Aircraft landing assistance method, associated computer program and device

Abstract

Aircraft Landing Assistance Method, Computer Program, and Associated Device. The invention relates to a method for assisting the landing of a civil aircraft on a potential landing area divided into sub-areas. It comprises a development step (101) of a digital surface model (M) of the potential landing area using topographic terrain data (D1). In a localization step (102), the digital surface model (M) is positioned and / or oriented in space to obtain a localized digital surface model (M'). In a calculation step (106), an overall risk level (R) is calculated for each sub-area from the localized digital surface model (M') and sub-area characterizations, including topography, accessibility, loss of visibility, and presence of living beings.A feedback step (112) includes the generation of an information signal dependent on the overall risk levels (R); and / or the transmission of a control signal dependent on the overall risk levels (R). Figure for the abstract: Figure 2.

Description

Title of the invention: Method for assisting the landing of an aircraft, computer program and associated device

[0001] The present invention relates to a method for assisting the landing of a civil aircraft on a potential landing area, implemented by an electronic landing aid device intended to be carried on board the civil aircraft.

[0002] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method. It also relates to such an electronic device for assisting the landing of a civil aircraft on a potential landing zone.

[0003] The invention relates to the field of landing assistance for civil aircraft on unprepared runways. Indeed, when landing on an unprepared runway, the pilot of a civil aircraft, such as a helicopter or airplane, generally applies a Method of Approach and Takeoff Reasoning (MRAD). To do this, they must analyze their environment to determine the precise point where they will touch down, the approach path taking into account wind and obstacles, and the type of approach they will use. This complex analysis involves a significant cognitive load for the pilot, in addition to being dependent on weather conditions that can degrade visibility. This leads to a high level of uncertainty associated with a risk of an accident during landing, which can result in material and / or human losses.

[0004] To assist the pilot in this phase of landing in unprepared terrain, we know from US document 8,521,343 B2 an autopilot system determining an optimal landing trajectory taking into account the terrain profile and possible obstacles.

[0005] However, this system only takes into account a limited number of parameters and does not reflect the complexity of the field analysis performed by an experienced pilot. Furthermore, this solution requires the use of a predefined database.

[0006] The aim of the invention is therefore to propose a method of assisting the landing of an aircraft to help a pilot to land the aircraft in unprepared terrain, while limiting the risk of accident.

[0007] To this end, the invention relates to a method for assisting the landing of a civil aircraft on a potential landing zone, implemented by an electronic landing aid device intended to be carried on board the aircraft, the potential landing zone being divided into sub-zones, the method comprising:

[0008] - a step in developing a digital surface model of the landing zone potential using topographic field data;

[0009] - a step of localizing the digital surface model, during which the digital surface model is positioned and / or oriented in space using attitude and / or positioning data of the aircraft, to obtain a localized digital surface model;

[0010] - a risk level calculation step, during which a risk level global is calculated for each sub-zone from at least the localized digital surface model and taking into account at least two characterizations of the sub-zone from among a topography characterization, an accessibility characterization, a loss of visibility characterization and a characterization of the presence of living beings; and

[0011] - a risk level restitution step, including the implementation of less one action from the group consisting of: the generation, for at least one aircraft operator, of an information signal dependent on overall risk levels; and the transmission, to at least one avionics system, of a control signal dependent on overall risk levels.

[0012] Thanks to the invention, the decision to make an unprepared landing on a given terrain and the choice of the landing point are significantly facilitated via the restitution of the risk level, the risk level taking into account different parameters which no longer have to be analyzed visually by the pilot, which significantly reduces the cognitive load of the pilot in critical phase, and consequently decreases the risk of accident.

[0013] According to other advantageous aspects of the invention, the method comprises one or more of the following features, taken individually or in all technically possible combinations:

[0014] - the risk level calculation step includes, for each sub-area, a sub-step of determining at least one unit risk level for each respective characterization, then a sub-step of calculating the overall risk level from the unit risk levels associated with the different characterizations.

[0015] - the restitution step includes the implementation of at least one action among the group consisting of: the generation, for at least one aircraft operator, of a secondary information signal dependent on at least one unit risk level; and the transmission, to at least one avionics system, of a secondary control signal dependent on at least one unit risk level.

