method for generating a protection ray in case of RAIM unavailability

Abstract

Method comprising a current iteration, the current iteration comprising: availability evaluation (100, 200) of an input protection radius () associated with a current instant (t), resulting from an autonomous receiver integrity check, RAIM, implemented by a satellite signal receiver, and relating to the estimate of a navigation quantity of a carrier; selection (102, 202) of a data item, in which: when the input protection radius () is available, the selected data item is a current data item obtained at the current iteration depending on the input protection radius (), otherwise the selected data item is a previous data item selected during a selection made at a previous iteration; determining (108, 114, 208, 212) an exit protection radius (HPL(), HPL(t)) from the selected data and an estimated covariance matrix resulting from a last prediction implemented by a Kalman filter.Figure for abstract: Figure 2.

Description

Title of the invention: Method for generating a protection beam in the event of RAIM unavailability Technical field

[0001] The present disclosure relates to a method generating protection rays. The integrity control finds advantageous application in assisting the navigation of a wearer. STATE OF THE ART

[0002] A GNSS receiver is a device capable of providing an estimate of the position of a carrier based on signals emanating from a satellite constellation. This estimate is affected by a greater or lesser error compared to the actual position of the carrier.

[0003] The position estimate provided by a GNSS receiver is conventionally merged with other data to generate a carrier navigation solution. Such data fusion is commonly referred to as "hybridization" or "coupling". In particular, when the other data comes from an inertial unit, it is generally referred to as "IRS / GNSS" fusion.

[0004] It is also known to incorporate into a GNSS receiver a RAIM (“Receiver Autonomous Integrity Monitoring”) algorithm, which aims, as its name indicates, to control the integrity of the position developed by the GNSS receiver. In particular, a function of this RAIM control is to calculate a protection radius relating to the estimate of the position of the carrier. In a manner known per se, the protection radius is an estimated limit of the maximum tolerable error between the estimate of the position provided by the GNSS receiver and the true position of the carrier.

[0005] However, it may happen that a GNSS receiver equipped with a RAIM algorithm is not capable of producing, at a given moment, such a protection radius. We then say that the integrity function is unavailable. Such a situation occurs for example when the GNSS receiver receives signals from an insufficient number of satellites (in other words, the GNSS receiver does not "see" enough satellites).

[0006] The DO-384 standard (RTCA document "Minimum Operating Performance Standard for GNSS aided inertial System") defines three different categories of integrity algorithms in an IRS / GNSS fusion context. • Category 0: IRS / GNSS fusion does not provide additional satellite integrity and failure detection (SISF) beyond the capabilities of a GNSS receiver implementing RAIM. • Category 1: IRS / GNSS fusion provides integrity and detection of additional satellite outage beyond the capabilities of the GNSS receiver, and continues to provide a protective radius. • Category 2: IRS / GNSS fusion provides additional integrity, satellite failure detection and exclusion capability over and above the capabilities of the GNSS receiver, and continues to provide a protection radius.

[0007] Categories 1 and 2 of the DO-384 standard are thus intended to extend the integrity capabilities of the GNSS receiver. However, to achieve this goal, it is necessary to implement complex architectures with multiple filters, which are very expensive in terms of computational load. Statement of the invention

[0008] An aim of the present disclosure is to provide a protection radius even when the RAIM of a GNSS receiver is not available, without implementing a complex autonomous failure detection or exclusion function.

[0009] For this purpose, according to a first aspect, a computer-implemented iterative method is proposed, the method comprising a current iteration associated with a current instant and a previous iteration associated with a previous instant, the current iteration comprising the following steps: • reception of Kalman data including: • an estimated covariance matrix, the estimated covariance matrix resulting from a last prediction implemented by a Kalman filter, • assessment of the availability of an input protection radius associated with the current instant, resulting from an autonomous receiver integrity check, RAIM, implemented by a satellite signal receiver, and relating to the estimate of a navigation quantity of a carrier, the estimate being provided by the satellite signal receiver, • selection of data based on the availability assessment, in which: • when the input protection radius is available, the selected data is a current data obtained at the current iteration, and dependent on the input protection radius, • when the input protection radius is unavailable, the selected data is a previous data having been selected during an implementation of the selection step carried out during the previous iteration, • determination of an exit protection radius, from the selected data and the Kalman data.

[0010] The method according to the first aspect may also comprise the following optional features, taken alone or combined with each other whenever this is technically feasible.

[0011] Preferably: • the estimated covariance matrix is associated with an arrival time of the last prediction implemented by the Kalman filter, • in the determination step, an exit protection radius associated with the arrival time is determined, from the selected data and the estimated covariance matrix.

