Method for determining position in vehicle by receiving GNSS signals by calibrating GNSS receiving antenna based on multiple antenna

Abstract

The invention relates to a method for determining a position in a vehicle by receiving GNSS signals while calibrating a GNSS receiving antenna on the basis of a plurality of antenna error influence maps, the following steps being carried out: a) ascertaining at least one signal parameter of at least one GNSS signal; b) selecting at least two antenna error impact maps (1, 2) from the plurality of antenna error impact maps (1, 2); c) ascertaining at least one correction value for correcting the position determination from each of the selected at least two antenna error influence maps (1, 2), taking into account the signal parameters ascertained in step a); and d) performing the position determination using a Kalman filter, the correction value ascertained in step c) being taken into account.

Description

Technical Field

[0001] The invention relates to a method for determining a position in a vehicle by receiving GNSS signals while calibrating a GNSS receiving antenna based on a plurality of antenna error influence maps stored in the vehicle. In addition, a control device, a computer program and a machine-readable storage medium are described. The invention can be used in particular for GNSS-supported positioning systems for automatic or semi-automatic driving. Background Art

[0002] The Global Navigation Satellite System (abbreviation: GNSS) has a plurality of GNSS satellites that orbit the earth and transmit electromagnetic GNSS signals. The vehicle can be positioned and navigated by receiving the GNSS signals using an antenna.

[0003] When receiving GNSS signals using an antenna, it is important to consider that group delay variations or antenna error effects related to the azimuth and elevation angles relative to the GNSS satellites may impair the measurement accuracy of the GNSS signals (e.g. the accuracy of the transit time measurement). In other words, this means that the measurement accuracy of the GNSS signals is related to the relative position between the antenna and the corresponding GNSS satellite.

[0004] In addition to group delay variations as well as azimuth and elevation angles, the measurement accuracy of GNSS signals may additionally be impaired by the vehicle body surface, since different paint types, paint colors, dirt and / or weather conditions (e.g. wet paint when it rains, snowy paint when it snows) may have different reflection and attenuation characteristics, so that GNSS signals may be reflected by the vehicle body surface (especially in the case of bodies with silver metallic paint) and / or attenuated (especially in the case of snow-covered roofs) when received.

[0005] In the context of safety-critical automated driving functions, the positioning accuracy must be in the centimeter range. It is therefore desirable to take the above-mentioned impairments into account and compensate for them in the calculation of the navigation solution (eg position, velocity, time). Summary of the invention

[0006] Starting from this, the object of the present invention is to alleviate or at least partially solve the problems described with respect to the prior art. In particular, a method for determining the position in a vehicle by receiving GNSS signals while calibrating a GNSS receiving antenna taking into account vehicle properties, in particular the body surface, is to be specified. This is particularly important for automated driving, since automated driving places particularly high demands on the accuracy of the navigation solution.

[0007] This is facilitated by a method for determining a position in a vehicle by receiving GNSS signals while calibrating a GNSS receiving antenna based on a plurality of different antenna error influence maps stored in the vehicle, wherein the following steps are carried out:

[0008] a) obtaining at least one signal parameter of at least one GNSS signal;

[0009] b) selecting at least two antenna error influence diagrams from the plurality of antenna error influence diagrams;

[0010] c) determining at least one correction value for correcting the position determination from each of the at least two selected antenna error influence maps, taking into account the signal parameters determined in step a); and

[0011] d) Carrying out a position determination using a Kalman filter, taking into account the correction value ascertained in step c).

[0012] As mentioned at the beginning, the measurement accuracy of GNSS signals depends on a number of factors. Important factors are, for example, the azimuth and elevation of the corresponding GNSS satellites (i.e. the relative position between the antenna and the GNSS satellites) as well as atmospheric influences, reflections and deflections of GNSS signals, etc. It has been shown that the measurement accuracy of GNSS signals is also dependent on vehicle characteristics (e.g. different paint jobs), and in particular there is also a correlation with the orientation of the vehicle in space (i.e. the azimuth and elevation). Depending on the orientation of the vehicle relative to the current GNSS satellite constellation, different signal deviations occur, which lead to erroneous or shifted positioning / position determination. With the proposed method, GNSS signals can be measured and position determination can be performed using the measured GNSS signals not only while taking into account the azimuth and elevation (relative to the vehicle orientation or antenna orientation), but also while taking into account the vehicle characteristics, so that the positioning accuracy and the overall performance of GNSS-supported navigation can be improved. Here, the vehicle characteristics are taken into account based on signal parameters, wherein in this case, the signal parameters are especially the azimuth and elevation.