[0016] - the method further comprises a step of acquiring at least one terrain feature from a sensor, during which at least one terrain feature is acquired from the sensor and positioned and / or oriented in space using aircraft attitude and / or positioning data to obtain a respective localized terrain feature, and the overall risk level is calculated from the localized digital surface model and at least one localized terrain feature; each terrain feature being chosen from the group comprising: a color characteristic of the subzone, a thermal characteristic of the subzone, a radar reflectivity of the subzone and a movement characteristic of at least one obstacle on the surface of the subzone.

[0017] - at least one terrain characteristic is chosen from the group comprising the color characteristic of the subzone, thermal characteristic of the subzone and radar reflectivity of the subzone; and the visibility loss characterization includes a soil classification made from at least one terrain characteristic, with at least one unit risk level associated with the visibility loss characterization being increased if the soil classification corresponds to soil likely to generate visibility loss.

[0018] - at least one terrain characteristic is chosen from the group comprising the thermal characteristic of the subzone and the obstacle movement characteristic(s) in the subzone; and at least one unit risk level associated with the characterization of the presence of a living being is higher if at least one terrain characteristic corresponds to a thermal or dynamic signature of a living being and lower otherwise.

[0019] - the topography characterization of the sub-zone includes at least one parameter of characterization among the group comprising:

[0020] + a standard deviation of altimetry data relative to a mean plane of the sub-zone, at least one unit risk level associated with topographic characterization being higher the higher the standard deviation; and

[0021] + a slope value, at least one unit risk level associated with the The higher the topographic characterization, the higher the slope value.

[0022] - the accessibility characterization of the subzone includes at least one parameter of characterization among the group comprising:

[0023] + an accessible land area value including the sub-zone, at least one level the unit risk associated with the characterization of accessibility is higher the smaller the accessible land area;

[0024] + the presence or absence of at least one accessible path of a length predetermined minimum including the sub-zone and an angle of incidence for landing according to at least one accessible path, the at least one unit risk level associated with the accessibility characterization being maximal in the absence of said at least one accessible path and all the lower as the angle of incidence is low in the presence of said at least one accessible path;

[0025] + the presence or absence of at least one accessible path and a width of at least one accessible path, the at least one unit risk level associated with the accessibility characterization being maximal in the absence of said at least one path accessible, and all the weaker the greater the width of the at least one accessible path in the presence of said at least one accessible path; and

[0026] + an angle between a plane of the terrain at the level of a laying point and a line connecting the landing point at the highest obstacle at a predetermined maximum distance from the landing point, the at least one unit risk level associated with the accessibility characterization being higher the larger the angle.

[0027] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a process as defined above.

[0028] The invention also relates to an electronic device for assisting the landing of an aircraft on a potential landing zone, the potential landing zone being divided into sub-zones, the device comprising:

[0029] - a processing module configured to process a digital surface model of the potential landing zone using topographic terrain data,

[0030] - a localization module, configured to position and orient the model digital surface in space using attitude and / or positioning data of the aircraft, to obtain a localized digital surface model;

[0031] - a calculation module configured to calculate an overall risk level for each sub-zone based on at least the localized digital surface model and taking into account at least two characterizations of the sub-zone from among a topography characterization, an accessibility characterization, a loss of visibility characterization and a characterization of the presence of living beings; and

[0032] - a reporting module configured to report overall risk levels via the implementation of at least one action from the group consisting of: the generation, for at least one aircraft operator, of an information signal dependent on the overall risk levels; and the transmission, to at least one avionics system, of a control signal dependent on the overall risk levels.

[0033] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0034] [Fig-1] [Fig.1] is a schematic view of a civil aircraft comprising a device landing aid according to the invention;

[0035] [Fig.2] [Fig.2] is a flowchart of a landing aid method according to the invention, the method being implemented by the landing aid device of [Fig. 1]; and

[0036] [Fig.3] [Fig.3] is an example of a screen implementing a rendering step of risk level according to the invention.

[0037] In [Fig.1], a civil aircraft 1 includes a landing aid device 3 and sensors 5, intended to be carried on board the civil aircraft 1.

[0038] Civil aircraft 1 is in particular a rotary-wing aircraft, such as a civil helicopter, as shown in [Fig. 1]. Alternatively, civil aircraft 1 is an airliner, or even a civil drone, remotely piloted or not by a remote operator.

