Patent Publication Number: US-2020283163-A1

Title: Flight vision system and method for presenting images from the surrounding of an airborne vehicle in a flight vision system

Description:
TECHNICAL FIELD 
     The present disclosure relates to a flight vision system and to a method for presenting images from the surrounding of an airborne vehicle in a flight vision system. It further relates to a computer program product, to a computer-readable storage medium, and to an airborne vehicle. 
     BACKGROUND ART 
     In flight vision systems, information regarding the surrounding of an airborne vehicle is presented to an observer. This information can originate from different sources, such as different sensors. The different sources usually have different properties. As an example, a first source might be able to provide high resolved images, but it not able to provide long range images during rain and/or fog. A second source might be able to provide long range images during rain and/or fog, but the provided images might be only low-resolved. When having access to different sources in the flight vision system, it is thus important to decide in a good way which of the sources should be used to provide images for flight vision system. In the above example, it might be a good choice to show images from the second source in case it is raining and/or foggy and in case the airborne vehicle is at long distance from a place to land, such as an airfield. If, on the other hand, there is no rain and/or fog or in case the airborne vehicle is close to a place to land, it might be a good choice to show images from the first sensor. 
     Often, one of two different ways is applied how to choose between different sources. One way is to switch between different sources is based on a distance to a place to land, such as a distance to a runway. As an example, when the airborne vehicle is situated closer than a pre-determined threshold from the runway, images from a first source are used in the flight vision system. In case the airborne vehicle is situated longer than the pre-determined threshold from the runway, images from a second source are used. This has the disadvantage that no consideration is given to the weather conditions. Since the threshold is pre-determined irrespective of the weather, the choice of sources to use for the flight vision system will usually not be optimal for all weather conditions. 
     This disadvantage has been solved by another way, relying on so-called runway visual range, RVR. A RVR-system might be installed at an airport and determine repeatedly the visibility from the runway. As an example, the RVR-system might repeatedly determine that the runway is visible from a certain distance. Since this determination is repeated, the determined distance might change in each run. Thus, consideration to the weather conditions can be given by a RVR-system. The determined distance is then transmitted from the RVR-system to the airborne vehicle and the source can be switched in the flight vision system of the airborne vehicle based on the determined distance. Although consideration to the weather is given, this way of switching between different sources has the disadvantage that it requires an installation of the RVR-system at the airport, or at any other place to land. Thus, this solution requires quite some resources and preparation. Therefore, RCR-systems are usually not installed at smaller airports and airborne vehicles thus do not always have the advantages of an RVR-system. 
     SUMMARY OF THE INVENTION 
     It is an objective of the present disclosure to present a flight vision system, an airborne vehicle, a method, a computer program product, and a computer-readable storage medium which alleviate at least some of the above mentioned disadvantages. 
     It is a further objective of the present disclosure to present a flight vision system, an airborne vehicle, a method, a computer program product, and a computer-readable storage medium which provide an alternative solution to the hitherto known flight vision systems. 
     It is yet a further objective of the present disclosure to present a flight vision system, an airborne vehicle, a method, a computer program product, and a computer-readable storage medium which present an optimal image to an observer. 
     It is yet even a further objective of the present disclosure to present a flight vision system, an airborne vehicle, a method, a computer program product, and a computer-readable storage medium which require a low amount of human interaction during operation to present an optimal image to an observer. 
     At least some of the aforementioned objectives are achieved by a method for presenting images from the surrounding of an airborne vehicle in a flight vision system. The method comprises the step of providing a plurality of images of the surrounding of the airborne vehicle. The plurality of images originates from at least two different sources. The method further comprises assessing a quality measure in each of the images out of the plurality of images. The quality measure relates at least to the visibility of objects in the plurality of images. The method further comprises to automatically deciding from which of the at least two different sources at least one image out of the plurality of images should be displayed. The decision is based at least on the assessed quality measure. The method yet even further comprises displaying the at least one automatically decided image in the flight vision system. 
