JP2021128095A - Positioning device for vehicles and positioning program for vehicles - Google Patents

Abstract

To provide a positioning device for vehicles which improves the accuracy of traveling trajectory of a vehicle immediately after the power supply of the vehicle is turned on.SOLUTION: A positioning device 50 includes a change unit. When the characteristic of a vehicle speed sensor 11 has changed while the power supply of the vehicle is turned on from an off state, the change unit changes a value pertaining to the likelihood of a movement distance measurement error out of the components of an error covariance matrix calculated by a Kalman filter 55. When the characteristic of a gyro sensor 12 has changed while the power supply of the vehicle is turned on from an off state, a value pertaining to the likelihood of a bearing change amount measurement error out of the components of an error covariance matrix calculated by the Kalman filter 55 is increased, separately from calculation of the error covariance matrix calculated by the Kalman filter 55, to be larger than before the power supply of the vehicle is turned off.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to a vehicle positioning device and a vehicle positioning program.

Conventionally, as described in Patent Document 1, a device for calculating a traveling locus of a vehicle while correcting an error by using a Kalman filter based on a signal from a vehicle speed sensor, a gyro sensor, and a GPS receiver is known. ..

JP-A-2015-94690

According to the study of the inventor, the sensor characteristics such as the distance coefficient of the vehicle speed sensor change relatively slowly due to, for example, changes in the tires of the vehicle and aging of the vehicle speed sensor. Further, sensor characteristics such as offset and gain of the gyro sensor change relatively slowly due to, for example, a temperature change of the gyro sensor. Therefore, in the device described in Patent Document 1, the error correction time by the Kalman filter can be relatively long in the state where the power of the vehicle is turned on, so that the reliability of the estimation error by the Kalman filter is relatively high. Can be high.

Further, in the device described in Patent Document 1, when the power of the vehicle is turned off, the estimation error by the Kalman filter immediately before the power of the vehicle is turned off is stored. As a result, the traveling locus of the vehicle can be continuously calculated from the estimation error by the Kalman filter immediately before the power of the vehicle is turned off.

Here, if the power of the vehicle is turned on when the sensor characteristics have changed while the power of the vehicle is turned on, the power of the vehicle is turned off immediately after the power of the vehicle is turned on. Since it is in the state immediately before it is performed, a sudden change occurs in the error. However, since the error correction time by the Kalman filter is relatively long as described above, it takes time for the estimation error by the Kalman filter to converge to an appropriate value. Therefore, immediately after the power of the vehicle is turned on, it is not possible to follow a sudden change in the error used in the Kalman filter, and the accuracy of the traveling locus of the vehicle may decrease.

An object of the present disclosure is to provide a vehicle positioning device that improves the accuracy of a traveling locus of a vehicle immediately after the power of the vehicle is turned on.

The invention according to claim 1 is a moving distance measuring unit (S130) that calculates a moving distance (L) of a vehicle based on a signal from a vehicle speed sensor (11) according to the rotation of a vehicle tire, and a gyro sensor. Based on the signal according to the yaw rate (R) of the vehicle from (12), the orientation change amount measuring unit (S130) that calculates the orientation change amount (D) of the vehicle, and the movement distance and the orientation change amount. A position estimation unit (S150) that calculates values related to the vehicle position and orientation (θd), a value related to the movement distance, a value related to the vehicle position and orientation (θd) calculated by the position estimation unit, and a signal from the satellite. Based on the values related to the position and orientation (θg) of the vehicle calculated based on, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the measurement error of the amount of change in orientation (εF, εS) value (σFF ^2, σSS ²⁾ relates to the probability of calculating the error covariance matrix (P) comprising a as a component, the Kalman filter for correcting a moving distance and orientation change amount based on the calculated error covariance matrix (S 210 to S270) and the value (σKK) related to the certainty of the movement distance measurement error (εK) among the components of the error covariance matrix when the characteristics of the vehicle speed sensor changed while the vehicle power was turned on. ² ) is a vehicle positioning device provided with a change unit (S320) that is different from the calculation of the error covariance matrix by the Kalman filter and is larger than before the power of the vehicle is turned off.

The invention according to claim 7 is a moving distance measuring unit (S130) that calculates a moving distance (L) of a vehicle based on a signal from a vehicle speed sensor (11) according to the rotation of a vehicle tire, and a gyro sensor. Based on the signal according to the yaw rate (R) of the vehicle from (12), the orientation change amount measuring unit (S130) that calculates the orientation change amount (D) of the vehicle, and the movement distance and the orientation change amount. A position estimation unit (S150) that calculates values related to the vehicle position and orientation (θd), a value related to the movement distance, a value related to the vehicle position and orientation (θd) calculated by the position estimation unit, and a signal from the satellite. Based on the values related to the position and orientation (θg) of the vehicle calculated based on, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the measurement error of the amount of change in orientation (εF, εS) value (σFF ^2, σSS ²⁾ relates to the probability of calculating the error covariance matrix (P) comprising a as a component, the Kalman filter for correcting a moving distance and orientation change amount based on the calculated error covariance matrix (S 210 to S270) and the certainty of the measurement error (εF, εS) of the amount of azimuth change among the components of the error covariance matrix when the characteristics of the gyro sensor changed while the power of the vehicle was turned on. This is a vehicle positioning device that includes a change unit (S340) that increases the values (σFF ² , σSS ² ) from the calculation of the error covariance matrix by the Kalman filter, and makes them larger than before the vehicle was turned off. ..

The invention according to claim 11 is a moving distance measuring unit (L) for calculating a moving distance (L) of a vehicle based on a signal from a vehicle speed sensor (11) according to the rotation of a vehicle tire. S130), azimuth change amount measuring unit (S130) that calculates the azimuth change amount (D) of the vehicle based on the signal corresponding to the yaw rate (R) of the vehicle from the gyro sensor (12), moving distance and azimuth change amount. Position estimation unit (S150) that calculates values related to the position and orientation (θd) of the vehicle based on, values related to the movement distance, values related to the position and orientation (θd) of the vehicle calculated by the position estimation unit, and satellites. Based on the value related to the position and orientation (θg) of the vehicle calculated based on the signal from, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the measurement error of the amount of change in orientation (εF). , ΕS) is calculated as an error covariance matrix (P) including the values related to the certainty (σFF ² , σSS ² ) as components, and the movement distance and the amount of change in orientation are corrected based on the calculated error covariance matrix. (S210 to S270), a value related to the certainty of the measurement error (εK) of the movement distance among the components of the error covariance matrix when the characteristics of the vehicle speed sensor are changing while the power of the vehicle is turned on. This is a vehicle positioning program that functions (σKK ² ) as a change unit (S320) that makes (σKK 2) larger than before the power of the vehicle is turned off, which is different from the calculation of the error covariance matrix by the Kalman filter.

