Method for improving sensor performance in a system comprising a plurality of platforms, each incorporating at least one sensor and one electronic processing module

Abstract

A method for improving the performance of sensors (20) comprising: capturing a first image by a first sensor (20) on a first platform (30) and programmed according to a first configuration of the first sensor; determining, by a first processing block (30) on the first platform (10), based on the first captured image, the location and size of an object detected on said first image; capturing a second image including said object by a second sensor (20) on a second platform and programmed based on the location and size of an object detected on said first image; determining, by the second processing block (30), based on the second captured image, the location and size of said object detected on said second image; evaluating the sizes and locations and selecting, based on this evaluation, one of the two sets of location and size;triggering the programming, according to a second configuration depending on the selection, of the first and second sensors (20) not programmed according to the selection and capture of a new image by said sensor thus programmed.;

Description

Title of the invention: Method for improving the performance of sensors in a system comprising a plurality of platforms, each incorporating at least one sensor and an electronic processing module technical field

[0001] The invention relates to the field of platform systems incorporating sensors operating over a common area of ​​interest. The invention is particularly applicable to cases where all these sensors constitute sensor chains, also known as sensor suites. The sensors considered include radars, for example, those with GMTI / SAR and / or ISAR modes, EO / IR optronic turrets, and / or radar detectors. These systems are preferably airborne, but ground-based use on mobile vehicles is also possible.

[0002] Such systems are found in the field of combat aircraft, maritime combat missions, satellite acquisitions or aeronautical embarked missions for example. Previous technique

[0003] The sensors on the platforms perform a set of actions essential to the success of the mission. Their functions cover a wide range of mission objectives, from image acquisition for intelligence gathering to the survival and security of their platform or platforms. Faced with this set of functions, a system operator attempts to make the best possible use of all available sensors in order to carry out the mission and optimize its results, that is, to achieve the primary objectives and accomplish as many opportunity objectives as possible.

[0004] The optimal use of sensors to fulfill a mission requires knowledge of their performance in order to make the right decisions. The performance of these sensors depends on parameters such as their physical placement on airborne platforms, the technologies used, and also the operating environment (weather, etc.). Typically, performance models based on equations are defined.

[0005] Radars are also known to have adaptive modes that improve clutter rejection. Usually, depending on the size and kinematics of the targets being sought, there are preset modes that allow the sensor to perform optimally. However, this is not always sufficient.

[0006] there is a need to improve the quality and relevance of the detections carried out at with the help of such systems. Summary of the invention

[0007] To this end, according to a first aspect, the present invention describes a method for improving the performance of sensors in a system comprising a plurality of platforms, each carrying at least one sensor and one electronic processing module, the platforms being connected by teletransmission links, said method comprising the following steps: - capture of a first image by a first sensor on a first platform of the system and programmed according to a first configuration of the first sensor; - determination, by a first processing block on the first platform, of a first set of information, based on the first captured image, said set of information including at least one location information of an object detected on said first image and one information indicating the size of said object; - teletransmission of said first set of information from the first platform to a second platform; - reception, by the second platform comprising a second sensor and a second processing block, of said first set of information and determination, by the second processing module, of a first configuration of the second sensor according to the information in the information set; - capture of a second image including said object by the second sensor programmed according to said first configuration of the second sensor determined; - determination, by the second processing block, of a second set of information, based on the second captured image, said second set of information including at least one location information for said object detected on said second image and one information indicating the size of said object; - evaluation, based on the first and second sets of information, by one of the first and second processing modules and selection, based on this evaluation, of one of the first and second sets of information; - triggering the programming, according to a second configuration depending on the selected information set, and on the first and second sensors not programmed according to said information set selected and capture of a new image by said sensor programmed according to said second configuration.

[0008] The invention enables the improvement of sensor performance, particularly in the context of decentralized collaborative platforms, and notably allows for the improvement of optical (EO / IR) quality of service through the use of RADAR (GMTI & SAR) means by considering common analysis areas. Targeted and statistical measurements can be carried out by area to take into account the specificities of the respective measurement conditions.

[0009] The measurements taken by the sensors are used to improve object identification, for example, by contour measurement or shadow measurement. Based on the measurements taken by one of the sensors (based on their quality, for example) on one platform, the technical parameters of at least one other sensor, located on another platform, are adjusted to increase its acquisition performance.

