EP2936205A2 - Procédé pour fournir un signal gnss - Google Patents

Procédé pour fournir un signal gnss

Info

Publication number
EP2936205A2
EP2936205A2 EP13805855.7A EP13805855A EP2936205A2 EP 2936205 A2 EP2936205 A2 EP 2936205A2 EP 13805855 A EP13805855 A EP 13805855A EP 2936205 A2 EP2936205 A2 EP 2936205A2
Authority
EP
European Patent Office
Prior art keywords
signal
vehicle
gnss
gnss signal
position data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP13805855.7A
Other languages
German (de)
English (en)
Inventor
Ulrich STÄHLIN
Klaus Rink
Marc Menzel
Michael ZALEWSKI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Continental Teves AG and Co OHG
Original Assignee
Continental Teves AG and Co OHG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Continental Teves AG and Co OHG filed Critical Continental Teves AG and Co OHG
Publication of EP2936205A2 publication Critical patent/EP2936205A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/22—Multipath-related issues
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/28—Satellite selection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled

Definitions

  • the invention relates to a method for providing a GNSS signal, a control device for carrying out the method and a vehicle with the control device.
  • a method for providing a GNSS signal for position determination of a vehicle comprises the steps of receiving the GNSS signal, filtering the GNSS signal based on an environmental condition about the vehicle, and outputting the filtered GNSS signal.
  • GNSS signal for example, the above-mentioned GPS signal, a rjioöajibHa «HaBnraunoHHafl CnyTHMKOBaa Cnc eMa signal, short GLONASS signal and / or a Galileo signal can be used.
  • the specified method is based on the consideration that in the above-mentioned navigation device, the statistical properties of the error must first be measured in order to use this to improve the position estimate based on the GNSS signal.
  • the method is also based on the consideration that the error condition for the vehicle, such as shading effects, which could only be taken into account in the context of the above-mentioned navigation device for improving the signal, if the GNSS signal becomes worse due to the shadowing effects and the error due to the statistical recording becomes more detectable occurs.
  • the error in whatever form, can be reacted only a posteriori to a worse signal quality of the GNSS signal.
  • the stated method is based on the consideration of estimating the signal quality based on the ambient conditions affecting the signal quality.
  • an expected value for the signal quality of the GNSS signal which can be interpreted as a statistical property of a fault of the GNSS signal in the case of a deteriorating signal quality.
  • this expected error may be a priori reacted before the expected deterioration of the signal quality of the GNSS signal actually occurs.
  • the ambient condition can be detected by means of environmental sensors on the vehicle.
  • environment sensors should be understood in the context of further training, especially sensors from which can estimate possible shadowing of the GNSS signal.
  • sensors from which can estimate possible shadowing of the GNSS signal For example, camera sensors, radar sensors, lidar sensors or V2X sensors can be used, which are present anyway on modern vehicles, and thus do not require any structural modifications to the vehicle to implement the specified method.
  • the GNSS signal for filtering is weighted based on an output signal of the environment sensor.
  • the output signal could describe an object on a road on which the vehicle is traveling.
  • the aforementioned weighting could be based on the structure of the detected objects, which then essentially provides information about how much the GNSS signal is being shadowed.
  • a tunnel is expected to result in complete shadowing of all available GNSS signals, while trees near the road will shade at least some GNSS signal weakly.
  • house walls near the road can lead to complete shadowing of some GNSS signals and / or reflections of some GNSS signals.
  • the reception angle of the GNSS signal may also be taken into account so as to further enhance the estimated value for the estimated shading from the position of the object and the reception angle.
  • any ambient conditions such as Störsignalfeider could be detected to estimate the signal quality of the GNSS signal, the signal quality of the
  • the GNSS signal is filtered on the basis of an expected degree of shading of the GNSS signal by the object as ambient condition.
  • a method for determining a position of a vehicle based on a GNSS signal comprises the steps of providing the GNSS signal with a specified method and determining the position of the vehicle based on the provided GNSS signal.
  • the specified method comprises the
  • GNSS signal can also be used to check the calculated position of the vehicle based on the
  • GNSS signal could be used.
  • a change of the lane of the vehicle on the road tracked and so the GNSS signal can be made plausible.
  • the specified method comprises the steps of recording reference position data of the vehicle and more precisely the position of the vehicle by filtering the detected position of the vehicle based on the reference position data.
  • the reference position data could be dependent, for example, on vehicle dynamics data and / or odometry data of the vehicle. This refinement is based on the consideration that the reference position data based on the GNSS signal could be specified in a fusion filter, for example. This could be done, for example, by contrasting the reference position data with the GNSS signal itself in a filter, or position data derived from the GNSS signal, as opposed to the measurement data in an observer. Under such an observer can fall any filter that allows an analog or digital state observation of the vehicle. For example, a Luenberger observer can be consulted. If the noise is taken into account, a Kalman filter could be considered. If the shape of the noise is also to be taken into account, then possibly a
