A deep-sea heterogeneous platform on-call search method, system and terminal

Abstract

The present invention discloses a deep-sea heterogeneous platform on-call search method, which relates to the field of hydroacoustics and underwater target search technology, so as to solve the problems of the detection capability, search capability and redundant search of the platform that are not comprehensively considered in the prior art, and the target escape problem caused by unreasonable planning of the target search path, and effectively improve the search accuracy, search efficiency and save detection resources. The deep-sea heterogeneous platform on-call search method includes: gridding the sea area; reconstructing the sea area environmental characteristics; constructing a platform detection capability objective function; constructing a platform search capability objective function; constructing a search redundancy objective function; using the three constructed objective functions to score the platform estimated position set, selecting the platform estimated position with the highest score, and obtaining the target search path. The present invention also provides a heterogeneous platform on-call search system and terminal. The method, system and terminal provided by the present invention are applied to underwater target search to achieve fast and accurate search of underwater targets.

Description

Technical Field

[0001] The present invention relates to the technical field of hydroacoustics and underwater target search, and in particular to a deep-sea heterogeneous platform on-call search method, system and terminal. Background Art

[0002] At present, the importance of underwater environmental safety is becoming increasingly prominent. With the rapid development of modern science and technology, submarines have made significant progress in concealment, maneuverability and attack range. Considering the severe challenges posed by submarines and other underwater threats to maritime traffic, on-call anti-submarine technology can grasp important information such as the position, speed and course of submarines, effectively curb submarine threats, and become an important part of ensuring maritime safety.

[0003] Existing sonar detection technology is one of the main means of on-call anti-submarine technology. However, in practical applications, the existing sonar detection technology directly dispatches the detection platform to search for targets. Because the target search path is not planned, this search method is time-consuming, and because accurate target movement information is not obtained, it is easy to cause the target to escape, resulting in low search accuracy.

[0004] The underwater target search method based on the wasp swarm algorithm in the prior art is based on the expanded search of the target appearance area, and uses the wasp swarm algorithm to complete the full coverage search of the target area. Although this method ensures the coverage rate of the target search, it does not comprehensively consider the detection and search capabilities of the platform, and has the defect of redundant search.

[0005] In summary, when performing target search in the prior art, there are problems such as low search efficiency or accuracy, redundant search, etc. Summary of the invention

[0006] The present invention proposes a deep-sea heterogeneous platform on-call search method, system and terminal, which can solve the problems of failure to comprehensively consider the platform's detection capability, search capability and redundant search in the prior art and the target escape problem caused by unreasonable planning of the target search path, effectively improve the search accuracy, search efficiency and save detection resources.

[0007] In a first aspect, the present invention provides a deep-sea heterogeneous platform on-call search method, comprising:

[0008] Given a search area, platform and target, configure the marine environment elements, calculate and obtain the marine environment characteristics based on the marine environment elements, and the marine environment characteristics at least include noise, reverberation, and sound propagation characteristics; configure platform parameters and target parameters, wherein the platform parameters include the number of platforms, initial positions and sonar parameters; the target parameters include the target initial position and target acoustic parameters;

[0009] Grid search of the sea area to obtain multiple sea area grids;

[0010] Determine the target transfer probability and platform transfer probability;

[0011] Based on the platform transfer probability, the platform estimated position at time k is inferred from the platform position at time k-1, and the platform estimated position set is obtained by arranging and combining multiple platform estimated positions;

[0012] Based on the target transfer probability, the estimated target position at time k is calculated from the target position at time k-1;

[0013] Based on the principle of proximity and uniqueness, all sea area grids are divided into each platform to obtain the detection area of ​​each platform;

[0014] Reconstruct the characteristics of the marine environment;

[0015] At time k, the target detection probability is determined based on the platform parameters, target parameters, estimated platform position and reconstructed sea environment characteristics; the predicted target probability distribution is obtained based on the target transfer probability; the updated target probability distribution is obtained based on the platform detection results and the predicted target probability distribution;

[0016] Based on the target detection probability and the detection area of ​​each platform, the platform detection capability objective function is constructed; based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform, the platform search capability objective function is constructed; based on the pheromone at each sea area grid, the search redundancy objective function is constructed;

[0017] Based on the platform detection capability objective function, platform search capability objective function and search redundancy objective function, the platform estimated position set is scored, and the platform estimated position with the highest score is selected to obtain the target search path.

[0018] As a possible implementation method, reconstructing the marine environment characteristics includes:

[0019] Dynamically simulate the acoustic propagation characteristics of targets, platforms, and sea environment coupling under sea grid;

[0020] Simulate the ocean environmental noise composed of comprehensive wind, rain and ship effects;

[0021] Calculate the acoustic energy scattered by all sea grids that contribute to reverberation, and accumulate them to obtain the seabed reverberation;

[0022] The sound propagation characteristics, ocean environmental noise and seabed reverberation are accumulated to obtain the reconstructed sea environment characteristics.

[0023] As a possible implementation method, based on the target detection probability and the detection area of ​​each platform, a platform detection capability objective function is constructed, including:

[0024] The target detection probability of each sea area grid in the detection area of ​​each platform at time k is weighted and summed to obtain the detection result of each platform;

[0025] Solve the weight value ω of the platform detection capability objective function p ;

[0026] The detection result of each platform is compared with the weight value ω of the platform detection capability objective function p The weighted sum is performed to obtain the platform detection capability objective function.

