Navigation and positioning device and method

Abstract

The invention relates to a navigation and positioning device comprising at least: - an inertial measurement unit, - a receiver for receiving GNSS satellite positioning measurements, - a closed-loop primary Kalman filter (32) configured to compute navigation data corrections by hybridizing satellite data and non-satellite data, as well as a bank (38) of N closed-loop secondary Kalman filters, each configured to compute navigation data corrections based solely on the non-satellite positioning data delivered at least by said inertial measurement unit, each secondary Kalman filter of index i, where 1 ≤ i ≤ N, being able to reconfigure itself to the primary Kalman filter at a time (i − 1)T from the start of navigation of the vehicle, and then periodically at a period NxT, T being a predetermined duration.

Description

[0001] TITLE: Navigation and positioning device and method

[0002] The present invention relates to a navigation and positioning device, suitable for being on board a vehicle suitable for moving between two distinct geographical positions, the device comprising at least: an inertial measurement unit suitable for providing navigation measurements, a receiver for positioning measurements by GNSS satellites, a main closed-loop Kalman filter configured to calculate corrections of navigation data by hybridization of satellite positioning data provided by said receiver and non-satellite positioning data provided at least by said inertial measurement unit, said corrections being reapplied by looping back to the input of said main Kalman filter.

[0003] The invention also relates to a vehicle comprising such a navigation and positioning device.

[0004] The invention also relates to a navigation and positioning method implemented by such a navigation and positioning device.

[0005] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a navigation and positioning method.

[0006] The present invention relates to the navigation of a vehicle capable of moving between two distinct geographical positions, such as a land vehicle, an aircraft, or preferably a naval vehicle such as a ship or even a naval vessel.

[0007] Currently, it is possible to determine the geographical position of such a vehicle using a GNSS (Global Navigation Satellite System) satellite navigation and positioning system. To do this, the vehicle generally carries a satellite navigation and positioning system receiver configured to determine, in particular by trilateration, a position (i.e. a geolocation position or a geolocation solution) of the aircraft using estimates of distances to the visible satellites of one or more satellite constellations of the satellite navigation and positioning system. Examples of satellite navigation systems are the American GPS system, the European GALILEO system, the Russian GLONASS system, or the Chinese BEIDOU system, etc.

[0008] In addition, vehicles also have other navigation systems such as one or more inertial measurement units (INS), baro-altimeters, anemometers, etc. An inertial measurement unit consists of a set of inertial sensors (accelerometers, gyrometers) associated with processing electronics, and provides low-noise and accurate information in the short term, but its performance deteriorates in the long term, in particular due to the sensors that compose it. Such vehicles then implement, for predetermined applications, a hybridization technique of position measurements known as INS / GNSS hybridization, capable of providing vehicle location with an accuracy of the same order of magnitude as GNSS location and very precise attitude and heading angles, while ensuring continuity of service during GNSS unavailability.

[0009] However, INS / GNSS hybridization implemented using current techniques is not optimal for protecting against GNSS errors in the event of satellite failure, GNSS software or hardware faults, or intentional or unintentional interference, nor for providing accurate positioning when such errors occur.

[0010] The aim of the invention is then to propose a navigation and positioning device which allows at least the integrity of the positioning to be maintained independently of the vulnerability of the GNSS measurements.

[0011] To this end, the invention relates to a navigation and positioning device, suitable for being mounted on board a vehicle suitable for moving between two distinct geographical positions, the device comprising at least:

[0012] - an inertial measurement unit capable of providing navigation measurements,

[0013] - a GNSS satellite positioning measurement receiver,

[0014] - a primary closed-loop Kalman filter configured to calculate navigation data corrections by hybridizing satellite positioning data provided by said receiver and non-satellite positioning data provided at least by said inertial measurement unit, the device further comprising a bank of N secondary closed-loop Kalman filters with N a predetermined integer such that each secondary Kalman filter being configured to calculate navigation data corrections solely from non-satellite positioning data provided at least by said inertial measurement unit, each secondary Kalman filter of index i, with being able to reconfigure itself on the main Kalman filter at a time (j - 1)T from the start of navigation of the vehicle, then periodically according to a period NxT, with T a predetermined duration, the N secondary Kalman filters being identical and independent, the period NxT corresponding to a period of verification of the integrity of said positioning measurements by GNSS satellites, the device also being configured to:

[0015] - checking, at each instant n + 1, the integrity of said GNSS satellite positioning measurements by comparing, with a predetermined threshold, the difference between the state of each secondary filter and the state of the main Kalman filter, and

[0016] - in the event of a deviation greater than said predetermined threshold, raise an alarm to signal a vulnerability of said GNSS satellite positioning measurements at said time n + 1.

[0017] Thus, the navigation and positioning device according to the present invention has a particular architecture where the secondary Kalman filters (i.e. Kalman sub-filters) are each capable of reconfiguring themselves periodically, according to a period NxT on the main Kalman filter (i.e. copying the state vector and the covariance matrix of the main Kalman filter into the state vector and the covariance matrix of the secondary filter) and this successively each in turn.

[0018] In other words, the particular architecture of the navigation and positioning device according to the present invention makes it possible to carry out a time-shifted recalibration.

[0019] Additionally, none of the secondary Kalman filters use GNSS measurements as input to calculate their navigation data corrections, making each of them invulnerable to possible GNSS error.

[0020] Furthermore, the long-term degradation of the performance of the secondary Kalman filters is limited. Indeed, this degradation is classically due to a drift of the position measurements, received as input, and obtained only from the non-satellite measurements provided at least by said inertial measurement unit, and, according to the present invention, is limited by means of said periodic reconfiguration on the main Kalman filter.