The invention according to claim 12 is a moving distance measuring unit (L) for calculating a moving distance (L) of a vehicle based on a signal from a vehicle speed sensor (11) according to the rotation of a vehicle tire. S130), azimuth change amount measuring unit (S130) that calculates the azimuth change amount (D) of the vehicle based on the signal corresponding to the yaw rate (R) of the vehicle from the gyro sensor (12), moving distance and azimuth change amount. Position estimation unit (S150) that calculates values related to the position and orientation (θd) of the vehicle based on, values related to the movement distance, values related to the position and orientation (θd) of the vehicle calculated by the position estimation unit, and satellites. Based on the value related to the position and orientation (θg) of the vehicle calculated based on the signal from, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the measurement error of the amount of change in orientation (εF). , ΕS) A Kalman filter that calculates an error covariance matrix (P) that includes values related to the certainty (σFF ² , σSS ² ) as components, and corrects the movement distance and the amount of azimuth change based on the calculated error covariance matrix. (S210 to S270), when the characteristics of the gyro sensor are changing while the power of the vehicle is turned on, the measurement error (εF, εS) of the amount of azimuth change among the components of the error covariance matrix is certain. A vehicle positioning program that functions as a change part (S340) that makes the values related to the uniqueness (σFF ² , σSS ² ) larger than before the vehicle was turned off, separately from the calculation of the error covariance matrix by the Kalman filter. Is.

As a result, when the sensor characteristics change while the power of the vehicle is turned on, the value related to the certainty of the measurement error by the sensor is increased even immediately after the power of the vehicle is turned on. be able to. Therefore, it is possible to shorten the time required for the estimation error by the Kalman filter to converge to an appropriate value. Therefore, the accuracy of the traveling locus of the vehicle can be improved.

The reference reference numerals in parentheses attached to each component or the like indicate an example of the correspondence between the component or the like and the specific component or the like described in the embodiment described later.

A block diagram of a vehicle navigation system in which a positioning device is used. The block diagram which shows the model of the Kalman filter of the positioning apparatus. A flowchart of the process executed by the positioning device. Sub-flow chart of the process executed by the positioning device. Sub-flow chart of the process executed by the positioning device.

Hereinafter, embodiments will be described with reference to the drawings. In each of the following embodiments, the same or equal parts are designated by the same reference numerals, and the description thereof will be omitted.

The positioning device 50 is used, for example, in the vehicle navigation system 10. First, the vehicle navigation system 10 will be described.

As shown in FIG. 1, the vehicle navigation system 10 includes an interface (not shown) for communicating with the tire pressure monitoring system 20. Further, the vehicle navigation system 10 includes a vehicle speed sensor 11, a gyro sensor 12, a gyro temperature sensor 13, a GNSS receiving unit 14, and a positioning device 50.

The tire pressure monitoring system 20 includes an air pressure sensor (not shown) and a tire replacement switch (not shown). The air pressure sensor outputs a signal corresponding to the air pressure of the tires of the vehicle to the positioning device 50 described later. The tire change switch outputs a signal to the positioning device 50 according to whether or not the tire has been changed by being operated by the driver of the vehicle. In the figure, the tire pressure monitoring system 20 is referred to as TPMS. TPMS is an abbreviation for Tire Pressure Monitoring System.

The vehicle speed sensor 11 outputs a pulse signal synchronized with the rotation of the tires of the vehicle to the positioning device 50.

The gyro sensor 12 outputs a signal corresponding to the yaw rate R of the vehicle to the positioning device 50.

The gyro temperature sensor 13 outputs a signal corresponding to the temperature of the gyro sensor 12 to the positioning device 50. Further, the gyro temperature sensor 13 is integrated with the gyro sensor 12 here. The gyro temperature sensor 13 may be separate from the gyro sensor 12.

The GNSS receiving unit 14 receives signals from a plurality of positioning satellites (not shown). Further, the GNSS receiving unit 14 calculates the GNSS absolute position, the GNSS absolute direction θg, the GNSS vehicle speed Vg, and the like of the vehicle based on the received signal. Further, the GNSS receiving unit 14 outputs the calculated GNSS absolute position, GNSS absolute direction θg, GNSS vehicle speed Vg, and the like to the positioning device 50. The positioning satellites used in the GNSS receiving unit 14 are, for example, GPS satellites, GLONASS satellites, Galileo satellites, quasi-zenith satellites, and the like.

The positioning device 50 corresponds to a vehicle speed characteristic determination unit, a gyro characteristic determination unit, a distance storage unit, a direction storage unit, a change unit, a reading unit, and the like, and is mainly composed of a microcomputer and the like. Further, the positioning device 50 includes a CPU, a ROM, a RAM, a flash memory, an I / O, a bus line connecting these configurations, and the like. Further, the positioning device 50 calculates the traveling locus of the vehicle by executing the program stored in the ROM. Specifically, the positioning device 50 includes a moving distance measuring unit 51, a directional change amount measuring unit 52, a relative locus calculation unit 53, an absolute position estimation unit 54, a Kalman filter 55, and the like as functional blocks.

The moving distance measuring unit 51 calculates the moving distance L of the vehicle based on the signal from the vehicle speed sensor 11 and the value to be corrected by the Kalman filter 55 described later.

The directional change amount measuring unit 52 calculates the directional change amount D of the vehicle based on the signal from the gyro sensor 12 and the value to be corrected by the Kalman filter 55 described later.

The relative locus calculation unit 53 calculates the relative locus of the vehicle and the vehicle speed Vc based on the movement distance L calculated by the movement distance measurement unit 51 and the direction change amount D calculated by the direction change amount measurement unit 52.

The absolute position estimation unit 54 calculates the absolute direction θd and the absolute position of the vehicle based on the movement distance L calculated by the movement distance measurement unit 51 and the direction change amount D calculated by the direction change amount measurement unit 52. ..

The Kalman filter 55 is a correction target to be described later based on the signal from the GNSS receiving unit 14, the vehicle speed Vc calculated by the relative trajectory calculation unit 53, and the absolute azimuth θd and the absolute position calculated by the absolute position estimation unit 54. Calculate the value.

Further, here, the Kalman filter 55 has a state quantity generation process and an observation process, as shown in FIG.

In the state quantity generation process, the state quantity x, which is an error estimated by the Kalman filter 55, is set. This state quantity x is updated by the equation of state shown in the relational expression (1-1) below. The details of this state quantity x will be described later. Further, in the following relational expression, t is a natural number and represents the number of times. x (t) is the state quantity x at the time of updating at the tth time. A (t + 1, t) is a state transition matrix A from x (t) to x (t + 1). v (t) is noise generated in the state quantity generation process when the t-th update is performed.

In the observation process, the observed value y, which is a value observed based on the signals from the vehicle speed sensor 11, the gyro sensor 12, and the GNSS receiving unit 14, is set. This observed value y is represented by the observation equation shown in the relational expression (1-2) below. As a result, the relationship between the relational expressions (1-1) and (1-2) is represented by the block diagram shown in FIG. The details of this observed value y will be described later. Further, in the following relational expression, C (t) is an observation matrix C that transforms x (t) into y (t). w (t) is noise generated in the observation process when the t-th update is performed.

Further, the Kalman filter 55 repeatedly estimates the state quantity x at a predetermined period Tm by using the following relational expressions (2-1) to (2-5). In the following relational expression, G (t) is the Kalman gain G at the time of updating at the t-th time. P is an error covariance matrix of the state quantity x. P (t | t-1) is a prior error covariance matrix, and is a predicted value of the error covariance matrix P when updating the tth time based on the information up to the (t-1) th update. .. P (t | t) is an error covariance matrix P based on the information up to the t-th time. A (t | t-1) is an estimated value of the state transition matrix A when updating the t-th time based on the information up to the (t-1) th update. V (t) is a covariance matrix of noise generated in the state quantity generation process when the t-th update is performed. W (t) is a covariance matrix of noise generated in the observation process when the t-th update is performed. x (t | t-1) is a pre-estimated value, and is a predicted value of the state quantity x in the t-th update based on the information up to the (t-1) th time. x (t | t) is an ex post facto estimated value, and is an optimal estimated value of the state quantity x based on the information up to the t-th update. I is an identity matrix. The superscript "T" means transposed matrix. The superscript "-1" means an inverse matrix.

