponds, and railroad crossings. Therefore, a walking passage (road) can be set in an indoor area such as a building, an underground passage, a station building, a store, a retail store, a house, a factory, an underground shopping area, or the inside of a building.

  FIG. 4 is an explanatory diagram showing an example of setting a road on a map. The example of FIG. 4 shows an example of setting a road around an intersection as a feature point on the road. As shown in FIG. 4, in the case of a main road where a sidewalk and a roadway are separated, a pedestrian road (sidewalk) and a pedestrian crossing can be set as roads. In the example of FIG. 4, the road is shown in two dimensions and the width of the road is set.

  FIG. 5 is an explanatory diagram showing another example of setting a road on a map. The example of FIG. 5 also shows an example of setting a road around an intersection as a feature point on the road. As shown in FIG. 5, in the case of a main road where a sidewalk and a roadway are separated, a pedestrian road (sidewalk) and a pedestrian crossing can be set as roads. In the example of FIG. 5, the road is shown in one dimension, and the road is set as a line segment. In this case, the width of the road can be set.

  The storage unit 15 stores various information received via the communication unit 12, positioning data measured by the positioning unit 13, processing results processed by the position detection processing unit 17, and the like. When the control unit 11, the position detection processing unit 17 and the like are constituted by a CPU, a RAM, and the like, a computer program that defines the processing procedures of the control unit 11 and the position detection processing unit 17 can be stored.

  The operation unit 16 includes various operation buttons and functions as a user interface between the pedestrian and the position detection device 10. For example, the operation unit 16 receives an operation for starting or stopping the operation of the position detection device 10 by an operation of a pedestrian.

  The position detection processing unit 17 may be configured by a dedicated hardware circuit, or may be configured to execute a computer program having a predetermined processing procedure.

  The distance calculation unit 171 uses the correlation parameter calculation unit 172 to correlate the pedestrian's stride and walking characteristics (for example, walking speed for each step, walking time for each step, walking intensity as an impact at landing for each step, etc.). The walking distance between two points necessary for calculating a parameter and for calculating a stride based on a correlation parameter and a walking characteristic (for example, walking speed, walking time, walking strength, etc. for each step) in the stride calculation unit 173 is calculated. calculate.

  FIG. 6 is an explanatory diagram illustrating an example of calculating the walking distance between two points. 6A, the two points A1 and B1 where the pedestrian walks can be measured using positioning data such as GPS 131, base station communication, distance sensor 132, direction sensor 133, or the like. Positioning can be performed by communication such as a beacon and a radio beacon.

  The distance calculation unit 171 calculates the distance L1 of the straight line between the two specified points A1 and B1, and calculates the distance D1 along the walking locus of the two specified points A1 and B1 for each step of the autonomous navigation. Calculate by sum. For example, when the positions of the points A1 and B1 are represented by (x, y, z) coordinates, the distance L1 of the straight line between the two specified points A1 and B1 is obtained by adding the square of the difference between the coordinate values to obtain the square root. It can be calculated by obtaining.

  Next, as shown in FIG. 6B, the distance calculation unit 171 specifies two points A2 and B2 on the map corresponding to the specified two points A1 and B1. In order to specify the positions A2 and B2 on the map corresponding to the positioning positions A1 and B1, for example, map matching can be used. For example, each of the points A2 and B2 can be based on position data of two points determined by a pedestrian turning on a road by map matching. The distance calculation unit 171 calculates the actual walking distance d between the two specified points by using, for example, L2 · D1 / L1 using the linear distance L2 between the two points (A2, B2) on the map.

  This compensates for the disadvantages of the map matching method and the self-contained navigation, and incorporates the advantages of each, so that the positioning position can be identified with high accuracy by map matching, and along the walking trajectory obtained by the self-contained navigation By considering the ratio of the distance and the straight line distance, the walking distance between the two specified points can be obtained with high accuracy even when the pedestrian walks without going straight. If the specified points A2 and B2 are not straight on the map (for example, there is a curve or a corner between A2 and B2), the above processing is performed for each point of the map that approximates the curve with a broken line. Accumulate. In addition, as another method, whether the walking between the two specified points is a substantially straight line is estimated by a walking trajectory obtained by self-contained navigation, or is determined by a corresponding road on the map and is a substantially straight line. Only data can be used.