[0012] Preferably: • the estimated covariance matrix is associated with an arrival time of the last prediction implemented by the Kalman filter, • Kalman data further includes: • an evolution matrix allowing the estimated covariance matrix to be propagated from the arrival time to the current time, • an evolution noise covariance matrix allowing the estimated covariance matrix to be propagated from the arrival time to the current time, • in the determination step, an output protection radius associated with the current time is determined, from the selected data, the estimated covariance matrix, the evolution matrix and the evolution noise covariance matrix.

[0013] In one embodiment, the current data is a current weighting coefficient calculated as a ratio between: • the input protection radius associated with the current time, and • a standard deviation relating to the carrier's navigation magnitude, calculated at from the estimated covariance matrix.

[0014] Preferably, the exit protection radius associated with the arrival time is calculated as a product between: • a weighting coefficient depending on the selected data, and • the standard deviation relating to the carrier's navigation magnitude.

[0015] Preferably, the exit protection radius associated with the current time is calculated as a product between: • a weighting coefficient depending on the selected data, and • a standard deviation associated with the current instant resulting from a propagation of the standard deviation relating to the carrier's navigation quantity using the evolution matrix and the evolution noise covariance matrix.

[0016] Preferably, the method according to the first aspect comprises steps of: • calculation of a reference coefficient KO from a predefined probability value p, the reference coefficient KO satisfying the following formula:

[0017] p = Probability ( [> KQ.o(tk) )

[0018] where e denotes the error affecting the estimate provided by the satellite signal receiver, and o(tk) denotes the standard deviation relating to the carrier's navigation magnitude, • determination of a maximum between the selected data and the reference coefficient, the maximum constituting the weighting coefficient depending on the selected data.

[0019] In another embodiment, wherein the current data is the entrance protection radius.

[0020] Preferably, the method according to the first aspect comprises steps of: • calculation of a reference coefficient from a predefined PO probability value, the reference coefficient satisfying the following formula: [0021 ] P() = Probability ( | e | > KQ / j ( tk ) )

[0022] where e denotes the error affecting the estimate provided by the satellite signal receiver, and tk) denotes a standard deviation relating to the carrier's navigation magnitude and calculated from the estimated covariance matrix, • calculation of an intermediate protection radius associated with the arrival time, the intermediate protection radius being calculated as a product between: • the reference coefficient, and • the standard deviation relating to the carrier's navigation magnitude, • the exit protection radius associated with the arrival time being a maximum between: • the selected data, and • the intermediate protection radius associated with the arrival time.

[0023] Preferably, the method according to the first aspect comprises steps of • calculation of a reference coefficient KO from a predefined probability value PO, the reference coefficient KO satisfying the following formula:

[0024] PO = Probability (|e| > KQ.O(tk))

[0025] where e denotes the error affecting the estimate provided by the satellite signal receiver, and tk) denotes a standard deviation relating to the carrier's navigation magnitude and calculated from the estimated covariance matrix, • calculation of an intermediate protection radius associated with the current time as a product between: • the reference coefficient KO, and • a standard deviation associated with the current instant resulting from a propagation of the standard deviation using the evolution matrix and the evolution noise covariance matrix, • the exit protection radius associated with the current time being a maximum between: • the selected data, and • the intermediate protection radius associated with the current time.

[0026] Preferably, wherein the navigation quantity of the carrier is a position of the carrier.

[0027] A second aspect of the present disclosure is a computer program product comprising program code instructions for executing the steps of the method according to the first aspect, when this program is executed by a processor.

[0028] A third aspect of the present disclosure is a carrier navigation aid system, the system comprising: • a satellite signal receiver configured to provide an estimate of a carrier navigation quantity, and to implement an autonomous receiver integrity check producing a protection radius relating to the estimate of the carrier movement data, • an inertial unit configured to provide another estimate of the carrier's navigation magnitude, • a hybridization module configured to couple data including the estimate provided by the satellite signal receiver and the other provided by the inertial unit, so as to produce a navigation solution including a consolidated estimate of the carrier's navigation magnitude, • a processing module configured to implement the iterative method according to the first aspect. DESCRIPTION OF FIGURES

[0029] Other characteristics, aims and advantages of the invention will emerge from the following description, which is purely illustrative and non-limiting, and which must be read in conjunction with the appended drawings in which:

[0030] [Fig.l] schematically illustrates a navigation aid system according to a first embodiment of the invention.

[0031] [Fig.2] is a flowchart of steps of a method according to a first embodiment of the invention.

