Method for elaborating and deploying an improved configuration of an active array of sonobuoys

Abstract

The invention relates to a method for elaborating (1) and deploying (2) an improved configuration (Rc) of an active array of sonobuoys in a geographical space divided into a grid of boxes, wherein: • the improved configuration is determined (1) iteratively; and • the improved configuration (Rc) is deployed (2).

Description

[0001] Method for developing and deploying an improved configuration of an active acoustic buoy network

[0002] The present invention relates to a method for developing and deploying an improved configuration of an active network of acoustic buoys.

[0003] The scope of the invention is that of acoustic buoy networks, or "sonobuoy" in English. The configuration of an acoustic buoy network refers to the positions of the individual acoustic buoys.

[0004] Anti-submarine warfare, abbreviated as ASW, refers to the search for submarines at sea, in deep waters, or in coastal waters, using both active and passive means. These means include sonar deployed from various vehicles, such as frigates or autonomous vessels, helicopters, aircraft, aerial drones, and submarines.

[0005] An active acoustic buoy network is defined as a network operating with active and / or passive acoustic buoys.

[0006] Maritime anti-submarine warfare (ASW) patrols rely on the use of acoustic buoys, deployed from the air and expendable (they are scuttled after use), from an aircraft or helicopter (more rarely from a ship). They can be released in large numbers and activated remotely (via UHF radio link), potentially long after deployment, following a period of inactivity. Their range varies depending on environmental conditions, the target, and the sonar modes used. The buoy signals are received by radio (VHF) and processed by the aircraft.

[0007] Acoustic buoys are devices with a surface component and a submerged component. The submerged component includes acoustic sensors that measure and record the underwater sound field at a specific depth and emit an acoustic wave at a specific frequency. The measured signal is transmitted to the surface component, which has an antenna for receiving the signal from an aircraft. Acoustic buoys are generally deployed by aircraft. An active sonar system consists of a transmitter and a receiver. When the transmitter and receiver are located on the same device (an acoustic buoy), it is called a "monostatic" system. When the transmitter and receiver are located on different devices at different locations, it is called a bistatic system. Multistatic systems involve a network of active and passive acoustic buoys, which can be used to create multiple sonar systems.During an operation, depending on its objectives, an aircraft will have to drop buoys, exploit them and decide in real time on the following tactical actions based on the result of the processing: detection or not, acquisition of kinematic elements of the target, .

[0008] Since the beginnings of airborne anti-submarine warfare (ASW) in the 1940s, and up to the present day, the number of buoys in the water has increased significantly, from just a few units at the start, to around two dozen in the 1990s, and now to several dozen, or even a hundred. Under these conditions, the time spent by the aircraft during deployment is no longer negligible, nor is the time required to traverse the area to be covered. This makes it impossible to simultaneously deploy the buoys, control all their modes and pulse transmissions, and monitor their signals.

[0009] During an operation, a carrier deploying acoustic buoys for their mission must be able to do so while maximizing their chances of success. To achieve this, the acoustic buoys are deployed according to a plan that maximizes a success criterion (called MOE for "Measurement of Efficiency"). 1 'in English, or Effectiveness Measurement), adapted to the mission.

[0010] For example, the success criterion may aim to ensure that the detection of an underwater vehicle in the surface of the mission area is safe, or that the detection of an underwater vehicle along the length of the barrier is safe.

[0011] Defining a useful deployment plan is complex due to the large number of possible combinations: there are a great many possible locations for acoustic buoys and a significant number of different sensor types, not all of which are compatible with each other. A discrete approach is taken by dividing the operational area into a grid of regular cells (e.g., rectangular, square, hexagonal, or any other tiling of the plane relevant to the problem). Denoting the number of cells as E and the number of acoustic buoys to be placed as B (using only one type of buoy for simplicity), the number of possible sonar systems is on the order of:

[0012] N = E B

[0013] For example, for an area 100 kilometers on each side, with a discretization step of 500 meters, we have: 200 cells per side, and E=40,000 cells in total. For 10 buoys (an aircraft payload can reach 100 buoys), we get an enormous figure:

[0014] N = 40,000 10 1.05 10 46 The number of calculations required to estimate the value of a solution is even greater, since it would be necessary to estimate the detection capacity of each buoy network at each point, i.e., N Calculs = N * E = E B+1

[0015] The number of sonar system configurations is gigantic, and a systematic approach cannot be achieved in a reasonable time.