  The correlation parameter calculation unit 172 correlates the pedestrian's stride and walking characteristics (for example, walking speed, walking time, walking strength, etc. for each step) based on the walking distance between the two points specified by the distance calculating unit 171. Calculate the parameters. Note that one of walking speed, walking time, walking strength, etc. for each step may be used as a walking characteristic, or some may be used in combination.

  First, a case where a correlation parameter between walking speed and step length for each step is calculated as walking characteristics will be described. That is, the correlation parameter calculation unit 172 calculates a correlation parameter between the pedestrian's stride and the walking speed for each step based on the walking distance between the two points specified by the distance calculation unit 171. It is reasonable to consider that the pedestrian's stride w has the relationship of the formula (14) between the pedestrian's walking speed v. Here, α and β are correlation parameters.

  The walking distance between two points (A2, B2 in the example of FIG. 6) specified by the map matching is d, the number of steps obtained by the distance sensor 132 between these two points is n, and the step length of each step between the two points Wi (i = 1, 2,... N) and the walking speed column for each step is vi (i = 1, 2,... N), the correlation between the stride and the walking speed is, for example, (15). That is, the stride can be expressed by a correlation equation in which the second correlation parameter (β) is added to the integrated value of the walking speed and the first correlation parameter (α).

  In this case, the linear regression line is expressed by Expression (16) to Expression (18). In the equations (17) and (18), ave represents an average operation. Necessary amounts of these data are collected, and correlation parameters α and β can be calculated by equations (19) to (22) as solutions of the linear regression line. Here, var indicates a distributed operation, and cov indicates a covariance operation.

  The stride calculation unit 173 acquires the number of steps and the walking speed for each step with the distance sensor 132 or the like, and calculates the step length for each step based on the acquired number of steps and the walking speed and the correlation parameters α and β. Thereby, even when the walking speed of a pedestrian fluctuates between two points, the stride can be calculated with high accuracy.

  The error calculation unit 174 calculates an error of the calculated stride. The error in the stride can be obtained from the variance around the regression line when specifying the correlation between the stride and the walking speed. For example, the error (y per step) of the above-described linear regression line can be expressed by the variance around the regression line expressed by Equation (23), and the average ratio e of the distance error per step is It can be calculated by the equation (24) as a level of 1 sigma. Since the distance error is a distance error calculated using a correlation parameter between the stride and the walking speed, it also has a meaning as a correlation error.

  The error per step calculated by Equation (24) can be used as e (t) in Equation (4). In this case, it is necessary to add the number of steps to the error per step. Thereby, when calculating the walking distance based on the stride, it is possible to grasp the degree of error of the calculated walking distance, and if the error exceeds the allowable range, the erroneous walking distance is identified, Or specification of the position of an incorrect pedestrian can be prevented. Further, when detecting the position of a pedestrian, it is possible to accurately grasp the position detection error range by taking into account other sensor errors and the like.

  In the above example, the correlation parameter is calculated on the assumption that the pedestrian's stride w is linearly related to the pedestrian's walking speed v. However, the present invention is not limited to this. It can also be assumed that there is a polynomial relationship with the walking speed v of the pedestrian. For example, as shown in Expression (25), it can be assumed that the relationship is a quadratic expression.

  In this case, Expression (26) is replaced with Expression (15), Expression (27) is replaced with Expression (16), Expression (28) and Expression (29) are replaced with Expression (17), Expression (18) ) Can be used instead of the formula (30). Further, the correlation parameters α, β, γ can be calculated using a technique such as multiple regression analysis. That is, the stride is obtained by adding the integrated value of the walking speed and the second correlation parameter (β) to the integrated value of the square of the walking speed and the first correlation parameter (α), and further adding the third correlation parameter. It can be expressed by a correlation equation obtained by adding (γ).

  Since the relationship between the pedestrian's stride and the walking speed can be assumed to be a polynomial relationship, the relationship is not limited to a quadratic relationship, but is an equation consisting of a combination of N-order equations (N = 2, 1, -1, or 0, that is, a constant term).