     This has the advantage that no extra equipment at the airport, such as an RVR-system is required. Further, no input from the operator of the airborne vehicle is required. The method can automatically adapt the flight vision system to the best possible view given the circumstances, such as the current weather conditions. It is in this relation not needed to know the weather conditions, as the method will automatically adapt to it. Since no extra equipment outside the airborne vehicle is required, the autonomous degree of the airborne vehicle can be increased. As a consequence, flexibility can be increased, and cost an needed pre-investments be reduced. 
     In one example, the step of assessing a quality measure comprises comparing images from different sources for finding differences between the images. This is a simple way of assessing a quality measure. 
     In one example, the step of assessing a quality measure comprises determining a qualitative measure for each of the images out of the plurality of images. This allows to have a specific expression for the quality. 
     In one example, at least one of the at least two different sources is a database, for example a database for synthetic vision. This allows for an applicability of the system even under bad weather conditions. 
     In one example, at least one of the at least two different sources is an image sensor. 
     In one example, the image sensor is an infrared sensor, a radar sensor, and/or a sensor for visual wavelengths. This allows using known sensors and gives good flexibility to adapt to different weather conditions. 
     In one example, the step of assessing a quality measure comprises comparing each image out of the plurality of images with a pre-determined pattern. This allows to easily detect objects of specific importance, such as runways. 
     In one example, the step of automatically deciding from which of the at least two different sources at least one image out of the plurality of images should be displayed comprises choosing only one out of the at least two different sources. This reduced latency and computation power. 
     In one example, the step of automatically deciding from which of the at least two different sources at least one image out of the plurality of images should be displayed comprises choosing a plurality out of the at least two different sources. This allows to maximise the information on the display since different information/objects from different sources can be combined. 
     At least some of the objectives are also achieved by a flight vision system for presenting images from the surrounding of an airborne vehicle. The flight vision system is arranged to receive a plurality of images of the surrounding of the airborne vehicle from at least two different sources. The flight vision system comprises a processor arrangement which is arranged to assess a quality measure in each of the images out of the plurality of images. The quality measure relates at least to the visibility of objects in the plurality of images. The processor arrangement is further arranged to automatically decide from which of the at least two different sources at least one image out of the plurality of images should be displayed. The decision is based at least on the assessed quality measure. The flight vision system also comprises at least one display which is arranged to display the at least one automatically decided image. 
     In one embodiment the processor arrangement is arranged to compare images from different sources for finding differences between the images. 
     In one embodiment the processor arrangement is arranged to determine a qualitative measure for each of the images out of the plurality of images. 
     In one embodiment, the flight vision system is further arranged to receive images from at least one database. At least one of the at least two different sources is a database, for example a database for synthetic vision. 
     In one embodiment, the flight vision system is further arranged to receive images from at least one image sensor. At least one of the at least two different sources is an image sensor. 
     In one embodiment, the image sensor is an infrared sensor, a radar sensor, and/or a sensor for visual wavelengths. 
     In one embodiment, the processor arrangement is arranged to compare each image out of the plurality of images with a pre-determined pattern. 
     In one embodiment, the processor arrangement is arranged to choose only one out of the at least two different sources when automatically deciding from which of the at least two different sources at least one image out of the plurality of images should be displayed. 
     In one embodiment, the processor arrangement is arranged to choose a plurality out of the at least two different sources when automatically deciding from which of the at least two different sources at least one image out of the plurality of images should be displayed. 
     At least some of the objectives are also achieved by a computer program product, comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to the present disclosure. 
     At least some of the objectives are also achieved by a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the present disclosure. 
     At least some of the objectives are also achieved by an airborne vehicle which comprises the flight vision system according to the present disclosure. 
     In relation to this disclosure the term computer can relate to what is commonly referred to as a computer and/or to an electronic control unit. 
     The system, the airborne vehicle, the computer program product and the computer-readable storage medium have corresponding advantages as have been described in connection with the corresponding examples of the method according to this disclosure. 
     Further advantages of the present invention are described in the following detailed description and/or will arise to a person skilled in the art when performing the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more detailed understanding of the present invention and its objects and advantages, reference is made to the following detailed description which should be read together with the accompanying drawings. Same reference numbers refer to same components in the different figures. In the following, 
         FIG. 1  shows, in a schematic way, an airborne vehicle according to one embodiment of the present disclosure; 
         FIG. 2  shows, in a schematic way, a flight vision system according to an embodiment of the present disclosure; 
         FIG. 3  shows, in a schematic way, a method according to an example of the present invention; and 
         FIG. 4  shows, in a schematic way, an example of a situation in which the present disclosure can be used. 