[0013] In order to prepare the proposed method, a plurality of different antenna error influence diagrams can be created in the case of different vehicle characteristics using measurement technology, for example, by determining the measurement errors of the GNSS signals on a rotating disk using vehicles with different paint finishes. By rotating the rotating disk, the measurement errors at different azimuths and elevations can be determined, and the measurement errors determined at the corresponding azimuths and elevations can be registered in the antenna error influence diagram. In the case of different vehicle characteristics, the measurement errors determined at the same azimuth and elevation can also have different sizes. These differences in the size of the measurement errors may be caused (only) by different vehicle characteristics, because the measurement errors at the same azimuth and elevation are determined under different vehicle characteristics. Therefore, the antenna influence diagrams used here may differ from each other, so that the measurement errors registered on different antenna influence diagrams may have different sizes, although these measurement errors are associated with the same azimuth and elevation.

[0014] In order to correctly measure the GNSS signals during vehicle travel while also taking into account the vehicle's characteristics, it is important to use at least two different antenna influence diagrams, which may be different from one another. Preferably, the at least two different antenna influence diagrams each depict the maximum possible error or a particularly extreme filtering in a specific situation (for a specific azimuth and elevation angle). Thus, the actual measured value of the GNSS signal can be corrected using the measurement error, which is read from the at least two different antenna error influence diagrams with respect to the same azimuth and elevation angle of the GNSS satellite transmitting the GNSS signal.

[0015] The measured value of the GNSS signal is, for example, the runtime of the GNSS signal from the corresponding GNSS satellite to the GNSS receiving antenna. Based on the measured runtime and the speed of light, the distance between the GNSS satellite and the GNSS receiving antenna can also be calculated. Therefore, the antenna error influence diagram proposed here may include runtime measurement errors and / or distance measurement errors associated with azimuth and elevation.

[0016] Vehicle properties that are relevant for the measurement accuracy of GNSS signals are, in particular, the vehicle body surface, which can reflect and / or attenuate the GNSS signals to be received differently with different paint types, different paint colors and / or different dirt, so that the GNSS signals can be received with extended or shortened runtimes and runtime measurement errors and distance measurement errors can occur. In addition, under different weather conditions, the vehicle body surface can also have different reflection and / or attenuation characteristics due to moisture / snow adhering to the vehicle body surface. For example, in the case of rain, a vehicle body surface with wet paint can affect the GNSS signal more strongly due to reflection than a vehicle body surface with dry paint. In the case of snow falling on the vehicle, a vehicle body surface with snow-covered paint and a snow-covered roof can affect the GNSS signal more strongly due to attenuation than a vehicle body surface and a roof without snow.

[0017] In principle, the run time measurement errors and / or distance measurement errors caused by the azimuth and elevation angles and the vehicle characteristics belong to so-called "systematic errors" (i.e. non-random errors) and may occur regularly under certain circumstances according to a deterministic pattern. It is therefore possible to detect such run time measurement errors and / or distance measurement errors in advance by experiments and to correct the currently measured run time and / or distance using the previously detected run time measurement errors and / or distance measurement errors while the vehicle is traveling.

[0018] It can be provided that previously detected run time measurement errors and / or distance measurement errors are displayed in the form of an antenna error influence diagram. Such an antenna error influence diagram can be stored in the control device. In order to be able to use the run time measurement errors and / or distance measurement errors read from the antenna error influence diagram to correct the measured run time and / or calculated distance or a specific position / position determination while the vehicle is traveling, the control device can be a control device of a GNSS positioning system, with which the vehicle is equipped.

[0019] According to step a), at least one signal parameter of at least one GNSS signal is obtained. The signal parameters are, for example, the azimuth and elevation of the GNSS satellite that transmits the GNSS signal. It is possible here to determine, for example, from the ephemeris contained in the received GNSS signal which GNSS satellite the received GNSS signal comes from and the current manner / position of the GNSS satellite in the sky. In addition, the current driving direction of the vehicle determined by a sensor (e.g., an IMU sensor) and / or a camera can be used to determine the azimuth and elevation of the vehicle's GNSS receiving antenna relative to the GNSS satellite that transmits the received GNSS signal.

[0020] According to step b), at least two antenna error influence diagrams are selected from a plurality of antenna error influence diagrams. It is possible here to select only two extreme antenna error influence diagrams that have the greatest difference from each other. For this purpose, for example, the following extreme antenna error influence diagram can be selected so that it is pre-created using a silver metallic vehicle, while another extreme antenna error influence diagram can be selected so that it is pre-created using a matte black vehicle. It has been confirmed that just this combination of antenna error influence diagrams (silver vehicle and matte black vehicle) correspondingly detects particularly extreme error influences. Therefore, the currently measured GNSS signal can only be corrected with the aid of these two extreme antenna error influence diagrams. This is particularly advantageous because computer resources (e.g., computing time, memory requirements) can thereby be reduced to a minimum. It is also conceivable to select more than two antenna error influence diagrams so that the measured GNSS signal can be corrected using more than two selected antenna error influence diagrams. This is particularly advantageous for further improving the accuracy of the entire navigation solution.

