An optimized layout method for multi-static underwater detection based on particle swarm algorithm

Abstract

The present invention discloses an optimized layout method for multi-base underwater detection based on the particle swarm algorithm, which belongs to the technical field of underwater target detection. The present invention includes the following steps: determining the optimization objective, which is to maximize the effective coverage rate; making simulation assumptions, defining constraint conditions, and performing optimal layout optimization through the particle swarm algorithm. Initialize the particle swarm: randomly generate a group of particles, and each particle represents a possible base station layout scheme; conduct simulation verification and analyze the results of the simulation experiment. The present invention realizes the maximization of the effective coverage area during underwater target detection, efficiently utilizes resources, and improves the accuracy and effectiveness of target detection. After defining the constraint conditions, the present invention conducts simulation verification through the particle swarm algorithm. For the layout scheme optimized by the particle swarm algorithm, the effective coverage rate is optimized, significantly improving the detection efficiency of the system. Finally, the optimal solution is verified through the iteratively optimized algorithm to determine the optimal effective coverage rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater target detection, and more specifically, relates to a multi-base underwater detection optimization layout method based on a particle swarm algorithm. Background Art

[0002] When detecting underwater targets, a sonar detection system is usually used for target detection. In underwater target detection, a single-base sonar system usually has a low detection probability, so multi-base sonar detection is often used. Although multi-base collaborative detection can greatly improve the probability and efficiency of detection, it is also subject to many constraints. The area of ​​a multi-base sonar detection system is closely related to the location of each base, and the location of the multi-base sonar detection system needs to be reasonably set to ensure that the entire detection system has a high detection probability.

[0003] At present, the use of multi-base collaborative detection has a low coverage rate and is subject to many constraints, such as the location and coordinate layout of the transmitting station and the receiving station. Once the location of the transmitting station and the receiving station changes, the detection coverage rate will change, and the layout of the transmitting station and the receiving station requires huge costs. Therefore, how to use limited transmitting stations and receiving stations to obtain the maximum detection coverage area has been a problem that has been plaguing the field. Summary of the invention

[0004] The present invention aims at solving the technical problems existing in the prior art and provides a multi-base underwater detection optimization layout method based on particle swarm algorithm.

[0005] To solve the above technical problems, the present invention comprises the following steps:

[0006] S1. Determine the optimization goal, which is to maximize the effective coverage rate;

[0007] S2. Make simulation assumptions and define constraints, which include: island shielding, sonar communication distance, and number of base stations;

[0008] S3, optimal layout optimization through particle swarm algorithm, including:

[0009] Initialize the particle swarm: randomly generate a group of particles, each particle represents a possible base station layout solution;

[0010] Calculate the fitness function: Use the inverse of the effective coverage area as the fitness function to evaluate the quality of each particle;

[0011] Update particle position: Update the particle position according to the particle's current position, speed and historical optimal position;

[0012] Iterative optimization: repeatedly calculate the fitness function and update the particle position until the convergence condition is met;

[0013] S4. Conduct simulation verification and analyze the results of the simulation experiment.

[0014] Preferably, the effective coverage rate is associated with the effective coverage area. The effective coverage area is defined as the area where the detection probability is greater than or equal to 0.7, and the effective coverage rate is defined as the ratio of the area where the detection probability is greater than or equal to 0.7 to the entire area. Its expression is:

[0015] Effective coverage rate = (Area where detection probability ≥ 0.7) / Entire area.

[0016] Preferably, when detecting underwater targets, the detection base stations of the base include a transmitting station and a receiving station. Three constraint conditions are defined as:

[0017] Island shielding: Assume the island is circular and sound waves propagate in a straight line. When the line connecting the transmitting station and the receiving station passes through the island, it is considered shielded and the receiving station cannot receive sound waves for detection;

[0018] Sonar communication distance: The working radius is limited between 3 km and 8 km;

[0019] Number of base stations: Assume the number of transmitting stations is 2 and the number of receiving stations is 6.

[0020] Preferably, assume the experimental scenario is around the island. Target detection is carried out through 2 transmitting stations and 6 receiving stations. The two transmitting stations are located on both sides of the island, and the transmitting stations and receiving stations are evenly distributed on different concentric circles. Due to the symmetry of the assumed layout, the optimization parameters can be changed from coordinate points to the radii of the concentric circles where the transmitting stations and receiving stations are located;

[0021] When assuming the location of the island shielding in the scenario, the included angle between the island, the transmitting station and the receiving station is used as the judgment criterion. Assume the scenario includes: transmitting station T , receiving station R , island center O and the tangent point of the transmitting station and the island D * , where the coordinates of the transmitting station are ( x T , y T ), the coordinates of the receiving station are ( x R , y R ), the coordinates of the island center are ( x O , y O ), and the coordinates of the tangent point of the transmitting station and the island are ( x D*, y D* );

[0022] Record , , , , α =∠ OTD * , , then and satisfy:

[0023]

[0024]

[0025] When , it is considered that the receiving station is masked by the island and cannot form a detection system with the transmitting station.

[0026] Preferably, when using the particle swarm algorithm for optimal layout planning, the update formula for updating the particle position is:

[0027]

[0028]

[0029] In the formula, v i is the velocity of the particle, x i is the position of the particle, pbest i is the historical optimal position of the particle, gbest is the global optimal position, w is the inertia weight, c 1, c 2 are learning factors, r 1, r 2 are random numbers.

[0030] Preferably, use multiple unmanned underwater vehicles as the carriers of the receiving stations for detection, plan their cruise paths, and under the condition of considering the island masking effect, adopt the obtained base station layout structure, and obtain the detection range results of different initial angles by changing the initial angles of the receiving stations;

[0031] Optimize with the effective detection area as the index, study the relationship between the initial angle and the effective detection area, and calculate the corresponding effective coverage area at all angles.