[0016] Furthermore, the deployment plan must also take into account the performance of the active sonar systems formed by the acoustic buoys (wireless sensors), and meet a multitude of constraints:

[0017] • Performance calculations must take into account underwater topography, including coastlines.

[0018] • The choice of acoustic buoys must take into account the compatibility or incompatibility of the sensors of the acoustic buoys with each other (frequency band, performance),

[0019] • The load of the carrier agent is fixed once on mission, and it is only possible to exploit the available acoustic buoys.

[0020] The problem is therefore complex. The computational context requires a solution to be proposed within a limited timeframe (30 minutes). An exact solution can only be proposed within a reasonable time for small areas, and thus a small number of cells to cover, which does not correspond to operational uses.

[0021] A multistatic sonar network, or MSN (Multistatic Sonar Network), is defined as a set of active sonar systems in monostatic and / or bistatic configurations. These sonar systems result from the combination of a source and a receiver originating from a single buoy or from two separate buoys.

[0022] In the scientific literature related to the deployment and configuration of wireless sensor networks or WSNs (Wireless Sensor Networks), of which MSNs are a special case of WSNs, we find the following three types of problems:

[0023] - Barrier cover, or BC for "Barrier Coverage" in English,

[0024] - Point (target) coverage, or PC for "Point (target) Coverage" in English, and

[0025] Area coverage, or AC, is an acronym for "Area (blanket) Coverage." For AC area coverage, a multi-objective particle swarm optimization (PSO) algorithm is known to be used to determine sensor placement, maximizing coverage while simultaneously minimizing the number of sensors required. A genetic algorithm is also known to be used to optimize both the placement of individual sensors (including depth) and the transmission times of individual acoustic buoys in a non-homogeneous environment (known as SCOUT). Various geometric arrangements have been studied in open water to find the most effective one for covering a large area. Some of these arrangements use a genetic algorithm to determine the positions of the different sensors.Some researchers derive an analytical theory to predict the probability of detection based on randomly deployed sensors and use it to determine optimal models. Building on this work, some use simulations to quantify the impact of the direct path effect on coverage. Some use simulations to quantify the coverage of a mobile source performing parallel scans in a stationary receiving field. Others propose several models with different linearizations and compare them in the context of two problems: maximizing the coverage area with a limited number of sensors and minimizing the network cost (the number of sensors) required to achieve 100% coverage (a fixed performance).

[0026] For barrier coverage in the field of multistatic radar networks, a method is known for the optimal placement of radars on a segment to maximize the detectability of intrusions in the worst case. A method based on the "equipartition strategy" is known for determining the optimal deployment schemes of multistatic radars for a subproblem. These models are then used in an integer linear program (ILP) and an exhaustive search to solve the overall problem. An ILP and an exhaustive search are known for determining several barrier coverages of unequal widths within the area of ​​influence (with different deployment sequences).

[0027] Regarding spot PC coverage, there is a known algorithm called DiBS, an acronym for "Divide Best Sector". 1In English, this refers to the placement of a single source when a number of receivers are already positioned (with an iterative extension for placing multiple sources). The optimal placement of sources and receivers for this type of problem in open water (without obstacles) is known, using two ILPs (DISC-LOC-M and DISC-LOC-ENUM) as well as two heuristics, Adapt-LOC and Iter-LOC, based on a procedure called LOC-GEN-II (an improved version of LOC-GEN). Also known are an exact OPT-LOC solution method and a greedy-LOC heuristic for source placement when receivers are already deployed.

[0028] One object of the invention is to provide a method for developing and deploying an improved configuration of an active network of acoustic buoys.

