JPH0634379A - Navigation device - Google Patents

Abstract

PURPOSE:To improve positional accuracy by finding a moving body position by means of output of an azimuth measuring means, output of a distance measuring means, an estimated scale factor error and bias. CONSTITUTION:An azimuth outputted by an earth magnetism sensor 4 is shown even by using an integral value outputted by an angular velocity sensor 3 and speed obtained by a position sensor 1, and a magnetization parameter estimating device 9 estimates the azimuth from an average value, and estimates a magnetization component and an installation angle error of the earth magnetism sensor 4 from the estimated azimuth. An output signal of the angular velocity sensor 3 is shown even by using a differential value of a distance sensor 2 and the speed obtained by the position sensor 1, and an angular velocity error parameter estimating device 8 estimates angular velocity, and estimates error parameter of the angular velocity sensor 3 of a scale factor, bias and so on from this estimated value. A distance movable between sampling intervals can be measured even by the position sensor 1 besides the distance sensor 2, and a distance error parameter estimating device 7 takes up an average value, and estimates the distance, and estimates error parameter of the distance sensor 2. In this way, errors of the respective sensors are corrected, and a position estimiting unit 10 calculates an accurate position.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a distance measuring means, an angular velocity measuring means, a geomagnetic measuring means correcting device mounted on a moving body and a navigation device using the same.

[0002]

2. Description of the Related Art Conventionally, as a navigation device for a mobile body, a hybrid navigation system has been adopted in which inertial navigation and radio navigation are combined to compensate for their respective drawbacks to improve the estimation accuracy of position, speed and the like.

The error in radio navigation falls within a substantially constant range regardless of the passage of time. GPS (Global Positioning System) and beacons are used for radio navigation. On the other hand, inertial navigation measures the traveling direction of a moving body and the distance traveled, and integrates the estimated position. Although the accuracy is good in a short time, the error in the estimated position diverges with time due to the effect of the error on the measured traveling direction and distance.

In order to solve such a problem, a position sensor (positional distance measuring means) and an angular velocity sensor (angular velocity distance measuring means) are disclosed, for example, in Japanese Patent Laid-Open No. 63-111115.
It is known that the output of the distance sensor (distance measuring means) is input to the Kalman filter to obtain the scale factor error of the distance sensor and the bias of the angular velocity sensor, and the respective sensor outputs are corrected.

[0005]

By the way, the distance sensor,
The output error of the angular velocity sensor consists of a scale factor error and a bias. When a geomagnetic sensor is used as the azimuth sensor, there are azimuth errors due to errors in magnetization and mounting angle, and influence (μ effect) on the magnetic field of the moving body to which the geomagnetic sensor is mounted.

However, in Japanese Patent Laid-Open No. 63-311115, which is the above-mentioned conventional technique, the output error of the distance sensor and the angular velocity sensor is corrected by the Kalman filter, but the characteristics of the above-mentioned sensor error are not sufficiently taken into consideration, and therefore the accuracy is accurate. It was impossible to correct such errors.

Therefore, an object of the present invention is to provide a navigation device for a mobile body which estimates an error of a distance sensor, an angular velocity sensor or a geomagnetic sensor.

[0008]

In order to estimate the scale factor error and bias of the distance measuring means, the azimuth measuring means, the position measuring means, the distance measuring means, and the position of the moving body from the outputs of these measuring means. A navigation apparatus for a moving body having a position estimating means for obtaining an error parameter estimating device for estimating a scale factor error and a bias of the distance measuring means from an output of the position measuring means and an output of the distance measuring means. And the position estimating means determines the position of the moving body from the output of the azimuth measuring means, the output of the distance measuring means, and the estimated scale factor error and bias. .

