An unmanned surface vehicle underwater target tracking method under sensor constraints

Abstract

The application discloses an unmanned ship underwater target tracking method under sensor constraint, comprising the following steps: S1, acquiring the parameter information of a ship-borne sensor, the motion parameter information of an unmanned ship, the motion parameter information of an underwater target and an expected tracking distance; S2, establishing a matrix game model according to the parameter information of the ship-borne sensor, the motion parameter of the unmanned ship and the motion parameter of the underwater target, and acquiring the comprehensive income of participants; S3, acquiring the expected distance between the unmanned ship and the underwater target based on the comprehensive income of the participants; S4, acquiring the expected speed and the expected heading of the unmanned ship based on a navigation following algorithm according to the updated expected distance between the unmanned ship and the underwater target, updating the motion parameter information of the unmanned ship, and tracking the underwater target according to the updated motion parameter information of the unmanned ship. The method can effectively detect and track the underwater target, has strong feasibility, and is suitable for unmanned ship underwater target tracking operation.

Description

Technical Field

[0001] This invention relates to the field of autonomous navigation planning and control technology, and in particular to a method for tracking underwater targets by unmanned surface vessels under sensor constraints. Background Technology

[0002] As an important component of the maritime system, unmanned surface vessels (USVs) demonstrate significant application value in areas such as marine environmental monitoring and underwater target tracking due to their autonomous operation capabilities. However, under the physical constraints of sensors, traditional navigation and following methods have significant technical bottlenecks: when current sonar is limited to a specific azimuth angle and a specific detection range, sudden maneuvers of the target will cause a motion coupling hysteresis effect in the following vessel, specifically manifested as a lag in course adjustment, causing the target to leave the sonar detection range.

[0003] While multi-vessel collaboration in existing solutions can expand detection coverage, it significantly increases system energy consumption. Therefore, there is an urgent need to construct a tracking architecture based on autonomous decision-making by a single vessel under sensor constraints to solve the tracking loss problem caused by large target maneuvers. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a sensor-constrained underwater target tracking method for unmanned surface vessels, addressing the deficiencies in the prior art.

[0005] The technical solution adopted by this invention to solve its technical problem is: This invention provides a sensor-constrained underwater target tracking method for unmanned surface vessels, comprising the following steps: S1: Acquire parameter information from the onboard sensors, motion parameters of the unmanned surface vessel, motion parameters of the underwater target, and the desired tracking distance; S2: Based on the parameter information of the onboard sensors, the motion parameters of the unmanned surface vessel, and the motion parameters of the underwater target, establish a matrix game model, and obtain the comprehensive benefits of the participants based on the matrix game model; S3: Based on the overall benefits to the participants, obtain the expected distance between the unmanned surface vessel and the underwater target; S4: Based on the updated expected distance between the unmanned surface vessel (USV) and the underwater target, obtain the expected speed and expected heading of the USV using the navigation and following algorithm, update the motion parameters of the USV, and track the underwater target based on the updated motion parameters of the USV.

[0006] Furthermore, the parameters obtained in step S1 of the present invention specifically include: The parameter information of the shipborne sensor includes: the detection range of the shipborne sensor. Detection angle of shipborne sensors ; The motion parameters of the unmanned surface vessel (USV) include: its position, speed, and heading; that is, acquiring all motion parameters of the USV is... , in Represents the x-coordinate value of the unmanned surface vessel in two-dimensional space. This represents the vertical coordinate value of the unmanned surface vessel in two-dimensional space. Indicates the speed of the unmanned surface vessel. Indicates the heading of the unmanned surface vessel; The underwater target's motion parameters include: the target's position, the target's speed, and the target's heading; that is, acquiring all the underwater target's motion parameter information is... , in Represents the x-coordinate value of the underwater target in two-dimensional space. Represents the ordinate value of the underwater target in two-dimensional space. Indicates the speed of an underwater target. Indicates the heading of an underwater target; The desired tracking distance is a constant defined by the tracking task. .

[0007] Furthermore, the matrix game model in step S2 of the present invention is specifically as follows: A matrix game model includes: players, a set of strategies, states, and a payoff function; where: The participants were unmanned surface vessels and underwater targets; The set of strategies adopted by the participants includes: unmanned surface vessel (USV) strategies and underwater target strategies; the USV strategies include maintaining the original expected tracking distance, reducing the original expected tracking distance by 20%, reducing the original expected tracking distance by 40%, reducing the original expected tracking distance by 60%, and reducing the original expected tracking distance by 80%; the underwater target strategies include maintaining the current course, turning counterclockwise relative to the current course, and turning the target clockwise relative to the current course. The status includes the current motion parameters of the unmanned surface vessel and the current motion parameters of the underwater target.

