WO2016041963A1 - Localisation redondante au moyen d'un signal gnss - Google Patents

Localisation redondante au moyen d'un signal gnss Download PDF

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Publication number
WO2016041963A1
WO2016041963A1 PCT/EP2015/071099 EP2015071099W WO2016041963A1 WO 2016041963 A1 WO2016041963 A1 WO 2016041963A1 EP 2015071099 W EP2015071099 W EP 2015071099W WO 2016041963 A1 WO2016041963 A1 WO 2016041963A1
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WO
WIPO (PCT)
Prior art keywords
data
filtering
gnss
vehicle
position data
Prior art date
Application number
PCT/EP2015/071099
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German (de)
English (en)
Inventor
Ulrich STÄHLIN
Original Assignee
Continental Teves Ag & 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 & Co. Ohg filed Critical Continental Teves Ag & Co. Ohg
Publication of WO2016041963A1 publication Critical patent/WO2016041963A1/fr

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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
    • G01S19/38—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
    • 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
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Definitions

  • the invention relates to a method for determining position data of a vehicle based on a GNSS signal received by a global navigation satellite system GNSS, a control device for performing the method and a vehicle with the control device.
  • a control device for performing the method
  • a vehicle with the control device From WO 2011/098 333 AI it is known to use different sensor sizes in a vehicle in order to improve existing sensor sizes or to generate new sensor sizes and thus to increase the detectable information. It is the task to improve the use of several sensor sizes to increase information.
  • a method for determining, in particular, a positional data of the vehicle which includes an absolute position of a vehicle based on a GNSS signal mentioned by a GNSS comprises the steps of determining a first position-dependent intermediate result based on a first filtering of the GNSS signal GNSS signal based on a second to a first Tasktra ⁇ cleaning function, determining a second off from the position data pending intermediate result
  • the given method is based on the consideration that modern localization systems rely on a combination of GNSS global navigation satellite systems such as GPS, GLONASS, Galileo, etc., and vehicle dynamics sensors such as yaw rate sensors, acceleration sensors, steering wheel angle sensors, wheel speed sensors, etc. are based.
  • GNSS global navigation satellite systems such as GPS, GLONASS, Galileo, etc.
  • vehicle dynamics sensors such as yaw rate sensors, acceleration sensors, steering wheel angle sensors, wheel speed sensors, etc. are based.
  • a typical solution for this is the loosly coupled GNSS system in which the position of the vehicle descriptive intermediate result is first determined by a GNSS which is subsequently oniert for precision with the measurement result ⁇ from the driving dynamics sensors of the vehicle fusi ⁇ .
  • Tightly Coupled GNSS system in which the raw data that can be received via the GNSS is directly fused with the driving dynamics sensors.
  • the stated method is based on the knowledge that although the redundancy of the various measurement data, ie GNSS data and vehicle dynamics data, there is no redundancy for the actual calculation rule for determining the position data.
  • the specified method attacks with the proposal, not just to set a calculation rule in the determination of the position data, so for example, the above-mentioned Loosly Coupled GNSS system or the Tightly Coupled GNSS system. Rather, different calculation rules should be used in parallel when determining the position data of the vehicle.
  • the first transfer function comprises an explicit calculation rule and / or the second transfer function comprises a recursive calculation rule.
  • an explicit history is formed, which means that the entspre ⁇ -reaching calculation rule is implemented in an arithmetic logic unit with no memory, so that there is no internal feedback of intermediate results in the calculation method. All data necessary for the determination of the position data must therefore be fed to the arithmetic unit in the case of an explicit history.
  • filters without recursion such as finite impulse response filters called FIR filters, operate with an explicit history.
  • FIR filters finite impulse response filters
  • Such rake ⁇ rules are generally based on state variables and can observers, Kalman filter, particle filter, alpha-beta filter, alpha-beta-gamma filter, or the like.
  • Kalman filter For example, recursive computational rules are implemented in infinite impulse response filters called HR filters.
  • the calculation rule in the first filtering and the second filtering comprises a position calculation rule for
  • the position calculation rule and the fusion prediction are loosely coupled in a transfer function and closely coupled in another transfer function. Under a loose coupling is to be understood hereinafter that the posi tion ⁇ calculation rule and the fusion procedure only have a low degree of interdependence. This includes, for example, the previously mentioned Loosly Coupled GNSS.
