EP3997479A1 - Method for determining the spatial coverage of a detection system - Google Patents

Abstract

The embodiments of the invention provide a method for determining a spatial coverage of a multi-sensor detection system comprising a plurality of sensors, the plurality of sensors being supported by supporting structures situated in a given geographical area, at least some of the supporting structures being movable, the method being characterised in that it comprises the steps of: - determining (201) a set of parameters representing the geographical area and comprising environmental data, data relating to the supporting structures and data relating to the sensors; - iteratively calculating (203) the detection probabilities for the plurality of sensors in the geographical area from at least some of the parameters; - determining (205) a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors; - determining (207) 3D shadow areas in the geographical area in which the detection probability is less than at least one given threshold; - selecting (209) the shadow areas located in at least one predefined security area of the system.

Description

DESCRIPTION

Title of the invention: Method for determining the spatial coverage of a detection system

[0001] Prior Art

[0002] The invention relates to detection systems, and in particular to the three-dimensional representation of the spatial coverage of a detection system.

[0003] Detection systems, such as sonar or radar are used in many fields of application today. For example, sonar is used in the field of underwater acoustics to detect and locate objects underwater.

The detection systems can be used by various surveillance infrastructures (for example for the detection of submarines or objects placed on the seabed, in the field of fishing for the detection of schools of fish, in the field of cartography to map a geographical area, the ocean floor and other bodies of water, or in the field of archaeologists, for example underwater and underwater archeology).

[0005] The detection systems are provided with antennas for transmitting and / or receiving signals.

[0006] Detection systems are conventionally used to detect threats in areas to be protected, for example in areas defined with respect to a sensitive infrastructure to be protected.

[0007] For example, in a sonar type detection system, it is known to use an anti-submarine warfare system comprising elements for managing naval air fleet scenarios, modeling the spatial coverage of the sonar and tactical decision support tools.

[0008] For example, in "PC Etter, Underwater Acoustic Modeling and Simulation, CRC Press, 5 ^th edition, 2018, Taylor & Francis Group," modeling and simulation technologies to help with tactical decision in the context of the Anti-submarine warfare has been proposed to improve the efficiency of the management and deployment of deployed resources. Such a solution uses a network of geographically distributed components comprising autonomous underwater vehicles (AUV), surface ships, submarines, airplanes, and / or satellites. Such network components deploy distributed sensors. The processing of the signals received by the detection system uses a signal processing step and an information processing step. The processing of the data collected by the various components of the detection system network makes it possible to represent the spatial coverage of the detection system from which an operator of the detection system can determine the dynamics and the trajectory of the target objects, the position of the objects. targets in distance, bearing and depth, as well as the nature of the target objects.

It is known to use a representation called "probabilities of detection or POD" ("Performance of the day" in Anglo-Saxon language). A POD representation is a representation of the probabilities of detection that allows you to determine the spatial coverage of a detection system and to estimate the ability of the detection system to detect target objects. Such a representation is generated from a data matrix of detection probabilities as a function of the distance from the position of the detection system antenna and the depth between the surface and the seabed, using a color coding. The probabilities of detection can be displayed graphically as data or colored elements, with each color representing a probability range varying from 0 to 10%, from 1 1 to 20%, up to the range of values [ 91, 100%].

[001 1] The shapes obtained by means of this representation are relatively complex and non-uniform due to the non-linearity of the propagation of the waves in the environment considered (for example the propagation of sound in water for a detection system sonar type) and the fact that the image which represents these detection probabilities only makes it possible to visualize part of the scene considered. In complex environments, the final three-dimensional (3D) shape can be relatively complex to interpret. Furthermore, this representation does not make it possible to highlight the dangerous areas representing a potential risk for the infrastructure using the detection system (platform or building to be protected for example). It also does not provide a complete three-dimensional overview of the geographic area covered by the detection system. In addition, such a representation does not cover a sufficiently large area compared to the scale of the infrastructure using the detection system.

For sonar type detection systems, tactical decision support tools have been proposed. However, such tools are suboptimal. Indeed, the known decision-support tools provide a modeling environment considering a single sensor at a time and over a distance linked to the maximum range of this sensor, which does not make it possible to have a global view of the sensor. geographic area covered by sonar and may expose the infrastructure to be protected (naval fleet for example) to the risk of attacks by threats that cannot be detected if they operate in areas not covered by the considered sensor.

[0013] In addition, in the existing solutions based on the representation of the detection probabilities to evaluate the detection capacity of the detection system, the representation takes into account the detection probabilities of a single sensor of the detection system at a time and does not not make it possible to generate a visualization of the entire area in a way that can be used by the operator of the detection system, which can limit the efficiency of the naval fleet.

[0014] There is therefore a need for an improved method and device capable of determining the spatial coverage of a detection system in an efficient manner. To this end, the subject of the invention is a method for determining the spatial coverage of a multi-sensor detection system comprising a plurality of sensors, the plurality of sensors being carried by supporting structures moving in a given geographical area, some at least of the supporting structures being mobile, the method being characterized in that it comprises the steps consisting in:

- determine a set of parameters representing the geographical area and comprising environmental data, data relating to supporting structures and data relating to sensors;

iteratively calculating the probabilities of detection of the plurality of sensors in the geographical area from at least some of the parameters;

- determining a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors;

- determine 3D shadow zones of the geographical area in which the probability of detection is less than at least a given threshold;

- select the shadow zones located in at least one predefined security zone of the system.

