JPH0868654A - Current position detector for vehicle - Google Patents

Abstract

PURPOSE: To reduce an error when the accuracy of azimuth/speed measured by GPS decreases when the estimation navigation is compensated by the GPS. CONSTITUTION: An offset error, a distance coefficient error, an absolute azimuth error, and an absolute position error in the estimation navigation are obtained for compensation by a Karman filter 6 according to the information of the position, azimuth, and vehicle speed obtained from the estimation navigation and the information of the position, azimuth, and vehicle speed outputted from GPS 3 for the estimation navigation by a distance sensor 1, a relative azimuth sensor 2, a relative trace operation part 4, and an absolute trace operation part 5. When the decrease in the accuracy of azimuth/speed measured by the GPS 3 is judged, an error is obtained only by the information of the position of the vehicle.

Description

Detailed Description of the Invention

[0001]

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

[0002]

2. Description of the Related Art Conventionally, in this type of vehicle-mounted navigation device, a relative direction sensor (gyro, steering sensor, wheel sensor, etc.) for detecting the amount of change in the direction of the vehicle.
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 etc. is used.

The dead reckoning navigation output (position, direction, vehicle speed, etc.) includes an error of the sensor, and thus an error occurs. In particular, the position / azimuth is obtained in an integrated manner, so the error gradually increases. On the other hand, since GPS can determine the absolute position, direction, and vehicle speed, GP
The correction can be made by matching the dead reckoning output with the GPS output when S is positioned. For example, when the difference between the position obtained by dead reckoning and the position obtained by aligning the road position on the road map by map matching and the position obtained by GPS becomes larger than a predetermined value, The position may be corrected to the position obtained by GPS.

[0004]

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

In order 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 vehicle azimuth, position and speed information obtained from dead reckoning and the vehicle azimuth, position and speed information output from GPS. In consideration of the above, an offset correction amount and a distance coefficient were corrected.

In the error correction by the Kalman filter, the accuracy of the azimuth / distance coefficient measured by GPS decreases as the vehicle speed decreases. In this case, if the error is corrected as it is, an error occurs in the direction / 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 decreases.

[0007]

In order to achieve the above object, the present invention provides a relative azimuth sensor (2) for outputting a signal according to an amount of azimuth change of a vehicle.
And a distance sensor (1) that outputs a signal according to the traveling speed of the vehicle, and based on a signal from the relative azimuth sensor, an offset correction amount is offset-corrected to obtain an azimuth change amount, and the vehicle is detected based on the azimuth change amount. Position detecting means (4, 5 and its) for detecting the position of the vehicle based on the azimuth and the moving distance of the vehicle by determining the azimuth of the vehicle, multiplying the signal from the distance sensor by the distance coefficient to obtain the moving distance of the vehicle. And a dead reckoning means (1, 2, 4, 5 and calculation processing for it), and G
By receiving satellite radio waves from the PS satellite,
A GPS (3) that outputs information related to speed and at least an offset error, a heading error of a vehicle, and a distance coefficient error as a state quantity X (t), and generate a process matrix φ that gives a temporal change of this error and a signal generation process. Signal ω, the state quantity X (t + 1) is related to φ · X (t) + ω, and the state quantity X (t) and the observed value Y
The dead reckoning navigation is based on a model that forms an observation process in which (t) and the observation matrix H and noise v generated in the observation process relate the observed value Y (t) with H · X (t) + v. Information of the direction, position and speed of the vehicle in the means and the GP
A state in which the observed value Y (t) is calculated from the difference between the vehicle azimuth, position, and speed information output from S, and the state quantity X (t) is calculated from the model based on the calculated value. Quantity calculating means (6 and 401 to 40 corresponding thereto)
8, 410, 411, 501-503) and the correction means for correcting the offset correction amount and the distance coefficient by the offset error and the distance coefficient error by the state quantity X (t) obtained by the state quantity calculating means. (409), the state quantity computing means, when determining a state in which the accuracy of the heading and speed output from the GPS is reduced, information on the heading and speed of the vehicle from the dead reckoning means, Excluding the information on the direction and speed output from the GPS, based on the difference in the position information of the vehicle,
The state quantities of the offset error and the distance coefficient error are calculated (501, 503).