     
    
    
     DETAILED DESCRIPTION 
     The term “link” refers herein to a communication link which may be a physical connection such as an opto-electronic communication line, or a non-physical connection such as a wireless connection, e.g. a radio link or microwave link. 
     The term “image” does not require a lifelike picture. Instead, the term “image” comprises also wrong-coloured images, stylised images, and the like. 
       FIG. 1  shows, in a schematic way, an airborne vehicle  100  according to one embodiment of the present disclosure. The airborne vehicle  100  can be any kind of aircraft, such as an airplane, a helicopter, an airship, or the like. The airborne vehicle  100  can be manned or unmanned. The airborne vehicle can comprise a flight vision system  299 , which is described in more detail in relation to  FIG. 2 . The airborne vehicle  100  can comprise any of the sources  210 ,  220  described in relation to  FIG. 2 . 
       FIG. 2  shows, in a schematic way, a flight vision system  299  according to an embodiment of the present disclosure. The flight vision system  299  can be a so-called enhanced flight vision system, EFVS. However, any other type of flight vision system might be used as well. The flight vision system  299  is arranged to present images from the surrounding of an airborne vehicle. 
     The flight vision system  299  comprises a processor arrangement. The processor arrangement comprises at least one processor. The processor arrangement can comprise a first control unit  200  and/or a second control unit  205 . In the following, an embodiment of the invention will be described based on the first and/or second control unit  200 .  205 . However, it should be understood that the anything attributed to the first and/or second control unit  200 ,  205  equally well can be applied to any other processor arrangement. 
     The flight vision system  200  can be connected to different image sources. In the following, two different kinds of image sources  210  and  220  are discussed. However, it should be emphasised that the present disclosure can be adapted to any other kind of image source as well. In one example, the flight vision system  299  is connected to at least one database  220 . The database  220  can be any database arranged to provide images of the surrounding of the airborne vehicle. In one example, the database  220  is a database for synthetic vision, SV. As an example, the database  200  can contain two-, or three-dimensional terrain data regarding the surrounding of the airborne vehicle. The database can contain images of the surrounding of the airborne vehicles and/or contain texture information of the terrain data. 
     The first control unit  200  can be connected to the database  220  via a link L 220 . The database  220  is arranged for communication with the first control unit  200 , for example via the link L 220 . The database  220  is arranged to transmit images of the surrounding of the airborne vehicle. The first control unit  200  is arranged to receive the images from the database  220 . In one example, the database  220  is arranged to store the images. In one example, the database  220  comprises processing means, such as at least one processor, which are arranged to produce the images out of data from the database. 
     In one example, the flight vision system  200  is connected to at least one image sensor, such as at least one image sensor out of a sensor array  210 . In the following, an embodiment of the disclosure is described in relation to the sensor array  210 . However, it should be understood that the sensor array in its simplest form comprises only one image sensor. The sensor array  210  can comprise a radar sensor  211 . The radar sensor  211  can be arranged to provide radar images of the surrounding of the airborne vehicle. The sensor array  210  can comprise a sensor for visible light  212 , such as a camera. The sensor for visible light  212  can be arranged to provide images of the visible spectrum of the surrounding of the airborne vehicle. The sensor array  210  can comprise an IR sensor array  215 . The IR sensor array  215  can comprise any number of IR sensors, such as a near IR sensor  216 , a short-wavelength IR sensor  217 , a mid-wavelength IR sensor  218 , and/or a long-wavelength IR sensor  219 . The sensor(s) in the IR sensor array can be arranged to provide a respective and/or a combined IR image of the surrounding of the airborne vehicle. It is well known in the art how any of the sensors described in relation to the sensor array  210  can provide respective images. Therefore, this is not described any further here. 
     The first control unit  200  can be connected to the sensor array  210 , and/or any of the sensors therein, via a link L 210 . The sensor array  210 , and/or any of the sensors therein, is arranged for communication with the first control unit  200 , for example via the link L 220 . The sensor array  210 , and/or any of the sensors therein, is arranged to transmit images of the surrounding of the airborne vehicle. The first control unit  200  is arranged to receive the images from the sensor array  210 , and/or any of the sensors therein. 
