Remote sensing satellite sea ship search method based on improved ant colony algorithm

Abstract

The application relates to a remote sensing satellite offshore ship searching method based on an improved ant colony algorithm and belongs to the technical field of imaging satellite ocean remote sensing, and solves the problem that the prior art cannot search for ships in time and quickly. Specifically, the method comprises the following steps: carrying out grid division on a sea area to be searched; obtaining displacement probability of a ship from a grid i to any reachable grid j at a next step at a current time based on the grid i where the ship is located at an initial time; obtaining transfer probability of an ant k from the grid i to any reachable grid j at the next step at the t n‑1 time; selecting a grid j' corresponding to maximum transfer probability, transferring the ant k to the grid j', updating a taboo list of the ant k, adding the grid j' to the taboo list of the ant k, and carrying out local updating of pheromone; obtaining an optimal path through ant colony searching based on the updated taboo list and the locally updated pheromone, and carrying out global updating of pheromone; and the remote sensing satellite searches for the ship based on the optimal path. The purpose of completely covering offshore ships and quickly searching for targets is achieved.

Description

Technical Field

[0001] This invention relates to the field of imaging satellite ocean remote sensing technology, and in particular to a remote sensing satellite method for searching for ships at sea based on an improved ant colony algorithm. Background Technology

[0002] High-orbit remote sensing satellites possess the advantages of wide sensor coverage and real-time visibility of targets within their coverage area. In peacetime, they can be used for searching for lost ships at sea and monitoring maritime hotspots; in wartime, they can be used for ship searches, playing a crucial role in the field of marine remote sensing. Traditional methods of searching large areas of sea are time-consuming and inefficient. Furthermore, it's possible that while searching area A, the target ship might be located in the adjacent area B, and then during a subsequent search of area B, the target ship might have sailed towards area A, resulting in the target being undetected. Summary of the Invention

[0003] Based on the above analysis, the embodiments of the present invention aim to provide a remote sensing satellite maritime vessel search method based on an improved ant colony algorithm, in order to solve the problem that existing methods cannot search for vessels in a timely and rapid manner.

[0004] This invention provides a remote sensing satellite method for searching for ships at sea based on an improved ant colony algorithm, specifically including:

[0005] The search area is divided into grids;

[0006] The displacement probability of the ship moving to any reachable grid j at the current moment is obtained based on the grid i where the ship is located at the initial moment;

[0007] Based on the displacement probability, t is obtained. n-1 The probability of ant k moving from grid i to any reachable grid j at time step;

[0008] Select the grid j' with the highest transition probability, move ant k to grid j', update ant k's tabu list, add grid j' to ant k's tabu list, and perform a local pheromone update.

[0009] Ant colony search is performed based on the updated taboo list and locally updated pheromones to obtain the optimal path, and then the pheromones are updated globally.

[0010] The remote sensing satellite searches for ships based on the optimal path.

[0011] Optionally, the displacement probability of the ship moving to any reachable grid j at the next moment based on the grid i where the ship is initially located includes:

[0012] Ship t n-1 At time i, after time t n-t n-1 Time in t n The displacement probability of moving to the grid j at the moment t is:

[0013]

[0014] Wherein: t n -t n-1 =m n Δt, m n is the calculation step, Δt is the time base unit, wherein Δt takes 1s;

[0015] r e is the radius of the earth, is the estimated speed of the ship; (x n-1 ,y n-1 ,z n-1 ) is the center point coordinate of the grid i; (x n ,y n ,z n ) is the center point coordinate of the grid j.

[0016] Optionally, the transition probability of the ant k from the grid i to any reachable grid j at the moment t is obtained based on the displacement probability, comprising:

[0017] The path distance d ij between the grid i and any reachable grid j is calculated;

[0018] The transition probability is obtained based on the path distance and the displacement probability by using the following formula:

[0019]

[0020] Wherein, α is a preset weight parameter; tabu k is the tabu list of the ant k, after the ant k passes through the grid j, the grid j is added to the tabu k , which indicates that the grid j cannot be selected to move next time; τ ij (t) represents the amount of pheromone between the grids i and j at the moment t; d ij is the path distance from the grid i to the grid j; d is is the path distance from the grid i to the grid s; τ is (t) represents the amount of pheromone between the grids i and s at the moment t; n represents the probability of the ant k moving from the grid i to the grid j at the moment t.

