method for generating a protection radius in case of RAIM unavailability

Abstract

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

Description

Title of the invention: Method for generating a protection radius in case of RAIM unavailability technical field

[0001] This disclosure relates to a method for generating protective beams. Integrity control finds advantageous application in assisting the navigation of a carrier. 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 subject to a greater or lesser error compared to the actual position of the carrier.

[0003] The position estimate provided by a GNSS receiver is conventionally fused with other data to generate a carrier navigation solution. Such data fusion is commonly called "hybridization" or "coupling." In particular, when the other data comes from an inertial measurement unit (IMU), 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, as its name indicates, aims to control the integrity of the position determined by the GNSS receiver. In particular, one function of this RAIM control is to calculate a protection radius related to the estimated position of the wearer. As is known per se, the protection radius is an estimated bound of the maximum tolerable error between the estimated position provided by the GNSS receiver and the true position of the wearer.

[0005] However, it may happen that a GNSS receiver equipped with a RAIM algorithm is unable to produce such a protection radius at a given time. In this case, the integrity function is said to be 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 fault detection (SISF) compared to the capabilities of a GNSS receiver implementing a RAIM. • Category 1: IRS / GNSS fusion provides integrity and detection of additional satellite failure compared to the capabilities of the GNSS receiver, and continues to provide a protection radius. • Category 2: IRS / GNSS fusion provides integrity, additional satellite failure detection, and exclusion capabilities beyond those of the GNSS receiver, while still providing a protection radius.

[0007] Categories 1 and 2 of the DO-384 standard thus aim to extend the integrity capabilities of the GNSS receiver. However, achieving this goal requires implementing complex, multi-filter architectures that are very computationally expensive. Description of the invention

[0008] One purpose of this disclosure is to provide a protection radius including when the RAIM of a GNSS receiver is not available, without implementing a complex autonomous fault detection or exclusion function.

[0009] To this end, according to a first aspect, an iterative process implemented by computer is proposed, the process comprising a current iteration associated with a current time and a previous iteration associated with a previous time, the current iteration comprising the following steps: • receiving Kalman data including: • an estimated covariance matrix, the estimated covariance matrix resulting from a final prediction implemented by a Kalman filter, • Evaluation of the availability of an entry protection radius associated with the current time, resulting from an autonomous receiver integrity check, RAIM, implemented by a satellite signal receiver, and relating to the estimate of a carrier navigation quantity, the estimate being provided by the satellite signal receiver, • selection of data based on availability assessment, in which: • When the entry protection radius is available, the selected data is a current data point obtained at the current iteration, and dependent on the entry protection radius. • When the entry protection radius is unavailable, the selected data is a previous data point that was selected during an implementation of the selection step performed during the previous iteration. • determination of an exit protection radius, from the selected data and Kalman data.

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

[0011] Preferably: • The estimated covariance matrix is ​​associated with the arrival time of the last prediction implemented by the Kalman filter. • at the determination stage, 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 the arrival time of the last prediction implemented by the Kalman filter. • Kalman data also includes: • an evolution matrix allowing the propagation of the estimated covariance matrix from the arrival time to the current time, • a noise evolution covariance matrix allowing the propagation of the estimated covariance matrix from the arrival time to the current time, • at the determination stage, an exit 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 entrance protection radius associated with the current time, and • a standard deviation relating to the carrier's navigation magnitude, calculated at starting from the estimated covariance matrix.

[0014] Preferably, the exit protection radius associated with the arrival time is calculated as a product of: • 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 instant is calculated as a product of: • a weighting coefficient depending on the selected data, and • a standard deviation associated with the current time 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 process according to the first aspect comprises the steps of: • Calculation of a KO reference coefficient from a predefined probability value p, the KO reference coefficient 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 quantity, • 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, in which the current data is the inlet protection radius.

[0020] Preferably, the process according to the first aspect includes the steps of: • calculation of a reference coefficient from a predefined probability value PO, 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 quantity 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 time of arrival.