[0032] [Fig. 3] is a flowchart of steps of a method according to a second embodiment of the invention.

[0033] [Fig.4] shows examples of curves showing the evolution of a position error and a protection radius relating to this error.

[0034] Throughout the figures, similar elements bear identical references. DETAILED DESCRIPTION OF THE INVENTION 1) Navigation aid system

[0035] With reference to [Fig. 1], a navigation aid system for a carrier such as an aircraft, a land vehicle or a ship, comprises a satellite signal receiver 1, an inertial measurement unit 2, a location unit 4, a fusion module 6, and an integrity module 8.

[0036] The navigation aid system is intended to be installed on the carrier to assist in its navigation.

[0037] The satellite signal receiver 1, more simply called receiver 1 in the following, is configured to produce an estimate Psat of a navigation quantity of the carrier at a given time t, and this from signals emanating from a constellation of navigation satellites, and by applying a known method. The receiver 1 is typically of the GNSS type.

[0038] The receiver 1 is further configured to produce a protection radius relating to the estimate. This protection radius is the result of an implementation of a processing also known as “receiver autonomous integrity monitoring” (in English, “Receiver Autonomous Integrity Monitoring” or RAIM). The protection radius is for example horizontal. It is denoted HPLRA1M in the following.

[0039] In an ideal situation, the receiver 1 produces estimates of the navigation quantity (and the associated protection radii) periodically, for example according to a first frequency of 1 Hz (one estimate per second).

[0040] However, as indicated in the introduction, it may happen that the receiver 1 is not capable of producing, at a given time t, the HPLRAIM protection radius. It is then said that the integrity function of the receiver 1 is unavailable. Such a situation occurs for example when the GNSS receiver 1 receives signals from an insufficient number of satellites (in other words, the GNSS receiver 1 does not “see” enough satellites).

[0041] The inertial measurement unit 2 (called in English “Inertial Measurement Unit”) is known per se. It is configured to provide inertial measurements. Typically, the inertial measurement unit comprises inertial sensors such as gyrometers and accelerometers. The inertial measurements can thus comprise angular velocities and accelerations.

[0042] The location module 4 is configured to produce another estimate of the same navigation quantity of the carrier. In other words, the location unit 4 and the satellite signal receiver 1 constitute two independent estimators of this same navigation quantity.

[0043] More generally, the location module 4 is configured to produce an estimate of a navigation state of the carrier, this navigation state not necessarily being limited to the navigation quantity under discussion, but being able to comprise a plurality of navigation quantities, such as a position of the carrier, a speed of the carrier, an attitude of the carrier, etc.).

[0044] The location module 4 produces estimates of the magnitude periodically, for example according to a second frequency different from that of the receiver 1, or even higher than that of the receiver 1. For example, the product frequency estimated by the location module 4 is 100 Hz (one hundred estimates per second).

[0045] The inertial measurement unit and the location module 4 together form an inertial unit.

[0046] The fusion module 6 is configured to produce a carrier navigation solution from estimated data provided by the receiver 1 and by the location module 4. The carrier navigation solution comprises a consolidated estimate of the carrier navigation magnitude under discussion.

[0047] In the following, we will focus on a non-limiting embodiment in which the navigation quantity is a position of the carrier. Thus, the satellite signal receiver 1 and the location module 4 provide different estimates of the position of the carrier, denoted Psat and Pin, and the fusion module 6 uses these positions among other things to generate a navigation solution which includes a consolidated position of the carrier.

[0048] The fusion carried out by the fusion module 6 is known. In the literature, we speak of inertial / satellite coupling or inertial / satellite hybridization.

[0049] In particular, the coupling implemented by the fusion module 6 may be a “loose” type coupling (“loose hybridization” in English), when the fusion is carried out from the position of the carrier. Alternatively, the coupling implemented by the fusion module 6 is of the “tight” type (“tight hybridization”). In this variant, it is possible to use the satellite pseudo-ranges as observation for the fusion.

[0050] The fusion module 6 and the localization module 4 jointly implement a Kalman filter.

[0051] As is known, a Kalman filter is an infinite impulse response estimator that estimates the state of a dynamic system. Here, the dynamic system is the carrier, and the estimated state is the navigation state mentioned above.

[0052] A Kalman filter implements two fundamental steps: a propagation step, also called a prediction step in the literature, and an update step.

[0053] The propagation step takes as input a previous a posteriori estimate of the navigation state (associated with an instant tk.i), and generates a predicted estimate or a priori of the navigation state (associated with an instant tk), using in particular a propagation matrix PHItk^tk.