[0010] The invention therefore proposes an automatic solution for adjusting the sensor according to the parameters and acquisitions of another sensor.

[0011] In embodiments, such a method will further comprise at least one of the following features:

[0012] - said information set further includes at least one piece of information relating image capture includes: sensor aperture, resolution, distance to object, object classification;

[0013] - the acquisition spectra of the first and second sensors are separated, by example one of the first and second sensors is radar type and the other of the first and second sensors is EO / IR type;

[0014] - the steps of the process are implemented cyclically between distinct pairs of system platforms;

[0015] - said method further comprises triggering the programming, according to a third configuration based on the selected information set, a sensor from a platform distinct from the first and second platforms and capture of a new image by said sensor programmed according to said third configuration.

[0016] According to another aspect, the invention describes a system comprising a plurality of platforms, each carrying at least one sensor and one electronic processing module, the platforms being connected by teletransmission links, adapted to implement the following operations: a first sensor on a first platform of the system and programmed according to a first configuration of the first sensor is adapted to capture a first image by; a first processing block on the first platform, is adapted to determine a first set of information, based on the first image captured, said set of information including at least one location piece of information for an object detected on said first image and one piece of information indicating the size of said object; the first platform is adapted to transmit said first set of information to a second platform; said second platform comprising a second sensor and a second processing block, is adapted to receive said first set of information and the second processing module is adapted to determine, a first configuration of the second sensor according to the information in the information set; the second sensor is adapted to, programmed according to said first determined configuration of the second sensor, capture a second image including said object; the second processing block is adapted to determine a second set of information, based on the second captured image, said second set of information comprising at least one location information for said object detected on said second image and one information indicating the size of said object; one of the first and second processing modules is adapted to perform an evaluation, based on the first and second sets of information, and to select, based on this evaluation, one of the first and second sets of information; said system being adapted to trigger the programming, according to a second configuration depending on the selected information set, of the first and second sensors not programmed according to said selected information set and to trigger the capture of a new image by said sensor programmed according to said second configuration.

[0017] In embodiments, such a system will further comprise at least one of the following features:

[0018] - said information set further includes at least one piece of information relating image capture includes: sensor aperture, resolution, distance to object, object classification;

[0019] - the acquisition spectra of the first and second sensors are separated, by example one of the first and second sensors is radar type and the other of the first and second sensors is EO / IR type;

[0020] - said system is adapted to cyclically implement said operations between distinct pairs of system platforms;

[0021] - said system is further adapted to trigger programming, according to a The third configuration depends on the selected information set, a sensor from a platform separate from the first and second platforms, and captures of a new image by said sensor programmed according to said third configuration. Brief description of the drawings

[0022] The invention will be better understood and other features, details and advantages will become clearer from the following description, given by way of non-limiting reason, and from the accompanying figures, given by way of example.

[0023] [Fig-1] Fig. 1 schematically represents a system of platforms sensors embedded in an embodiment of the invention;

[0024] [Fig.2] Fig.2 represents steps in a process for improving the performance formances of embedded sensors in an embodiment of the invention;

[0025] [Fig.3] The [Fig.3] illustrates the steps of calculating the contours of an object in an image using the Sobel filter;

[0026] [Fig.4] Figure 4 is a representation of the apparent surface '

[0027] [Fig.5] The [Fig.5] illustrates a calculation of the shadow of an object.

[0028] Identical references may be used in different figures when they refer to identical or comparable elements. Description of the implementation methods

[0029] Figure 1 schematically represents a system 1 of sensor platforms embedded in one embodiment of the invention. The system 1 comprises n platforms 10, designated PF1, ..., PFn, which may or may not be mobile, with n greater than or equal to 2. Depending on the embodiment, one or more platforms may be aircraft, land vehicles, maritime vessels, satellites, or mobile terrestrial elements. For example, in the case considered, the platforms are drones.

[0030] Each PFi platform 10, i = 1 to n, comprises at least one sensor 20, CPTi, and a processing module 30, TRTi. Depending on the embodiment, one or more sensors 20 are of the radar, lidar, sonar, or optical imaging type, for example of ElectroOptical or Infrared (EO / IR) or hyperspectral (HSI) type..... A radar sensor 20 is for example of the GMTI (Ground Moving Target Indicator) type, SAR (Synthetic Aperture Radar) type, ISAR (Inverse Synthetic Aperture Radar) type, etc.

