JP3218876B2 - Current position detection device for vehicles - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a current position detecting device for a vehicle which detects the current position of the vehicle based on the direction and the moving distance of the vehicle.

[0002]

2. Description of the Related Art Conventionally, in this kind of on-vehicle navigation device, a relative direction sensor (gyro, steering sensor, wheel sensor, etc.) for detecting an amount of change in direction of a vehicle is known.
And the output of a distance sensor (vehicle speed sensor, wheel sensor, etc.) that detects the speed (distance) of the vehicle,
Dead reckoning which detects vehicle speed and the like is used.

The output (position, direction, vehicle speed, etc.) of this dead reckoning includes an error of a sensor, so that an error occurs. In particular, since the position and orientation are obtained in an integral manner, errors gradually increase. On the other hand, the GPS can determine the absolute position, direction, and vehicle speed.
Correction is possible by matching the output of dead reckoning with the output of GPS when S is located. For example, when the difference between the position obtained by dead reckoning and the position obtained by aligning the position obtained by map matching with the road position on the road map and the position obtained by GPS becomes larger than a predetermined value, the position on the road map is displayed. The position can be corrected to the position obtained by GPS.

[0004]

However, the position determined by dead reckoning can be corrected by the output of the GPS, but cannot be corrected by the sensor. For this reason, there is a problem that the output accuracy of dead reckoning is poor due to an error in the sensor output when the GPS is not received. A first object of the present invention is to correct a sensor output error so as to obtain a current position.

[0005] To achieve the above object, the present inventors have
As will be described later, using a Kalman filter, the offset error and the distance coefficient error are determined by the difference between the azimuth, position, and speed information of the vehicle obtained from dead reckoning and the azimuth, position, and speed information of the vehicle output from GPS. In this case, the present inventors have conceived a method in which the offset correction amount and the distance coefficient are corrected.

In the error correction by the Kalman filter, the accuracy of the azimuth / distance coefficient measured by the GPS decreases as the vehicle speed decreases. In this case, if the error is corrected as it is, an error occurs in the bearing / distance coefficient of dead reckoning. Therefore, a second object of the present invention is to reduce the error in the azimuth / distance coefficient of dead reckoning when the accuracy of the azimuth / distance coefficient measured by GPS is reduced.

[0007]

According to the present invention, in order to achieve the above object, a relative azimuth sensor (2) for outputting a signal corresponding to an amount of change in azimuth of a vehicle.
A distance sensor (1) for outputting a signal corresponding to the traveling speed of the vehicle, and an azimuth change amount obtained by offset correction by an offset correction amount based on a signal from the relative azimuth sensor. Position detecting means (4, 5 and 4) for detecting the position of the vehicle based on the direction and the distance of the vehicle by determining the direction of the vehicle, multiplying the signal from the distance sensor by a distance coefficient to obtain the distance of the vehicle. Dead-reckoning means (1, 2, 4, 5, and the processing for the same) comprising:
Receiving satellite radio waves from PS satellites,
A GPS (3) for outputting information relating to speed, and at least an offset error, an azimuth error of a vehicle, and a distance coefficient error are defined as a state quantity X (t). A signal generation process that associates the state quantity X (t + 1) with φ · X (t) + ω by the noise ω, and the state quantity X (t) and the observed value Y
(T) is based on a model that forms an observation process in which an observation value Y (t) is related by H · X (t) + v by an observation matrix H and noise v generated in the observation process. Of the direction, position, speed of the vehicle in the means and the GP
A state in which the observed value Y (t) is calculated based on a difference from the information of the direction, position, and speed of the vehicle output from S, and a state quantity X (t) is calculated from the model based on the calculated value. Quantity calculation means (6 and 401 to 40
8, 410, 411, 501 to 503) and a correction means for correcting the offset correction amount and the distance coefficient by an offset error and a distance coefficient error based on the state quantity X (t) obtained by the state quantity calculation means. (409), when the state quantity calculating means determines that the accuracy of the azimuth and speed output from the GPS is degraded, information on the azimuth and speed of the vehicle from the dead reckoning means; Excluding information about the direction and speed output from the GPS, based on the difference between the information on the position of the vehicle,
A state quantity of the offset error and the distance coefficient error is calculated (501, 503).