Further, here, the Kalman filter 55 feeds back and corrects the estimated state quantity x. As a result, in the relational expression (2-4), x (t | t-1) can be assumed to be zero, so that the relational expression (2-4) is transformed as shown in the relational expression (3) below. be able to. Therefore, the Kalman filter 55 estimates the optimum estimated value of the state quantity x based on the Kalman gain G and the observed value y. Further, the Kalman filter 55 calculates a correction value described later based on the optimum estimated value of the estimated state quantity x.

As described above, the vehicle navigation system 10 is configured. In the vehicle navigation system 10, the positioning device 50 accurately calculates the traveling locus of the vehicle. In order to explain the calculation of the traveling locus of the vehicle, the processing of the positioning device 50 will be described with reference to the flowchart of FIG. Here, when the power of the vehicle is turned on, for example, when the ignition of the vehicle is turned on, the positioning device 50 executes the processing of the program stored in the ROM.

In step S100, the RAM as the work area of the positioning device 50 is initialized because the power of the vehicle is turned off. Therefore, the positioning device 50 reads back each data stored in the backup process in step S170, which will be described later, from the flash memory. Further, the positioning device 50 stores each of the read back data in the RAM.

Subsequently, in step S110, the positioning device 50 resets the error covariance matrix P based on the tire pressure of the vehicle output from the tire pressure sensor (not shown) of the tire pressure monitoring system 20. Further, the positioning device 50 resets the error covariance matrix P based on a signal output from a tire replacement switch (not shown) of the tire pressure monitoring system 20 depending on whether or not the tire has been replaced. Further, the positioning device 50 resets the error covariance matrix P based on the temperature of the gyro sensor 12 output from the gyro temperature sensor 13. The reset process will be described later.

Subsequently, in step S120, the positioning device 50 determines whether or not the power of the vehicle has been turned off. When the power of the vehicle is turned off, the process proceeds to step S170. Further, when the power of the vehicle is turned on, the process proceeds to step S130. Here, for convenience, the cycle Tm is defined as a series of operation periods from the start of the process of step S120 to the return to the process of step S120.

In step S130 following step S120, the positioning device 50 calculates the moving distance L and the directional change amount D of the vehicle.

Specifically, the moving distance measuring unit 51 of the positioning device 50 multiplies the number of pulses output from the vehicle speed sensor 11 within the period Tm by the distance coefficient K, as shown in the following relational expression (4). The moving distance L is calculated by. The distance coefficient K is a value indicating the amount of movement of the vehicle per pulse output from the vehicle speed sensor 11, and is subject to correction described later here. In the following relational expression, L (t) is the moving distance L when updated at the t-th cycle Tm. N (t) is the number of pulses output from the vehicle speed sensor 11 within the t-th cycle Tm. K (t) is a distance coefficient K used for calculating the moving distance L in the t-th cycle Tm.

Further, the directional change amount measuring unit 52 of the positioning device 50 multiplies the yaw rate R of the vehicle from the gyro sensor 12 by the period Tm, as shown in the relational expression (5) below. Further, the directional change amount measuring unit 52 subtracts the value obtained by multiplying the offset correction value F as a correction target, which will be described later, by the period Tm from the multiplied value, thereby performing offset correction of the multiplied value. As a result, when the yaw rate R is zero, the error due to the change in the output value of the corresponding gyro sensor 12 is corrected. Further, the directional change amount measuring unit 52 corrects the gain by multiplying the offset-corrected value by the gain correction value S as a correction target described later. As a result, the error due to the change in the output value of the gyro sensor 12 with respect to the yaw rate R is corrected. In the following relational expression, R (t) is the yaw rate R output from the gyro sensor 12 in the t-th cycle Tm. F (t) is an offset correction value F used for calculating the directional change amount D in the t-th period Tm. S (t) is a gain correction value S used for calculating the directional change amount D in the t-th period Tm.

Subsequently, in step S140, the positioning device 50 calculates the relative trajectory of the vehicle and the vehicle speed Vc.

Specifically, the relative trajectory calculation unit 53 of the positioning device 50 calculates the relative direction θs using the following relational expression (6). Here, the relative trajectory calculation unit 53 adds the direction change amount D calculated by the direction change amount measurement unit 52 in the step S130 to the relative direction θs calculated in the previous period Tm. The relative direction θs in the period Tm of is calculated. θs (t) is the relative direction θs calculated in the t-th period Tm.

Further, the relative locus calculation unit 53 calculates the movement amount of the X component of the relative position coordinates based on the calculated relative direction θs and the movement distance L calculated by the movement distance measurement unit 51 in step S120. .. Further, the relative locus calculation unit 53 adds the calculated movement amount of the X component to the X component of the relative position coordinates calculated in the previous period Tm, as shown in the following relational expression (7-1). Therefore, the X component of the relative position coordinates in the current period Tm is calculated. Similarly, as shown in the following relational expression (7-2), the relative locus calculation unit 53 calculates the amount of movement of the Y component in the current period Tm by the relative position coordinate Y calculated in the previous period Tm. By adding to the components, the Y component of the relative position coordinates in the current period Tm is calculated. In the following relational expression, rel_x (t) is the X component of the relative position coordinates calculated in the t-th period Tm. rel_y (t) is the Y component of the relative position coordinates calculated in the t-th period Tm.

Further, as shown in the relational expression (8) below, the relative locus calculation unit 53 divides the movement distance L calculated by the movement distance measurement unit 51 in step S120 by the period Tm to obtain the vehicle speed Vc. Is calculated. In the following relational expression, Vc (t) is the vehicle speed Vc calculated in the t-th cycle Tm.

Subsequently, in step S150, the positioning device 50 calculates the absolute orientation θd and the absolute position.

Specifically, the absolute position estimation unit 54 of the positioning device 50 measures the amount of change in direction in step S130 to the absolute direction θd calculated in the previous period Tm, as shown in the following relational expression (9). The directional change amount D calculated by the unit 52 is added. As a result, the absolute position estimation unit 54 calculates the absolute direction θd. In the following relational expression, θd (t) is the absolute bearing θd calculated in the t-th period Tm.

Further, the absolute position estimation unit 54 calculates the movement amount of the X component of the absolute position coordinates based on the calculated absolute orientation θd and the movement distance L calculated by the movement distance measurement unit 51 in step S120. Further, the absolute position estimation unit 54 adds the calculated movement amount of the X component to the X component of the absolute position coordinates calculated in the previous period Tm, as shown in the following relational expression (10-1). Therefore, the X component of the absolute position coordinates in the current period Tm is calculated. Similarly, as shown in the following relational expression (10-2), the absolute position estimation unit 54 calculates the amount of movement of the Y component in the current period Tm by the previous period Tm, and calculates the Y of the absolute position coordinates. By adding to the components, the Y component of the absolute position coordinates in the current period Tm is calculated. In the following relational expression, abs_x (t) is the X component of the absolute position coordinates calculated in the t-th period Tm. abs_y (t) is a Y component of absolute position coordinates calculated in the t-th period Tm.