     The first control unit  200  is arranged to assess a quality measure in each of the images received from the database  220 , the sensor array  210 , or from any other source. The quality measure relates at least to the visibility of objects in the received images. The assessing of the quality measure is described in more detail in relation to  FIG. 3 . In one example, the first control unit  200  is arranged to compare images from different sources for finding differences between the images. In one example, the first control unit  200  is arranged to determine a qualitative measure for each of the images out of the plurality of images. Examples of how a comparison and/or a qualitative measure can be performed are described in relation to  FIGS. 3 and 4 . 
     The first control unit  200  is further arranged to automatically decide from which of the different sources, such as the database  220 , any of the images sensors in the sensor array  210 , or any other source, at least one image out of the plurality of images should be displayed. The first control unit  200  is arranged to base the decision at least on the assessed quality measure. This is also further explained in relation to  FIGS. 3 and 4 . As an example, the first control unit  200  may be arranged to compare each image out of the plurality of images with a pre-determined pattern. This is further explained in relation to  FIGS. 3 and 4  as well. 
     In one example, the first control unit  200  is arranged to choose only one out of the different sources, such as only one of the images sensors in the sensor array  210 , or only the database  220 . This is especially useful in case it can be determined that this only one source provides an image of better quality in any respect. In one example, the first control unit  200  is arranged to choose a plurality out of the at least two different sources, such as a plurality of the image sensors in the sensor array  210  and possibly the database  220 , or such as only one of the image sensors in the sensor array  210  and the database  220 . This is especially useful in case it can be determined that at least one first source has a better quality in one respect and that at least one second source has a better quality in another respect. 
     The flight vision system  299  comprises at least one display  230 . The at least one display is, for example, a head up display, HUD, and/or a head down display, HDD. The first control unit  200  can be connected to the at least one display  230  via a link L 230 . The first control unit  200  is arranged for communication with the at least one display  230 , for example via the link L 230 . The first control unit  200  can be arranged to transmit at least one image to the at least one display  230 . The at least one image comprises at least the automatically decided image. The at least one display  230  is arranged to display the at least one automatically decided image. 
     A second control unit  205  might be arranged for communication with the first control unit  200  via a link L 205 . It may be a control unit external to the airborne vehicle  100 . It may be adapted to conducting the innovative method steps according to the invention. The second control unit  205  may be arranged to perform the inventive method steps according to the invention. It may be arranged for communication with the first control unit  200  via an internal network on board the airborne vehicle. It may be adapted to performing substantially the same functions as the first control unit  200 , such as controlling any of the elements of the system  299 . The innovative method may be conducted by the first control unit  200  or the second control unit  205 , or by both of them. 
       FIG. 3  shows, in a schematic way, a method  300  according to an example of the present invention. The method  300  is a method for presenting images from the surrounding of an airborne vehicle in a flight vision system. The method  300  starts with the step  310 . 
     Step  310  comprises providing a plurality of images of the surrounding of the airborne vehicle. The plurality of images originates from at least two different sources. In one example, at least one of the at least two different sources is a database, for example a database for synthetic vision. An example of such a database has been described in relation to  FIG. 2 . In one example, at least one of the at least two different sources is an image sensor. Examples of such image sensors have been discussed in relation to  FIG. 2  and can, for example, be an infrared sensor, a radar sensor, and/or a sensor for visual wavelengths. The at least two different sources can be sources which differ by the wavelength of the spectrum which they detect. The method continues with step  320 . 
     In step  320  a quality measure is assessed in each of the images out of the plurality of images which have been provided in step  310 . The quality measure relates at least to the visibility of objects in the plurality of images. It should be emphasised that the term visibility in relation to step  320  does not relate to any specific wavelength of the light. In that respect, the term visibility relates to the fact that objects can be identified as being objects, i.e. that they are not covered by fog, rain, or the like, in the respective image. In one example, step  320  comprises step  321 . 