According to a second aspect of the present invention, in the first aspect of the present invention, the state quantity computing means reduces the accuracy of the azimuth and speed output from the GPS when the speed of the vehicle is below a predetermined value. The feature is that the state in which it is operating is determined. According to a third aspect of the present invention, in the first aspect of the present invention, the state quantity computing means determines the accuracy of the azimuth and speed output from the GPS based on the information on the azimuth and speed from the GPS. It is characterized by determining the state of decrease.

The reference numerals in parentheses of the above-mentioned means indicate the correspondence with the concrete means described in the embodiments described later. Further, each step in the flowcharts described later constitutes a function realizing means for realizing each function.

[0010]

According to the invention as set forth in claims 1 to 3, 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 from the difference in the vehicle position information and the offset correction amount and the distance coefficient are corrected, the error in the sensor output with respect to the relative direction sensor and the distance sensor can be corrected. it can.

Further, when it is determined that the accuracy of the heading and speed output from the GPS is low, information regarding the heading and speed of the vehicle from the dead reckoning means and the GPS is excluded, and the position information of the vehicle is calculated. Since the state quantities of the offset error and the distance coefficient error are calculated based on the difference, those errors can be reduced in a state where the accuracy of the azimuth / distance coefficient measured by GPS is lowered.

[0012]

Embodiments of the present invention will be described below with reference to the drawings. This embodiment uses a Kalman filter in order to combine dead reckoning and GPS. The outline of this 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, if there is a linear system (φ) and a part of X (t) related by the observation matrix H can be observed for the state X (t) of the system, the filter of X (t) Gives an optimal 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 equation 1.

[0014]

## EQU1 ## X (t | t) = X (t | t-1) + K (t)
{Y (t) -HX (t | t-1)} where X (t | t-1) is a pre-estimated value and K (t) is a Kalman gain, which are represented by Equations 2 and 3, respectively. It

[0015]

## EQU00002 ## X (t | t-1) =. Phi.X (t-1 | t-1)

[0016]

[Number 3] 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 are represented by equations 4 and 5, respectively.

[0017]

[Number 4] P (t | t-1) = φP (t-1 | t-1) φ T + W

[0018]

## EQU5 ## P (t-1 | t-1) = (I-K (t-1)
H) P (t-1 | t-2) 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. In addition, A (i |
j) represents the estimated value of A at time i based on the information up to time j. The subscript ^T means a transposed matrix, and ^-1 means an inverse matrix. I is a unit matrix.

Further, V and W are white Gaussian noises 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 values of the state quantity X and the error covariance P and repeating the above calculation each time a new observation is made. To do. This embodiment applies such a Kalman filter to dead reckoning.

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

[0021]

[Equation 6] εG _t = εG _t-1 + ω _{0 There} is no definite change, and noise is added to the previous error. Absolute heading error (εA)

[0022]

In Equation 7] _{εA t = T × εG t-} 1 + εA t-1 + ω 1 last error, the azimuth error and noise to obtain over the elapsed time from the previous to the offset error is added. Distance coefficient error (εK)

[0023]

[Equation 8] εK _t = εK _t-1 + ω _{2 There} is no definite change, and noise is added to the previous error. Absolute position north direction error (εY)

[0024]

## EQU9 ## 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 The error generated by the azimuth error / 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 The error caused by the azimuth error / 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 the above equations are partially differentiated by the 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 the sensor error to the true absolute azimuth A _T , and is the absolute azimuth A obtained from the amount of azimuth change, as will be described later. Also, ω ₀
Indicates offset noise (change in offset due to temperature drift etc.), ω ₁ indicates absolute direction noise (gyro gain error), and ω ₂ indicates distance coefficient noise (aging).