  • Particle filter which has a basic set of available noise scenarios and selects the to be considered in the elimination Rauschen scenario example, by a Monte Carlo simulation.
  • the observer is preferably a Kalman filter that provides an optimal result in terms of its necessary computational resources.
  • the precise position is approximated depending on an information content of the specific position.
  • the specified development is based on the consideration that the reference data could represent redundant position data in order to correct the position data derived from the GNSS signal. For this correction, however, a difference between the reference position data and the position data derived from the GNSS signal is required, in the context of which it would initially be unclear in which of the two available data the error lies. However, this can be determined by the aforementioned plausibility of the position data derived from the GNSS signal, and the position data derived from the GNSS signal can be assigned an integrity measure that is dependent, for example, on the information technology content of the message.
  • a method for outputting a measurement signal in a vehicle comprises the steps:
  • the specified method is based on the consideration that in a fusion sensor, as is known, for example, from the document WO 2011/098 333 AI, errors can only be detected if they have already occurred since the fusion sensor compares the sensor signal and the comparison sensor signal , Differences between both sensor signals interpreted as errors and eliminates the error in the form of feedback. This leads to a corresponding dead time, which can be avoided by estimating the error in advance and thus taking it into account before it actually occurs.
  • One of the two sensor signals may, for example, represent the above-mentioned reference position data derived from vehicle dynamics data from an inertial sensor, while the other sensor signal may represent the position data derived from a GNSS signal, for example, both sensor signals and the measurement signal representing a position of the vehicle absolute position, speed, acceleration and heading of the vehicle.
  • the filtering of the two sensor signals can be done with the filter described above.
  • a control device is set up to perform one of the specified methods.
  • the specified device has a memory and a processor.
  • a computer program comprises program code means for performing all the steps of one of the specified methods when the computer program is executed on a computer or one of the specified devices.
  • a computer program product includes program code stored on a computer-readable medium and, when executed on a data processing device, performs one of the specified methods.
  • a vehicle includes a specified controller.
  • 1 is a schematic diagram of a vehicle on a road
  • FIG. 2 is a schematic diagram of a fusion sensor in the vehicle of FIG. 1.
  • Fig. 1 shows a schematic diagram of a vehicle 2 on a road 4.
  • the vehicle 2 moves on the road 4 in a direction of movement 6.
  • this direction of movement 6 at the edge of the road 4 in front of the vehicle 2 is an object in the form of a building 10, to which the vehicle 2 moves.
  • the vehicle 2 is to receive a GNSS signal 12 via an antenna 11 of global satellite navigation system, hereinafter called GNSS, via several GNSS satellites, of which a GNSS satellite 14 is shown in FIG Trilateration known per se in Fig. 2 indicated position 16 of the vehicle 2 on the road 4 certain.
  • GNSS global satellite navigation system
  • the signal quality of at least one of the GNSS signals becomes worse, which may affect the accuracy of the particular location 16 of the vehicle 2.
  • the GNSS satellite 12 shown is obscured by the building 10 as the vehicle 2 moves in the direction of travel 6, thus shadowing the GNSS signal 12 from the vehicle rather than providing a sufficiently high signal quality for accurate determination of the GNSS signal 12 Position of the vehicle 2 can be used.
  • measures are to be taken sufficiently quickly to minimize the effects of the shadowing of the GNSS signal 12 as far as possible.
  • a camera 18 shown in Fig. 2 is arranged in the vehicle 2, which receives an image 20, which is seen in the direction of movement 6 of the vehicle 2 in front of the vehicle 2.
  • this image 20 the building 10 can be seen, whereby the upcoming shadowing of the GNSS signal 12 is recognizable by the building.
  • FIG. 2 shows a fusion sensor 22 in the vehicle 2 of FIG.
  • the fusion sensor 22 receives in the present
  • the above-mentioned layer 16 of the vehicle 2 in the form of data which may include an absolute position of the vehicle 2 on a lane 4.
  • the position data 16 from the GNSS receiver 6 may also include a speed of the vehicle 2 and its heading to the GNSS satellite 14. Since the location data 16 is derived from the GNSS signals 12, they are hereinafter referred to as GNSS location data 16.
  • the fusion sensor 22 is configured to increase the information content of the GNSS location data 16 derived from the GNSS signal 12 in a manner to be described. On the one hand, this is necessary since the GNSS signal 12 has a very high signal / noise band spacing and can thus be very inaccurate. On the other hand, as already described, the GNSS signal 12 is not always available due to shadowing.
  • the vehicle 2 for this purpose has an inertial sensor 26, which detects vehicle dynamics data 28 of the vehicle 2.
  • vehicle dynamics data 28 includes a longitudinal acceleration, a transverse acceleration as well as a vertical acceleration and a roll rate, a pitch rate and a yaw rate of the vehicle 2.
  • vehicle dynamics data 26 are used in the present embodiment to increase the information content of the GNSS position data 16 and, for example, the position and to specify the speed of the vehicle 2 on the lane 4.
