JP6521853B2 - INFORMATION PROCESSING APPARATUS, POSITIONING SYSTEM, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM - Google Patents

Description

  The present invention relates to an information processing apparatus, a positioning system, an information processing method, and a computer program.

  Information processing apparatuses such as user terminals and tablet terminals are often equipped with a satellite positioning system (Global Positioning System: GPS) or the like. The information processing apparatus acquires position information using the GPS.

  A technology for acquiring positioning data from a positioning method selection apparatus is known as a technology for acquiring position information (see, for example, Patent Document 1). In this technique, one or more applications request positioning data from the positioning method selection device. The positioning method selection device provides positioning data to this application using one or more positioning methods according to the settings defined by the application and / or the user.

  The positioning method selection apparatus receives a positioning request from the application, forms a parameter representing the positioning quality requested by the application, and determines the quality of positioning data provided by the positioning method and the positioning quality requested by the application. Compare and send positioning data to the application in response to the positioning request.

JP, 2011-209298, A

  Among the positioning methods installed in the information processing apparatus, for example, GPS can accurately measure the position, but the power consumption is high. Furthermore, GPS can not be used indoors where GPS radio waves can not reach. Also, for example, Pedestrian Dead Reckoning (PDR) is not very accurate but has low power consumption. Furthermore, PDR can also be used indoors.

  On the other hand, there is known a technology for acquiring information on a user from an information processing apparatus and providing a service using the information on the user. For example, the information processing apparatus transmits position information to a web server (Web server), and the web server transmits information related to the position information.

  Since the information processing apparatus can obtain position information accurately by performing positioning based on GPS radio waves outdoors, the information processing apparatus can receive provision of correct information from the web server, but power consumption increases. Here, although an example of GPS is given, the same applies to Global Navigation Satellite System (s) (GNSS) such as GPS and quasi-zenith satellites (QZS).

  That is, since the information processing apparatus can obtain the position information with high accuracy by performing the positioning based on the radio wave of GNSS, the information processing apparatus can receive provision of the correct information from the web server, but the power consumption increases. On the other hand, indoors, since positioning is performed based on PDR, the accuracy of the position information is low, and there is a possibility that the erroneous information may be provided, but the power consumption is low.

  When the information processing apparatus switches the positioning method from GNSS to PDR, a reference position and a moving direction from the reference position are determined as input values of PDR. By improving the accuracy of the input value of this PDR, the accuracy of the positioning result by PDR can be improved.

  The present invention has been made to solve the above problems, and it is an object of the present invention to improve the accuracy of the reference position used as an input value of pedestrian autonomous navigation (PDR) and the moving direction from the reference position. Do.

(1) One aspect of the present invention, executes a principal component analysis input unit which determines the input value of the principal component analysis process including a plurality of positioning values of one object, the principal component analysis process using the input value And a principal component analysis unit that generates a regression line of the position of the object having the direction of the principal component vector as a result of the principal component analysis process in the direction of a straight line, and the input value of pedestrian autonomous navigation based on the regression line. and a determination section for determining the moving direction from the reference position and the reference position to be used, the principal component analysis input unit, additional processing of the input values of the principal component analysis process corresponding to the uncertainty likeness of positioning values An information processing apparatus that executes
(2) In one aspect of the present invention, in the information processing apparatus according to (1), the principal component analysis input unit is configured to measure a plurality of positioning values of one object and uncertainty of positioning values with respect to each positioning value. It is an information processing apparatus which determines the additional position which took the interval according to the input value of the principal component analysis process .
( 3 ) One embodiment of the present invention relates to the information processing apparatus according to any one of the above (1) or (2) , wherein the reference position determined by the determination unit and the moving direction from the reference position The information processing apparatus further includes a pedestrian autonomous navigation processing unit used for the input value.

( 4 ) One aspect of the present invention is an information processing apparatus according to any one of the above (1) or (2) , a reference position determined by the information processing apparatus, and a movement direction from the reference position as pedestrian autonomous navigation. And a pedestrian autonomous navigation processing device used for the input value of.

(5) One aspect of the present invention relates to an information processing apparatus, and principal component analysis input determining an input value of the principal component analysis process including a plurality of positioning values of one object, the information processing apparatus, the input Performing a principal component analysis process using values, generating a regression line of the position of the object having the direction of the principal component vector of the result of the principal component analysis process in the direction of a straight line; device, viewed contains a judgment step, the determining the direction of movement from the reference position and the reference position used to input values of the pedestrian autonomous navigation based on the regression line, the information processing apparatus, the main component It is an information processing method which performs addition processing of an input value of said principal component analysis processing according to uncertainty degree of a positioning value in an analysis input step .

(6) One aspect of the present invention, the computer, and principal component analysis input function to determine the input value of the principal component analysis process including a plurality of positioning values of one object, principal component analysis using the input value Processing for generating a regression line of the position of the object having the direction of the principal component vector of the result of the principal component analysis process in the direction of a straight line, and a pedestrian autonomous navigation system based on the regression line A computer program for realizing a reference position used for an input value and a determination function of determining a moving direction from the reference position , wherein the principal component analysis input function is based on the uncertainty of the positioning value. It is a computer program which performs the addition process of the input value of the said main component analysis process .

  According to the present invention, it is possible to improve the accuracy of the reference position used as the input value of pedestrian autonomous navigation (PDR) and the moving direction from the reference position.