[0027] As a possible implementation method, based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform, a platform search capability objective function is constructed, including:

[0028] The target detection probability of each sea area grid in the detection area of ​​each platform at time k is weighted and summed with the product of the predicted target probability distribution to obtain the search results of each platform;

[0029] Solve the weight value ω of the platform search capability objective function t ;

[0030] The search results of each platform are compared with the weight value ω of the platform search capability objective function t Weighted summation is performed to obtain the platform search capability objective function.

[0031] As a possible implementation method, the weight value ω of the platform search capability objective function is solved: t ,include:

[0032] The minimum and maximum weight values ​​of the platform search capability objective function are set based on the anti-submarine requirements, and the maximum distance between the platform and the target in the search area is set;

[0033] Based on the logarithmic function of the minimum weight value and the maximum weight value and the farthest distance, the weight curve coefficient of the platform search capability objective function is solved;

[0034] Solve the exponential function of the product of the weight curve coefficient and the distance between the platform and the target at the initial moment, multiply the exponential function by the maximum weight value, and obtain the weight value of the platform search capability objective function.

[0035] As a possible implementation method, based on the weight value ω of the platform search capability objective function t Solve the weight value ω of the platform detection capability objective function p , specifically: p =1-ω t .

[0036] As a possible implementation method, based on the pheromone at each sea area grid, a search redundancy objective function is constructed, including:

[0037] Using the pheromone, pheromone diffusion coefficient, and number of adjacent cells of each sea area grid at time k-1, the cumulative pheromone concentration of adjacent grids of each sea area grid at time k is calculated;

[0038] The pheromone evaporation coefficient, pheromone diffusion coefficient, pheromone of each sea grid at time k-1, pheromone release switch, pheromone release amount and cumulative pheromone concentration of adjacent grids of each sea grid at time k are used to solve the pheromone of each sea grid at time k;

[0039] The weight value of the search redundancy objective function is set to a value between 0 and 1 based on the acceptance degree of the search redundancy;

[0040] The search redundancy objective function is obtained by weighted summing the pheromone of each sea area grid at time k and the weight value of the search redundancy objective function.

[0041] As a possible implementation method, the platform estimated position set is scored based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, and the platform estimated position with the highest score is selected to obtain the target search path, including:

[0042] Based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, an overall objective function is obtained;

[0043] Based on the platform estimated position set and the total objective function, a total objective function group is obtained;

[0044] Normalizing the total objective function group to obtain the dimension-removed total objective function group;

[0045] Screening the maximum and minimum values ​​of the total objective function group after dimension removal, calculating the distance between each function value in the total objective function group after dimension removal and the maximum and minimum values, and obtaining the maximum spacing and the minimum spacing;

[0046] Solve the ratio of the minimum spacing to the sum of the maximum spacing and the minimum spacing to obtain the score of the platform estimated position set;

[0047] The estimated platform position with the highest score is selected to obtain the target search path.

[0048] In a second aspect, the present invention further provides a deep-sea heterogeneous platform on-call search system, comprising:

[0049] The estimated position solving unit calculates the estimated platform position at time k from the platform position at time k-1 based on the platform transfer probability, and calculates the estimated target position at time k from the target position at time k-1 based on the target transfer probability;

[0050] The detection area division unit divides all sea area grids into each platform based on the proximity principle and the unique principle to obtain the detection area of ​​each platform;

[0051] A sea area environment feature reconstruction unit, used to reconstruct the search sea area environment features;

[0052] The target detection probability solving unit determines the target detection probability based on the platform parameters, target parameters, the estimated platform position and the reconstructed sea environment characteristics;

[0053] A target probability distribution prediction unit, which obtains a predicted target probability distribution based on the target transfer probability;

[0054] A target probability distribution updating unit obtains an updated target probability distribution based on the platform detection results and the predicted target probability distribution;

[0055] The objective function solving unit constructs the platform detection capability objective function based on the target detection probability and the detection area of ​​each platform; constructs the platform search capability objective function based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform; and constructs the search redundancy objective function based on the pheromone at each sea area grid;

[0056] The target search path solving unit scores the platform estimated position set based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, selects the platform estimated position with the highest score, and obtains the target search path.

[0057] In a third aspect, the present invention further provides a terminal, comprising a processor and a communication interface coupled to the processor, wherein the processor is used to run a computer program or instruction to implement the deep-sea heterogeneous platform on-call search method provided in the first aspect.

[0058] Compared with the prior art, the beneficial effects of the present invention include:

[0059] 1. The deep-sea heterogeneous platform on-call search method proposed in the present invention applies the sea environment feature reconstruction technology to dynamically deduce the acoustic propagation characteristics of the target, platform, and sea environment coupling under the sea grid, unify and link the elements such as the target, platform, and search sea environment, and quickly reconstruct the sea environment features. It can provide a virtual deep-sea physical environment with multiple sea areas, multiple sea conditions, and multiple situations, provide realistic scenes for on-call search tasks, and improve search accuracy.

[0060] 2. The deep-sea heterogeneous platform on-call search method proposed in the present invention constructs the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, which can effectively balance the platform's search speed and detection performance, and effectively reduce the redundant search caused by the platform falling into the local optimal search position. Based on this, when collaboratively performing on-call search tasks in deep-sea areas, the search capabilities of heterogeneous platforms can be comprehensively considered, and the most suitable platform can be quickly dispatched to perform on-call search tasks, thereby improving search efficiency.