[0054] During the propagation step, the Kalman filter also takes as input an a posteriori covariance matrix estimated at the previous state, denoted Pk-ÿt-l and associated with the previous estimate of the navigation state and generates on its basis a predicted or a priori estimate of the covariance matrix, denoted Pj^-\ and associated with the estimate X / ^p. The Kalman filter uses for this the propagation matrix Pai as well as an evolution noise covariance matrix denoted Qk.

[0055] In theory, we have

[0056] PHItk_rtk =

[0057] where F verifies:

[0058]

[0059] In numerical mathematics, the integral appearing in this formula can be seen as a sum of elementary terms relating to contiguous elementary subintervals of the interval from 4-1 to 4- Thus, the propagation matrix can in practice be calculated as the product of elementary propagation matrices respectively associated with these elementary subintervals.

[0060] The update step generates an a posteriori estimate of the navigation state, noted from the a priori predicted estimate of the navigation state of observations Yk and an observation matrix H. The update step also generates an associated covariance matrix P^k, from the a priori covariant matrix P^-[ and the observation matrix.

[0061] The data x^ and P^ produced during the update are then used as input data in a new implementation of the Kalman filter propagation step.

[0062] In the navigation aid system shown in Figure 1, the steps of propagating and updating the covariance matrix P^ are implemented by the fusion module 6. Thus, the fusion module 6 uses the propagation matrix discussed previously.

[0063] Furthermore, the steps of propagation and updating of the navigation state are carried out by the localization module 4. The Kalman filter uses the data provided by the inertial measurement unit as observations during this update step.

[0064] Each time that the fusion module 6 implements a propagation in the Kalman sense, the fusion module 6 communicates to the integrity module 8 certain data, including the propagation matrix that it has just used for this propagation. In the Subsequently, these data are conventionally called “Kalman data” to indicate that they are data involved in the implementation of the Kalman filter.

[0065] The integrity module 8 is configured to implement an iterative processing which will be described below, based on data provided by the receiver 1, and data provided by the fusion module 6.

[0066] The integrity module 8 is timed to the operating period of the location module 4.

[0067] The integrity module 8 also has access to a read and write memory. This memory is adapted to store data received by the integrity module 8 or generated by the integrity module 8.

[0068] On the hardware level, the navigation aid system may comprise one or more processors to carry out the processing of the location module 4, the fusion module 6 and the integrity module 8. These modules may be different parts of a computer program executed by the or each processor. Alternatively, these modules are separate electronic circuits.

[0069] The navigation aid system further comprises a memory to which at least the integrity module 8 has access. The memory is adapted to store data received or generated by the integrity module. The memory is of any type: RAM, EEPROM, HDD, SDD, Flash, etc.

[0070] This memory typically stores a computer program comprising code instructions for the execution of a method implemented by the integrity module 8, and which will be described below. 2) Navigation aid process

[0071] A method implemented by the integrity module 8 is iterative, in the sense that it comprises successive iterations.

[0072] We will describe an iteration of this process, which is conventionally called the current iteration. This current iteration is preceded by a previous iteration.

[0073] The current iteration is associated with a current instant t at which the receiver 1 is supposed to provide a protection radius HPLRA1M relating to an estimate that the receiver 1 also provides to the fusion module 6.

[0074] The previous iteration is therefore similarly associated with a previous instant, separated from the current instant t by the operating period of receiver 1.

[0075] The current iteration associated with the current time t comprises the following steps.

[0076] The integrity module 8 receives Kalman data, which are provided by the fusion module 6. The Kalman data include in particular the a priori covariance matrix resulting from the last prediction implemented by the Kalman filter. However, it will be seen later that these Kalman data can also include other data.

[0077] The integrity module 8 evaluates whether a protection ray HPLRA1M(t) associated with the instant t is available. When this ray HPLRAIM(t) is available, this ray is received by the integrity module 8. The integrity module 8 therefore perceives the protection ray HPLRAlM as an entry protection ray associated with the instant t.

[0078] The integrity module 8 selects a data item based on the availability assessment. In other words, the selection made depends on whether the HPLRA1M ray is available or unavailable at time t. We will see below that the data item to be selected has different embodiments.

[0079] When the input protection radius HPLRA!M(j ) is available, the selected data is a current data obtained at the current iteration, and dependent on the input protection radius HPLRA1M( t) ■ By convention, in this text, “X depends on Y” covers the particular case X=Y. This means that the selected data can be HPLRA[M( t ), as we will see later.