It is a figure showing an example of composition of a positioning system concerning one embodiment. It is a figure showing the example of hardware constitutions of the information processor concerning one embodiment. It is a functional block diagram of an information processor concerning one embodiment. It is a figure which shows the flow of the whole information processing method which concerns on one Embodiment. It is a figure for explaining the information processing concerning one embodiment. It is a figure for explaining the information processing concerning one embodiment. It is a figure for explaining the information processing concerning one embodiment. It is a figure for explaining the information processing concerning one embodiment. It is a figure for explaining the information processing concerning one embodiment. It is a figure for explaining the information processing concerning one embodiment. It is a figure showing an example of composition of a positioning system concerning one embodiment. It is a figure showing the example of hardware constitutions of the terminal unit concerning one embodiment. It is a figure for explaining the information processing concerning one embodiment.

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

First Embodiment
FIG. 1 shows an exemplary configuration of a positioning system according to the present embodiment. The information processing apparatus 100 receives navigation signals wirelessly transmitted by a plurality of navigation satellites 300 (300a, 300b, 300c, 300d), and performs positioning of the information processing apparatus 100 based on the navigation signals. Get Further, the information processing apparatus 100 receives a downlink radio signal transmitted by the base station 200, and acquires position information attached to the downlink signal.

  Furthermore, the information processing apparatus 100 mounts one or more sensors, and obtains position information by performing positioning of the information processing apparatus 100 based on the information acquired by the one or more sensors. Hereinafter, performing positioning of the information processing apparatus 100 based on a wireless signal transmitted by a wireless communication device such as the base station 200 and the navigation satellite 300 is referred to as “first positioning” and is acquired by one or more sensors. Performing positioning of the information processing apparatus 100 based on the information is referred to as “second positioning”.

  Furthermore, the information processing apparatus 100 accumulates the accumulated error by accumulating the error estimated for the position obtained by the first positioning and the error estimated for the position obtained by the second positioning. Calculate The information processing apparatus 100 switches between the first positioning and the second positioning based on the accumulated error. The information processing apparatus 100 reduces the power consumption by switching between the first positioning in which the power consumption is high and the second positioning in which the power consumption is low. It can be kept in a predetermined range.

  Further, when switching from the first positioning to the second positioning, the information processing apparatus 100 determines a reference position and a moving direction from the reference position as input values for the second positioning. The accuracy of the positioning result by the second positioning can be increased by increasing the accuracy of the reference position serving as the input value of the second positioning and the moving direction from the reference position.

  An example of the information processing apparatus 100 is a smartphone, a tablet terminal, a portable PC, or the like. An example of a navigation satellite is the Global Navigation Satellite System (s) (GNSS), etc. An example of a GNSS is the Global Positioning System (GPS), quasi-zenith satellites (QZS), etc. An example of the sensor is an acceleration sensor, a gyro sensor, a geomagnetic sensor, an atmospheric pressure sensor, a proximity sensor, an illuminance sensor, and the like.

  FIG. 2 shows an example of the hardware configuration of the information processing apparatus 100. In the example shown in FIG. 2, a gyro sensor and an acceleration sensor are mounted on the information processing apparatus 100 as one or more sensors. The information processing apparatus 100 may be equipped with a sensor other than a gyro sensor and an acceleration sensor.

  The information processing apparatus 100 includes a navigation signal receiving unit 102, a gyro sensor 104, an acceleration sensor 106, a central processing unit (CPU) 108, a random access memory (RAM) 110, and an electrically erasable programmable read-only memory (EEPROM). 112, a wireless communication unit 116, a short distance wireless communication unit 118, and a bus line 150 such as an address bus or a data bus for electrically connecting the respective components as shown in FIG.

  The navigation signal receiving unit 102 receives a navigation signal transmitted by the navigation satellite 300 (300a, 300b, 300c, 300d) in response to a positioning command from the CPU 108, and demodulates the navigation signal. Then, the navigation signal receiving unit 102 obtains position information by performing positioning of the information processing apparatus 100 by performing predetermined positioning calculation based on the demodulated navigation signal.

  The navigation signal reception unit 102 receives navigation signals from the four navigation satellites 300 to obtain accurate reception times and receiver coordinates (points in a three-dimensional space) by a predetermined positioning operation. In addition, when the navigation signal reception unit 102 can not receive navigation signals from the four navigation satellites, positioning may be performed by using the position information of the base station 200 as auxiliary information. The navigation signal receiving unit 102 inputs, to the CPU 108, position information obtained by performing positioning.

The gyro sensor 104 measures an angular velocity. The gyro sensor 104 inputs information representing the angular velocity (hereinafter referred to as “angular velocity information”) to the CPU 108.
The acceleration sensor 106 is formed of, for example, a three-axis acceleration sensor, and measures the magnitudes of acceleration in directions of three axes orthogonal to each other. The acceleration sensor 106 inputs information (hereinafter referred to as “acceleration information”) representing the magnitude of acceleration in three axial directions to the CPU 108.

  The CPU 108 performs overall control of the information processing apparatus 100. The RAM 110 provides the CPU 108 with a working memory space. The EEPROM 112 is a type of non-volatile memory, and stores programs 114 executed by the CPU 108 and data.

  The wireless communication unit 116 performs wireless data communication between the information processing apparatus 100 and another device. For example, the information processing apparatus 100 performs wireless data communication with the base station 200 of the mobile phone in accordance with the communication standard of the mobile phone such as Long Term Evolution (LTE). The wireless communication unit 116 inputs, to the CPU 108, positional information attached to the downlink signal transmitted by the base station 200.