  • a close coupling should be understood to mean a high degree of dependence between the position calculation rule and the fusion rule.
  • This includes, for example, the above-mentioned Tightly Coupled GNSS.
  • the so-called Deeply Coupled GNSS and the so-called Ultra Coupled GNSS are also based on a tight coupling.
  • input data of the first filtering of input data of the second filtering differ from one another.
  • all possible usable input data could be used at one of the two filters, while in
  • a subset of the input data is used.
  • a subset is defined by special relationships of the sensors.
  • Another Ausgesaltung could include in the first subset GNSS and wheel speeds and in the second subset GNSS, accelerations and rotation rates.
  • parameters for filtering are specified explicitly and adaptive for further filtering.
  • Parameters are parameters in filters that are used to influence the input variables when converting them into output variables, for example by weighting.
  • Explicit parameters are usually set in the configuration of the arithmetic unit or filter executing the filtering, and the appropriate engineer can explain the parameters with physical relationships.
  • adaptive parameters are obtained by means of learning or adaptation methods, which are not necessarily direct Find correspondence in physical quantities. Examples of systems with adaptive parameters are polynomial approaches, neural networks or Bayesian filters.
  • the embodiments of the two filters for determining the insects can be combined in any way. Particularly advantageous are combinations of pliziter in ⁇ and explicit history, as this can be detected errors due to ill-defined internal historical data.
  • the specified method comprises the step of determining a tolerance between the two intermediate results.
  • the tolerance makes it possible to compare the two intermediate results. By comparing the intermediate results obtained with the different Filter ⁇ conclusions or transfer functions or calculation rules, it can be concluded on the quality of location data ultimately determined.
  • the intermediate results are compared, for example, by subtraction. Not too fast to be reliable
  • the different filterings are additionally assigned confidence values, the intermediate results from the filterings for a reliability evaluation being weighted with the respective confidence value.
  • driving dynamics sensors could be trusted more than the GNSS data or a tightly coupled GNSS system more than a Loosly Coupled GNSS system.
  • the intermediate results could be fused in a separate fusion filter.
  • the specified method comprises the step of outputting an error if the tolerance fulfills a predetermined condition.
  • the predetermined condition could be defined in such a way that one or more intermediate results fall out of the previously explained tolerance band in which the intermediate results should be located. This would be a way to respond to it if the results of ⁇ It is not within a tolerance band.
  • a further predetermined condition may be that the interim ⁇ rule results over a predefined period of time permanently outside the tolerance range.
  • the filters used could be evaluated, outputting the intermediate result of the "dominant" filtering, optionally combined with the lower reliability.
  • filtering using explicit history may be considered superior or "dominant" over filtering using an implicit history, and hence preferred when both have the same basic modeling accuracy, but it is reasonable to choose which filtering to pass and thus prefers to perform measurements with additional reference systems so as to perform the evaluation based on an objective comparison and obtain a preference derived therefrom.
  • the interim results from the first filtering and the second filtering should be as similar as possible.
  • the intermediate results could of course be even ⁇ already a Location Date as his example, the absolute position.
  • the intermediate results may comprise further measured variables, such as VELOCITY ⁇ speeds and / or accelerations and / or the time course of the measured values. If the temporal course of a measured variable dependent on the position data is also recorded in the intermediate results, additional statistical outliers (engl., Outlier) can be filtered out.
  • the evaluation could be carried out within the scope of the specified method with a further, broadly defined tolerance band. If there are again sufficient intermediate results in this further tolerance band, downstream devices using the position data in the vehicle can decide based on the broader defined tolerance band whether the accuracy now given will continue to be sufficient for their purposes. In this way, however, a 100% reliability can be achieved in combination with the greater inaccuracy.
  • reliability can also be provided based on a tolerance band taken from the accuracy of the filtering that has received the best rating and is therefore considered dominant.
  • multiple reliabilities may be provided, once based on a tight tight tolerance band and then based on tolerance bands derived from the accuracies of one or more methods.
  • the positional data actually to be determined on the basis of the intermediate results can be supplied in various ways together with the result of the previously explained evaluation:
  • a specific filtering could be selected and its intermediate result used as the basis for determining the positional data.
  • the location data could also be determined based on the intermediate value of the selected filtering.
  • the above-mentioned evaluation of the individual filters could be used, wherein preferably the most highly rated, ie the dominant filtering should be selected or the filtering that currently provides the greatest accuracy.
  • the location data could be determined based on the mean or median of the intermediate results from the different filters.
  • the individual intermediate results in the formation of the mean value can also be weighted with the weights to the individual filtering, for example, from ⁇ dependence of the current accuracy of the filtering and / or the number of the input variables and / or the distance can be set to the other values .
  • the individual weights could also be fixed, for example by empirical investigations in measurements with a reference measuring technique.
  • the two aforementioned concepts for outputting the position data can be combined based on the intermediate results. That is, first of all, a certain number of intermediate results could be selected from different filters based on given criteria and then these selected intermediate results could be averaged.
  • the intermediate results for determining the tolerance can be weighted.
  • a control device is set up to perform one of the specified methods.
  • the specified device has a memory and a processor on.
  • a specified method in the form of a computer program is stored in the memory and the processor is provided for carrying out the method when the computer program is loaded from the memory into the 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 comprises a program code which is stored on a data carrier and the compu ⁇ terlesbaren, when executed on a data processing device, carries out one of the methods specified.
  • a vehicle includes a specified controller.
  • Fig. 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, show.
  • Fig. 1 shows a schematic diagram of a vehicle 2 with a chassis 4, the wheels on the sixth worn.
  • a fusion sensor 8 is arranged in the vehicle 2.
  • the fusion sensor 8 receives in the present
  • Ausu-form via a known GNSS receiver 10 also raw data position data 12 of the vehicle 2, which can describe an absolute position of the vehicle 2, a speed of the vehicle 2 and / or an acceleration of the vehicle 2.
  • the location data 12 from the GNSS receiver 10 in the present embodiment is emitted from GNSS satellites in a manner known to those skilled in the art
  • GNSS location data 12 called.
  • GNSS location data 12 refers to the relevant literature on GNSS.
  • the fusion sensor 8 is designed in a manner to be described to increase the information content of the GNSS position data 12 derived from the GNSS signal 14. This is on the one hand necessary because the GNSS signal 14 have a very low Sig nal ⁇ / noise band gap and may be very inaccurate. On the other hand, the GNSS signal 14 is not always available.
  • the vehicle 2 for this purpose has a motion sensor in the form of an inertial sensor 18, which detects driving dynamics data 20 of the vehicle 2.
  • driving dynamics data 20 include, as is known, a longitudinal acceleration, a lateral acceleration as well as a vertical acceleration and a roll rate, a pitch rate and a yaw rate of the vehicle 2.
  • all of this vehicle dynamics data 20 is used, either all or only part thereof, for the information content of the GNSS position data To increase 12 and, for example, to specify the absolute position and the speed of the vehicle 2 on the roadway 13.
  • the more precise location data 22 can then be used by an application device (for example navigation device or driver assistance system) 24 even if the GNSS signal 14 is not available at all under a tunnel, for example.
  • an application device for example navigation device or driver assistance system
  • GNSS position data 12 can be used in the present embodiment nor wheel speed sensors 26 which detect the wheel speeds 28 of the individual wheels 6 of the vehicle 2 and are also fused in the fusion sensor 8 with the GNSS signal.
  • Fig. 2 shows a schematic diagram of the fusion sensor 8 of Fig. 1.
  • the measurement data already mentioned in FIG. 1 enter into the fusion sensor 8.
  • the fusion sensor 8 should output the precise position data 22.
  • the basic idea of the fusion sensor 8 is to compare the information from the GNSS position data 12, the driving dynamics data 20 from the inertial sensor 18 in a first filter 30 and a second filter 32 and thus a signal / noise band spacing in the GNSS position data 12 of the
  • the filters 30, 32 may be formed as desired, a Kalman filter solves this task most effectively with a relatively low computational resource claim. Therefore, the filters 30, 32 below should preferably be Kalman filters 30, 32.
  • Absolute position of the vehicle 2, the more precise Geschwin ⁇ speed of the vehicle 2 and / or the specified acceleration of the vehicle 2 are in the present invention via a first filtering 34 with the first Kalman filter 30 and a second filtering 36 with the second Kalman filter 32 determined.