[0061] 3. The deep-sea heterogeneous platform on-call search method proposed in the present invention introduces the platform detection capability objective function, platform search capability objective function and search redundancy objective function on the basis of reconstructing the characteristics of the marine environment. According to the on-call search command requirements, the weighted superior and inferior solution distance decision method is used to assign weights to the weight curves of the platform detection capability objective function and the platform search capability objective function, and the best platform search path is planned, which can effectively reduce the probability of target escape while improving the platform search efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0063] Figure 1 It is a flow chart of the on-call search method for deep-sea heterogeneous platforms in an embodiment of the present invention;

[0064] Figure 2 A schematic diagram of a grid search sea area in an embodiment of the present invention;

[0065] Figure 3 A motion model diagram based on Markov properties in an embodiment of the present invention;

[0066] Figure 4 It is a schematic diagram of dividing all sea area grids into each platform in an embodiment of the present invention;

[0067] Figure 5 A weight curve diagram of the platform search capability objective function in an embodiment of the present invention;

[0068] Figure 6 A heterogeneous platform path planning diagram at each time in an embodiment of the present invention;

[0069] Figure 7 This is a diagram showing that platform 1 is trapped in a local optimal array in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0071] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0072] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. The following at least one item (items) or similar expressions thereof refer to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one item (items) of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0073] In the target search methods provided by the prior art, sonar detection technology is generally used to directly dispatch a detection platform to search for a target, which is time-consuming. During the search process, the existing platform only relies on the target probability distribution and does not obtain accurate target motion information, which may cause the target to escape. In addition, the prior art has the defects of not comprehensively considering the detection and search capabilities of the platform and redundant search.

[0074] The embodiment of the present invention provides a deep-sea heterogeneous platform on-call search method, which can solve the problem of not comprehensively considering the platform's detection capability, search capability and redundant search in the prior art and the problem of target escape caused by unplanned target search path, thereby improving search accuracy and search efficiency.

[0075] In order to facilitate understanding of a deep-sea heterogeneous platform on-call search method provided by an embodiment of the present invention, it is described in detail below with specific examples. It should be understood that the following examples are only for explanation and not for specific limitation.

[0076] In a first aspect, an embodiment of the present invention provides a deep-sea heterogeneous platform on-call search method, see Figure 1 , specifically including the following steps:

[0077] S1. Given a search area, platform and target, configure the marine environment elements, calculate the marine environment characteristics based on the marine environment elements, and the marine environment characteristics at least include noise, reverberation, and sound propagation characteristics; configure platform parameters and target parameters, where the platform parameters include the number of platforms, initial positions and sonar parameters; and the target parameters include the target initial position and target acoustic parameters.

[0078] S2. Grid search the sea area to obtain multiple sea area grids.

[0079] As a possible implementation, Figure 2 As shown in the figure, given a search area A of M×N rectangular size, the grid method is used to evenly divide the search area into J sea grids of size Δx×Δy according to the step accuracy requirements. It needs to be further explained that the step accuracy refers to the accuracy of grid division. There is no specific requirement in practical applications. The user sets the step accuracy based on the amount of calculation and the requirements for the detailed display of the results.

[0080] S3. Determine the target transfer probability and the platform transfer probability.

[0081] As a possible implementation, Figure 3 As shown in the figure, a Markov-based platform and target motion model is constructed, and it is set that the platform and target positions at time k can only move from the positions of the platform and target at time k-1 to the adjacent grid or remain unchanged. Based on the above settings, the platform and target motion are modeled through the underlying Markov chain transition probability, and the platform transition probability Γ a It is expressed as:

[0082] Γ a =P(u(k)=j∈C g |u(k-1)=g),

[0083] Among them, u(k) is the grid where the platform is located at time k, C g is the set of adjacent grids when the platform is located at grid g, a represents the platform, j represents the jth grid in the J sea area grids, and u(k-1) is the grid where the platform is located at time k-1.

[0084] The target transition probability is expressed as:

[0085] Γ t =P(x(k)=s∈C h |x(k-1)=h),

[0086] Among them, x(k) is the grid where the target is located at time k, C h is the set of adjacent grids when the target is located at grid h, t represents the target, s represents the sth grid among J sea area grids, and u(k-1) is the grid where the target is located at time k-1.

[0087] S4. Based on the platform transfer probability, the platform estimated position at time k is inferred from the platform position at time k-1, and a set of platform estimated positions is obtained by arranging and combining multiple platform estimated positions.

[0088] S5. Based on the target transfer probability, the target estimated position at time k is calculated from the target position at time k-1.

[0089] S6. Based on the proximity principle and the uniqueness principle, all sea area grids are divided into each platform to obtain the detection area of ​​each platform.

[0090] As a possible implementation method, the number of platforms determined by S1 is set to m. Based on the number of platforms m and the estimated target position at time k calculated by S5, all sea area grids are divided into each platform based on the proximity principle and the uniqueness principle to obtain the detection area of ​​each platform.

[0091] As an example, Figure 4 As shown in the figure, at each moment k = {1, 2, ..., K}, the platform position is regarded as a Voronoi site. The proximity principle is to measure the distance between the sea grid j = 1, 2, ... J and the Voronoi site based on the Euclidean distance, and assign each sea grid to the nearest Voronoi site. The unique principle is that each sea grid is only assigned to a unique Voronoi site. The detection area of ​​each platform is obtained in, i represents the i-th platform.