[0021] In other words, the NxT period allows each secondary Kalman filter to take advantage of the short-term precision of the position measurements, received as input, and obtained only from the non-satellite measurements provided at least by said inertial measurement unit.

[0022] According to other advantageous aspects of the invention, the navigation and positioning device comprises one or more of the following characteristics, taken individually or in all technically possible combinations:

[0023] - the device is also configured to determine said predetermined threshold as a function of a probability of false alarm; - in the event of an alarm being raised, the main Kalman filter is also configured to reconfigure itself on a predetermined secondary Kalman filter of index p with

[0024] - said predetermined secondary Kalman filter of index p on which the main Kalman filter is capable of reconfiguring itself in the event of an alarm being raised, is the secondary Kalman filter among said N secondary Kalman filters whose reconfiguration on the main Kalman filter is temporally the furthest from the time of alarm raising;

[0025] - said main Kalman filter is configured to no longer use said GNSS satellite positioning measurements as input from the moment the main Kalman filter initiates its reconfiguration;

[0026] - in the event of an alarm being raised, each secondary Kalman filter of index i #= p is also configured to reconfigure itself on said predetermined secondary Kalman filter of index P;

[0027] - the device is also configured to determine a protection radius with respect to a vulnerability of said GNSS satellite positioning measurements, said protection radius ensuring that the value of the distance between the hybrid position provided from said main Kalman filter and the true position of said vehicle is less than the value of said protection radius, said protection radius depending on the number N of secondary Kalman filters;

[0028] - the device is also configured to provide, as output, in parallel, navigation solutions respectively associated with said bank of N secondary Kalman filters, and with said main Kalman filter.

[0029] The invention also relates to a vehicle comprising such a navigation and positioning device.

[0030] The invention also relates to a navigation and positioning method implemented by said navigation and positioning device and comprising the following steps implemented in parallel or successively one after the other or vice versa:

[0031] - localization of said vehicle using the corrections provided respectively by the main Kalman filter and by the bank of N secondary Kalman filters,

[0032] - verification of the integrity of said GNSS satellite positioning measurements, said verification comprising:

[0033] - determination of the state of each secondary filter and the state of the main Kalman filter,

[0034] - the determination of a threshold suitable for comparison with the difference between the state of each secondary filter and the state of the main Kalman filter,

[0035] - the comparison, at each instant n + 1, of the difference between the state of each secondary filter and the state of the main Kalman filter at said threshold, - in the presence of a difference greater than said predetermined threshold:

[0036] - the raising of an alarm capable of signaling a vulnerability of said GNSS satellite positioning measurements at said time n + 1,

[0037] - the reconfiguration of the main Kalman filter on a predetermined secondary Kalman filter of index p with 1 < p ≤ N,

[0038] - deselection of the main Kalman filter input dedicated to GNSS satellite positioning measurements from the moment the main Kalman filter initiates its reconfiguration,

[0039] - the reconfiguration of each secondary Kalman filter of index i #= p on said predetermined secondary Kalman filter of index p,

[0040] - in the absence of a deviation greater than said predetermined threshold, the determination of a protection radius with respect to a vulnerability of said GNSS satellite positioning measurements, said protection radius guaranteeing that the value of the distance between the hybrid position provided from said main Kalman filter and the true position of said vehicle is less than the value of said protection radius, said protection radius depending on the number of secondary Kalman filters.

[0041] According to a particular aspect of said method, said localization step comprises providing as output, in parallel, the navigation solutions respectively associated with said bank of N secondary Kalman filters, and with said main Kalman filter.

[0042] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a satellite navigation and positioning method as defined above.

[0043] These characteristics and advantages of the invention will appear more clearly on reading the description which follows, given solely by way of non-limiting example, and made with reference to the appended drawings, in which:

[0044] - [Fig 1] Figure 1 is a diagram illustrating a navigation and positioning device capable of implementing INS / GNSS hybridization, and optionally with additional measurements provided by equipment separate from a GNSS satellite positioning measurement receiver and separate from said inertial measurement unit;

[0045] - [Fig 2] Figure 2 is a diagram illustrating the architecture of the Kalman filter bank according to the present invention;

[0046] - [Fig 3] Figure 3 illustrates the closed loop principle;

[0047] - [Fig 4] Figure 4 is a flowchart of a navigation and positioning method according to the present invention.Figure 1 is an overall representation of a navigation and positioning device 10 according to the present invention, capable of implementing an INS / GNSS hybridization, and optionally with complementary measurements provided by equipment separate from a receiver of GNSS satellite positioning measurements and separate from said inertial measurement unit, and comprising at least one inertial measurement unit 12 capable of providing navigation measurements, in particular to a virtual platform 14 for calculation and location, a receiver 16 of GNSS satellite positioning measurements, and optionally a receiver 18 of complementary measurements provided by at least one equipment separate from said receiver 16 of GNSS satellite positioning measurements and separate from said inertial measurement unit 12, and finally a set K of Kalman filters.

[0048] The inertial measurement unit 12 consists of a set of inertial sensors such as gyrometers and accelerometers associated with processing electronics and is capable of providing increments 20 of angular rotation and speed of the vehicle in which the navigation and positioning device 10 is embedded.

[0049] The virtual computing platform 14 integrates such angular rotation and speed increments 20 to provide, as input to the set K of Kalman filters, navigation data 22, such as the orientation of the vehicle, in terms of roll, pitch, yaw, heading, etc., the speed of the vehicle, for example the speed Vnord in the North direction, the speed Vest in the East direction, the speed Vbas at the bottom of the trajectory, etc., and the position of the vehicle, for example in latitude, longitude, altitude.