[0032] Preferably, the receiving stations are evenly distributed, and it is calculated that the positional relationship of multiple unmanned underwater vehicles is periodic, and the number of repetitions between [0°, 360°] is the same as the number of receiving stations. Therefore, by virtue of the periodicity, only the optimal path between 0° and 60° needs to be calculated to obtain the overall optimal path;

[0033] It is stipulated that the patrol path is the path composed of the points that maximize the effective detection area at each angle.

[0034] Preferably, in the case of only considering noise limitation, the bistatic sonar equation is:

[0035] TL1 + TL2 = SL - NL + DI - DT + TS

[0036] In the formula, TL1 is the propagation loss from the transmitting station to the target, TL2 is the propagation loss from the target to the receiving station, SL is the source level of the transmitting sound source, NL is the ambient noise level, DI is the directivity gain of the receiving station, DT is the detection threshold, and TS is the target strength;

[0037] When the system parameters of the sonar system and the sea conditions are determined, the source level and the noise masking level are determined values, while TS, TL1, and TL2 change with the change of the geometric relationship between the target and the bistatic sonar;

[0038] The value of TS: Under bistatic conditions, the value of TS depends on the acoustic incidence angle and the separation angle. The TS values of each acoustic incidence angle and separation angle under bistatic conditions are statistically averaged according to the rules to obtain , and is used to replace the randomly distributed TS for range estimation. Finally, the coverage area of the sonar is a regularly shaped ellipse.

[0039] Preferably, it further includes:

[0040] Propagation loss and range:

[0041] TL1 is related to the distance r T between the transmitting station and the target, denoted as TL1(r T ), TL2 is related to the distance r R from the target to the receiving station, denoted as TL2(r R ). Under the condition that SL, NL, TS, DI, and DT are all determined, the bistatic sonar equation is:

[0042]

[0043] If the acoustic wave propagation loss is calculated according to spherical wave attenuation and absorption attenuation is ignored, then:

[0044]

[0045] Substituting into the bistatic sonar equation, we can obtain:

[0046]

[0047] Therefore, we can obtain:

[0048]

[0049] Detection range and detection area:

[0050] Define the source level and the receiver distance as D, and the equivalent radius of the bistatic system is ;

[0051] When D = 0, the detection range of the bistatic system is a circle with a radius of R; as the parameter D increases, when 0 < D < 1.41R, the detection range of the bistatic sonar system gradually evolves from a circle to an approximately elliptical shape; when D gradually becomes greater than 1.41R, the detection range of the bistatic sonar begins to distort; when D further increases to 2R, the detection range of the bistatic sonar system will be divided into two non-intersecting regions;

[0052] Therefore, define 0 < D < 1.41R as the preferred source level and receiver distance of the bistatic sonar. At this time, the detection range of the bistatic sonar system can be regarded as an ellipse.

[0053] Preferably, when performing data detection, it is necessary to perform data fusion processing on the results obtained by each detector, and use the OR criterion of the distributed structure to perform data fusion on the multi-static detection probability.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] In the embodiment of the present invention, by proposing an optimized layout method, the effective coverage area is maximized during underwater target detection, resources are efficiently utilized, and the accuracy and effectiveness of target detection are improved. In this embodiment, the optimization objective is first determined. After defining the constraint conditions, simulation verification is carried out through the particle swarm algorithm. Through the layout scheme optimized by the particle swarm algorithm, the effective coverage rate is optimized, and the detection efficiency of the system is significantly improved. Finally, the optimal solution is verified through the iteratively optimized algorithm, and the optimal effective coverage rate is determined. Brief Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 Schematic diagram of the optimization layout method proposed in the embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the positional relationship between the transmitting station and the receiving station under the island shielding condition of the present invention;

[0059] Figure 3 Simulation diagram of the detection range of different initial angles in the embodiment of the present invention Figure 1 ;

[0060] Figure 4 Simulation diagram of the detection range of different initial angles in the embodiment of the present invention Figure 2 ;

[0061] Figure 5 Simulation diagram of the detection range of different initial angles in the embodiment of the present invention Figure 3 ;

[0062] Figure 6 Schematic diagram of the relationship between the initial angle and the effective detection area in the embodiment of the present invention;

[0063] Figure 7 Schematic diagram of the flow of the particle swarm optimization algorithm proposed in the embodiment of the present invention;

[0064] Figure 8 Schematic diagram of the geometric relationship among the bistatic sound source, the target and the receiving station in the embodiment of the present invention Figure 1 ;

[0065] Figure 9 Schematic diagram of the geometric relationship among the bistatic sound source, the target and the receiving station in the embodiment of the present invention Figure 2 ;

[0066] Figure 10 Relationship curve between the number of iterations and the fitness function in the embodiment of the present invention;

[0067] Figure 11 Schematic diagram of the fusion result of the left base station of the optimized layout structure in the embodiment of the present invention;

[0068] Figure 12 Schematic diagram of the fusion result of the right base station of the optimized layout structure in the embodiment of the present invention;

[0069] Figure 13 Schematic diagram of the fusion result of the entire system of the optimized layout structure in the embodiment of the present invention;

[0070] Figure 14 Relationship curve between the optimized angle and the effective coverage area in the embodiment of the present invention;

[0071] Figure 15 Error curve after and before optimization in the embodiment of the present invention. Detailed implementation manners

[0072] In order to make the technical problems, technical solutions and beneficial effects to be solved by this application more clear and understandable, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0073] Embodiment

[0074] Please refer to Figure 1 , this embodiment provides a multi-base underwater detection optimization layout method based on the particle swarm algorithm, including the following steps:

[0075] S1. Determine the optimization objective, and the optimization objective is to maximize the effective coverage rate;

[0076] S2. Conduct simulation assumptions and define constraint conditions, where the constraint conditions include: island occlusion, sonar communication distance, and the number of base stations;

[0077] S3. Perform optimal layout optimization through the particle swarm algorithm, including:

[0078] Initialize the particle swarm: randomly generate a group of particles, and each particle represents a possible base station layout scheme;

[0079] Calculate the fitness function: use the reciprocal of the effective coverage area as the fitness function to evaluate the quality of each particle;

[0080] Update the particle position: update the position of the particle according to the current position, velocity and historical optimal position of the particle;

[0081] Iterative optimization: repeatedly calculate the fitness function and update the particle position until the convergence condition is met;

[0082] S4. Conduct simulation verification and analyze the results of the simulation experiment.

[0083] Through the proposed optimization layout method in the embodiment of the present invention, the effective coverage area is maximized during underwater target detection, resources are efficiently utilized, and the accuracy and effectiveness of target detection are improved. In this embodiment, the optimization objective is first determined, and after defining the constraint conditions, simulation verification is carried out through the particle swarm algorithm. The layout scheme optimized by the particle swarm algorithm optimizes the effective coverage rate, significantly improves the detection efficiency of the system, and finally verifies the optimal solution through the iteratively optimized algorithm to determine the optimal effective coverage rate.

[0084] In this embodiment, the optimization layout method is applied to the collaborative operation of multiple unmanned underwater vehicles (UUVs) for underwater target detection. When conducting underwater target detection, the detection base stations of the base include a transmitting station and a receiving station, and the actual application scenarios are mostly for target detection around islands such as national ocean test sites.

[0085] Specifically, the optimization layout method proposed in this embodiment includes the following steps:

[0086] S1. Determine the optimization objective

[0087] In this embodiment, the optimization objective determined by the optimization layout method is to maximize the effective coverage rate. The effective coverage rate is associated with the effective coverage area, and the effective coverage area is defined as the area where the detection probability is greater than or equal to 0.7. The effective coverage rate is defined as the ratio of the area where the detection probability is greater than or equal to 0.7 to the entire area, and its expression is:

[0088] Effective coverage rate = (area where detection probability ≥ 0.7) / entire area.

[0089] S2. Conduct simulation assumptions

[0090] S201. Conduct simulation scenario assumptions

[0091] Since the search space is a two-dimensional or three-dimensional space, and the number of multiple unmanned underwater vehicles (UUVs) responsible for transmitting and receiving is large, if the coordinates of the UUVs are used as the optimization variables, the solution time is long, the calculation amount is large, and it is not easy to obtain the optimal solution. Therefore, considering the actual application scenario, it is necessary to simplify the optimization variables.

[0092] The experimental scenario of this embodiment is set around an island. When detecting, it is necessary to prevent non-cooperative targets from entering the waters near the island to interfere with the detection operation. Considering that the island has an obstructive and masking effect on sound waves, at least two transmitting stations need to be set on both sides of the island to ensure full coverage of the entire operation area.

[0093] Target detection is carried out through 2 transmitting stations and 6 receiving stations. Assuming that the shape of the island is circular, the transmitting stations and receiving stations are evenly distributed on different concentric circles. At this time, due to the symmetry of the assumed layout, the optimization parameters can be changed from coordinate points to the radii of the concentric circles where the transmitting stations and receiving stations are located.

[0094] In the two-dimensional case, compared with using the two-dimensional coordinates of multiple UUVs as the optimization parameters, through simulation scenario assumptions, while being reasonable, it greatly reduces the complexity of optimization and can effectively improve the running speed of the algorithm.

[0095] When conducting scenario assumption positioning for island occlusion, for the occlusion of sound waves by the island, it is assumed that the sound waves propagate in a straight line and the diffraction phenomenon of sound waves is not considered. When the line connecting the transmitting station and the receiving station passes through the island, it is considered to be occluded and the receiving station cannot receive the sound wave for detection.

[0096] As Figure 2 shown, for simplicity of calculation, the angle between the island, the transmitting station, and the receiving station is used as the judgment criterion.

[0097] The assumed scenario includes: a transmitting station T , a receiving station R , the center of the island O and the tangent point between the transmitting station and the island D * . Among them, the coordinates of the transmitting station are ( x T , y T ), the coordinates of the receiving station are ( x R , y R ), the coordinates of the center of the island are ( x O , y O ), and the coordinates of the tangent point between the transmitting station and the island are ( x D* , y D* ).

[0098] Denote , , , , α =∠ OTD * , . Then and satisfy:

[0099]

[0100]

[0101] When , it is considered that the receiving station is blocked by the island and cannot form a detection system with the transmitting station.

[0102] S202. Design of UUV cruise plan

[0103] Since the object to be detected in reality may be moving, it is necessary to further mount the receiving station on the UUV, let it move, and then continue to use the particle swarm algorithm to plan the UUV cruise path to find the cruise path with the largest coverage rate. After this step of processing, the maximum coverage rate can be further improved compared with the above maximum coverage rate.

[0104] Comparing with the multi-static sonar system with a buoy as the carrier, the multi-static system with a UUV as the carrier can make the detection range more comprehensive through movement. Therefore, it is necessary to reasonably plan and design the movement path of the UUV. Detecting with multiple unmanned underwater vehicles as the carrier of the receiving station and planning their cruise paths. Under the condition of considering the island masking effect, using Figure 2 The obtained base station layout structure, by changing the initial angle of the receiving station, changing the initial angle between the first receiving station and the x-axis, the detection range results of different initial angles are obtained, as Figures 3 - 5 shown.