  Next, a case where a correlation parameter between the walking time (walking pace) for each step and the stride is calculated as a walking characteristic will be described. The walking pace is a numerical value based on the speed of foot rotation in walking. For example, the walking time tr required per step, the number of steps per unit time, or the origin of sensor data per unit time (almost data) This is the number of passes through the median.

  FIG. 7 is an explanatory diagram showing an example of a walking pace. In FIG. 7, the horizontal axis indicates time, and the vertical axis indicates vertical acceleration associated with walking. When data is acquired by a sensor that can detect vertical acceleration associated with walking of a pedestrian, as shown in FIG. 7, the origin of sensor data (acceleration data in the example of FIG. 7) or the approximate median value of the data The walking pace can be acquired by obtaining the number of times of passing. This is an effective and simple method when it is difficult to detect the number of steps. In the following description, the case of the walking time tr per step as the walking pace will be described. However, the number of steps per unit time or the number of times of passing the sensor data origin (almost the median value of data) per unit time. Is the same.

  The correlation parameter calculation unit 172 calculates a correlation parameter between the pedestrian's stride and the walking time for each step (walking pace) based on the walking distance between the two points specified by the distance calculation unit 171. It is reasonable to think that the pedestrian's stride w has the relationship of the formula (31) with the pedestrian's walking time v. Here, α ′ and β ′ are correlation parameters.

  The walking distance between two points (A2, B2 in the example of FIG. 6) specified by the map matching is d, the number of steps obtained by the distance sensor 132 between these two points is n, and the step length of each step between the two points Wi (i = 1, 2,... N) and the walking time sequence for each step is tri (i = 1, 2,... N), the correlation between the stride and the walking time is, for example: (32). That is, the stride can be expressed by a correlation formula in which the second correlation parameter β ′ is added to the integrated value of the walking time and the first correlation parameter α ′.

  In this case, the linear regression line is expressed by Expression (33) to Expression (35). Necessary amounts of these data are collected, and correlation parameters α ′ and β ′ as solutions of the linear regression line can be calculated by equations (19) to (22) as in the case of walking speed.

  The stride calculation unit 173 acquires the number of steps and the walking time for each step with the distance sensor 132 and the like, and calculates the step length for each step based on the acquired number of steps, the walking time, and the correlation parameter. Thereby, even when the walking time (walking pace) of a pedestrian fluctuates between two points, the stride can be calculated with high accuracy.

  The error calculation unit 174 calculates an error of the calculated stride. The error in the stride can be obtained from the variance around the regression line when specifying the correlation between the stride and the walking time. For example, the error (y per step) of the above-described linear regression line can be expressed by the variance around the regression line expressed by Equation (23), and the average ratio e of the distance error per step is It can be calculated by the equation (24) as a level of 1 sigma. The distance error has a meaning as a correlation error because it is a distance error calculated using a correlation parameter between the stride and the walking time.

  In the above-described example, the correlation parameter is calculated on the assumption that the pedestrian's stride w is linearly related to the pedestrian's walking time tr. However, the present invention is not limited thereto, and the pedestrian's stride w and It can also be assumed that there is a polynomial relationship with the walking time tr of the pedestrian. For example, as shown by the equation (36), it can be assumed that the relationship is a quadratic equation.

  In this case, Expression (37) is replaced with Expression (15), Expression (38) is replaced with Expression (16), Expression (39) and Expression (40) are replaced with Expression (17), Expression (18) ) Can be used instead of the formula (41). Further, the correlation parameters α ′, β ′, and γ ′ can be calculated using a technique such as multiple regression analysis. That is, the stride is obtained by adding the integrated value of the walking time and the second correlation parameter β ′ to the integrated value of the square of the walking time and the first correlation parameter α ′, and further adding the third correlation parameter γ ′. Can be represented by a correlation equation.

  Since the relationship between the pedestrian's stride and the walking time can be assumed to be a polynomial relationship, the relationship is not limited to a quadratic relationship, but is an equation consisting of a combination of N-order equations (N = 2, 1, -1, constant terms, etc.).