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

[0030]

[Equation 12]

However, the noise v generated in the observation process is GPS
Noise, which is defined as

[0032]

[Equation 13]

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

When the Kalman filter is applied to such a vehicle-mounted navigation device, the pre-estimation X (t shown in Formula 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]

[Mathematical formula-see original document] X (t | t) = K (t) Y (t) Therefore, the state quantity X by 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 this error covariance P, σ _GG ² represents an estimate of the magnitude of the offset error, σ _AA ² represents an estimate of the magnitude of the absolute azimuth error, and σ _KK ² is an estimate of the magnitude of the distance coefficient error. , Σ _YY ² represents an estimate of the absolute position northward error magnitude, and σ _XX ² represents an absolute position eastward error magnitude estimate. Other than that, σ _ij ² represents the cross-correlation value of the i-th row and the j-th column. For example, σ _AG ² represents the cross-correlation value between the offset error and the absolute azimuth error.

The value of this error covariance P is updated by the calculation of equation (4). In addition, in the initial value, 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 the cross-correlation values are all 0.
Set to. In addition, the matrix shown in Expression 12 is used for H in Expression 3, and the matrix shown in Expression 13 is used for V. Further, W in Expression 4 uses the variance of ω shown in Expression 11.

As the observed value Y in the observation process,
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 navigation based on signals from the vehicle speed sensor 1 and the gyro 2 at time t, and GPSt means a value output from _{GPS 3} at time t.

ΕA _DRt −εA _GPSt is the difference between the absolute azimuth determined by dead reckoning and the azimuth output from GPS3,
That is, the absolute azimuth obtained by dead-reckoning contains the true absolute azimuth and its error εA _DRt.
The true absolute azimuth and its error εA
_{Since GPSt} is included, εA _DRt −εA _GPSt can be obtained by taking the difference between them.

Similarly, εK _DRt −εK _GPSt is a distance coefficient error obtained from the difference between the speed obtained by dead reckoning and the speed output from GPS3. Specifically, (speed due to dead reckoning−speed due to GPS ) / (Speed by dead reckoning). Also, εY _DRt −εY _GPSt
Is the Y component of absolute position and GP obtained by dead reckoning
It is the difference in error of the Y component of the position output from S3, and ε
X _DRt −εX _GPSt is the difference between the error between the X component of the absolute position obtained by dead reckoning and the X component of the position output from the GPS 3.

Further, the noise v generated in the observation process shown in the equation 13 is the noise of the GPS 3 and is obtained as follows. Pseudo-range measurement error in GPS3 (UER
E) and HDOP (Horizontal Dilution of Presision)
Therefore, the positioning accuracy is calculated by UARE × HDOP, and by squaring the positioning accuracy, v _2t and v _3t are calculated. In addition, Doppler frequency measurement error and HD
The speed accuracy is calculated from the relationship of OP by the measurement error of the Doppler frequency x HDOP, and the distance coefficient measurement error is calculated by this speed accuracy / vehicle speed.
_1t is required. Further, the azimuth accuracy is calculated as tan ⁻¹ (speed accuracy / Vc) from the vehicle speed Vc and the speed accuracy,
The square of this azimuth accuracy gives v _0t .

Therefore, εA _DRt −εA in the observation process
_{GPSt, εK DRt -εK GPSt, εY} DR t -εY GPSt, εX
_DRt- εX _GPSt and the noise V are input, and by executing the _equations 3 to 5 and the equation 1, the state quantity X based on the five error values defined in the signal generation process is obtained. The distance coefficient correction of 1, the offset correction of the gyro 2, the absolute position correction, and the absolute azimuth correction are performed.