Further, in order to estimate the scale factor error and bias of the angular velocity measuring means, position measuring means,
What is claimed is: 1. A navigation device for a moving body, comprising: distance measuring means, angular velocity measuring means, and position estimating means for obtaining the position of the moving body from the outputs of these measuring means, the output of the position measuring means and the output of the angular velocity measuring means. From, with an error parameter estimation device for estimating the scale factor error and bias of the angular velocity measuring means, the position estimating means,
The position of the moving body is determined from the output of the distance measuring unit, the output of the angular velocity measuring unit, the estimated scale factor error and the bias.

Further, in order to estimate the azimuth error of the geomagnetic measuring means, it has a position measuring means, a distance measuring means, a geomagnetic measuring means, and a position estimating means for obtaining the position of the moving body from the outputs of these measuring means. A navigation device for a mobile body, having an error parameter estimating device for estimating a bearing error of the geomagnetic measuring means from the output of the position measuring means and the output of the geomagnetic measuring means, and the position estimating means,
The position of the moving body is determined from the output of the distance measuring means, the output of the geomagnetic measuring means, and the estimated azimuth error.

[0011]

In order to estimate the scale factor error and bias of the distance measuring means, the moving means has an azimuth measuring means, a position measuring means and a distance measuring means, and obtains the position of the moving body from the outputs of these measuring means. In the body navigation device, the error parameter estimation device estimates the scale factor error and the bias of the distance measuring means from the output of the position measuring means and the output of the distance measuring means. The position estimating means obtains the position of the moving body from the output of the azimuth measuring means, the output of the distance measuring means, and the estimated scale factor error and bias.

Further, in order to estimate the scale factor error and the bias of the angular velocity measuring means, position measuring means,
In a navigation device for a moving body, which has a distance measuring means and an angular velocity measuring means, and obtains the position of the moving body from the outputs of these measuring means, the error parameter estimation device has an output of the position measuring means and an angular velocity measuring means. The scale factor error and bias of the angular velocity measuring means are estimated from the output. The position estimating means obtains the position of the moving body from the output of the distance measuring means, the output of the angular velocity measuring means, the estimated scale factor error and the bias.

Further, in order to estimate the direction error of the geomagnetism measuring means, it has a position measuring means, a distance measuring means, and a geomagnetic measuring means, and for a moving body which obtains the position of the moving body from the outputs of these measuring means. In the navigation device, the error parameter estimation device estimates the azimuth error of the geomagnetic measuring means from the output of the position measuring means and the output of the geomagnetic measuring means. The position estimating means obtains the position of the moving body from the output of the distance measuring means, the output of the geomagnetism measuring means, and the estimated azimuth error.

[0014]

1 is a functional block diagram of a navigation device according to an embodiment of the present invention. In the figure, 1 is GP
It is a radio navigation device (position measuring means) that measures the position and speed of a moving body at a certain sampling interval using S, beacons, and the like. Reference numeral 2 is a distance sensor such as a vehicle speed sensor for measuring the distance traveled by the moving body in a certain time section.
Reference numeral 3 denotes an angular velocity sensor that measures the rotational angular velocity of the moving body at a certain sampling interval, such as an optical fiber gyro or a vibration gyro. Reference numeral 4 denotes an azimuth sensor such as a geomagnetic sensor that outputs the traveling direction of the moving body at a certain sampling interval.

First, a method of estimating the error of various sensors without using the Kalman filter will be described.

Position sensors such as GPS and beacons output position information such as the position and speed of the moving body at a certain time interval. With this as a reference, the error of the distance sensor, the angular velocity sensor, and the azimuth sensor (geomagnetic sensor) can be estimated.

The azimuth output by the geomagnetic sensor 4 can also be expressed by using the integrated value of the output of the angular velocity sensor 3 and the velocity obtained by the position sensor 1. Since the errors of these sensors are considered to be independent, the azimuth may be estimated by taking the average value of the output signals of the three sensors. The magnetization component and the mounting angle error of the geomagnetic sensor 4 can be estimated from this estimated orientation. The magnetization parameter estimation device 9 does this.