[0050] The GNSS satellite positioning measurement receiver 16 is capable of providing, according to arrow 24, information on the position and speed of the vehicle by triangulation from the signals emitted by satellites moving in view visible to the vehicle. The information provided may be temporarily unavailable because the receiver must have a minimum of four satellites of the positioning system in direct view to be able to make a point. They are also of variable precision, depending on the geometry of the constellation at the base of the triangulation, and noisy because they rely on the reception of very low level signals coming from distant satellites with low transmission power. But they do not suffer from long-term drift, the positions of the satellites moving in their orbits being known precisely over the long term.Noise and errors may be related to satellite systems, the receiver, or signal propagation between the satellite transmitter and the GNSS signal receiver. In addition, satellite data may be erroneous due to failures affecting the satellites. This non-integrated data must then be identified so as not to distort the position from the GNSS receiver. The optional receiver 18 of complementary measurements 26 provided by at least one piece of equipment separate from said receiver 16 of GNSS satellite positioning measurements and separate from said inertial measurement unit 12 provides, for example, zero-displacement recalibration when the vehicle is stationary, an Electromagnetic Log measurement and a dynamic model of the vehicle, a Doppler log or a water speed measurement when the equipment is a DVL (Doppler Velocity Log), a depth measurement, recalibration by radar, by imaging, by opportunity signals, etc.

[0051] The hybridization implemented by the set K of Kalman filters consists of mathematically combining the measurements 22, 24, 26 provided respectively by the inertial measurement unit 12, the receiver 16 of GNSS satellite positioning measurements, and the optional receiver 18 of complementary measurements 26 to obtain position and speed information by taking advantage of the three elements 12, 16 and 18.

[0052] Kalman filtering is based on the possibilities of modeling the evolution of the state of a physical system considered in its environment, by means of a so-called "evolution" equation (a priori estimation), and of modeling the relationship of dependence existing between the states of the physical system considered and the measurements of an external sensor, by means of a so-called "observation" equation to allow a recalibration of the states of the filter (a posteriori estimation). In a Kalman filter, the effective measurement or "measurement vector" makes it possible to carry out an a posteriori estimate of the state of the system which is optimal in the sense that it minimizes the covariance of the error made on this estimate. The estimator part of the filter generates a posteriori estimates of the state vector of the system by using the difference observed between the effective measurement vector and its a priori prediction to generate a corrective term, called innovation.This innovation, after multiplication by a gain vector of the Kalman filter, is applied to the a priori estimate of the state vector of the system and leads to obtaining the optimal a posteriori estimate.

[0053] The Kalman filtering implemented by the set K of Kalman filters models the evolution of the errors of the inertial measurement unit 12 and delivers the a posteriori estimate of these errors which is used to correct the positioning and velocity point of the inertial measurement unit 12.

[0054] The correction 28 of the errors by means of their estimation made by the set K of Kalman filters is then carried out at the input of the virtual platform 14 according to a so-called “closed loop” architecture as illustrated by FIG. 1 making it possible to keep navigation errors low and therefore to remain in the linear domain of the set K of Kalman filters. The virtual platform 14 uses such a correction 28 to develop the optimal estimate 30 of the position and speed of the vehicle. The hybridization is said to be “loose” (or hybridization in geographic axes) when the receiver 16 of GNSS satellite positioning measurements provides the position and speed of the vehicle resolved by the GNSS receiver.

[0055] Hybridization is said to be "tight" when the GNSS satellite positioning measurement receiver 16 provides the information extracted upstream by the GNSS receiver, namely the pseudo-distances and pseudo-speeds (quantities directly derived from the measurement of the propagation time and the Doppler effect of the signals emitted by the satellites in the direction of the receiver).

[0056] With such a closed-loop INS / GNSS hybrid navigation and positioning device 10 where the point resolved by the GNSS receiver 16 is used to recalibrate the information coming from the inertial measurement unit 12, it is necessary to monitor the faults affecting the information provided by the satellites because the receiver 16 which receives them will propagate these faults to the inertial measurement unit 12, causing poor recalibration of the latter.

[0057] To do this, the set K of Kalman filters according to the present invention has a particular architecture illustrated by figure 2.

[0058] The set K firstly comprises a main closed-loop Kalman filter 32 configured to implement a hybridization of the satellite positioning data provided by said receiver and non-satellite positioning data provided at least by said inertial measurement unit, in other words a hybridization of position measurements, received as input, and obtained respectively from said GNSS satellite positioning measurements 24, and measurements 34 provided both by said inertial measurement unit 12 and by said optional receiver 18 of complementary measurements, in order to calculate corrections 36 of navigation data.

[0059] The set K further comprises, according to the present invention, a bank 38 of N secondary Kalman filters SF1, SF2, ...SFi , SF i+ i, ...SF N , operating at the deviations (i.e. the correction established by the main filter being applied, as detailed below, to the propagation phase of each secondary Kalman filter) with N a predetermined integer such that N > 1, or even preferentially N>1, each secondary Kalman filter of index i, with 1 < i ≤ N, being capable of reconfiguring itself (i.e. of copying the state vector and the covariance matrix of the main Kalman filter 32), according to the arrow 40 on the main Kalman filter at an instant (j - 1)T from the start of navigation of the vehicle, then periodically according to a period NxT, with T a predetermined duration.

[0060] For example if T = 2 hours and N = 12, the first secondary Kalman filter (i.e. sub-filter) SF 1(i=i) reconfigures itself on the main Kalman filter 32 at the start of navigation then 2x12 = 24 hours after the start of navigation and so on. The second secondary Kalman filter SF2(i=2) reconfigures itself two hours after the start of navigation then 2x12 + 2 = 26 hours after the start of navigation and so on.

