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
  • 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

Definitions

• the present invention relates to hybridization using a
• Kalman information delivered by a satellite positioning receiver with information coming from an inertial unit.
• Kalman filtering which appeared in 1960, is a signal processing technique making it possible to optimally reduce the noises of various origins accompanying, inevitably, the signals generated by an equipment, from a modeling of the evolution of the state of the equipment considered within its environment and of a modeling of the dependence relationship existing between the measurable signals coming from the equipment and its state. It implements a recursive filter whose realization is greatly facilitated by the generalization of digital signal processing. Its field of application is very wide since it applies to any system whose evolution of the state and the relation of dependence between its state and signals accessible to the measurement can be modeled. It is widely used in aeronautics, for the treatment of noises affecting measurements renewed periodically and for the fusion of information of the same type coming from measurements coming from several pieces of equipment implementing different physical principles.
• Kalman filters An important field of use of Kalman filters is that of navigation computers which must develop information for piloting assistance such as position, attitudes (roll, pitch and heading) and speed vector with the best possible precision, despite noises measurement and faults specific to each sensor.
• the Kalman filter makes it possible to make short-term predictions on the errors affecting the expected inertial data (position, attitudes, and speed vector) from the estimates of the values taken by these same errors in a recent past and their known laws of evolution according to the environment of the flight computer, and to deduce from these short-term forecasts, corrections to be made to the information drawn from the measurements coming from the sensors to make them optimal, it that is, as precise as possible. -.
• Kalman filtering technique will not be detailed here since it is within the competence of a person skilled in the art and has already been the subject of an abundant literature.
• the Kalman filtering technique is based on the possibility of modeling the evolution of the state of a physical system considered in its environment, by means of an equation called “evolution”, and on the possibility of modeling the dependence relation existing between the state of the physical system considered and the signals or "measure” by which it is perceived from the outside, by means of an equation called “observation” to make a forecast a priori of the state of the system and of the measurement, taking into account the history
• the adaptive adjustment of the Kalman filter is obtained using a servo loop tending to minimize the cova ⁇ ance of the error made on the posterior estimation of the state vector of the system. Kalman which are well known, will be recalled later
• the Kalman filter is used to provide an optimal estimate of the errors affecting the information extracted from the measurements delivered by the inertial unit by the navigation computer.
• the system whose dynamic behavior is modeled, has as state vector, a vector having as components the errors on the information made by the computer from the measurements of the inertial unit and for vector of measurement a vector having as components the errors measured on information accessible to the measurement.
• the modeling of its dynamic behavior is defined by means of an evolution equation translating, as faithfully as possible, the deterministic laws known to be followed by the errors considered in the environment in which the system is immersed, a function for example of the kinematics of the movement of the carrier of the inertial unit, and by an observation equation translating the r dependency relationships existing between the errors on the information accessible to the measurement and the errors taken into account in the state vector
• the measurement vector which does not necessarily have the same dimension as the state vector, is made up
• Kalman filter Another known application of the Kalman filter concerns the filtering of measurement noise in the processing part of a GNSS receiver.