  Next, a case will be described in which a correlation parameter between the walking strength (walking strength) as the impact at the time of landing for each step and the stride is calculated as the walking characteristics. The walking intensity indicates, for example, an impact when a foot is landed on the ground every time a pedestrian walks. FIG. 8 is an explanatory diagram showing an example of walking strength. In FIG. 8, the horizontal axis indicates time, and the vertical axis indicates vertical acceleration associated with walking. As shown in FIG. 8, the walking intensity is a variable included in the acceleration in the vertical direction caused by walking, and is, for example, the maximum absolute value of the difference between the acceleration for each step and the stationary acceleration (gravity acceleration g). Sm1 or Sm2, or an average value of maximum values (Sm1 + Sm2) / 2, or an integrated value of acceleration at each step, for example, areas S1 and S2 indicated by hatching in FIG. 8, or a sum of areas (S1 + S2). .

  The correlation parameter calculation unit 172 calculates a correlation parameter between the pedestrian's stride and the walking strength (walking strength) for each step based on the walking distance between the two points specified by the distance calculation unit 171. It is reasonable to think that the pedestrian's stride w has the relationship of the formula (42) with the pedestrian's walking strength st. Here, α ″ and β ″ are correlation parameters.

  The walking distance between two points (A2, B2 in the example of FIG. 6) specified by the map matching is d, the number of steps obtained by the distance sensor 132 between these two points is n, and the step length of each step between the two points Wi (i = 1, 2,..., N) and the walking intensity column for each step as sti (i = 1, 2,... N), the correlation between the stride and the walking intensity is, for example, (43). That is, the stride can be expressed by a correlation formula obtained by adding the second correlation parameter β ″ to the integrated value of the walking intensity and the first correlation parameter α ″.

  In this case, the linear regression line is expressed by Expression (44) to Expression (46). Necessary amounts of these data are collected, and correlation parameters α ″ and β ″ as solutions of the linear regression line can be calculated by equations (19) to (22) as in the case of walking speed.

  The step length calculation unit 173 acquires the number of steps and the walking strength for each step with the distance sensor 132 and the like, and calculates the step length for each step based on the acquired number of steps and the walking strength and the correlation parameter. Thereby, even when the walking time (walking pace) of a pedestrian fluctuates between two points, the stride can be calculated with high accuracy.

  The error calculation unit 174 calculates an error of the calculated stride. The error in the stride can be obtained from the variance around the regression line when specifying the correlation between the stride and the walking strength. For example, the error (y per step) of the above-described linear regression line can be expressed by the variance around the regression line expressed by Equation (23), and the average ratio e of the distance error per step is It can be calculated by the equation (24) as a level of 1 sigma. The distance error has a meaning as a correlation error because it is a distance error calculated using a correlation parameter between the stride and the walking strength.

  In the above example, the correlation parameter is calculated on the assumption that the pedestrian's stride w is linearly related to the pedestrian's walking strength st. However, the present invention is not limited to this. It is assumed that there is a polynomial relationship between the pedestrian's stride w and the pedestrian's walking strength st, similarly to the relationship between the pedestrian's walking speed v or the stride w and the walking time tr. You can also.

  In calculating the correlation parameters, α, β, γ, α ′, β ′, γ ′, α ″, β ″, etc. described above, a lot of data collected and stored in the past is used, but the data is stored. When (accumulating), it is not always necessary to store all data. That is, if the number of data, the sum of each of x and y, the sum of the squares of x and the sum of the squares of y, and the sum of x and y are stored, the equations (19) to (24) Each time it is obtained, it can be calculated incrementally.

  Note that the above-described distance calculation unit 171, correlation parameter calculation unit 172, stride length calculation unit 173, and error calculation unit 174 constitute a stride calculation device according to the present invention as a whole. Further, the correlation parameter calculation unit 172 may be configured by a CPU, a RAM, and the like, and a computer program that defines a correlation parameter calculation processing procedure may be executed.