[0021] ​Optionally, the grid j' corresponding to the maximum transition probability is selected, the ant k is transferred to the grid j', the taboo list of the ant k is updated, the grid j' is added to the taboo list of the ant k, and local pheromone update is performed, including:

[0022] Initialize algorithm parameters; set the initial cycle number Nc=0, set the maximum cycle number Nmax, initialize the iteration coefficient k=0, and set the longest running time T of the algorithm; the initial amount of pheromone τ ij1 =C; randomly place m different ants in the grid;

[0023] Set the ant taboo list; the taboo list is used to record the grids that have been walked by the ant, and the first element of the taboo list of each ant is set to the initial grid where the ant is located. During one cycle of the algorithm, the ant cannot repeatedly select the grids in the taboo list;

[0024] Calculate the grid j' corresponding to the maximum transition probability according to the ant k transition probability formula;

[0025] Add the grid j' corresponding to the maximum transition probability to the ant taboo list, and update the pheromone of the path selected by the ant.

[0026] Optionally, the optimal path is obtained by performing ant colony search based on the updated taboo list and the locally updated pheromone, and global pheromone update includes:

[0027] According to the path obtained by each ant, the total distance of the path of each ant is obtained;

[0028] Compare the total distance of the path of each ant to obtain the shortest path as the optimal path;

[0029] Update the pheromone of all sub-paths in the optimal path; wherein the global pheromone update includes pheromone update and local pheromone update of all sub-paths in the optimal path.

[0030] Optionally, the path distance d ij between the grid i and any reachable grid j is calculated.

[0031]

[0032] Wherein, is the satellite attitude maneuvering rate.

[0033] Optionally, the local pheromone update includes:

[0034] τ i,j =(1-ξ)τ i,j +ξτ0

[0035] Wherein, τ0 is the pheromone increment, and ξ is a preset parameter.

[0036] Optionally, the pheromone updating of all sub-paths in the optimal path comprises:

[0037] The pheromone on the optimal path is updated based on the local pheromone updating:

[0038]

[0039] Wherein, τ i,j' represents the pheromone of the sub-path of the optimal path, is the increment of the pheromone on the optimal path, and ρ is a preset parameter.

[0040] The increment of the pheromone on the optimal path is:

[0041]

[0042] Wherein, L gb represents the optimal path, and ρ is a preset parameter.

[0043] Optionally, the local pheromone comprises the original pheromone on the path before the ants walk and the pheromone left after the current ants walk.

[0044] Optionally, the search of all ants is taken as a circulation process, in which, based on the maximum transition probability calculated, the ants are moved to the grid with the maximum transition probability and the ant tabu list is updated to obtain a new ant tabu list, and the circulation process is started again.

[0045] Compared with the prior art, the present application can realize at least one of the following beneficial effects:

[0046] 1. The transition probability between different positions of the ship in a large sea area is calculated, the transition probability of the ant colony algorithm is improved through the calculated transition probability, the ant colony algorithm is used to design the scanning path of the satellite sensor, and the target is searched as soon as possible.

[0047] 2. The position at the next moment is obtained by using the coordinate time method according to the initial position of the ship, and the optimal path is obtained by using the ant colony algorithm, the optimal path information is transmitted to the satellite, and thus the complete coverage of the ship at sea and the fast target search are realized.

[0048] In the present application, the above technical solutions can be combined with each other to realize more preferred combination solutions. Other features and advantages of the present application will be set forth in the subsequent description, and some advantages can become apparent from the description, or can be understood by implementing the present application. The objects and other advantages of the present application can be realized and obtained from the contents particularly pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0050] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the global multi-target ant colony algorithm in an embodiment of the present invention. Detailed Implementation

[0052] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0053] A specific embodiment of the present invention discloses a remote sensing satellite method for searching for ships at sea based on an improved ant colony algorithm, such as... Figure 1 As shown, it specifically includes:

[0054] The search area is divided into grids;

[0055] The displacement probability of the ship moving to any reachable grid j at the current moment is obtained based on the grid i where the ship is located at the initial moment;

[0056] Based on the displacement probability, t is obtained. n-1 The probability of ant k moving from grid i to any reachable grid j at time step;

[0057] Select the grid j' with the highest transition probability, move ant k to grid j', update ant k's tabu list, add grid j' to ant k's tabu list, and perform a local pheromone update.

[0058] Ant colony search is performed based on the updated taboo list and locally updated pheromones to obtain the optimal path, and then the pheromones are updated globally.