The above-mentioned relative locus calculation, absolute position calculation and Kalman filter are carried out by the calculation processing by the microcomputer, so that they will be described below. FIG. 2 shows the arithmetic processing of the main routine of dead reckoning. Step 1
At 00, the azimuth change amount / movement distance is calculated. Details of this processing are shown in FIG. First, in step 101, the output angular velocity of the gyro 2 is multiplied by the start cycle T _M of the main routine to calculate the bearing change amount. In the next step 102,
The offset correction amount (the correction amount will be described later) multiplied by the start cycle T _M of the main routine is subtracted from the direction change amount to perform the offset correction of the direction 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 this step 100, step 20
A relative locus calculation process of 0 is performed. Details of this processing are shown in FIG. First, in step 201, the relative azimuth is updated based on the azimuth change amount (obtained in step 102).
The relative position coordinates are updated in step 202 based on the updated relative azimuth and the moving distance obtained in step 103. This update is based on the relative azimuth X,
This is performed by adding the Y component to the relative position coordinates up to that point. This relative position coordinate is used to determine the relative trajectory, and so-called map matching is performed based on the relationship between the relative trajectory and the road shape.

After step 200, step 30
The absolute azimuth and absolute position of 0 are calculated. Details of this processing are shown in FIG. First, in step 301, the absolute azimuth is updated based on the azimuth change amount (obtained in step 102). This updated absolute orientation and step 10
In step 202, the absolute position coordinates are updated based on the movement distance obtained in 3. The absolute azimuth A and the absolute position updated in the processing of this step 200 are used in the composite processing with GPS described later.

FIG. 6 shows details of step 400 for carrying out 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. This performs the process of correcting the dead reckoning error in steps 403 to 409 every time the GPS3 positioning is performed. However, when the GPS3 positioning cannot be performed, the error becomes large. Therefore, the prediction calculation of the error corresponding thereto is performed. Step 4
It is provided to perform regularly at 10 and 411.

When the determination in step 401 is NO, the scan
Whether there is positioning data from GPS3 at step 402
Do that. If there is positioning data from GPS3,
The processing proceeds to the Kalman filter calculation processing after step 403. Well
First, in step 403, the observation value Y is calculated. this
Is the speed, position, and direction data output from GPS3.
And obtained in the process of step 300 in dead reckoning
Absolute azimuth, absolute position and speed calculation processing not shown
Based on the vehicle speed pulse from the vehicle speed sensor 1,
Therefore, εA shown in Equation 12_DRt-ΕA _GPSt, ΕK_DRt
-ΕK_GPSt, ΕY_DRt-ΕY_GPSt, ΕX_DRt-ΕX_GPSt
As well as calculating
Noise v is calculated based on GPS3 positioning data and the like.

In step 404, the process matrix φ is calculated. This is the process shown in Formula 11 according to the moving distance L from the time when the previous process matrix was calculated, the elapsed time T (these are separately obtained by the measurement process not shown), and the absolute azimuth A obtained in step 301. Find the matrix φ. Based on the observation value Y and the process matrix φ calculated in this way, the above-described calculations of the expressions 3 to 5 are performed to obtain the state quantity X shown in the expression 14. That is, in step 405, the prediction calculation of the error covariance P is performed by the equation 3. In step 406, the Kalman gain K is calculated by the equation 4.
Calculate. In step 407, the error covariance P is calculated by the 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 the calculation of Expression 14. This state quantity X is, as shown on the left side of the equation 11, offset error (εG), absolute heading error (εA), distance coefficient error (εK), absolute position north direction error (εY), absolute position east direction error ( εX) is represented.

Due to these errors, in step 409, the dead reckoning error is corrected by the calculation shown in the figure, that is, the gyro 2 offset correction, the vehicle speed sensor 1 distance coefficient correction, absolute azimuth correction, and absolute position correction are performed. . 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 direction correction is performed in step 301 by the absolute direction correction. The absolute orientation A used is modified, and the absolute position correction modifies the absolute position used in step 302.

The above process is repeated every time there is positioning data from the GPS 3 to correct the above error, and more accurate dead reckoning data can be obtained. Also, G
If positioning of PS3 is not possible for a long time, step 401
If the determination is YES, the process proceeds to steps 410 and 411 to calculate the process matrix φ and predict the error covariance P. This makes it possible to perform an error covariance prediction calculation corresponding to an error when the GPS 3 cannot be positioned, and to accurately perform the Kalman filter process performed when the GPS 3 is subsequently positioned.