[0031] Each sensor 20, CPTi, is adapted to perform measurements of physical quantities according to its technology (echoes for radars, sonars, lidars; brightness, temperature ... for others) and to convert the measurements into an electrical output signal and provide them as input to the processing module 30, TRTi.

[0032] The processing module 30, TRTi, is adapted to perform algorithmic processing based on this input electrical signal. It is particularly adapted to implement the steps described below with reference to [Fig. 2] and which are its responsibility. In one embodiment, the processing module 30, TRTi, includes a memory and a processor (not shown). The steps mentioned are implemented, for example, by executing software instructions stored in memory on the processor. Alternatively, they are implemented by dedicated hardware, typically a digital integrated circuit, either specific (ASIC) or based on programmable logic (e.g., FPGA / Field Programmable Gate Array).

[0033] In the case considered, the sensor 20 CPT1 is of the RADAR type in SAR mode and the sensor 20 CPT2 is of the EO / IR type. The sensors 20 target a common area of ​​interest 60, Z.

[0034] There are teletransmission links (not shown), wired or wireless, between the platforms 10, in particular between any two platforms involved in the process described below.

[0035] A method 100 for improving the performance of the embedded sensors of system 1 is now described in an embodiment of the invention, with reference to [Fig.2],

[0036] In a step 100_l, a primary detection is carried out by the sensor 20 CPT1 (RADAR) following a phase of analysis of the echoes received following a radar emission: the presence of an object is determined on the detection zone Z 60 according to these echoes by the sensor 20 CPT1.

[0037] In a step 101, substeps 101a, 101b, 101c, lOld are then implemented within the PF1 platform.

[0038] In substep 101a, the 20 CPT1 radar performs SAR imaging on the detection zone Z 60. In substep 101b, from the obtained SAR image, the 30 TRTI processing module determines the object's location (in the form of geographic coordinates) and performs algorithmic processing to determine the object's contours on the SAR image. In substep 101c, based on the determined contours, the 30 TRTI processing module then determines the object's dimensions, for example here by calculating the object's Equivalent Area, and deduces the pair of determined data {Location; object dimensions (here Equivalent Area)}.

[0039] In the substep lOld, the processing module 30 TRTI transmits to the processing module 30 TRT2 the determined data pair {Location; dimensions of the object (here Equivalent Surface)}.

[0040] In a step 102, substeps 102a, 102b, 102c are then implemented within the PF2 platform.

[0041] Upon receiving the pair of data {Location; Equivalent Area} transmitted in substep 102a, the processing module 30 TRT2 automatically calculates the The 20 CPT2 sensor's line of sight (EO / IR) and the FOV (Field of View, which includes resolution calculation) are determined based on the object's location and dimensions (here, Equivalent Surface Area) so that the entire object is within the FOV. The EO / IR image capture parameters of the 20 CPT2 sensor are adjusted to maintain this line of sight and FOV, and an EO / IR image of the object is then captured by the 20 CPT2 sensor.

[0042] In substep 102b, from the obtained EO / IR image, the processing module 30 TRT2 determines the location of the object (in the form of geographic coordinates), and performs an algorithmic process to determine the contours of the object on the EO / IR image. In substep 102c, based on the determined contours, the processing module 30 TRT2 then determines the dimensions of the object, for example here in the form of calculating the Equivalent Surface of the object.

[0043] These same calculations as those previously carried out in step 101 thus allow at the end of step 102c to determine a new value of the location and a new value of the dimensions, i.e. a pair {Location; Equivalent Area}.

[0044] A set of 200 steps is then implemented, comprising steps 201 and 202. In the embodiment considered, this set of steps is implemented by the TRT2 30 processing module of the PF2 platform. In another embodiment, it is carried out by the TRT1 30 processing block of the PF platform after the data from the PF2 platform has been transmitted from the PF2 platform to the PF1 platform.

[0045] In step 201, the pairs {Location ; Equivalent Area] calculated respectively by the processing modules 30 TRT1, TR2, i.e. respectively {LocalisationCPTi ; Equivalent AreaCPTi}, {LocalisationCPT2 ; Equivalent AreaCPT2}. are compared.