• a GNSS receiver is made up of a reception part which provides, for each satellite in the constellation of the positioning system from which the transmission signals are received, the data transmitted by this satellite and two kinds of measurements which are pseudodistance and pseudo- speed of the receiver with respect to the satellite considered, and of a calculation part which provides from this data and measurements, the position and the speed vector of the receiver carrier as well as the time
• the calculation part operates on the basis of the data transmitted by the visible satellites and measurements of the reception part
• the system which the calculation part must solve to obtain the position (or the speed) has four unknowns (position x, y, z, and time t or speed Vx, Vy, Vz and clock drift) and has as many equations as of visible satellites
• a GNSS receiver is therefore capable of delivering a position and speed vector measurement of its carrier, as well as of time, as soon as it receives four satellites from the positioning system In practice, it often receives more than four
• the number of equations then being greater than the number of unknowns, the resolution can be carried out by the method of least squares, which makes it possible to minimize the vanance of errors on the position and the resolved speed, due to measurement noises
• the precision on the position and the resolved speed depends only on the geometry of the constellation of the visible satellites, characterized by the parameter DOP (Dilution Of Precision in English language) -saxon)
• DOP Deution Of Precision in English language
• Loose hybridization consists in using, with a navigation computer equipped with an inertial measurement unit, a complete GNSS receiver, with its specialized computer often provided at the output with a first Kalman filter usually degenerated into a least squares filter , without memory effect, and to provide the navigation computer with an error estimator, in the form of a second Kalman filter based on the modeling of the dynamic behavior of a physical system made up of the inertial measurement unit , the navigation computer and the part of the GNSS receiver delivering the clock signal
• the modeled system used for the design of this second Kalman filter has, among the components of its state vector, the expected errors on the position coordinates of the mobile carrying the navigation computer and the GNSS receiver and the expected errors on the components of the mobile speed vector, taken from the acceleration and angular velocity components delivered by the navigation computer
• the measurement vector adopted to operate this second Kalman filter has, among the components of its measurement vector, the differences between two versions of the position coordinates and of the components of the speed vector, one delivered by the GNSS receiver, the other delivered by the mertiel navigation computer
• the modeling of the dynamic behavior of the adopted system is defined, in the usual way, by an evolution equation translating the deterministic laws of evolution known for the components of its state vector and by an observation equation translating the relations of dependence between the measurement vector and the state vector
• the tight hybridization between a GNSS receiver and an inertial reference consists in operating at the level of the basic information available in a GNSS receiver that are the pseudo-distances or relative distances separating the GNSS receiver from the received satellites and the pseudo-speeds or speeds of the relative displacements of the satellites received by report to the GNSS receiver and therefore to short-circuit, for the resetting of the navigation computer the calculation part of the receiver
• the Kalman filter used in the navigation computer is then based on the modeling of the dynamic behavior of a system defined by
• the navigation computer performs the processing necessary to extract measurements from the inertial measurement unit, the position, speed vector and attitude information of the wearer II then uses this position and speed vector information of the wearer as well that information on the positions and speed vectors of the satellites of the positioning system supplied to it by the GNSS receiver from the data transmitted by these satellites themselves to calculate the values of the pseudo-distances and pseudo-speeds between the carrier and these satellites
• the object of the present invention is to avoid the abovementioned drawbacks
• It relates to a hybridization device, within a navigation computer, of a satellite positioning receiver with an inertial unit equipping a mobile, comprising a filter
• Kalman with an input and an output, based on the modeling of the dynamic behavior of a system and defined by a state vector with, among its components, the expected errors on the position coordinates and on the components of the speed vector, extracted from the acceleration and angular velocity components delivered by the navigation computer, an evolution equation translating the known evolution laws of the components of its state vector,
• an observation equation translating the dependence relationships existing between the measurement vector and the state vector characterized in that it comprises, inserted before the input of the Kalman filter, a circuit for synthesizing the differences between pseudo -distances and pseudo-speeds operating from the two versions of position information and speed vector of the mobile delivered one, by the satellite positioning receiver and the other, by the navigation computer, and using, for do the direction cosines of the axes connecting the mobile to the visible satellites of the positioning system, deduced from the positions of said visible satellites extracted from the data transmitted by the latter and captured by the satellite positioning receiver and from one of the available versions of the position information of the mobile
• the Kalman filter has, among the components of its state vector, the bias and the drift of the receiver clock.
• the synthesis circuit deduces the directing cosines of the axes connecting the mobile to the visible satellites of the positioning system, from the positions of said visible satellites extracted from the data transmitted by the latter and picked up by the satellite positioning receiver, and from the version d position information of the mobile delivered by the navigation computer
• the synthesis circuit deduces the director cosines of the axes connecting the mobile to the visible satellites of the positioning system, from the positions of said visible satellites extracted from the data transmitted by the latter and picked up by the receiver of satellite positioning, and the mobile position information version from the satellite positioning receiver
• the synthesis circuit receives the values of the directing cosines of the axes connecting the mobile to the visible satellites of the positioning system directly from the satellite positioning receiver which itself deduces them from the positions of said visible satellites extracted from the data transmitted by the latter and its own version of information on the position of the mobile delivered by the navigation computer
• a hybridization is thus obtained, having the same advantages from the point of view of a criterion of least squares (optimality) as tight hybridization while not using sensitive or not necessarily accessible data from the reception part of the GNSS receiver.