[0061] Additionally, each secondary Kalman filter SF1, SF2, ... SFi, SF i+ i, ... SF Nis configured to calculate corrections 42 of navigation data, solely from non-satellite positioning data provided at least by said inertial measurement unit, in particular here by hybridization of position measurements, received as input, and obtained solely from measurements 34 provided by said inertial measurement unit 12 and provided by said optional receiver 18 of complementary measurements, and does not accept as input, unlike the main Kalman filter 32, the positioning measurements 24 by GNSS satellites.

[0062] According to a more basic embodiment, not shown, each secondary Kalman filter SF1, SF2, ...SFi, SF i+ i, ... SF Nis configured to calculate corrections 42 of navigation data, solely from the non-satellite positioning data provided by said inertial measurement unit, while ignoring the non-satellite data provided by the optional receiver 18.

[0063] According to an optional variant, the N secondary Kalman filters SF1, SF2, ... SFi, S +1, ... SF N are identical and independent, the NxT period corresponding to a period of verification of the integrity of said GNSS satellite positioning measurements 24.

[0064] According to a particular variant of the present invention, the device 10, part of which is shown in FIG. 2, is also configured to control, at each instant n + 1, the integrity of said GNSS satellite positioning measurements by comparing, with a predetermined threshold, the difference between the state 44 of each secondary filter SF1, SF2, ... SFi, SF+1, ... SFN and the state 46 of the main Kalman filter 32. The element 48 of FIG. 2 determines such a difference and compares it with a predetermined threshold 50.

[0065] In the event of a deviation greater than said predetermined threshold 50, the device 10 is configured to raise, according to the arrow 52 of FIG. 2, an alarm capable of signaling a vulnerability of said GNSS satellite positioning measurements at said instant n + 1.

[0066] As an optional addition, the device 10 is also configured, by means of a calculation tool not shown in FIG. 2, to determine said predetermined threshold 50 as a function of a probability of false alarm, as detailed below in relation to FIG. 4.

[0067] As an optional addition, as illustrated by the embodiment represented by Figure 2, in the event of an alarm being raised illustrated by arrow 52, ​​the main Kalman filter 32 is also configured to reconfigure itself, according to arrow 54, on a secondary Kalman filter SF P predetermined index p with 1 < p ≤ N. By "reconfigure", we mean that the main Kalman filter 32 is capable of copying the state vector and the covariance matrix of the secondary Kalman filter SF P .

[0068] According to a complementary variant of this optional addition, said SF filter Pof predetermined secondary Kalman filter of index p on which the main Kalman filter 32 is capable of reconfiguring itself, according to arrow 54, in the event of an alarm being raised 52, is the secondary Kalman filter among said N secondary Kalman filters whose reconfiguration 40 on the main Kalman filter 32 is temporally the furthest from the instant n + 1 of alarm raising.

[0069] According to another complementary variant of this optional addition, said main Kalman filter 32 is configured to no longer use as input said GNSS satellite positioning measurements 24 from the moment when the main Kalman filter 32 initiates its reconfiguration. In other words, as soon as the reconfiguration of the main Kalman filter 32 is commanded, the GNSS satellite positioning measurements 24 (whose vulnerability is detected) are no longer used, for example by sending a command to deselect these measurements as input to the set K of Kalman filters.

[0070] According to another complementary variant of this optional addition, in the event of alarm 52 being raised, each secondary Kalman filter of index i #= p is also configured to reconfigure itself on said predetermined secondary Kalman filter of index p, which makes it possible to restore a set K of main Kalman filters 32 and secondary Kalman filters SF1, SF2, ... SFi , SF i+ i, ... SF N completely healthy.

[0071] According to another optional complementary aspect, the device 10 is also configured to determine a protection radius 58 with respect to a vulnerability of said GNSS satellite positioning measurements, said protection radius ensuring that the value of the distance between the hybrid position 30 provided from said main Kalman filter 32 and the true position of said vehicle is less than the value of said protection radius 58, said protection radius 58 depending on the number N of secondary Kalman filters.

[0072] Indeed, to quantify the integrity of a position measurement in applications such as naval or aeronautical applications, where integrity is critical, such a parameter of protection radius of the position measurement is generally used. The protection radius generally corresponds to a maximum position error for a given probability of error occurrence, that is to say that the probability that the position error exceeds the announced protection radius without an alarm being sent to a navigation system, is lower than this given probability value. The calculation is based on two types of error which are on the one hand normal measurement errors and on the other hand errors caused by an operating anomaly of the satellite constellation, for example a satellite failure.The value of the protection radius of a positioning system is a key value specified by clients wishing to acquire a positioning system. The evaluation of the value of the protection radius generally results from probability calculations using the statistical characteristics of the precision of GNSS measurements and the behavior of inertial sensors. These calculations are explained formally and allow simulations for all GNSS constellation cases, for all possible positions of the positioning system on the Earth and for all possible trajectories followed by the positioning system. The results of these simulations make it possible to provide the client with protection radius characteristics guaranteed by the proposed positioning system.Most often these characteristics are expressed in the form of a protection radius value for 100% availability or an unavailability duration for a required protection radius value.

[0073] The determination of the protection radius implemented according to the present invention using the particular architecture of the set K of Kalman filters mentioned above is described below in more detail in relation to figure 4.

[0074] According to another optional complementary aspect, the device 10 is also configured to provide, at output, in parallel, navigation solutions 60 and 62 respectively associated with said main Kalman filter, and with said SF bank of N secondary Kalman filters.