[0059] The remote sensing satellite searches for ships based on the optimal path. The high-orbit remote sensing satellite determines the search order of the grid within the sea area according to the optimal path and performs the ship search.

[0060] Specifically, the search area R is divided into a grid set according to preset rules (e.g., 100 km × 100 km). In a three-dimensional Cartesian coordinate system, the ship t... n-1 At time i, the coordinates of the grid center point (x) n-1 ,y n-1 ,z n-1 ), after t n -t n-1 Time, in tn The transfer probability of time instant to the grid j, the grid center point coordinate (x n ,y n ,z n ) is:

[0061]

[0062] Wherein: t n -t n-1 =m n Delta t, m n is the calculation step, Delta t is the time base unit, wherein,

[0063] Delta t takes 1s;

[0064] R e is the earth radius, is the ship estimated speed, is a preset constant;(x n-1 ,y n-1 ,z n-1 ) is the center point coordinate of the grid i;(x n ,y n ,z n ) is the center point coordinate of the grid j.

[0065] As Figure 2 shown, in the application of ant colony algorithm, the process that ants transfer between different grids to find the optimal search path of the sensor, in addition to considering the calculated path distance, making the total distance of the search path shorter (i.e. completing the full coverage of the search sea area as soon as possible), the calculated ship transfer probability between different grids is also considered, that is, the optimal path search process of the ant considers the motion characteristics of the ship, and the ant colony algorithm is improved according to the above, and finally the time t n The probability that the ant k transfers from the grid i to the grid j.

[0066] Specifically, the transfer probability of the ship between different grids i, j contained in the to-be-searched R can be calculated by formula (1), and the optimal ship search path in the sea area is found by the ant colony algorithm, so that the success rate of searching the ship is higher, and the time consumed for completing the coverage of the search sea area is shorter.

[0067] The path distance between the grids i, j can be represented by the time consumed for the satellite to perform attitude maneuver from pointing to the grid i to pointing to the grid j:

[0068]

[0069] Wherein, is the satellite attitude maneuver speed.

[0070] Based on the path distance and displacement probability, a transition probability is obtained by using the following formula

[0071]

[0072] wherein, a is a preset weight parameter; tabu k is a tabu table of the ant k, after the ant k passes through the grid j, the grid j is added to the tabu k , which indicates that the grid j cannot be selected again next time; τ ij (t) represents the amount of pheromone between the grids i and j at time t; d ij is the time consumption of the attitude maneuver of the grid i to the grid j; d is is the time consumption of the attitude maneuver of the grid i to the grid s; τ is (t) represents the amount of pheromone between the grids i and s at time t; d n represents the probability of the ant k transferring from the grid i to the grid j at time t.

[0073] According to the selection of the grid j' corresponding to the maximum transition probability, the ant k is transferred to the grid j', the tabu table of the ant k is updated, the grid j' is added to the tabu table of the ant k, and local pheromone updating is performed, including:

[0074] initializing algorithm parameters; setting an initial time cycle number Nc=0, and setting a maximum cycle number Nmax, initializing an iteration coefficient k=0, and an algorithm running longest time consumption T; an initial pheromone amount τ ij1 =C; m different ants are randomly placed in the grid;

[0075] setting an ant tabu table; the tabu table is used for recording the grids that have been passed through by the ant, and the first element of the tabu table of each ant is set as the initial grid where the ant is located, and in the process of one cycle of the algorithm, the ant cannot repeatedly select the grid in the tabu table;

[0076] calculating the grid j' with the maximum transition probability according to the ant k transition probability formula;

[0077] adding the grid j' with the maximum transition probability to the ant tabu table, and updating the pheromone of the path selected by each ant.

[0078] In this process, the optimal path is obtained by performing ant colony search based on the updated tabu table and the locally updated pheromone, and global pheromone updating is performed, including:

[0079] obtaining the total distance of the path of each ant according to the path obtained by each ant;

[0080] comparing the path with the shortest total distance of the path of each ant as the optimal path;​

[0081] updating pheromone of all sub-paths in the optimal path; wherein the global pheromone updating includes pheromone updating and local pheromone updating of all sub-paths in the optimal path.

[0082] The local pheromone includes original pheromone on the path before the ants walk and pheromone left after the current ants walk.

[0083] The local pheromone updating includes:

[0084] τ i,j = (1-ξ)τ i,j +ξτ0

[0085] Wherein τ0 is pheromone increment, and ξ is a preset parameter.