• FIG. 2 represents the block diagram of a “loose” hybridization between an inertial unit and a GNSS receiver according to the state of the prior art
• FIG. 3 shows the block diagram of a "tight" hybridization between an inertial unit and a receiver
• the index i / i means that we are dealing with an a posteriori estimate on step ia starting from information available in step i while a double index ⁇ / ⁇ -1 means that we are dealing with an a priori forecast on step i from information coming from step ⁇ -1
• a Kalman filter makes it possible to obtain an estimate of the posterior state of an optimal system in the sense that the probability of the error made on this posterior estimate is minimal II is based on a modeling of the evolution of the system and on a modeling of the relation existing between the measurement and the state of the system
• the modeling of the relation existing between the measurement and the state of the system is defined by another first order differential equation which is expressed, after discretization of the continuous model, by an equation of observation of the form
• H ⁇ X , + V ⁇ z being a measurement vector of dimension m, H, a matrix defining the relation between the measurement vector and the state vector during the same step and v a noise of measurement during a step assumed to be white and Gaussian with zero mean value
• the Kalman filter is recursive. It is based on the determination of an a priori estimate,sko_, of the state vector of the system in step i from a posteriori knowledge of the vector d state of the system *, _,, _, in the previous step ⁇ -1 and of the application to this estimated a priori ⁇ -,,,, of a corrective term depending on the difference found between the vector measure r, observed during this step i and its estimated a priori f,, _, deduced from the a priori estimate v ,,, of the state vector
• the a priori estimate z,, _, of the measurement vector in step i is determined by applying the evolution and observation equations to the posterior estimate ⁇ ,,,, of the state vector and at control vector II. corresponding to step ⁇ -1 above
• This operation is illustrated in your figure 1 by the presence of a feedback loop consisting of a delay circuit 1, three filters 2, 3 4 and a summator 5
• the circuit delay 1 connected at the output of the Kalman filter makes it possible to store the posterior estimate v,,, _, of the state vector available at the output of the Kalman filter during the previous step ⁇ -1
• the two filters 2 and 3 and the summator 5 make it possible, by implementing the observation equation, to obtain the a priori estimate v, - of the state vector in step i, from of the posterior estimate î,,,, of the state vector of the system in the previous step ⁇ -1
• Filter 2 provides the component of the a priori estimate J 1 / ( _, of the state vector in step i depending on the a posteriori estimate £, _ ,,, _, of the state vector at l step ⁇ -1 Its transfer function is defined by the matrix E, _, of the observation equation
• the filter 3 provides the component of the a priori estimate x ⁇ _ ] of the state vector in step i depending on the control vector w, _, corresponding to step ⁇ -1 Its transfer function is defined by the matrix
• This a priori estimate ,,,, of the state vector in step i is then used to obtain, by means of a third filter 4, the a priori estimate i ; / ⁇ _, of the measurement vector at l step i by application of the evolution equation
• This third filter 4 has, to do this a transfer function defined by the matrix H, of the observation equation
• the a priori estimate z lh of the measurement vector in step i obtained in the feedback loop is applied, at the input of the Kalman filter, to a subtractor circuit 8 which also receives the measurement vector actually measured during step i, z t and which delivers an error vector /; attesting to the error made during the a priori estimation
• This error vector r is transformed by a fourth filter 6 into a correction vector
• This correction vector is added by a second summator 7 to the a priori estimate v ,, of the state vector for step i from the first summator 5, to obtain the posterior estimate x,, of the state vector which constitutes the output of the Kalman filter
• the fourth filter 6, which provides the corrective term, is known as the registration gain filter II has a transfer function defined by a matrix K t determined so as to minimize the cova ⁇ ance of the error made on the estimate. a posteriori
• Kalman showed that the optimal gain matrix K, could be determined by a recursive method from the equations of the cova ⁇ ance matrix of the a priori estimate of the state vector.