  The short distance wireless communication unit 118 performs wireless data communication between the information processing apparatus 100 and another device using a short distance wireless communication technology such as wireless LAN or Bluetooth (registered trademark) Low Energy (BLE). For example, the information processing apparatus 100 performs wireless data communication with a wireless LAN base station according to a standard of wireless communication technology such as wireless fidelity (Wi-Fi), for example, IEEE 802.11 standard, and performs Bluetooth (registration The wireless data communication is performed with the transmitter by BEL. The short distance wireless communication unit 118 inputs, to the CPU 108, positional information of a beacon transmitter attached to a signal transmitted by a wireless LAN base station or a beacon transmitter such as a Bluetooth (registered trademark) transmitter.

  In this case, wireless LAN and Bally are used as an example of the short distance wireless communication technology, but in addition, IRD (Infrared Data Association: IrDA), RFID (Radio Frequency Identification), transfer jet (TransferJet), WiMedia Alliance, ZigBee, etc. can be applied.

  FIG. 3 is a functional block diagram of the information processing apparatus 100 according to the present embodiment. The information processing apparatus 100 includes a principal component analysis input unit 402, a principal component analysis unit 404, a determination unit 406, a pedestrian autonomous navigation (PDR) processing unit 408, and a movement direction change measurement unit 410. Prepare. The functions of the units shown in FIG. 3 are realized by the CPU 108 shown in FIG. 2 executing a program 114 stored in the EEPROM 112.

  The principal component analysis input unit 402 uses a plurality of positioning values of one object as input values of principal component analysis processing. In the present embodiment, one object is the information processing apparatus 100. The principal component analysis unit 404 executes principal component analysis processing using input values input from the principal component analysis input unit 402. The determination unit 406 determines, based on the result of the principal component analysis process, one object, that is, the reference position regarding the movement of the information processing apparatus 100 and the movement direction from the reference position.

  The pedestrian autonomous navigation processing unit 408 uses the reference position determined by the determination unit 406 and the moving direction from the reference position as input values of the pedestrian autonomous navigation. The pedestrian autonomous navigation processing unit 408 executes predetermined pedestrian autonomous navigation processing by using the input value, and outputs a pedestrian autonomous navigation processing result as a result of the processing. In the present embodiment, pedestrian autonomous navigation is used for the second positioning.

  The movement direction change measurement unit 410 measures a change in the movement direction of one object, that is, the information processing apparatus 100, and outputs the measurement data. Based on the measurement data output from the moving direction change measurement unit 410, the principal component analysis input unit 402 selects one object, that is, a plurality of positioning values of the information processing apparatus 100 as input values for the same principal component analysis process. Select the positioning value to use.

  Next, the overall flow of the information processing method performed by the information processing apparatus 100 will be described with reference to FIG. FIG. 4 shows the overall flow of the information processing method according to the present embodiment. The process shown in FIG. 4 is executed at a predetermined timing. The information processing apparatus 100 may execute the process shown in FIG. 4 at a predetermined cycle, for example, or execute the process shown in FIG. 4 in response to a request from a user or an application provided in the information processing apparatus 100. It is also good.

(Step S1) The principal component analysis input unit 402 determines an input value to the principal component analysis process based on the positioning result of the first positioning. The positioning result of the first positioning is output from, for example, the navigation signal receiving unit 102.

(Step S2) The principal component analysis unit 404 executes principal component analysis processing using the input value determined by the principal component analysis input unit 402.

(Step S3) Based on the result of the principal component analysis process performed by the principal component analysis unit 404, the determination unit 406 determines the reference position regarding the movement of the information processing apparatus 100 and the movement direction from the reference position.

(Step S4) The pedestrian autonomous navigation processing unit 408 uses the reference position determined by the determination unit 406 and the movement direction from the reference location as input values of the pedestrian autonomous navigation, and a predetermined pedestrian autonomous navigation is performed. The processing is executed, and the pedestrian autonomous navigation processing result which is the result of the processing is output.

  Next, information processing performed by the information processing apparatus 100 according to the present embodiment will be described in detail with reference to FIGS. 5 to 10.

Principal component analysis
The process according to the principal component analysis of the present embodiment will be described with reference to FIGS. 5 and 6. 5 and 6 are diagrams for explaining the information processing according to the present embodiment. First, with reference to FIG. 5, an example 1 of processing relating to principal component analysis will be described. FIG. 5 shows positioning values P101, P102 and P103, which are positioning results of the first positioning. In FIG. 5, the Y axis indicates latitude, and the X axis indicates longitude.

  In the present embodiment, the principal component analysis unit 404 executes principal component analysis processing on input values including a plurality of positioning values that are positioning results of the first positioning. In this principal component analysis process, principal component regression analysis is performed. The principal component analysis unit 404 obtains a regression line as a result of the principal component regression analysis. The direction of the regression line determined by the principal component analysis unit 404 can be used for the direction in which the information processing apparatus 100 moves (hereinafter, referred to as “initial direction”). The direction of the regression line determined by the principal component analysis unit 404 is the direction of the principal component vector. By performing principal component analysis processing on input values including a plurality of positioning values to obtain an initial orientation, the plurality of positioning values are used as they are without performing principal component analysis processing, and an initial orientation is obtained from orientations between positioning values Compared to the case of determination, it is possible to prevent the decrease in the accuracy of the initial direction due to the measurement error of the first positioning.

  Furthermore, in the present embodiment, an additional process of input values of the principal component analysis process is executed. In FIG. 5, eight additional input values are shown at equal intervals on the circumference of the circles Ca, Cb and Cc centered on the respective positioning values P101, P102 and P103. Additional input values Pa1 to Pa8 are shown at equal intervals on the circumference of the circle Ca centered on the positioning value P101. On the circumference of the circle Cb centered on the positioning value P102, input values Pb1 to Pb8 of the main component analysis process are shown at equal intervals. Additional input values Pc1 to Pc8 are shown at equal intervals on the circumference of the circle Cc centered on the positioning value P103.