[0086] The pheromone on the optimal path is updated based on the local pheromone updating:

[0087]

[0088] Wherein τ i,j' represents pheromone of the sub-path of the optimal path, is pheromone increment of the optimal path, and ρ is a preset parameter.

[0089] The pheromone increment of the optimal path is:

[0090]

[0091] Wherein L gb represents the optimal path, and ρ is a preset parameter.

[0092] When all ants complete a search as a cycle process, in the cycle process, based on the calculated maximum transition probability, the ants are moved to the maximum transition probability grid while updating the ant taboo list to obtain a new ant taboo list, and a new cycle process is started.

[0093] The new cycle process takes the current grid j' as the initial grid; when all grids are traversed and all ants complete the search, the search is ended. After the search is ended, the path total distance of each ant is calculated by comparing the path obtained by each ant, and the shortest path is selected as the optimal path.

[0094] Example 1

[0095] (1) Initialize algorithm parameters. Set initial cycle number Nc=0, set maximum cycle number Nmax, k=0, longest running time T of the algorithm; pheromone initial concentration τ ij=C; randomly place m different ants into the grid; in this method, the path of the ants walking between the grids represents the search order of the high-orbit remote sensing satellite for the grids in the sea area.

[0096] (2) Increase the number of cycles, i.e. Nc=Nc+1.

[0097] (3) Initialize the ant tabu list, which is used to record the grids that have been walked through by the ants, and set the first element of the tabu list of each ant to the initial grid where the ant is located. During one cycle of the algorithm, the ant cannot select the grids in the tabu list again.

[0098] (4) k=k+1.

[0099] (5) The kth ant calculates the transition probability of the ant from the current grid i to all the reachable grids (i.e. the next grids that can be imaged by the satellite sensor) according to (formula 3), selects the grid j' with the maximum transition probability, and transfers the ant k to the grid j', updates the tabu list of the ant k, and adds the grid j' to the tabu list of the ant k.

[0100] (6) Update the amount of pheromone on the path locally.

[0101] The pheromone on the path is updated after each node is selected by the ant. The pheromone on the current path mainly includes two parts: the original pheromone on the path before the current ant walks, and the pheromone left by the current ant after walking. The former gradually volatilizes and disappears as time moves forward, and the volatilization amount is related to the volatilization factor of the pheromone; the latter is newly added, and the amount of increase is related to the heuristic information on the path. The sum of the two is the total amount of pheromone on the path after local updating. The local pheromone updating formula is as follows:

[0102] τ i,j = (1-ξ)τ i,j + ξτ0

[0103] Where τ0 is the pheromone increment, and ξ is a preset parameter.

[0104] (7) If the running time of the current algorithm is less than T, go to step (4) and continue to execute, otherwise go to step (8).

[0105] (8) If all the grids have not been traversed, go to step (5) and continue to execute, otherwise go to step (9).

[0106] (9) If the number of ants k=m is true, i.e. all the ants have completed the search, go to step (10), otherwise go to step (4).

[0107] (10) compare the path of each ant, according to (formula 2) to calculate the total distance of each ant path, select the shortest path as the optimal path L gb , update the pheromone of all sub-paths contained in the optimal path L gb :

[0108]

[0109]

[0110] Wherein, τ i,j' represents the pheromone of the optimal path sub-path, is the increment of the pheromone on the optimal path, and ρ is a preset parameter.

[0111] If Nc≥Nmax, the loop ends, and the result is output; otherwise, the information in the tabu table is emptied, and Δτ i,j (the pheromone increment) between each grid is reset to 0, and step (2) is continued to continue the algorithm loop.

[0112] The ship transfer probability between different positions in a large area of sea is calculated, the transfer probability of the ant colony algorithm is improved by the calculated transfer probability, the satellite sensor scanning path is designed by using the ant colony algorithm, and the target is searched as soon as possible.

[0113] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. The computer readable storage medium is a disk, an optical disk, a read-only memory or a random access memory, etc.