Concretely, the procedure is as follows. The direction θ of the moving body is

[0019]

[Number 1] _{^{θ = tan ~ 1 (V ygps}} / V xgps) than can be calculated from the output of the position sensor. further

[0020]

(2) V _x = r _x cos (θ + α) + mx

[0021]

V _y = r _y sin (θ + α) + my where V _x , V _y : Geomagnetic sensor output mx, my: Magnetization component r _x , r _y : μ effect of vehicle body α: Mounting angle to vehicle body If the equations are solved as simultaneous equations at two or more times, the magnetization of the geomagnetic sensor, the μ effect of the vehicle body, and the mounting angle can be estimated.

Besides the above method, it is of course not necessary to take the average.

Next, since the angular velocity can be expressed by acceleration and velocity, the output signal of the angular velocity sensor 3 can be expressed by using the differential value of the distance sensor 2 and the velocity obtained by the position sensor 1. Since the errors of these sensors are considered to be independent, the angular velocity is estimated by taking an average value, and the error parameters of the angular velocity sensor 3, such as the scale factor and the bias, can be estimated from this estimated value. This is done by the angular velocity error parameter estimation device 8.

Concretely, the procedure is as follows. Angular velocity ω
Is

[0025]

[Equation 4]

It can be calculated from the output of the position sensor. further

[0027]

Ω = (1 + a _g ) ω _g + b _g where ω _g : angular velocity sensor output a _g , b _g : angular velocity sensor bias, scale factor error If the equation is solved as a simultaneous equation at two or more times, the angular velocity is solved. Sensor bias and scale factor error can be estimated.

Besides the above method, it is of course not necessary to take an average.

The distance moved during the sampling interval can be measured by the position sensor 1 as well as the distance sensor 2. Therefore, it is possible to estimate the distance by taking the average value and to estimate the error parameters of the distance sensor 2 such as the scale factor and the bias. This processing is performed by the distance error parameter estimation device 7.

Specifically, it is carried out as follows. The distance traveled between them can be calculated from the difference in the position output of the position sensor or from the speed output. For example,

[0031]

√ (V ² _xgps + V ² _ygps ) = (1 + a _od ) d _od + b _od where V _xgps , V _ygps : GPS speed output (x direction,
y direction) d _od : Distance sensor output a _od , b _od : Distance sensor bias, scale factor error If the equation (2) is solved as a simultaneous equation at two or more times, the distance sensor bias and scale factor error can be estimated.

By the above means, the geomagnetic sensor 4, the angular velocity sensor 3 and the distance sensor 2 can be corrected for errors, and the accurate position can be calculated by the position estimator (position estimating means) 10.

Besides the above method, it is of course not necessary to take the average.

When the position sensor cannot be used, the angular velocity sensor and the azimuth sensor (geomagnetic sensor) may be used to mutually estimate the error. For example, when estimating the bias and scale factor error of the angular velocity sensor, Equation 5 is used to estimate the bias and scale factor error of the angular velocity sensor. The angular velocity ω is obtained from the difference of the direction sensor.

On the contrary, when estimating the magnetization of the geomagnetic sensor, the μ effect of the vehicle body, and the mounting angle, the magnetization of the geomagnetic sensor, the μ effect of the vehicle body, and the mounting angle are estimated from Equations 2 and 3. The azimuth θ of the moving body is obtained by integrating the output of the angular velocity sensor.

Next, a method of using the Kalman filter for the calculation of each sensor error parameter estimation device will be described. In order to estimate the position and sensor error parameters by Kalman filter, it is necessary to model the navigation system. First, each sensor error is modeled.

The vehicle speed sensor is of a type that detects the number of rotations of the wheel of the automobile and calculates the vehicle speed. The output is the distance traveled by the sensor in one sampling interval.
Therefore, a change in the wheel diameter due to wear of the tire or a change in the load amount corresponds to a scale factor error, and slip or the like corresponds to a bias.