[0075] In other words, the device 10 according to the present invention allows parallel navigation by distinct Kalman filters, namely via the navigation solutions 62 associated with the N secondary Kalman filters SF1, SF2, ... SF i , SF i+ i, ... SF N and via the navigation solutions 60 respectively associated with the main Kalman filter 32. Such an architecture thus makes it possible to provide secondary navigation solutions, in terms of position, speed, attitude, in parallel with the solution provided by the main filter, such secondary navigation solutions being suitable for being useful for certain types of navigation, particularly underwater.

[0076] For example, for the position, at each cycle of the main Kalman filter 32, the device 10 according to the present invention is capable of applying the correction Cor SF of each secondary Kalman filter (i.e. sub-filter) SF1, SF2, ... SFi, SF i+i , ... SFN in the main position state X n+1 / n (ie associated with the main INS / GNSS solution delivered by the main Kalman filter 32, n and n + 1 being successive time instants) to obtain the position state associated with the SF sub-filter: and the same for latitude Lat SF , there longitude Lon SF , and the altitude Alt SF , such as:

[0077] According to a variant not shown, the navigation and positioning device 10 according to the present invention comprises a processing unit formed for example of a memory and a processor associated with the memory, and the device 10 is at least partly produced in the form of software, or a software brick, executable by the processor, in particular the set K of Kalman filters, the virtual platform 14 for calculation and localization, the element 48 of FIG. 2 configured to determine a difference between the state of each secondary filter and the state of the main Kalman filter, and compare this difference to a predetermined threshold 50, and optionally the calculation tool configured to determine said threshold 50. The memory of the navigation and positioning device 10 is then capable of storing such software or software bricks, and the processor is then capable of executing them.

[0078] In a variant not shown, the set K of Kalman filters, the virtual platform 14 for calculation and localization, the element 48 of FIG. 2 configured to determine a difference between the state of each secondary filter and the state of the main Kalman filter, and to compare this difference to a predetermined threshold 50, and optionally the calculation tool configured to determine said threshold 50 are each produced in the form of a programmable logic component, such as an FPGA (Field Programmable Gate Array), or in the form of a dedicated integrated circuit, such as an ASIC (Application Specific Integrated Circuit).

[0079] When a part of the navigation and positioning device 10 according to the present invention is produced in the form of one or more software programs, that is to say in the form of a computer program, this part is furthermore capable of being recorded on a medium, not shown, readable by a computer. The computer-readable medium is, for example, a medium capable of storing electronic instructions and of being coupled to a bus of a computer system. By way of example, the readable medium is an optical disk, a magneto-optical disk, a ROM memory, a RAM memory, any type of non-volatile memory (for example EPROM, EEPROM, FLASH, NVRAM), a magnetic card or even an optical card. A computer program comprising software instructions is then stored on the readable medium.

[0080] Figure 3 illustrates the principle of the closed loop applied to the main Kalman filter 32, with the position state X of the main Kalman filter 32, and P its covariance matrix. A propagation module 64 of the main Kalman filter 32 is configured to propagate the state using the navigation equations, and a recalibration module 66 makes it possible to estimate the state using the GNSS measurements provided by said receiver 16 of GNSS satellite positioning measurements and the measurements of the optional receiver 18 of complementary measurements provided by at least one piece of equipment separate from said receiver 16 of GNSS satellite positioning measurements and separate from said inertial measurement unit 12. The closed-loop propagation and recalibration equations are for the recalibration implemented by the module 66: and for the propagation implemented by module 64: with F the propagation matrix, Q the model noise matrix, R the measurement noise covariance matrix, H the observation matrix, K the Kalman gain and Z the observation vector obtained from receivers 16 and 18, X n+1 / n the propagated position state vector after propagation between the two successive time instants n and n + 1. The Cor correction n+i is applied by a correction module 68 to the navigation data to obtain the X position state n+1 / n+1 and stored in memory M2, as well as the covariance matrix P n+1 / n in memory Mi, for a following iteration at time n + 1.

[0081] This principle also applies to each secondary Kalman filter (i.e. sub-filter) SF1, SF2, ... SFi, SF i+i , ... SF N , each sub-filter SF1, SF2, ... SFi, SF i+i , ... SF Nusing the observation matrix H, the measurement noise R and the measurements Z of the observations from the receiver 18 of complementary measurements provided by at least one piece of equipment separate from said receiver 16 of GNSS satellite positioning measurements and separate from said inertial measurement unit 12, a set K of Kalman filters, but in no case the GNSS measurements from the receiver 16 of GNSS satellite positioning measurements.

[0082] More precisely, for each secondary Kalman filter (i.e. sub-filter) SF1, SF2, ... SFi, SF i+ i, ... SFN, we use the classical Kalman filter equations by applying the Cor INS / GNSS correction of the main Kalman filter 32 at the time of propagation.

[0083] The propagation and recalibration equations are therefore implemented for the recalibration within each secondary Kalman filter operating at the deviations (i.e. the correction established by the main filter being applied, as detailed below, to the propagation phase of each secondary Kalman filter): and for propagation: with Cor n the correction from the main Kalman filter 32, Z SF the observation vector which is a subset of Z of the main Kalman 32 containing only the observations obtained from the receiver 18, and not from the receiver 16 of GNSS satellite positioning measurements, H SF the observation matrix which contains the lines of H of the main Kalman filter 32 linked to the observations of the secondary filter considered in turn (i.e. in other words H SF contains zeros for the part associated with GNSS satellite positioning measurements) K SFis the gain of the Kalman filter for the secondary sub-filter considered, P SF is the covariance matrix of the Kalman filter for the secondary sub-filter considered.