[0015] According to some embodiments, the method further comprises determining an intersection of the selected shadow areas forming an access channel to safe areas.

[0016] According to certain embodiments, the detection system can be a sonar type detection system, the environment being a maritime environment, said environment data comprising data which vary in space and in time and represent a profile of the seabed in a geographical area defined, a temperature profile of the water volume, information defining the state of the sea, and information related to sea noise, the data of supporting structures operating in the marine domain defining one or more mobile platforms and / or one or more background objects.

[0017] According to some embodiments, said calculation can be carried out periodically according to a chosen period or continuously.

[0018] According to certain embodiments, the spatial coverage of the detection system is represented according to a given position and a given viewing orientation, the step of determining a three-dimensional representation of the spatial coverage of the detection system comprising, for each sensor of said detection system, the steps consisting in:

Receive a set of data comprising a probability of detection of said sensor in said geographical area, the probability of detection being determined in a calculation area associated with said sensor and included in said geographical area, the data of said set comprising position data of the area calculation and dimension data of the calculation area,

determining a 3D rendering of said probability of detection of said sensor on the basis of at least some of the probability data, the position data of the calculation zone and the dimension data.

[0019] According to certain embodiments, the method further comprises a step consisting in determining a main data structure having at least three dimensions from said probability data.

[0020] According to certain embodiments, the method may further comprise a step consisting in determining a depth rendering of the volume encompassing the calculation area from the position data of the calculation area, the dimension data, and of viewing position and orientation.

According to some embodiments, the 3D rendering can be volume, the method further comprising a step of determining a volume rendering of the probability of detection according to at least one function in a given color space from said structure of main data and said depth rendering.

[0022] According to certain embodiments, the step of determining a volume rendering can comprise the steps consisting in: - determining a volume rendering in gray levels of the detection probability from said 3D data structure and from said depth rendering;

determining a volume rendering in colors of the detection probability from the volume rendering in gray levels.

[0023] According to some embodiments, the step of determining a three-dimensional representation of the spatial coverage of the detection system comprises a step of generating the display of said three-dimensional representation of the spatial coverage, of the shadow zones inside safety zones and access channels to safety zones on a screen or in augmented reality mode or in virtual reality mode.

[0024] Advantageously, the embodiments of the invention allow real-time calculation of the probabilities of detection associated with a multi-sensor detection system.

Advantageously, the embodiments of the invention allow real-time representation of the detection probabilities associated with a multi-sensor detection system for a real three-dimensional scene.

Advantageously, the embodiments of the invention can provide a mode of representation of the probabilities of detection in volume form, which allows a 3D display of the spatial coverage of the detection system in the entire geographical area considered. .

[0027] Advantageously, the embodiments of the invention allow a three-dimensional representation of the spatial coverage of the detection system on a display device such as a screen or an augmented reality device.

The representation of the spatial coverage of the detection system on an augmented reality device allows in particular a better perception of the three-dimensional representation, the sharing of the same tactical situation between several actors or control systems, and visualization of a hologram on a tactical table without requiring the addition of additional screens in the operational environment considered.

[0029] The embodiments of the invention also make it possible to determine actions to be implemented in response to the identification of gray areas of non-coverage by the detection of the system located in security zones in the geographical area, or an access channel constructed by the sum of the shadow areas no coverage leading to these safety zones, regardless of the complexity of the shape of these shadow zones or these access channels.

Brief description of the drawings

[0031] Other characteristics and advantages of the invention will become apparent from the following description, made with reference to the accompanying drawings, given by way of example, and which represent, respectively:

[0032] [Fig. 1] is an example of an environment for using a method for determining spatial coverage of a sonar type detection system, according to some embodiments.

[0033] [Fig.2] is a flowchart showing a method for determining a spatial coverage of a multi-sensor detection system, according to certain embodiments of the invention.

[0034] [Fig.3] is a flowchart representing a method for determining a three-dimensional representation of the spatial coverage of a multi-sensor detection system, according to some embodiments of the invention.

[0035] [Fig.4] is a schematic view showing an accumulation ray tracing algorithm ("Ray Marching" in English), according to certain embodiments of the invention.

[0036] [Fig.5] shows an example of spatial coverage of a sonar type detection system used for object detection, obtained using prior art spatial coverage display methods.

[0037] [Fig.6] is a schematic representation of the theoretical target detection zones in an example of application to the detection of objects in an underwater environment.

[0038] [Fig.7] is a schematic representation of realistic detection zones in an example of application to the detection of objects in an underwater environment.

[0039] [Fig.8] represents an example of display on screen of the spatial coverage of a multi-sensor detection system of sonar type obtained from the volume rendering of the probabilities of detection, according to certain embodiments of the 'invention.