[0039] The civil aircraft 1 is operated by an operator, typically a pilot.

[0040] The electronic landing aid device 3 comprises an elaboration module 7, a localization module 9, a calculation module 11 and a restitution module 13.

[0041] The development module 7 is configured to develop a digital surface model M of the potential landing area 23 using topographic terrain data DI.

[0042] The localization module 9 is configured to position and orient the digital surface model M in space using attitude and / or positioning data of the aircraft 1, to obtain a localized digital surface model M'.

[0043] The calculation module 11 is configured to calculate an overall risk level R for each sub-zone 25 from at least the localized digital surface model M' and taking into account at least two characterizations of the sub-zone from among a topography characterization, an accessibility characterization, a loss of visibility characterization and a characterization of the presence of a living being.

[0044] The restitution module 13 is configured to restore the overall risk levels R via the implementation of at least one action from the group consisting of: the generation, for at least one operator of the aircraft 1, of an information signal dependent on the overall risk levels R; and the transmission, to at least one avionics system, of a control signal dependent on the overall risk levels R.

[0045] The details of the operations carried out by each of these modules 7, 9, 11 and 13 are described later in the description, in particular during the description of the steps of the landing assistance process according to the invention.

[0046] In the example of [Fig.1], the electronic landing aid device 3 includes an information processing unit 15 formed for example of a memory 17 and a processor 19 associated with the memory 17.

[0047] In the example of [Fig. 1], the processing module 7, the localization module 9, the calculation module 11, and the retrieval module 13 are each implemented as a software program, or a software component, executable by the processor 19. The memory 17 of the electronic landing aid device 3 is then capable of storing processing software, localization software, calculation software, and retrieval software. The processor 19 is then capable of executing each of the software programs from among the development software, location software, calculation software and rendering software.

[0048] In an alternative not shown, the processing module 7, the localization module 9, the calculation module 11 and the output module 13 are each implemented as a programmable logic component, such as an FPGA (Field Programmable Gate Array), or as a dedicated integrated circuit, such as an ASIC (Application-Specific Integrated Circuit).

[0049] When the electronic landing aid device 3 is implemented in the form of one or more software programs, i.e., in the form of a computer program, it is also capable of being stored on a computer-readable medium (not shown). A computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. For example, a readable medium is an optical disc, a magneto-optical disc, a ROM, a RAM, any type of non-volatile memory (e.g., EPROM, EEPROM, FLASH, NVRAM), a magnetic card, or an optical card. A computer program comprising software instructions is then stored on the readable medium.

[0050] The electronic landing aid device 3 further comprises a feedback device 21. In the example shown in [Fig. 1], the feedback device 21 is a human-machine interface and typically includes a display. In an alternative not shown, the human-machine interface may include an indicator light, a loudspeaker, or a haptic interface. According to another alternative, the feedback device 13 may be a connection interface to one or more avionics systems not shown.

[0051] The sensors 5 are connected to the electronic landing aid device 3 via a wired or wireless link. Advantageously, the sensors 5 comprise primary sensors, capable of acquiring topographic terrain data D1, and secondary sensors, capable of acquiring terrain features D2, distinct from the topographic terrain data DL

[0052] Primary sensors are, for example, cameras in the visible range, radars or lidars.

[0053] Secondary sensors are, for example, cameras in the infrared range or motion sensors.

[0054] In an alternative not shown, other sensors, not carried on the civil aircraft 1 but carried on another aircraft or on a satellite and also providing topographic terrain data or terrain characteristics, are also linked to the electronic landing aid device 3.

[0055] During a mission, the civil aircraft 1 may have to land on unprepared ground. The unprepared ground on which the aircraft is likely to land, called the potential landing area 23 and shown in [Fig. 1], is divided into sub-areas 25. The potential landing area 23 may include features likely to cause an accident during landing, for example, terrain features 27, obstacles 29 or a type of ground, for example sandy, likely to generate a loss of visibility during landing (brown out, white out).