[0008] Furthermore, the comprehensive benefits for participants in step S2 of the present invention are specifically as follows: The overall payoff for all participants is the payoff function of the matrix game model, specifically:

[0009] In the formula, For heading consistency benefits, A weighting factor representing the benefits of consistent course alignment; To detect the profit, This represents the proportion of the gains from the exploration. Indicates the overall benefits for participants; in,

[0010] In the formula, This represents the absolute value of the difference between the unmanned surface vessel's heading and the underwater target's heading. Take the remainder;

[0011] In the formula, This indicates the relative distance between the unmanned surface vessel and the underwater target.

[0012] Furthermore, the method for obtaining the desired distance in step S3 of the present invention is specifically as follows: Based on the comprehensive payoff of the participants in the matrix game model, the mixed strategy Nash equilibrium is solved to obtain the strategy selection probability of the unmanned surface vessel (USV). Based on the strategy selection probability of the USV, the final strategy adopted by the USV is determined. Based on the strategy selection probability of the unmanned surface vessel (USV), the strategy when the strategy selection probability of the USV is maximized is obtained, and this strategy is taken as the final strategy adopted by the USV, thereby updating the expected distance between the USV and the underwater target.

[0013] Furthermore, the specific implementation method of step S3 of the present invention is as follows: The mixed Nash equilibrium in the matrix game model is solved by linear programming. The solution of the mixed Nash equilibrium corresponds to the selection probability of each strategy in the unmanned surface vessel's strategy set, thus obtaining the final strategy selection probability value of the unmanned surface vessel. The probabilities of the unmanned surface vessel's strategy selection are obtained as follows:

[0014] In the formula: The number of strategies that unmanned surface vessels can employ; The number of strategies that can be adopted for underwater targets; For unmanned surface vessels, take the first The probability of each strategy; For unmanned surface vessels, take the first The strategy and the objective are the first The overall return value for each strategy; To calculate the total revenue of the unmanned surface vessel during the process; This is the current probability value for each strategy chosen by the unmanned surface vessel during the solution process.

[0015] Furthermore, the method for obtaining the desired speed and desired heading of the unmanned surface vessel in step S4 of the present invention is specifically as follows: The lateral error of the tracking task is obtained as follows:

[0016] The lateral error of the tracking task is obtained as follows:

[0017] In the formula, The updated expected distance between the unmanned surface vessel and the underwater target; The desired angle between the unmanned surface vessel and the underwater target; The expected speed of the unmanned surface vessel in the next moment is obtained as follows:

[0018] The expected course of the unmanned surface vessel in the next moment is as follows:

[0019] In the formula, This is a hyperparameter.

[0020] Furthermore, the method for updating the motion parameter information of the unmanned surface vessel in step S4 of the present invention is specifically as follows: The motion parameters of the unmanned surface vessel will be updated as follows:

[0021]

[0022] In the formula, This represents the lateral position of the unmanned surface vessel at the next moment. This represents the longitudinal position of the unmanned surface vessel at the next moment.

[0023] This invention provides a sensor-constrained underwater target tracking system for unmanned surface vessels, comprising: Memory, used to store executable computer programs; The processor, when executing an executable computer program stored in the memory, implements the sensor-constrained underwater target tracking method for unmanned surface vessels.

[0024] The present invention provides a computer-readable storage medium, characterized in that it stores a computer program for implementing the sensor-constrained underwater target tracking method of an unmanned surface vessel when executed by a processor.

[0025] The beneficial effects of this invention are: This invention discloses an underwater target tracking method for unmanned surface vessels (USVs) under sensor constraints. The tracking distance is determined according to mission requirements. Based on the traditional navigation and following algorithm, a game theory model is established according to the motion parameters of the USV, the sensor parameters of the USV, and the target motion parameters. The optional strategies and payoff functions of the USV are set, and the tracking distance is dynamically adjusted by solving the Nash equilibrium. This ensures that the target will not leave the sensor detection range during large maneuvers and turns, and has strong feasibility, making it suitable for underwater target tracking operations of USVs.