[0029] According to one aspect of the invention, a method is proposed for developing and deploying an improved configuration of an active network of acoustic buoys over a geographical area divided into a grid of squares, in which:

[0030] The improved configuration is determined through the iterative steps of:

[0031] - updating a current configuration initialized by an initial coverage rate configuration;

[0032] - determination of a first neighborhood corresponding to the application of a first neighborhood operator from an ordered increasing list of neighborhood operators;

[0033] - updating a current neighborhood initialized by the first current neighborhood; - random selection of a random configuration in the current neighborhood;

[0034] - determination of an improved sub-configuration by the iterative steps of: o updating a current sub-configuration initialized by the random configuration;

[0035] o determination of a first sub-neighborhood corresponding to the application of a first neighborhood operator from an increasing ordered sub-list of neighborhood operators;

[0036] o updating a current sub-neighborhood initialized by the first current sub-neighborhood;

[0037] o determination of a candidate neighboring subconfiguration by exploring the current subneighborhood;

[0038] o first test if the coverage rate of the candidate sub-configuration is greater than the coverage rate of the current sub-configuration;

[0039] o if the first test is negative, the coverage rate of the candidate subconfiguration being lower than the coverage rate of the current subconfiguration, a second test is performed if all sub-neighborhoods have been explored;

[0040] o if the second test is negative, the current subneighborhood is expanded by applying the next neighborhood operator of the ordered sublist which is passed to the update of the current subneighborhood; o if the first test is positive, the value of the candidate neighbor subconfiguration is assigned to the current subconfiguration by returning to the update step;

[0041] o if the second test is positive, the coverage rate of the neighboring candidate subconfiguration being less than the coverage rate of the current subconfiguration, we leave the iterative determination step for a third test step;

[0042] o third test if the coverage rate of the current sub-configuration is greater than the coverage rate of the current configuration;

[0043] o if the third test is negative, the rate of the current configuration being greater than the coverage rate of the current sub-configuration, a fourth test is performed if all neighborhoods have been explored;

[0044] o if the fourth test is negative, the current neighborhood is expanded by applying the next neighborhood operator from the ordered list which is passed on to the update of the current neighborhood;

[0045] o If the third test is positive, and the current configuration's coverage rate is lower than the current sub-configuration's coverage rate, the current configuration's value is updated with the current sub-configuration's value; and

[0046] o if the fourth test is positive, the improved current configuration is deployed.

[0047] In one embodiment, the increasing ordered list of neighborhood operators and / or the ordered sublist of neighborhood operators, includes knm-shift operators, corresponding to the shift of k buoy(s) in a ring corresponding to a ring of squares located around the buoy in question at a distance of n squares to m squares inclusive, ordered according to the growth of k, and between shift operators of identical values ​​of k, according to the decrease of n, and between shift operators of identical values ​​of k and n, according to the growth of m.

[0048] Alternatively, the increasing ordered list of neighborhood operators and / or the ordered sublist of neighborhood operators, includes k-swap exchange operators, corresponding to k exchanges of positions of k pair(s) of buoys, ordered according to the growth of k, and ordered after any possible offset operators.

[0049] According to one embodiment, the descent-through-exploration step of the current subneighborhood is a simple descent in which the first improving subconfiguration is chosen in the current subneighborhood.

[0050] Alternatively, the descent determination step by exploration of the current subneighborhood is a deep descent in which the best of the improving subconfigurations in the current subneighborhood is chosen. In one embodiment, the method includes a step of testing another stopping criterion including the attainment of a threshold computation time, and / or a number of iterations, and / or a successive number of iterations without improvement in the coverage rate, and / or an average improvement in the coverage rate over a successive number of iterations less than a threshold, and / or a threshold coverage rate attained.

[0051] According to one embodiment, a step of determining isolated buoys and modifying their position by relocation, using a contribution score.

[0052] In one embodiment, the geographical space is divided into a grid of square-shaped cells.

[0053] According to one embodiment, the geographical space is divided into a grid of hexagonal squares.

[0054] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0055] - Figure 1 schematically illustrates a method for developing and deploying an improved configuration of an active network of acoustic buoys over a geographical area divided into a grid of squares, according to one aspect of the invention;

[0056] - Figure 2 schematically illustrates a neighborhood operator, with a grid of square cells, according to one aspect of the invention; and

[0057] - Figure 3 schematically illustrates a neighborhood operator, with a grid of hexagonal cells, according to one aspect of the invention.

[0058] Across all figures, elements with identical references are similar.