[0046] Then in the performance evaluation step 202, the TRT2 30 processing module evaluates, based on this comparison, which of the component values ​​of the two pairs are optimal, i.e. which corresponds to better localization and / or better image resolution (sensor performance analysis).

[0047] Here, "best localization" means the localization provided by the sensor with the best accuracy (it is generally provided by the manufacturer of the equipment; it generally varies from 1 to 10 degrees relative to the actual position of the target depending on the equipment used).

[0048] The performance evaluation includes updating the values ​​of the "localization" and "equivalent area" components by retaining, for each component, the optimal value in terms of localization accuracy (localization component) and resolution of the observed object (equivalent area component). Following the comparison of the results (step 201), the pair {Location, Equivalent Area} is updated. (and then stored in memory) if at least the value of one of the two components from CPT2 is evaluated as "better" than those calculated by CPT1. For example, if CPT2 gives a better resolution or equivalent area of ​​the object, but not a more precise location, the new pair becomes {LocalisationCPi ; Surface EquivalenteCPT2}-

[0049] Thus, it is determined which of the two locations, CPT1 and CPT2, provides the best localization accuracy, and it is evaluated whether the new measurement of the equivalent surface area results in an improvement in the resolution and / or the size of the object. For example, CPT1 may provide the equivalent surface area, but the resolution may be improved by CPT2.

[0050] Resolution is considered better when the number of pixels characterizing the same object, obtained following edge detection, is greater.

[0051] The best localization accuracy corresponds to the best sensor accuracy between those of CPT1 and CPT2, which are known.

[0052] In one embodiment, this comparison / evaluation is carried out on more than two pairs, for example N pairs from N platforms, with N >2.

[0053] The optimal torque is: - referred back to processing module 30 of the platform PF1, PF2 that was not set according to the optimal torque, to determine a programming adaptation of the CPT sensor 20 based on this optimal torque and to implement the programming adaptation of the CPT sensor 20 to obtain better image quality; for example, here the optimal torque is the torque determined in step 102c in the PF2 platform, this torque is transmitted to the PF1 platform for implementation of the corresponding settings on the CPT1 sensor; in particular, if the optimal torque is that of the PF2 platform, it is provided to the PF1 platform to be taken into account for programming; and / or - provided to (at least) a third platform to perform its sensor programming based on this optimal torque.

[0054] For example, if the comparison shows that the torque provided by CPT1 is better, the precision / resolution offered by CPT1 (which will be, where applicable, that given to CPT3) is kept unchanged by CPT1.

[0055] In one embodiment, this process is implemented cyclically between distinct pairs of platforms from the set of platforms constituting system 1. Thus, for example, the optimal pair is provided as input to at least one PFi platform, i being an integer between 2 and n, and a step lOi, similar to step 102, is implemented in the PFi platform, followed by a set of steps 200. This allows the PFi platform to benefit from the improvements determined using the two PFI platforms, PF2.

[0056] In one embodiment, instead of the pair {location; dimensions of the object], a set of data is transmitted by the PF1 platform to the step lOld, then considered at the set 200 of steps comprising, in addition to the location and the dimensions, one or more additional elements having an impact on the adjustment parameters of the sensors 20, for example the sensor aperture, the resolution, the distance to the object, the classification of the object.

[0057] In the preceding, a radar image is acquired first. In another embodiment, an EO / IR image is acquired first, followed by a radar image. In another embodiment, the CPT1 and CPT2 sensors capture images of the same type, i.e., radar or EO / IR type.

[0058] The performance of EO / IR sensors is highly dependent on the environment and shooting conditions. These conditions can change with:

[0059] - weather: day / night, sunshine, clouds, fog, mist, snow / frost, rain, wind ;

[0060] - the shooting conditions, in particular the interaction of the wearer's movements, the height and distance to the observed target;

[0061] - the optical sensor adjustment conditions, possibly including added filters can affect the quality of the videos and therefore the possibilities for detection, analysis and automatic recognition.

[0062] These conditions do not have the same effects on electromagnetic sensors, particularly day / night conditions, sunlight, etc. Unlike EO / IR sensors, radar in SAR mode illuminates the scene. It is therefore less dependent on external conditions. It experiences attenuation due to rain, but it is also capable of measuring the amount of rain and estimating performance under these conditions. Transmission and reception are co-located (except in the specific case of bistatic and passive radars). The shadows cast by the radar are therefore deterministic.