• P being the cova ⁇ ance matrix of the state vector, either estimated a priori for step i from step ⁇ -1 if P is affected by the index ⁇ / ⁇ -1, or estimated a posteriori if P is assigned the index ⁇ -1, R being the cova ⁇ ance matrix of observation noises w t , Q being the cova ⁇ ance matrix of state noises v,
• the covanance matrix of the state vector and the state vector are taken equal to their most likely estimates.
• the covanance matrix is a diagonal matrix with infinite or very large terms (so to have very large standard deviations in front of the domain in which the state vector is likely to evolve) and the estimated state vector the null vector, when we have no idea about the initial values
• the correction gain of a Kalman filter is "proportional" to the uncertainty on the a priori estimate of the state of the system and "inversely proportional" to the uncertainty in the measurement If the measurement is very uncertain and the estimate of the state of the system relatively precise, the difference observed is mainly due to the measurement noise and the resulting correction must be low On the contrary, if the uncertainty on the measurement is low and that on the estimation of the state of the system large, the difference observed is very representative of the estimation error and must lead to a strong correction. the behavior towards which we tend with the Kalman filter
• the construction of a Kalman filter requires the definition of two matrices F and L corresponding to the evolution equation defining the evolution of the state vector of the modeled physical system, of a matrix H corresponding to the observation equation defining the relations allowing to pass from the measurement vector to the state vector, and from the gain matrix K updated using an iterative process bringing into play the covanance matrix of the vector of state P itself updated during the iterative process and Q and R covanance matrices of state and observation noise
• the Kalman filtering technique is often used when it is a question of seeking an optimal estimate of the same physical quantity, several estimates of which are available from several pieces of equipment operating according to different physical principles and not presenting the same defects.
• the Kalman filtering is carried out at the level of the discrepancies noted between two versions of information of the same type coming from one of the inertial unit and, the other of the GNSS receiver, because it allows to work on variables with more restricted areas of variation on which the linear approximation can be used to simplify the modeling and evolution equations
• FIG. 2 represents the diagram of the principle of a loose hybridization between an inertial unit and a GNSS receiver.
• the inertial measurement unit UMI 10 which delivers increments of angles ⁇ ⁇ A ⁇ ⁇ to ⁇ . and speed increments AV x , V y , AV l according to an orientation t ⁇ edre x, y, z linked to its sensors
• the navigation computer 1 1 which receives the increments of angles and speeds coming from the UMI 10 and extract from it the position P, the speed vector V and the attitudes Att of the wearer and, - the GNSS receiver 13 which provides another version of the position P and the speed vector V
• the GNSS receiver 13 is shown in a representation detailing its reception part 13a delivering the pseudo-distances pd and pseudo-speeds pv of the carrier relative to the visible satellites and its computer part 13b extracting from 1 D
• position and speed vector from the navigation computer 11 and the GNSS receiver 13 are subjected to a subtractor 14 determining their deviations
• the deviations observed in position ⁇ P and speed vector ⁇ V feed a Kalman filter 15 which extracts them an optimal posterior estimate of the measurement bias of the sensors of the inertial measurement unit UMI 10 and of the errors made on the position P, the speed vector V and the attitudes of the wearer by the navigation computer, and which applies this optimal estimate a posteriori to the navigation computer in order to readjust it
• the Kalman filter 15 used here is based on an evolution equation corresponding to a modeling of the drifts of the system consisting of the navigation computer 1 1 supplied with information by the inertial measurement unit 10 and on an observation equation modeling the relationships between the measurement vector and the state vector
• the computer 13b of the GNSS receiver 13 generally uses a Kalman filter often reduced to a least squares filter without memory which performs spatial filtering in order to extract, from the information of pseudo-distance pd and pseudo-speed pv, a single information of position and vector speed of the carrier and time which is as precise as possible by reducing the measurement noise of the GNSS receiver
• a Kalman filter often reduced to a least squares filter without memory which performs spatial filtering in order to extract, from the information of pseudo-distance pd and pseudo-speed pv, a single information of position and vector speed of the carrier and time which is as precise as possible by reducing the measurement noise of the GNSS receiver
• FIG. 3 represents the block diagram of a close hybridization between an inertial unit and a GNSS receiver
• the UMI 10 delivers increments of angles ⁇ (- * ⁇ , ⁇ ⁇ * 0 v , ⁇ ! - / * > _ and increments of speed AV, Al, V. according to an orientation t ⁇ èdre x , y, z linked to its sensors to a navigation computer 1 1 responsible for extracting from these increments of angles and speed, the position P, the speed vector V and Attitudes of the wearer, while the GNSS receiver 13 provides another version of the position P and of the speed vector V of the wearer
• the GNSS receiver 13 is used to readjust the navigation computer 1 1 in a manner different from the loose hybridization.
