DE19636425C1 - Navigation method using complementary measurement methods for motor vehicle - Google Patents

Abstract

The method involves acquiring sensor signals using two complementary methods which exploit available measurement data and accuracy respectively. The measurement signals of the movements of an object (1) of a first sensor (2) are used in a simulation (5) and appropriate data are acquired. The movement data are fed into a simulation (6) of the second measurement method for generating simulated measurement signals of the second sensor arrangement (3). The simulated signals are compared with the actual measurement signals from the second sensor arrangement. The movement simulation is corrected according to the comparison results and its output signals used as navigation method output data.

Description

The invention relates to a method for navigating with one another additional evaluations of data from different measurement methods for a movable object, with a first measurement method advantageous in terms of availability of measurement signals a first sensor arrangement and with a second measuring method measurement signals of two advantageous in terms of accuracy th sensor arrangement can be processed.

It is known to use data from various sensor arrangements for the Use navigation, taking regular data from one Sensor arrangement, for example by an on-board self-sufficient System that is constantly available while data is a two th sensor arrangement, the determination of movement data, including location data, more accurate ben, are dependent on external transmissions and therefore due to faults, shadowing, etc. not always available To be available. The data of the second sensor arrangement are used to support the data of the first sensor arrangement, to increase their accuracy.  

Common self-sufficient sensors are, in particular, inertial sensors, but also, for example, wheel sensors. Coming as support systems especially the satellite and radio navigation in question, whose Sensors thus have antennas.

In the known navigation methods of this type, the Measurement data of the first sensor arrangement, for example with the help a "strap-down calculation" movement data determined. Output The point for the evaluation is that, based on the measurement data, the movement data determined in the first sensor arrangement is faulty are. A linearized error model is therefore used for the calculation correction. By applying the second measurement method as a support system, especially a Satelli navigation method, the faulty movement data according to the first measurement method with output data from the second Measuring method compared and an error value is formed from this. For Adaptation of the linearized error model to the current who te becomes the error predicted in the linearized error model converted with a linearized support model so that a predicted error value arises, which corresponds to the value of Ver equal to the error value determined using the two measurement methods becomes. Via a controller based, for example, on the Kalman filter principle is designed with the determined ver equivalent to the linearized error model on the one hand, the actual strap-down calculation on the other hand corrected. Since the faulty, determined by the strap-down calculation Movement data for determining the working point both on the linearized error model and the linearized support model as well as get to the controller, there is a multiple change interaction between the strap-down calculation on the one hand and the Control loop for adapting the linearized error model and of the linearized support model on the other hand. This external cor rectification of the Kalman filter is actually foreign to the system and may cause unstable behavior of the rule circle and thus the navigation procedure.  

Navigation systems for motor vehicles are known at which a usual location determination with on-board sensors and possibly a satellite navigation receiver. over a switch can be found in DE 44 28 009 A1, EP 0 519 593 A2 or JP 07 311 043 A (Patent Abstracts of Japan) Known systems put into a simulation mode the one in which a journey from the current starting point to one simulated selected end point and darge using a map is posed. The simulation mode influences the actual location determination not.

The present invention therefore addresses the problem reasons, the navigation method of the type mentioned above train that an improvement of the control system is achieved will and possible instabilities due to the previous Training of the control system are possible can be avoided nen.

Based on this problem, according to the invention Procedure for navigation with complementary evaluations of data from different measuring methods for a movable Item specified where

• - with a first measurement method regarding availability advantageous measurement signals of a first sensor arrangement and with a second method of measuring accuracy advantageous measurement signals of a second sensor arrangement ge be won,
• - The measurement signals of the first sensor arrangement on a simula tion of movement of the object applied and from it Movement data are determined,
• - The determined movement data in a simulation of the two th measurement method for generating simulated measurement signals of the second sensor arrangement can be entered,
• - The simulated measurement signals with actual measurement signals the second sensor arrangement are compared,
• - from the comparison the underlying simulation of the movement correcting the object and
• - he by simulating the movement of the object averaged movement data as the starting data for navigation proceedings are issued.

The essence of the invention is that the entire object movement in a movement model is mathematically simulated in order to required movement data, including sth if necessary auxiliary data. The estimated movement data as a result of the navigation process. Since the original estimated movement data are highly error-prone, ge they reach a model of the support system in which the movement Transfor data to simulated measurement data of the support system be lubricated. These are compared with the real measurement data of the support system compared. With the result In the comparison, a controller is controlled, which in turn controls the complete movement model changed so as to be more appreciated To get movement data.