[0092] S7. Reconstruct the environmental characteristics of the sea area.

[0093] As a possible implementation method, reconstructing the marine environment characteristics includes the following sub-steps:

[0094] S70, Dynamically simulate the acoustic propagation characteristics of the target, platform and sea environment coupling under the sea grid;

[0095] As an example, the acoustic propagation characteristics of the coupled targets, platforms, and sea environment under the sea grid are dynamically simulated based on the BELLHOP acoustic toolbox.

[0096] S71, simulate the ocean environmental noise composed of comprehensive wind, rain and ship effects;

[0097] As an example, the ocean environmental noise composed of wind, rain and ship effects is simulated by empirical formulas, including:

[0098] Wind-generated noise high frequency band (10-1000kHz) model The calculation formula is as follows:

[0099]

[0100] In the formula, represents the high-frequency model of wind-generated noise; v is the wind speed, and v is required to be ≥ 1m / s; f is the noise frequency, in kHz; δ(ΔT) represents the definition of the ΔT value range; ΔT is the temperature difference between air and water, and there are:

[0101] ΔT=T air -T water .

[0102] The calculation formula for wind-induced noise in the low-frequency band below 400 Hz is as follows:

[0103]

[0104] In the formula, Represents the low-frequency band model of wind-generated noise.

[0105] Combining the calculation formulas of low and high frequency bands, a smooth transition model between the two is obtained:

[0106]

[0107] NL wind Represents the wind-generated noise model.

[0108] The calculation formula for the spectrum level of rain noise in the frequency range of 1-100kHz is as follows:

[0109]

[0110] In the formula, Represents the rain noise model; f is the frequency, unit is kHz.

[0111] The calculation formula for shipping noise is as follows:

[0112]

[0113] In the formula, represents the shipping noise model; f is the frequency, unit is Hz.

[0114] S72, calculating the sound energy scattered by all sea area grids that contribute to the reverberation, and accumulating them to obtain the seabed reverberation;

[0115] As an example, the formula for calculating the reverberation intensity is as follows:

[0116]

[0117] Where N is the number of sea area grids that contribute to reverberation, I i is the emission sound intensity corresponding to each sea area grid, σ i is the seabed acoustic scattering coefficient of each sea grid, r1 is the distance from the sea grid to the transmitter, r2 is the distance from the sea grid to the receiver, α is the medium acoustic absorption coefficient, and are the directivities of the transmitting end and the receiving end respectively, and Δs is the area of ​​the sea grid.

[0118] S73. Accumulate the sound propagation characteristics, ocean environmental noise and seabed reverberation to obtain reconstructed ocean environment characteristics.

[0119] S8. At time k, the target detection probability is determined based on the platform parameters, target parameters, estimated platform position and reconstructed sea environment characteristics, and the predicted target probability distribution is obtained based on the target transfer probability; the updated target probability distribution is obtained based on the platform detection results and the predicted target probability distribution.

[0120] As a possible implementation method, S8 specifically includes the following sub-steps:

[0121] S80, at time k, determining the target detection probability based on the platform parameters, the target parameters, the estimated platform position and the reconstructed sea environment characteristics;

[0122] As an example, determine whether the interference affecting sonar detection is reverberation or noise, and use the sonar equation combined with the platform parameters, target parameters, platform estimated position and reconstructed sea environment characteristics input by S1 to determine the target detection probability.

[0123] Active sonar equation when reverberation is the main interference:

[0124] SNR=SL-TL1-TL2+TS-RL,

[0125] Active sonar equation when noise is the main interference:

[0126] SNR=SL-TL1-TL2+TS-(NL-DI),

[0127] Where SL is the sound source level, TL1 and TL2 are the propagation losses between the transmitter and the receiver and the target, TS is the target strength, RL is the reverberation level, NL is the noise interference level, DI is the receiving directivity index, and SNR is the signal-to-noise ratio at the receiver.

[0128] Passive sonar equation when noise is interference:

[0129] SNR = SL1-TL-(NL-DI),

[0130] Where SL1 is the target radiation source level, TL is the propagation loss between the target and the receiving end, NL is the noise interference level, DI is the receiving directivity index, and SNR is the signal-to-noise ratio at the receiving end.

[0131] According to the sonar parameters input by S1, including the false alarm probability and the upper and lower limits of the receiver curve, the receiver characteristic curve is established to establish the relationship between the signal-to-noise ratio SNR at the receiving end and the target detection probability. The target detection probability p d The calculation expression is as follows:

[0132]

[0133] Where Q is the Malkum function, R is the signal-to-noise ratio, and p f is the false alarm probability.

[0134] Since the sea area grid is divided according to the number of platforms, the most "loose" detection criterion is adopted, that is, as long as one platform detects the target, it is considered that the system detects the target, and the probability that platform i detects the target is greater than P d0 =0.7 is the effective detection area ζ i (k).

[0135] S81, obtaining a predicted target probability distribution based on the target transfer probability; obtaining an updated target probability distribution based on the platform detection result and the predicted target probability distribution;

[0136] As an example of obtaining a predicted target probability distribution based on the target transition probability, according to the updated target probability distribution π(k-2│k-2) at time k-2 and the target transition probability Γ t Preliminary prediction of target probability distribution at time k-1:

[0137] π(k-1│k-2)=Γ t π(k-2│k-2).