  The principal component analysis input unit 402 provides the 24 additional input values Pa1 to Pa8, Pb1 to Pb8 and Pc1 to Pc8 as input values used for the principal component analysis process together with the positioning values P101, P102 and P103. The principal component analysis input unit 402 transmits the positioning values P101, P102 and P103 and the additional input values Pa1 to Pa8, Pb1 to Pb8, and Pc1 to Pc8 to the principal component analysis unit 404 as input values for the principal component analysis process. Output. The principal component analysis unit 404 performs principal components with respect to the input values “positioning values P101, P102 and P103, additional input values Pa1 to Pa8, Pb1 to Pb8, and Pc1 to Pc8” of the 27 principal component analysis processes. Execute analysis processing. As a result of the principal component analysis process, a regression line L shown in FIG. 5 is obtained. The principal component analysis unit 404 includes information indicating the regression line L in the result of the principal component analysis process and outputs the result.

  The radius of the circle Ca centered on the positioning value P101 is a length according to the uncertainty of the positioning value P101. The radius of the circle Cb centered on the positioning value P102 is a length corresponding to the uncertainty of the positioning value P102. The radius of the circle Cc centered on the positioning value P103 is a length corresponding to the uncertainty of the positioning value P103. That is, the larger the uncertainty of the positioning value, the larger the radius of the circle centered on the positioning value. Therefore, the additional input value for a certain positioning value reflects the uncertainty of the positioning value. Thereby, in the principal component analysis process performed by the principal component analysis unit 404, the uncertainty of each positioning value can be reflected.

  Generally, in principal component analysis, it is important from the viewpoint of analysis accuracy that the error at each point to be analyzed is the same. For this reason, if there is a difference in the error of each positioning value, the accuracy of the principal component analysis may be reduced. According to the additional processing of the input value of the principal component analysis processing of the present embodiment, in the principal component analysis processing executed by the principal component analysis unit 404, the accuracy of the principal component analysis is achieved by reflecting the uncertainty of each positioning value. It is possible to prevent the decline.

  The principal component analysis input unit 402 determines the uncertainty of a certain positioning value based on the measurement error of the first positioning at the measurement time of the first positioning at which the positioning value is obtained. That is, the larger the measurement error of the first positioning at the measurement time of the first positioning at which a certain positioning value is obtained, the greater the uncertainty of the positioning value. The error information indicating the measurement error of the first positioning at the measurement time of the first positioning is included in the positioning result of the first positioning. That is, the positioning result of the first positioning includes the positioning value and error information indicating the measurement error of the first positioning at the measurement time of the first positioning at which the positioning value is obtained. The positioning result of the first positioning is output from, for example, the navigation signal receiving unit 102. The navigation signal reception unit 102 transmits the delay of the radio wave propagation velocity caused by the change of radio wave characteristics in the ionosphere and the troposphere in the route where the navigation signal arrives from the navigation satellite 300 to the navigation signal reception unit 102 and the navigation signal transmitted. Based on the number of satellites and the delay of radio wave propagation speed caused by the arrangement, error information indicating the measurement error of the first positioning is generated.

  The addition process of the input value of the principal component analysis process described with reference to FIG. 5 is an example, and is not limited thereto. For a certain positioning value, additional input values may be provided at a position corresponding to the uncertainty of the positioning value. Furthermore, an additional input value may be provided in a range including the positioning value of a size corresponding to the uncertainty of the positioning value. Further, additional input values may be provided equally in the range. Also, the range may be a circle centered on the positioning value of a radius corresponding to the uncertainty of the positioning value. Furthermore, as illustrated in FIG. 5, additional input values may be provided at equal intervals around the circumference of the circle.

  Next, with reference to FIG. 6, an example 2 of processing relating to principal component analysis will be described. In FIG. 6 (1) and (2), positioning values P201 to P206, which are positioning results of the first positioning, are shown respectively. In FIG. 6 (1) and (2), the Y-axis shows the latitude and the X-axis shows the longitude. In FIG. 6 (1) and (2), the broken line Rt shows the movement path | route of the information processing apparatus 100 actual.

  FIG. 6A is an explanatory diagram for comparison with Example 2 of the process related to principal component analysis. In FIG. 6A, six positioning values P201 to P206 present in the range A210 are selected as positioning values to be used as input values for the same principal component analysis process. Thereby, the regression line L1 shown in FIG. 6 (1) is obtained as a result of the principal component analysis process. Then, the direction of the regression line L1 (the direction of the arrow of the regression line L1 in FIG. 6 (1)) is the initial direction. However, the initial direction based on the regression line L1 is largely different from the moving direction of the broken line Rt. This is because the movement direction of the information processing apparatus 100 largely changes at the point Pt, but the positioning values before and after the point Pt are used as the input value of the same principal component analysis process.

  In order to cope with this, the principal component analysis input unit 402 executes example 2 of the process related to principal component analysis. In example 2 of the process related to principal component analysis, the principal component analysis input unit 402 selects one of the six positioning values P201 to P206 based on the measurement data of the change in the movement direction of the information processing apparatus 100. Select the positioning value to be used for the input value of analysis processing. Measurement data on a change in the movement direction of the information processing apparatus 100 is input from the movement direction change measurement unit 410 to the principal component analysis input unit 402. The moving direction change measurement unit 410 measures a change in the moving direction of the information processing apparatus 100 based on angular velocity information and acceleration information included in the sensor detection result, and outputs the measurement data. The principal component analysis input unit 402 determines the magnitude of the change in the movement direction of the information processing apparatus 100 based on the measurement data. When the magnitude of the change in the movement direction of the information processing apparatus 100 is equal to or greater than a predetermined value as a result of the determination, the principal component analysis input unit 402 is the same as the positioning value selected as the input value of the principal component analysis processing Selection of the positioning value to be used for the input value of the principal component analysis process is ended.

