Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
  • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
  • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
  • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
  • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
  • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
  • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
  • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
  • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
  • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
  • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
  • G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
  • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
  • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
  • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
  • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
  • G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
  • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
  • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
  • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
  • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
  • G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
  • G01C21/185—Compensation of inertial measurements, e.g. for temperature effects for gravity

Definitions

• the present invention relates to the field of vehicle navigation methods. It relates more particularly to so-called hybrid navigation methods.
• Hybrid navigation methods are methods in which measurements from several sensors (accelerometers, gyroscopes, GPS, etc.) are merged in order to determine kinematic variables or information defining the state of a device implementing the method.
• kinematic variables are, for example, a position, a speed or an orientation of the device.
• the measurements are, for example, inertial measurements, for example obtained from accelerometers and gyroscopes, such as the specific force, the angular speed or speed of rotation of the device, speed measurements or position measurements of the device.
• the specific force is the sum of the external forces experienced by the device, other than gravitational, divided by the mass. This quantity therefore has the dimension of an acceleration
• invariant filtering simplifies the alignment phase for the hybridizations to which his theory applies, but also has limitations in the case of inertia-altimetry fusion. More precisely, the combination of invariant filtering with long phases of inertia-altimetry fusion interspersed with rare position measurements does not provide theoretical guarantees of filtering convergence. Generally the performances of the invariant filtering are inferior to a classical extended Kalman filter for the inertia-altimetry fusion.
• the invention proposes to remedy the aforementioned drawbacks.
• the invention proposes, according to a first aspect, a method for aiding the navigation of a vehicle equipped with a navigation device comprising the following steps: acquisition of a priori values of kinematic variables of the navigation device , determination of respective current values of the kinematic variables of the navigation device and of a current uncertainty matrix representative of an uncertainty of the respective current values of the kinematic variables, from respective previous values of the kinematic variables, of a matrix of previous uncertainty representative of an uncertainty respective previous values of the kinematic variables and of a fictitious model of an earth gravity undergone by the navigation device, an intensity of the modeled gravity being increasing with an altitude of the navigation device, determining a correction from the values respective current values of the kinematic variables and of a measurement and updating of the respective current values of the kinematic variables from the correction and the current uncertainty matrix.
• this method makes it possible to determine the value of the kinematic variables of the navigation device.
• it makes it possible to control the duration of the initial static phase, also called alignment, at start-up.
• the kinematic variables comprise an orientation of the navigation device, a current value of which is a current orientation matrix and a previous value of which is a previous orientation matrix, a speed of the navigation device, a current value of which is a current speed vector and a previous value is a previous speed vector and a position of the navigation device, a current value of which is a current position vector and a previous value is a previous position vector.
• the current uncertainty matrix is representative of an uncertainty of the current orientation matrix, of the current velocity vector and of the current position vector
• the previous uncertainty matrix is representative of an uncertainty of the previous orientation matrix , the previous speed vector and the previous position vector.
• the current values are associated with a current instant and the previous values are associated with a previous instant.
• the determination of the current values of the kinematic variables comprises a determination of the current speed vector by adding to the previous speed vector an integration, over a time interval between the previous instant and the current instant, of a sum of a force specific to navigation device and gravity modeled, a determination of the current position vector by adding to the previous position vector an integration, over the time interval, of the previous velocity vector, a determination of the current orientation matrix by multiplication of the previous orientation matrix with a matrix representative of a rotation of the navigation device, a determination of the current uncertainty matrix from the previous uncertainty matrix.
• the determination of the correction includes a subtraction of the current velocity vector and the measurement, and a multiplication by a gain matrix.
• the determination of the correction includes a subtraction of the current position vector and the measurement, and a multiplication by a gain matrix.
• the correction is a correction vector
• the update comprises a sub-step of updating the current orientation matrix by multiplying a rotation matrix of a first part of the vector of correction and the current orientation matrix, a sub-step of updating the current speed vector by adding to the speed vector a multiplication of the current rotation matrix and a second part of the correction vector and a substep of updating the current position vector by adding to the current position vector a multiplication of the current rotation matrix and a third part of the correction vector.
• the determination of kinematic variables of the navigation device comprises a step of determining the fictitious model of the gravity undergone using the formula where g n (X n ) is a modeled gravity vector, g real is an opposite of a module of an earth gravity from a physically consistent model, r T is an earth radius,
• X n is the current position vector and is a measured altitude of the device.