  • the refined position data 30 can then be used by a navigation device 32 in the vehicle 2, for example, even if the GNSS signal 12 is no longer available, for example, by the shadowing building 10.
  • GNSS position data 16 can optionally also be used for wheel speed sensors 34, which detect the wheel speeds 36 of the individual wheels of the vehicle 2 which are not referred to in greater detail.
  • the filter 38 may be formed arbitrarily, a Kalman filter solves this task most effectively with a comparatively low computational resource claim. Therefore, the filter 38 below should preferably be a Kalman filter 38.
  • the caiman filter 38 is preceded by the more precise position data 30 of the vehicle 2 and comparison position data 40 of the vehicle 2.
  • the more precise position data 30 are generated in the present embodiment in a strapdown algorithm 42, known for example from DE 10 2006 029 148 A1, from the vehicle dynamics data 28. They contain more precise position information about the vehicle, but also other position data about the vehicle 2, such as its speed, its acceleration and its heading.
  • the comparison position data 40 are obtained from a model 44 of the vehicle 2, which is initially fed from the GNSS receiver 24 with the GNSS position data 16. From this GNSS position data 16, the comparison position data 40 which contains the same information as the specified position data 30 is then determined in the model 44.
  • the specified position data 30 and the comparison position data 40 differ only in their values.
  • the Kalman filter 38 calculates, based on the refined position data 30 and the comparison position data 40, a error budget 46 for the specified position data 30 and a error budget 48 for the comparison position data 40.
  • An error budget is understood below to mean a total error in a signal. which consists of several individual errors in the acquisition and transmission of the signal.
  • a corresponding error budget can be composed of errors in the satellite orbit, the satellite clock, the remaining refraction effects and errors in the GNSS receiver 24.
  • the error budget 46 of the refined position data 18 and the error budget 48 of the comparison position data 34 are then supplied according to the strapdown algorithm 36 and the model 44 for correcting the specified position data 30 and the comparison position data 40, respectively. That is, the refined location data 30 and the comparison location data 40 are iteratively adjusted for their errors. In an analogous manner, the error budget 48 of the comparison position data 40 also the
  • GNSS receiver 24 may be supplied so that it can erase the above-mentioned errors of the satellite orbit, the satellite clock and the remaining refraction effects iteratively.
  • GNSS system is also called GNSS.
  • the GNSS receiver 24 to a selection and correction device 50 and a
  • Trilateration device 52 on.
  • the selection and correction device 50 selects four GNSS signals 12 from all received GNSS signals 12. From the thus selected GNSS signals 54, not all of which are provided with a reference numeral in FIG. 2 for the sake of clarity, the GNSS position data 16 of the vehicle 2 are then determined in the trilateration device 52 in a manner known to the person skilled in the art.
  • the aforementioned selection of the GNSS signals 12 is made in the present embodiment based on a weighting of the GNSS signals 12, wherein the individual weighting factors may be determined based on the error budget 48. For this weighting, however, an error must be present that could be fed back. Until the If errors present back into the selection and correction device 50 of the GNSS receiver 24 are coupled, a time known to the person skilled in the art in which a faulty GNSS signal always misses an error in the GNSS position data 16 and thus in the specified position data 30 passes further enlarged.
  • the shadowing of the GNSS satellite 14 also represents a previously mentioned error, which would be reflected in the error budget 48 and thus in the fed back error.
  • the dead time can be bridged by the error is already detected in advance on the image 20, which receives the mentioned camera 18 in the direction of movement 6 in front of the vehicle 2.
  • the GNSS signals 12 could also be weighted and selected so that a faulty GNSS satellite 14 could be proactively detected. In this way, for example, the weighting of the in Fig. 1 until the GNSS satellite 14 is sorted out in time by the selection and correction device 50 of the GNSS receiver 24 before introducing errors into the GNSS position data 16 due to its shadowing.
  • the selection and correction device 50 of the GNSS receiver 24 receives the image 20 and carries out an object recognition (not shown in detail) on the image 20, which is known per se to a person skilled in the art.
  • the object recognition can be performed with respect to certain classes of objects. For example, these object classes can be split as follows:
  • GNSS signals 12 only partially shading trees. If a potential shading object, such as, for example, the building 10 shown in FIG. 1, is detected, then based on the comparison location data 40 (or other available location data of the vehicle 2), for example
  • the selector and corrector 50 of the GNSS receiver 24 may then successively increase the weighting of the affected GNSS signal 12 in the manner described above.
  • the above-described idea could alternatively or additionally also be implemented in the Kalman filter 38 (or any of the above-mentioned arbitrary other filters) in order to detect an error in the comparison position data 40 in comparison with the precisely defined data 30 before its occurrence.
  • the Kalman filter 38 could receive the image 20, recognize the shading objects in the image 20 and weight the comparison position data 40 in the event of an error such that the comparison position data 40 is less strong in the filtering of the comparison position data 40 and the specified position data 30 be taken into account.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