Subsequently, in step S160, the positioning device 50 performs a compounding process with the GNSS. In order to explain this process, it will be described with reference to the sub-flow chart of FIG.

In step S200, the positioning device 50 determines whether or not the data from the GNSS receiving unit 14, here, the GNSS absolute position, the GNSS absolute direction θg, the GNSS vehicle speed Vg, and the like are newly acquired within a predetermined time. When the positioning device 50 newly acquires the data from the GNSS receiving unit 14 within a predetermined time, the process proceeds to step S210. Further, when the positioning device 50 has not newly acquired the data from the GNSS receiving unit 14 even after the predetermined time has elapsed, the process proceeds to step S280.

In step S210 following step S200, the Kalman filter 55 of the positioning device 50 calculates an observed value y which is a signal observed based on the signals from the vehicle speed sensor 11, the gyro sensor 12, and the GNSS receiving unit 14.

Here, since the Kalman filter 55 corrects the error, the observed value y has the following error in order to relate it to the error of the state quantity x described later. Specifically, the observed value y is an absolute orientation observation error εθd−εθg, a distance coefficient observation error εKd−εKg, a northward observation error εYd−εYg, and an eastward observation error εXd−εXg.

Further, here, the absolute direction θd calculated by the absolute position estimation unit 54 includes the true value θv of the absolute direction of the vehicle and the error εθd calculated by the absolute position estimation unit 54. Further, the GNSS absolute bearing θg output from the GNSS receiving unit 14 includes the true value θv of the absolute bearing of the vehicle and the error εθg calculated by the GNSS receiving unit 14. Therefore, the Kalman filter 55 subtracts the GNSS absolute azimuth θg output by the GNSS receiving unit 14 from the absolute azimuth θd calculated by the absolute position estimation unit 54 in step S150, whereby the absolute azimuth observation error εθd− Calculate εθg.

Similarly, the Kalman filter 55 subtracts the GNSS vehicle speed Vg output from the GNSS receiving unit 14 from the vehicle speed Vc calculated by the relative trajectory calculation unit 53 in step S140. As a result, the Kalman filter 55 calculates the distance coefficient observation error εKd−εKg. Further, the Kalman filter 55 observes in the north direction by subtracting the Y component of the absolute position output by the GNSS receiving unit 14 from the Y component of the absolute position calculated by the absolute position estimating unit 54 in step S150. Calculate the error εYd−εYg. Further, the Kalman filter 55 observes in the east direction by subtracting the X component of the absolute position output by the GNSS receiving unit 14 from the X component of the absolute position calculated by the absolute position estimating unit 54 in step S150. Calculate the error εXd−εXg.

Subsequently, in step S220, the Kalman filter 55 calculates the state transition matrix A, which is a matrix for converting the state quantity x.

Here, in order to explain this state transition matrix A, the details of the state quantity x will be described. Here, since the Kalman filter 55 corrects the error, the state quantity x includes the offset error εF, the gain error εS, the absolute orientation error εθ, the distance coefficient error εK, and the absolute orientation northward error εY. There are six errors with the absolute eastward error εX.

The offset error εF corresponds to the measurement error of the directional change amount D, and is represented by the following relational expression (11-1). The gain error εS corresponds to the measurement error of the directional change amount D, and is expressed as shown in the following relational expression (11-2). The absolute bearing error εθ is expressed by the following relational expression (11-3). The distance coefficient error εK corresponds to the measurement error of the moving distance L, and is expressed as shown in the following relational expression (11-4). The absolute azimuth north direction error εY is expressed as shown in the following relational expression (11-5). The absolute directional east direction error εX is expressed as shown in the following relational expression (11-6). In the following relational expression, ε0 is the amount of offset change in the output value of the gyro sensor 12 due to the temperature change of the gyro sensor 12, for example, the amount of change in the output value of the gyro sensor 12 corresponding to when the yaw rate R is zero. be. ε1 is the amount of change in the gain of the output value of the gyro sensor 12 due to the temperature change of the gyro sensor 12, for example, the amount of change in the output value of the gyro sensor 12 with respect to the yaw rate R. ε2 is the amount of change due to cross-coupling or the like, which is the sensitivity of the gyro sensor 12 on other axes different from the X direction and the Y direction. ε3 is the amount of change due to changes in the tires of the vehicle and changes over time in the vehicle speed sensor 11. ε0, ε1, ε2, and ε3 are preset by experiments, simulations, and the like. τ is the time elapsed since the end of the previous cycle Tm. Further, the absolute azimuth θd is a value obtained by adding a sensor error to the true value θv of the absolute azimuth, and is expressed as the following relational expression (11-7). Here, the absolute position in step S150 is described above. A value calculated by the estimation unit 54 based on the amount of change in direction D is used.

Further, the relational expression (1) can be transformed into the following relational expression (12) by partially differentiating the relational expressions (11-1) to (11-6) with respect to each state quantity x.

Therefore, the state transition matrix A is a matrix whose components are the movement distance L, the amount of change in direction D, τ which is the time elapsed since the end of the (t-1) th period Tm, and the absolute direction θd. It has become. Therefore, the Kalman filter 55 is in a state based on τ, the movement distance L and the amount of change in direction D calculated in step S130, and the absolute direction θd calculated by the absolute position estimation unit 54 in step S150. The transition matrix A is calculated.

Subsequently, in step S230, the Kalman filter 55 is based on the state transition matrix A calculated in step S220 and the observation noise w which is the noise generated in the observation process, using the relational expression (2-1). The prior error covariance matrix P (t | t-1) is estimated.

Here, the observed noise w is generated in the observation process in which the observed value y is calculated, and here, it is the noise of the GNSS receiving unit 14. Therefore, the observed noise w includes a GNSS absolute orientation error εθg, a GNSS distance coefficient error εKg, a GNSS north direction error εYg, and a GNSS east direction error εXg. Then, the GNSS absolute orientation error εθg, the GNSS distance coefficient error εKg, the GNSS north direction error εYg, and the GNSS east direction error εXg are calculated as follows.

The GNSS absolute directional error εθg is calculated by the Kalman filter 55, for example, by squared the directional accuracy σb as shown in the following relational expression (13-1). The directional accuracy σb is calculated based on the GNSS vehicle speed Vg and the vehicle speed accuracy σv, which are the vehicle speeds calculated by the GNSS receiving unit 14, as shown in the relational expression (13-2) below. Further, the vehicle speed accuracy σv is calculated by multiplying the error of the Doppler frequency detected by the GNSS receiving unit 14 and the HDOP indicating the degree of decrease in the positioning accuracy σm due to the arrangement of the positioning satellites. Further, HDOP is an abbreviation for Horizontal Dilution of Precision.

The GNSS distance coefficient error εKg is calculated by the Kalman filter 55, for example, by dividing the above-mentioned vehicle speed accuracy σv by the above-mentioned GNSS vehicle speed Vg, as shown in the following relational expression (14).

Further, the GNSS north direction error εYg is calculated by the Kalman filter 55 by squared the Y component of the positioning accuracy σm as shown in the following relational expression (15-1). Further, the GNSS eastward error εXg is calculated by the Kalman filter 55 by squared the X component of the positioning accuracy σm as shown in the following relational expression (15-2). The positioning accuracy σm is calculated by multiplying UERE, which is a pseudo-distance error, by HDOP described above. Here, the pseudo distance is a distance from a positioning satellite (not shown) to an antenna (not shown) of the GNSS receiving unit 14. UERE is an abbreviation for User Equivalent Range Error. Further, in the following relational expression, σm_y is a Y component having a positioning accuracy of σm. σm_x is an X component having a positioning accuracy of σm.