[0056] A landing aid method 100, intended to help the pilot of the aircraft 1 or an autonomous system to achieve a safe landing in the potential landing zone 23, is then implemented by the electronic landing aid device 3. In particular, this method includes determining a level of risk of accident during landing for each sub-zone 25 of the potential landing zone 23, enabling the pilot or the autonomous system to be guided in choosing the sub-zone 25 in which to land the aircraft 1. This method is described below with reference to [Fig.2].

[0057] During a first step 101 of the development of a digital surface model, implemented by the development module 7, the topographic terrain data DI from the primary sensors are used to develop a digital surface model M of the potential landing area 23.

[0058] This digital surface model M is advantageously developed using photogrammetry. Photogrammetry is a technique that, through the capture of images, determines the position, size, and volume in space of a subject, in this case the potential landing zone 23, as well as its specific characteristics. The digital surface model M is a three-dimensional representation of a topographic profile of the potential landing zone 23, as well as all the elements present in this zone, such as buildings or vegetation.

[0059] During a localization step 102 of the digital surface model, implemented by the localization module 9, the digital surface model M is positioned in space by georeferencing the topographic terrain data DI. Onboard computing capabilities (edgecomputing) allow, for example, these calculations to be performed in real time. This localization step 102 provides a localized digital surface model M'.

[0060] Advantageously, as an optional complement, the landing aid method 100 also includes a terrain feature acquisition step 104, during which at least one terrain feature is acquired from a respective sensor 5, and then positioned and / or oriented in space using attitude and / or positioning data of the aircraft to obtain a respective localized terrain feature D2. The localized terrain features D2 include preferably a colour characteristic, a thermal characteristic, a radar reflectivity and a movement characteristic of at least one obstacle.

[0061] In an alternative not shown, the terrain features acquired during step 104 (terrain feature acquisition) are positioned and / or oriented in space simultaneously with the digital surface model M during step 102 (localization of the digital surface model). Those skilled in the art will understand that this alternative corresponds to the case where the terrain features are acquired before the localization step 102, then merged with the digital surface model M developed during the development step 101 to form an enriched model. The enriched model is then positioned in space during the localization step 102 to obtain the localized digital surface model M', which also includes the localized terrain features D2, also called the enriched localized digital surface model.

[0062] The localized digital surface model M' and the localized terrain features D2 are then used during a risk level calculation step 106, implemented by the calculation module 11. During this calculation step 106, an overall risk level R is calculated for each sub-zone 25, taking into account at least two characterizations of the sub-zone 25 from among a topography characterization, an accessibility characterization, a loss of visibility characterization and a characterization of the presence of a living being.

[0063] Alternatively, step 106 of risk level calculation also uses data from one or more database(s).

[0064] The overall risk level R is expressed, for example, as a percentage, with a value of 0% corresponding to the riskiest conditions, i.e., a maximum risk level, and a value of 100% corresponding to the least risky conditions, i.e., a minimum risk level. In other words, a high risk level corresponds to a percentage close to 0, while a low risk level corresponds to a percentage close to 100. An "increase in the risk level" therefore means a decrease in this percentage. Conversely, a "decrease in the risk level" means an increase in this percentage.

[0065] Advantageously, step 106 of risk level calculation is broken down into a substep 108A, 108B, 108C or 108D of calculation of unit risk level RA, RB, RC or RD for each of the aforementioned characterizations, and a substep 110 of calculation of the overall risk level R from the unit risk levels RA, RB, RC and RD.

[0066] The 108A-D risk level calculation substeps may be four in number, as in the example shown in [Fig.2], or more generally in a number equal to the number of distinct characterizations used.

[0067] The unit risk levels RA, RB, RC and RD are for example expressed in the same form as the overall risk level R. Each risk level RA, RB, RC or RD corresponds to a unit risk level associated with the characterization or the multiplication of different unit risk levels associated with the same characterization.

[0068] In an unrepresented variant, the same substep 108A, 108B, 108C and / or 108D of unit risk level calculation provides several unit risk levels associated with the characterization concerned.

[0069] Advantageously, the overall risk level R is the result of multiplying the individual risk levels RA, RB, RC and RD. Thus, if one of the criteria gives a maximum risk level (risk level value equal to 0%), then the overall risk level R is also maximum (risk level value also equal to 0%).