L'invention concerne un procédé pour fournir un signal de système mondial de satellites de navigation (54), ci-après dénommé signal GNSS (54), en vue de déterminer une position (30) d'un véhicule, ledit procédé consistant : - à recevoir un signal GNSS non filtré (12), - à filtrer le signal GNSS non filtré (12) sur la base d'un élément d'environnement (20) à proximité du véhicule (2), et - à produire en sortie le signal GNSS filtré (54).
EP13805855.7A 2012-12-20 2013-12-12 Procédé pour fournir un signal gnss Withdrawn EP2936205A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102012224104.3A DE102012224104A1 (de) 2012-12-20 2012-12-20 Verfahren zum Bereitstellen eines GNSS-Signals
PCT/EP2013/076359 WO2014095558A2 (fr) 2012-12-20 2013-12-12 Procédé pour fournir un signal gnss

Publications (1)

Publication Number Publication Date
EP2936205A2 true EP2936205A2 (fr) 2015-10-28

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP13805855.7A Withdrawn EP2936205A2 (fr) 2012-12-20 2013-12-12 Procédé pour fournir un signal gnss

Country Status (7)

Country Link
US (1) US10353076B2 (fr)
EP (1) EP2936205A2 (fr)
JP (1) JP6815078B2 (fr)
KR (1) KR102200624B1 (fr)
CN (1) CN104871032A (fr)
DE (1) DE102012224104A1 (fr)
WO (1) WO2014095558A2 (fr)

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Also Published As

Publication number Publication date
CN104871032A (zh) 2015-08-26
KR102200624B1 (ko) 2021-01-08
US20150309181A1 (en) 2015-10-29
US10353076B2 (en) 2019-07-16
KR20150097712A (ko) 2015-08-26
WO2014095558A2 (fr) 2014-06-26
DE102012224104A1 (de) 2014-06-26
JP2016504584A (ja) 2016-02-12
JP6815078B2 (ja) 2021-01-20
WO2014095558A3 (fr) 2014-09-12

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