     In step  321 , a qualitative measure is determined for each of the images out of the plurality of images which have been provided in step  310 . The determined qualitative measure can then be the assessed quality measure. In principle, any qualitative measure relating at least to the visibility can be used. Examples of such qualitative measures are known in the art. As an example, the qualitative measure can be a measure relating to the contrast in each of the images. In practice, images not disclosing objects have usually low contrast. As an example, an image only showing fog will in general have low contrast. On the other side, in practice, images disclosing objects, i.e. images where objects are visible, do often have in comparison high contrast as the contours of the objects will contribute to the contrast. 
     In one example, step  320  comprises step  322 . In step  322 , images from different sources are compared. This comparison is performed for finding differences between the images. As an example, the differences can relate to the presence of objects in the images. The comparison can be performed for determining whether objects are present in at least one of the images which are not present in at least one other of the images, and/or vice versa. It should be understood that different cases are possible. In a first example, one image out of the plurality of images shows all the objects which can be detected in the plurality of images. In a second example, a first image out of the plurality of images shows at least one first object which is not shown in a second images out of the plurality of images, whereas the second image shows at least one second object which is not shown in the first image. Such an example is discussed in more detail in relation to  FIG. 4 . The assessed quality measure might relate to the number and/or the kind and/or properties of visible objects in a respective image. The properties of the visible objects might, for example, relate to anyone of size, distance, velocity, position, temperature, or any other property of the object. 
     In one example, step  320  comprises step  323 . In step  323  each image out of the plurality of images is compared with a pre-determined pattern. The pre-determined pattern can be a pattern which is expected to be seen in an image. As an example, the light pattern at a runway is often standardised. Thus, when approaching a runway, it is expected that the pre-determined light pattern at some point of time will be present in any of the images. The assessed quality measure can then relate to the fact whether one, or several, pre-determined patterns are present at an image. As an example, it might be assessed that an image which shows the expected light pattern of the runway may have better quality than an image which does not show that pattern. A reason can be that the pilot of the vehicle is interested in seeing the runway whenever possible. 
     It should be understood that the assessed quality measure in step  320  is not necessarily a one-dimensional quantity. Instead, the quality measure can have different aspects and/or can be two- or more dimensional. As an example, a first image might have a better quality than a second image in one respect, but a better quality than the second image in another respect. 
     After step  320 , the method  300  continues with step  330 . 
     In step  330 , it is automatically decided from which of the at least two different sources at least one image out of the plurality of images should be displayed. This decision is based at least on the assessed quality measure. As an example, in case it is determined that the images of a specific source show a specific pre-determined pattern, it might be decided that images from this source should be displayed. As an example, in case it is determined that the images of a specific source show objects which are not visible in the images from any other source, it might be decided that the images of this source should be displayed. As an example, in case it is determined that the images of a specific source show objects more clearly than the images from any other source, it might be decided that the images of this source should be displayed. 
     In one example, only one out of the at least two different sources is chosen to be displayed. This might, for example, be the case in case it is decided that images from this source have better quality than images from other sources in any respect. Another reason might be that it is decided that images from only one source should be displayed in any case. This might be advantageous to reduce complexity in computational power and/or to reduce latency in the system. 
     In one example, at least two different sources out of the plurality of sources are chosen to be displayed. This might, for example, be the case in case it is decided that images from a first source have better quality than images from a second sources in one respect, while it is vice versa in another respect. Then it can be decided that images from both sources are displayed. This has the advantage that more information will be available for an operator. On the other side, this usually requires to decide how the images from the different sources should be combined. As an example, it might be decided that the images from the different sources should be combined into a combined image. The combined image might be combined in such a way that all the objects which are visible by at least one of the sources can be seen in the combined image. The method continues with step  340 . 
     In step  340 , the at least one automatically decided image is displayed in the flight vision system. In one example, the at least one automatically decided image relates to the combined image from step  330 . After step  340  the method  300  ends. 
     In a preferred embodiment, the method  300  is performed repeatedly. It should be emphasised that there is quite some freedom to define suitable quality measures. There is further quite some freedoms to define suitable rules which decide on which image should be displayed in the flight vision system, where the decision is based on the assessed quality measures. Both the quality measures and the rules might vary between different kinds of sources and might have to be adapted to the specific sources at hand in a given setup and/or to the specific properties of the airborne vehicles and/or the tasks to be performed by the airborne vehicle. 