Therefore, the Kalman filter 55 is a covariance matrix of the observed noise w from the observed noise w including the calculated GNSS absolute orientation error εθg, the GNSS distance coefficient error εKg, the GNSS northward error εYg, and the GNSS eastward error εXg. W is calculated. Therefore, the Kalman filter 55 estimates the prior error covariance matrix P (t | t-1) by substituting the calculated W and the state transition matrix A calculated in step S220 into the above relational expression (3). do. As a result, the prior error covariance matrix P (t | t-1) is based on the movement distance L and the direction change amount D because the state transition matrix A is calculated based on the movement distance L and the direction change amount D. Is estimated.

Subsequently, in step S240, the Kalman filter 55 calculates the Kalman gain G using the above relational expression (2-2).

Here, the prior error covariance matrix P (t | t-1) in the relational expression (2-2) is calculated in step S230. Further, V, which is a covariance matrix of the state quantity generation noise v, is calculated based on ε0, ε1, ε2, and ε3 using the above relational expression (12). Further, the relational expression (1-2) can be transformed into the following relational expression (16) by the above-mentioned observed value y and the observed noise w. Therefore, the observation matrix C is set as shown in the relational expression (16) below.

Therefore, the Kalman filter 55 has the above relational expression between V, which is a covariance matrix of the state quantity generation noise v, the prior error covariance matrix P (t | t-1) calculated in step S230, and the observation matrix C. The Kalman gain G is calculated by substituting into (2-2). As a result, the Kalman gain G is calculated based on the movement distance L and the directional change amount D because the prior error covariance matrix P (t | t-1) is calculated based on the movement distance L and the directional change amount D. Calculated.

Subsequently, in step S250, the Kalman filter 55 calculates the posterior error covariance matrix P (t | t) using the above relational expression (2-5). Specifically, the Kalman filter 55 includes an identity matrix I, a Kalman gain G calculated in step S240, a prior error covariance matrix P (t | t-1) calculated in step S230, and an observation matrix. Substitute C into the above relational expression (2-5). As a result, the Kalman filter 55 calculates the posterior error covariance matrix P (t | t). Further, the posterior error covariance matrix P (t | t) is calculated based on the movement distance L and the direction change amount D because the Kalman gain G is calculated based on the movement distance L and the direction change amount D. ..

Further, the calculated error covariance matrix P is expressed by, for example, the following relational expression (17). In the following relational expression, σFF ² is a value relating to the certainty of the offset error εF, and is represented here by, for example, the variance with respect to the offset error εF. σSS ² is a value relating to the certainty of the gain error εS, and is represented here by, for example, the variance with respect to the gain error εS. σθθ ² is a value related to the certainty of the absolute orientation error εθ, and is represented here by, for example, the variance with respect to the absolute orientation error εθ. σKK ² is a value relating to the certainty of the distance coefficient error εK, and is represented here by, for example, the variance with respect to the distance coefficient error εK. σYY ² is a value relating to the certainty of the absolute bearing north direction error εY, and is represented here by, for example, the variance with respect to the absolute bearing north direction error εY. σXX ² is a value relating to the certainty of the absolute eastern error εX, and is represented here by, for example, the variance with respect to the absolute eastern error εX. Further, σij ² other than these is a covariance of i rows and j columns. For example, σFS ² indicates the degree of correlation between the offset error εF and the gain error εS.

Subsequently, in step S260, the Kalman filter 55 calculates the state quantity x by substituting the Kalman gain G calculated in step S240 and the observed value y calculated in step S210 into the above relational expression (3). do. As a result, the offset error εF, which is the state quantity x, the gain error εS, the absolute orientation error εθ, the distance coefficient error εK, the absolute orientation northward error εY, and the absolute orientation eastward error εX are calculated.

Subsequently, in step S270, the Kalman filter 55 corrects the value to be corrected using the state quantity x calculated in step S260. Specifically, the Kalman filter 55 corrects the values of the offset correction value F, the gain correction value S, the distance coefficient K, the absolute orientation θd, the Y component of the absolute position coordinates, and the X component of the absolute position coordinates, which are the correction targets.

For example, the Kalman filter 55 corrects the offset correction value F using the offset error εF calculated in step S260, as shown in the relational expression (18-1) below. Further, the Kalman filter 55 corrects the gain correction value S using the gain error εS calculated in step S260, as shown in the relational expression (18-2) below. Further, the Kalman filter 55 corrects the distance coefficient K by using the distance coefficient error εK calculated in step S260, as shown in the relational expression (18-3) below. Further, the Kalman filter 55 corrects the absolute azimuth θd using the absolute azimuth error εθ calculated in step S260, as shown in the relational expression (18-4) below. Further, the Kalman filter 55 corrects the Y component of the absolute position coordinates by using the absolute azimuth north direction error εY calculated in step S260, as shown in the relational expression (18-5) below. Further, as shown in the relational expression (18-6) below, the Kalman filter 55 corrects the X component of the absolute position coordinates by using the absolute azimuth east direction error εX calculated in step S260.

As a result, in the next cycle Tm, errors are corrected for each of the movement distance L, the amount of change in direction D, the relative trajectory of the vehicle, the absolute direction θd, and the absolute position. Accurate data can be obtained by repeating this error correction. After that, the process returns to step S120.

In step S280 following step S200, the Kalman filter 55 calculates the state transition matrix A in the same manner as in step S220.

Subsequently, in step S290, the Kalman filter 55 estimates the prior error covariance matrix P (t | t-1) in the same manner as in step S230. After that, the process returns to step S120. As a result, when the Kalman filter 55 cannot obtain the data from the GNSS receiving unit 14, it is possible to prevent the error from becoming large without doing anything. Therefore, when the data of the GNSS receiving unit 14 is obtained from the state where the Kalman filter 55 cannot obtain the data of the GNSS receiving unit 14, each data is calculated with high accuracy.

In step S170 following step S120, the positioning device 50 stores each data before the stop in the flash memory. Specifically, the positioning device 50 stores the directional change amount D and the moving distance L calculated in step S130 described above in the flash memory immediately before the power of the vehicle is turned off. Further, the positioning device 50 stores the relative locus calculated in step S140 described above in the flash memory immediately before the power of the vehicle is turned off. Further, the positioning device 50 stores the absolute orientation θd and the absolute position calculated in step S150 described above in the flash memory immediately before the power of the vehicle is turned off. Further, the positioning device 50 stores the posterior error covariance matrix P (t | t) calculated in step S250 described above in the flash memory immediately before the power of the vehicle is turned off. Further, the positioning device 50 has an offset correction value F, a gain correction value S, a distance coefficient K, an absolute azimuth θd, a Y component of absolute position coordinates, and an absolute position calculated in step S270 described above immediately before the power of the vehicle is turned off. The value of the X component of the coordinates is stored in the flash memory. After that, the process ends.

As described above, when the power of the vehicle is turned on, the processing of the positioning device 50 is performed.

Next, the details of the reset process in step S110 of the positioning device 50 will be described with reference to the sub-flow chart of FIG. In step S110, as described above, the positioning device 50 resets the error covariance matrix P based on the air pressure of the tires of the vehicle, whether or not the tires have been replaced, and the temperature of the gyro sensor 12. ..