  FIG. 6 (2) shows an example 2 of the process related to principal component analysis. In FIG. 6 (2), the principal component analysis input unit 402 detects that the movement direction of the information processing apparatus 100 is largely changed at the point Pt based on the measurement data input from the movement direction change measurement unit 410. Do. Then, the principal component analysis input unit 402 ends the selection of the positioning value to be used as the input value of the principal component analysis processing which is the same as the positioning values P201 and P202 before the point Pt. Accordingly, the positioning values P203 to P206 after the point Pt do not become input values of the same principal component analysis process as the positioning values P201 and P202. Then, the principal component analysis input unit 402 performs positioning using four positioning values P203 to P206 after the point Pt existing in the range A220 as input values of the principal component analysis process different from the positioning values P201 and P202. Select to value. Thereby, a regression line L2 shown in FIG. 6 (2) is obtained as a result of the principal component analysis process. Then, the direction of the regression line L2 (the direction of the arrow of the regression line L2 in FIG. 6 (2)) is the initial direction. The initial direction based on the regression line L2 approximates the moving direction after the point Pt of the broken line Rt.

[Determination of reference position and movement direction]
A process related to the determination of the reference position and the movement direction of the present embodiment will be described with reference to FIGS. 7 to 9. 7 to 9 are diagrams for explaining the information processing according to the present embodiment.

FIG. 7 shows positioning values P101, P102 and P103, which are positioning results of the first positioning, and a regression line L obtained as a result of the principal component analysis process of the principal component analysis unit 404. In FIG. 7, the Y-axis indicates latitude and the X-axis indicates longitude. The result of the principal component analysis process including information for specifying the regression line L is input from the principal component analysis unit 404 to the judgment unit 406. The determination unit 406 determines an intersection point of the regression line L and a perpendicular from the reference positioning value P103 to the regression line L as a reference position Pb regarding the movement of the information processing apparatus 100. The regression line L is a line of principal component vectors as a result of the principal component analysis process of the principal component analysis unit 404. Further, the determination unit 406 determines the direction of the regression line L (the direction of the arrow of the regression line L in FIG. 7) as the movement direction from the reference position Pb. The determination unit 406 outputs information indicating the determined reference position Pb and the moving direction from the reference position Pb to the pedestrian autonomous navigation processing unit 408.
According to the determination processing of the reference position of the present embodiment, the influence of the error of the positioning value on the reference position can be reduced as compared to the case where the positioning value is directly used as the reference position.

  Note that the determination unit 406 may select the reference positioning value used to determine the reference position from among a plurality of positioning values of the information processing apparatus 100 based on the time of positioning and the error in the positioning. . An example of a method of selecting a reference positioning value will be described with reference to FIG. FIG. 8 shows positioning values P301 and P302, which are positioning results of the first positioning, and a regression line L3 obtained as a result of the principal component analysis process of the principal component analysis unit 404. In FIG. 8, the Y-axis indicates latitude and the X-axis indicates longitude. The measurement time of the positioning value P301 is “t0”, and the measurement time of the positioning value P302 is “t0 + Δ1”. The position P_A is an intersection point of the regression line L3 and a perpendicular from the positioning value P301 to the regression line L3. The position P_B is an intersection point of the regression line L3 and a perpendicular from the positioning value P302 to the regression line L3.

  Here, the case where the reference positioning value is selected from the positioning values P301 and P302 will be described as an example. The determination unit 406 obtains an estimation error E_B at the position P_B when the position P_A is set as a reference position. The estimation error E_B is expressed by the following equation.

  Estimation error E_B = "Measurement error of positioning value P302" + "Direction error of position P_B" + "Measurement error by pedestrian autonomous navigation from position P_B to position P_A"

  The error information indicating the measurement error of the positioning value P302 is included together with the positioning value P302 in the positioning result of the first positioning. The measurement error by the pedestrian autonomous navigation from the position P_B to the position P_A is calculated according to the error inherent to the pedestrian autonomous navigation and the movement distance of the information processing apparatus 100. An error inherent to pedestrian autonomous navigation is preset. The determination unit 406 calculates the distance D from the position P_B to the position P_A. The determination unit 406 uses the distance D from the position P_B to the position P_A and the error inherent to the pedestrian autonomous navigation set in advance to determine the measurement error due to the pedestrian autonomous navigation from the position P_B to the position P_A. Calculated using the formula for The calculation formula of the measurement error by pedestrian autonomic navigation is determined in advance such that the measurement error by pedestrian autonomic navigation becomes larger as the movement distance becomes longer.

  Here, with reference to FIG. 9, an estimation method of the azimuth error will be described. FIG. 9 shows an example of a method of estimating a heading error. FIG. 9 shows positioning values P300, P301, P302 and P303, which are positioning results of the first positioning, and a regression line L3. In FIG. 9, a circle C_300 centered on the positioning value P300, a circle C_301 centered on the positioning value P301, a circle C_302 centered on the positioning value P302, and a circle C_303 centered on the positioning value P303 It is shown. The radius of each circle C_300, C_301, C_302, C_303 is a length according to the uncertainty of each positioning value P300, P301, P302, P303 of the center of the circle. That is, the larger the uncertainty of the positioning value, the larger the radius of the circle centered on the positioning value. The uncertainty of a certain positioning value is determined based on the measurement error of the first positioning at the measurement time of the first positioning at which the positioning value is obtained. The error information indicating the measurement error of the first positioning at the measurement time of the first positioning is included in the positioning result of the first positioning. That is, the positioning result of the first positioning includes the positioning value and error information indicating the measurement error of the first positioning at the measurement time of the first positioning at which the positioning value is obtained.