[0084] An example of operation of the navigation and positioning device 10 according to the present invention is now described below in relation to FIG. 4.

[0085] More specifically, the navigation and positioning method 70 implemented by said navigation and positioning device 10 comprises the steps described below implemented in parallel or successively one after the other or vice versa.

[0086] According to step 72, as indicated previously, the navigation and positioning device 10 according to the present invention implements a localization of said vehicle using the corrections provided respectively by the main Kalman filter and by the bank of N secondary Kalman filters.

[0087] According to an optional aspect, such a location is suitable for allowing parallel navigation by distinct Kalman filters, namely via the navigation solutions 60 associated with the N secondary Kalman filters SF1, SF2, ... SFi, SF i+ i, ... SF N and via the navigation solutions 62 respectively associated with the main Kalman filter 32.

[0088] In parallel with step 72, or successively to this step 72 or vice versa (i.e. before this step 72), a step 74 of verifying the integrity of said GNSS satellite positioning measurements is implemented by the navigation and positioning device 10 according to the present invention.

[0089] According to a particular embodiment illustrated by figure 2, said verification 74 notably comprises a sub-step 76 of determining the state of each secondary filter and the state of the main Kalman filter, between two successive time instants n and n + 1, and the difference E between the state of each secondary filter and the state of the main Kalman filter.

[0090] Optionally, said verification step 74 also comprises a sub-step 78 of determining a threshold S suitable for being compared to the difference E between the state of each secondary filter and the state of the main Kalman filter.

[0091] As an alternative, not shown, to sub-step 78 of determining a threshold S, said threshold S is directly provided and determined outside of said navigation and positioning device 10.

[0092] Then, during step 80, the navigation and positioning device 10 according to the present invention implements the comparison, at each instant n + 1, of the difference E, between the state of each secondary filter SF1, SF2, ... SFi, SF i +1, ... SFN and the state of the main Kalman filter 32, at said threshold S.

[0093] During a step 82, the lifting of an alarm A is triggered or not.

[0094] More precisely, in the absence of a deviation greater than said predetermined threshold S, said absence being represented by the branch 84, no alarm is raised, and during a sub-step 86, the navigation and positioning device 10 then implements the determination RP of a protection radius with respect to a vulnerability of said GNSS satellite positioning measurements, said protection radius guaranteeing that the value of the distance between the hybrid position provided from said main Kalman filter 32 and the true position of said vehicle is less than the value of said protection radius, said protection radius depending on the number of secondary Kalman filters.

[0095] On the other hand, in the presence of a deviation greater than said predetermined threshold, said presence being represented by branch 88, the raising of an alarm capable of signaling a vulnerability of said GNSS satellite positioning measurements at said instant n + 1 is triggered as well as a subsequent step 90 of reconfiguration of the navigation and positioning device 10.

[0096] Step 90 comprising a first sub-step 92 of reconfiguration Ri of the main Kalman filter 32 on a predetermined secondary Kalman filter of index p with 1 < p ≤ N, a second sub-step 94 of reconfiguration R2 of each secondary Kalman filter of index i #= p on said predetermined secondary Kalman filter of index p, a third sub-step 96 of deselection GNSS_D of the input of the main Kalman filter dedicated to the positioning measurements by GNSS satellites from the moment when the main Kalman filter initiates its reconfiguration.

[0097] Below, steps of said method 70 according to the present invention are detailed in more detail. In particular, during sub-step 76, the difference between the state of each secondary filter and the state of the main Kalman filter is determined because if a GNSS measurement is erroneous, it will corrupt the main INS / GNSS solution from the main Kalman filter 32, but not certain solutions from the sub-solutions provided by the N secondary filters capable of carrying out a time-shifted recalibration according to the present invention. Such a difference between the different solutions respectively associated with the different filters of the set K of Kalman filters will result in a gap between the position states of the sub-filter X FF 1 / n and the main filter X n+1 / nMore or less important depending on the state studied, and inconsistent with the covariance of the deviation of the states, the X position states in question corresponding for example to heading states, position states, speed states, etc.

[0098] During sub-step 80, we seek to control over time for each sub-filter the deviation by comparing it, via a predetermined threshold, to the covariance of the deviation of the states.

[0099] Indeed, considering that the observation matrices of each sub-filter H SF and measurement noise R SFare sub-matrices of the observation matrices H and measurement noise R of the main Kalman filter 32 where the rows (respectively columns) related to the GNSS measurements have been set to zero (the rest being identical between sub-filter and main filter), and that the propagation matrices F and model noise Q are identical between sub-filter and main filter, then it is demonstrable by recurrence by the person skilled in the art that the expectation is equal to the difference of matrix of covariance P SF - P, which when developed comes back to an expectation of equal to P the covariance matrix of the main Kalman filter 32.

[0100] According to an optional additional aspect, as described previously, the navigation and positioning device 10 according to the present invention itself determines during step 78, said threshold used to compare the difference X n+1 / n to the covariance of the deviation of the states.

[0101] In particular, during this sub-step 78, according to a false alarm probability noted P fa we seek to establish a real threshold value such that: being respectively provided by the secondary Kalman filter considered and by the primary Kalman filter, the secondary Kalman filter containing less information than the primary Kalman filter 32, then by construction Furthermore, the difference also follows, by construction of the Kalman filters a centered Gaussian law of standard deviation Considering for example, a distribution of a Gaussian law centered at zero and with a standard deviation equal to one. The detection threshold is chosen for this example so that 1% of the time an error is detected which is not present (P fa = 0.01), which in this example leads to what amounts to mathematically for a centered variable X and standard deviation equal to one and therefore to the threshold S such that: Thus, as illustrated by test substep 82, when according to branch 84, no alarm is raised because there is then no problem detected on the GNSS measurements.