[0040] [Fig.9] shows an example of display in augmented reality mode of the spatial coverage of a multi-sensor detection system of sonar type obtained from the volume rendering of the probabilities of detection, according to certain embodiments of the invention. [0041] [Fig.10] is a schematic view of a device for determining the spatial coverage of a detection system, according to certain embodiments of the invention.

[0042] Detailed description

[0043] The embodiments of the invention provide a method for determining the spatial coverage of a multi-sensor detection system deploying a plurality of sensors and operating in a given geographic area. The geographical area includes a calculation area, the calculation area being a restricted geometric area included in the geographical area such that it includes all of the non-zero calculated detection probabilities of the detection system and determined by a center and a frame of reference (for example a Cartesian frame of reference).

[0044] FIG. 1 shows an example of an environment 100 in which a method is used for determining a three-dimensional representation of the spatial coverage of a detection system operating in a geographical area.

The detection system can be any detection system carried by a supporting structure capable of operating in the geographical area such as a radar or a sonar for example. In the example of FIG. 1, the detection system is a sonar carried by a supporting structure of the surface building type 101. The spatial coverage of the detection system is represented according to a given position and viewing orientation (the whole position and orientation data is also called "point of view"). The data of the set of data associated with a detection system carried by a supporting structure include, by way of nonlimiting example, position data of the calculation area, data of dimensions of the calculation area, and information on the supporting structure and the sensors.

The detection system can be used in various systems or infrastructures for the detection and location of objects in the geographical area considered (for example in water for a sonar type detection system). For example, the detection system can include one or more acoustic detection devices (for example sonars 107, 108 and / or 109) used:

- for the detection of submarines or surface vessels and / or threats (eg mines) or objects placed on the seabed;

- for the detection of schools of fish, in the field of maritime and river navigation;

- in hydrography to map the bottom of the oceans and other bodies of water, - in underwater and underwater archeology or in aquatic pollution sensors.

[0047] The detection system can also be used to locate the detected objects.

In the example of the environment 100 of Figure 1 (representing an anti-submarine warfare device), other auxiliary elements can be deployed to implement preventive, defensive, or offensive actions in depending on the object detected. The auxiliary elements can include, for example, a surface ship 101 equipped with one or more sonars, one or more maritime patrol aircraft, and one or more attack submarine. For example, sonars may include active bow sonar 107, towed active sonar 108, and wet active sonar 109 deployed by helicopter 103.

The different elements of the environment 100 can be controlled by an operator or a control system present for example in the surface vessel 101, to monitor and protect the elements of the environment 100 against threats by implementing operations or actions. The operator or the control system can, for example, implement preventive, defensive or offensive actions.

The description of the embodiments of the invention which follows will be made mainly with reference to a detection system of the sonar type implementing at least one passive or active multi-sensor sonar to facilitate understanding of the embodiments of the invention, by way of nonlimiting example. However, those skilled in the art will easily understand that the invention applies more generally to any detection system capable of detecting an object in water or in any other environment.

FIG. 2 represents a method for determining a spatial coverage of a multi-sensor detection system deploying a plurality of sensors carried by supporting structures moving in a given geographical area is illustrated. A supporting structure can carry one or more sensors. A sensor can operate in passive, mono-static, or multistatic mode.

According to certain embodiments, the supporting structures can be independent or not independent.

In step 201, a set of parameters representing the given geographical area comprising environmental data (for example the underwater, marine or terrestrial environment), data relating to the supporting structures (for example the speed of the supporting structure) and data relating to the sensors of the multi-sensor detection (for example for a sonar-type detection system, the data relating to the sensors may include the passive or active mode of the sonar, the frequency (s) of the sonar, the maximum range of the sonar, the gain of the sonar). The environmental data may further include tactical data. Such parameters make it possible to define the location of the detection system, the environmental conditions relating to this location, the initial trajectories followed by different platforms of the detection system, the threats, the units to be protected, and / or the equipment of the suitable platforms. to ensure this protection.

[0054] In a sonar type detection system, the environmental data can include marine and / or terrestrial environment data which vary in space and time and represent:

- the profile of the seabed in a defined geographical area (which can be chosen with high resolution, for example up to 20cm); this profile can be supplemented by the type of bottom (combinations of mud, sand and / or rock);

- a temperature profile of water volume and salinity, water volume temperature and salinity influencing the propagation of acoustic waves;

- information defining the state of the sea (for example the height of the waves and their direction) and having an influence on the way in which the acoustic waves are reflected on the surface;

- information related to sea noise (for example rain, traffic noise, diffuse biological noise, seismic, etc.

According to some embodiments, the data relating to the supporting structures moving in the given geographical area can be associated with the bearing structures moving in the geographical area marine domain such as one or more mobile platforms (for example, surface vessels , submarines, drones, airplanes, helicopters, sonar drifting buoys, biological entities, etc.) and / or one or more bottom objects (e.g. mines, static acoustic detection systems, wrecks, or any other background object having an influence on the detections). The supporting structures can have a predefined trajectory, be controlled by an operator of the detection system or by a control system. The load-bearing structures can have their own noise (due for example to machines and / or noises of flow in water), can be equipped with sensors of any type, and / or can also emit sounds. Bottom objects can be equipped with sensors, behavioral automatons and explosive charges. In an exemplary embodiment, the tactical data can be associated with one or more threats to be detected (compound fleet for example), and the supporting structures can include one or more High Value units (in English 'High Value Unit' or HUV), one or more protective elements of the HUV units arranged in surveillance zones or at the level of Limits of Approach (LLA).