  By collecting predetermined data and obtaining correlation parameters, the walking distance specifying unit 175 specifies the walking distance based on the number of steps of the pedestrian and the stride calculated by the stride calculating unit 173. Thereby, the walking distance can be obtained with high accuracy.

  By collecting predetermined data and obtaining correlation parameters, the position specifying unit 176 specifies the position of the pedestrian based on the positioning position determined by the positioning unit 13 and the walking distance specified by the walking distance specifying unit 175. To do. Thereby, the position of a pedestrian can be calculated | required accurately.

  The display unit 18 is a liquid crystal display panel, for example, and displays its position on a map to a pedestrian.

  When the pedestrian's position is displayed on the display unit 18, the audio output unit 19 outputs audio or sound to notify the pedestrian of necessary information or to call attention.

  Next, a procedure for calculating a correlation parameter and calculating a stride will be described. FIG. 9 is a flowchart showing the steps of the stride calculation process. The control unit 11 measures the position of the pedestrian (S11), determines whether or not the two specific points have been passed (S12), and if the two points have not been passed (NO in S12), Process and continue positioning.

  When passing through two points (YES in S12), the control unit 11 calculates the walking distance between the two points (S13), acquires the number of steps between the two points (S14), and walks between the two points. Characteristics (for example, walking speed, walking time, walking strength, etc. for each step) are acquired (S15).

  The control unit 11 determines whether or not there is sufficient data necessary for calculating the correlation parameter (S16). If the data is not sufficient (NO in S16), the acquired data is stored (S17), and the steps after step S11 are performed. Continue processing.

  When there is sufficient data (YES in S16), the control unit 11 calculates a correlation parameter (S18), and based on the calculated correlation parameter and walking characteristics (for example, walking speed, walking time or walking strength for each step). The stride is calculated (S19), and the process ends.

  Note that the control unit 11 specifies the walking distance based on the stride calculated by the above process and the detected number of steps. Moreover, the control part 11 specifies the position of a pedestrian based on the measured positioning position and the specified walking distance.

Embodiment 2
The walking characteristics of the pedestrian described in the first embodiment may be slightly different between when the pedestrian walks on a flat road and when walking on a hill. Therefore, the correlation parameter between the pedestrian's stride and walking characteristics may be different between a flat road and a slope. Hereinafter, the case where the inclination of the road between two points where the pedestrian walked is considered will be described.

  The configuration of the position detection device 10 is the same as that in the example of FIG. 1, but the map database 14 is used as a wide range of map information, for example, in addition to a defined road distance and direction, as appropriate for roads as inclination information. The altitude information of the point, the difference information of the altitude, or the inclination angle obtained by dividing the altitude difference between the appropriate points by the distance between the end points is stored.

  Note that the position detection device 10 may be provided with an altitude sensor, and the tilt angle may be acquired using altitude information detected by the altitude sensor. Further, the inclination information may be received from the outside via the communication unit 12. Hereinafter, the case where the inclination angle between two points is used as the inclination information will be described.

  First, the case where the correlation parameter between the stride and the walking speed for each step is calculated in consideration of the inclination angle will be described. That is, the correlation parameter calculation unit 172 calculates a correlation parameter between the pedestrian's stride and the walking speed for each step based on the walking distance between the two points specified by the distance calculation unit 171 and the inclination angle between the two points. . It is reasonable to consider that the pedestrian's stride w has the relationship of the formula (14) between the pedestrian's walking speed v. Here, α (φ) and β (φ) are correlation parameters, and φ is an inclination angle between two points. Note that the inclination angle φ can be uphill when positive and downhill when negative.

  The walking distance between two points (A2, B2 in the example of FIG. 6) specified by the map matching is d, the number of steps obtained by the distance sensor 132 between these two points is n, and the step length of each step between the two points Wi (i = 1, 2,... N) and the walking speed column for each step is vi (i = 1, 2,... N), the correlation between the stride and the walking speed is, for example, (48). That is, the stride can be expressed by a correlation equation in which the second correlation parameter β (φ) is added to the integrated value of the walking speed and the first correlation parameter α (φ).

