Electronic device for detecting GNSS interference, associated vehicle, method and computer program

Abstract

The present invention relates to a device (10) for detecting GNSS interference, comprising at least: • an inertial measurement unit (12), • a GNSS receiver (16), • a Kalman filter (20) configured to cyclically calculate an innovation vector (22) by hybridisation of data provided by: • the inertial measurement unit, and • the GNSS receiver, and additionally, at the output of the Kalman filter: • a filtering module (26) applying at least one filtering on each component of the innovation vector provided at each calculation cycle, • an interference detection module (30) detecting interference when, during Q cycles and on a number U of components of the innovation vector, the absolute value of the output (28) of the filtering module, for at least one filtering, is greater than a predetermined threshold, the filtering module (26) being configured to apply at least one filtering with predetermined filtering coefficients, using a finite impulse response filter, the N filtering coefficients of which, for each component of the innovation vector, are calculated with the predetermined filtering coefficients, the filtering coefficients being predetermined for each type of interference of a set of predetermined interference types and for each type of navigation phase of a set of predetermined navigation phase types associated with the vehicle.

Description

[0001] DESCRIPTION

[0002] TITLE: Electronic device for detecting GNSS interference(s), vehicle, method and associated computer program

[0003] The present invention relates to an electronic device for detecting GNSS interference(s) suitable for being on board a vehicle suitable for moving between two geographical positions, the device comprising at least: an inertial measurement unit suitable for providing navigation measurements, a GNSS receiver for positioning measurements by satellite(s), a Kalman filter configured to cyclically calculate an innovation vector by hybridization of data provided, at the input of said Kalman filter, both by said inertial measurement unit and by said GNSS receiver.

[0004] The invention also relates to a vehicle comprising such an electronic device for detecting GNSS interference(s).

[0005] The invention also relates to a method for detecting GNSS interference(s), said method being implemented by an electronic device for detecting GNSS interference(s) suitable for being on board a vehicle suitable for moving between two geographical positions.

[0006] The invention also relates to a computer program comprising software instructions, which when executed by a computer implement such a method of detecting GNSS interference(s).

[0007] The present invention relates to the navigation of a vehicle (also called a carrier) capable of moving between two distinct geographical positions, such as a land vehicle corresponding in particular to a car, a truck, or such as an aircraft or any type of carrier.

[0008] To do this, the vehicle generally carries a satellite navigation and positioning system receiver configured to determine, in particular by trilateration, a positioning (i.e. a geolocation position or a geolocation solution) of the vehicle 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 (from the English "Global Positioning System"), the European GALILEO system, the Russian GLONASS system, or the Chinese BEIDOU system, etc. More specifically, the present invention relates to the detection of interferences hereinafter called "GNSS interferences" from the hybridization between the inertial data and the data received by the GNSS receiver, also hereinafter called inertial / GNSS hybridization.

[0009] Subsequently, "GNSS interference" means interference that consists of sending erroneous radio frequency signals to the GNSS receiver. The GNSS receiver then uses these erroneous signals to calculate the navigation data of the vehicle in which it is embedded, for example its position and speed, which will then be different from the exact (i.e. "true") data, because of errors in the radio frequency signals.

[0010] For example, such GNSS interference occurs when, inside hangars located at airports, GNSS signal repeaters, including GPS, are used to propagate these signals inside the hangars, so that they can be used to assist an aircraft pilot located inside the hangars to position himself.

[0011] However, when the doors of these hangars are open, the signals from these repeaters are likely to propagate outside the hangars and be picked up by aircraft located outside, which must use the direct GNSS signals to position themselves, and not the signals from these repeaters, which then constitute interference for aircraft outside the hangars.

[0012] To address this, a current technique for detecting such GNSS interference is described in the DO-229D standard "Minimum Operational Performance Standards for Global Positioning System / Satellite-Based Augmentation System Airborne Equipment" and in particular, in appendix R 3 1 1 , and consists of using hybridization between inertial data and GPS data in order to detect the interference using a Kalman filter.

[0013] More precisely, this current technique first calculates, during hybridization, the innovation vector which constitutes the difference between on the one hand the measured difference between the GNSS data and the inertial data, and on the other hand the difference predicted according to the hybridization model between these data.

[0014] Then, this technique then implements the comparison of the different components of this innovation vector with the expected standard deviation on each component of the innovation vector, this expected standard deviation being calculated from the covariance matrix P of the Kalman filter used for the calculation of the innovation vector, and when the absolute value of one or more components of the innovation vector are much higher than the expected standard deviation, it disables the use of the data from the GNSS receiver because there is potentially a problem with the GNSS data, coming from interference or another phenomenon.This current technique is however not satisfactory, because interference on the GNSS data can cause an error that gradually increases over time without causing, at each calculation cycle of the Kalman filter, excessive changes in value at the level of the components of the innovation vector, so that the values ​​of the components of this innovation vector will be of the same order of magnitude as those present in the absence of interference and will not be much higher than the standard deviation calculated from the covariance matrix P of the Kalman filter used in the calculation of the innovation vector. Thus, such interference on the GNSS data will not be detected most of the time, or at least not immediately or quickly after the appearance of such interference.