[0057] In an exemplary embodiment, step 201 can be performed by a scenario generator in the environment considered (marine environment for example).

In step 203, a detection probability can be calculated iteratively, for example at predefined time intervals or according to a fixed period or continuously, for the plurality of sensors of the multi-sensor detection system from certain or less of the set of parameters determined in step 201. A detection probability, denoted p (D), denotes the probability that an actual target echo will be detected when an actual target echo exists. In a passive sonar type detection system, the detection probability represents the probability of detecting an actual target object which emits a given noise level at different frequencies. In an active sonar-type detection system, the detection probability represents the probability of obtaining a viable echo through emission of acoustic waves by the active sonar. The probability that an actual target echo will not be detected while the target echo exists is 1 -p (D). The probability of false alarm, denoted p (FA), designates the probability that a false echo (or spurious echo) is detected, i.e. the probability that there is a false detection of an echo which or really noise.

The probability of detection calculated for the plurality of sensors can be determined by combining the elementary probabilities of detection of the different sensors.

The detection probabilities can change at any time taking into account the profile of the environment in the vicinity of the supporting structure (bottom under the platform considered, for example), the movements of the supporting structure and the environmental conditions. In one embodiment, the detection probabilities can be updated periodically over a chosen period of less than one minute, for example every 30 seconds (or more quickly depending on the available computing power), taking into account the speeds of the structures. carriers (the speed is low, for example for naval platforms) and the fact that the detection probabilities vary according to certain quantities relating to the environment (for example, they decrease when the noise of water flowing over Sonar type sensors increases). Step 203 may include a step of saving data representing the probabilities of detection calculated for the plurality of sensors in a file. In one embodiment, the detection probability data may be represented by a probability data matrix. The position of the center of this matrix (center of the reference frame of the matrix) can then be located at the position of the supporting structure of the detection system.

[0062] In step 205, a three-dimensional representation of the spatial coverage of the detection system can be determined for the plurality of sensors. The three-dimensional representation of the spatial coverage according to the embodiments of the invention makes it possible to represent the detection probabilities for the set of sensors of the multi-sensor detection system and over the entire geographical area, taking into account the parameters representing the geographic area. For example, in an application of the invention to a sonar type detection system, the representation of the spatial coverage in three dimensions can take into account the relief of the seabed, the supporting structures evolving in the geographical area in a marine environment. , and tactical zones determined in step 201. The relief of the environment under consideration, such as for example the relief of a seabed, can be imported from maps such as nautical charts used for example for S57 type navigation. The relief can undergo a smoothing and lighting treatment with a shadow treatment to be able to optimize the representation of the entire area. The load-bearing structures can be associated with a fairly faithful 3D model given the scale of the objects. The position of the supporting structures can be updated periodically, such as for example every 200 ms, the period being chosen or determined to be adapted to the fluid movements of the supporting structures taking account of their speeds.

According to some embodiments, step 205 may include a display of the three-dimensional representation of the spatial coverage of the detection system on a rendering device such as a screen, an augmented reality rendering device or yet another virtual reality rendering device. The display in augmented reality mode advantageously makes it possible to improve the perception of the three-dimensional representation thanks to holograms, the sharing of the same tactical situation between several operators equipped with rendering devices allowing them to visualize the holograms with dots different views, and to transfer the hologram to a tactical table without requiring the addition of additional screens in the already crowded operational environment. The vision of the hologram can be synchronized with the data of the tactical table. In step 207, the three-dimensional representation of the spatial coverage can be analyzed to determine 3D shadow zones of the geographic zone, the shadow zones being zones in which the probability of detection is lower. at at least a given threshold.

[0065] In step 209, the shadow areas can be located in at least one predefined security area of the system. Safety zones can be selected based on strategic criteria.

The safety zone with respect to a structure to be protected by the multi-sensor detection system can be substantially a sphere centered on the infrastructure to be protected, the radius of the sphere being for example of the order of 30 km.

A 3D shadow zone can be, for example, a non-detection cuvette.

[0068] According to some embodiments, step 209 may include determining an intersection of the selected shadow zones forming an access channel to safety zones, a channel designating the sum of the shadow zones leading to the security zone. A risk zone is a gray area within the safety zone.

According to some embodiments, the three-dimensional representation of the spatial coverage of the multi-sensor sonar in step 205 can be based on the determination of a 3D rendering of the detection probabilities calculated for the set of sensors. in step 203. In one embodiment, the step of determining the 3D rendering (in three dimensions) of the probability of detection can also take into account the visualization data (position and orientation data).