  In this case, the linear regression line is expressed by Expression (49) to Expression (51). In the formulas (50) and (51), ave represents an average operation. Necessary amounts of these data are collected, and correlation parameters α (φ) and β (φ) can be calculated by equations (52) to (55) as solutions of the linear regression line. Here, var indicates a distributed operation, and cov indicates a covariance operation.

  The stride calculation unit 173 acquires the number of steps and the walking speed for each step with the distance sensor 132 and the like, and calculates the step length for each step based on the acquired number of steps and the walking speed and the correlation parameters α (φ) and β (φ). Thereby, even when the pedestrian walks not only on a flat road but also on a slope between two points, the stride can be calculated with high accuracy.

  The error calculation unit 174 calculates an error of the calculated stride. The error in the stride can be obtained from the variance around the regression line when specifying the correlation between the stride and the walking speed. For example, the above-mentioned linear regression line y error (error per step) can be expressed by the variance around the regression line represented by equation (56), and the average ratio e of distance error per step is: It can be calculated by the equation (57) as a level of 1 sigma. Since the distance error is a distance error calculated using a correlation parameter between the stride and the walking speed, it also has a meaning as a correlation error.

  The error per step calculated by Expression (57) can be used as e (t) in Expression (4). In this case, it is necessary to add the number of steps to the error per step. Thereby, when calculating the walking distance based on the stride, it is possible to grasp the degree of error of the calculated walking distance, and if the error exceeds the allowable range, the erroneous walking distance is identified, Or specification of the position of an incorrect pedestrian can be prevented. Further, when detecting the position of a pedestrian, it is possible to accurately grasp the position detection error range by taking into account other sensor errors and the like.

  In the above example, the correlation parameter is calculated on the assumption that the pedestrian's stride w is linearly related to the walking speed v of the pedestrian, but the present invention is not limited to this. Similarly, it can be assumed that there is a polynomial (Nth order equation) relationship between the pedestrian's stride w and the pedestrian's walking speed v. For example, in the case of a quadratic expression, the correlation parameters α (φ), β (φ), and γ (φ) can be calculated for each inclination angle φ using a technique such as multiple regression analysis. That is, the stride is obtained by adding the integrated value of the walking speed and the second correlation parameter β (φ) to the integrated value of the square of the walking speed and the first correlation parameter α (φ), and It can be expressed by a correlation formula obtained by adding the correlation parameter γ (φ).

  In addition to the walking speed for each step, walking time (walking pace) or walking strength (walking strength) for each step can be used as the walking characteristics.

  As described above, the correlation parameters α (φ), β (φ), γ (φ) and the like obtained for each inclination angle φ change minutely according to the change in the inclination angle φ. [phi], [beta] ([phi]), [gamma] ([phi]), etc. may be in a linear relationship with the tilt angle [phi]. That is, Formula (58)-Formula (60) are materialized. Here, α1 and α2 are correlation parameters (correction correlation parameters) for specifying a new second correlation that can correct the correlation parameter α (φ) with respect to an arbitrary inclination angle φ, and β1 , Β2 are correlation parameters (correction correlation parameters) that specify a new second correlation that can correct the correlation parameter β (φ) with respect to an arbitrary tilt angle φ, and γ1, γ2 are This is a correlation parameter (correction correlation parameter) that specifies a new second correlation that can correct the correlation parameter γ (φ) with respect to an arbitrary inclination angle φ.

  Further, since the average ratio e (φ) of the distance error (correlation error) per step obtained for each inclination angle φ also changes minutely according to the change of the inclination angle φ, the distance error (correlation error). It can be assumed that e (φ) has a linear relationship with the inclination angle φ, and Equation (61) is established. Here, e1 and e2 are new parameters (correlation error correction parameters) that can correct the distance error (correlation error) e (φ) with respect to an arbitrary inclination angle φ. Note that the correlation parameter calculation unit 172 can calculate the correction correlation parameter and the correlation error correction parameter.

  The above equations (58) to (60) relate the correlation parameter and the inclination angle. If the correlation parameters α (φ), β (φ), and γ (φ) corresponding to several tilt angles φ are known in advance, the correlation described above is used for correlation with an arbitrary tilt angle. Parameters can be determined. The same applies when using walking time (walking pace) and walking strength (walking strength) as walking characteristics.