[0015] Another technique is also described in the document "Fault Detection, Integrity Monitoring, and Testing" by Groves Paul D which deals with the integrity control of navigation systems, however this technique is not optimal in terms of filtering.

[0016] The aim of the invention is therefore to propose an electronic device for detecting GNSS interference(s) which makes it possible to overcome the drawbacks of the current technique by increasing the efficiency and responsiveness of GNSS interference detection.

[0017] To this end, the invention relates to an electronic device for detecting GNSS interference(s), suitable for being on board a vehicle suitable for moving between two geographical positions, the device comprising at least:

[0018] - an inertial measurement unit capable of providing navigation measurements,

[0019] - a GNSS receiver for positioning measurements by satellite(s),

[0020] - a Kalman filter configured to cyclically calculate an innovation vector by hybridization of data provided, at the input of said Kalman filter, by both:

[0021] - said inertial measurement unit, and

[0022] - said GNSS receiver, the device further comprising, at the output of said Kalman filter:

[0023] - a filtering module configured to apply at least one filter to each component of the innovation vector provided at each calculation cycle by said Kalman filter,

[0024] - an interference detection module configured to detect interference when during Q cycles and on a number U of components of the innovation vector, Q and U each being an integer greater than or equal to one, the absolute value of the output of the filtering module, for at least one filtering applied by said module, is greater than a predetermined threshold.

[0025] Furthermore, the filtering module is configured to apply at least one filtering with predetermined filtering coefficients, using a finite impulse response filter, the N filtering coefficients of which, for each component of the innovation vector, are calculated with said predetermined filtering coefficients, said filtering coefficients being predetermined for each type of interference of a predetermined set of interference types and for each type of navigation phase of a predetermined set of navigation phase types associated with said vehicle.

[0026] Thus, the electronic GNSS interference detection device according to the present invention has a particular architecture making it possible to detect interference by using not directly the components of the innovation vector but the output of a filtering on these components.

[0027] In other words, the present invention consists of using an electronic device for detecting GNSS interference(s) capable of applying one or more filterings to each component of the innovation vector, and of using the output of these filterings to detect GNSS interference.

[0028] Indeed, the present invention aims to exploit the fact that the evolution over time of the components of the innovation vector is different depending on whether interference at the level of the GNSS data is present or not. The filtering used then makes it possible, specifically according to the present invention, at the level of the output returned by the filtering, to highlight this difference in evolution over time.

[0029] Furthermore, advantageously, for each navigation phase considered and each type of interference considered, the method of calculating the predetermined filtering coefficients is optimized, in order to obtain, as described below, a minimum probability of non-detection, the predetermined filtering coefficients being capable of differing according to the type of interference and / or according to the type of navigation phase.

[0030] According to other advantageous aspects of the invention, said predetermined threshold depends on the type of filtering applied by the filtering module and / or the current calculation cycle.

[0031] The invention also relates to a vehicle comprising such an electronic device for detecting GNSS interference(s).

[0032] The invention also relates to a method for detecting GNSS interference(s), said method being implemented by an electronic device for detecting GNSS interference(s) suitable for being on board a vehicle suitable for moving between two geographical positions, the method comprising at least the following steps:

[0033] - cyclic calculation of an innovation vector, implemented by a Kalman filter of said device, by hybridization of data provided, at the input of said Kalman filter, both by an inertial measurement unit capable of providing navigation measurements and by a GNSS receiver of positioning measurements by satellite(s); - application of at least one filtering on each component of the innovation vector provided at each calculation cycle by said Kalman filter, with predetermined filtering coefficients, using a finite impulse response filter, the N filtering coefficients of which, for each component of the innovation vector, are calculated with said predetermined filtering coefficients, said filtering coefficients being predetermined for each type of interference of a predetermined set of interference types and for each type of navigation phase of a predetermined set of navigation phase types associated with said vehicle;

[0034] - detection of interference when during Q cycles and on a number U of components of the innovation vector, Q and U each being an integer greater than or equal to one, the absolute value of the filtering output, for at least one filtering, is greater than a predetermined threshold.

[0035] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method for detecting GNSS interference(s) as defined above.