  • the two filtering operations 34, 36 give as a result of the above measurement data corresponding to a first intermediate result 38 and a second intermediate result from 40, both 38, 40 already write Zvi ⁇ rule results the position of the vehicle 2 loading.
  • the two intermediate results 38, 40 are then compared in an evaluation device 42 and based on this comparison, the actual precise position data 22 are formed. Before going any further should be gone first, the two filters 34, 36 will be explained in more detail.
  • the first intermediate result 38 and first comparison position data 44 of the vehicle 2 enter the first Kalman filter 30.
  • the first intermediate result 22 is generated in the present embodiment in a strapdown algorithm 46, known for example from DE 10 2006 029 148 A1, from the vehicle dynamics data 20.
  • the first comparison position data 44 are obtained from a first model 48 of the vehicle 2, which is initially fed with position data 50, which are determined from the GNSS position data 12, ie the GNSS raw data, in a position determination device 52.
  • the first comparison position data 44 From these position data then in the model 48 determines that contain the same information as the first intermediate result 38.
  • the first interim ⁇ rule result 38 and the first comparison position data 44 differ from the first model 30 is ultimately only in their values ,
  • the first Kalman filter 30 calculates, based on the first intermediate result 38 and the comparison position data 44, a fault budget 54 for the first intermediate result 38 and a fault budget 56 for the comparison situation data 44.
  • a fault budget is understood to mean a total error in a signal resulting from various single errors in the acquisition and transmission of the signal.
  • a corresponding error budget can be composed of errors of the satellite orbit, the satellite clock, the remaining refraction effects and errors in the GNSS receiver 10.
  • the error budget 54 of the first intermediate result 38 and the error budget 56 of the comparison position data 44 are then supplied according to the strapdown algorithm 46 and the model 48 for correcting the first intermediate result 38 and the comparison position data 44, respectively. That means the first one Interim result 38 and the comparison data 48 are iteratively adjusted for their errors.
  • the first filtering 34 in the fusion filter 8 operates on the basis of a Loosly Coupled GNSS principle, within which the determination of the position data 50 from the GNSS position data 12 and thus from the GNSS raw data and the precision of the position data 50 based on the vehicle dynamics data 20 in succession happen.
  • a Loosly Coupled GNSS filtering like the first filtering 34, can in the manner described above, the driving dynamics data 20 of the vehicle 2, by the
  • Inertialsensor 18 are detected based on the
  • the GNSS position data 12 and thus the GNSS raw data enter into the second Kalman filter 32 in addition to the second intermediate result 40 and second comparison position data 58 of the vehicle 2.
  • the second intermediate result 40 is generated again in a strap-down algorithm 46 from the driving dynamics data ⁇ 20th
  • the second comparison position data 58 are obtained from a second model 60 of the vehicle 2, which in the present embodiment, the second comparison position data 58 based on the still to be described error budget 62 from the second Kaiman filter 32 and the wheel speeds 28, without the GNSS Position data 12, that is, the GNSS raw data determined.
  • the second intermediate result 38 and the second comparison position data 58 differ only in their values.
  • the second Kalman filter 32 is calculated based on the second intermediate result 40 and the second comparison position data 58 to the error budget 62 for the second comparison position data and an error budget 64 for the second intermediate result 40.
  • Ge ⁇ contrast to the first Kalman filter 30 in the first filtering 34
  • the second Kalman filter 32 also takes into account the GNSS signal 12 and thus the GNSS raw data when generating the error households 62, 64. That is, the absolute position of the vehicle 2 derivable from the GNSS is taken into account for the first time in the error budget 62, 64 during the second filtering 36, while the absolute position within the first filtering 34 is already taken into account in the first model 48 before the modeling.
  • the error budget 64 of the second intermediate result 40 and the error budget 62 of the second comparison position data 58 are then corresponding to one
  • the second filtering 34 in the fusion filter 8 operates on the basis of a tightly coupled GNSS principle, in the context of which the determination of the absolute position of the vehicle from the
  • the redundant intermediate results 38, 40 can then be monitored in a tolerance monitor 66 in the evaluation device 42 become.
  • the tolerance monitor 66 may then, for example, output the tolerance between the two intermediate results 38, 40 as information for all downstream devices using the more precise location data 22, such as the application device 24.
  • the tolerance monitor 66 may also generate an error signal 68 if the tolerance exceeds a predetermined level.
  • the intermediate results 38, 40 can be weighted, for example, application-specifically with corresponding weighting factors 70, 72 and then averaged in an average value filter 74.