In contrast to the prior art, the evaluation of the Measuring signals of the first sensor arrangement only in one simulated movement model instead, in its entirety is changeable by the controller. When training the Bewe thus finds the model within a Kalman filter Correction by regulating the Kalman filter instead and not - as before - at least partially outside the Kalman filter. Therefore, the control loop provided according to the invention can be sta build more easily because of instabilities due to external correction the strap-down calculation, for example using a Kalman filter, be avoided.

The complete simulation of the movement according to the invention as well of the support model in particular also enables the arrangement of the Sensor arrangements for the two measuring methods at a distance from each other, which cannot be assumed to be zero must, but in the simulation of the second measurement method (the Support system) is taken into account.

The invention is illustrated below with the aid of drawings positions explained in more detail. Show it:

Fig. 1 is a block diagram schematically illustrating the inventive method,

FIG. 2 shows a block diagram for an exemplary embodiment of a movement model from FIG. 1.

Fig. 1 is divided by a horizontal dashed line into an area "Reality" and an area "Simulation".

As a moving object, a vehicle 1 is adopted in FIG. 1, the (changing) movement data of which is part of x. The vehicle 1 is provided with a first sensor arrangement 2 and a second sensor arrangement 3 , the first sensor arrangement 2 being an inertial sensor system and the second sensor arrangement 3 being an antenna system for a satellite navigation receiver. The data received by the second sensor arrangement 3 , in particular satellite data, are evaluated as a support system with an evaluation system 4 belonging to the satellite receiver, and support data y is thereby formed. The support data are usually y oblique distances to different satellites.

The first sensor arrangement 2 supplies output data u that arrive at a mathematical movement model 5 . At the output of the movement model 5 there are estimated data, movement data with auxiliary data, which are directed to a support model 6 as a simulated support system. Support model 6 transforms the estimated data into estimated support data. The estimated data are compared with the measured data y in a difference stage 7 . The output signal arrives at a controller 8 , which controls the movement model 5 as a function of the input signal, in order to arrive at better estimates for the data. The result data E ascertained by the method are also present at the output of the movement model 5 and are therefore formed exclusively in the simulated part of the navigation device.

The task of the motion model 5 is to simulate the vehicle / sensor movement and, if necessary, sensor properties. Vectorial or scalar variables such as position, speed, acceleration, position, rotation rate, sensor error etc. can be taken into account. Ordinary differential equations, which come from different areas, serve as the mathematical basis, such as strap-down technology, coupling navigation, sensor error models, etc. With the help of the motion model 5 , the motion data are estimated for the acceleration, speed and position vectors , Oblique distances and their changes over time, distance differences, directions, distances or speeds along railways etc. can be used.

It is the job of the controller to ensure that the ge Estimated the course of the actual in reasonable amounts Recreate wise. For the design of the controller, for example show the theories of a Kalman filter or another Optimal filter and a Luenberger or non-linear observer be used aft.

The task of the support model 6 is the simulation of the support sizes and, if necessary, the fulfillment of mathematical constraints. Depending on the application, motion data such as acceleration, speed and position vectors, rotation speed and rotation vectors, oblique distances and their changes over time, distance differences, directions, distances or speeds along orbits etc. can be used. The mathematical basis of the model is expediently a set of equations.

The estimated movement data is sent to the controller. Here only the operating point of the controller 8 is influenced, but not the actual control function.

Fig. 2 shows an embodiment of a motion model 5 for measuring signals and a Inertialmeßsystems. The following stipulations have been chosen for naming sizes and coordinate systems:

The measurement signal u, for example of an inertial measurement system, reaches inverse sensor models 10 , 10 '. At the output of the sensor model 10 there is an inertial acceleration vector _b in the body-fixed system. This is transformed into the ECEF system by a matrix C _eb . In a subsequent compensation stage, apparent accelerations, such as Coriolis and centrifugal accelerations, are eliminated during the transformation. There follows an integration 13 to form the associated speed vector and an integration 14 to form the associated location vector. The location vector data and speed vector data arrive at the support model as part of the movement data. For the output of the result E, a conversion 15 is carried out into an earth-solderable system. In a similar manner, a transformation 16 of the speed vector for the output of the result E is carried out.

With the inverse sensor model 10 'rotational speeds are formed, which are processed in an integration stage 17 and converted in a conversion stage 18 into the usual yaw angle Ψ, pitch angle θ and roll angle Φ using the earth-fixed position from the output of the conversion stage 15 and also as part of the result E be issued. The data available before the conversion is transferred to the support model. For feedback purposes, the motion model also provides a transformation stage 19 through which a model of the acceleration due to gravity reaches the compensation stage 12 .