[0036] 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:

[0037] - [Fig 1] Figure 1 is a diagram illustrating an electronic device for detecting GNSS interference(s);

[0038] - [Fig 2] Figure 2 is a flowchart representative of the GNSS interference detection method according to the present invention;

[0039] - [Fig 3] Figure 3 illustrates, under the effect of interference, the k ième normalized component of the innovation vector as well as the weighted sum of this normalized component obtained at the output of the filtering implemented according to an embodiment of the invention;

[0040] - [Fig 4] [Fig 5] Figures 4 and 5 illustrate the predetermined filter coefficients, the k ième normalized component of the innovation vector as well as the output of the filtering with said predetermined filtering coefficients implemented according to another embodiment of the invention and under the effect of interference occurring from separate calculation cycles.

[0041] Figure 1 is an overall representation of an electronic device 10 for detecting GNSS interference(s) according to the present invention. Such an electronic device 10 for detecting GNSS interference(s) is suitable for being installed on board a vehicle suitable for moving between two geographical positions.

[0042] As illustrated by FIG. 1, such a device 10 firstly comprises an inertial measurement unit 12 capable of providing navigation measurements 14.

[0043] In addition, such a device 10 comprises a GNSS receiver 16 for satellite positioning measurements 18.

[0044] Such a device 10 also comprises a Kalman filter 20 configured to cyclically calculate an innovation vector 22 by hybridization of data provided, at the input of said Kalman filter, both by said inertial measurement unit 12 and by said GNSS receiver 16.

[0045] In other words, the electronic device 10 for detecting GNSS interference(s) is firstly configured to use the inertial data (i.e. the navigation measurements 14) and the data from the GNSS receiver (i.e. the satellite positioning measurements 18) by carrying out a hybridization between these data, thanks to a Kalman filter activated at different calculation cycles, these calculation cycles representing the time which elapses.

[0046] Thus, the hybridization implemented by the Kalman filter 20 consists of mathematically combining the measurements 14 and 18 provided respectively by the inertial measurement unit 12 and the receiver 16 of GNSS satellite positioning measurements of said vehicle to obtain position and speed information by taking advantage of these two sources 12 and 16 of measurements 14 and 18 respectively.

[0047] 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, subsequently called the innovation vector, after multiplication by a gain matrix of the Kalman filter, is, in a manner not shown, suitable for being applied to the a priori estimate of the state vector of the system and leads to obtaining the optimal a posteriori estimate. The Kalman filtering implemented by the Kalman filter 20 is thus also suitable for modeling the evolution of the errors of the inertial measurement unit 12 and for delivering, in a manner not shown, the a posteriori estimate of these errors which is used to correct the positioning and speed point of the inertial measurement unit 12.

[0048] More precisely, at each calculation cycle n, we calculate the observation vector Z n which constitutes the difference between the inertial data 14 (i.e. navigation measurements 14) and the GNSS data 18 (i.e. satellite positioning measurements 18).

[0049] Such an observation vector Z ncorresponds according to a first example to the difference between the inertial position associated with the inertial data 14 and the GNSS position associated with the GNSS data 18, or according to another example to the difference between the pseudo-distance (from the English pseudo-range) associated with the measurements 18 of positioning by satellite(s) corresponding to the distance between the GNSS receiver of the carrier and the satellites emitting the radio frequency signals, and the pseudo-distance (from the English pseudo-range) calculated from the inertial position of the carrier and the position of the satellites emitting the radio frequency signals.

[0050] The classical operation of the Kalman filter in the field of the invention is recalled below.

[0051] The Kalman filter 20 is first configured to define the state vector X n , with NE components, which gathers a certain number of unknown states that we wish to estimate, a component of the vector X nbeing for example the error on the inertial position.

[0052] Then, the Kalman filter 20 is configured to write the relationship between the observation vector Z n , with No components, this vector being known, and the state vector X n to be estimated in the form Z n = H n *X n + v n , with H n the known observation matrix, v n the unknown measurement noise, considered as Gaussian noise, but whose variance R n is known.

[0053] Then, the Kalman filter 20 is configured to calculate the vector X n |ni corresponding to the estimate of the vector X n from observations Zi, Z2, ... Z n -i, then to calculate the innovation vector Y n = Z n - H n *X n | n -i.

[0054] This innovation vector Y n is therefore itself the gap between on the one hand Z nwhich corresponds to the difference obtained between the inertial data 14 and the GNSS data at cycle n, and on the other hand H n *X n | n .i which corresponds to the predicted deviation from the estimate X n |ni- In other words, Y n corresponds to the deviation predicted from the hybridization model.

[0055] The Kalman filter 20 is also suitable for generating the elements 24 a n (k) with k varying between 1 and N o (N o being the number of components of the innovation vector Y n ), each element has n (k) corresponding to the expected standard deviation, in the absence of interference, for the component k of the innovation vector Y n , the calculation of a n (k) calling on R n the variance of the measurement noise v n .