According to a second aspect of the present invention, in the first aspect of the present invention, the state quantity calculating means reduces the accuracy of the azimuth and speed output from the GPS when the speed of the vehicle is equal to or less than a predetermined value. It is characterized by judging the state of the operation. According to a third aspect of the present invention, in the first aspect of the present invention, the state quantity calculating means determines the accuracy of the azimuth and the speed output from the GPS based on the information on the azimuth and the speed from the GPS. It is characterized in that a state in which the temperature has decreased is determined.

The reference numerals in parentheses of the above means indicate the correspondence with the concrete means described in the embodiments described later. Each step in the flowchart described below constitutes a function realizing unit that realizes each function.

[0010]

According to the first to third aspects of the present invention, information relating to the direction and speed of the vehicle from the dead reckoning means and information relating to the direction and speed output from the GPS are excluded. Since the offset error and the distance coefficient error are obtained based on the difference in the information on the position of the vehicle and the offset correction amount and the distance coefficient are corrected, it is possible to correct the error of the sensor output with respect to the relative direction sensor and the distance sensor. it can.

Further, when it is determined that the accuracy of the azimuth and speed output from the GPS is reduced, the information on the azimuth and speed of the vehicle from the dead reckoning means and the GPS is excluded, and the information on the position of the vehicle is excluded. Since the state quantities of the offset error and the distance coefficient error are calculated based on the difference, it is possible to reduce those errors when the accuracy of the azimuth / distance coefficient measured by the GPS is reduced.

[0012]

BRIEF DESCRIPTION OF THE DRAWINGS FIG. This embodiment uses a Kalman filter to combine dead reckoning and GPS. The outline of the Kalman filter will be described. This Kalman filter is divided into a signal generation process and an observation process, as shown in FIG. In the figure, when there is a linear system (φ) and a part of X (t) related by an observation matrix H can be observed with respect to the state X (t) of the system, the filter is a function of X (t). Give the best estimate. Here, ω is noise generated in the signal generation process, and v is noise generated in the observation process. The input of this filter is Y (t) and the output is the optimal estimate of X (t).

The optimum estimated value of the state X using the information up to the time t, that is, the state quantity X (t | t) is obtained by the following equation (1).

[0014]

X (t | t) = X (t | t-1) + K (t)
{Y (t) -HX (t | t-1)} Here, X (t | t-1) is a pre-estimated value, and K (t) is a Kalman gain, which are expressed by Equations 2 and 3, respectively. You.

[0015]

X (t | t-1) = φX (t-1 | t-1)

[0016]

K (t) = P (t | t-1) H ^T (HP (t |
t-1) H ^T + V) ^-1 where P is the error covariance of the state quantity X, and P (t | t
-1) is the predicted value of the error covariance, and P (t-1 | t-1) is the error covariance, which is expressed by Equations 4 and 5, respectively.

[0017]

P (t | t−1) = φP (t−1 | t−1) φ ^T + W

[0018]

P (t-1 | t-1) = (I−K (t−1))
H) P (t-1 | t-2) where V is the variance of noise v generated in the observation process, and W is the variance of noise ω generated in the signal process. A (i |
j) represents the estimated value of A at time i based on the information up to time j. Note that the subscript ^T indicates a transposed matrix, and ^-1 indicates an inverse matrix. I is a unit matrix.

Further, V and W are white Gaussian noise having an average of 0 and are uncorrelated with each other. In the Kalman filter as described above, the accuracy of the state quantity X is improved by giving an appropriate error to the initial value of the state quantity X and the error covariance P, and repeating the above calculation each time a new observation is performed. I do. This embodiment applies such a Kalman filter to dead reckoning.

First, the definition of the above signal generation process will be described. Since the Kalman filter in dead reckoning aims to correct errors in dead reckoning, the state quantity X defines the following five error values. What gives a temporal change of this error value is a process matrix φ. Offset error (εG)

[0021]

ΕG _t = εG _t-1 + ω _{0 There} is no definite change, and noise is added to the previous error. Absolute bearing error (εA)

[0022]

ΕA _t = T × εG _t-1 + εA _t-1 + ω ₁ A direction error and noise obtained by multiplying the offset error by the elapsed time from the previous time are added to the previous error. Distance coefficient error (εK)

[0023]

ΕK _t = εK _t-1 + ω _{2 There} is no deterministic change, and noise is added to the previous error. Absolute position north error (εY)

[0024]

ΕY _t = sin (A _T + εA _t-1 + εG _t-1 ×
T / 2) × L × (1 + εK _t−1 ) −sin (A _T ) × L
+ ΕY _t−1 An error caused by an azimuth error and a distance error is added to the previous error. Absolute position east error (εX)

[0025]

ΕX _t = cos (A _T + εA _t−1 + εG _t−1)
× T / 2) × L × (1 + εK _t-1 ) -cos (A _T ) ×
L + εX _t−1 An error caused by an azimuth error and a distance error is added to the previous error. In the above definition, _AT is the true absolute azimuth, L is the moving distance from the previous time, and T is the elapsed time from the previous time.