Fig. 2 illustrates that the controller 8 both on the Sensormo delle 10 , 10 'and the integrators 8 , 13 , 14 acts, that is, on all stages of the motion model 5 , which serve the modeling of the movement and not just one To calculate in different coordinate systems.

In order to supplement the qualitative explanation of the movement model according to FIG. 2 and the method according to FIG. 1, algorithms used according to standing are specified.

1. Position angle calculation

2. Speed, position calculation

3. Gravitational acceleration

With the names and numerical values from [4], the following applies according to [18]:

4. Watch model

Clock error Δt, derivative t = Δt _p , white noise w

5. Sensor models Support sizes

1. Position, speed support: (GPS, INS, MLS, camera support) conversions: ϕ, λ, h into [r _{x, e, e} , r _{y, e, e} , r _{z, e, e} ] ^T

6. Measuring functions

r _{x, e, e} INS position; Antenna position r * (GPS, DME,...): Gln. (48) (44)
r _{y, e, e} INS position; Antenna position r * (GPS, DME,...): Gln. (48) (44)
r _{z, e, e} INS position; Antenna position r * (GPS, DME,...): Gln. (48) (44)
v _{x, e, e} INS position; Antenna position r * (GPS, DME,...): Gln. (48) (44)
v _{y, e, e} INS position; Antenna position r * (GPS, DME,...): Gln. (48) (44)
v _{z, e, e} INS position; Antenna position r * (GPS, DME,...): Gln. (48) (44)

2. Range, range rate support: (GPS, DME, LORAN, laser tracker support) For each satellite / ground station, with the satellite position _{s, e} ,
Satellite clock error Δt _s

Measuring function range

see equations (48),
where Δt _{s is known} from the ephemeris
and Δt <0 means: Receiver clock comes first.
For laser trackers, Δt ≡ 0, Δt _s ≡ 0.

Measuring function range rate

see equations (48), (49) (addition of the weighted with direction cosine Components of the relative speed satellite receiver antenna)

Measuring function range difference

With difference vector INS-GPS antenna: _{d, b} = [r _{x, d, b} , r _{y, d, b} , r _{z, d, b} ] ^T applies

3. Height support

Measurement function: iteration according to equations (38)

4. Position support

GPS: position difference of several antennas to be displayed in the ECEF system:
C _{eb d, b} → Equations (48) with r _{x, e, e} = r _{y, e, e} = r _{z, e, e} = 0 apply
INS, magnetic field probe, etc.

The following applies: C _gb = C _ge · C _eb

5. Wheel sensor support

Supporting the route is only a "capped" speed support or - at well-known railway - a position support.

6. Angular support

Elevation angle: (ILS, laser tracker)

The same applies to the azimuth against the north direction (VOR, laser tracker)

Equation 48 in particular clarifies the calculation distance between the inertial sensor system (INS) and the satellite antenna (GPS) used in a commercial aircraft can be more than 30 m. While trying so far has been arranged to place both sensors at the same location if possible, to neglect a distance between the two sensors can, it is an inventive finding that the distance should be chosen as large as possible, as Improved care and the occurrence of singularities is suppressed more securely.

Claims (2)

1. A method for navigation with complementary evaluations of data from different measurement methods for a movable object ( 1 ), wherein

• - With a first measurement method with regard to the availability, advantageous measurement signals of a first sensor arrangement ( 2 ) and with a second measurement method, the accuracy of advantageous measurement signals of a second sensor arrangement ( 3 ) can be obtained,
• - The measurement signals (U) of the first sensor are applied to a simulation ( 5 ) of the movement of the object and movement data () are determined therefrom,
• the determined movement data () are entered into a simulation ( 6 ) of the second measurement method for generating simulated measurement signals () of the second sensor arrangement ( 3 ),
• - The simulated measurement signals () with actual measurement signals (y) of the second sensor arrangement ( 3 ) are compared,
• - from the comparison, the underlying simulation ( 5 ) of the movement of the object is corrected and
• - The movement data determined by the simulation ( 5 ) of the movement of the object ( 1 ) are output as output data (E) of the navigation method.

2. The method according to claim 1, characterized in that the sensor arrangements ( 2 , 3 ) for the two measuring methods are arranged at a distance from one another and that the distance for the simulation ( 6 ) of the second measuring method is taken into account.