• the determination of kinematic variables of the navigation device comprises a step of determining the fictitious model of the gravity undergone using the formula where g n (X n ) is a modeled gravity vector, real g is an earth gravity vector from a physically consistent model, r T is an earth radius, X n is the current position vector, is a measured altitude of the navigation device, alt(X n ) is an altitude of the navigation device determined from the current position vector and is a modified position vector in which the altitude is the measured altitude.
• a navigation device comprising a processing unit, three accelerometers and three gyroscopes.
• the navigation device also includes a measuring device.
• the processing unit is configured for the implementation of the navigation aid method described above.
• the navigation device further comprises a device for measuring an altitude of the navigation device.
• Another aspect of the invention relates to a computer program product comprising program code instructions for executing the steps of the navigation aid method described previously, when the latter is executed by a processor.
• Figure 1 shows a navigation system of the invention.
• FIG. 2 represents a navigation method of the invention.
• Figure 3 shows a linear Kalman filter.
• Figure 4 shows an extended Kalman filter.
• FIG. 1 schematically represents a navigation device.
• This navigation device DISP comprises a processing unit UNIT.
• This UNIT processing unit comprises a general or specific purpose processor or microcontroller and a memory.
• the processor or the microcontroller can be an integrated circuit specific to an application (Application-Specific Integrated Circuit for ASIC in English), it can also be a programmable logic circuit or programmable logic network (Field-Programmable Gate Array for FPGA in English).
• the memory can be fixed or removable and include different memory units which can include a combination of units allowing volatile and non-volatile storage.
• the memory is configured to store software code that can be used by the processor or the microcontroller to carry out a method for determining respective values of kinematic variables of the navigation device DISP.
• the values of the kinematic variables allow the location of the navigation device DISP and therefore the navigation of the wearer of this device.
• the navigation device DISP also comprises three accelerometers 101-a to 101-c, three gyroscopes 102-a to 102-c and a device for measuring, for example a physical quantity, for example dependent on one or more kinetic variables or depending a bias of one of the accelerometers or one of the gyroscopes.
• This measuring device is for example a measuring device 103-a of a position of the navigation device DISP and/or a measuring device 103-b of a speed of the navigation device DISP.
• the navigation device DISP can also include other devices for measuring a kinematic variable of the navigation device DISP.
• the navigation device DISP can also comprise a device 104 for measuring an altitude of the navigation device DISP.
• This measuring device 104 is for example an altimeter 104.
• the three accelerometers 101-a to 101-c are capable of delivering specific force data.
• the three accelerometers are respectively associated with three axes which may be mutually orthogonal.
• the three gyroscopes 102-a to 102-c are capable of delivering angular position data.
• the three gyroscopes are respectively associated with three axes which may be mutually orthogonal.
• the accelerometers measure a specific force fn of the navigation device DISP and the gyroscopes measure an angular speed of the navigation device DISP. This angular velocity is then transformed into a rotation matrix ⁇ n representative of the rotation of the device. The time interval between two measurements is denoted dt.
• Accelerometers and gyroscopes can either provide specific forces and angular velocities, or directly variations in speed and angle.
• the device 103-a for measuring a position of the navigation device DISP is for example:
• satellite navigation receiver for example a GPS type receiver for Global Positioning System in English or a Galileo type receiver,
• the device 103-b for measuring a speed of the navigation device DISP is for example:
• zupt for Zero velocity UPdaTe
• the data delivered by the three accelerometers 101-a to 101-c, by the three gyroscopes 102-a to 102-c, by the measuring device 103-a of a position or by the measuring device 103-b of a speed and possibly by the altimeter 104 are received by the processing unit UNIT.
• the processing unit UNIT is configured by implementing the method for determining respective values of kinematic variables of the navigation device DISP represented in FIG. 2. This method therefore allows the location of the navigation device DISP and therefore the navigation of the wearer of this device.
• This method of Figure 2 comprises:
• step 202 for determining respective current values of kinematic variables of the navigation device DISP and of a current uncertainty matrix representative of an uncertainty of the respective current values of the kinematic variables, from respective previous values of the kinematic variables of the DISP device and from a fictitious model of an earth attraction undergone by the DISP device in which the modeled attraction is of increasing intensity with an altitude of the DISP device, and from a previous uncertainty matrix representative of 'an uncertainty of the respective previous values of the kinematic variables,
• step 203 for determining a correction from the current values of the kinematic variables and from a measurement
• step 204 for updating or correcting the current values of the kinematic variables and of the current uncertainty matrix from the correction.