[0070] The content of substeps 108A, 108B, 108C, and 108D is detailed below. Any numerical criteria given in these explanations are provided solely by way of example and are advantageously adjustable in the electronic landing aid device 3 according to the specific needs of each user. Furthermore, various characterization parameters are explained in this section and can be combined in any technically feasible way to form different embodiments of the invention.

[0071] The first sub-step 108A of unit risk level calculation corresponds for example to the topographic characterization of sub-zone 25.

[0072] A first unit risk level RA associated with the topographic characterization corresponds, for example, to the slope of sub-zone 25. This unit risk level is higher the greater the slope value of sub-zone 25. For example, the unit risk level is 0% if the slope value exceeds a predetermined threshold, 100% if the slope value is 0°, and its evolution as a function of the slope value between these two extremes follows a predetermined law, for example, linear or logarithmic. The value of the predetermined threshold is typically derived from charts provided by the aircraft manufacturer 1, and is, for example, on the order of 12°.

[0073] A second unit risk level RA associated with topographic characterization corresponds, for example, to the flatness of sub-zone 25 and involves a standard deviation of altimetry data relative to a mean plane of sub-zone 25. The mean plane is advantageously obtained by linear regression on a set of altimetry data obtained by radar in sub-zone 25 or over a given surface, for example, in such a way as to minimize the sum of the squares of the distances of each altimetry data point to the mean plane. In other words, a least-squares regression is typically performed on this set of altimetry data in three dimensions. To obtain the mean plane, this regression allows us to map the mean plane onto this set of points in three dimensions, using the least squares method. The surface is, for example, a square with a length equal to the length of aircraft 1 and can be extended by adding neighboring surfaces if the slope of the mean plane of the neighboring surface is similar to the slope of the mean plane of the surface under consideration (for example, if the difference in slope is less than 1°), and if the standard deviation of the altimetry data relative to the mean plane of the neighboring surface is similar to the standard deviation of the altimetry data relative to the mean plane of the surface under consideration (for example, if the difference in standard deviation is less than 5 cm). This level of risk per unit is higher the larger the standard deviation.For example, the value of the unit risk level is 0% for a standard deviation greater than 50 cm, 100% for a standard deviation less than 5 cm, and its evolution as a function of the standard deviation between these two extrema follows a predetermined law, for example linear or logarithmic.

[0074] The second sub-step 108B of unit risk level calculation corresponds for example to the accessibility characterization of sub-zone 25.

[0075] A first unit risk level RB associated with the accessibility characterization involves, for example, a value for the accessible terrain area including sub-zone 25. This unit risk level is higher the smaller the accessible terrain area. The accessible terrain area is advantageously defined as the area of ​​the largest terrain including the sub-zone and not including any obstacle larger than or equal to 50 cm relative to the mean plane of the sub-zone in which the obstacle is located. For example, the unit risk level value is 0% if a diameter of the largest circle contained within the accessible terrain area is less than or equal to a maximum length of aircraft 1, 100% if this diameter is greater than three times the maximum length of aircraft 1, and the evolution of this unit risk level as a function of the diameter between these two extremes follows a predetermined law, for example, linear or logarithmic.

[0076] A second and third unit risk level RB associated with the accessibility characterization depend, for example, on the presence or absence of at least one accessible path of a predetermined minimum length including sub-zone 25. Preferably, the second and third unit risk levels RB associated with the accessibility characterization depend more precisely on the presence or absence of two accessible paths of the predetermined minimum length and substantially perpendicular to each other. The predetermined minimum length is, for example, on the order of 200 m.

[0077] The second unit risk level RB associated with the accessibility characterization further depends on an angle of incidence for landing along at least one path accessible, if it exists. This unit risk level is maximal in the absence of at least one such accessible path and is lower the smaller the angle of incidence in the presence of at least one such accessible path. For example, the risk level value is 0% in the absence of an accessible path or for an angle of incidence greater than 90°, 100% in the presence of an accessible path and for an angle of incidence equal to 0°, and the evolution of this unit risk level as a function of the angle of incidence between these two extremes in the presence of an accessible path follows a predetermined law, for example linear or logarithmic.