[0080] When the input protection radius HPLR^IM( t) is unavailable, the selected data is a previous data item having been selected during an implementation of the selection step carried out during the previous iteration. This mechanism thus makes it possible to go back to the corresponding data item which depended on the last protection radius provided by the receiver, even if the receiver 1 has switched to a state of unavailability and is still in this state at the current iteration.

[0081] Then, the integrity module 8 determines at least one output protection radius from the selected data and the Kalman data. We will see below that this determination step also has different embodiments.

[0082] 2.1) Navigation assistance method - Embodiment 1

[0083] [Fig.2] shows a first embodiment of the navigation aid method, the general principles of which have been described above.

[0084] The availability evaluation step is referenced 100, and the selection step is referenced 102 in this figure.

[0085] In this first embodiment, the data which is the subject of the selection 102 is the input protection radius HPL^^Çt) itself, when it is available for the current instant t. If this radius is not available, then the protection radius HPLRAJM is selected which had itself been selected during the same selection step of the previous iteration. With this mechanism, the protection radius HPLraim selected is the last protection radius provided by the receiver 1, just before the receiver 1 switches to an unavailability phase.

[0086] After selection 102, the stored HPLraim is updated with the HPLRAJM that was selected.

[0087] In the context of the description of this first embodiment, the following are adopted: following notations: • HPLRAIM(t): protection radius provided by receiver 1 for time t, if available • HPLRA1M stored: protection radius having been selected at the previous iteration (therefore associated with the instant preceding t) and stored in the memory accessible by the integrity module 8. • tk: arrival time of the last prediction made by the Kalman filter, within fusion module 6. This time tk precedes the current time t. • <7 ( ): standard deviation relating to the navigation quantity, calculated from the covariance matrix P^ estimated by the Kalman filter during the last prediction made. This estimated covariance matrix Pty is part of the data resulting from this last prediction; it is therefore associated with the arrival time tk. This estimated covariance matrix P^ is part of the Kalman data that the integrity module 8 receives as part of the current iteration associated with time t. Alternatively, the integrity module 8 directly receives the standard deviation <7(4) which results from it. • PHItk-*t: evolution matrix allowing to propagate the estimated covariance matrix from the arrival time tk to the current time t. This matrix can be provided by the fusion module 6, in which case it is part of the Kalman data. • Q: evolution noise covariance matrix allowing the covariance matrix to be propagated from the arrival time tk to the current time t. This matrix is provided by the fusion module 6, it is part of the Kalman data.

[0088] The calculation of the standard deviation ) from the covariance matrix P^ can be carried out as follows. The covariance matrix comprises one or more diagonal terms which relate specifically to the navigation quantity considered (here, the position of the carrier). For example, if the position of the carrier is a triplet of coordinates, three diagonal terms of the covariance matrix P^ relate to the position, the standard deviation cr(^) is calculated as the root of one of these diagonal terms, for example the one which is of maximum value.

[0089] It should also be noted that the instant t is not normally one of the instants seen by the Kalman filter. We therefore have in principle: îk<'i ^+1- Now, we have seen previously that the evolution matrix PHItk.c*tk is calculable as the product of elementary matrices. The matrix PHIt^t is calculable in the same way, on the basis of the elementary matrices relating to the elementary subintervals which form the interval from tk to t. We therefore have:

[0090] l . PHI^tk+l

[0091] This equation illustrates the fact that PHIt^t constitutes a “partial” evolution matrix with respect to the matrix P used by the Kalman filter.

[0092] In a step 104, the integrity module 8 calculates a reference coefficient KO from a predefined probability value PO.

[0093] The reference coefficient KO is calculated so as to satisfy the following formula:

[0094] PO - Probability ( | e | > TTO.cr (t))

[0095] In this equality, e designates the error which affects the estimate provided by the receiver 1 of satellite signals at the current time t.

[0096] Thus, the reference coefficient KO that is calculated in step 104 is such that the value PO is equal to the probability that the absolute value of the error that affects the estimate provided by the receiver 1 of satellite signals at the current time t is greater than KO times the standard deviation a1) ■

[0097] Generally speaking, the formula for calculating the protection radius as a function of the probability density law f followed by the random variable x consisting of the error whose amplitude must be limited by a value “B” (generally noted “Protection radius”) is written:

[0098] „ / t.,. x / . । \ , Probability (\e\ > B) -1- ]Bf(x)dx

[0099] When the random variable under discussion is of dimension 1 and it follows a Gaussian probability density law, and in particular we can write:

[0100] , . x PQ=. Probai H >K{}.ait) ) = 1- J_K(hjoydx

[0101] It is enough to invert this formula to calculate the KO bound as a function of the probability PO (by choosing B = K().ai tk ) ):

[0102] B = erfaKPO)

[0103] where “erfc” is the complementary error function, for example in tabulated form.