• the fictitious model will be indifferently called “fictitious model of terrestrial attraction” and “fictitious model of terrestrial gravity”. This model does have a fictional character, due to the fact that the modeled pull is of increasing intensity with an altitude of the DISP device, which is the opposite of a realistic gravity/earth pull model in which a such intensity would decrease with such altitude.
• the measurement is for example the measurement of a physical quantity, for example dependent on one or more kinematic variables or depending on a bias of one of the accelerometers or one of the gyroscopes. This measurement is for example the position or the speed of the navigation device DISP.
• the modeled attraction coincides with the real attraction at the measured altitude of the wearer.
• Device kinematic variables include:
• an uncertainty matrix P representative of an uncertainty of the kinematic variables is used.
• This matrix is a covariance matrix.
• variables (matrix or vector) bearing a circumflex accent represent estimated variables, the corresponding real variables are noted without a circumflex accent.
• the method includes determining the value of these variables which are denoted respectively .
• the method also includes determining the covariance matrix representative of the uncertainty of the current estimate.
• a covariance matrix representing the initial uncertainty is available at the start of the navigation.
• the index n here represents the time step and, conventionally in Kalman filtering, the index n
• the method of the invention uses a Kalman filter (advantageously the invention uses an invariant Kalman filter), where successive propagation phases (including the determination 202) using the inertial measurements and the fictitious attraction model terrestrial, and update phases (comprising the determination 203 of a correction and the correction 204) using the position data delivered by the device 103-a for measuring a position or the speed data delivered by the device 103-b for measuring a speed.
• update phases comprising the determination 203 of a correction and the correction 204 using the position data delivered by the device 103-a for measuring a position or the speed data delivered by the device 103-b for measuring a speed.
• other types of measurements are used to determine the correction 203 and perform the update 204.
• the method uses, during the determination 202 of a state of the device, a measurement of altitude to feed a non-physical gravity model.
• This altitude measurement can be supplied by the altimeter 104 if it is present, for example if the navigation system is present in an aircraft whose altitude may vary. This altitude measurement can also be known a priori in the case of a boat.
• Determination 202 uses the following equations:
• - real g is a physically coherent model of the opposite of a module of gravity.
• physically coherent we understand a model in which the intensity of gravity is only a function of the distance from a point to the center of the earth and in which the intensity of gravity decreases with altitude.
• this fictitious model we make the approximation that gravity is oriented towards the center of the Earth and that its modulus is only a function of the distance to the center of the Earth.
• Q n is a covariance matrix representing the uncertainty added by each stage of propagation of the kinematic variables.
• the main source of this uncertainty is the inaccuracy of measurements from accelerometers and gyroscopes.
• the exact values to be given to Q n are generally difficult to choose but can be chosen using the specifications supplied by the manufacturer of the navigation device DISP.
• - ⁇ is the radius of the earth (the distance to the center of the earth corresponding to zero altitude).
• - P is a covariance matrix whose values on the diagonal represent the uncertainties of each variable of the state and whose values which are not on the diagonal represent the uncertainties crossed between the kinematic variables. is the previous uncertainty matrix, is the current uncertainty matrix.
• This fictitious model is based on a realistic model but deviates from it.
• real g is an opposite of a realistic spherical model in which the intensity of gravity is only a function of the distance from a point to the center of the Earth. Actual gravity at a point
• X n is then written where is a unit vector pointing in the direction of X n .
• g' reaI is a vector of a terrestrial gravity resulting from a physically coherent model.
• r t is the local radius of curvature of the Earth in is the point whose latitude and longitude are respectively the latitude and longitude of x n and whose altitude is h n .
• alt(X n ) is the altitude of x n .
• step 203 of determining a correction ds comprises:
• R n a covariance matrix used to represent the measurement errors and the non-modeled quantities. It may or may not depend on the estimated kinematic variables.
• this matrix makes it possible to link the measured position Yn to the other kinematic variables of the device DISP,
• - K n is a gain matrix or transformation of the error on the position vector into a correction to be applied to the other kinematic variables.