• the final information on the position P and the speed vector V of the carrier delivered by its computer 13b, are not used for registration They are replaced by the intermediate measurements of pseudo-distances pd and pseudo-speeds pv with respect to the different visible satellites of the constellation of the positioning system which are available at the output of its reception part 13a
• a synthesizer circuit S 20 is added at the output of the navigation computer 1 1 II receives from the GNSS receiver, by a particular link, additional information on the positions and speeds of the satellites extracted from the data transmitted by the satellites themselves and calculates, from this additional information and information of position P and speed V of the carrier delivered by the navigation computer 1 1, estimates pd ei pv of the pseudo-distances and pseudo-speeds
• the two versions of pseudo-distances pd and pseudo-pv speeds available, one calculated by the synthesizer S 20 and the other measured by the reception part 13a of the GNSS receiver 13 are subjected to a deviation detector 21
• the observed deviations ⁇ pd and ⁇ pv between the two versions of each pseudo-distance and each pseudo-speed are applied at the input of a Kalman filter 22 which delivers, at the output, an optimal estimate a posteriori of s measurement bias of the sensors of the inertial measurement unit UMI 10 and of the errors made on the position P, the speed vector
• K being the gain matrix
• the determination method of which, described in relation to FIG. 1 uses the covanance matrix P of the state vector updated according to an iterative process, the cova ⁇ ance matrix R of the observation noises , to the observation matrix H and to the measurement or observation vector Z
• the Kalman filter is perfectly defined by means of its estimated state vector X , of the matrix F defining by modeling, the evolution of its state vector, of its observation vector
• ⁇ v x , ⁇ Vy, ⁇ v z being the components of the difference between the real speed vector of the carrier and an a priori estimate of this speed vector
• d being the drift of the clock of the GNSS receiver, made homogeneous at a speed by multiplication by the speed of light
• v x , v y , v z being the a priori estimate of the components of the speed vector of the carrier, given by the GNSS receiver before correction, deduced for example from the speed vector estimated at the previous resolution
• v x ⁇ , v y ⁇ , v z being the components of the speed vector of the satellite system of the positioning system extracted from the data of the satellite itself, expressed in the coordinate system used for the speed vector of the carrier
• dp being the pseudo-speed between the carrier and the satellite lemme calculated from the a priori speed vector of the carrier and the satellite data
• pv being the pseudo-speed between the carrier and the satellite lemme measured by the GNSS receiver
• ⁇ X [ ⁇ ⁇ ⁇ t ⁇ . ⁇
• A V ( v - ⁇ *) 2 + C y -,) : + (- - : ⁇ x , ⁇ y , ⁇ z being the differences between the coordinates of the actual position of the carrier and the coordinates of the position of the carrier given by the navigation computer, t being the error made over time by the clock of the GNSS receiver, made homogeneous at a distance by multiplication by the speed of light, ⁇ i. ⁇ i. ⁇ , being the direction cosines of the axis connecting the carrier to the lemma satellite in the coordinate system used by the navigation computer, x, y, z being the coordinates of the position of the carrier given by the navigation computer, x mecanicyoffice z, being the coordinates of the position of the same satellite of the positioning system extracted from the data of the lth satellite itself and expressed in the reference frame used for the position of the carrier, p, being the pseudo-distance calculated between the carrier and the i-th satellite, pd, being the pseudo-di
• FIG. 4 To improve the hybridization between an inertial unit and a GNSS receiver, a new arrangement is proposed, shown schematically in FIG. 4.