[0105] From Figures 3 - 5 it can be seen that the extreme value situation obtained by using the particle swarm optimization algorithm is that the first transmitting station and the first receiving station are both located on the polar axis. When the initial angle of the receiving station changes, the shape of the effective coverage area will change, and its effective coverage area will also change. Therefore, only making circular motion cannot meet the situation of obtaining the maximum value at each position, and it is impossible to have the maximum coverage area during the UUV cruise process. It is necessary to plan the movement path.

[0106] Optimizing with the effective detection area as the index, first study the relationship between the initial angle and the effective detection area, calculate the corresponding effective coverage area at all angles, and obtain the results as Figure 6 shown.

[0107] From Figure 6 it can be seen that there are large differences in the effective areas at different angles. At the same time, due to the uniform distribution of the receiving stations, the positional relationship of the UUVs is periodic, and the number of repetitions between [0°, 360°] is the same as the number of receiving stations. Therefore, by virtue of the periodicity, only the optimal path between 0° and 60° needs to be calculated to obtain the overall optimal path.

[0108] It is stipulated that the patrol path is: the path composed of the points that make the effective detection area the largest at each angle.

[0109] Since the starting point has been calculated by the PSO algorithm, therefore, at a certain angle, it can be traversed around the initial point as the center within the appropriate movement range of the UUV, and it can be traversed within a certain movement range of the UUV to obtain the optimal distance at the corresponding angle. Considering that the distance of the transmitting station from the center of the sea area is small, the cruise time and distance are short, the initial point of the transmitting station is optimized at different angles to obtain the optimal points at several key positions. The UUV carrying the transmitting sonar cruises towards the optimal point position as the target position, and the UUV cruises towards the point position to improve the effective coverage rate of the entire cruise process.

[0110] S3. Define the constraint conditions

[0111] In this embodiment, the method defines three constraint conditions, specifically:

[0112] Island shielding: Assume the island is circular and sound waves propagate in a straight line. When the line connecting the transmitting station and the receiving station passes through the island, it is considered shielded and the receiving station cannot receive sound waves for detection.

[0113] The method adopted in this embodiment has verified its effectiveness and feasibility in actual projects. Since there are no completely regular circular islands in reality, the island is generally assumed to be circular when making experimental assumptions in the field to facilitate simulation experiments and without affecting actual target detection.

[0114] Sonar communication distance: The working radius of the UUV is limited between 3 km and 8 km.

[0115] Number of base stations: Assume the number of transmitting stations is 2 and the number of receiving stations is 6.

[0116] Furthermore, in this embodiment of the present invention, during the actual application of the project, considering cost, if you want the transmitted signal to cover the entire perimeter of the island, at least two transmitting stations need to be deployed. In actual engineering, it is generally required that the number of receiving stations be as small as possible. In this experiment, it is assumed that there are 6 receiving stations to meet the requirements of minimizing cost and maximizing coverage. The deployment of 6 receiving stations is already a relatively low number in the industry. In the actual application process, if you want to change the number of base station deployments and, without considering cost, further improve the coverage rate, the method provided by the present invention is also applicable. You can choose to increase the number of corresponding transmitting stations and receiving stations as needed. You only need to change the set number of receiving stations and transmitting stations in the code of the layout method, and the algorithm will enumerate each deployment situation and find the optimal solution through calculation.

[0117] S4. Optimization algorithm confirmation

[0118] This embodiment uses the Particle Swarm Optimization (PSO) algorithm to perform the optimal layout planning of base stations for multi-base collaborative detection. PSO is a population-based optimization algorithm that finds the optimal solution by simulating the foraging behavior of bird flocks.

[0119] The process of the Particle Swarm Optimization algorithm is as Figure 7 shown.

[0120] The specific steps are as follows:

[0121] Initialize the particle swarm: Randomly generate a group of particles. Each particle represents a possible base station layout plan, and the position and velocity of the particles are initialized with random values.

[0122] Calculate the fitness function: Use the reciprocal of the effective coverage area as the fitness function to evaluate the quality of each particle.

[0123] Since the particle swarm algorithm is used for optimal layout optimization in this embodiment, the design of the fitness function often depends on specific objectives. The effective detection area can be selected as the evaluation criterion. Considering that the primary task of the multi-UUV system is to detect targets, the effective detection area is determined as the index for optimization.

[0124] Define the effective coverage rate as the ratio of the area of the region where the detection probability is greater than or equal to 0.7 to the entire region area. Then the fitness function is the reciprocal of the effective coverage area.

[0125] Update the particle position: Update the particle position according to the current position, velocity, and historical optimal position of the particle. The update formula is as follows:

[0126]

[0127]

[0128] In the formula, v i is the velocity of the particle, x i is the position of the particle, pbest i is the historical optimal position of the particle, gbest is the global optimal position, w is the inertia weight, c 1, c 2 is the learning factor, r 1, r 2 is a random number.

[0129] Iterative optimization: Repeatedly calculate the fitness function and update the particle position until the convergence condition is met.

[0130] Furthermore, the experimental principle of UUV target detection in this embodiment is as follows:

[0131] The monostatic active sonar equation is:

[0132] SL - 2TL + TS = NL - DI + DT

[0133] In the formula, SL is the transmitting source level, TL is the propagation loss, TS is the target strength, NL is the noise level, DI is the receiving directivity index, and DT is the detection threshold.

[0134] Under bistatic conditions, a complex triangular relationship is formed among the sound source, target, and receiver. The basic geometric relationship is as Figure 8 , Figure 9 shown, and the detection range of the sonar is an ellipse.