[0059] Figure 1 schematically represents a method for developing and deploying an improved configuration of an active network of acoustic buoys over a geographical area divided into a grid of squares, according to one aspect of the invention,

[0060] We iteratively determine the improved configuration 1, and we perform the deployment 2 of the improved configuration.

[0061] We choose an initial Rinit configuration with an initial coverage rate TCinit, for example at random from acceptable configurations.

[0062] Alternatively, it is also possible to use Rinit as the initial configuration, the result of a known configuration development process, for example that disclosed in the article "An improved two-phase heuristic for active multistatic sonar networkconfiguration" by Owein Thuillier, Nicolas Le Josse, Alexandru-Liviu Olteanu, Marc Sevaux, and Hervé Tanguy, Expert Systems with Applications 238 (2024), page(s): 121985, issn: 0957-4174, doi: 10.1016 / j.eswa.2023.121985.

[0063] The improved configuration Rc of an active network of acoustic buoys is determined over a geographical space divided into a grid of squares, by the iterative steps of:

[0064] - update 3 of a current configuration Rc initialized by the initial configuration Rinit of initial coverage rate TCinit;

[0065] - determination 4 of a first neighborhood V1 corresponding to the application of a first neighborhood operator of an increasing ordered list LO of neighborhood operators;

[0066] - update 5 of a current neighborhood Vc initialized by the first current neighborhood V1;

[0067] - random selection 6 of a random RA configuration in the current neighborhood Vc; - determination 7 of an improved sub-configuration through the iterative steps of:

[0068] o update 8 of a current sub-configuration RD initialized by the random configuration RA;

[0069] o determination 9 of a first sub-neighborhood SV1 corresponding to the application of a first neighborhood operator of an increasing ordered sub-list SLO of neighborhood operators;

[0070] o update 10 of a current subneighborhood SVc initialized by the first current subneighborhood SV1;

[0071] o determination 11 of a candidate neighboring subconfiguration RL by exploration Expl of the current subneighborhood SVc;

[0072] o first test 12 if the coverage rate of the candidate sub-configuration RL is greater than the coverage rate of the current sub-configuration RD;

[0073] o if the first test 12 is negative, the coverage rate of the candidate sub-configuration RL being less than the coverage rate of the current sub-configuration RD, a second test 13 is carried out if all sub-neighborhoods have been explored;

[0074] o if the second test 13 is negative, we expand 14 the current subneighborhood by applying the next neighborhood operator of the ordered SLO sublist which is passed to the update 10 of the current subneighborhood SVc; o if the first test 12 is positive we assign the value of the candidate neighbor subconfiguration RL to the current subconfiguration RD by returning to the update step 8;

[0075] o if the second test 13 is positive, the coverage rate of the neighboring candidate sub-configuration RL being less than the coverage rate of the current sub-configuration RD, we leave the iterative determination step 7 for a third test step 16;

[0076] o third test 16 if the coverage rate of the current sub-configuration RD is greater than the coverage rate of the current configuration Rc;

[0077] o if the third test 16 is negative, the rate of the current configuration Rc being greater than the coverage rate of the current sub-configuration RD, a fourth test 17 is carried out if all sub-neighborhoods have been explored;

[0078] o if the fourth test 17 is negative, we widen 18 the current neighborhood Vc by applying the next neighborhood operator from the ordered list LO which is passed to the update 5 of the current neighborhood Vc;

[0079] o if the third test 16 is positive, the rate of the current configuration Rc being less than the coverage rate of the current sub-configuration RD, we update 3 the value of the current configuration Rc with the current sub-configuration RD; and o if the fourth test 17 is positive, we carry out the deployment 2 of the improved current configuration Rc.

[0080] By "a second test 13 is carried out if all sub-neighborhoods have been explored", it is understood that we check, by this second test 13, whether all sub-neighborhoods have been explored, and therefore, by "if the second test 13 is negative", it will be understood that all sub-neighborhoods have not yet been explored, and by "if the second test 13 is positive", it will be understood that all sub-neighborhoods have been explored.

[0081] In other words, the second test 13 verifies that all neighborhood operators in the ascending ordered SLO sublist have been unsuccessfully used to find an improving RD subconfiguration. If it is positive, this means that the ascending ordered SLO sublist is completely exhausted, and if it is negative, this means that this SLO sublist contains neighborhood operators that have not yet been used to explore subneighborhoods that would allow the discovery of an improving RD subconfiguration.