[0138] As an example of obtaining an updated target probability distribution based on the platform detection results and predicted target probability distribution, assuming that platform i is located at grid j and the target is located at grid h at time k-1, the detection result of platform i is represented by o i (k-1)∈{0,1}, where 0 means the target is not detected and 1 means the target is detected. The target detection probability is The false alarm probability is d indicates detection and f indicates false alarm.

[0139] The target detection probability As prior information, combined with the preliminary predicted k-1 time target probability distribution, the updated k-1 time target probability distribution is obtained. Specifically,

[0140] If the target is within the effective detection area of ​​platform i The detection result is o i (k-1)=0, and the updated target probability distribution is obtained:

[0141]

[0142] If the target is not in the effective detection area, the updated target probability distribution is expressed as:

[0143]

[0144] If the target is within the effective detection area of ​​platform i The observation result is o i (k-1)=1, then the updated target probability distribution is expressed as:

[0145]

[0146] If the target is not in the effective detection area, the updated target probability distribution is expressed as:

[0147]

[0148] Based on the updated target probability distribution π(k-1|k-1) at time k-1 and the target transfer probability, the target probability distribution at time k is preliminarily predicted:

[0149]

[0150] In the formula, is the target transition probability Γ t The transposed matrix of , T stands for transpose.

[0151] S9. Based on the target detection probability and the detection area of ​​each platform, the platform detection capability objective function is constructed. Based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform, the platform search capability objective function is constructed. Based on the pheromones at each sea area grid, the search redundancy objective function is constructed.

[0152] As a possible implementation method, S9 specifically includes the following sub-steps:

[0153] S90, constructing a platform detection capability objective function based on the target detection probability and the detection area of ​​each platform;

[0154] As a possible implementation manner, S90 specifically includes the following sub-steps:

[0155] S900, taking the weighted sum of the target detection probability of each sea area grid in the detection area of ​​each platform at time k, and obtaining the detection result of each platform;

[0156] As an example, the platform detection capability objective function requires the detection performance to be as good as possible on the basis of ensuring the search speed. The detection performance is expressed as the numerical value of the target detection probability. Therefore, the target detection probability of each platform in the assigned area is integrated as the detection capability. The target detection probability of each sea area grid in the detection area of ​​each platform at time k is weighted and summed to obtain the detection result of each platform, which is expressed as:

[0157]

[0158] S901. Calculate the weight value ω of the platform detection capability objective function p ;

[0159] As an example, ω p =1-ω t ,ω t The weight value of the platform search capability objective function.

[0160] S902: Compare the detection result of each platform with the weight value ω of the platform detection capability objective function p Weighted summation is performed to obtain the platform detection capability objective function;

[0161] As an example, based on the superior and inferior solution distance method, the detection result of each platform is compared with the weight value ω of the platform detection capability objective function. p Weighted summation is performed to obtain the platform detection capability objective function r p (k):

[0162]

[0163] S91. Based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform, construct the platform search capability objective function.

[0164] As a possible implementation manner, S91 specifically includes the following sub-steps:

[0165] S910, performing a weighted summation of the product of the target detection probability of each sea area grid in the detection area of ​​each platform at time k and the predicted target probability distribution, to obtain the search result of each platform;

[0166] As an example, in order to achieve the purpose of searching as quickly as possible, the platform should search for locations with large target probability distribution, so the target probability distribution and target detection probability of each platform's detection area are integrated as the search capability. The target detection probability of each sea area grid in the detection area of ​​each platform at time k is weighted and summed with the product of the predicted target probability distribution to obtain the search result of each platform, which is expressed as:

[0167]

[0168] S911. Solve the weight value ω of the platform search capability objective function t , including: setting the minimum weight value and maximum weight value of the platform search capability objective function based on the anti-submarine requirements, and setting the farthest distance between the platform and the target in the search area; solving the weight curve coefficient of the platform search capability objective function based on the logarithmic function of the minimum weight value and the maximum weight value and the farthest distance; solving the exponential function of the weight curve coefficient and the product of the distance between the platform and the target at the initial moment, multiplying the exponential function by the maximum weight value to obtain the weight value of the platform search capability objective function.

[0169] As a weight value of the objective function of solving the platform search capability, ω t For example, the weight value is calculated according to the distance of each platform from the target position at the initial moment. The platform with a closer distance is more focused on rapid exploration, ω t The weight value is large, ω p The weight value is small; the platform far away is more inclined to conduct a comprehensive search to prevent the target from escaping, ω p The weight value is large, ω t The weight value is small. Set the minimum weight value of the platform search capability objective function to ω min , the maximum weight value is ω max , the maximum distance between the platform and the target in the search area is Dmax. t The weight value changes with distance as shown in Figure 4 As shown, it is specifically expressed as:

[0170] ω t =ω max exp(c·d i0 )

[0171]

[0172] In the formula, c represents the weight curve coefficient of the platform search capability objective function, d i0 is the distance between platform i and the target at the initial moment.

[0173] S912: Compare the search results of each platform with the weight value ω of the platform search capability objective function t Weighted summation is performed to obtain the platform search capability objective function;

[0174] As an example, based on the superior and inferior solution distance method, the search results of each platform are compared with the weight value ω of the platform search capability objective function. t Weighted summation to obtain the platform search capability objective function r t (k), expressed as:

[0175]

[0176] S92. Construct a search redundancy objective function based on the pheromones at each sea area grid.