[0023] Preferably, the process according to the first aspect comprises steps of • Calculation of a KO reference coefficient from a predefined PO probability value, the KO reference coefficient 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 quantity and calculated from the estimated covariance matrix, • Calculation of an intermediate protection radius associated with the current instant as a product of: • the KO reference coefficient, 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 instant being a maximum between: • the selected data, and • the intermediate protection radius associated with the current instant.

[0026] Preferably, which the carrier's navigation magnitude is a carrier position.

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

[0028] A third aspect of this 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 autonomous receiver integrity control producing a protection radius relating to the estimated carrier motion data, • an inertial navigation system 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 navigation system, 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 process according to the first aspect. DESCRIPTION OF THE FIGURES

[0029] Other features, objectives and advantages of the invention will become apparent from the following description, which is purely illustrative and not limiting, and which should be read in conjunction with the accompanying drawings on which:

[0030] Fig. 1 schematically illustrates a navigation aid system according to a first embodiment of the invention.

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

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

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

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

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

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

[0037] The satellite signal receiver 1, more simply referred to as receiver 1 hereafter, is configured to produce a Psat estimate of a carrier navigation quantity at a given time t, based on signals emanating from a navigation satellite constellation, and by applying a known method. Receiver 1 is typically of the GNSS type.

[0038] Receiver 1 is further configured to produce a protection radius related to the estimate. This protection radius is the result of implementing a process also known as "Receiver Autonomous Integrity Monitoring" (RAIM). The protection radius is, for example, horizontal. It is denoted HPLRA1M hereafter.

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

[0040] However, as mentioned in the introduction, it may happen that the receiver 1 is unable to produce the HPLRAIM protection beam at a given time t. In this case, the integrity function of the receiver 1 is said to be 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 in itself. It is configured to provide inertial measurements. Typically, the inertial measurement unit includes inertial sensors such as gyroscopes and accelerometers. The inertial measurements can thus include angular velocities and accelerations.

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

[0043] More generally, the localization 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 include 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 localization module 4 produces estimates of the quantity periodically, for example at a second frequency different from that of the receiver 1, or even higher than that of the receiver 1. For example, the frequency of product of estimated by the localization module 4 is 100 Hz (one hundred estimates per second).

[0045] The inertial measurement unit and the localization 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 localization module 4. The carrier navigation solution includes a consolidated estimate of the carrier navigation magnitude under discussion.

[0047] In what follows, we will focus on a non-limiting embodiment in which the navigation parameter is a position of the carrier. Thus, the satellite signal receiver 1 and the localization module 4 provide different estimates of the carrier's position, denoted Psat and Pin, and the fusion module 6 uses these positions, among others, to generate a navigation solution that includes a consolidated position of the carrier.

[0048] The fusion performed by the fusion module 6 is known. In the literature, it is referred to as inertial / satellite coupling or inertial / satellite hybridization.

[0049] In particular, the coupling implemented by the fusion module 6 can be a "loose" type coupling ("loose hybridization"), where fusion is performed from the carrier position. Alternatively, the coupling implemented by the fusion module 6 is a "tight hybridization" type. In this variant, it is possible to use satellite pseudo-ranges as observations for 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 dynamical system. Here, the dynamical 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 a time tk.i), and generates a predicted or a prior to the navigation state (associated with a time tk), using in particular a propagation matrix PHItk^tk.

[0054] During the propagation step, the Kalman filter also takes as input a posterior 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 purpose the propagation matrix Painsi 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 a posteriori estimate of the navigation state, denoted 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 covariance matrix P^-[ and the observation matrix.

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

[0062] In the navigation aid system shown in Figure 1, the propagation and update steps of 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 propagation and navigation state update steps 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 the fusion module 6 implements Kalman propagation, the fusion module 6 communicates certain data to the integrity module 8, including the propagation matrix it just used for that propagation. In the Consequently, this data is conventionally called "Kalman data" to indicate that it is 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, from data provided by the receiver 1, and from data provided by the fusion module 6.

[0066] The integrity module 8 is timed to the operating period of the localization 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] In terms of hardware, the navigation assistance system may include one or more processors to perform the processing of the localization module 4, the fusion module 6, and the integrity module 8. These modules may be different parts of a computer program executed by the processor(s). Alternatively, these modules are separate electronic circuits.