When each of the above equations is partially differentiated by a state quantity and linearized, the signal generation process is defined as follows.

[0027]

[Equation 11]

The above A is the absolute azimuth A _T + εA _t−1 + εG
_It means _t-1 × T / 2. This value is obtained by adding a sensor error to the true absolute azimuth _AT and is set to an absolute azimuth A obtained from the azimuth change amount, as described later. Also, ω ₀
Is (variation of the offset due to temperature drift, etc.) offset noise, omega ₁ is the absolute azimuth noise (gyro gain specific errors), omega ₂ denotes a distance coefficient noise (aging).

Next, the definition of the observation process will be described. Observed values are obtained from the difference between the output of dead reckoning navigation and the output of GPS. Since each output includes an error, a sum of a dead reckoning error and a GPS error is obtained in the observed value. The observed value Y is related to the state quantity X, and is defined as shown in Expression 12.

[0030]

(Equation 12)

However, the noise v generated in the observation process is GPS
And is defined as in Equation 13.

[0032]

(Equation 13)

Dead reckoning using a Kalman filter will be described based on the above definitions. FIG. 1 shows a schematic configuration in this embodiment. As shown in this figure, based on signals from the vehicle speed sensor 1 and the gyro 2, the relative trajectory calculation unit 4,
Calculations are performed in the absolute position calculation unit 5, and the vehicle speed, the relative trajectory, the absolute position, and the absolute direction are output by the calculations (dead reckoning calculation). In addition, from GPS3,
The output of the vehicle speed is obtained. The Kalman filter 6 corrects the distance coefficient of the vehicle speed sensor 1, the offset correction of the gyro 2, the absolute position based on the vehicle speed, absolute position and absolute direction information obtained by dead reckoning navigation and the vehicle speed, position and direction information from the GPS 3. Performs correction and absolute azimuth correction.

When a Kalman filter is applied to such an on-vehicle navigation device, the prior estimation X (t) shown in Equation 2 is obtained by the distance coefficient correction of the vehicle speed sensor 1, the offset correction of the gyro 2, the absolute azimuth correction, and the absolute position correction.
| T-1) becomes 0. Therefore, Equation 1 becomes as shown in Equation 14.

[0035]

X (t | t) = K (t) Y (t) Therefore, the state quantity X based on the five error values defined in the signal generation process is the Kalman gain obtained by the equations (3) to (5). It is determined by K (t) and the observed value Y (t).

Here, the error covariance P in Equation 3 is defined by Equation 15.

[0037]

(Equation 15)

In the error covariance P, σ _GG ² represents the estimation of the magnitude of the offset error, σ _AA ² represents the estimation of the magnitude of the absolute azimuth error, and σ _KK ² represents the estimation of the magnitude of the distance coefficient error. Σ _YY ² represents an estimate of the magnitude of the absolute position northward error, and σ _XX ² represents an estimate of the magnitude of the absolute position eastward error. The other σ _ij ² represent the cross-correlation value between the i-th row and the j-th column. For example, σ _AG ² represents a cross-correlation value between the offset error and the absolute azimuth error.

The value of the error covariance P is updated by the calculation of Expression 4. In the initial values, σ _GG ² , σ
_The values of _AA ² , σ _KK ² , σ _YY ² , and σ _XX ² are set to values that maximize the error, and all the cross-correlation values are set to 0.
Set to. Further, the matrix shown in Equation 12 is used for H in Equation 3, and the matrix shown in Equation 13 is used for V. Further, W in Equation 4 uses the variance of ω shown in Equation 11.

The observation value Y in the observation process is given by
As shown in FIG. 2, εA _DRt −εA _{GP St} , εK _DRt −εK
_GPSt , εY _DRt −εY _GPSt , εX _DRt −εX _GPSt are used. Here, the subscript _DRt means a value obtained by dead reckoning based on signals from the vehicle speed sensor 1 and the gyro 2 at time t, and GPSt means a value output from the _{GPS 3} at time t.