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

If the GPS correction is not performed when the vehicle speed is low, the performance in an urban area or the like where GPS positioning accuracy is low will deteriorate. Therefore, if the vehicle speed decreases and the accuracy of the direction and speed measured by GPS decreases,
This is not used as a reference for correction, and is used when the vehicle speed is high and the azimuth / speed accuracy measured by GPS is high.

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, and the observed value Y is calculated by Expression 12, and the Kalman filter is used for correction. In this case, it is the same as that described above.

On the other hand, if the vehicle speed is lower than the specified value, step 5
When the determination in 01 is NO, the process proceeds to step 503, the observed value Y is calculated by the equation 16, and the Kalman filter is used for correction.

[0057]

[Equation 16]

That is, when the vehicle speed is low, the azimuth / speed accuracy is low, so the observed value Y is calculated without using the azimuth / distance coefficient. Then, the state quantity X is calculated based on the observation value Y obtained in this way, and the correction in step 409 is performed. Note that the determination in step 501 may be performed by determining the state in which the accuracy of the azimuth / speed of the vehicle obtained from the reception data from the GPS 3 is reduced, and therefore, in addition to determining that the vehicle speed is reduced,
It is also possible to compare v _0t and v _1t indicating the azimuth accuracy and the speed accuracy with a predetermined comparison value to determine the accuracy decrease of the azimuth / distance coefficient of the vehicle.

[Brief description of drawings]

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

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

FIG. 3 is a flowchart showing a calculation process of a direction change amount / movement distance.

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

FIG. 5 is a flowchart showing a calculation process of absolute azimuth and absolute position.

FIG. 6 is a flowchart showing a combination process with GPS.

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

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

[Explanation of symbols]

 1 Distance Sensor 2 Relative Direction Sensor 3 GPS 4 Relative Trajectory Calculation Section 5 Absolute Position Calculation Section 6 Kalman Filter

Claims (3)

[Claims] 1. A relative azimuth sensor that outputs a signal in accordance with the amount of change in the azimuth of the vehicle, a distance sensor that outputs a signal in accordance with the traveling speed of the vehicle, and an offset correction amount based on the signal from the relative azimuth sensor. Offset correction is performed to obtain the azimuth change amount, and the azimuth change amount of the vehicle is specified, and the signal from the distance sensor is multiplied by the distance coefficient to obtain the moving distance of the vehicle. Dead reckoning means composed of position detecting means for detecting the position of the vehicle based on the GPS, GPS for receiving satellite radio waves from GPS satellites and outputting information on the azimuth, position and speed of the vehicle, at least an offset error, The heading error and the distance coefficient error of the vehicle are taken as the state quantity X (t), and the process matrix φ that gives a temporal change of this error and the coarseness generated in the signal generation process. By the sound ω, the state quantity X (t + 1) is calculated as φ · X (t) + ω
, The state quantity X (t) and the observed value Y (t) are related to each other by the observation matrix H and the noise v generated in the observed process. ) + V based on a model forming an observation process, which is related to the direction, position, and speed information of the vehicle in the dead reckoning means and the difference between the direction, position, and speed information of the vehicle output from the GPS. , The observation value Y (t) is calculated, and
A state quantity calculating means for calculating the state quantity X (t) from the model based on the calculated value, and the offset correction by the offset error and the distance coefficient error due to the state quantity X (t) obtained by the state quantity calculating means. An amount and a correction means for correcting the distance coefficient, and when the state quantity calculation means determines a state in which the accuracy of the azimuth and speed output from the GPS is reduced, Information about bearing and speed,
Current position detection for vehicle characterized in that the state quantities of the offset error and the distance coefficient error are calculated based on the difference in the information on the position of the vehicle, excluding the information on the azimuth and speed output from the GPS. apparatus. 2. The state quantity calculating means determines a state in which the accuracy of the azimuth and speed output from the GPS is reduced when the speed of the vehicle is equal to or lower than a predetermined value. The vehicle current position detection device described. 3. The state quantity calculating means determines a state in which the accuracy of the azimuth and speed output from the GPS is reduced based on the information on the azimuth and speed from the GPS. The vehicle current position detection device according to claim 1.