[0135] In the figure, T represents the sound source, which can work only in the transmitting state, emitting acoustic pulses into the water to irradiate the target, or can work in the monostatic mode, having the functions of both emitting sound waves and receiving target echoes; R is the receiver of the receiving station, which is separated from the transmitter of the transmitting station by a certain distance and works only in the listening mode, and its position is not easily detected by the enemy.

[0136] In a bistatic sonar system, R is generally composed of a UUV or an airborne dipping sonar, S is the underwater target, r T is the distance from the transmitter to the target, r R is the distance from the target to the receiver, and D is the baseline length. What is measured at the receiving end is the sum of the distances, that is, r Σ = r T + r D .

[0137] Under bistatic conditions, the baseline length is defined as the distance between the transmitting station and the receiving station, θ T is the beam pointing angle measured at the acoustic wave transmitting end, θ R is the receiving beam pointing angle, and β is the separation angle, which is defined as the angle between the lines connecting the transmitting base, the receiving base and the target with the target as the vertex.

[0138] The bistatic sonar has the working characteristics of both active sonar and passive sonar at the same time, including the transmitting station emitting acoustic pulses, irradiating the target, the acoustic waves being scattered by the target to generate target echoes, and the receiving station receiving the target echoes at different positions. The transmitting station works in an active mode, while the receiving station works in a passive state because it only receives the target echoes. Therefore, according to the working characteristics of the bistatic sonar, there are also two forms of its sonar equation.

[0139] For the case of noise limitation, the bistatic sonar equation is:

[0140] SL - TL1 - TL2 + TS - NL + DI = DT

[0141] For the case of reverberation limitation, the sonar equation is:

[0142] SL - TL1 - TL2 + TS - RL + DI = DT

[0143] In the formula, TL1 and TL2 are the propagation losses from the sound source to the target and from the target to the receiver respectively. Under bistatic conditions, the two propagation distances and the absorption factors on the two propagation paths experienced by the acoustic waves can be different, and TL1 and TL2 will not be exactly the same.

[0144] Under bistatic conditions, the propagation loss is:

[0145] TL = TL1 + TL2

[0146] Wherein, TL1 represents the propagation loss from the sound source to the target, which is related to the distance r from the transmitter to the target, denoted as TL1(r T ); TL2 represents the propagation loss from the target to the receiver, which is related to the distance r from the target to the receiver, denoted as TL2(r T ). Under the condition that SL, NL, TS, DI, and DT are all determined, the operating range of the bistatic sonar satisfies: R TL1(r R ) + TL2(r

[0147] ) = SL + TS - NL + DI - DT = constant T ) + TL2(r R ) = SL + TS - NL + DI - DT = constant

[0148] At this time, the operating range of the sonar only depends on the value of r R .

[0149] Therefore, in the above formula, the smaller r R is, the larger r T is. However, in actual situations, r R is on the one hand an unknown quantity, and on the other hand, it cannot be too small, otherwise it will be difficult to separate the direct wave and the target echo in the time domain. Assuming that through signal processing methods, the direct wave and the echo can be separated in the spatial domain, then after setting a certain value of r R , the operating range of the bistatic sonar can be estimated. The lower the operating frequency, the smaller the propagation loss, and the better the effect of the bistatic sonar; the higher the sonar figure of merit, the more obvious the effect.

[0150] Furthermore, through the above theoretical derivation, it is obtained that:

[0151] In the case of only considering noise limitation, the bistatic sonar equation is:

[0152] TL1 + TL2 = SL - NL + DI - DT + TS

[0153] Wherein, TL1 is the propagation loss from the transmitting station to the target, TL2 is the propagation loss from the target to the receiving station, SL is the sound source level of the transmitting sound source, NL is the ambient noise level, DI is the directivity gain of the receiving station, DT is the detection threshold, and TS is the target strength;

[0154] When the system parameters of the sonar system and the sea state are determined, the sound source level and the noise masking level are determined values, while TS, TL1, and TL2 change with the change of the geometric relationship between the target and the bistatic sonar.

[0155] Value of TS: Under bistatic conditions, the value of TS depends on two quantities, the acoustic incident angle and the separation angle. As these two quantities vary in combination, the bistatic target strength may be less than or greater than the monostatic target strength. When estimating the range, different TS values at different incident angles and separation angles will calculate different operating ranges, and the coverage area of the sonar is an irregularly shaped figure, which will lead to the inability to calculate the detection area.

[0156] Therefore, the TS values at various incident angles and separation angles under bistatic conditions are statistically averaged according to certain rules (such as ±2.5°) to obtain , and use to replace the randomly varying TS for range estimation. Finally, the coverage area of the sonar is a regularly shaped ellipse, and the coverage area of the ellipse is equivalent to the coverage area of the aforementioned irregularly shaped figure.

[0157] Propagation loss and operating range:

[0158] TL1 is related to the distance r T between the transmitting station and the target, denoted as TL1(r T ). Similarly, TL2 is related to the distance r R from the target to the receiving station, denoted as TL2(r R ). Under the conditions that SL, NL, TS, DI, and DT are all determined, the bistatic sonar equation is:

[0159]

[0160] If the acoustic wave propagation loss is calculated according to spherical wave attenuation and absorption attenuation is ignored, then:

[0161]

[0162] Substituting into the bistatic sonar equation gives:

[0163]

[0164] Further derivation gives:

[0165] .

[0166] Detection range and detection area:

[0167] Define the distance between the source level and the receiver as D, and the equivalent radius of the bistatic system as .

[0168] When D = 0, the detection range of the bistatic system is a circle with a radius of R; as the parameter D increases, when 0 < D < 1.41R, the detection range of the bistatic sonar system gradually evolves from a circle to an approximately elliptical shape; when D gradually becomes greater than 1.41R, the detection range of the bistatic sonar begins to distort; when D further increases to 2R, the detection range of the bistatic sonar system will be divided into two non-intersecting regions.