[0026] This invention is particularly applicable to unmanned surface vessels (USVs) equipped with forward-looking sonar, whose sensor constraints mainly include maximum detection radius and horizontal detection angle. Different sensors, such as flank sonar arrays and towed sonar arrays, all share common constraints such as limited detection range and azimuth angle limitations. By adaptively adjusting the payoff function in the game theory model of this method, the detection parameters of different sensors can be adapted, exhibiting good scalability. Attached Figure Description

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of an underwater target tracking method for unmanned surface vessels under sensor constraints according to the present invention. Figure 2 This is a schematic diagram of the sensor detection range in an embodiment of the present invention; Figure 3 This is a schematic diagram a of the underwater target tracking process in an embodiment of the present invention; Figure 4 This is a schematic diagram (b) of the underwater target tracking process in an embodiment of the present invention; Figure 5 This is a schematic diagram (c) of the underwater target tracking process in an embodiment of the present invention; Figure 6 This is a probability distribution diagram of each strategy changing over time after solving the Nash equilibrium of the hybrid strategy in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] Example 1 This invention provides a sensor-constrained underwater target tracking method for unmanned surface vessels, specifically including the following steps: S1: Acquire parameter information from the onboard sensors, motion parameters of the unmanned surface vessel, motion parameters of the underwater target, and the desired tracking distance; The parameter information of the shipborne sensor includes the detection range of the shipborne sensor. Detection angle of shipborne sensors As attached Figure 1 As shown; The motion parameters of the unmanned surface vessel include its position, speed, and heading. The motion parameters of the underwater target include the target's position, target's speed, and target's heading; The desired tracking distance is a constant defined by the tracking task. ; Specifically, obtaining all motion parameter information of the unmanned surface vessel is as follows: , in Represents the x-coordinate value of the unmanned surface vessel in two-dimensional space. This represents the vertical coordinate value of the unmanned surface vessel in two-dimensional space. Indicates the speed of the unmanned surface vessel. Indicates the heading of the unmanned surface vessel; Obtain all motion parameter information of the underwater target as follows , in Represents the x-coordinate value of the underwater target in two-dimensional space. Represents the ordinate value of the underwater target in two-dimensional space. Indicates the speed of an underwater target. Indicates the heading of an underwater target; S2: Based on the parameter information of the onboard sensors, the motion parameters of the unmanned surface vessel, and the motion parameters of the underwater target, establish a matrix game model to obtain the comprehensive benefits of the participants; Specifically, the matrix game model includes participants, a set of strategies, states, and a payoff function; The participants in the matrix game model are unmanned surface vessels and underwater targets. The set of strategies adopted by the participants in the matrix game model includes unmanned surface vessel (USV) strategies and underwater target strategies. The USV strategies include maintaining the original expected tracking distance, reducing the original expected tracking distance by 20%, reducing the original expected tracking distance by 40%, reducing the original expected tracking distance by 60%, and reducing the original expected tracking distance by 80%. The target strategies include maintaining the current course, turning counterclockwise relative to the current course, and turning clockwise relative to the current course. The states in the matrix game model are the current motion parameters of the unmanned surface vessel and the current motion parameters of the underwater target. The payoffs in the matrix game model are obtained as follows:

[0030] In the formula, For heading consistency benefits, A weighting factor representing the benefits of consistent course alignment; To detect the profit, This represents the proportion of the gains from the exploration. This represents the overall return value; in,

[0031] In the formula, This represents the absolute value of the difference between the unmanned surface vessel's heading and the underwater target's heading. Take the remainder;

[0032] In the formula, Indicates the relative distance between the unmanned surface vessel and the underwater target; S3: Based on the overall benefits to the participants, obtain the expected distance between the unmanned surface vessel and the underwater target; Based on the combined payoffs of the participants' strategies in the matrix game model, the mixed-strategy Nash equilibrium is solved to obtain the strategy selection probability of the unmanned surface vessel (USV). Based on the strategy selection probability of the USV, the final strategy adopted by the USV is determined. Specifically, in this embodiment, the mixed Nash equilibrium in the matrix game model is solved using linear programming. The solution of the mixed Nash equilibrium corresponds to the selection probability of each strategy in the unmanned surface vessel's strategy set, thus obtaining the final strategy selection probability value of the unmanned surface vessel. Preferably, the strategy selection probability of the unmanned surface vessel is obtained as follows:

[0033] In the formula: The number of strategies that unmanned surface vessels can employ; The number of strategies that can be adopted to achieve the goal; For unmanned surface vessels, take the first The probability of each strategy; For unmanned surface vessels, take the first The strategy and the objective are the first The overall return value for each strategy; To calculate the total revenue of the unmanned surface vessel during the process; The current probability values ​​for each strategy chosen by the unmanned surface vessel during the solution process; Based on the strategy selection probability of the unmanned surface vessel (USV), the strategy when the strategy selection probability of the USV is maximized is obtained, and this strategy is taken as the final strategy adopted by the USV to update the expected distance between the USV and the underwater target. S4: Based on the updated expected distance between the unmanned surface vessel (USV) and the underwater target, obtain the expected speed and expected heading of the USV using the navigation and following algorithm, and update the motion parameters of the USV. Specifically, the lateral error of the tracking task is obtained as follows:

[0034] The lateral error of the tracking task is obtained as follows:

[0035] In the formula, The updated expected distance between the unmanned surface vessel and the underwater target; The desired angle between the unmanned surface vessel and the underwater target; The expected speed of the unmanned surface vessel in the next moment is obtained as follows:

[0036] The expected course of the unmanned surface vessel in the next moment is as follows:

[0037] In the formula, For hyperparameters; The motion parameters of the unmanned surface vessel will be updated as follows:

[0038]

[0039] In the formula, This represents the lateral position of the unmanned surface vessel at the next moment. This represents the longitudinal position of the unmanned surface vessel at the next moment.

[0040] Example 2 Appendix Figures 3-6 This is an embodiment of a simulation using Matlab of an unmanned surface vessel (USV) underwater target tracking method under sensor constraints according to the present invention. In this embodiment, the USV is equipped with a forward-looking sonar with a maximum detection range of 300m and a horizontal detection angle of 60°. In the game theory model, the USV's optional strategy set is {maintain original distance, reduce distance by 20%, reduce distance by 40%, reduce distance by 60%, reduce distance by 80%}; the target vessel's optional strategy set is {maintain heading, turn left with maximum angular velocity, turn right with maximum angular velocity}. In the payoff function, the heading consistency weight is set to 0.6; the detection payoff weight is set to 0.4. The game theory model solves its mixed-policy Nash equilibrium using linear programming, selecting the strategy with the highest probability as the USV's tracking distance command.

[0041] At the start of the mission, the unmanned surface vessel (USV) is located at (590, 774), with a heading angle of 69°, a speed of 5 m / s, and a maximum speed of 13 m / s; the underwater target is located at (686, 1057), with an initial heading angle of 71° and a constant speed of 5 m / s. The expected tracking distance is 250 m.

[0042] At the start of the mission, the underwater target was within the detection range of the unmanned surface vessel's (USV) sensors. 180 seconds into the mission, the target performed a large maneuver, turning 70° clockwise from its current course. At this point, the mission state changed significantly, and the payoff matrix of the game theory model abruptly changed. After solving for the Nash equilibrium, the USV was most likely to choose the strategy of "reducing the tracking distance by 40%". Based on this decision, the USV dynamically adjusted its desired tracking distance, reducing its speed to 2 m / s and simultaneously correcting its course to ensure the target remained within the effective detection range of the sensors. 205 seconds into the mission, the target completed the large maneuver, and the mission state was updated again. After solving for the Nash equilibrium strategy, the USV switched to the strategy of "maintaining the original tracking distance", gradually increasing its speed to 13 m / s, and then stabilizing at 5 m / s after recovering to the desired tracking distance, thus achieving continuous and stable tracking of the large maneuvering underwater target.

[0043] To visually demonstrate the game decision-making process, Figure 6 The probability distribution diagram of each strategy changing over time after solving the Nash equilibrium of the hybrid strategy is given, which clearly reflects the dynamic regulation effect of the Nash equilibrium on the tracking strategy in different task stages.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0045] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. For those skilled in the art, improvements or modifications can be made based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims of this invention.

Claims

1. A sensor-constrained underwater target tracking method for unmanned surface vessels, characterized in that, The method includes the following steps: S1: Acquire parameter information from the onboard sensors, motion parameters of the unmanned surface vessel, motion parameters of the underwater target, and the desired tracking distance; S2: Based on the parameter information of the onboard sensors, the motion parameters of the unmanned surface vessel, and the motion parameters of the underwater target, establish a matrix game model, and obtain the comprehensive benefits of the participants based on the matrix game model; S3: Based on the overall benefits to the participants, obtain the expected distance between the unmanned surface vessel and the underwater target; S4: Based on the updated expected distance between the unmanned surface vessel (USV) and the underwater target, obtain the expected speed and expected heading of the USV using the navigation and following algorithm, update the motion parameters of the USV, and track the underwater target based on the updated motion parameters of the USV.

2. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 1, characterized in that, The parameters obtained in step S1 specifically include: The parameter information of the shipborne sensor includes: the detection range of the shipborne sensor. Detection angle of shipborne sensors ; The motion parameters of the unmanned surface vessel (USV) include: its position, speed, and heading; that is, acquiring all motion parameters of the USV is... , in Represents the x-coordinate value of the unmanned surface vessel in two-dimensional space. This represents the vertical coordinate value of the unmanned surface vessel in two-dimensional space. Indicates the speed of the unmanned surface vessel. Indicates the heading of the unmanned surface vessel; The underwater target's motion parameters include: the target's position, the target's speed, and the target's heading; that is, acquiring all the underwater target's motion parameter information is... , in Represents the x-coordinate value of the underwater target in two-dimensional space. Represents the ordinate value of the underwater target in two-dimensional space. Indicates the speed of an underwater target. Indicates the heading of an underwater target; The desired tracking distance is a constant defined by the tracking task. .

3. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 2, characterized in that, The matrix game model in step S2 is specifically as follows: A matrix game model includes: players, a set of strategies, states, and a payoff function; where: The participants were unmanned surface vessels and underwater targets; The set of strategies adopted by the participants includes: unmanned surface vessel (USV) strategies and underwater target strategies; the USV strategies include maintaining the original expected tracking distance, reducing the original expected tracking distance by 20%, reducing the original expected tracking distance by 40%, reducing the original expected tracking distance by 60%, and reducing the original expected tracking distance by 80%; the underwater target strategies include maintaining the current course, turning counterclockwise relative to the current course, and turning the target clockwise relative to the current course. The status includes the current motion parameters of the unmanned surface vessel and the current motion parameters of the underwater target.

4. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 3, characterized in that, The specific comprehensive benefits for participants in step S2 are as follows: The overall payoff for all participants is the payoff function of the matrix game model, specifically: In the formula, For heading consistency benefits, A weighting factor representing the benefits of consistent course alignment; To detect the profit, This represents the proportion of the gains from the exploration. Indicates the overall benefits for participants; in, In the formula, This represents the absolute value of the difference between the unmanned surface vessel's heading and the underwater target's heading. Take the remainder; In the formula, This indicates the relative distance between the unmanned surface vessel and the underwater target.

5. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 1, characterized in that, The method for obtaining the desired distance in step S3 is as follows: Based on the comprehensive payoff of the participants in the matrix game model, the mixed strategy Nash equilibrium is solved to obtain the strategy selection probability of the unmanned surface vessel (USV). Based on the strategy selection probability of the USV, the final strategy adopted by the USV is determined. Based on the strategy selection probability of the unmanned surface vessel (USV), the strategy when the strategy selection probability of the USV is maximized is obtained, and this strategy is taken as the final strategy adopted by the USV, thereby updating the expected distance between the USV and the underwater target.

6. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 5, characterized in that, The specific implementation method of step S3 is as follows: The mixed Nash equilibrium in the matrix game model is solved by linear programming. The solution of the mixed Nash equilibrium corresponds to the selection probability of each strategy in the unmanned surface vessel's strategy set, thus obtaining the final strategy selection probability value of the unmanned surface vessel. The probabilities of the unmanned surface vessel's strategy selection are obtained as follows: In the formula: The number of strategies that unmanned surface vessels can employ; The number of strategies that can be adopted for underwater targets; For unmanned surface vessels, take the first The probability of each strategy; For unmanned surface vessels, take the first The strategy and the objective are the first The overall return value for each strategy; To calculate the total revenue of the unmanned surface vessel during the process; This is the current probability value for each strategy chosen by the unmanned surface vessel during the solution process.

7. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 6, characterized in that, The method for obtaining the desired speed and desired heading of the unmanned surface vessel in step S4 is as follows: The lateral error of the tracking task is obtained as follows: The lateral error of the tracking task is obtained as follows: In the formula, The updated expected distance between the unmanned surface vessel and the underwater target; The desired angle between the unmanned surface vessel and the underwater target; The expected speed of the unmanned surface vessel in the next moment is obtained as follows: The expected course of the unmanned surface vessel in the next moment is as follows: In the formula, This is a hyperparameter.

8. The underwater target tracking method for unmanned surface vessels under sensor constraints according to claim 7, characterized in that, The method for updating the motion parameters of the unmanned surface vessel in step S4 is as follows: The motion parameters of the unmanned surface vessel will be updated as follows: In the formula, This represents the lateral position of the unmanned surface vessel at the next moment. This represents the longitudinal position of the unmanned surface vessel at the next moment.

9. A sensor-constrained underwater target tracking system for unmanned surface vessels, characterized in that, include: Memory, used to store executable computer programs; The processor, when executing an executable computer program stored in memory, implements the sensor-constrained underwater target tracking method for unmanned surface vessels as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the sensor-constrained underwater target tracking method for unmanned surface vessels as described in any one of claims 1 to 8.

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