In step S300, the positioning device 50 determines whether or not the tire pressure Pt has changed by a predetermined value or more. Specifically, the positioning device 50 acquires the current tire pressure Pt from the tire pressure monitoring system 20. Further, the positioning device 50 calculates the amount of change in air pressure | ΔPt | by, for example, calculating the absolute value of the difference between the acquired tire air pressure Pt and the tire air pressure Pt read back in step S100. Further, the positioning device 50 determines whether or not the calculated air pressure change amount | ΔPt | is equal to or greater than the air pressure threshold value ΔPt_th. When the amount of change in air pressure | ΔPt | is equal to or greater than the air pressure threshold value ΔPt_th, the process proceeds to step S320. Further, when the amount of change in air pressure | ΔPt | is less than the air pressure threshold value ΔPt_th, the process proceeds to step S310.

In step S310 following step S300, the positioning device 50 determines whether or not the tire has been replaced. Specifically, a tire replacement switch (not shown) of the tire pressure monitoring system 20 outputs a signal indicating that the tire has been replaced to the positioning device 50 when operated by the driver of the vehicle. When the positioning device 50 receives this signal, the process proceeds to step S320. Further, when the positioning device 50 has not received this signal, the process proceeds to step S330.

In step S320, since the amount of change in air pressure | ΔPt | is equal to or greater than the air pressure threshold value ΔPt_th or the tires of the vehicle have been replaced, the diameter of the tires of the vehicle is different from that before the power of the vehicle is turned on. As a result, the amount of movement of the vehicle per rotation of the tire is different, so that the distance coefficient K indicating the amount of movement of the vehicle per pulse output from the vehicle speed sensor 11 is changed. At this time, the distance coefficient observation error εKd-εKg of the observed value y calculated by subtracting the GNSS vehicle speed Vg output from the GNSS receiving unit 14 from the vehicle speed Vc calculated by the relative locus calculation unit 53 suddenly changes. do.

Further, here, since the state immediately after the power of the vehicle is turned on and before the power of the vehicle is turned off is read back, among the components of the error covariance matrix P as the prediction error of the state quantity x. ^{The variance σKK 2} , which indicates the certainty of the distance coefficient error εK, is relatively small. Therefore, the Kalman gain G corresponding to the distance coefficient error εK becomes relatively small. Therefore, at this time, it may not be possible to follow the sudden change in the observed value y because it takes time for the distance coefficient error εK to be corrected to an appropriate value.

Therefore, the positioning device 50 turns off the power of the vehicle with ^{respect to σKK 2} , which is a variance indicating the certainty of the distance coefficient error εK among the components of the posterior error covariance matrix P (t | t) read out in step S100. Make it larger than just before. Here, the positioning device 50 initializes the ^{σKK 2.} The initial value of σKK ² is set by experiments, simulations, etc. so that the distance coefficient error εK converges to an appropriate value relatively quickly. For example, ^{the initial value of σKK 2} is the value in the initial state when the positioning device 50 is shipped from the factory or mounted on the vehicle, and is relatively larger than the value immediately before the power of the vehicle is turned off. Set to a value. As a result, the certainty of the distance coefficient error εK becomes low, so that the Kalman gain G becomes large in an attempt to significantly correct the distance coefficient error εK. Therefore, the distance coefficient error εK is relatively fast and converges to an appropriate value. After that, the process proceeds to step S330.

In step S330, the positioning device 50 determines whether or not the change in the gyro temperature Hg, which is the temperature of the gyro sensor 12, is equal to or greater than a predetermined value. Specifically, the positioning device 50 acquires the current gyro temperature Hg from the gyro temperature sensor 13. Further, the positioning device 50 calculates the temperature change amount | ΔHg | by calculating the absolute value of the difference between the acquired gyro temperature Hg and the gyro temperature Hg read back in step S100. Further, the positioning device 50 determines whether or not the calculated temperature change amount | ΔHg | is equal to or greater than the temperature threshold value ΔHg_th. When the temperature change amount | ΔHg | is equal to or greater than the temperature threshold value ΔHg_th, the process proceeds to step S340. When the temperature change amount | ΔHg | is less than the temperature threshold value ΔHg_th, the process proceeds to step S120.

In step S340, since the temperature change amount | ΔHg | is equal to or greater than the temperature threshold value ΔHg_th, the characteristics of the gyro sensor 12 are changed. Specifically, the offset of the gyro sensor 12, here, the output value of the corresponding gyro sensor 12 changes when the yaw rate R is zero. As a result, the offset correction value F has changed as compared with before the power of the vehicle was turned on. Further, since the gain of the gyro sensor 12, here, the output value of the gyro sensor 12 with respect to the yaw rate R has changed, the gain correction value S has changed as compared with before the power of the vehicle was turned on.

Further, here, the directional change amount D is calculated based on the offset correction value F and the gain correction value S. Further, the absolute azimuth θd is calculated by the absolute position estimation unit 54 based on the calculated directional change amount D. Therefore, at this time, the absolute orientation observation error εθd− of the observed value y calculated by subtracting the GNSS absolute orientation θg output by the GNSS receiving unit 14 from the absolute orientation θd calculated by the absolute position estimation unit 54. εθg changes rapidly.

Further, here, since the state immediately after the power of the vehicle is turned on and before the power of the vehicle is turned off is read back, among the components of the error covariance matrix P as the prediction error of the state quantity x. ^{The variance σFF 2} , which indicates the certainty of the offset error εF, is relatively small. Therefore, the Kalman gain G corresponding to the offset error εF becomes relatively small. Further, among the components of the error covariance matrix P as the prediction error of the state quantity x, the variance σSS ² indicating the certainty of the gain error εS is relatively small, so that the Kalman gain G corresponding to the gain error εS is relatively small. Is relatively small. Therefore, at this time, it may not be possible to follow the sudden change in the observed value y because it takes time for the offset error εF and the gain error εS to be corrected to appropriate values.

Therefore, the positioning device 50 immediately before the power of the vehicle is turned off ^{, the σFF 2} which is the variance indicating the certainty of the offset error εF among the components of the posterior error covariance matrix P (t | t) read out in step S100. Make it larger than when. Here, the positioning device 50 initializes the ^{σFF 2. Further, the positioning device 50 makes σSS 2} , which is a variance indicating the certainty of the gain error εS among the components of the error covariance matrix P, larger than that immediately before the power of the vehicle is turned off. Here, the positioning device 50 initializes the ^{σSS 2.} After that, the process proceeds to step S120. The initial value of σFF ² is set by experiments, simulations, etc. so that the offset error εF converges to an appropriate value relatively quickly. Further, ^{the initial value of σSS 2} is set by experiments, simulations, etc. so that the gain error εS converges to an appropriate value relatively quickly. For example, the initial value and the initial value of ShigumaSS ² of ShigumaFF ² is positioning device 50 is a value of the initial state when mounted on a vehicle and when it is shipped from the factory, immediately before the power source of the vehicle is turned off It is set to a relatively larger value than when. As a result, the certainty of the offset error εF and the gain error εS becomes low, so that the offset error εF and the gain error εS are to be corrected significantly. Therefore, since the Kalman gain G becomes large, the offset error εF and the gain error εS converge to appropriate values relatively quickly. After that, the process proceeds to step S120.