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  • 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)

Abstract

L'invention concerne un procédé permettant de déterminer des données de position (22) d'un véhicule (2) sur la base d'un signal GNSS (système mondial de navigation par satellite) (12) reçu d'un système GNSS, le procédé comprenant les étapes suivantes : détermination d'un premier résultat intermédiaire (38) fonction des données de position (22) sur la base d'un premier filtrage (34) du signal GNSS (12) au moyen d'une première fonction de transmission, détermination d'un second résultat intermédiaire (40) fonction des données de position (22) sur la base d'un second filtrage (36) du signal GNSS (12) selon une seconde fonction de transmission différente de la première fonction de transmission, et production d'un résultat final pour les données de position (22) sur la base d'une comparaison (42) entre le premier résultat intermédiaire (38) et le second résultat intermédiaire (40).
PCT/EP2015/071099 2014-09-17 2015-09-15 Localisation redondante au moyen d'un signal gnss WO2016041963A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102014218703.6 2014-09-17
DE102014218703.6A DE102014218703A1 (de) 2014-09-17 2014-09-17 Redundante Lokalisierung mittels GNSS-Signal

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Families Citing this family (3)

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Publication number Priority date Publication date Assignee Title
DE102016122193A1 (de) * 2016-11-18 2018-05-24 Valeo Schalter Und Sensoren Gmbh Funktionsüberwachung einer Sensoreinrichtung eines Kraftfahrzeugs
DE102018204304A1 (de) * 2018-03-21 2019-09-26 Robert Bosch Gmbh Verfahren zur satellitengestützten Ermittlung einer Fahrzeugposition mittels eines Bewegungs- und Positionssensors
DE102022214432A1 (de) 2022-12-29 2024-07-04 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Überwachen einer Eigenbewegungszustandsschätzung eines Fahrzeugs

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EP1205732A2 (fr) * 2000-11-10 2002-05-15 Thales Centrale inertielle de navigation comportant un récepteur GPS intégré
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US20070222674A1 (en) * 2006-03-24 2007-09-27 Containertrac, Inc. Automated asset positioning for location and inventory tracking using multiple positioning techniques
US20100049439A1 (en) * 2006-11-07 2010-02-25 Electronics And Telecommunications Research Institute Apparatus for integrated navigation based on multi filter fusion and method for providing navigation information using the same
US20130282875A1 (en) * 2008-05-02 2013-10-24 James Aweya Method and apparatus for time and frequency transfer in communication networks
DE102012224109A1 (de) * 2012-12-20 2014-06-26 Continental Teves Ag & Co. Ohg Vorrichtung zum Orten eines Fahrzeuges

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DE102006029148B4 (de) 2006-06-24 2010-08-05 Lfk-Lenkflugkörpersysteme Gmbh Verfahren zur Überprüfung einer inertialen Messeinheit von Fahrzeugen, insbesondere von Luftfahrzeugen, im stationären Zustand
DE102010063984A1 (de) 2010-02-11 2011-08-11 Continental Teves AG & Co. OHG, 60488 Fahrzeug-Sensor-Knoten
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US6408245B1 (en) * 2000-08-03 2002-06-18 American Gnc Corporation Filtering mechanization method of integrating global positioning system receiver with inertial measurement unit
EP1205732A2 (fr) * 2000-11-10 2002-05-15 Thales Centrale inertielle de navigation comportant un récepteur GPS intégré
US20070222674A1 (en) * 2006-03-24 2007-09-27 Containertrac, Inc. Automated asset positioning for location and inventory tracking using multiple positioning techniques
US20100049439A1 (en) * 2006-11-07 2010-02-25 Electronics And Telecommunications Research Institute Apparatus for integrated navigation based on multi filter fusion and method for providing navigation information using the same
US20130282875A1 (en) * 2008-05-02 2013-10-24 James Aweya Method and apparatus for time and frequency transfer in communication networks
DE102012224109A1 (de) * 2012-12-20 2014-06-26 Continental Teves Ag & Co. Ohg Vorrichtung zum Orten eines Fahrzeuges

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