[0078] The third unit risk level RB associated with the accessibility characterization further depends on the width of at least one accessible path, if any. This unit risk level is highest in the absence of such an accessible path and lowest as the width of the accessible path increases in the presence of at least one such accessible path. For example, the risk level value is 0% in the absence of an accessible path or for an accessible path width less than 1.5 times the rotor diameter of aircraft 1, 100% in the presence of an accessible path and for an accessible path width greater than 5 times the rotor diameter of aircraft 1, and the evolution of this unit risk level as a function of the path width between these two extremes in the presence of an accessible path follows a predetermined law, for example, linear or logarithmic.

[0079] A fourth unit risk level RB associated with the accessibility characterization depends, for example, on an angle between a ground plane at a landing point and a line connecting the landing point to the highest obstacle at a predetermined maximum distance from the landing point. This unit risk level is greater the larger the angle. For example, the unit risk level value is 0% if the angle is greater than 30°, 100% if the angle is 0°, and its evolution as a function of the angle between these two extremes follows a predetermined law, for example, linear or logarithmic.

[0080] The third sub-step 108C of unit risk level calculation corresponds for example to the characterization of loss of visibility of sub-zone 25.

[0081] A unitary risk level RC associated with the visibility loss characterization, for example, incorporates a color characteristic of sub-zone 25, a thermal characteristic of sub-zone 25, and a radar reflectivity of sub-zone 25. This unitary risk level defaults to 100% (minimum risk) and is increased by decreasing this percentage if the ground classification corresponds to ground likely to generate a visibility loss. Advantageously, this classification is performed using a combination of localized ground characteristics and the localized digital surface model. According to a first embodiment, the increase in the risk level is determined by an algorithm Machine learning takes as input localized terrain characteristics and the localized digital surface model, combined into an enriched digital surface model. In a second embodiment, the risk level is increased based on the soil type according to a predetermined database of different soil types. For example, yellow / beige soil is identified as sand or dust and assigned a unit risk level of 50% due to the risk of brownout. White soil with a temperature below 5°C is identified as ice and also assigned a unit risk level of 50%.Ground exhibiting several layers detected by radar, with a difference between 10 cm and 20 cm between each layer, gives a unit risk of 50%. The same ground with a difference greater than 50 cm between layers gives a unit risk of 0%. The evolution of the unit risk as a function of the difference between these two extremes follows a predetermined, for example, linear, law. Very flat ground, exhibiting little echo and appearing blue, is identified as a body of liquid water and gives a unit risk level of 0%.

[0082] The fourth sub-step 108D of unit risk level calculation corresponds for example to the characterization of the presence of a living being in sub-zone 25.

[0083] A unit risk level RD associated with the characterization of the presence of a living being involves, for example, a thermal characteristic of sub-zone 25 and a movement characteristic of obstacle(s) in sub-zone 25. This unit risk level is higher when the terrain characteristics correspond to a thermal and dynamic signature of a living being. For example, a moving object with a volume greater than 20 liters gives a unit risk level of 25% if it has a temperature between 35 and 40 degrees Celsius, and 50% otherwise. The presence of at least two moving objects, each with a volume greater than 20 liters, gives a risk level of 0%.

[0084] Substep 110 of calculating the overall risk level R from the unit risk levels RA, RB, RC and RD involves, for example, a multiplication of the unit risk levels RA, RB, RC and RD. Thus, if only one of the risk levels is zero (corresponding to the riskiest conditions), then the overall risk level is also zero.

[0085] Alternatively, substeps 108A, 108B, 108C and 108D for calculating the unit risk level, as well as substep 110 for calculating the overall risk level, are performed by a machine learning algorithm taking as input the localized terrain characteristics D2 and the localized digital surface model M'. For substep 110 for calculating the overall risk level, the use of machine learning makes it possible to determine a model for calculating the overall score with optimal weights associated with each of the unit risk levels.

[0086] Alternatively, machine learning is used only for steps 108A, 108B, 108C and 108D of calculating unit risk level.

[0087] According to another variant, step 106 of calculating the overall risk level is carried out by a machine learning algorithm taking as input the localized terrain characteristics and the localized digital surface model, without calculating the unit risk levels RA, RB, RC and RD.

[0088] The landing aid method 100 includes a risk level restitution step 112, implemented by the restitution module 13 for the restitution device 21. During this restitution step 112, a signal dependent on the overall risk level R of each sub-zone 23 is generated by the restitution module 21.