[0102] On the other hand, according to branch 88, when a anomaly in GNSS measurements is detected and an alarm is raised.

[0103] It should be noted that test 82 is performed, depending on the application, on certain controlled states such as position, speed, attitudes, sensor fault states, etc., and this for the N sub-filters of the architecture.

[0104] As indicated previously, in the absence 84 of a deviation greater than said predetermined threshold S, no alarm is raised, and during a sub-step 86, the navigation and positioning device 10 then implements the determination of a protection radius with respect to a vulnerability of said GNSS satellite positioning measurements, said protection radius guaranteeing that the value of the distance between the hybrid position provided from said main Kalman filter 32 and the true position of said vehicle is less than the value of said protection radius, said protection radius depending on the number of secondary Kalman filters.

[0105] To determine such a protection radius, which is fully predictive, from the covariances of the main Kalman filter 32 and the secondary Kalman filters SF1, SF2, ... SFi, SF+1, ... SF N , the navigation and positioning device 10 introduces a probability of non-detection Pnd .

[0106] In particular, taking as an example the state of latitude noted X iat , the protection radius R p is then defined as follows: with the sum over the N secondary Kalman filters, P iat the diagonal of the covariance matrix corresponding to the latitude state

[0107] Considering the simplest case where N =1, which amounts to using a single secondary Kalman filter, \X iat - lat vraie | is bounded with the state of this secondary Kalman filter which potentially can detect the GNSS failure (i.e. a vulnerability of the GNSS measurements):

[0108] Moreover, as seen previously, at the time of detection so that the whole point of the protection radius lies in the fact that at this same moment of detection:

[0109] So taking and considering that suit une centered Gaussian law of standard deviation The determination of the protection radius comes down to finding the coefficient such that: so that it is then possible to guarantee that if the gap is lower than at the time of the GNSS failure, then with a probability of non-detection of the failure of P nd and then:

[0110] Considering the most complex case where N #1, which amounts to using a plurality of secondary Kalman filters, making no assumption about the duration of the failure except that the failure cannot be undetected for (N - 1). T hours, where T is the reconfiguration period of the sub-filters, then the protection radius preferably corresponds to the maximum value of the protection radii of each of the sub-filters such that: and this as long as the failure detection did not raise an alarm. Once the alarm is raised, the protection radius can propagate with the error value in the following manner: so it is then preferable to stop the reconfiguration mechanism according to arrow 40 of figure 2 of each secondary Kalman filter SF1, SF2, ... SF i , SF i+ i, ... SF N on the main Kalman filter 32.

[0111] It should be noted that the previous example of determining the protection radius developed from the latitude state can be generalized to other states such as other position states (longitude, altitude) or even to speed or attitude and heading states.

[0112] Regarding the reconfiguration sub-step 90, it should be noted that the proposed Kalman filter set K architecture has the ability to provide a navigation solution not corrupted by the GNSS failure. Indeed, once the alarm is raised, just like the secondary Kalman filters SF1, SF2, ... SFi, SF i+ i, ... SF Nwere, prior to this alarm being raised, periodically and offset reconfigured on the main Kalman filter 32, it is possible to reconfigure the main Kalman filter 32 on an uncorrupted secondary Kalman filter, the choice of which depends on the desired application, a preferential and conservative choice being to take the secondary Kalman filter which was reset the earliest on the main Kalman filter 32 assuming that the GNSS failure cannot go undetected for more than (N - l). T hours. Thus during the reconfiguration, the covariance matrix of the main Kalman filter 32 is overwritten by that of the selected healthy sub-filter.

[0113] The architecture of the navigation and positioning device 10 according to the embodiment of Figures 1 and 2 was tested by considering the application example considering the trajectory of a vehicle corresponding to a surface vessel at 10 m / s (19.4 kts) for 4 days, a GNSS longitude drift command after 69.4 hours (250,000 seconds) of navigation, the drift taking the value 0.25 m / s (0.46 kts), the use, within this vehicle, of a high-performance INS inertial unit having a typical gyrometric drift of 0.01 7h, a multi-filter architecture of the set K as described previously and consisting of twelve secondary Kalman filters (sub-filters) reset every 24 hours with a 2-hour time lag between them, a false alarm probability P fa taken at 10 -5 / hour and a probability of non-detection taken at 0.1% to calculate the detection threshold S and the protection radius.

[0114] As a result, such an architecture of the navigation and positioning device 10 according to the embodiment of Figures 1 and 2 detects a GNSS longitude drift after 3 hours 30 minutes, i.e. a position drift of 1.6 NM, and the determined protection radius converges towards 6.2 NM and is much greater than the difference between the hybrid position and the true position at the time of detection. In addition, in the absence of a GNSS failure, the hybrid position provided by the main Kalman filter 32 and the GNSS position provided, for example, by a GPS have less than 10 meters of error relative to the true position, while the positions provided by the twelve secondary Kalman filters are within a radius of 0.5 NM around the true position in accordance with the inertial performance of the chosen INS inertial unit. Thus, the positions are all contained within the protection radius determined according to the present invention.