• the inertial measurement unit UMI 10 delivers increments of angles ⁇ J X , ⁇ 9 V , ⁇ 9. and speed increments ⁇ F ⁇ ⁇ J- ⁇ ⁇ F. according to an orientation t ⁇ èdre x, y, z linked to its sensors, to a navigation computer 1 1 responsible for extracting from these increments of angles and speed, the position P, the speed vector V and the attitudes Att of the carrier, while the GNSS receiver 1 3 provides another version of the position P and the speed vector V of the carrier
• the GNSS receiver 13 is again considered in its integrity, as in the case of loose hybridization, without direct access to the intermediate information delivered by its reception part, which are its pseudo-distance and pseudo-speed measurements, this which avoids the problems linked to the output of its sensitive information unit Only the final information, that is to say position, speed vector, clock information and the data transmitted by the satellites of the positioning system which are delivered by its calculation part 13b As in the context of the loose hybridization described with reference to FIG.
• the synthesizer circuit S ⁇ receives the two versions of the position P and speed vector information V delivered one by the navigation computer 1 1 and the other by the satellite positioning receiver 13 with in addition the positions P, and speed V vectors, satellites visible from the positioning system extracted from the data transmitted by these satellites captured and decoded in the satellite positioning receiver 13
• the direction cosines ⁇ ,, ⁇ ,, ⁇ , of the satellite axes uses the coordinates x ,, y ,, z, the positions of the satellites visible from the positioning system and the coordinates x, y, z of the position of the carrier
• the coordinates of the positions xonuly,, z, of the visible satellites are supplied to it by the GNSS receiver
• the coordinates x, y, z of the position of the carrier he has the choice between two sources either, the GNSS receiver 13 or, the navigation computer 1 1
• the synthesizer circuit S ⁇ 30 it is advantageous for the synthesizer circuit S ⁇ 30 to use, when determining the director cosines of the satellite axes, the information relating to the x, y, z coordinates of the position of the carrier coming from the navigation computer 11, rather than that from the GNSS receiver 13 because they are always available, which allows the synthesizer circuit S ⁇ 30 to calculate the direction cosines of the directions of all the visible satellites and therefore to arrive at the pseudo-distances and pseudo-speeds of these satellites, even s '' they are insufficient in number to allow the GNSS receiver to provide an estimate of the position of the carrier.However, it is possible for the latter to use, when determining the cosines of the satellite axes, the information relating to the x coordinates. , y, z of the position of the carrier from the satellite positioning receiver II is even possible that the directing cosines of the directio ns visible satellites are directly supplied to it by the satellite positioning receiver because
• the Kalman filter 32 is analogous to that (22 FIG. 3) described in the context of "tight" hybridization, with the exception of the observation vector which has, as components for the pseudo-distances the quantities
• the diagonal term of the state noise matrix Q corresponding to the estimated clock bias is increased, for example to a value greater than 10 6 , so as to not take into account the time t estimated at the previous registration because there is no seeks more to model the clock of the GNSS receiver in this kind of hybridization) and, as regards pseudo-speeds, the quantities
• the diagonal term of the state noise matrix Q corresponding to the estimated clock drift is also increased, for example to a value greater than 10 6. so as to not take account of the drift d estimated at the previous registration since we no longer seek to model the clock of the GNSS receiver in this kind of hybridization)
• the new hybridization arrangement between an inertial unit and a GNSS receiver illustrated in FIG. 4 presents the cumulative advantages of tight hybridization and loose hybridization when the information on the position and the speed vector of the carrier used by the synthesizing circuit for calculating the directing cosines of the directions of visible satellites originate from the navigation computer
• the precision of the information delivered by the GNSS receiver which is very dependent on the geometry of the constellation of satellites that '' it uses at the time of its triangulation (Dilution Of Precision) It also allows a certain registration of the navigation computer as soon as at least one of the satellites of the positioning system is visible from the GNSS receiver, this registration improving with the increase the number of satellites received (when the number of available satellites is less than four, the receiver's computer provides a partially readjusted position from an a priori position, deduced for example from the previous resolution)
• the navigation computer, the synthesizer circuit and the Kalman filter used to correct the errors of the inertial unit have been represented in the form of an assembly of separate boxes. It should not be concluded that this is necessarily the case in practice Indeed, as we are dealing with signals which are very often digitized, all the processing and functions performed in these different boxes can be done by means of one or more processors controlled by software and grouped in the navigation calculator

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