     The present disclosure also relates to a computer program product and to a computer-readable storage medium. The computer program product and the computer-readable storage medium can comprise instructions which, when the program is executed by a computer, cause the computer to carry out the method  300 . As an example, the execution might be performed on the system  299  described in relation to  FIG. 2 , for example on the first and/or second control unit  200 ,  205 . 
       FIG. 4  shows, in a schematic way, an example of a situation in which the present disclosure can be used. In  FIG. 4 a   , the overall situation is depicted. It should be emphasised that the figure is drawn to best explain the context of the present disclosure. The figure is not to scale. 
     An airborne vehicle  100  is approaching a runway  413 . To start with, the airborne vehicle is at a first position  420  with a first distance to the runway. This situation is further described in relation to  FIG. 4 b   . It then approaches the runway and will after some time be at a second position  421  with a second distance to the runway. This situation is further described in relation to  FIG. 4 c   . When further approaching the runway, the airborne vehicle will reach a third position  422  with a third distance to the runway  413 . This situation is further described in relation to  FIG. 4 d   . A tree  410  is placed in between the position of the airborne vehicle  100  and the intended position to land on the runway  413 . The tree  410  exemplifies a first object and can in principle be any other object or group of objects. On the runway  413 , a person  411  is situated. The person  411  is an example of a second object or group of objects. It could equally well be larger animal such as a moose or the like, or any other object. A tank truck  412  is also placed on the runway  413 . The tank truck  412  is an example of a third object or group of objects. It could be equally well any other object, such as any other vehicle. As can be seen from the example, it might be advantageous for an operator of the aircraft to detect any of the objects  410 ,  411 , and  412  as early as possible as any of these objects might prevent a safe landing on the runway  413 . 
     In the depicted situation, the airborne vehicle  100  is equipped with three sources: an infrared sensor, a sensor for visual wavelengths, and a database for synthetic vision. The infrared sensor provides a first image  430 . The sensor for visual wavelengths provides a second image  431 . The database for synthetic vision provides a third image  432 . 
     Now the situation at the first, second, and third position  420 ,  421 ,  422  will be described in relation to  FIGS. 4 b , 4 c , and 4 d   , respectively. In practice, the images at the three positions will not show the objects in the same size in all three positions as the scale will change when approaching the runway. However, this effect is not depicted as it would decrease legibility of the objects in the images at the first and the second position. The image which will be shown in the flight vision system is depicted a fourth image  435 . It might be one of the first, second, or third image  430 ,  431 ,  432 , or a combination thereof. 
     The database for synthetic vision might contain both the tree  410  and the runway  413  as they are stationary. The database for synthetic vision will, however, not contain the person  411  and the tank truck  412  as they are non-stationary objects and were not present at the time the database for synthetic vision was created. At all three positions  420 ,  421 , and  422  the third image  432  from the database will show the tree  410  and the runway  413 . This might be due to the fact that the database for synthetic vision usually does not change during the flight and that the database is usually not weather dependent. 
     At the first position  420 , heavy rain clouds  440  are situated between the airborne vehicle and the runway. These heavy rain clouds  440  prevent both the sensor for visible wavelengths and the infrared sensor from providing any images disclosing objects. The images from these sensors might thus be quite blurred and with low contrast. As a consequence, none of the three objects  410 ,  411 , and  412 , and not the runway  413  either will be visible in the first and the second image  430  and  431  in  FIG. 4 b   . As only one of the three images will show any of the objects, it will be assessed that the third image  432 , i.e. the only image showing some objects, has best quality. It can then be decided that only this image should be displayed, i.e. that only the database for synthetic vision should provide images which are shown on the flight vision system. Thus, the fourth image  435  will be identical to the third image  432 . This is indicated in  FIG. 4   b.    