The relationship between the vehicle speed sensor output l (measured value) and the true value l ₀ is

[0039]

## _EQU7 ## It can be expressed as l = (1 + a _od / (1-a _od )) l ₀ + b _od / (1-a _od ) + n _od . However, a _od / (1-a _od ) is a scale factor error, b _od / (1-a _od ) is a bias, and _nod is a random error. The relationship between the measured value of the vehicle speed sensor and the true value assumed here can be expressed as shown in FIG. The value measured by the vehicle speed sensor is always larger than the true value, and there is almost no bias error when the vehicle speed is below a certain value, but when it exceeds the certain value, the scale factor changes and a bias error occurs. Therefore, it is necessary to estimate both the scale factor and the bias error in real time in order to accurately estimate the distance. Transforming number 1

[0040]

## _EQU8 ## l = l ₀ + a _od l + b _od + n _od . If a _od and b _od are estimated, the scale factor error and the error due to the bias can be estimated using the measured value l.

Next, an error model of the angular velocity sensor will be described. The angular velocity sensor is a sensor that detects the angular velocity, and the angle can be known by integrating the sensor. It is assumed that the error of the angular velocity sensor is also composed of the scale factor error and the bias like the error of the vehicle speed sensor. The relationship between the angular velocity output ω of the angular velocity sensor and its true value ω ₀ is

[0042]

It can be written as ω = (1 + a _g / (1-a _g )) ω ₀ + b _g / (1-a _g ) + _ng . a _g / (1-a _g ) and b _g / (1-a _g ) are the scale factor error and bias, respectively. n _g is a random error. If we estimate a _g and b _g , we transform Equation 3

[0043]

[Equation 10] ω = ω ₀ + _ag ω + _bg + _ng can be used to obtain the error of the angular velocity sensor from the measured value.

The error of the geomagnetic sensor is considered as follows.
The secondary output voltages of the geomagnetic sensor are V _x and V _y , respectively. The relationship between these and the azimuth θ of the geomagnetic sensor is

[0045]

[Formula 11] V _x = a ・ cos (θ + α) + m _x + n _x

[0046]

[Expression 12] V _y = b · sin (θ + α) + m _y + n _y m _x and m _y are error components due to the magnetization of the geomagnetic sensor, and n _x and n _y are random errors. α is an angular error when the geomagnetic sensor is attached. Azimuth θ is

[0047]

[Equation 13] Determined by θ = tan ~ ¹ (V _y / V _x ). Ideally (when there is no magnetizing of the geomagnetic sensor, mounting angle error, and μ effect of movement), if the sensor is rotated 360 degrees horizontally with a = b and m _x = m _y = 0, the secondary output becomes V _x . V _y draws a circle centered on the reference voltage. In reality, the geomagnetic sensor is magnetized by the influence of a magnetic field other than the geomagnetism, and the above conditions are not satisfied. There is also an angular error when mounting the sensor. Therefore, a, b, m _x and m _y are estimated and the correct azimuth is obtained.

A state equation is established based on the above sensor error model. Estimates include moving body position, speed,
It is an error parameter of acceleration and sensor, and the state variables are as follows.

[0049]

X = (x, y, v _x , v _y , a _x , _{ay y} , a _g , b _g , a _od , b _od , m _x , my _y , a, b, α) 'Equation of state Can be written as

[0050]

[Equation 15] X (k + 1) = AX (k) + BU (k) + v (k)

[0051]

[Equation 16]

[0052]

[Equation 17]

[0053]

[Equation 18] a ₁₂ = O ⁶⁹ (6-row 9-column zero matrix)

[0054]

[Formula 19] a ₂₁ = O ⁹⁶ (zero matrix of 9 rows and 6 columns)

[0055]

[Formula 20] a ₂₂ = E ⁹⁹ (9-by-9 identity matrix)

[0056]

[Equation 21]

[0057]

U (k) = (a _x a _y ) ′ The acceleration can be calculated from the outputs of the vehicle speed sensor, the angular velocity sensor and the geomagnetic sensor, or can be obtained by using the acceleration sensor. Also, v (k) is system noise. T is a sampling interval.