[0063] A method according to the invention is relevant for processing acquisitions in a detection zone Z common to heterogeneous platforms / sensors. When the sensors 20 CAPT1 on PF1 and CAPT 2 on PF2 point towards this zone, they each allow the recovery, in addition to the information specific to the detections, of secondary information which corresponds to degradations in the quality of detection caused by the environment (for example, in maritime surveillance, SAR can estimate a wind speed based on the detection noise caused by the waves).

[0064] In one embodiment, by exploiting, or even merging, the secondary information from at least one of the sensor types, or even both types of sensors, it is determined, by any one of the processing modules 30, for example TRT1, TRT2, a state of the weather environment of zone Z. When the weather conditions of the zone are known (or estimated from secondary information from a sensor, for example the radar sensor), it is possible to calculate a performance forecast (estimate of degradations due to weather conditions) for another, for example 1 EO / IR sensor, as well as the optimal settings (or processing) associated with it.

[0065] Secondary radar sensor information includes, for example, detection noise caused by waves and target velocity vectors. This information is measured by the radar sensor, for example. Knowing the environment allows for the correction of certain undesirable effects on image quality.

[0066] Secondary EO / IR sensor information includes, for example, the measurement of the outside temperature and knowledge of the image distortion as a function of this temperature; the sensor can use correction curves on the image if available.

[0067] By way of illustration, the radar sensor exhibits significant speckle, degrading the object's equivalent surface area. Similarly, in high temperatures, the EO / IR image is severely degraded due to atmospheric distortions. To improve the accuracy of determining the object's equivalent surface area based on measurements taken from the captured image, it is further determined by calculating the dimensions of the shadows cast on the image.

[0068] In the context of the process represented in [Fig.2], this action can be done in the processing phase operated by TRT1 30, respectively TRT2 30 according to the sensor CPT1, respectively CPT2.

[0069] The trigger action on the measurement or knowledge of external temperatures which statistically should modify the shape of the object. Similarly, for RADAR, knowing the angle of incidence of acquisition, one can presuppose knowledge of the degradation on the SAR image.

[0070] The settings and / or the location / dimension combination of CPT1 transmitted to the sensor on the other platform, CPT2, are used to automatically program sensor CPT2 and potentially improve its detection quality. A feedback loop then analyzes the new detection quality to verify that it has been improved as expected, or if not, to use the new secondary information obtained to improve the data acquisition of the area by CPT1 (radar) and thus reproduce the settings optimization process.

[0071] The advantage of the solution is that it can transmit the characteristics of targets (size, shape, etc.) to each drone, thereby optimizing the programming of the onboard sensor. The solution increases the autonomy of the onboard systems and automatically optimizes performance. sensors on remote platforms, which is particularly useful in areas where Human-System interactions are not always available.

[0072] In one embodiment of the invention, the sensor chain 20 of the system 1 is adapted to optimize the sensors and estimate the possible performance according to the operational constraints (climate, interference, possible failures, sub-optimization of settings...).

[0073] Depending on the characteristics of the target sought and the estimated performance, it is determined, in one embodiment, by any of the processing modules 30, whether the detection mission is possible or utopian.

[0074] Methods for calculating object dimensions

[0075] Methods for calculating object dimensions are now described which are used for example by the processing modules in steps 101, 102, or lOi, i between 1 and n.

[0076] In order to define the characteristics of the object and improve its detection, post-processing of the acquired image is applied, as shown with reference to [Fig. 2], to define the contours of the object. This contour calculation phase improves image quality by eliminating environmental noise.

[0077] In the literature, most edge detection treatments are based on edge detection, and more particularly on the Sobel filter, and rely on the study of the image gradient: we are in the presence of an edge if in one of the two directions of the image we have a strong variation in the amplitude of the pixels.

[0078] A Sobel filter is generally applied to calculate the contours (thus also eliminating sensor aberrations: chromatic, thermal, and speckle aberrations); then hysteresis filtering of the contours is implemented. A Harris detection algorithm then detects the objects of interest (it allows for the detection of corners in an image); a measurement of the dimensions of the detected objects of interest is then performed.