[0177] As a possible implementation manner, S92 specifically includes the following sub-steps:

[0178] S920, using the pheromone of each sea area grid at time k-1, the pheromone diffusion coefficient, and the number of adjacent cells, calculate the cumulative pheromone concentration of adjacent grids of each sea area grid at time k;

[0179] As an example of calculating the cumulative pheromone concentration g(l, k) of adjacent grids in each sea area at time k,

[0180]

[0181] Where N(l) represents the set of sea grids adjacent to sea grid l, |N(l)| represents the number of adjacent sea grids, and λ is the release decision variable. When λ(l,k-1)=1, grid l releases pheromones to attract the platform to search in this area; when λ(l,k-1)=0, the platform does not need to search in this location.

[0182] S921, using the pheromone evaporation coefficient, the pheromone diffusion coefficient, the pheromone of each sea area grid at time k-1, the pheromone release switch, the pheromone release amount, and the cumulative pheromone concentration of the adjacent grids of each sea area grid at time k to solve the pheromone of each sea area grid at time k;

[0183] As an example, under the premise that the platform has good target detection and search capabilities, it is also necessary to ensure that the platform searches for sea grids with higher uncertainty to avoid the platform falling into the local optimal sea grid and causing repeated searches of sea grids. The pheromone s(l, k) of grid l at time k is expressed as follows:

[0184] s(l,k)=(1-E){(1-G)[s(l,k-1)+λ(l,k)·d]+g(l,k)},

[0185] Where E∈(0,1) represents the evaporation coefficient of pheromone, G∈(0,1) represents the diffusion coefficient of pheromone, λ is the pheromone release switch, which is used to control whether grid l releases pheromone, d is the pheromone release amount, which is usually a constant, and g(l,k) is the accumulated pheromone concentration of adjacent grids in each sea area at time k.

[0186] S922, based on the acceptance degree of search redundancy, the weight value of the search redundancy objective function is set to a value between 0 and 1, expressed as ω s ∈[0,1];

[0187] S923, weighted sum of the pheromone of each sea area grid at time k and the weight value of the search redundancy objective function to obtain the search redundancy objective function, which is expressed as:

[0188]

[0189] S10. Score the platform estimated position set based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, select the platform estimated position with the highest score, and obtain the target search path.

[0190] As a possible implementation manner, S10 specifically includes the following sub-steps:

[0191] S100, based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, the overall objective function is obtained, which is expressed as:

[0192] R=[r p (k),r t (k),r s (k)].

[0193] S101, based on the platform estimated position set and the total objective function, obtain the total objective function set R'=[r p '(k),r t '(k),r s '(k)].

[0194] As an example, the arrangement and combination of the movable positions of the platform can obtain n groups of total objective functions, and the total objective function group R'=[r p '(k),r t '(k),r s '(k)],

[0195] S102, normalizing the total objective function group to obtain the dimension-removed total objective function group.

[0196] As an example, the total objective function group is normalized to remove the dimension, and the total objective function group R after removing the dimension is obtained. s =[r ps '(k),r ts '(k),r ss '(k)], the specific normalization method is as follows:

[0197]

[0198] S103, screening the maximum and minimum values ​​of the total objective function group after dimension removal, calculating the distance between each function value and the maximum and minimum values ​​in the total objective function group after dimension removal, and obtaining the maximum spacing and the minimum spacing.

[0199] As an example, the distance between each function value and the maximum value is expressed as follows:

[0200]

[0201] The distance between each function value and the minimum value is expressed as follows:

[0202]

[0203] In the formula, r + is the maximum spacing, r - is the minimum spacing, and are the maximum and minimum values ​​of the platform detection capability objective function in the total objective function group after removing the dimension, and are the maximum values ​​of the platform search capability objective function in the total objective function group after removing the dimension, and They are respectively the maximum value of the redundant objective function searched in the total objective function group after removing the dimension, and They are respectively the platform detection capability objective function value, platform search capability objective function value and search redundancy objective function value in the total objective function group after removing the dimension.

[0204] S104. Calculate the ratio of the minimum spacing to the sum of the maximum spacing and the minimum spacing to obtain a score of the platform estimated position set.

[0205] As an example, the score of the platform estimated position set is expressed as:

[0206]

[0207] S105: Select the platform estimated position with the highest score to obtain the target search path.

[0208] In a possible implementation, the path planning of the heterogeneous platform at each time is as follows: Figure 5 As shown, the target is set to move to the right at a uniform speed, but no prior information about the target's movement direction is given to the platform, that is, the provided target transfer probability indicates that the target can move from the initial position to all adjacent grids.

[0209] Figure (a) At the initial time t = 1s, platform 1 is closer to the target, so the platform search capability objective function has a large weight, and platform 1 tends to quickly explore. Figure (b) At t = 4s, platform 1 reaches the position with the maximum target detection probability, achieving rapid detection of the target. However, due to the movement of the target and the lack of prior information, platform 1 did not detect the target at that location. If only the target search capability is considered, it may lead to search redundancy or target escape, that is, platform 1 falls into a local optimal position, such as Figure 6 shown.