[0169] Therefore, 0 < D < 1.41R is defined as the distance between the optimal sound source level and the receiver of the bistatic sonar. At this time, the detection range of the bistatic sonar system can be regarded as an ellipse.

[0170] Furthermore, taking specific parameters as an example, the detection areas of the monostatic sonar system and the bistatic sonar system are theoretically deduced and explained below.

[0171] Assume that the target strength is 20 dB for the monostatic sonar and 15 dB for the bistatic sonar, and the sound source level SL = 210 dB. Noise source masking level:

[0172]

[0173] For better realizability, the acoustic wave propagation loss is considered as spherical spreading plus seawater absorption, that is:

[0174]

[0175] In the formula, α is the acoustic absorption coefficient.

[0176] Substitute the above data into the bistatic sonar equation to get:

[0177]

[0178] 20lg( r T + r R )+ α ( r T + r R ) = 45

[0179] In the frequency range of 0.5 kHz to 100 kHz, the approximate formula for the absorption coefficient in seawater is:

[0180]

[0181] When the frequency is 10 kHz:

[0182]

[0183] Therefore:

[0184] 20lg( r T + r R ) + 1.18( r T + r R ) = 45

[0185] When the distance between the transmitting sonar and the non - cooperative target is 1 km, i.e., r T = 1 km, substituting it into the above equation can obtain r R which is approximately 16.2 km.

[0186]

[0187] r T ≈ 16.2

[0188] At this time, the detection radius of the bistatic is 10.8 times that of the monostatic detection radius of 1.5 km.

[0189] Furthermore, since it is generally known in the industry that the bistatic is used as a typical representative for experimental verification, and all multistatic systems are derived from the bistatic, and the parameters of the bistatic need to be calculated using the values of the monostatic. During the detection process of the actual project, the representative monostatic detection radius obtained from actual tests is 1.5 km. Therefore, this value is directly used for comparison in this embodiment.

[0190] Furthermore, as a preferred embodiment of the present invention, in order to enable the multistatic sonar to obtain better detection effects than the monostatic sonar, it is necessary to perform data fusion processing on the results obtained by each detector. Through data fusion detection, the multistatic sonar detection can obtain better reliability and accuracy than the monostatic sonar detection, and at the same time, the detection range is also greatly improved, thus giving full play to the advantages of the multistatic system.

[0191] The commonly used multistatic fusion detection methods are divided into two types: distributed and centralized. The centralized method is to transmit the echo information received by each base to the fusion center for unified processing, which has high requirements for the communication system and is not suitable for use in an underwater environment with limited communication; the distributed method is to perform a decision on the echo at each base first and only transmit the decision results to the decision center for fusion decision.

[0192] Therefore, this method adopts a distributed structure for data fusion detection, and specifically uses the OR criterion to perform data fusion on the multistatic detection probability.

[0193] S5. Conduct simulation verification

[0194] Obtain the results through simulation experiments to verify the effectiveness of the optimal layout scheme.

[0195] This method uses the PSO algorithm to simulate and verify the optimal layout, and uses a constant false alarm rate detector to simulate and analyze the detection efficiency of a multi-static sonar. The parameters of the sonar equipment, environment, and target are shown in Table 1.

[0196] Table 1 Parameters of Sonar Equipment, Environment, and Target

[0197]

[0198] Considering the influence of the underwater acoustic communication distance and the actual working scenario, it is assumed that the UUV carrying the sonar and hydrophone operates in a sea area with a radius ranging from 3 km to 8 km, and the radius of the island does not exceed 3 km. It is assumed that the parameters of the sonar equipment, environment, and target are the same as those in Table 1 except for the acoustic wave frequency. The selected acoustic wave frequency makes the acoustic absorption coefficient α equal to 1. The UUV carrying the sonar is the transmitting station, and the UUV carrying the hydrophone is the receiving station. The number of transmitting stations is 2, and the number of receiving stations is 6. The radius of the island is 2 km.

[0199] In the particle swarm algorithm, due to the small search space, the number of particle swarms is considered to be 5, and the number of iterations is 500. The weight ω is set to 1, and the learning factors c 1 and c 2 are both set to 2. The variables to be optimized are the radius of the transmitting station and the radius of the receiving station, that is, the distance from the transmitting station to the center of the sea area and the distance from the receiving station to the center of the sea area.

[0200] To improve the optimization speed and ensure a sufficiently large detection area, the OR criterion is used to fuse the multi-static detection probabilities. For the two fusion results obtained from the two transmitting stations, the maximum value is taken to obtain the fused result. The results optimized by the PSO algorithm are that the radius of the transmitting station is 6.23 km, the radius of the receiving station is 8.00 km, the minimum value of the fitness function is 4.45, and the effective coverage rate at this time is 0.2245.

[0201] The curve of the relationship between the number of iterations and the fitness function is as shown in Figure 10 Figure.

[0202] From Figure 10 it can be seen that as the number of iterations increases, the fitness function gradually decreases. Considering both the number of iterations and the value of the fitness function, the number of iterations is set to 500.

[0203] From the figure, it can be seen that after 100 iterations, as the number of iterations increases, the change in the fitness function is not significant. Therefore, when using it, the number of iterations can be considered to be set to more than 200 times. So it is reasonable to set the number of iterations to 500 times.

[0204] S6. Analyze the results of the simulation experiment.

[0205] The PSO algorithm is used to obtain the optimal layout with the effective coverage area as the index, and the detection coverage area is as Figures 11 - 13 shown.

[0206] The transmitting stations are evenly distributed on both sides of the island. Figure 11 and Figure 12 are the fusion results of the left and right transmitting stations with other non-shaded receiving stations under the condition of island shading respectively. Figure 13 is the detection area of the fusion result of the whole system.