[0079] In method 100, the size of the object of interest is estimated from the contours and resolution of the image taken by a sensor. This size information allows for the automatic programming of the pointing and aperture of another sensor.

[0080] Figure 3 illustrates and describes steps for calculating the contours of objects of interest from of an EO / IR image (the steps are similar in the case of a SAR image).

[0081] Contour calculation allows the observed object to be isolated from its environment in order to have an effective measurement of: its length, Lohj, its width lobj and its apparent surface, 7^' • The apparent surface of an object of interest is calculated from the number of pixels (X,Y) and the radial resolution of the image, ôr.

[0082] Thus:

[0083] Lobj = X*Ôr(l)

[0084] and

[0085] lohj = Y*ôr&

[0086] Figure 4 illustrates this calculation. The apparent surface, Tobj' along an axis, corresponds to the surface of the white rectangle.

[0087] Calculation of cast shadows

[0088] Similarly, to improve the performance of each sensor, it is possible to determine the size and shape of the object based on the shadow cast by each sensor. Indeed, as mentioned above, speckle effects for RADAR or image distortions due to weather conditions (for example, atmospheric distortions) can affect contour measurements (excessively blurry image, poor object measurement by the SAR, etc.). To improve object measurement, it is possible to measure the shadow cast by each sensor, thus allowing for a better assessment of the object of interest.

[0089] As a result, processing of cast shadows also makes it possible to validate the heights of targets (buildings, vehicle, etc.) and allows a correlation of 3D information between the zenithal illumination of the optics and the cast shadows of the radar (radial with respect to the illumination): see [Fig. 5] containing an image of the same object of interest (pick-up) in SAR imaging (top) and in EO / IR (bottom).

[0090] Knowing the height of the shot, the size of the object is determined according to the measurement of the shadow cast.

[0091] In optics, depending on the respective positions of the sun, the carrier in relation to the target, the height of the target can be determined by calculating the size of its projected shadow.

[0092] A concatenated image with additional information of the consolidated and conjugate shadows allows the 3D representation to be validated ([Fig.5]).

[0093] Modification of sensor parameters: determination of camera resolution and aperture

[0094] Knowing the image resolution, it is possible to determine the size of the object. Knowing the altitude of the aircraft making the acquisition, one can determine the camera aperture necessary for effective pointing at the object.

[0095] A camera has the following elements:

[0096] The output image size in X*Y pixels (X and Y are respectively the number of horizontal and vertical pixels in an image) - the pixel size: xx pm (micrometer) - the focal length of the lens, F - Instantaneous Field of View (IFOV) in rad.

[0097] The goal is to determine the size of an object, knowing the number of pixels, the distance to the object.

[0098] Let / 5 correspond to the size in radians of the object in the image [°0"] P_ jj “ ttiri ll

[0100] with

[0101] D: distance to the target

[0102] T^'f ' fault along an axis (or Apparent Surface) (what we are looking for)

[0103] To obtain the pixel size of the object in the image, the IFOV of the camera is used, such as :

[0104] Re / =_A_(5) JPOV

[0105] Thus, knowing the distance to the object, we can determine the corresponding size:

[0106] = DW (Repxl*IFOV) (6)

[0107] Performance calculation (processing carried out during step 202)

[0108] It is possible to use statistical methods for measuring performance.

[0109] In this case, the optical part will detect the movements of targets in the widest possible area.

[0110] The radar part will isolate targets detected in the same area.

[0111] Velocity processing is carried out: tangential velocities are less precise in radar, radial velocities are more difficult to estimate in optics.

[0112] The quantity of targets detected in each of the CPT1 and CPT2 sensors is compared, taking into account the intrinsic parameters of the sensors. A sorting based on velocity vectors will be performed, allowing for consideration of the differences between the sensors.

[0113] For example, vectors at + / -45° will be weighted more heavily because the information is comparable.

[0114] A target detection density will be performed by the two sensors with a comparison by zones.

[0115] The acquisition conditions of the optical part will be taken into account for this comparison.

[0116] From these measurements, it is possible to determine the exact shape and / or size(s) of the object which will improve the performance of the sensors by modifying the programming parameters of the sensors according to the exact shape and / or size(s).