The positioning values P300, P301, P302, and P303 are included in the input value of the same principal component analysis process. A regression line L3 is obtained as a result of the principal component analysis process. Among the positioning values included in the input value of the principal component analysis process, the positioning value foremost in time is the positioning value P300, and the positioning value foremost in time is the positioning value P303. The coordinates of the positioning value P300 are coordinates “x _s , y _s ”. The radius of a circle C_300 centered on the positioning value P300 is Ac _s . The coordinates of the positioning value P303 are coordinates "x _e , y _e ". Radius of the circle centered on the positioning values P303 C_303 is Ac _e. The components of the principal component vector indicating the direction of the regression line L3, ie, the initial orientation, are "P _x , P _y ".
Among the positioning values included in the input value of the principal component analysis process for which the regression line L3 is obtained, the determining unit 406 determines between the positioning value P300 that is the foremost in time and the positioning value P303 that is the foremost in time. Calculate the distance D_a of
The determination unit 406 calculates the angle θ _p by the following equation (1). The angle θ _p is an angle formed by the regression line L3 and the line L5. The straight line L5 is a straight line including a positioning value P300 which is the foremost in time and a positioning value P303 which is the most in time among the positioning values included in the input value of the principal component analysis process for which the regression line L3 is obtained. It is.

Determination unit 406 calculates an estimated value (the estimated azimuth error) E _v of the heading error by the following equation (2).

9 shows, by a line L4 at an angle represented by the initial orientation by the estimated azimuth error E _v, when the position information obtained by determining the positioning value P303 for positioning value of the reference is most deviated from the initial orientation The orientation of the is shown.

Description will be returned to FIG.
The determination unit 406 obtains the heading error (estimated heading error E _v ) of the position P_B by the heading error estimation method of FIG. 9 described above.
Further, the determination unit 406 obtains an estimation error E_A at the position P_A when the position P_A is set as a reference position. The estimation error E_A is “a measurement error of the positioning value P301”. The error information indicating the measurement error of the positioning value P301 is included together with the positioning value P301 in the positioning result of the first positioning.

  The determination unit 406 compares the estimation error E_A with the estimation error E_B to determine which estimation error is smaller. When determining that estimation error E_A is smaller, determination unit 406 determines positioning value P301 as a reference positioning value. Thus, the position P_A is determined to be the reference position. On the other hand, when determining that the estimation error E_B is smaller, the determination unit 406 determines the positioning value P302 as the reference positioning value. Thus, the position P_B is determined to be the reference position.

  Note that as another determination method of the reference positioning value, a temporally closest positioning value among positioning values of the positioning result of the first positioning may be determined as the reference positioning value.

[Pedestrian autonomous navigation processing]
The pedestrian autonomous navigation processing of the present embodiment will be described with reference to FIG. FIG. 10 is a diagram for explaining the information processing according to the present embodiment. In FIG. 10, the Y axis indicates latitude, and the X axis indicates longitude. FIG. 10 shows positioning values P101, P102 and P103, which are positioning results of the first positioning, and a regression line L obtained as a result of the principal component analysis process of the principal component analysis unit 404. Further, FIG. 10 shows the reference position Pb determined by the determination unit 406 and the moving direction from the reference position Pb.

  The pedestrian autonomous navigation processing unit 408 uses the reference position Pb indicated by the information input from the determination unit 406 and the movement direction from the reference position Pb as input values of the pedestrian autonomous navigation, and performs predetermined pedestrian autonomous operation. The navigation processing is executed, and the pedestrian autonomous navigation processing result that is the result of the processing is output. In the pedestrian autonomous navigation processing, the pedestrian autonomous navigation processing unit 408 calculates the relative movement amount from the reference position Pb based on the angular velocity information and the acceleration information included in the sensor detection result. The pedestrian autonomous navigation processing unit 408 determines the position to which the information processing device 100 has moved based on the reference position Pb and the relative movement amount. The pedestrian autonomous navigation processing unit 408 includes the position of the information processing apparatus 100 as the determination result in the pedestrian autonomous navigation processing result and outputs the result. In FIG. 10, a locus (PDR locus) according to the position of the information processing apparatus 100 as the pedestrian autonomous navigation processing result is shown.

  According to the information processing apparatus 100 according to the present embodiment, principal component analysis processing is performed using the input values of the principal component analysis processing of the positioning values of the plurality of first positioning of the information processing apparatus 100 Based on the result of the analysis process, a reference position regarding the movement of the information processing apparatus 100 and a movement direction from the reference position are determined. Then, the information processing apparatus 100 uses the reference position and the moving direction from the reference position as input values for pedestrian autonomous navigation.

  Thereby, without performing the principal component analysis process, when using the positioning values of the plurality of first positioning of the information processing apparatus 100 as it is and obtaining the reference position and the moving direction from the reference position from the orientation between the positioning values. On the other hand, since it is possible to prevent the reference position due to the measurement error of the first positioning and the decrease in the accuracy of the moving direction from the reference position, the reference position used as an input value of pedestrian autonomous navigation and the reference position It is possible to improve the accuracy with the moving direction from the And since the precision of the input value of pedestrian autonomy navigation can be improved, the precision of the positioning result by pedestrian autonomy navigation can be improved.