[0210] As can be seen from Figure (c) at t = 6s, the method provided by the present invention can effectively avoid repeated searches. The initial position of platform 2 is far from the target, and the platform detection capability objective function has a large weight, which is more inclined to target detection capability to prevent the target from escaping. Therefore, when planning the path, it does not go directly to the position with the largest target probability distribution, but searches in the periphery. At t = 8s, it can be seen that platform 2 detects the target, effectively preventing the target from escaping, and the target search ends. It verifies that the present invention can effectively integrate the platform's detection capability and search capability as well as the degree of search redundancy, ensure the on-call search capability of heterogeneous platforms, and improve search accuracy and efficiency.

[0211] The embodiment of the present invention further provides a deep-sea heterogeneous platform on-call search system, comprising:

[0212] The estimated position solving unit calculates the estimated platform position at time k from the platform position at time k-1 based on the platform transfer probability, and calculates the estimated target position at time k from the target position at time k-1 based on the target transfer probability;

[0213] The detection area division unit divides all sea area grids into each platform based on the proximity principle and the unique principle to obtain the detection area of ​​each platform;

[0214] A sea area environment feature reconstruction unit, used to reconstruct the search sea area environment features;

[0215] The target detection probability solving unit determines the target detection probability based on the platform parameters, target parameters, the estimated platform position and the reconstructed sea environment characteristics;

[0216] A target probability distribution prediction unit, which obtains a predicted target probability distribution based on the target transfer probability;

[0217] A target probability distribution updating unit obtains an updated target probability distribution based on the platform detection results and the predicted target probability distribution;

[0218] The objective function solving unit constructs the platform detection capability objective function based on the target detection probability and the detection area of ​​each platform; constructs the platform search capability objective function based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform; and constructs the search redundancy objective function based on the pheromone at each sea area grid;

[0219] The target search path solving unit scores the platform estimated position set based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, selects the platform estimated position with the highest score, and obtains the target search path.

[0220] An embodiment of the present invention further provides a terminal, including a processor and a communication interface coupled to the processor, wherein the processor is used to run a computer program or instruction to implement the deep-sea heterogeneous platform on-call search method of S1 to S10.

[0221] Compared with the prior art, the present invention has the following technical effects:

[0222] 1. The deep-sea heterogeneous platform on-call search method proposed in the present invention applies the sea environment feature reconstruction technology to dynamically deduce the acoustic propagation characteristics of the target, platform, and sea environment coupling under the sea grid, unify and link the elements such as the target, platform, and search sea environment, and quickly reconstruct the sea environment features. It can provide a virtual deep-sea physical environment with multiple sea areas, multiple sea conditions, and multiple situations, provide realistic scenes for on-call search tasks, and improve search accuracy.

[0223] 2. The deep-sea heterogeneous platform on-call search method proposed in the present invention constructs the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, which can effectively balance the platform's search speed and detection performance, and effectively reduce the redundant search caused by the platform falling into the local optimal search position. Based on this, when collaboratively performing on-call search tasks in deep-sea areas, the search capabilities of heterogeneous platforms can be comprehensively considered, and the most suitable platform can be quickly dispatched to perform on-call search tasks, thereby improving search efficiency.

[0224] 3. The deep-sea heterogeneous platform on-call search method proposed in the present invention introduces the platform detection capability objective function, platform search capability objective function and search redundancy objective function on the basis of reconstructing the characteristics of the marine environment. According to the on-call search command requirements, the weighted superior and inferior solution distance decision method is used to assign weights to the weight curves of the platform detection capability objective function and the platform search capability objective function, and the best platform search path is planned, which can effectively reduce the probability of target escape while improving the platform search efficiency.

Claims

1. A deep-sea heterogeneous platform on-call search method, characterized in that: The steps include: Given a search sea area, platform and target, configure marine environmental elements, calculate and obtain marine environmental characteristics based on the marine environmental elements, wherein the marine environmental characteristics at least include noise, reverberation and sound propagation characteristics; configure platform parameters and target parameters, wherein the platform parameters include the number of platforms, initial positions and sonar parameters; the target parameters include the target initial position and target acoustic parameters; Grid search of the sea area to obtain multiple sea area grids; Determine the target transfer probability and platform transfer probability; Based on the platform transfer probability, the platform estimated position at time k is calculated from the platform position at time k-1, and a set of platform estimated positions is obtained by arranging and combining a plurality of the platform estimated positions; Based on the target transfer probability, the target estimated position at time k is calculated from the target position at time k-1; Based on the principle of proximity and the principle of uniqueness, all the sea area grids are divided into each platform to obtain the detection area of ​​each platform; Reconstruct the characteristics of the marine environment; At time k, the target detection probability is determined based on the platform parameters, target parameters, estimated platform position and reconstructed sea environment characteristics; the predicted target probability distribution is obtained based on the target transfer probability; and the updated target probability distribution is obtained based on the platform detection results and the predicted target probability distribution; Based on the target detection probability and the detection area of ​​each platform, a platform detection capability objective function is constructed; based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform, a platform search capability objective function is constructed; based on the pheromone at each sea area grid, a search redundancy objective function is constructed; The platform estimated position set is scored based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, and the platform estimated position with the highest score is selected to obtain a target search path.