[0070] The determination of the 3D rendering of the probabilities of detection can be carried out using 3D image synthesis techniques to convert the raw data of the probabilities of detection into a 3D image to generate a display of the 3D image on the device. chosen rendering (a 3D device on screen, augmented reality device or virtual reality device).

The 3D representation of the spatial coverage can be advantageously superimposed with a representation of the geographic area on the rendering device, which allows a 3D display of the spatial coverage of the detection system in the geographic area considered.

The creation of a synthetic image can be broken down into three main stages: the geometric modeling of the objects of the scene to be represented, the creation of the scene, and the rendering. Geometric modeling makes it possible to define the geometric properties of objects either by a mathematical representation from the definitions or from the mathematical systems which describe them, or by a representation by construction tree by representing the complex objects as a composition of objects simple called primitives, or by a representation which represents an object by materializing the limit between the interior of the object and the exterior of the object by a series of geometric elements linked together (generally triangles).

The scene creation step makes it possible to define the appearance of the objects and to determine the non-geometric parameters of the scene to be displayed. The appearance of objects is defined by determining surface or volume properties of objects including optical properties, color and texture. Non-geometric scene parameters include the position and type of light sources, the position and orientation of the scene visualization forming the chosen vantage point.

[0075] The assignment of a color to an object according to the embodiments of the invention is based on the use of a color space taking into account the opacity. For example, data conversion can be achieved by using the RGBA color coding format which is an extension of the RGB format, taking into account the notion of transparency. In such an example, each pixel displayed on the image represents a vector of four components, comprising a first R value representing a red color value (Red), a second G value representing a green color value (Green), a third B value representing a blue color value (Blue), and a fourth A value representing a transparency component (or alpha component). Using the geometric representation in Euclidean space, each pixel is associated with three geometric dimensions (x, y, z) in an XYZ frame of reference comprising a width x (or abscissa of the frame of reference), a depth y (or ordinate of the frame of reference) and a height z (or side of the frame of reference). The XYZ frame of reference is defined by the calculation area and can be centered at the center of the calculation area.

[0076] As used here, a 3D texture refers to a data structure allowing the representation of a synthetic image.

A rendering method refers to a computer method consisting in converting the model of the objects into an image displayable on the chosen rendering device.

The determination of the spatial coverage of the three-dimensional detection system from the detection probabilities can use the transformation (or conversion) of the raw data of the probabilities of detection into a structure usable by the rendering calculation functions.

[0079] FIG. 3, a flowchart representing the step 205 of determining a representation of the spatial coverage of a three-dimensional multi-sensor detection system operating in a geographical area according to certain embodiments.

The steps of Figure 3 are performed for each sensor of the detection system.

In step 301, data from the set of data comprising a probability of the sensor in the geographical area are received, the detection probability being determined in a calculation area associated with the considered sensor and included in said geographical area . The data of the dataset further includes the dimension of the calculation area associated with the considered sensor, and position data of the calculation area. In particular, the data in the dataset can be read or extracted. In step 301, visualization data (position and orientation) can also be received.

A 3D rendering of the detection probability of the sensor is then determined from at least some of the probability data, the position data of the calculation zone and the dimension data.

In one embodiment, to generate the 3D rendering, the method can comprise a step 303 in which a main data structure having at least three dimensions (in particular 3D or 4D), also conventionally called 3D Texture 'is determined at from the probability of detection data. The data structure can be for example a matrix.

[0084] In one embodiment, the input data set may further include the input resolution, the input resolution corresponding to the distance between two points in the computation area. Step 303 of determining the data structure can then include the steps of:

- generate an auxiliary data structure from the detection probability data, the auxiliary data structure having dimensions defined from the probability, and the input resolution, and

- determine the 3D texture using a conversion of the data from the auxiliary structure into colorimetric data. In step 305, a depth rendering of the volume encompassing the calculation area can be determined from the position and dimension data of the calculation area, and the display position and orientation data.

In particular, the step 305 of determining the depth rendering can comprise the determination of a first depth image of a cube including the data structure and of a second depth image of the rear face of the cube. . The depth of the cube including the main data structure (3D Texture) representing the distance of the surface of the cube from the position and orientation of visualization (Z depth or 'Z-depth' in English), the depth rendering comprising the first depth image and the second depth image. A depth image includes a set of surfaces associated with positional information. In one embodiment, the second depth image can be determined as the depth image of the cube whose normals have been inverted, corresponding to the depth image of the rear face of the cube at the point of view.

In one embodiment, the 3D rendering can be a volume rendering. In such an embodiment, at step 307, a volume rendering of the probability of detection following at least one function in a given color space is determined from the main data structure (3D texture) and the rendering of depth.

In one embodiment, the functions used to determine the volume rendering can include at least one transfer function defined from a minimum probability bound, a maximum probability bound, a colorimetry bound. minimum, and a maximum colorimetry bound.

Each information of a depth image associated with x, y, z geometric dimensions can be defined in a color space, for example in the RGBA coding format.