The error parameters of the vehicle speed sensor, the angular velocity sensor, and the geomagnetic sensor are all modeled with constants. Although these parameters actually change with time, they can be estimated with sufficient accuracy because they change more slowly than the estimation speed by the Kalman filter.

Next, an observation equation is created. The observed amount is a position information obtained from a position sensor such as GPS or a beacon, speed information and a signal obtained from a vehicle speed sensor, an angular velocity sensor, and a geomagnetic sensor. Arrange these and

[0060]

Equation 23] _{Y = (x GPS, y GPS} , v xGPS, v yGPS, ω, l, V x, V y) is defined by '. The relationship between the observed quantity and the state quantity is w _i (i = 1, ...
・, 8) was modeled as the observation noise as follows.

[0061]

[Equation 24] x _GPS = x + w ₁

[0062]

[Equation 25] y _GPS = y + w ₂

[0063]

[ _Expression 26] v _xGPS = v _x + w ₃

[0064]

[ _Formula 27] v _yGPS = v _y + w ₄

[0065]

Ω = (a _y v _x -a _x v _y ) / (v _x ² + v _y ² ) + a _g ω ₀ + b _g + w ₅

[0066]

[Equation 29]

[0067]

[Expression 30] V _x = a ・ cos (tan ~ ¹ (v _y / v _x ) + α) + _mx
+ W ₇

[0068]

[Formula 31] V _y = b · sin (tan ⁻¹ (v _y /
_{v x) + α) + m} y + w 8 collectively these can have the following observation equation.

[0069]

[Expression 32] Y = f (X) + w where w = (w ₁ w ₂ w ₃ w ₄ w ₅
w ₆ w ₇ w ₈ ) 'Calculate the Kalman filter for the above model and estimate the position and sensor error parameters. The optimum estimated value X (k | k) by the Kalman filter is obtained by the following recurrence formula.
Since the observation equation is non-linear, the extended Kalman filter algorithm is used.

[0070]

[Expression 33] X (k + 1 | k) = AX (k | k) + BU (k)

[0071]

[Expression 34] X (k | k) = X (k | k-1) + K (k) [Y (k)-f (X (k | k-1))]

[0072]

[Equation 35] P (k + 1 | k) = AP (k | k) A '+ V (k)

[0073]

(36) P (k | k) = P (k | k-1)-K (k) C (k) P (k | k-1)

[0074]

K (k) = P (k | k-1) C '(k) [C (k) P (k | k-1) C' (k) + W (k)] ~ ¹ where The subscript (k | k) is the optimum value by the Kalman filter based on the observation value at time k, and (k | k-1) is the value at time k-1 predicted by the equation of state from the value at time k-1. Is. Further, V (k) and W (k) are the covariance matrices of system noise and observation noise, respectively.

P is a covariance matrix of the estimation error, which is a measure of the accuracy of estimation by the Kalman filter. K is a Kalman filter gain, which is calculated when the observed quantity is obtained, and is a quantity that determines whether to weight the equation of state or the observed value by comparing the statistics of the system noise and the observed noise. Expression 34 is an expression for calculating the optimum estimated value. Times of Day
Predict the value at time k according to the state equation using the optimal estimate at k-1. The optimum estimated value is the internal division point of the Kalman filter gain K of this predicted value and the observed value at time k. The matrix C (k) is the Jacobian of f (X (k | k-1)) used for linearization of the observation equation.

FIG. 2 is a block diagram showing one implementation method using the Kalman filter of FIG. Acceleration calculator 1
1 is equipped with an acceleration sensor and uses its output, or calculates the acceleration by obtaining the angular velocity, the angle, the distance and their differential values from the distance sensor 2, the angular velocity sensor 3, and the geomagnetic sensor 4. The state quantity predictor 12 performs the operation of Expression 32 based on the acceleration information calculated by the acceleration calculator 11. Next, in Equation 23 of the observation equation, Equation 3 is used around the predicted state quantity.
The observation matrix of 2 is linearized and the observation matrix is obtained. Equation 37 is calculated by the filter gain calculator from the error covariance matrix obtained by performing the calculation of Equation 35 by the observation matrix and the error covariance matrix predictor obtained here. The optimum value updater 17 calculates optimally by performing the calculation of Expression 37 from the observed amount obtained from the position sensor 1, the distance sensor 2, the angular velocity sensor 3, and the geomagnetic sensor 4 and the predicted state amount obtained from the state amount predictor 12. Output the value. The error covariance matrix updater 15 calculates the variance of the estimation error of the optimum estimated value from the calculation of Expression 36.