[0082] For example, this second test 13 is implemented by an algorithm for tracking the exploration state of neighborhood operators within the SLO sublist. Similarly, by "we perform a fourth test 17 if all subneighborhoods have been explored", it is understood that we check, by this fourth test 17, whether all subneighborhoods have been explored, and therefore, by "if the fourth test 17 is negative", it will be understood that all subneighborhoods have not yet been explored, and by "if the fourth test 17 is positive", it will be understood that all subneighborhoods have been explored.

[0083] This fourth test 17 is for example implemented by an algorithm for tracking the exploration state of neighborhood operators within the LO list.

[0084] Furthermore, by "first test 12 if the coverage rate of the candidate subconfiguration RL is greater than the coverage rate of the current subconfiguration RD", it is understood that we check, by this first test 12, whether the coverage rate of the candidate subconfiguration RL is greater than the coverage rate of the current subconfiguration RD.

[0085] By "third test 16 if the coverage rate of the current sub-configuration RD is greater than the coverage rate of the current configuration Rc", it is understood that we check, by this third test 16, whether the coverage rate of the current sub-configuration RD is greater than the coverage rate of the current configuration Rc.

[0086] The coverage rate is for example clearly defined in the article ("An improved two-phase heuristic for active multistatic sonar network configuration", by Owein Thuillier, Nicolas Le Josse, Alexandru-Liviu Olteanu, Marc Sevaux, and Hervé Tanguy, Expert in Systems with Applications 238 (2024), page(s): 121985, issn: 0957-4174, doi: 10.1016 / j.eswa.2023.121985).

[0087] The coverage rate is defined as the proportion of maritime squares covered by the multistatic sonar array. A square is considered covered if the probability of detecting a target positioned at the center of that square is greater than a threshold <|) (in our case, for example, <|) = 0.95). The probability of detecting a target at the center of a square t is calculated by the following steps:

[0088] - For a compatible transmitter s / receiver r pair;

[0089] - Calculate the equivalent acoustic distance:

[0090]

[0091] Calculate the probability of detection: Pd(t,s,r) (equation 7 from the cited article):

[0092]

[0093] - The probability of detecting a target positioned in the middle of cell t by the co network is calculated by assuming that it would be detected at least once (equation 8 of the cited article):

[0094]

[0095] >

[0096] - The number of squares covered is calculated using the following formula (equation 3 from the cited article):

[0097]

[0098] - I being the function that returns 1 if the argument is greater than a cp value, and 0 otherwise.

[0099] The increasing ordered LO list of neighborhood operators and / or the ordered SLO sublist of neighborhood operators, includes knm-shift operators, corresponding to the shift of k buoy(s) in a ring corresponding to a ring of squares located around the buoy in question at a distance of n squares to m squares inclusive, ordered according to the growth of k, and between shift operators of identical values ​​of k, according to the decrease of n, and between shift operators of identical values ​​of k and n, according to the growth of m.

[0100] The increasing ordered LO list of neighborhood operators and / or the ordered sublist (SLO) of neighborhood operators, includes k-swap exchange operators, corresponding to k exchanges of positions of k pair(s) of buoys, ordered according to the growth of k, and ordered after any offset operators.

[0101] The current subneighborhood exploration descent step is a simple descent in which the first improving subconfiguration in the current subneighborhood is chosen. Alternatively, the current subneighborhood exploration descent step is a deep descent in which the best subconfiguration in the current subneighborhood is chosen. The process may include a step 19 for testing another stopping criterion, including reaching a threshold computation time, and / or a number of iterations, and / or a successive number of iterations without improvement in the coverage rate TC, and / or an average improvement in the coverage rate over a successive number of iterations below a threshold, and / or a threshold coverage rate being reached. In which case, the value of the current subconfiguration RD is assigned to the current configuration Rc.

[0102] The process may include a step 20 of determining isolated buoys and modifying their position by relocation, using a contribution score.

[0103] Relocation uses the calculation of a contribution score or isolation score for a given buoy and its comparison to a threshold. If the score is below the threshold, then the buoy is considered isolated (i.e., it contributes little or nothing to the detection of targets in the area).