As described above, the processing of the positioning device 50 is performed. Further, in the positioning device 50, the accuracy of the traveling locus of the vehicle immediately after the power of the vehicle is turned on is improved. Hereinafter, this improvement in accuracy will be described.

When the characteristics of the vehicle speed sensor 11 change when the power of the vehicle is turned on from off, the positioning device 50 sets the value related to the certainty of the distance coefficient error εK among the components of the error covariance matrix P in step S320. Change a certain σKK ^2. Here, the positioning device 50 changes the σKK ² to an initial value in order to make it larger than that immediately before the power of the vehicle is turned off. As a result, σKK ² can be made larger than that immediately before the power of the vehicle is turned off, and the certainty of the distance coefficient error εK can be lowered. Therefore, the Kalman gain G for correcting the distance coefficient error εK can be increased. Therefore, the distance coefficient error εK can be converged to an appropriate value relatively quickly. Therefore, the accuracy of the traveling locus of the vehicle immediately after the power of the vehicle is turned on is improved.

Further, in step S340, the positioning device 50 is a value related to the certainty of the offset error εF among the components of the error covariance matrix P when the characteristics of the gyro sensor 12 change when the power of the vehicle is turned on from off. ΣFF ² is changed. Further, at this time, the positioning device 50 changes ^{σSS 2} , which is a value related to the certainty of the gain error εS. Here, the positioning device 50 changes σFF ² and σSS ² to initial values in order to make them larger than those immediately before the power of the vehicle is turned off. As a result, similarly to the above, the σFF ² and σSS ² can be made larger than those immediately before the power of the vehicle is turned off to reduce the certainty of the offset error εF and the gain error εS. Therefore, the Kalman gain G for correcting the offset error εF and the gain error εS can be increased. Therefore, the offset error εF and the gain error εS can be converged to appropriate values relatively quickly. Therefore, the accuracy of the traveling locus of the vehicle immediately after the power of the vehicle is turned on is improved.

(Other embodiments)
The present disclosure is not limited to the above embodiment, and can be appropriately modified with respect to the above embodiment. Further, in each of the above embodiments, it goes without saying that the elements constituting the embodiment are not necessarily essential except when it is clearly stated that they are essential and when they are clearly considered to be essential in principle. stomach.

The measuring unit, arithmetic unit, guessing unit, changing unit, etc. and methods thereof described in the present disclosure constitute a processor and memory programmed to execute one or more functions embodied by a computer program. It may be realized by a dedicated computer provided by the above. Alternatively, the measuring unit, arithmetic unit, guessing unit, changing unit, etc. and methods thereof described in the present disclosure are realized by a dedicated computer provided by configuring a processor with one or more dedicated hardware logic circuits. May be good. Alternatively, the measuring unit, computing unit, guessing unit, changing unit, etc. and methods thereof described in the present disclosure include a processor and memory programmed to perform one or more functions and one or more hardware logics. It may be realized by one or more dedicated computers configured in combination with a processor configured by a circuit. Further, the computer program may be stored in a computer-readable non-transitional tangible recording medium as an instruction executed by the computer.

(1) In the above embodiment, the moving distance L according to the output of the vehicle speed sensor 11 is calculated as the distance traveled by both of them during the period Tm. On the other hand, the moving distance L according to the output of the vehicle speed sensor 11 is not limited to the distance traveled by both of them during the period Tm, and may be calculated as, for example, the distance traveled by the vehicle in a unit time, that is, the vehicle speed. good.

(2) In the above embodiment, the directional change amount D according to the output of the gyro sensor 12 is calculated as the angle at which the directional change in the traveling direction of the vehicle changes during the period Tm. On the other hand, the directional change amount D according to the output of the gyro sensor 12 is not limited to the angle at which the directional direction of the vehicle changes during the period Tm. For example, the directional change of the vehicle in a unit time. May be calculated as the changed angle, i.e. yaw rate R.

(3) In the above embodiment, the pulse signal of the vehicle speed sensor 11 is directly input to the positioning device 50. On the other hand, the pulse signal of the vehicle speed sensor 11 is not limited to being directly input to the positioning device 50, and the pulse signal of the vehicle speed sensor 11 is a positioning device via an in-vehicle communication network, for example, CAN or LIN. It may be input to 50.

(4) In the above embodiment, the gyro sensor 12 may be replaced with a steering angle sensor that detects and outputs a turning angle of the steering wheel of the vehicle. This is because there is a correlation between the steering angle of the steering wheel and the yaw rate R.

(5) In the above embodiment, the positioning device 50 calculates the absolute value of the difference between the tire pressure Pt before the vehicle power is turned off and the tire pressure Pt when the vehicle power is turned on. The amount of change in air pressure | ΔPt | is calculated. On the other hand, the positioning device 50 is not limited to calculating the air pressure change amount | ΔPt | by calculating the absolute value of the above difference. For example, the positioning device 50 may calculate the amount of change in air pressure | ΔPt | by dividing the tire air pressure Pt when the power of the vehicle is turned on by the tire air pressure Pt before the power of the vehicle is turned off. .. Similarly, the positioning device 50 calculates the absolute value of the difference between the gyro temperature Hg before the vehicle power is turned off and the gyro temperature Hg when the vehicle power is turned on, so that the amount of temperature change | ΔHg | Is calculated. On the other hand, the positioning device 50 is not limited to calculating the temperature change amount | ΔHg | by calculating the absolute value of the above difference. For example, the positioning device 50 may calculate the temperature change amount | ΔHg | by dividing the gyro temperature Hg when the vehicle power is turned on by the gyro temperature Hg before the vehicle power is turned off. ..

(6) In the above embodiment, the Kalman filter 55 is a loose coupling type in which the position of the vehicle is an observed value. On the other hand, the Kalman filter 55 is not limited to the loose coupling type, and may be, for example, a tight coupling type in which the pseudo distance calculated by the GNSS receiving unit 14 and the Doppler frequency are observed values.

11 Vehicle speed sensor 12 Gyro sensor 13 Gyro temperature sensor 14 GNSS receiver 50 Vehicle positioning device 51 Movement distance measurement unit 52 Direction change amount measurement unit 53 Relative trajectory calculation unit 54 Absolute position estimation unit 55 Kalman filter

Claims (12)