[0104] This calculation can be generalized to dimensions greater than 1. This calculation is within the reach of those skilled in the art.

[0105] In a step 106, the integrity module 8 calculates an intermediate protection radius noted HPLFF(tk), associated with the arrival time tk, as the product between the reference coefficient KO and the standard deviation. We therefore have:

[0106] HPLFF(tk ) =

[0107] In a step 108, the integrity module 8 determines the maximum between the intermediate protection radius HPLFF(tk) and the protection radius HPLRAIM selected in the selection step (which is either the current HPLRAIMit available or the HPLRAIM remembered from the previous iteration in case of unavailability).

[0108] The selected maximum constitutes an output protection radius HPL(tk) associated with the arrival time tk. This protection radius is an output data item from the integrity module 8 updated with respect to the last known HPLRA]M, following unavailability of the receiver 1.

[0109] In a step 110, the integrity module 8 calculates a standard deviation cr(f) associated with the current instant t, by a propagation of the standard deviation cr^) using the evolution matrix PHItk-*t and the evolution noise covariance matrix Q. The standard deviation (y(t) is calculated from the covariance matrix P(tltk) which is the solution of the following equation:

[0110] Pt^) = PHI^F^t^PHI^ + Q

[0111] In this equation the prime “'” designates the transpose operator.

[0112] In a step 112, the integrity module 8 calculates an intermediate protection radius HPLFF(t) associated with the current instant t as a product between the reference coefficient KO and the standard deviation <!--?( Z ) • On a donc :<br-->

[0113] HPLFF ( t ) = K&o(t)

[0114] In a step 114, the integrity module 8 determines the maximum between the intermediate protection radius HPLFF(t) and the protection radius HPLRA1M selected in the selection step 112 (which is either the current HPLR41M(t) available or the HPLraim stored in the previous iteration in the event of unavailability).

[0115] The selected maximum constitutes an output protection radius HPL(t) associated with the current time t. This protection radius is an output data item from the integrity module 8 updated with respect to the last known HPLRA]M, following an unavailability of the receiver 1, and more recent than the output protection radius HPL(tk), given that tk is earlier than t (some time may have elapsed since the last prediction made by the Kalman filter within the fusion module 6).

[0116] 2.2) Navigation assistance method - embodiment 2

[0117] [Fig.3] shows a second embodiment of the navigation aid method, the general principles of which have been described above.

[0118] The steps of this second embodiment are as follows.

[0119] The step of evaluating the availability of the protection radius HPLRA1M(t), noted 200, is implemented by the integrity module 8.

[0120] If the input protection radius HPLRA]M(t ) is found to be available, the integrity module 8 calculates in a step 201 a current weighting coefficient Kl(t) as a ratio between the input protection radius HPLRAIM(t) associated with the current instant t and the standard deviation (7( / ) already discussed in the context of the description of the first embodiment. We therefore have:

[0121] =

[0122] The selection step is referenced 202 in this figure.

[0123] In this second embodiment, the data which is the subject of the selection 202 is the current weighting coefficient Kl(t) calculated in step 201 and which depends on the input protection radius HPLRAIM(7 ) • If this radius HPLRAlM( t ) is not available, then a stored coefficient Kl is selected in step 202, which was selected during the same selection step of the previous iteration. With this mechanism, the selected coefficient Kl is a coefficient which depends on the last protection radius provided by the receiver 1, just before the receiver 1 switches into an unavailability phase.

[0124] After selection 202, the stored Kl is updated with the Kl that was selected.

[0125] In a step 204, the integrity module 8 calculates the reference coefficient KO from the predefined probability value PO. This step is identical to step 104 of the first embodiment.

[0126] In a step 206, the integrity module 8 determines the maximum, noted Kmax, between the reference coefficient KO and the selected coefficient Kl.

[0127] In a step 208, the integrity module 8 determines an output protection radius HPL(tk) associated with the current instant tk, by calculating the product between the coefficient Kmax and the standard deviation <7 (tk) • We therefore have:

[0128] HPL(tk) - Kmax.&(tk)

[0129] As in the first embodiment, this protection radius HPL(tk) is an output data of the integrity module 8 updated with respect to the last known HPLRAiM, following unavailability of the receiver 1.