[0207] It can be seen from the figure that within the detectable range, the vast majority of the area is effective area, and there is no situation where the effective coverage area is low due to the excessive distance between the base stations, thus verifying the correctness and feasibility of the optimal layout scheme proposed by the present invention with the effective coverage area as the index.

[0208] Furthermore, conduct a simulation analysis on the UUV cruise path. Assume that the number of receiving stations is 6 and they are evenly distributed on a concentric circle 8 km away from the center of the island, and the number of transmitting stations is 2 and they are evenly distributed on a concentric circle 6.23 km away from the center of the island. The allowable path deviation range during the UUV cruise is 6.23 km ± 1.5 km, and the simulation step size is 50 m. According to symmetry, only the initial angle needs to vary within the range of 60°, with a step size of 5°, calculate the radius corresponding to the optimal coverage area of the cruise occupancy points, and obtain the optimized coordinate points as shown in Table 2.

[0209] Table 2 Radius of key points after optimization

[0210]

[0211] The first point in Table 2 is the starting point and is also obtained by local calculation. The result is similar to that of the PSO algorithm, indicating that the result obtained by the PSO algorithm is optimal and effective.

[0212] In this embodiment, during the scenario assumption and layout process, it is first assumed that the receiving stations are fixed. Through the particle swarm optimization algorithm, the layout with the largest effective coverage rate in all cases can be calculated, that is, the radius of the transmitting stations is set on a circle 6.23 km away from the center of the island, with a symmetric layout. There is no need for fixed points, just a symmetric layout on the circle. Similarly, the optimal layout positions of the receiving stations obtained by calculation are distributed on a circle 8 km away from the center of the island, evenly distributed, with an angle of 60° between adjacent receiving stations. Similarly, there is no need for fixed points, just an even distribution on the circle.

[0213] Then, since the detection target may be constantly moving during the actual application process, if the receiving station is set to move, the effective coverage rate can be further improved. Therefore, further experiments are carried out by mounting the receiving station on the UUV. Since the receiving stations in this embodiment are evenly distributed on a circle, the activity range of each UUV carrying a receiving station is a 60° sector. Taking 5° as the step size, the radius corresponding to the optimal coverage area of the cruise is calculated. As shown in Table 2, the distances from the center of the island obtained every 5° can be obtained, and connecting them together is the UUV cruise path within the 60° sector range.

[0214] Assume that the movement of the UUV in actual operation is a linear movement towards the key points, then linear interpolation can be performed between the points to describe this movement. Interpolation is carried out according to the point coordinates to obtain the relationship between the optimized effective coverage area and the angle within [0°, 60°]. Using the symmetry of the cruise movement, the relationship between the effective coverage area and the angle within the entire cruise period is obtained, as Figure 14 shown.

[0215] Due to the influence of linear interpolation, Figure 14 the results shown are not smooth, and there are sudden changes at some angles. However, the overall shape of the image is similar to Figure 6 Therefore, the optimized result is subtracted from the pre-optimized result to obtain the error as Figure 15 shown.

[0216] From Figure 15 it can be seen that, ignoring the sudden change values, the errors at most angles are greater than 0, that is, the optimized effective coverage area has been effectively improved.

[0217] In this embodiment, pre-optimization refers to the situation where the receiving station is fixed and no UUV cruise movement path analysis is carried out. Post-optimization refers to the situation after UUV cruise. If the detection target is fixed during the actual application process, there is no need to perform the second optimization, and only the fixed receiving station is required. The method has strong versatility.

[0218] If it is necessary to further improve the effective coverage during cruising and reduce the sudden change phenomenon, the number of occupancy points can be increased to perform a more refined path planning.

[0219] In view of the problems existing in the prior art, the present invention proposes a general multi-base underwater detection optimization layout method based on the particle swarm algorithm. By conducting mathematical modeling through actual engineering projects, the transmitting stations and receiving stations are arranged through the particle swarm algorithm to maximize the effective coverage area during underwater target detection, obtain the maximum effective coverage rate, efficiently utilize resources, and improve the accuracy and effectiveness of target detection. The present invention first determines the optimization objective, conducts simulation verification through the particle swarm algorithm after defining the constraint conditions. For the layout scheme optimized by the particle swarm algorithm, the effective coverage rate is optimized, significantly improving the detection efficiency of the system. Moreover, the particle swarm algorithm converges after 500 iterations, and the fitness function value tends to be stable, indicating that the algorithm can find the optimal solution. In addition, the cruise path optimized by the algorithm of the present invention can significantly increase the effective coverage area, further enhancing the detection efficiency of the system.

[0220] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present application.