[0056] It is worth noting that it can be shown that in the absence of interference, for each value of k between 1 and N o (N obeing the dimension of the innovation vector), Y n (k), when n varies, corresponds to a white Gaussian noise of standard deviation a n (k). It is also worth noting that it can be shown that, under the effect of interference, for each value of k between 1 and No, the k ième component of the innovation vector, when n varies, can be expressed as a sum of the effect due solely to interference on the one hand and the white Gaussian noise of standard deviation a n (k) calculated by the Kalman filter 20.

[0057] Specifically according to the present invention, as illustrated by FIG. 1, the electronic device 10 for detecting GNSS interference(s) further comprises, at the output of said Kalman filter 20, a filtering module 26 configured to apply at least one filtering to each component k of the innovation vector Y n 22 provided at each calculation cycle by said Kalman filter 20.

[0058] Indeed, as described below in relation to Figures 3 to 5, such a filtering module aims to use the fact that at the beginning of the application of the interference, the normalized components of the innovation vector Y n (k) / o n (k) exhibit behavior that is inconsistent with what they would have in the absence of interference.

[0059] More precisely, the filtering module 26 is configured to apply one or more filters, via one or more filters respectively, to each cycle n of calculation of the Kalman filter 20, on each component of the innovation vector.

[0060] The output of the filter of index J or of a filter of index J of the filters of the filtering module 26 is noted: S J n (k).

[0061] In the case where the filter of index J (also called J-filter hereinafter) corresponds to a finite impulse response filter, which corresponds to the fact that the output of this filter J at an instant depends only on the last N values ​​of the filter input, we can write S J n (k) as follows: which amounts to:

[0062] S J n(k) = a J 0,n(k) * Yn(k) + a J 1,n(k)* Yn-l(k) +... + a J N-1,n(k)* Y n -(N-1)(k), with Y n (k), the k ième component of the innovation vector calculated in the current cycle n, Y n .-i(k) is the k ième component of the innovation vector calculated in the previous cycle n-1, and a J o, n (k), a J i, n (k),... , a J N -i,n(k) the coefficients of the filter J, which potentially depend on the said filter J considered, of the k ièmecomponent of the innovation vector considered, and of the current cycle n.

[0063] Furthermore, specifically according to the present invention, as illustrated by FIG. 1, the electronic device 10 for detecting GNSS interference(s) further comprises an interference detection module 30 configured to detect interference when during Q cycles and on a number U of components of the innovation vector, Q and U each being an integer greater than or equal to one, the absolute value of the output 28 of the filtering module 26, for at least one filtering applied by said module, is greater than a predetermined threshold.

[0064] In other words, such an interference detection module 30 is capable of deciding that interference has been detected if, during Q consecutive calculation cycles and on a number U of components of the innovation vector, the absolute value of the filtering output |S J n(k)| is greater than a predetermined threshold denoted by the following Threshold J n for the threshold associated with filter J, and this for one or more of the filters used by the filtering module 26.

[0065] In the event of effective detection, the interference detection module 30 is capable of generating and emitting a signal 32 representative of the presence of such interference, in particular to alert the user.

[0066] As an optional addition, each predetermined threshold depends on the type of filtering applied by the filtering module 26 and / or the current calculation cycle n.

[0067] In other words, the Seu it J n associated with filter J depends on the filtering implemented by this filter J considered (i.e. is specific to the filter J considered) and the calculation cycle n considered and is a predefined value.

[0068] More precisely, to predefine such threshold values ​​associated respectively with each filter suitable for use within the filtering module 26, for a given filtering J, for which it is desired that it detects a given interference, when the application of this given interference is proven (for example in the test phase or on a computer simulation of the hybridization), the maximum value |S J n (k)| ma x obtained by the output |S J n (k)| of the filter J is first determined, and the Threshold value J n of the threshold associated with the filter J is by definition less than the maximum value |S J n (k) | ma x obtained by the output |S J n (k)| of the filter J.

[0069] Thus, according to the present invention, having defined the type of filtering applied by the filtering module 26, the numbers Q, U and the Threshold J nwhen the filtering module 26 implements only one filtering using the filter J, it is possible to analyze the interference detection performance by further determining the false alarm probability for the filtering J, this false alarm probability being determined using the fact that each component of the innovation vector, in the absence of interference, corresponds to white Gaussian noise, when the calculation cycle varies. The false alarm probability for the filtering J corresponds to the probability that during Q consecutive calculation cycles and over a number U of components of the innovation vector, |S J n (k)| is greater than the threshold Threshold J n , while there is no interference, and the detection of interference is all the more efficient as the probability of false alarm is low.