[0069] The navigation assistance system further includes 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, SSD, Flash, etc.

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

[0071] A process implemented by the integrity module 8 is iterative, in the sense that it includes 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 is provided by the fusion module 6. The Kalman data includes, in particular, the prior covariance matrix resulting from the last prediction implemented by the Kalman filter. However, it will be seen later that this Kalman data can also include other data.

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

[0078] Integrity module 8 selects data based on availability evaluation. In other words, the selection depends on whether the HPLRA1M radius is available or unavailable at time t. It will be seen later that the data to be selected has different embodiments.

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

[0080] When the HPLR^IM(t) input protection radius is unavailable, the selected data is a previous data item that was selected during an implementation of the selection step performed during the previous iteration. This mechanism thus makes it possible to return to the corresponding data item that depended on the last protection radius provided by the receiver, even if the receiver 1 has switched to an unavailable state and is still in that state in the current iteration.

[0081] Next, the integrity module 8 determines at least one exit protection radius from the selected data and the Kalman data. It will be seen later that this determination step also has different embodiments.

[0082] 2.1) Navigation aid method - Embodiment 1

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

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

[0085] In this first embodiment, the data subject to selection 102 is the inlet protection radius HPL^^Çt) itself, when it is available at the current time t. If this radius is not available, then the HPLRAJM protection radius that was itself selected during the same selection step of the previous iteration is selected. With this mechanism, the selected HPLraim protection radius is the last protection radius provided by receiver 1, just before 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 describing this first embodiment, we adopt the following notations: • HPLRAIM(t): protection radius provided by receiver 1 at time t, if available • HPLRA1M memorized: protection radius having been selected in the previous iteration (therefore associated with the instant preceding t) and memorized in the memory accessible by the integrity module 8. • tk: arrival time of the last prediction made by the Kalman filter, within the fusion module 6. This tk time precedes the current t time. • <7 ( ): standard deviation relating to the navigation magnitude, calculated from The covariance matrix P^ estimated by the Kalman filter during the last prediction. 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 in the current iteration associated with time t. Alternatively, the integrity module 8 directly receives the standard deviation <7(4) derived from it. • PHItk-*t: evolution matrix allowing propagation of 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 propagation of the covariance matrix 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 includes one or more diagonal terms that relate specifically to the navigation quantity under consideration (here, the carrier's position). For example, if the carrier's position 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 square root of one of these diagonal terms, for example, the one with the maximum value.

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

[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 KO reference coefficient from a predefined PO probability value.

[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 denotes 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 affecting the estimate provided by the satellite signal receiver 1 at the current time t is greater than KO times the standard deviation ait) ■

[0097] In general, the formula for calculating the protection radius as a function of the probability density function f followed by the random variable x, which represents the error whose amplitude must be bounded by a value "B" (generally denoted "Protection Radius"), is written:

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

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

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

[0101] It suffices to invert this formula to calculate the KO bound as a function of the PO probability (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 a person skilled in the art.

[0105] In a step 106, the integrity module 8 calculates an intermediate protection radius denoted 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 available HPLRAIMit or the HPLRAIM is stored in 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 of the integrity module 8 updated with respect to the last known HPLRA]M, following an unavailability of receiver 1.

[0109] In a step 110, the integrity module 8 calculates a standard deviation cr(f) associated with the current time t, by propagating 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 a solution of the following equation:

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

[0111] In this equation the prime "'" denotes 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 available HPLR41M(t) or the HPLraim stored in the previous iteration in case 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 of 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 prior to t (some time may have elapsed since the last prediction made by the Kalman filter within the fusion module 6).

[0116] 2.2) Navigation aid method - embodiment 2

[0117] Figure 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 HPLRA1M(t) protection radius availability evaluation step, denoted 200, is implemented by the integrity module 8.

[0120] If the inlet protection radius HPLRA]M(t) is available, the integrity module 8 calculates in a step 201 a current weighting coefficient Kl(t) as a ratio between the inlet protection radius HPLRAIM(t) associated with the current time t and the standard deviation (7( / ) already discussed in the description of the First embodiment. Therefore, we have:

[0121] =

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

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

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

[0125] In 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, denoted Kmax, between the reference coefficient KO and the selected coefficient Kl.