[0090] In one embodiment, step 307 of determining a volume rendering can comprise:

- a step 3071 of determining a volume rendering in gray levels of the probabilities of detection from the 3D texture and the depth rendering, and

a step 3073 for determining a volume rendering in colors of the detection probabilities from the volume rendering in gray levels. In one embodiment, the volume rendering in gray levels can be determined from the 3D texture, from the first depth image, and the second depth image by applying an algorithm (or technique) for calculating volume rendering of the accumulation ray tracing type ('Ray Marching' in English).

A Ray Marching type algorithm is illustrated in FIG. 4 in an embodiment using RGBA coding. A Ray Marching-type algorithm is based on geometric optics to simulate the path of light energy in the image to be displayed. A projection plane 403, placed in front of a viewpoint 401, represents the visualized image (that is, the volume rendering in grayscale). Each point of the projection plane 403 corresponds to a pixel of the volume rendering in gray levels. The implementation of step 307 by applying an algorithm of the Ray Marching type according to the embodiments of the invention can comprise the generation of a ray 407 (defined by a point of origin and a direction) for each pixel the desired image of the volume rendering in grayscale. The radius can be sampled in regular steps within volume 405 and the values in the color space (RGBA colors for example) of the various pixels thus calculated can be summed in proportion to their contribution of Alpha transparency. The algorithm traverses, in the direction of the projection plane 403, the oriented volumes of the results representing the detection probabilities, while collecting the probability values step by step. The value of a displayed pixel is the result of a function (eg transfer function) of the values collected.

For each point of the projection plane, each point corresponding to a pixel of the volume rendering, the Ray Marching type algorithm can be applied to calculate a probability value accumulated by a radius 407 starting from the front of the enclosing cube 3D texture to the back of the cube.

In one embodiment, the application of a Ray Marching type algorithm in step 307 can include the operations consisting in, for each pixel of the volume rendering and for each update of the detection probabilities:

- determining the start and arrival 3D positions of ray 407 from the read values of the textures of the first depth image and of the second depth image for the selected pixel;

- calculate a step-by-step displacement vector during a given or predetermined number of iterations, from the direction displacement vector of the radius 407, of the distance of the iterations step determined by dividing the total distance by the number iterations, and multiplying the direction displacement vector by the step distance.

For each iteration, the determination of a color representing a probability value for a selected pixel can comprise the operations consisting in:

- update the position of the path of radius 407 by adding the displacement vector;

- associate a gray level value with the probability color corresponding to the updated position in the 3D texture, such a color representing a gray level associated with the corresponding probability: for example, a 'black' value can be associated with a probability close to zero (Ό ') and a' white 'value can be associated with a probability close to one (' 1 ');

- determine the alpha transparency component of the color value;

- apply a function in the color space (eg transfer);

- add the determined color to the color of the pixel resulting from the algorithm.

In one embodiment, the transfer function can be a software function configured to perform linear or non-linear interpolation, between a minimum color boundary and a maximum color boundary, the color space in which is determined the volume rendering representing a detection probability lying between a minimum probability limit and a maximum probability limit.

For example, at step 3073, a color volume rendering of the detection probabilities is determined from the gray level volume rendering of the detection probabilities determined in sub-step 3071, the color volume rendering can be determined by applying a color transfer function to the grayscale volume rendering, the color transfer function using the minimum probability bound, the maximum probability bound, the minimum color bound, and the maximum color bound. The color transfer function can then for example perform a linear interpolation, between the minimum color bound and the maximum color bound, the colors of the volume rendering representing a detection probability between the minimum probability bound and the probability bound maximum.

In another embodiment, the 3D rendering can be surface. The method then comprises, as an alternative to steps 303, 305 and 307, a step 31 1 consisting in determining a surface rendering from the probabilities of detection, of the data of position of the computation area, dimension data, at least one detection probability threshold value, and viewing position and orientation.

In particular, step 31 1 of determining the surface rendering can comprise the generation of polygonal objects from the three-dimensional data matrix to approximate at least one iso-surface conducted from at least one given detection probability threshold ('Marching cube' in English).

The functions of calculating the probabilities of detection and of determining the texture can be software functions executed by a processing unit or processor. The functions implemented in the rendering process can be software programs called "shaders" or "shaders" in English, executed by a graphics processing unit.

[0101] The method according to the various embodiments of the invention can be implemented in a detection system in "mission preparation" mode or in "online" mode. The "mission preparation" mode optimizes the movement of the detection system by simulating a scenario based on a future mission. The "online" mode consists in implementing the process by coupling the detection system to the tactical links. In this 'online' mode, the actual positions, speeds, and headings of the various supporting structures as well as the use of their various sensors can be used to update the parameters defining the environment and making it possible to generate a representation. in real time. Data collected on-board the 'online' detection system can be converted into information that can be used for detection probability updates.

[0102] FIG. 5 represents an example of spatial coverage of a detection system of sonar type used in an anti-submarine warfare device obtained by using a detection probability display technique of the prior art. . This representation is generated from a matrix of detection system detection probabilities as a function of distance from the detection system antenna and depth between the surface and the seabed, using with coding colors used to assess the sonar's ability to detect a threat. In the example of figure 5, zone 1 which corresponds to a zone associated with 100% probability of detecting a threat (an enemy platform for example) and zone 2 corresponds to a zone associated with 0% probability of detecting a threat. The shapes thus obtained are complex and non-uniform.