ΕA _DRt -εA _GPSt is a difference between the absolute azimuth obtained by dead reckoning navigation and the azimuth output from GPS3,
That is, the absolute azimuth obtained by dead reckoning includes the true absolute azimuth and its error εA _DRt ,
3 is the true absolute direction and its error εA
_{Since GPSt} is included, εA _DRt −εA _GPSt is obtained by taking the difference between them.

Similarly, εK _DRt -εK _GPSt is a distance coefficient error obtained from a difference between the speed obtained by dead reckoning and the speed output from the GPS 3. ) / (Speed by dead reckoning). Also, εY _DRt -εY _GPSt
Is the Y component of the absolute position obtained by dead reckoning and GP
It is the difference between the error of the Y component at the position output from S3 and ε
X _DRt -εX _GPSt is the difference between the X component of the absolute position obtained by dead reckoning and the X component of the position output from the GPS 3.

The noise v generated in the observation process shown in Expression 13 is the GPS3 noise, and is obtained as follows. Measurement error of pseudorange in GPS3 (UER
E) and HDOP (Horizontal Dilution of Presision)
, The positioning accuracy is obtained by UERE × HDOP, and v _2t and v _3t are obtained by squaring the positioning accuracy. In addition, Doppler frequency measurement error and HD
From the relationship of OP, the speed accuracy is obtained by Doppler frequency measurement error × HDOP, and the distance coefficient measurement error is obtained by this speed accuracy / vehicle speed.
_1t is required. Further, the azimuth accuracy is obtained as tan ^-1 (speed accuracy / Vc) from the vehicle speed Vc and the speed accuracy,
By squaring this azimuth accuracy, v _0t is obtained.

Therefore, εA _DRt −εA in the observation process
_{GPSt, εK DRt -εK GPSt, εY} DR t -εY GPSt, εX
_DRt- εX _GPSt and the above-mentioned noise V are input, and _Equations 3 to 5 and _Equation 1 are executed to obtain a state quantity X based on five error values defined in the signal generation process. 1, the distance coefficient correction, the gyro 2 offset correction, the absolute position correction, and the absolute azimuth correction are performed.

The relative trajectory calculation, the absolute position calculation, and the Kalman filter are performed by a microcomputer, and will be described below. FIG. 2 shows the arithmetic processing of the main routine of dead reckoning navigation. Step 1
At 00, the azimuth change amount and the movement distance are calculated. The details of this processing are shown in FIG. First, in step 101, the azimuth change amount is calculated by multiplying the output angular velocity of the gyro 2 by the activation cycle T _M of the main routine. In the next step 102,
The offset correction amount (this correction amount will be described later) multiplied by the activation period T _M of the main routine is subtracted from the azimuth change amount to perform offset correction of the azimuth change amount. In the next step 103, the moving distance is calculated by multiplying the number of vehicle speed pulses from the vehicle speed sensor 1 by a distance coefficient (this distance coefficient will also be described later).

After step 100, step 20
A relative trajectory calculation process of 0 is performed. The details of this process are shown in FIG. First, in step 201, the relative azimuth is updated based on the azimuth change amount (determined in step 102).
In step 202, the relative position coordinates are updated based on the updated relative orientation and the moving distance obtained in step 103. This update includes X,
This is performed by adding the Y component to the relative position coordinates up to that time. The relative position coordinates are used to determine a relative locus, and so-called map matching is performed based on the relationship between the relative locus and the road shape.

After step 200, step 30
Calculation processing of absolute azimuth and absolute position of 0 is performed. The details of this process are shown in FIG. First, in step 301, the absolute azimuth is updated based on the azimuth change amount (determined in step 102). This updated absolute direction and step 10
In step 202, the absolute position coordinates are updated based on the movement distance obtained in step 3. The absolute azimuth A and the absolute position updated in the process of step 200 are used in the later-described compounding process with the GPS.

FIG. 6 shows the details of step 400 for performing the compounding process with the GPS. First, in step 401, it is determined whether or not T1 seconds have elapsed since the last positioning or prediction calculation. In this method, every time the GPS3 positioning is performed, the process of correcting the dead reckoning navigation error is performed in steps 403 to 409. However, when the GPS3 positioning is not possible, the error becomes large. Step 4
It is provided to perform the processing periodically at 10, 411.