Second Embodiment
FIG. 11 shows a configuration example of a positioning system according to the second embodiment. In FIG. 11, parts corresponding to the parts in FIG. 1 are given the same reference numerals, and the description thereof will be omitted. The information processing apparatus 700 according to the second embodiment can apply FIG. 2. The information processing apparatus 700 according to the second embodiment is different from the information processing apparatus according to the first embodiment in the processing of the principal component analysis input unit 402, the principal component analysis unit 404, the determination unit 406, and the movement direction change measurement unit 410. The apparatus 800 is made to execute. In the second embodiment, the information processing device 700 functions as a pedestrian autonomous navigation processing device. The information processing apparatus 700 implements the function of the pedestrian autonomous navigation processing unit 408 among the units shown in FIG. 3 by the CPU 108 shown in FIG. 2 executing the program 114 stored in the EEPROM 112. An example of the information processing apparatus 700 is a smartphone, a tablet terminal, a portable PC, or the like.

  The information processing apparatus 700 and the other information processing apparatus 800 are connected by wire or wirelessly. Here, as an example, a case where the information processing apparatus 700 and the other information processing apparatus 800 are connected by wireless will be described. The information processing apparatus 700 and the other information processing apparatus 800 may be connected by wire.

  An example of another information processing apparatus 800 is a terminal apparatus such as a PC or a server (hereinafter referred to as a terminal apparatus 800). FIG. 12 shows an example of the hardware configuration of the terminal device 800 according to the second embodiment. The terminal device 800 includes a wireless communication unit 802, a CPU 804, a RAM 806, an EEPROM 808, and a bus line 850 such as an address bus or a data bus for electrically connecting the respective components as shown in FIG.

  The wireless communication unit 802 performs data communication with the information processing apparatus 700. For example, the information processing apparatus 700 performs wireless data communication via a mobile phone base station or a base station such as an access point. The wireless communication unit 802 inputs information transmitted by the information processing apparatus 700 to the CPU 804.

  The CPU 804 performs overall control of the terminal device 800. The RAM 806 provides the CPU 804 with a working memory space. The EEPROM 808 is a type of non-volatile memory, and stores programs 810 executed by the CPU 804, data, and the like. The terminal device 800 executes the program 810 stored in the EEPROM 808 by the CPU 804, thereby the principal component analysis input unit 402, the principal component analysis unit 404, the judgment unit 406, and the movement direction change measurement among the units shown in FIG. The functions of the unit 410 are realized.

  The information processing device 700 transmits the positioning result of the first positioning and the sensor detection result to the terminal device 800. The terminal device 800 uses the positioning result and sensor detection result of the first positioning received from the information processing device 700 to execute the principal component analysis input unit 402, the principal component analysis unit 404, the judgment unit 406, and the movement direction change measurement unit Execute processing with 410. The terminal device 800 transmits, to the information processing device 700, information indicating the reference position determined by the process of the determination unit 406 and the moving direction from the reference position. The information processing device 700 uses the reference position indicated by the information received from the terminal device 800 and the moving direction from the reference position as input values for pedestrian autonomous navigation to execute predetermined pedestrian autonomous navigation processing. And outputs the pedestrian autonomous navigation processing result which is the result of the processing.

  According to the second embodiment, the pedestrian is caused to execute the processing of the principal component analysis input unit 402, the principal component analysis unit 404, the determination unit 406, and the movement direction change measurement unit 410 by the information processing device 800. The load on the information processing device 700 functioning as an autonomous navigation processing device can be reduced.

  Although the embodiments of the present invention have been described in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and design changes and the like within the scope of the present invention are also included.

  For example, a geomagnetic sensor, an air pressure sensor, a proximity sensor, an illuminance sensor, or the like may be mounted on the information processing apparatus 100 or the information processing apparatus 700 instead of the gyro sensor and the acceleration sensor or together with the gyro sensor and the acceleration sensor.

  And, by a geomagnetic sensor, a barometric pressure sensor, a proximity sensor, an illuminance sensor or the like mounted on the information processing apparatus 100 or the information processing apparatus 700 instead of the gyro sensor and the acceleration sensor or together with the gyro sensor and the acceleration sensor Positioning may be performed.

  By mounting a geomagnetic sensor, an atmospheric pressure sensor, a proximity sensor, an illuminance sensor, and the like together with a gyro sensor and an acceleration sensor, it is possible to improve the positioning accuracy by pedestrian autonomous navigation. For this reason, even if the positioning based on pedestrian autonomous navigation is performed over a long distance, the increase in accumulated error can be suppressed, and therefore the power consumption of the information processing apparatus 100 or 700 can be further reduced.

[Another example of estimation method of orientation error]
Another example of the estimation method of the orientation error will be described with reference to FIG. FIG. 13 shows an example of a method of estimating a heading error. FIG. 13 shows positioning values P401, P402, P403 and P404, which are positioning results of the first positioning, and a regression line L10. Further, in FIG. 13, a circle C_401 centered on the positioning value P401, a circle C_402 centered on the positioning value P402, a circle C_403 centered on the positioning value P403, and a circle C_404 centered on the positioning value P404 It is shown. The radius of each circle C_401, C_402, C_403, C_404 is a length according to the uncertainty of the positioning value P401, P402, P403, P404 of the center of the circle. That is, the larger the uncertainty of the positioning value, the larger the radius of the circle centered on the positioning value. The uncertainty of a certain positioning value is determined based on the measurement error of the first positioning at the measurement time of the first positioning at which the positioning value is obtained. The error information indicating the measurement error of the first positioning at the measurement time of the first positioning is included in the positioning result of the first positioning. That is, the positioning result of the first positioning includes the positioning value and error information indicating the measurement error of the first positioning at the measurement time of the first positioning at which the positioning value is obtained.