2. The deep-sea heterogeneous platform on-call search method according to claim 1, characterized in that: The reconstructed marine environment characteristics include: Dynamically simulate the acoustic propagation characteristics of targets, platforms, and sea environment coupling under sea grid; Simulate the ocean environmental noise composed of comprehensive wind, rain and ship effects; Calculate the acoustic energy scattered by all sea grids that contribute to reverberation, and accumulate them to obtain the seabed reverberation; The sound propagation characteristics, ocean environmental noise and seabed reverberation are accumulated to obtain reconstructed ocean environment characteristics.

3. The deep-sea heterogeneous platform on-call search method according to claim 2, characterized in that: Based on the target detection probability and the detection area of ​​each platform, a platform detection capability objective function is constructed, including: The target detection probability of each sea area grid in the detection area of ​​each platform at time k is weighted and summed to obtain the detection result of each platform; Solve the weight value ω of the platform detection capability objective function p ; The detection result of each platform and the weight value ω of the platform detection capability objective function p The weighted sum is performed to obtain the platform detection capability objective function.

4. The deep-sea heterogeneous platform on-call search method according to claim 3, characterized in that: Based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform, a platform search capability objective function is constructed, including: The target detection probability of each sea area grid in the detection area of ​​each platform at time k is weighted and summed by the product of the predicted target probability distribution to obtain the search result of each platform; Solve the weight value ω of the platform search capability objective function t ; The search results of each platform are compared with the weight value ω of the platform search capability objective function t Weighted summation is performed to obtain the platform search capability objective function.

5. The deep-sea heterogeneous platform on-call search method according to claim 4, characterized in that: Solve the weight value ω of the platform search capability objective function t ,include: The minimum weight value and the maximum weight value of the platform search capability objective function are set based on the anti-submarine requirement, and the maximum distance between the platform and the target in the search area is set; Based on the logarithmic function of the minimum weight value and the maximum weight value and the farthest distance, solving the weight curve coefficient of the platform search capability objective function; Solve the exponential function of the product of the weight curve coefficient and the distance between the platform and the target at the initial moment, multiply the exponential function by the maximum weight value, and obtain the weight value of the platform search capability objective function.

6. The deep-sea heterogeneous platform on-call search method according to claim 5, characterized in that: The weight value ω based on the platform search capability objective function t Solve the weight value ω of the platform detection capability objective function p , specifically: oh p =1-h t 。 7. The deep-sea heterogeneous platform on-call search method according to any one of claims 1 to 6, characterized in that: Based on the pheromone at each sea area grid, a search redundancy objective function is constructed, including: Using the pheromone, pheromone diffusion coefficient, and number of adjacent cells of each sea area grid at time k-1, the cumulative pheromone concentration of adjacent grids of each sea area grid at time k is calculated; The pheromone of each sea area grid at time k is solved by using the pheromone evaporation coefficient, the pheromone diffusion coefficient, the pheromone of each sea area grid at time k-1, the pheromone release switch, the pheromone release amount, and the cumulative pheromone concentration of the adjacent grids of each sea area grid at the said time k; The weight value of the search redundancy objective function is set to a value between 0 and 1 based on the acceptance degree of the search redundancy; The pheromone of each sea area grid at time k is weighted and summed with the weight value of the search redundancy objective function to obtain the search redundancy objective function.

8. The deep-sea heterogeneous platform on-call search method according to claim 7, characterized in that: Scoring the platform estimated position set based on the platform detection capability objective function, the platform search capability objective function, and the search redundancy objective function, selecting the platform estimated position with the highest score, and obtaining a target search path, including: Based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, an overall objective function is obtained; Based on the platform estimated position set and the total objective function, obtaining a total objective function group; Normalizing the total objective function group to obtain a dimension-removed total objective function group; Screening the maximum value and the minimum value of the total objective function group after dimension removal, calculating the distance between each function value in the total objective function group after dimension removal and the maximum value and the minimum value, and obtaining the maximum spacing and the minimum spacing; Solving the ratio of the minimum spacing to the sum of the maximum spacing and the minimum spacing to obtain a score of the platform estimated position set; The estimated platform position with the highest score is selected to obtain the target search path.

9. A deep-sea heterogeneous platform on-call search system, characterized in that: include: The estimated position solving unit calculates the estimated platform position at time k from the platform position at time k-1 based on the platform transfer probability, and calculates the estimated target position at time k from the target position at time k-1 based on the target transfer probability; The detection area division unit divides all sea area grids into each platform based on the proximity principle and the unique principle to obtain the detection area of ​​each platform; A sea area environment feature reconstruction unit, used to reconstruct the search sea area environment features; The target detection probability solving unit determines the target detection probability based on the platform parameters, target parameters, the estimated platform position and the reconstructed sea environment characteristics; A target probability distribution prediction unit, which obtains a predicted target probability distribution based on the target transfer probability; A target probability distribution updating unit obtains an updated target probability distribution based on the platform detection results and the predicted target probability distribution; The objective function solving unit constructs a platform detection capability objective function based on the target detection probability and the detection area of ​​each platform; constructs a platform search capability objective function based on the predicted target probability distribution, target detection probability and the detection area of ​​each platform; and constructs a search redundancy objective function based on the pheromone at each sea area grid; The target search path solving unit scores the platform estimated position set based on the platform detection capability objective function, the platform search capability objective function and the search redundancy objective function, selects the platform estimated position with the highest score, and obtains the target search path.

10. A terminal comprising a processor and a communication interface coupled to the processor, wherein the processor is used to run a computer program or instruction to implement the deep-sea heterogeneous platform on-call search method as described in any one of claims 1 to 8.

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