[0070] An example of operation of the electronic device 10 for detecting GNSS interference(s) is described below in relation to FIG. 2.

[0071] More specifically, the method 40 for detecting GNSS interference(s) implemented by said electronic device 10 for detecting GNSS interference(s) comprises the steps described below implemented successively.

[0072] According to step 42, as indicated previously, the electronic device 10 for detecting GNSS interference(s) according to the present invention implements a cyclic calculation C of an innovation vector V, implemented by a Kalman filter 20 of said device 10, by hybridization of data provided, at the input of said Kalman filter 20, both by an inertial measurement unit 12 capable of providing navigation measurements 14 and by a GNSS receiver of satellite positioning measurements 18.

[0073] Then according to step 44, as indicated previously, the electronic device 10 for detecting GNSS interference(s), via its filtering module 26, according to the present invention applies at least one filtering to each component, represented by an arrow, of the innovation vector provided at each calculation cycle by said Kalman filter 20. It should be noted that it is possible to apply different filtering to the same component.

[0074] It should be noted that, according to two distinct embodiments, two possible types of filtering are capable of being implemented distinctly during this step 44, by way of example.

[0075] It should be noted that other types of filtering, known to those skilled in the art, are also suitable for being respectively implemented according to other associated embodiments, not shown in Figure 2 for the sake of simplicity.

[0076] According to a first embodiment, the filtering module 26 is configured to apply, during a step 46, a weighted sum filtering, called F_S_P filtering, on each component (i.e. each component being represented by an arrow in FIG. 2) of the innovation vector provided at each calculation cycle by said Kalman filter 20.

[0077] According to a second embodiment, the filtering module 26 is configured to apply, during a step 48, at least one filtering with predetermined filtering coefficients, called F_C_P filtering.

[0078] Whatever the embodiment 46 or 48 of the filtering step 44, the filtering output S is obtained at the end of this step.

[0079] Finally, according to step 50, the electronic device 10 for detecting GNSS interference(s) according to the present invention implements, via its detection module, a step D of detecting an interference when during Q cycles and on a number U of components of the innovation vector, Q and II each being an integer greater than or equal to one, the absolute value of the filtering output S, for at least one filtering, is greater than a predetermined threshold and this for one or more of the filters used by the filtering module 26.

[0080] Each of the embodiments 46 and 48 is described in more detail below in relation to FIGS. 3 to 5.

[0081] According to the first embodiment, the filtering module 26 is configured to apply, during a step 46, a weighted sum filtering, called F_S_P filtering, on each component (i.e. each component being represented by an arrow in FIG. 2) of the innovation vector V provided at each calculation cycle by said Kalman filter 20.

[0082] More precisely, according to this embodiment, a single filtering is carried out, so that it is not necessary to specify the index of this single filter for this first embodiment.

[0083] Such filtering 46 consists of using a finite impulse response filter whose N filter coefficients, for each component of the innovation vector V, are suitable for summing the last N normalized input values ​​of said filter, and for weighting said resulting sum by a predetermined weighting coefficient.

[0084] More precisely, the coefficients of such a filter associated with the F_S_P filtering are suitable for being expressed in the following form ai n (k) = — - *M k ' M k being any coefficient ° -iW not depending on the index i (i ranging from 0 to N-1, N being the number of filtering coefficients) and on the index n but which may depend, as an optional complement, on the index k of said component considered of the innovation vector.

[0085] Thus, the filtering output S at cycle n of component k is expressed in the following form: S n (k) = filtering thus consists firstly in carrying out, for each component k of the innovation vector, at each cycle n of calculation, the sum of the last N standardized innovations, a standardized innovation corresponding to the value of the innovation divided by the corresponding standard deviation, and secondly in weighting this sum by the coefficient M k .

[0086] It should be noted that in the absence of interference, the Y component n (k), when n varies, corresponds to a white Gaussian noise of standard deviation c n (k), so that the normalized component of innovation Y n (k) / o n (k) is also a white Gaussian noise with a standard deviation equal to 1, and that consequently, the filtering output signal S n (k) is also a Gaussian noise of standard deviation M k *N 1 / 2 .

[0087] In Figure 3, view 52 represents, on the ordinate, the value of the normalized k component Y n (k) / o n (k) of the innovation vector as a function of the value of the calculation cycle on the abscissa, in the presence of GNSS interference, appearing from the calculation cycle n=900, and view 54 represents, on the ordinate, the value of the filtering output S n(k) corresponding to the weighted sum of this normalized component by taking the number N of filtering coefficients such that N=20 and M k = - =.