[0114] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A remote sensing satellite method for searching for ships at sea based on an improved ant colony algorithm, characterized in that, include: The search area is divided into grids; The displacement probability of the ship moving to any reachable grid j at the current moment is obtained based on the grid i where the ship is located at the initial moment; Based on the displacement probability, t is obtained. n-1 The probability of ant k moving from grid i to any reachable grid j at time step; Select the grid j' with the highest transition probability, move ant k to grid j', update ant k's tabu list, add grid j' to ant k's tabu list, and perform a local pheromone update. Ant colony search is performed based on the updated taboo list and locally updated pheromones to obtain the optimal path, and then the pheromones are updated globally. The remote sensing satellite searches for ships based on the optimal path; The steps of selecting the grid j' corresponding to the maximum transition probability, transferring ant k to grid j', updating ant k's tabu list, adding grid j' to ant k's tabu list, and performing a local pheromone update include: Initialize algorithm parameters; set the initial number of iterations Nc = 0, the maximum number of iterations Nmax, the iteration coefficient k = 0, and the maximum execution time T; initial pheromone quantity τ. ij1 =C; Randomly place m different ants into the grid; Set up an ant tabu list; the tabu list is used to record the grids that the ant has already visited. Set the first element of the tabu list of each ant to the initial grid where the ant is located. During one loop of the algorithm, the ant cannot repeatedly select a grid in the tabu list. The grid j' with the maximum transfer probability is calculated based on the ant k transfer probability formula; Add the grid j' with the highest transition probability to the ant tabu list, and update the pheromone for each node chosen by the ant; Each search completed by all ants constitutes one loop. During one loop, based on the calculated maximum transition probability, the ants are moved to the grid with the maximum transition probability, and the ant tabu table is updated to obtain a new ant tabu table, thus starting another loop.

2. The maritime vessel search method based on the improved ant colony algorithm according to claim 1, characterized in that, The displacement probability of the ship moving to any reachable grid j at the next moment, based on the grid i where the ship is initially located, includes: Ship t n-1 At time i, after time t n -t n-1 Time in t n The probability of displacement at time j is: Where: t n -t n-1 =m n Δt,m n To calculate the step size, Δt is the time base unit, where Δt is taken as 1 second; r e For the Earth's radius, Estimate the ship's speed; (x n-1 ,y n-1 ,z n-1 (x) represents the coordinates of the center point of grid i; n ,y n ,z n ) represents the coordinates of the center point of grid j.

3. The maritime vessel search method based on the improved ant colony algorithm according to claim 2, characterized in that, The transition probability of ant k moving from grid i to any reachable grid j at time t based on the displacement probability includes: Calculate the path distance d between grid i and any reachable grid j. ij ; Based on the path distance and displacement probability, the transition probability is obtained using the following formula. Where α is the preset weight parameter; tabu k Given the taboo list for ant k, after ant k passes through grid j, it adds grid j to the tabu list. k In the middle, it means that grid j cannot be selected for movement again in the next move; τ ij (t) represents the pheromone content between grids i and j at time t; d ij d is the path distance from grid i to grid j; is τ is the path distance from grid i to grid s; is (t) represents time t n The amount of pheromone between grids i and s; Let represent the probability that ant k moves from grid i to grid j at time t.

4. The remote sensing satellite maritime vessel search method based on the improved ant colony algorithm according to claim 1, characterized in that, The step of obtaining the optimal path through ant colony search based on the updated taboo table and locally updated pheromones, and then performing a global pheromone update, includes: Based on the path obtained by each ant, the total distance of each ant's path is obtained; The path with the shortest total distance for each ant is selected as the optimal path. The pheromone is updated for all sub-paths in the optimal path; the global pheromone update includes updating the pheromone for all sub-paths in the optimal path and updating the local pheromone.

5. The maritime vessel search method based on the improved ant colony algorithm according to claim 3, characterized in that, The path distance d between computation grid i and any reachable grid j ij include: in, This represents the satellite's attitude maneuver rate.

6. The maritime vessel search method based on the improved ant colony algorithm according to claim 4, characterized in that, The local pheromone update includes: t i,j =(1-ξ)τ i,j +ξτ0 Where τ0 is the pheromone increment and ξ is a preset parameter.

7. The maritime vessel search method based on the improved ant colony algorithm according to claim 4, characterized in that, The step of updating the pheromone content of all sub-paths in the optimal path includes: Update the pheromone levels based on the optimal path obtained from the local pheromone update: Where, τ i,j' The pheromone representing the sub-path of the optimal path. ρ is the increment of pheromone on the optimal path, and it is a preset parameter. The pheromone increment for the optimal path is: Among them, L gb This represents the optimal path, where ρ is a preset parameter.

8. The maritime vessel search method based on the improved ant colony algorithm according to claim 4, characterized in that, The local pheromones include the pheromones that were present on the path before the ant left and the pheromones left behind after the ant has passed.

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