[0021] According to step c), at least one correction value for correcting the position determination is determined from each of the at least two antenna error influence diagrams selected, taking into account the signal parameters determined in step a). In this case, the signal parameters serve as a bridge between the actual measurement value and the previously detected measurement error as a correction value. The correction value can be, for example, a runtime measurement error of the GNSS signal or a distance measurement error calculated from the runtime measurement error and the speed of light between the corresponding GNSS satellite and the GNSS receiving antenna, while the signal parameters determined in step a) are the azimuth and elevation of the corresponding GNSS satellite. For this purpose, the runtime measurement error and / or the distance measurement error associated with the azimuth and elevation angle can be read from the corresponding antenna influence diagram selected in step b) according to the azimuth and elevation angle determined in step a). The read runtime measurement error and / or the distance measurement error are used in step d) to determine the position.

[0022] Preferably, a correction value is determined for each GNSS signal considered. In this context, the correction values ​​determined from the antenna error influence diagrams are also referred to as a "set" of correction values. Preferably, for each GNSS signal used for determining the position, a correction value is determined using each of the at least two antenna error influence diagrams as a function of the corresponding signal parameters, in particular as a function of the corresponding azimuth and elevation angle relative to the vehicle. Thus, for a "set" of GNSS signals used for determining the position, preferably after step c) there are a plurality of correction value sets (based on the individual antenna error influence diagrams).

[0023] According to step d), position determination is performed using a Kalman filter, which includes combining the data corrected and / or estimated using the correction values ​​ascertained in step c).

[0024] A conventional Kalman filter essentially fulfills two tasks or performs two different determinations. The Kalman filter makes a prediction of the expected measurements in the next (future) time interval. The Kalman filter combines the predicted measurements with the actual measurements to determine the position to be output to the vehicle.

[0025] In order to use the correction value determined according to step c) for position determination, two different methods are proposed here.

[0026] According to a first possibility, in step d), correction values ​​from different antenna error influence maps are used together in a Kalman filter for position determination.

[0027] According to a second possibility, in step d), first positions are first determined separately from one another using a Kalman filter for each antenna error influence map selected in step b), and then the first positions are combined to form a position to be output.

[0028] In the first possibility, different antenna errors affect Figure 1 In principle, correction values ​​are set, which are respectively derived from particularly extreme antenna error influence maps, and the corrected measured values ​​are then combined with the predicted measured values ​​to form the position to be output.

[0029] In a second possibility, for example, each of the correction values ​​determined in step c) is first used to estimate the corresponding first position, and the estimated first positions are then combined to form the position to be output. The combination of the first positions to form the output position can also take place outside the Kalman filter, for example in the Kalman filter, the output position forms the intermediate position.

[0030] The described method allows measurement errors caused by the vehicle's body surface to be compensated purely on a software level without hardware outlay, without having to replace the GNSS receiving antenna already present in the vehicle (e.g., the vehicle antenna) with, for example, a high-precision geodetic antenna. Such compensation of measurement errors is particularly advantageous for automated driving, which requires positioning accuracy in the centimeter range.

[0031] It is also preferred that the first positions are weighted using the probability values ​​and that the weighted first positions are averaged to obtain the position to be output.

[0032] The first position and the position to be output have the same timestamp. The first position is only estimated internally. The position to be output is the final position and is sent to the vehicle by the GNSS control device. The first position is only an estimated possible position. A plurality of first positions can be combined, averaged and / or weighted to obtain the position to be output, wherein each first position is estimated using correction values ​​obtained from the selected antenna error influence map.

[0033] The estimated first positions are preferably weighted using the probability values, and the weighted first positions are averaged to form the position to be output.

[0034] If the estimated first positions do not differ significantly from each other, then alternatively the first positions may be directly averaged to obtain the position to be output (ie without weighting).

[0035] Preferably, the first position is weighted by means of the likelihood value, and the weighted first position is averaged to obtain the position to be output. The following sub-steps can be implemented here:

[0036] - an estimate of the first position,

[0037] - Obtain residuals using actual measured values,

[0038] - taking the likelihood from the residuals and from the measurement covariance, and

[0039] - determining the position to be output, averaging the first positions to obtain the position to be output, wherein the first positions are weighted by means of the likelihood value.