  In the second embodiment, in the calculation of the correlation parameters α (φ), β (φ), γ (φ) and the like described above, a lot of data collected and stored in the past is used, but the data is stored ( In the case of accumulation), it is not always necessary to store all data. That is, if the number of data for each inclination angle φ, the sum of x and y, the sum of x squares and the sum of y squares, and the sum of x · y are stored, formula (52) to formula (57 ) Can be calculated incrementally each time data is obtained. Then, using the results, correction correlation parameters (α1, α2, etc.) and correlation error correction parameters (e1) in the equations (58) to (60) and (61) showing the relationship between the correlation parameter and the tilt angle. , E2, etc.) may be calculated.

  Regarding the procedure for calculating the correlation parameter and calculating the stride, in addition to the walking distance between the two points, the number of steps and the walking characteristics, the inclination angle between the two points is acquired, and the correlation parameter is set according to the acquired inclination angle. The other points are the same as in the example of FIG.

  As described above, according to the present invention, the walking speed, walking pace (walking time) or walking strength (walking strength) varies for each step, the walking characteristics change, and the pedestrian's step length varies. Even in such a situation, the pedestrian's stride can be calculated more accurately than in the past. Moreover, even when the pedestrian walks not only on a flat road but also on a slope, the pedestrian's stride can be calculated with higher accuracy than in the past. In addition, the walking distance and position of the pedestrian can be specified with high accuracy by calculating the stride, and the accuracy of position detection by the hybrid type with the independent navigation, the map matching method, and the satellite navigation such as GPS is improved. be able to.

  The position detection device described above can be applied to an information terminal device such as a mobile phone, a PDA (Personal Digital Assistant), a PHS, a notebook personal computer, a music player, and a mobile game device, or a mobile terminal device.

  In the above-described embodiment, the position detection device may include an inclination angle sensor. As a result, when the position detection device tilts due to vibration or posture change of the position detection device accompanying walking, taking out, operation, etc. of the pedestrian, the function stops or the performance deteriorates depending on the type of the direction sensor or the distance sensor. There are things to do. Accordingly, the tilt angle can be detected by the tilt angle sensor, and the azimuth sensor or the distance sensor can be corrected.

  The formulas for estimating the correlation parameters, the stride, and the pedestrian position shown in the above-described embodiment are examples, and the formulas are not limited to these, and formulas appropriately modified can be used.

  The disclosed embodiments are to be considered in all respects as illustrative and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

It is a block diagram which shows an example of a structure of the position detection apparatus which is a position identification apparatus based on this invention. It is explanatory drawing which shows the example of the error range of a positioning position. It is a schematic diagram which shows an example of map information. It is explanatory drawing which shows an example of the setting of the road on a map. It is explanatory drawing which shows the other example of the setting of the road on a map. It is explanatory drawing which shows an example which calculates the walking distance between two points. It is explanatory drawing which shows an example of a walking pace. It is explanatory drawing which shows an example of walking intensity | strength. It is a flowchart which shows the procedure of a step calculation process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Position detection apparatus 11 Control part 12 Communication part 13 Positioning part 131 GPS
132 distance sensor 133 orientation sensor 14 map database 15 storage unit 16 operation unit 17 position detection processing unit 171 distance calculation unit 172 correlation parameter calculation unit 173 step calculation unit 174 error calculation unit 175 walking distance specification unit 176 position specification unit 18 display unit 19 Audio output section

Claims (12)