[0088] On view 52, ​​before the application of the interference (i.e. before the cycle n=900 on the abscissa), the normalized innovation corresponds to a white noise of standard deviation 1, then at the beginning of the application of the interference, between n=900 and n= 1000 on the abscissa, the normalized innovation has a behavior which does not correspond to a white noise, finally after n= 1000, the normalized innovation again has a behavior which corresponds to a white noise.

[0089] It should be noted that the value of the normalized k component Y n (k) / o n (k) is a dimensionless quantity because it is the innovation component divided by the corresponding standard deviation.

[0090] This figure also illustrates that in the absence of interference (i.e. before the application of the interference) the signal S n (k) is a Gaussian noise of standard deviation 1, and that under the effect of the interference the maximum reached by the weighted sum, of the order of 8, on view 54 corresponding to the output of the weighted sum filtering, is much higher than the maximum reached by the normalized component worth 3.4 on view 52.

[0091] Thus, if we consider the case where Q=1 and U=1, which amounts to the fact that the interference is detected if for a component the output of the filter is greater than the threshold over a calculation cycle, we see the interest in using the output of the filter to detect the interference and not simply the normalized component of the innovation.

[0092] If, to detect the interference, we simply use the normalized component of the innovation, as currently carried out according to the state of the art mentioned above, we therefore need a threshold less than or equal to 3.4 to detect the interference, whereas by using the output of the filter associated with the F_S_P filtering to detect the interference, in other words with a weighted sum as filtering, we need a threshold less than or equal to 8 to detect the interference, so that the output of the filter according to the present invention makes it possible to detect interference with thresholds that are significantly higher than with the simple normalized component of the innovation.

[0093] In the absence of interference, the output of the filter as the normalized component being Gaussian noises of standard deviation 1, the fact of being able to use a higher detection threshold makes it possible to drastically reduce the probability of associated false alarm.

[0094] According to the example illustrated by Figure 3, the probability of false alarm associated, according to the current state of the art, with a threshold of 3.4 would in fact have been equal to 6.7*10 -4 , which amounts to the fact that a Gaussian noise of standard deviation 1 has a probability of 6.7*10 -4 to exceed (in absolute value) the value 3.4, whereas the probability of false alarm associated with a threshold of 8 obtained according to the present invention is advantageously equal to 1.2*10 -15 , which amounts to the fact that a Gaussian noise of standard deviation 1 has a probability of 1.2*10 -15 to exceed (in absolute value) the value 8.

[0095] According to the second embodiment preferably used according to the present invention, the filtering module 26 is advantageously configured to apply, during a step 48 of FIG. 2, at least one filtering with predetermined filtering coefficients, called F_C_P filtering.

[0096] In this example, it is possible to use one or more filters, for example F filters, the j ième filter being of index J. Each of the F filters being a finite impulse response filter with N coefficients, the equation of which is expressed in the following form for the filter J of index J: with M J k, n being any coefficient not depending on the index i (i ranging from 0 to N-

[0097] 1, N being the number of filter coefficients) but may depend on the index k of the component considered of the innovation vector, the index n of the calculation cycle and the index J of the filter considered in the filtering module 26 and c (for i between 0 and N-1) of the pre-determined coefficients. The coefficients a J i, n (k) of this filter are therefore expressed in the following form: c 1 has J i n (k) = ' * M J k nhence the name of such a filter, namely a filter with predetermined filter coefficients.

[0098] In the absence of interference, the k ième Y component n (k) of the innovation vector, when n varies, is a white Gaussian noise of standard deviation o n (k), and the output S n (k) of such a filter F_C_P according to the second embodiment with predetermined filter coefficients, when

[0099] / ( c r \ +1 / 2 n varies, is a Gaussian noise of standard deviation f I * M J k, n , the unit in which the filtering coefficients cH are expressed, being the one in which the innovation is expressed.

[0100] As an optional addition, the N coefficients cH of such a filter with predetermined filtering coefficients, for each component of the innovation vector, are predetermined for each type of interference of a predetermined set of interference types and for each type of navigation phase of a predetermined set of navigation phase types associated with said vehicle.

[0101] Indeed, it is established that, for a given hybrid inertial / GNSS navigation system (i.e. implementing an inertial / GNSS hybridization) in a given navigation phase, for a given interference L, on the calculation cycles which follow the start of the interference, the effect due to the interference on the innovation can approximately be written in the form Y= ai_* u n with u n a time signal with a maximum equal to 1, u nvarying according to the calculation cycle n considered, and likely to depend on the navigation phase considered while not depending on the component considered in the innovation vector, nor on the interference considered but just on its type.