• step 203 of determining a correction ds performs:
• ds is a vector of size 9.
• the first three components (ds1:3) correspond to the rotational error.
• the next three components (ds4:6) correspond to the speed error.
• the last three components (ds7:9) correspond to the position error.
• the matrix H comprises the concatenation of two null matrices of size 3 by 3 and an identity matrix of size 3 by 3.
• This step 203 of determining a correction makes it possible to determine the deviation of all the kinematic variables of the navigation device from the value of only one of the kinematic variables of the device.
• This determination of the deviation is carried out by the gain matrix K, which takes into account the uncertainties on the kinematic variables of the device DISP. If there is a low uncertainty, the position measurement Y n is taken into account to a small extent and, if there is a large uncertainty, the position measurement Y n is taken into account to a large extent. By taking the position measurement Y n into account to a small extent, it is understood that the value of the inputs of the matrix K is low. By taking the position measurement Yn into account in a significant way, it is understood that the value of the inputs of the matrix K is significant.
• Update 204 uses ds correction to achieve the following equations:
• - R is a function which allows, from a vector, to obtain a rotation matrix of the vector
• - ds 7:9 is the part of the correction vector relating to the position correction is the corrected velocity vector, is the corrected position vector, is the corrected orientation matrix, is the corrected uncertainty matrix.
• the 204 update uses the ds correction to achieve the following equations:
• Steps 202 to 204 of the method are repeated throughout the navigation.
• the corrected velocity vector becomes the next vector previous velocity
• the corrected position vector becomes the next previous position vector
• the corrected orientation matrix becomes the next previous orientation matrix
• This method uses the matrix P which is the covariance matrix and the set of operations applied to P over time are called “Riccati equation”.
• the kinematic variables never appear in the matrix P (or only in the matrices Q n and R n ). Thus this process shares an important property of linear systems. In more complex embodiments these kinematic variables may appear but the method of the invention makes it possible to reduce the negative effects of this dependence.
• the merging technique using the fictitious Earth attraction model is an invariant filter.
• other registration methods can be used, for example conventional extended Kalman filter smoothing on sliding window constant gain filter particle filter
• Figure 3 shows a linear Kalman filter.
• the estimated kinematic variables which undergo a series of propagations (using measurements from accelerometers and gyroscopes) and updates (using an additional sensor such as a speed or position measuring device), are represented on the middle line.
• the updates are corrections of the estimated state taking into account the new measurement from the additional sensor.
• the sensor does not directly give the correction to be made, it only gives a measurement.
• the difference of this measure with the expected measure is called innovation.
• the gain matrix K is needed. It is calculated from the Riccati equation appearing on the bottom line.
• This equation updates the covariance matrix P which represents an uncertainty on the kinematic variables.
• This covariance matrix P makes it possible to construct the gain matrix K. If the estimate of the state is false, the measurements combined with the gain matrices make it possible to correct the estimate of the state over time.
• Figure 4 shows a nonlinear or extended Kalman filter.
• This Kalman filter makes it possible to manage the nonlinear aspect of the state of the navigation device.
• the difference between Figure 3 and Figure 4 is the addition of the feedback from the middle line to the bottom line.
• the estimated state is used to calculate the uncertainty and the gains.
• This feedback can cause a reduction in filter performance.
• An error on the state of the system induces an error on the gains, which in turn induces an error on the estimated state.
• all of the operations involving the matrix P do not reveal the estimated state of the navigation device DISP (or only in the matrices Q n and R n ). Thus we place our in the same case as for a linear system and the feedback of FIG. 4 has disappeared.

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• Engineering & Computer Science (AREA)
• Radar, Positioning & Navigation (AREA)
• Remote Sensing (AREA)
• Automation & Control Theory (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Navigation (AREA)

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• 2021
  • 2021-03-11 FR FR2102417A patent/FR3120689B1/fr active Active
• 2022
  • 2022-03-11 CN CN202280029813.6A patent/CN117203493A/zh active Pending
  • 2022-03-11 EP EP22713980.5A patent/EP4305383A1/de active Pending
  • 2022-03-11 US US18/281,446 patent/US20240159539A1/en active Pending
  • 2022-03-11 WO PCT/FR2022/050442 patent/WO2022189760A1/fr active Application Filing

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