Here, it is assumed that the output timings of the sensors are synchronized. Actually, information from position sensors such as GPS and beacons is irregularly obtained every few seconds. Since other sensors can obtain data at a faster cycle, they are measured at a cycle independent of the position sensor and stored in the memory. When the information is obtained from the position sensor, the memory is searched to obtain the sensor data having the measurement time closest to the time, and the observation value of the Kalman filter used in Equation 34 is prepared. Then, one step of iterative calculation of the Kalman filter is executed.

Regarding the initial value, the initial value of the state quantity is set as the output of the state quantity predictor 12, and the initial value of the error covariance matrix is set as the output of the error covariance matrix predictor 13, and the iterative calculation is started. . The initial value of the state quantity is measured by the position sensor when the position sensor is available for the position and speed, and when it is not available or the accuracy is low, the last data estimated during the previous run is stored. Use it aside As the sensor error parameter, the last data estimated when the vehicle last ran was stored and used, or a value representing a state without error was used.

[0079]

EFFECT OF THE INVENTION Distance sensor, scale factor error of angular velocity sensor, bias and magnetization component of geomagnetic sensor,
By estimating the mounting angle error and the μ effect of the moving body and correcting each sensor, the position and the traveling direction of the moving body can be accurately obtained.

[Brief description of drawings]

FIG. 1 is a block diagram showing an embodiment of a sensor error correction device for a moving body and a navigation device using the same according to the present invention.

FIG. 2 is a block diagram showing an embodiment using a Kalman filter.

FIG. 3 is a graph showing a relationship between a measured value of a distance sensor and a true value.

[Explanation of Codes] 1 ... Position sensor, 2 ... Distance sensor, 3 ... Angular velocity sensor, 4 ... Geomagnetic sensor, 5 ... Differentiator, 6 ... Integrator,
7 ... Distance error parameter estimation device, 8 ... Angular velocity error parameter estimation device, 9 ... Magnetization parameter estimation device, 10 ... Position estimator, 11 ... Acceleration calculator, 12 ... State quantity predictor, 13 ... Error covariance matrix predictor, 14 ... Observation equation linearizer, 15 ... Error covariance matrix updater, 16 ... Filter gain calculator, 17 ... Optimal value updater

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Yasuhiro Gunji 4026 Kuji Town, Hitachi City, Ibaraki Prefecture, Hitachi Research Laboratory, Hitachi, Ltd. (72) Inventor Jiro Takezaki, 560-2 Okazu Furuhisa, Atsugi City, Kanagawa, Ltd. In informatics (72) Inventor Kenji Takano 560-2 Furuhisa Okazu, Atsugi City, Kanagawa Stock Company, Xinavi Informatics

Claims (12)