[0102] Typically, if we consider the type of interference where the error in the radio frequency signals causes the appearance of an error in the calculated position increasing linearly in time, u nwill not depend on the steering coefficient giving the increase, as a function of time, of the error on the calculated position. ai_ is a coefficient which depends on the component considered in the innovation vector and on the interference considered (on the value of the errors applied to the radiofrequency signals), without depending on the navigation phase considered or on the calculation cycle n. For example, in the case of the type of interference where the error on the radiofrequency signals causes the appearance of an error on the calculated position increasing linearly over time, oti_ depends on the steering coefficient giving the increase (as a function of time) of the error on the calculated position.

[0103] According to this optional addition, a first list of possible types of interference is established, including for example a type of interference consisting of the appearance of a fixed error on the calculated position, another type of interference consisting of the appearance of an error increasing linearly over time on the calculated position, etc.

[0104] In addition, a second list of the different navigation phases of the carrier using GNSS data and inertial data is also established.

[0105] From these two lists, for each type of interference and each navigation phase, we determine predetermined filtering coefficients cH from the evolution obtained on a component of the innovation vector just after having applied the interference of the type considered, when the carrier is in the navigation phase considered.

[0106] In this case, the application of the interference of the type considered is effectively proven and the filtering coefficients cH are suitable for being determined either in the test phase or on a computer simulation of the hybridization implemented by the Kalman filter 20, considering that advantageously, for each navigation phase considered, there are as many filters as there are types of interference considered. In other words, the predetermined filtering coefficients are, according to the present invention, advantageously optimized according to the type of interference and / or the type of navigation phase, and thus suitable for differing according to the type of interference and / or according to the type of navigation phase. More precisely, for the same type of navigation phase, two distinct types of interference are suitable for being associated with distinct sets of filtering coefficients.Similarly, for the same type of interference, two distinct types of navigation phases are likely to be associated with distinct sets of filtering coefficients.

[0107] Indeed, for a given navigation phase, the filtering coefficients cH are specific, for example, to be predetermined, for each possible type of interference, by designating, for example, J the interference of the type considered, first of all by applying this interference J to an inertia / GNSS hybridization in the calculation cycle n1, then by selecting a component k0 of the innovation vector by recording the N values ​​Y ni (k0), Y n i+i(ko), Yni +N -i(ko) successive of the component k0, so that: C j o = Yn1 + Nl(ko), C J 1 = Ynl + N-2(ko), C J N -1= Y n i(k0).

[0108] Such coefficients associated with said given navigation phase are then used, when the carrier (i.e. the vehicle) is in said given navigation phase, and this by considering all possible types of interference, to define, for each component k of the innovation vector, the coefficients a J i, n (k) of the filter J according to the formula a J i, n (k) c 1

[0109] = * MJ k,n, filter which will be applied to the k component of the innovation vector and which

[0110] °n-iW will contribute, via its output S, to the subsequent detection of the presence or absence of this type of interference J. Thus, for each component of the innovation vector, there are, according to this second embodiment, as many filters as there are types of interference considered.

[0111] Such a second embodiment 48 based on the use of at least one filtering with predetermined filtering coefficients, called F_C_P filtering, has the advantage that for each navigation phase considered and each type of interference considered, the method for calculating the predetermined coefficients cH is extracted from the method which itself defines a filter, which makes it possible, when the wearer is in the navigation phase considered and for the type of interference considered, for a given false alarm probability (the false alarm probability being the probability of detecting interference when there is none) to obtain the minimum probability of non-detection (the non-detection probability being the probability of not detecting this type of interference when it is present).

[0112] Figures 4 and 5 are associated with such a second preferred embodiment 48 according to the present invention by considering for simplicity a single filter J with predetermined filter coefficients, Figure 4 representing the effect of an interference appearing from the calculation cycle n=900, while Figure 5 represents the effect of an interference appearing from the calculation cycle n=1200. In Figure 4 view 58 represents, again as in view 52 of Figure 3, on the ordinate the value of the normalized k component Y n (k) / o n (k) of the innovation vector as a function of the value of the calculation cycle on the abscissa, in the presence of GNSS interference, (already illustrated by figure 3) appearing from the calculation cycle n=900.

[0113] View 60 of Figure 4 represents, in the presence of GNSS interference, appearing from the calculation cycle n=900, on the ordinate the value of the filtering output S Jn (k) of the filter J with the predetermined filter coefficients (the coefficients cH used for this filter are illustrated by view 56 of figure 4) and: such that in the absence of interference, the output of the filter is a Gaussian noise of standard deviation 1.

[0114] In Figures 4 and 5, the same filter J with predetermined filtering coefficients is applied, so that views 56 of Figure 4 then 62 of Figure 5, representing the coefficients cH of this filter J as a function of the index i on the abscissa, are identical.

[0115] In figure 5 view 64 represents on the ordinate the value of the normalized k component Y n (k) / c> n (k) of the innovation vector as a function of the value of the calculation cycle on the abscissa, in the presence of GNSS interference, appearing from the calculation cycle n=1200.