In the stride calculation device that calculates the stride of a pedestrian,
A step acquisition means for acquiring the number of steps of the pedestrian;
A walking distance calculating means for calculating a walking distance between two points among points where the pedestrian walks;
A walking characteristic acquisition means for acquiring a walking characteristic of a pedestrian;
Correlation specifying means for specifying the correlation between the stride and the walking characteristics based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking characteristics;
A stride calculation device comprising: stride calculation means for calculating a stride based on the correlation and walking characteristics specified by the correlation specifying means. A walking speed acquisition means for acquiring a walking speed for each step as the walking characteristic;
The correlation specifying means includes
It is configured to specify the correlation between the stride and the walking speed based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking speed for each step between the two points. Yes,
The stride calculation means includes
The stride calculation device according to claim 1, wherein a stride is calculated based on the correlation specified by the correlation specifying means and the walking speed. A walking time acquisition means for acquiring a walking time for each step as the walking characteristics,
The correlation specifying means includes
It is configured to identify the correlation between the stride and the walking time based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking time for each step between the two points. Yes,
The stride calculation means includes
The stride calculation device according to claim 1, wherein a stride is calculated based on the correlation specified by the correlation specifying means and the walking time. An impact acquisition means for acquiring an impact at the time of landing for each step as the walking characteristic,
The correlation specifying means includes
The correlation between the stride and the impact at the time of landing is specified based on the walking distance between the two points, the number of steps of the pedestrian between the two points, and the impact at the time of landing for each step between the two points. Configured
The stride calculation means includes
The stride calculation device according to claim 1, wherein the stride is calculated based on the correlation specified by the correlation specifying means and the impact at the time of landing. Inclination information acquisition means for acquiring inclination information relating to the inclination between the two points,
The correlation specifying means includes
5. The apparatus according to claim 1, wherein a correlation between a stride and a walking characteristic is specified in accordance with the inclination information acquired by the inclination information acquisition unit. Stride calculation device. Storage means for storing map information;
A first specifying means for specifying two points where the pedestrian walks;
First distance calculating means for calculating a straight distance between two points specified by the first specifying means;
Second distance calculating means for calculating a distance along the walking locus of the two points specified by the first specifying means by self-contained navigation;
A second specifying means for specifying two points on the map by a map matching method using the map information stored in the storage means for the two points specified by the first specifying means;
A third distance calculating means for calculating a straight line distance between the two points specified by the second specifying means;
The walking distance calculating means includes
Based on the distance calculated by the first distance calculating means, the second distance calculating means and the third distance calculating means, the walking distance between the two points where the pedestrian walks is calculated. The stride calculation device according to any one of claims 1 to 5. The correlation specifying means includes
It is configured to specify a correlation parameter indicating a correlation between the stride and the walking characteristic according to the inclination information acquired by the inclination information acquisition unit,
A second correlation specifying unit for specifying a second correlation between the correlation parameter and the tilt information based on the correlation parameter according to the tilt information specified by the correlation specifying unit and the tilt information;
The correlation specifying means includes
6. The stride calculation device according to claim 5, further comprising: a correlation parameter for arbitrary inclination information is specified based on the second correlation specified by the second correlation specifying means. .   The stride calculation device according to any one of claims 1 to 7, further comprising error calculation means for calculating a step error calculated by the stride calculation means. A stride calculation device according to any one of claims 1 to 8,
A walking distance specifying device, comprising: a walking distance specifying means for specifying a walking distance based on the number of steps of a pedestrian and the stride calculated by the stride calculating device. The walking distance specifying device according to claim 9;
Positioning means for positioning the position of the pedestrian,
A position specifying device comprising: position specifying means for specifying a position of a pedestrian based on a positioning position measured by the positioning means and a walking distance specified by the walking distance specifying device. A computer program for causing a computer to acquire the number of steps and walking characteristics of a pedestrian and to function as a means for specifying a correlation between a pedestrian's step length and walking characteristics,
Computer
A walking distance calculating means for calculating a walking distance between two points among points where the pedestrian has walked;
It is made to function as a correlation specifying means for specifying the correlation between the stride and the walking characteristic based on the calculated walking distance between the two points, the number of steps of the pedestrian between the two points, and the walking characteristic. Computer program. In the stride calculation method for calculating the stride of the pedestrian with the stride calculation device,
The stride calculation device
Get pedestrian steps,
Calculate the walking distance between two points where the pedestrian walks,
Get walking characteristics of pedestrians,
Identify the correlation between the stride and the walking characteristics based on the walking distance between the two points, the number of steps of the pedestrian between the two points and the walking characteristics,
A stride calculation method characterized by calculating a stride based on the identified correlation and walking characteristics.

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