[0221] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0222] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An optimized layout method for multi-static underwater detection based on particle swarm algorithm, characterized in that, It includes the following steps: S1. Determine the optimization objective, which is to maximize the effective coverage rate; S2. Conduct simulation assumptions and define constraint conditions. The constraint conditions include: island shielding, sonar communication distance, and the number of base stations. When conducting underwater target detection, the detection base stations of the base include a transmitting station and a receiving station. The three constraint conditions are defined as: Island shielding: Assume the island is circular and sound waves propagate in a straight line. When the line connecting the transmitting station and the receiving station passes through the island, it is considered shielded and the receiving station cannot receive sound waves for detection; Sonar communication distance: The working radius is limited between 3 km and 8 km; Number of base stations: Assume the number of transmitting stations is 2 and the number of receiving stations is 6; Assume the experimental scenario is around the island, and target detection is carried out through 2 transmitting stations and 6 receiving stations. The two transmitting stations are located on both sides of the island respectively, and the transmitting stations and receiving stations are evenly distributed on different concentric circles. Due to the symmetry of the assumed layout, the optimization parameters can be changed from coordinate points to the radii of the concentric circles where the transmitting stations and receiving stations are located; When performing scene hypothesis to locate island occlusion, the included angle between the island, the transmitting station, and the receiving station is used as the judgment criterion. The assumed scenes include: the transmitting station T , the receiving station R , the center of the island O , and the tangent point between the transmitting station and the island D * . Among them, the coordinates of the transmitting station are ([[]] x T , y T ), the coordinates of the receiving station are ([[]] x R , y R ), the coordinates of the center of the island are ([[]] x O , y O ), and the coordinates of the tangent point between the transmitting station and the island are ([[]] x D* , y D* ); Record , , , , α =∠ OTD * , , then and satisfy: When it is considered that the receiving station is masked by the island and cannot form a detection system with the transmitting station; S3. Conduct optimal layout optimization through the particle swarm algorithm, including: Initialize the particle swarm: Randomly generate a group of particles, and each particle represents a possible base station layout scheme; Calculate the fitness function: Use the reciprocal of the effective coverage area as the fitness function to evaluate the quality of each particle; Update the particle position: Update the particle position according to the current position, velocity, and historical optimal position of the particle; Iterative optimization: Repeat calculating the fitness function and updating the particle position until the convergence condition is met; S4. Conduct simulation verification and analyze the results of the simulation experiment.

2. The multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 1, wherein, The effective coverage rate is associated with the effective coverage area. The effective coverage area is defined as the area of the region where the detection probability is greater than or equal to 0.7, and the effective coverage rate is defined as the ratio of the area of the region where the detection probability is greater than or equal to 0.7 to the area of the entire region. Its expression is: Effective coverage rate = (Area of the region where the detection probability ≥ 0.7) / Area of the entire region.

3. A multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 1, characterized in that, When using the particle swarm algorithm for optimal layout planning, the update formula for updating the particle position is: In the formula, v i is the velocity of the particle, x i is the position of the particle, pbest i is the historical optimal position of the particle, gbest is the global optimal position, w is the inertia weight, c 1, c 2 are the learning factors, r 1, r 2 are random numbers.

4. A multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 1, characterized in that, Use multiple unmanned underwater vehicles as the carriers of the receiving stations for detection, and plan their cruise paths. Under the condition of considering the island masking effect, adopt the obtained base station layout structure, and obtain the detection range results of different initial angles by changing the initial angles of the receiving stations; Optimize with the effective detection area as an index, study the relationship between the initial angle and the effective detection area, and calculate the corresponding effective coverage area at all angles.

5. A multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 4, characterized in that, The receiving stations are arranged in a uniform distribution. It is calculated that the positional relationship of the multiple unmanned underwater vehicles is periodic, and the number of repetitions between [0°, 360°] is the same as the number of receiving stations. Therefore, relying on the periodicity, only the optimal path between 0° and 60° needs to be calculated to obtain the overall optimal path; Specify the patrol path as: the path composed of the points where the effective detection area is the largest at each angle.

6. The multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 4, wherein, In the case of only considering noise limitation, the bistatic sonar equation is: TL1 + TL2 = SL - NL + DI - DT + TS Wherein, TL1 is the propagation loss from the transmitting station to the target, TL2 is the propagation loss from the target to the receiving station, SL is the sound source level of the transmitting sound source, NL is the ambient noise level, DI is the directivity gain of the receiving station, DT is the detection threshold, and TS is the target strength; When the system parameters of the sonar system and the sea state are determined, the sound source level and the noise masking level are determined values, while TS, TL1, and TL2 change with the change of the geometric relationship between the target and the bistatic sonar; Value of TS: Under bistatic conditions, the value of TS depends on the acoustic incident angle and the separation angle. The TS values for each incident angle and separation angle under bistatic conditions are statistically averaged according to the rules to obtain , and is used to replace the randomly distributed TS for range estimation. Finally, the coverage area of the sonar is a regularly shaped ellipse.

7. A multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 6, characterized in that It also includes: Propagation loss and operating range: TL1 is related to the distances r between the transmitting station and the target, denoted as TL1(r T ), TL2 is related to the distance r from the target to the receiving station, denoted as TL2(r T ). Under the condition that SL, NL, TS, DI, and DT are all determined, the bistatic sonar equation is as follows: R ), TL2 is related to the distance r from the target to the receiving station, denoted as TL2(r R ). Under the condition that SL, NL, TS, DI, and DT are all determined, the bistatic sonar equation is as follows: If the acoustic wave propagation loss is calculated according to the spherical wave attenuation and the absorption attenuation is ignored, then: Substituting into the bistatic sonar equation, we can get: Therefore, we can obtain: Detection range and detection area: Define the sound source level and the distance between the receiver as D, and the equivalent radius of the bistatic system is ; When D = 0, the detection range of the bistatic system is a circle with a radius of R; as the parameter D increases, when 0 < D < 1.41R, the detection range of the bistatic sonar system gradually evolves from a circle to an approximately elliptical shape; when D gradually becomes greater than 1.41R, the detection range of the bistatic sonar gradually starts to distort from the ellipse; when D further increases to 2R, the detection range of the bistatic sonar system will be divided into two non-intersecting regions; Therefore, 0 < D < 1.41R is defined as the optimal sound source level and receiver distance of the bistatic sonar. At this time, the detection range of the bistatic sonar system can be regarded as an ellipse.

8. A multi-base underwater detection optimization layout method based on the particle swarm algorithm according to claim 1, characterized in that, When performing data detection, it is necessary to perform data fusion processing on the results obtained by each detector, and use the OR criterion of the distributed structure to perform data fusion on the multi-static detection probability.

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