[0104] In more detail, the relocation is carried out as follows:

[0105] - We choose a buoy to test b. This choice can be made by drawing lots, or by a systematic analysis of all the buoys.

[0106] - We calculate the list L(b) of all sonar systems having at least one source or receiver belonging to the buoy of interest.

[0107] - The contribution score S is calculated as follows:

[0108] o S = sum h(7' s / r ' ) (t)) P our all the boxes t (where a target could be located), and for all sonar systems (source-receiver s,r) in the list L(b)

[0109] The function h is calculated by: h(p) = log (1-p) / log(1 -<())

[0110] o <|) is a parameter representing the desired detection probability at each point (threshold), and for example chosen to be 0.95. If the score S is less than a threshold epsilon, the buoy b is relocated.

[0111] o The epsilon threshold is a parameter of the algorithm.

[0112] The epsilon threshold value can be high, to systematically move an isolated buoy. However, the higher the threshold, the less chance there is of finding new configurations (sonars will not be able to be temporarily isolated).

[0113] o Buoy b will be moved in the vicinity of a compatible buoy bc (Tx or TxRx if b is Rx or TxRx, and Rx or TxRx if b is Tx or TxRx, and in compatible frequency ranges)

[0114] ■ We calculate the LC(b) list of compatible buoys

[0115] ■ A buoy bc is randomly selected in LC(b) ■ A neighborhood V(bc) of type 1-5-1 shift is calculated around the buoy bc

[0116] ■ A position is randomly selected in the neighborhood V(bc) ■ Buoy b is moved to this position (in this way, we ensure that its contribution in its new location will be significant)

[0117] Geographic space can be divided into a grid of square boxes, as illustrated in Figure 2, or into a grid of hexagonal boxes, as illustrated in Figure 3, or into boxes of other shapes.

[0118] The present invention allows for simplicity, with few parameters, and the use of a choice between a few variants (deterministic or stochastic operation, systematic or limited local search).

[0119] This is a process for which the computational budget can be fixed in advance, and the best solution obtained at the end of this computational budget can be accepted (an "anytime" algorithm).

[0120] The method of the invention allows a choice of expressive and varied neighborhoods, which allows for an efficient exploration of the solution space and a better quality of solutions.

[0121] The solutions proposed by this algorithm are close enough to the optimum to be used by an aircraft dropping a network of acoustic buoys.

[0122] The neighborhood variation method allows for the generation of multiple candidate solutions with varying characteristics. This gives an operator a choice between several solutions of equivalent quality.

[0123] The reliability of the proposed process could make it possible to consider integrating such a system into a drone operating autonomously, without human supervision.

[0124] The process is ideally implemented on an onboard computer (with a programming language adapted to scientific calculations), and connected to the mission system: - The operator inputs the tactical situation, and in particular the area of ​​interest (its size) and the nature of the target being sought, as this determines the performance of the sonars available in the payload,

[0125] - The operator defines the calculation conditions: the discretized model, for example a square, rectangular, or hexagonal pattern; the cell size, for example: cells of 500m per side, or less, or more (for example, from 1 meter to 10 kilometers); and the computational budget (number of iterations, maximum computation time). - The mission system performs the optimization, using a neighborhood-based metaheuristic that utilizes the neighborhoods described previously.

[0126] - The operator chooses from the algorithm's proposals and transmits his order to the mission system, which plans the deployment of the buoys by the aircraft.

[0127] The aircraft could also be a remotely piloted or autonomously operating drone. In this case, the robustness of the process would allow the drone to automatically and autonomously calculate the optimal configuration for an acoustic buoy network.