     At the second position  421 , there is no longer any rain between the airborne vehicle  100  and the runway. However, still clouds  445  are present between the second position  421  and the runway  413 . As a consequence, still none of the three objects  410 ,  411 , and  412 , and neither the runway  413  will be visible in the second image  431 . This is since the clouds  445  prevent light at visible wavelengths to penetrate them. Some infrared, IR, wavelengths, however, might penetrate the clouds  445 . As a consequence, the runway  413  might be, at least indirectly, visible in the first image  430 . As an example, the runway might be equipped with light sources forming a pre-determined light pattern and also emitting at IR-wavelengths which penetrate the clouds. At the airborne vehicle, the image from the IR sensor might be analysed to recognise pre-determined patterns and the light pattern of the runway  413  might be detected, and thus the runway  413 . The tree  410  is not visible at the first images  430  since it is not emitting differently at IR-wavelengths compared to the surrounding of the tree. The person  411  is not visible on the first image  430 . Although the person  411  will emit IR-radiation which differs from the surrounding, the IR-sensor might have limited resolution so that the person  411  will not be visible yet at the second position  421 . The tank truck  412 , however, might emit differently at IR-wavelengths from the surrounding and is of a big enough size to be visible on the first image. 
     It can then be assessed that the first and the third image are of better quality than the second image. In the third image  432 , one object, i.e. the tree  410 , is visible which is not visible in the first image  430 . On the other hand, an object, i.e. the tank truck  412  is visible in the first image  430  which is not visible in the third image  432 . Thus, it can be decided that both images from the IR sensor and from the database should be displayed. In the shown example, these images are combined and the combined image which is displayed, i.e. the fourth image  435 , shows all objects which are visible in at least one of the first, second, and third images  430 ,  431 , and  432 . 
     At the third position  422 , there are no clouds any longer between the airborne vehicle  100  and the runway. As a consequence, the sensor for visible wavelengths will be able to see all objects and thus all objects will be present in the second image  431 . The first image  430  will be the same as in the second position  421 , with the addition that the person  411  now also is visible due to the fact that the distance to the person is shorter so that the sensor can resolve the person. However, the tree  410  is still not visible in the first image since it does not emit IR-radiation differently than the surrounding of the tree. One image, i.e. the second image  431 , shows all objects, whereas the first and the third image  430 ,  432  do not. It can thus be assessed that the second image  432  has best quality. As a consequence, it can be decided that the displayed image, i.e. the fourth image  435 , corresponds to the second image  432 . 
     Thus, depending on the position of the airborne vehicle and the weather conditions, images from different sources are displayed in the flight vision system. The sources change as the airborne vehicle approaches the runway. The decision which sources should be used for displaying does neither require input from an operator of the airborne vehicle, nor does it require any equipment at the airport such as a RVR-system, nor any information regarding the weather for proper functioning. Instead, the flight vision system adapts automatically so as to present an optimised image to the operator. 
     The situation described in relation to  FIG. 4  is only an example. Other combinations of sensors and/or databases are possible. Which sources to choose might depend on the sources available aboard the airborne vehicle. As an example, a combination of a radar sensor, an infrared sensor and a database might be used as sources. Another example is a combination of a radar sensor, a visible sensor and a database. A third example is a combination of a radar sensor, an infrared sensor, a visible sensor, and possibly a database. The number of sources combined can be larger or smaller than three sources in the example of  FIG. 4 . When referring to an infrared sensor this might be an arbitrary infrared sensor. Examples of infrared sensors have been discussed in relation to the infrared sensor array  215 . 
     It should especially be noted that the flight vision system according to the present disclosure can be arranged to perform any of the steps or actions described in relation to the method  300 . It should also be understood that the method according to the present disclosure can further comprise any of the actions attributed to an element of the flight vision system  299  described in relation to  FIG. 2 . The same applies to the computer program product and the computer-readable storage medium. 
     LIST OF ELEMENTS 
     
         
           100  Airborne vehicle 
           200  First control unit 
           205  Second control unit 
           210  Image sensor array 
           211  Radar sensor 
           212  Sensor for visible light 
           215  IR sensor array 
           216  Near IR sensor 
           217  Short-wavelength IR sensor 
           218  Mid-wavelength IR sensor 
           219  Long-wavelength IR sensor 
           220  Database 
           230  Display 
           299  Flight vision system 
           410  Tree 
           411  Person 
           412  Tank truck 
           413  Runway 
           420  First position 
           421  Second position 
           422  Third position 
           430  First image 
           431  Second image 
           432  Third image 
           435  Fourth image 
           440  Rain clouds 
           445  Clouds