[0116] View 66 of Figure 5 represents, in the presence of GNSS interference, appearing from the calculation cycle n=1200, on the ordinate the value of the filtering output S J n (k) of the filter J with the predetermined filter coefficients illustrated by view 56 of the figure such that in the absence of interference, the filter output is Gaussian noise of standard deviation 1.

[0117] In the two cases illustrated by figures 4 and 5 associated with distinct interference occurrence cycles n=900 and n=1200, it is first noted that in the absence of interference, the output of the filtering as well as the normalized component are Gaussian noises of standard deviation 1. In addition, it is noted that the maximum, of the order of 9 in figure 4 and 11 in figure 5, reached by the output, in absolute value, of the filtering implemented specifically according to the present invention, on views 60 of figure 4 as 66 of figure 5, under the effect of the interference is clearly higher than the maximum, of the order of 3.4 in figure 4 and 3.5 in figure 5, reached by the normalized component of the innovation, in absolute value, on views 58 of figure 4 and 64 of figure 5.We can thus see the advantage of using the output of the filter of the filtering module 26 to detect the interference and not simply the standardized component of the innovation as proposed according to the current state of the art.

[0118] Indeed, by using the filter output, we can therefore detect interference with thresholds significantly higher than the simple standardized component of the innovation, which advantageously reduces the probability of associated false alarms.

[0119] 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.

[0120] The present invention thus proposes a particular architecture of an electronic device for detecting GNSS interference(s) not using the components of the innovation vector directly but using the output of a filtering of these components, advantageously optimized according to the type of interference and / or the type of navigation phase, which makes it possible to exploit the fact that the evolution over time of the components of the innovation vector is different depending on whether interference of the GNSS data is present or not. The filtering 44 used in fact makes it possible to highlight this difference in evolution over time, at the level of the output returned by the filtering.

Claims

CLAIMS 1. Electronic device (10) for detecting GNSS interference(s), suitable for being mounted on board a vehicle suitable for moving between two geographical positions, the device comprising at least: - an inertial measurement unit (12) capable of providing navigation measurements (14), - a GNSS receiver (16) for satellite positioning measurements (18), - a Kalman filter (20) configured to cyclically calculate an innovation vector (22) by hybridization of data provided, at the input of said Kalman filter, both by: - said inertial measurement unit (12), and - said GNSS receiver (16), the device (10) being characterized in that it further comprises, at the output of said Kalman filter: - a filtering module (26) configured to apply at least one filtering to each component of the innovation vector provided at each calculation cycle by said Kalman filter, - an interference detection module (30) configured to detect interference when, during Q cycles and on a number U of components of the innovation vector, Q and U each being an integer greater than or equal to one, the absolute value of the output (28) of the filtering module, for at least one filtering applied by said module, is greater than a predetermined threshold, the filtering module (26) being configured to apply at least one filtering with predetermined filtering coefficients, using a finite impulse response filter, the N filtering coefficients of which, for each component of the innovation vector, are calculated with said predetermined filtering coefficients, said filtering coefficients being predetermined for each type of interference of a predetermined set of interference types and for each type of navigation phase of a predetermined set of navigation phase types associated with said vehicle.

2. Device (10) according to claim 1, wherein said predetermined threshold depends on the type of filtering applied by the filtering module (26) and / or the current calculation cycle.

3. Vehicle capable of moving between two geographical positions, said vehicle being characterized in that it comprises an electronic device (10) for detecting GNSS interference(s) according to any one of the preceding claims.

4. Method for detecting GNSS interference(s), said method being implemented by an electronic device (10) for detecting GNSS interference(s) suitable for being on board a vehicle suitable for moving between two geographical positions, the method comprising at least the following steps: - cyclic calculation of an innovation vector, implemented by a Kalman filter of said device, by hybridization of data provided, at the input of said Kalman filter, both by an inertial measurement unit capable of providing navigation measurements (14) and by a GNSS receiver of positioning measurements (18) by satellite(s); - applying at least one filtering to each component of the innovation vector provided at each calculation cycle by said Kalman filter, with predetermined filtering coefficients, using a finite impulse response filter, the N filtering coefficients of which, for each component of the innovation vector, are calculated with said predetermined filtering coefficients, said filtering coefficients being predetermined for each type of interference of a predetermined set of interference types and for each type of navigation phase of a predetermined set of navigation phase types associated with said vehicle; - detection of interference when during Q cycles and on a number U of components of the innovation vector, Q and U each being an integer greater than or equal to one, the absolute value of the filtering output, for at least one filtering, is greater than a predetermined threshold.

5. Computer program comprising software instructions which, when executed by a computer, implement the method of detecting interference(s) according to claim 4.

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