[0089] According to a first embodiment, the signal is an information signal intended for an aircraft operator. Figure 3 gives an example of an information signal in the form of a map image of the potential landing zone, on which each sub-zone is colored according to its overall risk level. In the example in Figure 3, zone 212 is a high-risk zone (overall risk level R between 0% and 33%) due to the proximity of an obstacle. The dark, triangular fill of the high-risk zone 212 in Figure 3 corresponds, for example, to a red display. Zone 220 is a medium-risk zone (overall risk level R between 34% and 66%). The striped fill of the medium-risk zone 220 corresponds, for example, to a yellow display. In the example in Figure 3, the signal is an information signal intended for an aircraft operator.[3] Zone 220, representing a medium risk level, results from the combination of two zones: Zone 214, representing a medium risk level due to the average distance to an obstacle, and Zone 216, representing a medium risk level due to unfavorable terrain topography. Zone 218 is a low risk zone (overall risk level R between 67% and 100%). The dotted line filling Zone 218, representing a low risk level, corresponds, for example, to a green color display.

[0090] Alternatively, the information signal may be an audible signal, a light or haptic feedback (such as a vibration of an aircraft component) depending on the risk level of the sub-zone 23 overflown at a given time by the aircraft 1.

[0091] According to a second embodiment, the signal is a command signal to an avionics system, enabling aircraft 1 to perform all or part of landing 1 autonomously, without operator intervention. For example, the command signal can be addressed to an automatic landing system to indicate the nearest sub-zone 25 with the lowest risk level.

[0092] Advantageously, in the two aforementioned embodiments, the output module 21 also generates a secondary signal, dependent on at least one of the unit risk levels RA, RB, RC, and / or RD. For example, a secondary information signal for an aircraft operator is an image of a map similar to [Fig. 3] displaying only the unit risk level associated with the topography of the sub-area. This map is, for example, accessible via a menu allowing the operator to select the information to be displayed.

[0093] In all cases, the information or command thus restored is an aid to the pilot or to the control system to carry out the unprepared landing of the aircraft 1, thereby reducing the risk of an accident.

[0094] Any feature described above for an example embodiment or variant can also be implemented in the other examples embodiments and variants above, as far as technically possible.

Claims

Demands

1. A method (100) for assisting the landing of a civil aircraft (1) on a potential landing area (23), implemented by an electronic landing aid device (3) intended to be carried on board the aircraft (1), the potential landing area (23) being divided into sub-areas (25), the method (100) comprising: • a step of developing (101) a digital surface model (M) of the potential landing area (23) using topographic terrain data (D1); characterized in that the method (100) further comprises: • a step of locating (102) the digital surface model (M), during which the digital surface model (M) is positioned and / or oriented in space using attitude and / or positioning data of the aircraft (1), to obtain a localized digital surface model (M');• a risk level calculation step (106), during which an overall risk level (R) is calculated for each sub-area (25) from at least the localized digital surface model (M') and taking into account at least two characterizations of the sub-area (25) from among a topography characterization, an accessibility characterization, a loss of visibility characterization and a characterization of the presence of a living being; and • a risk level restitution step (112), comprising the implementation of at least one action from the group consisting of: the generation, for at least one aircraft operator (1), of an information signal dependent on the overall risk levels (R); and the transmission, to at least one avionics system, of a control signal dependent on the overall risk levels (R).

2. A method (100) according to claim 1, wherein the risk level calculation step (106) comprises, for each sub-zone (25), a sub-step (108A, 108B, 108C, 108D) for determining at least one unit risk level (RA, RB, RC, RD) for each respective characterization, then a sub-step (110) of calculation of the overall risk level (R) from the unit risk levels (RA, RB, RC, RD) associated with the different characterizations.

3. A method (100) according to claim 2, wherein the restitution step (112) comprises the implementation of at least one action from the group consisting of: the generation, for at least one aircraft operator (1), of a secondary information signal dependent on at least one unit risk level (RA, RB, RC, RD); and the transmission, to at least one avionics system, of a secondary control signal dependent on at least one unit risk level (RA, RB, RC, RD).