Claims

Demands

1. A method for improving the performance of sensors (20) in a system (1) comprising a plurality of platforms (10) each carrying at least one sensor (20) and an electronic processing module (30), the platforms being connected by teletransmission links, said method comprising the following steps: - capture of a first image by a first sensor (20) on a first platform (30) of the system and programmed according to a first configuration of the first sensor; - determination, by a first processing block (30) on the first platform (10), of a first set of information, based on the first captured image, said set of information including at least one location information of an object detected on said first image and one information indicating the size of said object; - teletransmission of said first set of information from the first platform (10) to a second platform (10); - reception, by the second platform (10) comprising a second sensor (20) and a second processing block (30), of said first set of information and determination, by the second processing module (30), of a first configuration of the second sensor (20) according to the information of the information set; - capture of a second image including said object by the second sensor (20) programmed according to said first configuration of the second sensor determined; - determination, by the second processing block (30), of a second set of information, based on the second captured image, said second set of information comprising at least one location information for said object detected on said second image and one information indicating the size of said object; - evaluation, based on the first and second sets of information, by one of the first and second processing modules (30) and selection, based on this evaluation, of one of the first and second sets of information; - triggering the programming, according to a second configuration function of the selected information set, of the first and second sensors (20) not programmed according to said selected information set and capture of a new image by said sensor programmed according to said second configuration.

2. A method for improving the performance of sensors (20) in a system (1) according to claim 1, wherein said information set further comprises at least one piece of information relating to image capture from among: sensor aperture, resolution, distance to object, object classification.

3. A method for improving the performance of sensors (20) in a system (1) according to claim 1 or 2, wherein the acquisition spectra of the first and second sensors are separated, for example one of the first and second sensors is radar type and the other of the first and second sensors is EO / IR type.

4. A method for improving the performance of sensors (20) in a system (1) according to any one of the preceding claims, wherein the steps of the method are implemented cyclically between distinct pairs of platforms (10) of the system.

5. A method for improving the performance of sensors (20) in a system (1) according to any one of the preceding claims, further comprising triggering the programming, according to a third configuration depending on the selected information set, of a sensor (20) of a platform (10) distinct from the first and second platforms and capturing a new image by said sensor programmed according to said third configuration.

6. A system (1) comprising a plurality of platforms (10), each carrying at least one sensor (20) and one electronic processing module (30), the platforms being connected by teletransmission links, said system comprising: - a first sensor (20) on a first platform (10) of the system and programmed according to a first configuration of the first sensor, adapted to capture a first image; - a first processing block (30) on the first platform, adapted to determine a first set information, based on the first image captured, said information set including at least one location piece of information for an object detected on said first image and one piece of information indicating the size of said object; the first platform (10) is adapted to teletransmit said first set of information to a second platform (10); said second platform (10) comprising a second sensor (20) and a second processing block (30), is adapted to receive said first set of information and the second processing module is adapted to determine, a first configuration of the second sensor according to the information of the information set; the second sensor (20) is adapted to, programmed according to said first determined configuration of the second sensor, capture a second image including said object; the second processing block (30) is adapted to determine a second set of information, based on the second captured image, said second set of information comprising at least one location information for said object detected on said second image and one information indicating the size of said object; one of the first and second processing modules (30) is adapted to perform an evaluation, based on the first and second sets of information, and to select, based on this evaluation, one of the first and second sets of information; said system (1) being adapted to trigger the programming, according to a second configuration depending on the selected information set, of the first and second sensors not programmed according to said selected information set and to trigger the capture of a new image by said sensor programmed according to said second configuration.

7. System (1) according to claim 6, wherein said information set further comprises at least one piece of information relating to image capture includes: sensor aperture, resolution, distance to object, object classification.

8. System (1) according to claim 6 or 7, wherein the acquisition spectra of the first and second sensors are separated, for example one of the first and second sensors is radar type and the other of the first and second sensors is EO / IR type.

9. System (1) according to any one of claims 6 to 8, adapted to cyclically implement said operations between distinct pairs of system platforms.

10. System (1) according to any one of the preceding claims 6 to 9, further adapted to trigger the programming, according to a third configuration depending on the selected information set, of a sensor (20) of a platform (10) distinct from the first and second platforms and capture of a new image by said sensor (20) programmed according to said third configuration.

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