  On the circumference of each circle C_401, C_402, C_403, C_404, eight additional input values are shown at equal intervals. A set of input values of the principal component analysis process consisting of all additional input values shown on the circumference of each circle C_401, C_402, C_403 and C_404 and positioning values P401, P402, P403 and P404 is taken as pn Do. As a result of the principal component analysis process in which the set pn is an input value, a regression line L10 indicating an initial direction is obtained.

  The determination unit 406 calculates the average error Ac based on the value obtained by dividing the sum of squares of the vertical distances between the input values included in the set pn and the regression line L10 by the degrees of freedom. The value obtained by dividing the sum of squares of the vertical distances between each input value included in the set pn and the regression line L10 by the degrees of freedom represents the variation of the set pn with respect to the regression line L10.

The straight lines L11 and L12 are straight lines parallel to the regression line L10. The interval between each straight line L11, L12 and the regression line L10 is the average error Ac. A set of intersections of the regression line L10 and the perpendicular lines from the input values included in the set pn to the regression line L10 is taken as pn '. The determination unit 406 calculates the longest distance D_b of the distances between any two intersections included in the set pn ′. Determination unit 406 calculates an estimated value (the estimated azimuth error) E _v of the heading error by the following equation (3).

Figure 13 is the straight line L13 at an angle from the initial orientation (the direction of the regression line L10) represented by the estimated azimuth error E _v, deviation estimated from the initial orientation is illustrated.

Alternatively, a computer program for realizing the functions of the information processing apparatus described above may be recorded in a computer readable recording medium, and the computer system may read and execute the program recorded in the recording medium. . Note that the “computer system” referred to here may include an OS and hardware such as peripheral devices.
The “computer readable recording medium” is a writable non-volatile memory such as a flexible disk, an optical magnetic disk, a ROM, a flash memory, etc., a portable medium such as a DVD (Digital Versatile Disc), etc. Storage devices such as hard disks.

Furthermore, the “computer-readable recording medium” is a volatile memory (for example, DRAM (Dynamic Memory) inside a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line). As Random Access Memory), it is assumed that the program which holds the program for a fixed time is included.
The program may be transmitted from a computer system in which the program is stored in a storage device or the like to another computer system via a transmission medium or by transmission waves in the transmission medium. Here, the “transmission medium” for transmitting the program is a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
Further, the program may be for realizing a part of the functions described above. Furthermore, it may be a so-called difference file (difference program) that can realize the above-described functions in combination with a program already recorded in the computer system.

DESCRIPTION OF SYMBOLS 100, 700, 800 ... Information processing apparatus, 102 ... Navigation signal receiving part, 104 ... Gyro sensor, 106 ... Acceleration sensor, 108, 804 ... CPU, 110, 806 ... RAM, 112, 808 ... EEPROM, 114, 810 ... Program 116, 802 Wireless communication unit 118 Short-range wireless communication unit 150, 850 Bus line 200 Base station 300 (300a, 300b, 300c, 300d) Navigation satellite 402 Main component analysis input unit , 404: principal component analysis unit, 406: judgment unit, 408: pedestrian autonomous navigation processing unit, 410: movement direction change measurement unit

Claims (6)

A principal component analysis input unit that determines input values of principal component analysis processing including a plurality of positioning values of one object;
A principal component analysis unit that executes principal component analysis processing using the input values and generates a regression line of the position of the object having the direction of the principal component vector of the result of the principal component analysis processing in the direction of a straight line ;
A reference position to be used for an input value of pedestrian autonomous navigation based on the regression line, and a determination unit that determines a moving direction from the reference position ,
The principal component analysis input unit executes additional processing of the input value of the principal component analysis processing according to the uncertainty of the positioning value,
Information processing device. The principal component analysis input unit determines, as input values of the principal component analysis processing, a plurality of positioning values of one object and an additional position at which each positioning value is spaced according to the uncertainty of the positioning value. Do,
An information processing apparatus according to claim 1. The pedestrian autonomous navigation processing unit according to any one of claims 1 or 2 , further comprising: a pedestrian autonomous navigation processing unit that uses the reference position determined by the judgment unit and the movement direction from the reference position as an input value of pedestrian autonomous navigation. Information processing device. An information processing apparatus according to any one of claims 1 or 2 .
A pedestrian autonomous navigation processing device that uses a reference position determined by the information processing device and a movement direction from the reference position as an input value of pedestrian autonomous navigation;
Positioning system comprising: A principal component analysis input step in which the information processing apparatus determines input values of principal component analysis processing including a plurality of positioning values of one object;
The information processing apparatus, using said input values perform principal component analysis process to generate a regression line of the position of the object with the direction of the result of the principal component vector of principal component analysis process in the direction of the straight line main Component analysis step,
The information processing apparatus, viewed contains a judgment step, the determining the direction of movement from the reference position and the reference position used to input values of the pedestrian autonomous navigation based on the regression line,
The information processing apparatus executes, in the main component analysis input step, an additional process of the input value of the main component analysis process according to the uncertainty of the positioning value.
Information processing method. On the computer
A principal component analysis input function that determines an input value of principal component analysis processing including a plurality of positioning values of one object;
A principal component analysis function of executing principal component analysis processing using the input values and generating a regression line of the position of the object having the direction of the principal component vector of the result of the principal component analysis processing in the direction of a straight line ;
A computer program for realizing a determination function of determining a reference position used for an input value of pedestrian autonomous navigation based on the regression line and a moving direction from the reference position ,
The principal component analysis input function executes additional processing of the input value of the principal component analysis processing according to the uncertainty of the positioning value,
Computer program.

Images