If the determination in step 401 is NO,
Whether or not there is positioning data from GPS3 at step 402
Or do. If there is positioning data from GPS3,
The process proceeds to the Kalman filter calculation process after step 403. Ma
First, at step 403, the observation value Y is calculated. this
Is the speed, position, azimuth data and output from GPS3.
And the value obtained in the processing of step 300 in dead reckoning navigation.
Absolute azimuth, absolute position, and speed calculation (not shown)
Vehicle speed based on the vehicle speed pulse from the vehicle speed sensor 1
From equation (12), εA_DRt-ΕA _GPSt, ΕK_DRt
−εK_GPSt, ΕY_DRt−εY_GPSt, ΕX_DRt-ΕX_GPSt
, And occurs during the observation process shown in Equation 13.
Is calculated based on GPS3 positioning data and the like.

In step 404, a process matrix φ is calculated. This is based on the moving distance L, the elapsed time T from the previous calculation time of the process matrix, the elapsed time T (these are separately obtained by measurement processing not shown), and the absolute azimuth A obtained in step 301, and the process represented by the equation 11 is performed. Find the matrix φ. Based on the observation value Y and the process matrix φ calculated in this way, the above-described calculations of Expressions 3 to 5 are performed to obtain the state quantity X shown in Expression 14. That is, in step 405, the prediction calculation of the error covariance P is performed by the equation (3). In step 406, Kalman gain K is calculated according to equation
Is calculated. In step 407, the error covariance P is calculated according to equation (5). After that, based on the Kalman gain K and the observed value Y, in step 408, the state quantity X is obtained by calculation of Expression 14. As shown on the left side of Expression 11, the state quantity X is represented by an offset error (εG), an absolute azimuth error (εA), a distance coefficient error (εK), an absolute position northward error (εY), an absolute position eastward error (εY). εX).

Based on these errors, at step 409, dead reckoning errors are corrected by the calculation shown in the figure, that is, offset correction of the gyro 2, correction of the distance coefficient of the vehicle speed sensor 1, correction of the absolute direction, and correction of the absolute position. . The offset correction amount used in step 102 is corrected by the offset correction of the gyro 2, the distance coefficient used in step 103 is corrected by the distance coefficient correction of the vehicle speed sensor 1, and the absolute azimuth correction is performed in step 301. The absolute direction A used is corrected, and the absolute position used in step 302 is corrected by the absolute position correction.

The above processing is repeated every time there is positioning data from the GPS 3, and the above error correction is performed, so that more accurate dead reckoning data can be obtained. G
Step 401 when positioning of PS3 cannot be performed for a long time.
Are YES, the process proceeds to steps 410 and 411, where the calculation of the process matrix φ and the prediction calculation of the error covariance P are performed. As a result, the prediction calculation of the error covariance corresponding to the error when the positioning of the GPS 3 cannot be performed is performed, and the processing of the Kalman filter performed when the positioning of the GPS 3 can be performed thereafter can be accurately performed.

In the above-described error correction using the Kalman filter, the azimuth and speed measured by the GPS 3 decrease in accuracy as the vehicle speed decreases. According to the Kalman filter theory, in such a case, normal operation is possible if the observation noise is increased to an amount that is actually appropriate. However,
The theory of the Kalman filter is based on the premise that the observation noise is white Gaussian noise, and the actual GPS error is S
When there is an influence such as A (intentional deterioration of precision), the distribution is not white Gaussian noise but is biased. In this case, if the processing is continued only with the observation noise as described above, an error occurs in the azimuth / distance coefficient of dead reckoning.

Further, if the configuration is such that the correction by the GPS is not performed when the vehicle speed is low, the performance in a city area or the like where the positioning accuracy of the GPS is low is deteriorated. Therefore, if the vehicle speed decreases and the accuracy of the azimuth and speed measured by GPS decreases,
This is not used as a reference for correction, and when the vehicle speed is high and the accuracy of the azimuth and speed measured by GPS is high, this is used.

Specifically, the processing shown in FIG. 8 is performed. FIG.
In step 501, it is determined whether the vehicle speed is equal to or higher than a specified value. The vehicle speed is obtained from the output from the vehicle speed sensor 1. If this determination is YES, the process proceeds to step 502, where the observation value Y is calculated according to Expression 12, and correction using the Kalman filter is performed. In this case, it is the same as described above.

On the other hand, if the vehicle speed is lower than the specified value,
If the determination of 01 is NO, the process proceeds to step 503, where the observation value Y is calculated according to Expression 16, and correction is performed using a Kalman filter.