[0040] Preferably, the weighting of the first position is filtered by means of a PT1 filter with a time constant. Here, the time constant can be at least 30 seconds.

[0041] Preferably, the time constant is optimized with the aid of machine learning.

[0042] Preferably, the at least one signal parameter comprises a reception direction of the GNSS signal.

[0043] Preferably, the vehicle characteristics include at least a vehicle body surface.

[0044] Preferably, the antenna error influence map is created using the following steps:

[0045] i) providing a vehicle with a GNSS receiving antenna and a specific body surface on a rotatable plate,

[0046] ii) When the rotating plate rotates, the GNSS signal is measured using the GNSS receiving antenna,

[0047] iii) Calculate the measurement error based on the measured GNSS signal,

[0048] iv) creating an antenna error influence diagram which shows the measurement errors determined in step iii) in relation to different reception directions, and

[0049] v) Repeating steps i) to iv) to create multiple antenna error influence maps for vehicles with different body surfaces.

[0050] Furthermore, it is preferred that in step v) an antenna error influence map is created taking into account different weather conditions, wherein the weather conditions are simulated using snowy paint, wet paint or dry paint.

[0051] Since the reception of GNSS signals may be affected by the relative position between the GNSS receiving antenna and the corresponding GNSS satellite, according to step i), a vehicle having a GNSS receiving antenna is provided on a rotatable plate.

[0052] By rotating the plate, the vehicle rotates together. Using theodolite measurements, different orientations of the vehicle and different real positions and reception directions of the GNSS receiving antenna can be determined when the vehicle rotates.

[0053] According to step ii), the GNSS signal is measured using the GNSS receiving antenna, wherein in particular the transit time of the GNSS signal between the GNSS receiving antenna and the corresponding GNSS satellite is measured.

[0054] According to step iii), a measurement error between the measured value and the true value is determined. The measurement error is, for example, a run-time measurement error or a distance measurement error calculated using the run-time measurement error. The true value can be determined using known methods, for example based on the ephemeris of the corresponding GNSS satellite.

[0055] According to step iv), the measurement errors detected during the rotation in the different receiving directions are entered in the antenna error influence map according to the corresponding receiving direction. In the antenna error influence map, the different receiving directions can be shown by the corresponding azimuth and elevation angles.

[0056] As mentioned above, vehicle body surfaces with different coating types and / or colors may have different effects on the vehicle antenna receiving GNSS signals. In addition, the reception of GNSS signals may be affected by the coating being covered with more or less snow, moisture and / or dirt.

[0057] Thus, according to step v), different antenna influence diagrams can be created with different vehicles, which are of the same model but have different body surfaces. The body surfaces may differ in at least one of the following aspects: coating type, color, snow, moisture and dirt.

[0058] Preferably, the vehicle body surface is selected such that it differs as significantly as possible in its reflection and / or attenuation properties in order to detect as many extreme measurement errors of the GNSS signal as possible by the measurement, which extreme measurement errors can then be further processed in the form of extreme antenna influence diagrams. The detected extreme measurement errors can represent limit values ​​of the actual measurement error which should not be exceeded during driving. The actual measurement error of the GNSS signal during driving is therefore usually between the detected extreme measurement errors, so that the actual measurement error or the actual measurement value can be compensated or corrected by mathematical methods, such as interpolation, merging, averaging and / or weighting.

[0059] For example, in the simplest case, an antenna error influence map can be created using a silver metallic wet vehicle, which may contain extreme measurement errors due to extreme reflection (compared to other colors). In contrast, another antenna error influence map can be created using a matte black dry vehicle, which may contain extreme measurement errors due to extreme attenuation (compared to other colors). During driving, the two extreme antenna error influence maps can be used to correct the current measurement values.

[0060] The control device is preferably designed to carry out the described method.

[0061] Furthermore, it is preferred to use a computer program for executing the method described herein. In other words, this relates in particular to a computer program (product) comprising instructions which, when the program is executed by a computer, cause the computer to execute the method described herein.

[0062] Furthermore, it is preferred to use a machine-readable storage medium on which the computer program proposed here is stored. Typically, a machine-readable storage medium is a computer-readable data carrier. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Below with the help of Figure 1 The present invention and technical environment are described. It should be pointed out that Figure 1 is indicative. It should also be noted that Figure 1 The features shown do not necessarily have to be used in the combinations shown; instead, some features can be extracted and combined with other descriptions of the specification. Other contents should only apply if this is explicitly stated as a mandatory combination of features.