A moving distance measuring unit (S130) that calculates the moving distance (L) of the vehicle based on a signal from the vehicle speed sensor (11) according to the rotation of the tires of the vehicle.
A directional change amount measuring unit (S130) that calculates the directional change amount (D) of the vehicle based on a signal from the gyro sensor (12) according to the yaw rate (R) of the vehicle.
A position estimation unit (S150) that calculates values related to the position and direction (θd) of the vehicle based on the movement distance and the amount of change in direction.
A value related to the movement distance, a value related to the position and orientation (θd) of the vehicle calculated by the position estimation unit, and a value related to the position and orientation (θg) of the vehicle calculated based on a signal from a satellite. Based on, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the value related to the certainty of the measurement error (εF, ^{εS) of the amount of change in orientation (σFF 2} , σSS ² ) are components. A Kalman filter (S210 to S270) that calculates the error covariance matrix (P) including
When the characteristics of the vehicle speed sensor are changing while the power of the vehicle is turned on, a value (σKK) related to the certainty of the measurement error (εK) of the travel distance among the components of the error covariance matrix. ² ) is different from the calculation of the error covariance matrix by the Kalman filter, and is made larger than before the power of the vehicle is turned off (S320).
Vehicle positioning device equipped with. ^{A value (σKK 2} ) relating to the certainty of the measurement error (εK) of the travel distance among the components of the error covariance matrix before the power of the vehicle is turned off when the power of the vehicle is turned off. ), The distance storage unit (S170) that stores the movement distance corrected before the power of the vehicle is turned off, and
^{Among the components of the error covariance matrix before the power of the vehicle is turned off, the value (σKK 2} ) related to the certainty of the measurement error (εK) of the moving distance, which is stored in the distance storage unit, and the above. A reading unit (S100) that reads back the travel distance corrected before the vehicle is turned off, and
The vehicle positioning device according to claim 1. Whether or not the characteristics of the vehicle speed sensor changed during the time when the power of the vehicle was turned on, depending on whether or not the diameter of the tire changed while the power of the vehicle was turned from off to on. The vehicle positioning device according to claim 1 or 2, further comprising a vehicle speed characteristic determination unit (S300, S310) for determining whether or not the tire is. The vehicle speed characteristic determination unit (S300) determines the power supply of the vehicle based on the change in the air pressure (ΔPt) when the power of the vehicle is turned on with respect to the air pressure of the tire before the power of the vehicle is turned off. The vehicle positioning device according to claim 3, wherein it is determined whether or not the diameter of the tire has changed while the tire is turned from off to on. The vehicle speed characteristic determination unit (S310) determines whether or not the diameter of the tire has changed while the power of the vehicle is turned on based on a signal according to whether or not the tire has been replaced. The vehicle positioning device according to claim 3 or 4. The change unit (S320) has a measurement error (εK) of the movement distance among the components of the error covariance matrix when the characteristics of the vehicle speed sensor are changed while the power of the vehicle is turned on. The vehicle positioning device according to any one of claims 1 to 5, wherein the ^{value (σKK 2} ) relating to the certainty of) is changed to an initial value. A moving distance measuring unit (S130) that calculates the moving distance (L) of the vehicle based on a signal from the vehicle speed sensor (11) according to the rotation of the tires of the vehicle.
A directional change amount measuring unit (S130) that calculates the directional change amount (D) of the vehicle based on a signal from the gyro sensor (12) according to the yaw rate (R) of the vehicle.
A position estimation unit (S150) that calculates values related to the position and direction (θd) of the vehicle based on the movement distance and the amount of change in direction.
A value related to the movement distance, a value related to the position and orientation (θd) of the vehicle calculated by the position estimation unit, and a value related to the position and orientation (θg) of the vehicle calculated based on a signal from a satellite. Based on, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the value related to the certainty of the measurement error (εF, ^{εS) of the amount of change in orientation (σFF 2} , σSS ² ) are components. A Kalman filter (S210 to S270) that calculates the error covariance matrix (P) including
Regarding the certainty of the measurement error (εF, εS) of the azimuth change amount among the components of the error covariance matrix when the characteristics of the gyro sensor are changed while the power of the vehicle is turned on. A change part (S340) that makes the values (σFF ² , σSS ² ) larger than before the power of the vehicle is turned off, which is different from the calculation of the error covariance matrix by the Kalman filter.
Vehicle positioning device equipped with. A value relating to the certainty of the measurement error (εF, εS) of the directional change amount among the components of the error covariance matrix before the power of the vehicle is turned off when the power of the vehicle is turned off. A directional storage unit (S170) that stores (σFF ² , σSS ² ) and the directional change amount corrected before the vehicle is turned off.
^{Values (σFF 2} , σSS) related to the certainty of the measurement error (εF, εS) of the directional change amount among the components of the error covariance matrix before the power of the vehicle is turned off stored in the directional storage unit. ² ), a reading unit (S100) that reads back the amount of change in direction corrected before the power of the vehicle is turned off, and
7. The vehicle positioning device according to claim 7. While the vehicle is powered off and on, based on the change in temperature (ΔHg) when the vehicle is powered on relative to the temperature of the gyro sensor before the vehicle is powered off. The vehicle positioning device according to claim 7 or 8, further comprising a gyro characteristic determination unit (S330) for determining whether or not the characteristics of the gyro sensor have changed. When the characteristics of the gyro sensor are changed while the power of the vehicle is turned on, the changing unit (S340) has a measurement error of the directional change amount among the components of the error covariance matrix (S340). The vehicle positioning device according to any one of claims 7 to 9, wherein the ^{values (σFF 2 , σSS 2} ) related to the certainty of εF, εS) are changed to initial values. Positioning device for vehicles,
A moving distance measuring unit (S130) that calculates the moving distance (L) of the vehicle based on a signal from the vehicle speed sensor (11) according to the rotation of the tires of the vehicle.
A directional change amount measuring unit (S130) that calculates a directional change amount (D) of the vehicle based on a signal from the gyro sensor (12) according to the yaw rate (R) of the vehicle.
Position estimation unit (S150), which calculates values related to the position and direction (θd) of the vehicle based on the movement distance and the amount of change in direction.
A value related to the movement distance, a value related to the position and orientation (θd) of the vehicle calculated by the position estimation unit, and a value related to the position and orientation (θg) of the vehicle calculated based on a signal from a satellite. Based on, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the value related to the certainty of the measurement error (εF, ^{εS) of the amount of change in orientation (σFF 2} , σSS ² ) are components. The Kalman filter (S210 to S270), which calculates the error covariance matrix (P) including the above and corrects the movement distance and the azimuth change amount based on the calculated error covariance matrix.
When the characteristics of the vehicle speed sensor are changing while the power of the vehicle is turned on, a value (σKK) related to the certainty of the measurement error (εK) of the travel distance among the components of the error covariance matrix. ²⁾ separately from the calculation of the error covariance matrix by the Kalman filter, the changing unit to be larger than before the power of the vehicle is turned off as (S320), the vehicle positioning program to function. Positioning device for vehicles,
A moving distance measuring unit (S130) that calculates the moving distance (L) of the vehicle based on a signal from the vehicle speed sensor (11) according to the rotation of the tires of the vehicle.
A directional change amount measuring unit (S130) that calculates a directional change amount (D) of the vehicle based on a signal from the gyro sensor (12) according to the yaw rate (R) of the vehicle.
Position estimation unit (S150), which calculates values related to the position and direction (θd) of the vehicle based on the movement distance and the amount of change in direction.
A value related to the movement distance, a value related to the position and orientation (θd) of the vehicle calculated by the position estimation unit, and a value related to the position and orientation (θg) of the vehicle calculated based on a signal from a satellite. Based on, the value related to the certainty of the measurement error (εK) of the movement distance (σKK ² ) and the value related to the certainty of the measurement error (εF, ^{εS) of the amount of change in orientation (σFF 2} , σSS ² ) are components. The Kalman filter (S210 to S270), which calculates the error covariance matrix (P) including the above and corrects the movement distance and the azimuth change amount based on the calculated error covariance matrix.
Regarding the certainty of the measurement error (εF, εS) of the azimuth change amount among the components of the error covariance matrix when the characteristics of the gyro sensor are changed while the power of the vehicle is turned on. Positioning for vehicles that causes the ^{values (σFF 2 , σSS 2} ) to function as a change unit (S340) that is different from the calculation of the error covariance matrix by the Kalman filter and is larger than before the power of the vehicle is turned off. program.

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