[0115] Furthermore, upon detection of a GNSS failure, the GPS position and the hybrid 62 position provided by the primary Kalman filter drifted by approximately 1.6 NM in longitude while the 60 positions provided by the secondary Kalman filters SF1, SF2, ... SF i , SE+1 , ... SF N generally remained within a radius of 0.5 NM around the true position. Only a secondary Kalman filter was partially trained because it was reset on the primary Kalman filter after the occurrence of the drift on the GNSS longitude. Thus, the protection radius determined according to the present invention covers the hybrid position well.

[0116] Those skilled in the art will understand that the invention is not limited to the embodiments described, nor to the particular examples of the description, the embodiments and variants mentioned above being suitable for being combined with each other to generate new embodiments of the invention.

[0117] The present invention thus proposes an architecture of a set of time-shifted Kalman filters making it possible to maintain the integrity of the positioning independently of the vulnerability of the GNSS measurements by comparison - on the one hand of the primary position resulting from the classic INS / GNSS hybridization carried out by means of a main Kalman filter 32 using as input all the available measurements: GNSS, external position, zero displacement recalibration when the vehicle is stationary, electromagnetic log measurement and a dynamic model of the vehicle, Doppler log or DVL, depth measurement, recalibration by radar, by imaging, recalibration by opportunity signals, etc.

[0118] - with on the other hand a subset of secondary positions provided by a bank of N secondary Kalman filters not using GNSS measurement input for a certain duration (eg of the order of a few hours), which amounts to using partial measurements.

[0119] Such control is not carried out on only one satellite at a time, which avoids limiting the application area.

[0120] In addition, the navigation and positioning device 10 is, according to an optional aspect, capable of permanently offering a set of navigation solutions, some of which have not used GNSS measurements for a certain time, typically from several hours to several days.

[0121] Furthermore, according to an optional embodiment, the navigation and positioning device 10 is also capable of providing a protection beam against a vulnerability of the GNSS measurements and capable of triggering a reconfiguration of the main Kalman filter on a solution of a secondary Kalman filter if a vulnerability of the GNSS measurements is detected. Thus, a healthy fallback solution is permanently available in the event of detection of a problem on the GNSS signals.

[0122] In other words, the present invention makes it possible to maintain the integrity of the location, to warn when the GNSS signals are not reliable, to reconfigure itself on a solution not tainted by the vulnerability of the GNSS measurements, in other words, to reconfigure the main Kalman filter on a "healthy" secondary Kalman filter, and to have available a panel of navigation solutions deduced from secondary Kalman filters having navigated without the GNSS measurement for a variable time.

Claims

CLAIMS 1. Navigation and positioning device (10), suitable for being mounted on board a vehicle suitable for moving between two distinct geographical positions, the device comprising at least: - an inertial measurement unit (12) capable of providing navigation measurements, - a receiver (16) for positioning measurements by GNSS satellites, - a closed-loop main Kalman filter (32) configured to calculate navigation data corrections by hybridization of satellite positioning data provided by said receiver and non-satellite positioning data provided at least by said inertial measurement unit, the device being characterized in that it further comprises a bank (38) of N secondary Kalman filters (SF1, SF2, ... SF i , SF i+ i, ...SF N) in closed loop with N a predetermined integer such that N ≥ 1, each secondary Kalman filter being configured to calculate navigation data corrections only from non-satellite positioning data provided at least by said inertial measurement unit, each secondary Kalman filter of index i, with being able to reconfigure itself on the main Kalman filter at an instant from the start of vehicle navigation, then periodically according to a period NxT, with T a predetermined duration, the N secondary Kalman filters (SF1, SF2, ... SFi, SF i+ i, ... SF N ) being identical and independent, the period NxT corresponding to a period of verification of the integrity of said positioning measurements by GNSS satellites, the device (10) also being configured to: - check, at each instant n + 1, the integrity of said GNSS satellite positioning measurements by comparing, with a predetermined threshold, the difference between the state of each secondary filter (SF1, SF2, ...SFi, SF i+ i, ... SF N ) and the state of the main Kalman filter (32), and - in the event of a deviation greater than said predetermined threshold, raise an alarm to signal a vulnerability of said GNSS satellite positioning measurements at said time n + 1.

2. Device (10) according to claim 1, wherein the device (10) is also configured to determine said predetermined threshold as a function of a probability of false alarm.

3. Device (10) according to claim 1 or 2, wherein, in the event of an alarm being raised, the main Kalman filter (32) is also configured to reconfigure itself on a predetermined secondary Kalman filter of index p with 1 ≤ p ≤ N.

4. Device (10) according to claim 3, in which said predetermined secondary Kalman filter of index p on which the main Kalman filter (32) is capable of reconfiguring itself in the event of an alarm being raised, is the secondary Kalman filter among said N secondary Kalman filters whose reconfiguration on the main Kalman filter is temporally the furthest from the time of alarm raising.

5. Device (10) according to claim 3 or 4, wherein said main Kalman filter is configured to no longer use said GNSS satellite positioning measurements as input from the moment when the main Kalman filter (32) initiates its reconfiguration.

6. Device (10) according to any one of claims 3 to 6, wherein in the event of an alarm being raised, each secondary Kalman filter of index i #= p is also configured to reconfigure itself on said predetermined secondary Kalman filter of index p.

7. Device (10) according to any one of the preceding claims, wherein the device is also configured to determine a protection radius with respect to a vulnerability of said GNSS satellite positioning measurements, said protection radius ensuring that the value of the distance between the hybrid position provided from said main Kalman filter and the true position of said vehicle is less than the value of said protection radius, said protection radius depending on the number N of secondary Kalman filters.

8. Device (10) according to any one of the preceding claims, in which the device (10) is also configured to provide, at output, in parallel, navigation solutions respectively associated with said bank of N secondary Kalman filters, and with said main Kalman filter (32).

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