Claims

DEMANDS 1. Method for developing (1) and deploying (2) an improved configuration (Rc) of an active network of acoustic buoys over a geographical area divided into a grid of squares, wherein: The improved configuration (1) is determined by the iterative steps of: - update (3) of a current configuration (Rc) initialized by an initial configuration (Rinit) of initial coverage rate (TCinit); - determination (4) of a first neighborhood (V1) corresponding to the application of a first neighborhood operator of an increasing ordered list (LO) of neighborhood operators; - update (5) of a current neighborhood (Vc) initialized by the first current neighborhood (V1); - random choice (6) of a random configuration (RA) in the current neighborhood (Vc); - determination (7) of an improved sub-configuration through the iterative steps of: o update (8) of a current subconfiguration (RD) initialized by the random configuration (RA); o determination (9) of a first sub-neighborhood (SV1) corresponding to the application of a first neighborhood operator of an increasing ordered sub-list (SLO) of neighborhood operators; o update (10) of a current subneighborhood (SVc) initialized by the first current subneighborhood (SV1); o determination (11) of a candidate neighboring subconfiguration (RL) by exploration (Expl) of the current subneighborhood (SVc); o first test (12) if the coverage rate of the candidate subconfiguration (RL) is greater than the coverage rate of the current subconfiguration (RD); o if the first test (12) is negative, the coverage rate of the candidate subconfiguration (RL) being less than the coverage rate of the current subconfiguration (RD), a second test (13) is performed if all subneighborhoods have been explored; o if the second test (13) is negative, we expand (14) the current subneighborhood by applying the next neighbor operator of the ordered sublist (SLO) which is passed to the update (10) of the current subneighborhood (SVc); o if the first test (12) is positive we assign the value of the candidate neighbor subconfiguration (RL) to the current subconfiguration (RD) by returning to the update step 8; o if the second test (13) is positive, the coverage rate of the neighboring candidate sub-configuration (RL) being less than the coverage rate of the current sub-configuration (RD), we leave the iterative determination step (7) for a third test step (16); o third test (16) if the coverage rate of the current subconfiguration (RD) is greater than the coverage rate of the current configuration (Rc); o if the third test (16) is negative, the rate of the current configuration (Rc) being greater than the coverage rate of the current sub-configuration (RD), a fourth test (17) is performed if all neighborhoods have been explored; o if the fourth test (17) is negative, we widen (18) the current neighborhood (Vc) by applying the next neighborhood operator from the ordered list (LO) which is passed to the update (5) of the current neighborhood (Vc); o if the third test (16) is positive, the current configuration rate (Rc) being less than the current sub-configuration coverage rate (RD), the value of the current configuration (Rc) is updated (3) with the current sub-configuration (RD); and o if the fourth test (17) is positive, the deployment (2) of the current configuration Rc improved is carried out.

2. A method according to claim 1, wherein the increasing ordered list (LO) of neighborhood operators and / or the ordered sublist (SLO) of neighborhood operators, comprises knm-shift operators, corresponding to the shift of k buoy(s) in a ring corresponding to a ring of squares located around the buoy in question at a distance of n squares to m squares inclusive, ordered according to the growth of k, and between shift operators of identical values ​​of k, according to the decrease of n, and between shift operators of identical values ​​of k and n, according to the growth of m.

3. A method according to claim 1 or 2, wherein the ascending ordered list (OL) of neighborhood operators and / or the ordered sublist (SOL) of neighborhood operators comprises k-swap exchange operators, corresponding to k17 exchanges of positions of k pair(s) of buoys, ordered according to the growth of k, and ordered after any shift operators.

4. A method according to any one of claims 1 to 3, wherein the determination step (11) by exploration descent (Expl) of the current subneighborhood (SVc) is a simple descent in which the first improving subconfiguration is chosen in the current subneighborhood.

5. A method according to any one of claims 1 to 3, wherein the determination step (11) by exploration descent (Expl) of the current subneighborhood (SVc) is a deep descent in which the best of the subconfigurations in the current subneighborhood is chosen.

6. A method according to any one of the preceding claims, comprising a test step (19) of another stopping criterion comprising reaching a threshold computation time, and / or a number of iterations, and / or a successive number of iterations without improvement in the coverage rate (CR), and / or an average improvement in the coverage rate over a successive number of iterations below a threshold, and / or a threshold coverage rate reached.

7. A method according to any one of the preceding claims, comprising a step of determining (20) isolated buoys and modifying their position by relocation, using a contribution score.

8. A method according to any one of claims 1 to 6, wherein the geographical space is divided into a grid of square-shaped cells.

9. A method according to any one of claims 1 to 6, wherein the geographical space is divided into a grid of hexagonal-shaped squares.

Images