[0103] FIG. 6 is a schematic representation of the theoretical target detection zones in an anti-submarine warfare device using circles to identify the target. intended protection of a naval fleet against a possible threat and Figure 7 is a schematic representation of realistic detection areas obtained using state of the art spatial coverage display methods. The two figures show that the actual global detection zone is far from the desired theoretical global detection zone, which does not allow effective preventive surveillance to be ensured in order to protect the naval fleet and may represent a danger in the presence of threats. possible.

[0104] FIG. 7 represents an example of a display on a screen of a spatial coverage of a detection system of the multi-sensor sonar type obtained according to an embodiment with volume rendering of the detection probabilities of the detection system. As illustrated by FIG. 7, the three-dimensional representation of the spatial coverage according to the invention makes it possible to display a global view of the complete zone in three dimensions, to cover a very large zone on the scale of the naval fleet, and to highlight non-insonified dangerous areas and non-detection cuvettes.

[0105] FIG. 8 represents an example of display in augmented reality mode of a multi-sensor spatial coverage obtained according to the volume rendering modes of the probabilities of detection of the sonar according to the embodiments of the invention.

[0106] The invention also provides a device for determining a spatial coverage of a multi-sensor detection system comprising a plurality of sensors, said plurality of sensors being carried by supporting structures moving in a given geographical area, some at least. bearing structures being mobile, characterized in that the device is configured for:

- determining a set of parameters representing said geographical area and comprising environmental data, data relating to said supporting structures and data relating to said sensors;

iteratively calculating the probabilities of detection of said plurality of sensors in the geographical area from at least some of said parameters;

- determining a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors;

- determining 3D shadow zones of said geographical zone in which said probability of detection is less than at least a given threshold;

- select the shadow zones located in at least one predefined security zone of the system. [0107] The invention further provides a computer program product for determining a spatial coverage of a multi-sensor detection system comprising a plurality of sensors, said plurality of sensors being carried by supporting structures moving in a geographical area. data, at least some of the supporting structures being mobile, characterized in that the computer program product comprising computer program code instructions which, when executed by one or more processors cause the processor (s) to:

- determining a set of parameters representing said geographical area and comprising environmental data, data relating to said supporting structures and data relating to said sensors;

iteratively calculating the probabilities of detection of said plurality of sensors in the geographical area from at least some of said parameters;

- determining a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors;

- determining 3D shadow zones of said geographical zone in which said probability of detection is less than at least a given threshold;

select the shadow areas located in at least one predefined security zone of the system.

FIG. 10 represents the device 4000 for determining a three-dimensional representation of the spatial coverage of a detection system (for example of the sonar type) operating in a geographical area (for example marine scene) from the set of data comprising the detection probabilities of the detection system. The data set can be saved for example in a memory 430 or in a mass memory device 420. Device 4000 can be any type of device or computer system referred to as a computer. The device 4000 may include at least one processing unit 4010 configured to determine a 3D rendering of the probabilities of detection from at least some of the probability data, the position data of the calculation area and the dimension data. In one embodiment, the 3D (three-dimensional) rendering of the probabilities of detection can further be determined from the visualization data (position and orientation data).

[0109] The device 4000 can further include a memory 430, a database 420 forming part of a mass storage memory device, an I / O input / output interface 470, and a Human-Machine interface. 410 to receive entries or return outputs from / to a detection system operator. The interface 410 can be used for example to configure or configure various parameters or functions used by the method for determining representation according to certain embodiments of the invention, such as the configuration of the volume rendering of the probabilities of detection. External resources may include, but are not limited to, servers, databases, mass storage devices, edge devices, cloud network services, or any other suitable computing resource that may be used with device 4000.

[01 10] The processing unit 4010 may include one or more devices selected from microprocessors, microcontrollers, digital signal processors, microcomputers, central processing units, programmable gate arrays, logic devices. programmable, defined state machines, logic circuits, analog circuits, digital circuits, or any other device used to manipulate signals (analog or digital) based on operating instructions stored in memory 430. Memory 430 may include a single device or a plurality of memory devices, including but not limited to read-only memory (ROM), random access memory (RAM )), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory or any other e device capable of storing information. The mass storage device 420 may include data storage devices such as a hard disk, an optical disk, a magnetic tape drive, a volatile or non-volatile solid state circuit, or any other device capable of storing informations. A database may reside on the mass memory storage device 420.

[01 1 1] The processing unit 4010 can operate under the control of an operating system 440 which resides in the memory 430. The operating system 440 can manage the computer resources in such a way that the program code of the computer, integrated in the form of one or more software applications, such as the application 450 which resides in the memory 430, can have instructions executed by the processing unit 4010. The device 4000 can comprise a graphics processing unit 4030 implemented on a graphics card, on a motherboard, or in a central processing unit. The graphics processing unit can generate a display of the 3D rendering on a display device.