[0064] Figure 1 Schematic diagram showing two exemplary antenna error effects Figure 1 , 2, which are created using vehicles with different paint jobs. DETAILED DESCRIPTION

[0065] exist Figure 1 It can be seen that the relative position between the vehicle or the vehicle antenna and the corresponding GNSS satellite is shown by a circle with an angle between 0 and 360 degrees. The center point of the circle indicates, for example, that the GNSS satellite is located just above the vehicle or the vehicle antenna. The intersection of the diameter with the circumference at 90 degrees indicates that the GNSS satellite is directly above the horizontal line to the right of the vehicle. The intersection of the diameter with the circumference at 0 degrees indicates that the GNSS satellite is directly above the horizontal line in the direction of travel of the vehicle.

[0066] exist Figure 1 It can be further seen that under the same circumstances, the two antenna errors affect Figure 1 , 2 can have different distance measurement errors 3. For example, the first antenna error affects Figure 1 It is shown that the distance measurement error 3 in the lower left range between 180 degrees and 270 degrees is much larger (almost more than 1 meter) than the distance measurement error 3 in the same area of ​​the second antenna error influence diagram 2. These differences may be caused by different paints. During driving, the two antenna influences Figure 1, 2 can be used to correct the actual measurement value. In the correction, according to the relative position between the vehicle antenna and the GNSS satellite, the error influence of the two antennas is taken into account. Figure 1 , 2 Reading the associated distance measurement error. The relative position can be described in different known forms, for example using, for example, a coordinate system in the vehicle Figure 1 The angles shown are described as azimuth and elevation or receiving direction in the astronomical coordinate system. By using these two antenna error effects Figure 1 2. The measurement of GNSS signals and the determination of position using the measured GNSS signals can be performed taking into account relative position and taking into account vehicle characteristics, especially paint, thereby improving the positioning accuracy and overall performance of GNSS-supported navigation.

Claims

1. A method for determining a position in a vehicle by receiving GNSS signals while calibrating a GNSS receiving antenna based on a plurality of different antenna error influence maps (1, 2) stored in the vehicle, wherein: Implement the following steps: a) obtaining at least one signal parameter of at least one GNSS signal; b) selecting at least two antenna error influence diagrams (1, 2) from a plurality of antenna error influence diagrams (1, 2); c) determining at least one correction value for correcting the position determination from each of the at least two selected antenna error influence maps (1, 2) taking into account the signal parameters determined in step a); and d) Carrying out a position determination using a Kalman filter, taking into account the correction value ascertained in step c).

2. The method according to claim 1, wherein: In step d), correction values ​​from different antenna error influence maps (1, 2) are used together in said Kalman filter for position determination.

3. The method according to claim 1, wherein: In step d), firstly a first position is determined separately from one another for each antenna error influence map (1, 2) selected in step b) using a Kalman filter and then the first positions are combined into a position to be output.

4. The method according to claim 3, wherein: The first positions are weighted using the probability values, and the weighted first positions are averaged to obtain the position to be output.

5. The method according to claim 4, wherein: The first positions are weighted using the likelihood values, and the weighted first positions are averaged to obtain the position to be output.

6. The method according to claim 3 or 4, wherein: The weighting of the first position is filtered by means of a PT1 filter with a time constant.

7. The method according to claim 6, wherein: The time constants are optimized with the aid of machine learning.

8. A method according to any one of the preceding claims, wherein: The at least one signal parameter ascertained in step a) and taken into account in step c) comprises the direction of reception of the GNSS signal.

9. A method according to any one of the preceding claims, wherein: The vehicle characteristics include at least a vehicle body surface.

10. A method according to any one of the preceding claims, wherein: The antenna error influence diagrams (1, 2) are created using the following steps: i) providing a vehicle with a GNSS receiving antenna and a specific body surface on a rotatable plate, ii) when the rotating plate rotates, measuring the GNSS signal using the GNSS receiving antenna, iii) obtaining the measurement error (3) based on the measured GNSS signal, iv) creating an antenna error influence diagram (1, 2) which shows the measurement errors determined in step iii) in relation to different reception directions, and v) Repeat steps i) to iv) to create multiple antenna error influence maps (1, 2) for vehicles with different body surfaces.

11. The method according to claim 10, wherein: In step v), the antenna error influence maps (1, 2) are created taking into account different weather conditions, wherein the weather conditions are simulated using snowy paint, wet paint or dry paint.

12. A method according to any one of the preceding claims, wherein: In step b), two antenna error images (1, 2) having the largest difference with each other are selected. 13 . A control device, which is designed to execute the method according to claim 1 .

14. A computer program for executing the method according to any one of claims 1 to 9.

15. A machine-readable storage medium on which the computer program according to claim 14 is stored.

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