[0130] In a step 210, the integrity module 8 calculates the standard deviation cr(f) associated with the current instant t, by a propagation of the standard deviation <t( ) à l’aide de la matrice d’évolution phl^t et covariance bruit d'évolution q. cette étape 210 est identique l’étape 110 du premier mode réalisation.

[0131] In a step 212, the integrity module 8 determines an output protection radius HPL(t) associated with the current instant t, by calculating the product between the coefficient Kmax and the standard deviation) • We therefore have:

[0132] H PL (t) = Kmaxxy (t)

[0133] This protection radius HPL(t) is an output data of the integrity module 8 updated with respect to the last known HPLRAlM, following an unavailability of the receiver 1, and more recent than the output protection radius HPL(tk), given that tk is prior to t.

[0134] It can be seen that the method according to the second embodiment produces two protection rays for the instants tk and t, like the method according to the first embodiment. However, the second embodiment provides a notable advantage over the first embodiment: it makes it possible to ensure continuity in the output data of the integrity module 8 during a loss of availability, which the first embodiment does not make it possible to do.

[0135] [Fig.4] shows curves that illustrate this continuity. The irregular curve represents the evolution over time of the error that affects the estimate provided by receiver 1 to the fusion module (typically a carrier position error). Of course, the true position of the carrier is not known, so this error is not known either. Two periods are distinguished in time, separated by a vertical dotted line: on the left, a period of receiver availability, and on the right, a period of receiver unavailability. The black dots represent the instants perceived by the Kalman filter; these instants are therefore separated temporally according to the second frequency discussed previously. The white dot designates a current instant t during the unavailability period. It is clear that t is later than tk, and earlier than tk+i, because the receiver operates at a first frequency different from the second frequency.The dotted curve is a curve showing the evolution of the protection radius generated by the integrity module 8. In this schematic example, it is assumed that this protection radius remains constant during the period of availability of the receiver (to the left of the dotted vertical line). This curve continues continuously into the period of unavailability of the receiver 1, to the right of the dotted vertical line. This continuity is obtained thanks to the steps of the method according to the embodiment. 2.3) Other embodiments

[0136] In the first embodiment and in the second embodiment discussed above, the integrity module 8 generates two distinct protection rays: the protection radius HPL(tk) associated with the instant tk and the protection radius HPL(t) associated with the current instant t. Alternatively, the integrity module generates only the protection radius HPL(tk) or only the protection radius HPL(t).

[0137] In the second embodiment, steps 204 and 206 are optional. In the absence of these steps, the coefficient Kl selected in the selection step 202 is directly used instead of Kmax in steps 208 and 212. Nevertheless, these steps have the advantage of increasing the protection radius with a conservative value of the coefficient K and thus providing an increase in the protection radius.

[0138] Furthermore, the protection rays can be horizontal protection rays (hence the H in their name) or alternatively be protection rays vertical.

Claims

Claims

1. A computer-implemented iterative method, the method comprising a current iteration associated with a current time (t) and a previous iteration associated with a previous time, the current iteration comprising the following steps: • receiving Kalman data comprising: • an estimated covariance matrix, the estimated covariance matrix resulting from a last prediction implemented by a Kalman filter, • availability assessment (100, 200) of an input protection radius (HPLRAIM{t)) associated with the current time (t), resulting from an autonomous receiver integrity check, RAIM, implemented by a satellite signal receiver, and relating to the estimate of a navigation quantity of a carrier, the estimate being provided by the satellite signal receiver, • selecting (102, 202) a data item on the basis of the availability assessment,in which: • when the input protection radius (HPLRAIM(t)) is available, the selected data is a current data item obtained at the current iteration, and dependent on the input protection radius (HPLRA1M (t)), • when the input protection radius (HPLRAJM (t)) is unavailable, the selected data item is a previous data item having been selected during an implementation of the selection step carried out during the previous iteration, • determination (108, 114, 208, 212) of an output protection radius (HPL(4), HPL(t)) from the selected data item and the Kalman data.,

2. Method according to the preceding claim, in which: • the estimated covariance matrix is associated with an arrival time (4) of the last prediction implemented by the Kalman filter,

3.

4.