[01 12] In general, the routines executed to implement the embodiments of the invention, whether they are implemented within the framework of a system operating or a specific application, a component, a program, an object, a module or a sequence of instructions, or even a subset of these , may be referred to as “computer program code” or simply “program code”. Program code typically includes computer readable instructions that reside at various times in various memory and storage devices in a computer and which, when read and executed by one or more processors in a computer, cause the computer to perform the operations necessary to perform the operations and / or elements specific to the various aspects of the embodiments of the invention. The instructions of a program, readable by computer, for carrying out the operations of the embodiments of the invention may be, for example, assembly language, or else a source code or an object code written in combination with one or several programming languages.

[01 13] The invention is not limited to the embodiments described above by way of non-limiting example. It encompasses all the variant embodiments which may be envisaged by those skilled in the art.

Claims

1. Method for determining the spatial coverage of a multi-sensor detection system comprising a plurality of sensors, said plurality of sensors being carried by supporting structures moving in a given geographical area, at least some of the supporting structures being mobile, the method being characterized in that it comprises the steps of: - determining (201) a set of parameters representing said geographical area and comprising environmental data, data relating to said supporting structures and data relating to said sensors; - iteratively calculating (203) the probabilities of detection of said plurality of sensors in the geographical area from at least some of said parameters; determining (205) a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors; - determining (207) 3D shadow zones of said geographical zone in which said probability of detection is less than at least a given threshold; - select (209) the shadow zones located in at least one predefined security zone of the system. 2. Method according to claim 1, characterized in that it further comprises determining an intersection of the selected shadow areas forming an access channel to safety areas. 3. Method according to claim 1, characterized in that said detection system is a sonar type detection system, said environment being a maritime environment, said environment data comprising variable data in space and time and representing a profile of the seabed in a defined geographical area, a temperature profile of the volume of water, information defining the state of the sea, and information related to sea noise, said supporting structure data evolving in said marine domain defining one or more mobile platforms and / or one or more bottom objects. 4. Method according to any one of the preceding claims, characterized in that said calculation (203) is carried out periodically according to a selected period or continuously. 5. Method according to any one of the preceding claims, characterized in that the spatial coverage of the detection system is represented according to a given position and viewing orientation, the step (205) of determining a three-dimensional representation. the spatial coverage of the detection system comprising, for each sensor of said detection system, the steps consisting in: Receive a set of data comprising a probability of detection of said sensor in said geographical area, the probability of detection being determined in a calculation area associated with said sensor and included in said geographical area, the data of said set comprising position data of the area calculation and dimension data of the calculation area, determining a 3D rendering of said probability of detection of said sensor on the basis of at least some of the probability data, the position data of the calculation zone and the dimension data. 6. Method according to claim 5, characterized in that it further comprises a step of determining (303) a main data structure having at least three dimensions from said probability data. 7. Method according to one of claims 5 and 6, characterized in that it further comprises a step of determining (305) a depth rendering of the volume encompassing the calculation area from the position data of the area. calculation, dimension data, and visualization position and orientation. 8. Method according to claims 2 and 3, characterized in that said 3D rendering is volume and in that the method further comprises a step of determining (307) a volume rendering of the probability of detection according to at least one function in a given color space from said main data structure and said depth rendering. 9. The method of claim 8, characterized in that said step of determining a volume rendering (307) comprises the steps of: - determining a volume rendering in gray levels of the detection probability from said 3D data structure and from said depth rendering; determining a volume rendering in colors of the detection probability from the volume rendering in gray levels. 10. Method according to one of the preceding claims, characterized in that the step of determining (205) a three-dimensional representation of the spatial coverage of the detection system comprises a step of generating the display of said representation. in three dimensions of the spatial coverage, shadow areas inside security zones and access channels to security zones on a screen or in augmented reality mode or in virtual reality mode. 1 1. Device for determining a spatial coverage of a multi-sensor detection system comprising a plurality of sensors, said plurality of sensors being carried by supporting structures moving in a given geographical area, at least some of the supporting structures being mobile, characterized in that that the device is configured for: - determining a set of parameters representing said geographical area and comprising environmental data, data relating to said supporting structures and data relating to said sensors; iteratively calculating the probabilities of detection of said plurality of sensors in the geographical area from at least some of said parameters; - determining a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors; - determining 3D shadow zones of said geographical zone in which said probability of detection is less than at least a given threshold; - select the shadow zones located in at least one predefined security zone of the system. 12. Computer program product for determining the spatial coverage of a multi-sensor detection system comprising a plurality of sensors, said plurality of sensors being carried by supporting structures moving in a given geographic area, at least some of the supporting structures being mobile, characterized in that the computer program product comprising computer program code instructions which, when executed by one or more processors cause the processor (s) to: - determining a set of parameters representing said geographical area and comprising environmental data, data relating to said supporting structures and data relating to said sensors; iteratively calculating the probabilities of detection of said plurality of sensors in the geographical area from at least some of said parameters; - determining a three-dimensional representation of the spatial coverage of the detection system for the plurality of sensors; - determining 3D shadow zones of said geographical zone in which said probability of detection is less than at least a given threshold; - select the shadow zones located in at least one predefined security zone of the system.