[0057]

(Equation 16)

That is, when the vehicle speed is low, the accuracy of the azimuth and speed is reduced. Therefore, the observation value Y is calculated without using the azimuth and distance coefficients. Then, the state quantity X is calculated based on the observation value Y obtained in this way, and the correction is performed in step 409. It should be noted that the determination in step 501 can be made by determining a state in which the accuracy of the azimuth and speed of the vehicle obtained from the data received from the GPS 3 is reduced.
_{Alternatively} , v _0t and v _1t indicating the azimuth accuracy and the speed accuracy may be compared with predetermined comparison values to determine a decrease in the accuracy of the azimuth / distance coefficient of the vehicle.

[Brief description of the drawings]

FIG. 1 is a schematic configuration diagram showing one embodiment of the present invention.

FIG. 2 is a flowchart showing a calculation process of a main routine of dead reckoning navigation.

FIG. 3 is a flowchart illustrating a calculation process of an azimuth change amount and a movement distance.

FIG. 4 is a flowchart showing a relative trajectory calculation process.

FIG. 5 is a flowchart showing an absolute azimuth / absolute position calculation process.

FIG. 6 is a flowchart showing a process of compounding with a GPS.

FIG. 7 is a configuration diagram illustrating a model of a Kalman filter.

FIG. 8 is a flowchart illustrating a Kalman filter process performed based on the accuracy determination of the azimuth / distance coefficient.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Distance sensor 2 Relative direction sensor 3 GPS 4 Relative trajectory calculation part 5 Absolute position calculation part 6 Kalman filter

──────────────────────────────────────────────────続 き Continuation of the front page (56) References JP-A-6-341847 (JP, A) JP-A-6-288776 (JP, A) JP-A-63-311115 (JP, A) JP-A-3-3 49000 (JP, A) JP-A-6-229772 (JP, A) JP-A-7-301541 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) G01C 21/00-21 / 20 G01S 5/00-5/14

Claims (3)

(57) [Claims] 1. A relative azimuth sensor for outputting a signal corresponding to an amount of change in azimuth of a vehicle, a distance sensor for outputting a signal corresponding to a running speed of the vehicle, and an offset correction amount based on a signal from the relative azimuth sensor Offset correction is performed to obtain the azimuth change amount and the azimuth change of the vehicle is specified by the azimuth change amount, and the signal from the distance sensor is multiplied by a distance coefficient to obtain the moving distance of the vehicle. Dead reckoning means comprising position detecting means for detecting the position of the vehicle based on the position of the vehicle, a GPS receiving satellite radio waves from GPS satellites and outputting information on the direction, position, and speed of the vehicle; The azimuth error and the distance coefficient error of the vehicle are defined as a state quantity X (t). By the sound ω, the state quantity X (t + 1) is changed to φ · X (t) + ω
, And the state quantity X (t) and the observed value Y (t) are converted into the observed value Y (t) by the observation matrix H and the noise v generated in the observation process. ) Based on a model forming an observation process related by + v, the difference between the information of the direction, the position, and the speed of the vehicle in the dead reckoning means and the information of the direction, the position, and the speed of the vehicle output from the GPS is calculated. , And the above observation value Y (t) is calculated,
A state quantity calculating means for calculating a state quantity X (t) from the model based on the calculated value; and the offset correction by an offset error and a distance coefficient error based on the state quantity X (t) obtained by the state quantity calculating means. Correction means for correcting the amount and the distance coefficient, wherein the state quantity calculation means determines a state in which the accuracy of the azimuth and speed output from the GPS is reduced, and the state of the vehicle from the dead reckoning means is determined. Bearing and speed information,
Current position detection for a vehicle, wherein a state quantity of the offset error and the distance coefficient error is calculated based on a difference in information of the position of the vehicle, excluding information on a direction and a speed output from the GPS. apparatus. 2. The apparatus according to claim 1, wherein the state quantity calculating means determines a state in which the accuracy of the azimuth and the speed output from the GPS is reduced when the speed of the vehicle is equal to or less than a predetermined value. A current position detecting device for a vehicle according to the above. 3. The method according to claim 1, wherein the state quantity calculating means determines a state in which the accuracy of the azimuth and the speed output from the GPS is reduced based on information on the azimuth and the speed from the GPS. The vehicle current position detecting device according to claim 1.