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

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

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

[0130] In a step 210, the integrity module 8 calculates the standard deviation cr(f) associated with the current time 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 exit protection radius HPL(t) associated with the current time t, by calculating the product between the coefficient Kmax and the standard deviation. Therefore:

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

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

[0134] It can be seen that the process according to the second embodiment produces two protection radii for times tk and t, like the process according to the first embodiment. However, the second embodiment provides a significant advantage over the first embodiment: it ensures continuity in the output data of the integrity module 8 during a loss of availability, which the first embodiment cannot do.

[0135] Figure 4 shows curves illustrating this continuity. The irregular curve represents the evolution over time of the error affecting the estimate provided by receiver 1 to the fusion module (typically a carrier position error). Of course, the carrier's true position is unknown, so this error is also unknown. Two periods are distinguished in time, separated by a vertical dashed line: on the left, a period of receiver availability, and on the right, a period of receiver unavailability. The black dots represent the times perceived by the Kalman filter; these times are therefore separated temporally according to the second frequency discussed previously. The white dot designates a current time 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 dashed curve represents 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 receiver's availability period (to the left of the vertical dashed line). This curve continues continuously during the receiver 1's unavailability period, to the right of the vertical dashed line. This continuity is achieved through the process steps 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 radii: the HPL(tk) protection radius associated with the time tk and the HPL(t) protection radius associated with the current time t. Alternatively, the integrity module generates only the HPL(tk) protection radius or only the HPL(t) protection radius.

[0137] In the second embodiment, steps 204 and 206 are optional. In the absence of these steps, the coefficient Kl selected in selection step 202 is used directly 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 protective beams can be horizontal protective beams (hence the H in their name) or, alternatively, protective beams vertical.

Claims

Demands

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 evaluation (100, 200) of an entry 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 carrier navigation quantity, the estimate being provided by the satellite signal receiver, • selection (102, 202) of a data point based on the availability evaluation,in which: • when the inlet protection radius (HPLRAIM(t)) is available, the selected data is a current data obtained in the current iteration, and dependent on the inlet protection radius (HPLRA1M(t)), • when the inlet protection radius (HPLRAJM(t)) is unavailable, the selected data is a previous data that was selected during an implementation of the selection step performed during the previous iteration, • determination (108, 114, 208, 212) of an outlet protection radius (HPL(4), HPL(t)) from the selected data and the Kalman data.

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

3.

4.

5. at the determination stage, 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 one of the preceding claims, wherein: the estimated covariance matrix is ​​associated with an arrival time (4) of the last prediction implemented by the Kalman filter, Kalman's data also includes: an evolution matrix (PHZf^) allowing propagation of the estimated covariance matrix from the arrival time (4) to the current time (t), an evolution noise covariance matrix (Q) allowing propagation of the estimated covariance matrix from the arrival time (4) to the current time (t), at the determination stage, an exit 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 one of the preceding claims, in where 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 instant, and a standard deviation (<7(4 ) ) relating to the carrier's navigation magnitude, calculated from the estimated covariance matrix. A method according to the preceding claim in its dependence on the re claim 2, in which 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 carrier. A method according to any one of claims 4 and 5 in their dependence on claim 3, wherein the exit protection radius (HPL(t)) associated with the current time (t) is calculated (212) as a product of: • a weighting coefficient (Kmax) depending on the selected data, and • a standard deviation (cr(f )) associated with the current time (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 evolution (Q). A method according to any one of claims 5 and 6, comprising the steps of: • calculation (204) of a KO reference coefficient from a predefined probability value p, the KO reference coefficient 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 quantity, • 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. The current data is the entrance protection radius (HPLRAIM(t))-

9. A method according to the preceding claim in its dependence on claim 2, comprising the 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 in their dependence on claim 3, • calculation (104) of a KO reference coefficient from a predefined PO probability value, the KO reference coefficient 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 exit 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 one of the preceding claims, wherein the carrier's navigational magnitude is a carrier position.

12. Product computer program comprising program code instructions for carrying out the steps of the process according to any one of the preceding claims, when such program is executed by a computer.

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