4. A method (100) according to any one of the preceding claims, wherein the method further comprises an acquisition step (104) of at least one terrain feature from a sensor (5), during which at least one terrain feature is acquired from the sensor (5) and positioned and / or oriented in space using attitude and / or positioning data from the aircraft (1) to obtain a respective localized terrain feature (D2), and the overall risk level (R) is calculated from the localized digital surface model (M') and at least one localized terrain feature (D2); each terrain feature being selected from the group comprising: a color feature of the sub-area (25), a thermal feature of the sub-area (25), a radar reflectivity of the sub-area (25), and a motion feature of at least one obstacle on the surface of the sub-area (25).

5. A method (100) according to claims 2 and 4, wherein at least one terrain feature is selected from the group comprising the color feature of the sub-area (25), the thermal feature of the sub-area (25), and the radar reflectivity of the sub-area (25); and wherein the visibility loss characterization includes a soil classification performed from at least one terrain feature, the at least one unit risk level (RA, RB, RC, RD) associated with the visibility loss characterization being increased if the soil classification corresponds to soil likely to generate a visibility loss.

6. A method (100) according to claim 4 or 5, taken in conjunction with claim 2, wherein at least one field characteristic is selected from the group comprising the thermal characteristic of the subzone (25) and the obstacle movement characteristic(s) in the subzone (25); and in which at least one unit risk level (RA, RB, RC, RD) associated with the characterization of the presence of a living being is higher if at least one terrain characteristic corresponds to a thermal or dynamic signature of a living being and lower otherwise.

7. A method (100) according to any one of the preceding claims, taken with claim 2, wherein the topography characterization of the subzone comprises at least one characterization parameter from the group comprising: • a standard deviation of altimetry data relative to a mean plane of the subzone (25), the at least one unit risk level (RA, RB, RC, RD) associated with the topography characterization being higher the higher the standard deviation; and • a slope value, the at least one unit risk level (RA, RB, RC, RD) associated with the topography characterization being higher the higher the slope value.

8. A method (100) according to any one of the preceding claims, taken together with claim 2, wherein the accessibility characterization of the sub-area (25) comprises at least one characterization parameter from the group comprising: • an accessible ground area value including the sub-area (25), the at least one unit risk level (RA, RB, RC, RD) associated with the accessibility characterization being higher the smaller the accessible ground area; • the presence or absence of at least one accessible path of a predetermined minimum length including the sub-area (25) and an angle of incidence for landing along the at least one accessible path, the at least one unit risk level (RA, RB, RC, RD) associated with the accessibility characterization being maximum in the absence of said at least one accessible path and lower the smaller the angle of incidence in the presence of said at least one accessible path; • the presence or absence of at least one accessible path and the width of that at least one accessible path, with the at least one unit risk level (RA, RB, RC, RD) associated with the accessibility characterization being maximal in the absence of said at least one accessible path and all the lower the greater the width of the at least one accessible path in the presence of said at least one accessible path; and • an angle between a plane of the ground at the level of a landing point and a line connecting the landing point to the highest obstacle at a predetermined maximum distance from the landing point, the at least one unit risk level (RA, RB, RC, RD) associated with the accessibility characterization being higher the larger the angle.

9. Computer program comprising software instructions which, when executed by a computer, implement a method (100) according to any one of the preceding claims.

10. An electronic landing aid device (3) for an aircraft (1) on a potential landing area (23), the potential landing area (23) being divided into sub-areas (25), the device comprising: • a development module (7) configured to develop a digital surface model (M) of the potential landing area (23) using topographic terrain data (Dl), characterized in that the device (100) further comprises: • a localization module (9), configured to position and orient the digital surface model (M) in space using attitude and / or positioning data of the aircraft (1), to obtain a localized digital surface model (M'); • a calculation module (11) configured to calculate an overall risk level (R) for each sub-zone (25) from at least the localized digital surface model (M') and taking into account at least two characterizations of the sub-zone from among a topography characterization, an accessibility characterization, a characterization of loss of visibility and a characterization of the presence of a living being; and a rendering module (13) configured to render the overall risk levels (R) via the implementation of at least one action from the group consisting of: the generation, for at least one aircraft operator (1), of an information signal dependent on the overall risk levels (R); and the transmission, to at least one avionics system, of a control signal dependent on the overall risk levels (R).

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