5. in the determination step, an exit protection radius (HPL(4)) associated with the arrival time (4) is determined (108, 208), from the selected data and the estimated covariance matrix. A method according to any preceding claim, wherein: the estimated covariance matrix is associated with an arrival time (4) of the last prediction implemented by the Kalman filter, Kalman data further includes: an evolution matrix (PHZf^) allowing to propagate the estimated covariance matrix from the arrival time (4) to the current time (t), an evolution noise covariance matrix (Q) allowing to propagate the estimated covariance matrix from the arrival time (4) to the current time (t), in the determination step, an output protection radius (HPL(t)) associated with the current time (t) is determined (114, 212), from the selected data, the estimated covariance matrix, the evolution matrix and the evolution noise covariance matrix (Q). A method according to any preceding claim, in which the current data is a current weighting coefficient (current Kl) calculated (201) as a ratio between: the entrance protection radius (HPLRAÏM( t)) associated with the current time, and a standard deviation (<7(4 ) ) relating to the carrier's navigation magnitude, calculated from the estimated covariance matrix. Method according to the preceding claim in its dependence on the re claim 2, wherein the exit protection radius (HPL(4)) associated with the arrival time (4) is calculated (208) as a product

6.

7. between : • a weighting coefficient (Kmax) depending on the selected data, and • the standard deviation (cr()) relating to the navigation quantity of the bearer. Method according to any one of claims 4 and 5 in their dependence on claim 3, in which the exit protection radius (HPL(t)) associated with the current time (t) is calculated (212) as a product between: • a weighting coefficient (Kmax) depending on the selected data, and • a standard deviation (cr(f )) associated with the current instant (t) resulting (200) from a propagation of the standard deviation ( <t(^)) se rapportant à la grandeur de navigation du porteur à l’aide de la matrice d’évolution et de la matrice de covariance de bruit of evolution (Q). A method according to any one of claims 5 and 6, comprising steps of: • calculation (204) of a reference coefficient KO from a predefined probability value p, the reference coefficient KO satisfying the following formula: p = Probability ( |e| > ) where e denotes the error affecting the estimate provided by the satellite signal receiver, and ) denotes the standard deviation relating to the carrier's navigation magnitude, • determination (206) of a maximum (Kmax) between the selected data and the reference coefficient (KO), the maximum constituting the weighting coefficient depending on the selected data. current data is the input protection radius (HPLRAIM( t))-

9. Method according to the preceding claim in its dependency on claim 2, comprising steps of: • calculation (104) of a reference coefficient (KO) from a predefined probability value PO, the reference coefficient (KO) satisfying the following formula: PO = Probability ( |e| > KQ.u( tk) ) where e denotes the error affecting the estimate provided by the satellite signal receiver, and 17(1^ ) denotes a standard deviation relating to the carrier's navigation magnitude and calculated from the estimated covariance matrix, • calculation (106) of an intermediate protection radius (HPLFF(4)) associated with the arrival time (4), the intermediate protection radius being calculated as a product between: • the reference coefficient (KO), and • the standard deviation (cr( tk ) ) relating to the carrier's navigation magnitude, • the exit protection radius (HPL(4)) associated with the arrival time 0k) being a maximum (108) between: • the selected data, and • the intermediate protection radius (ÏHPLFFO / J) associated with the arrival time (4).

10. A method according to any one of claims 8 and 9 as dependent on claim 3, • calculation (104) of a reference coefficient KO from a predefined probability value PO, the reference coefficient KO satisfying the following formula: PO = Probability ( H > tk) ) where e denotes the error affecting the estimate provided by the satellite signal receiver, and <7(4) denotes a standard deviation relating to the carrier's navigation magnitude and calculated from the estimated covariance matrix, • calculation (112) of an intermediate protection radius (HPLFF(t)) associated with the current time (t) as a product between: • the reference coefficient KO, and • a standard deviation associated with the current time (t) resulting from a propagation of the standard deviation (¢7(4)) using the evolution matrix and the evolution noise covariance matrix (Q), • the output protection radius (HPL(t)) associated with the current time (t) being a maximum (114) between: • the selected data, and • the intermediate protection radius ((HPLFF(t))) associated with the current time (t).

11. A method according to any preceding claim, wherein the navigational quantity of the carrier is a position of the carrier.

12. A computer program product comprising program code instructions for executing the steps of the method according to one of the preceding claims, when this program is executed by a computer.

13. A system for assisting the navigation of a carrier, the system comprising: • a satellite signal receiver (1) configured to provide an estimate of a navigation quantity of the carrier, and to implement an autonomous receiver integrity check (RAIM) producing a protection radius relating to the estimate of the movement data of the carrier, • an inertial unit (2, 4) configured to provide another estimate of the navigation quantity of the carrier, • a hybridization module (6) configured to couple data including the estimate provided by the satellite signal receiver and the other provided by the inertial unit, so as to produce a navigation solution including a consolidated estimate of the navigation quantity of the carrier, • a processing module (8) configured to implement the iterative method according to any one of the preceding claims.

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