[Claims] 1. A navigation system for a mobile body, comprising: an azimuth measuring means, a position measuring means, a distance measuring means, and a position estimating means for obtaining the position of the mobile body from the outputs of these measuring means. From the output of the measuring means and the output of the distance measuring means,
An error parameter estimating device for estimating a scale factor error and a bias of the distance measuring means is provided, and the position estimating means has an output of the azimuth measuring means, an output of the distance measuring means, and the estimated scale factor. A navigation device for a mobile body, wherein the position of the mobile body is obtained from an error and a bias. 2. A navigation device for a mobile body, comprising: position measuring means, distance measuring means, angular velocity measuring means, and position estimating means for obtaining the position of the mobile body from the outputs of these measuring means. An error parameter estimating device for estimating a scale factor error and a bias of the angular velocity measuring means from the output of the measuring means and the angular velocity measuring means is provided, and the position estimating means includes the output of the distance measuring means and the angular velocity. A navigation device for a mobile body, wherein the position of the mobile body is obtained from the output of the measuring means and the estimated scale factor error and bias. 3. A navigation system for a moving body, comprising: a position measuring means, a distance measuring means, a geomagnetism measuring means, and a position estimating means for obtaining the position of the moving body from outputs of these measuring means. An error parameter estimation device for estimating the azimuth error of the geomagnetic measurement means based on the output of the means and the output of the geomagnetism measurement means, the position estimation means including the output of the distance measurement means and the output of the geomagnetism measurement means. And a navigation device for a mobile body, wherein the position of the mobile body is obtained from the estimated heading error. 4. A navigation device for a mobile body, comprising a distance measuring means, an angular velocity measuring means, a geomagnetism measuring means, and a position estimating means for obtaining the position of the mobile body from the outputs of these measuring means. From the output of the means, an error parameter estimation device for estimating the scale factor error and bias of the angular velocity measuring means is provided, and the position estimating means estimates the output of the distance measuring means and the output of the angular velocity measuring means. A navigation device for a mobile body, wherein the position of the mobile body is obtained from the scale factor error and the bias. 5. A navigation device for a moving body, comprising a distance measuring means, an angular velocity measuring means, a geomagnetism measuring means, and a position estimating means for obtaining the position of the moving body from the outputs of these measuring means. An error parameter estimation device for estimating a heading error of the geomagnetic measuring means from the output of the means, the position estimating means, the output of the distance measuring means, the output of the geomagnetic measuring means, the estimated heading A navigation device for a mobile body, wherein the position of the mobile body is obtained from the error. 6. The navigation device for a mobile body according to claim 1, 2, 3, 4, or 5, wherein the error parameter estimation device estimates an error using a Kalman filter. . 7. A navigation method for a mobile body for obtaining the position of a mobile body by means of azimuth measurement, position measurement and distance measurement, wherein scale factor error of the distance measurement is obtained from the position measurement and distance measurement. A navigation method for a mobile body, which comprises estimating a bias, determining the position of the mobile body from the azimuth measurement, the distance measurement, the estimated scale factor error and the bias. 8. A navigation method for a mobile body for determining a position of a mobile body by position measurement, distance measurement, and angular velocity measurement, wherein scale factor error of the angular velocity measurement is obtained from the position measurement and angular velocity measurement. A navigation method for a mobile body, which comprises estimating a bias, obtaining the position of the mobile body from the distance measurement, the angular velocity measurement, the estimated scale factor error and the bias. 9. A navigation method for a mobile body, which obtains the position of a mobile body from position measurement, distance measurement, and geomagnetic measurement, wherein a direction error of the geomagnetic measurement is estimated from the position measurement and geomagnetic measurement. A navigation method for a mobile body, wherein the position of the mobile body is obtained from the distance measurement, the geomagnetic measurement, and the estimated azimuth error. 10. A navigation method for a mobile body for determining the position of a mobile body from distance measurement, angular velocity measurement and geomagnetic measurement, wherein scale factor error of the angular velocity measurement is obtained from the geomagnetic measurement and angular velocity measurement. A navigation method for a mobile body, comprising estimating a bias, determining the position of the mobile body from the distance measurement, the angular velocity measurement, and the estimated scale factor error and bias. 11. A navigation method for a mobile body for obtaining the position of a mobile body from these measurement outputs based on distance measurement, angular velocity measurement, and geomagnetic measurement, wherein the geomagnetic measurement is performed by the angular velocity measurement and the geomagnetic measurement. The method for navigating a moving body, wherein the position of the moving body is obtained from the distance measurement, the geomagnetic measurement, and the estimated direction error. 12. The navigation method for a mobile body according to claim 7, 8, 9, 10, or 11, wherein the error estimation is performed using a Kalman filter.