A multi-static detection performance evaluation method based on marine environment data

Abstract

The application relates to a multi-base station detection efficiency evaluation method based on marine environment data, the algorithm calculates propagation loss based on actual marine environment data and sound propagation characteristics, the error of the propagation loss calculated by an empirical formula method is reduced, and the influence of specific sea area conditions on the detection efficiency can be reflected. The algorithm adopts a Monte Carlo simulation and a cumulative detection probability mode, simulates the changes of target intensity and detection probability caused by changes in target motion posture, and increases the reliability of the multi-base station detection efficiency evaluation result.

Description

Technical Field

[0001] This invention relates to the field of marine exploration technology, and in particular to a method for evaluating the effectiveness of multi-site exploration based on marine environmental data. Background Technology

[0002] As submarine radiated noise continues to decrease, the detection range of passive sonar becomes increasingly limited, while active sonar is prone to revealing its own position. Multistatic detection systems combine the advantages of both active and passive sonar. They typically consist of one or more active sound sources emitting signals, while multiple passive receiving sonars are deployed at different locations to receive and analyze target echoes. The performance of a multistatic detection system is closely related to the multistatic sonar array configuration and the target scattering and motion characteristics. Therefore, the multistatic sonar array parameters and target scattering and motion characteristics should be fully considered when evaluating the effectiveness of a multistatic detection system.

[0003] Traditional multistatic detection performance evaluation methods do not consider the changes in target scattering intensity caused by alterations in the incident and exit angles due to the target's motion. Furthermore, in practice, to ensure the reliability of sonar detection results, cumulative detection probability is often used. Simultaneously, traditional detection performance evaluation methods frequently utilize empirical formulas for extended absorption to calculate propagation loss, neglecting the influence of actual marine environment and sound propagation characteristics. This results in significant errors in propagation loss estimation and fails to reflect the impact of specific sea area conditions on detection performance. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-base detection performance evaluation method based on marine environmental data. This method combines actual marine environmental data and acoustic field models to calculate acoustic propagation loss and uses Monte Carlo simulation to calculate the success rate of detecting targets in different motion states, thereby solving the problems encountered in the aforementioned background technology.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] A method for evaluating the effectiveness of multi-site detection based on marine environmental data includes the following steps:

[0007] Step 1: Array Parameter Settings: Set the deployment parameters for the multi-base sonar array, including the total number of sonars, the latitude and longitude of each sonar, the active and passive operating status and operating parameters of each sonar;

[0008] Step 2: Model the target trajectory: Assume that the target maintains uniform linear motion during its movement. Its initial parameters include three parameters: initial position, initial velocity, and initial heading. Detect whether the target's linear motion trajectory intersects with the distribution area of ​​the multi-base detection nodes. If it does, accept the three randomly generated parameters; otherwise, regenerate a set of sampling parameters.

[0009] Step 3: Calculate propagation loss: The propagation loss calculation is based on actual marine environmental data, the location of the sonar and the target, and the sonar operating parameters, and is performed using the publicly available BELLHOP acoustic propagation calculation model;

[0010] Step 4: Instantaneous Detection Probability Assessment of Multistatic Detection: The instantaneous detection probability assessment of multistatic detection is calculated based on the multistatic active sonar equations;

[0011] Step 5: Detect the target using the KOFN model: The probability of target confirmation is calculated based on the permutation and combination relationship between N and K. The probability of each permutation and combination is calculated using the instantaneous detection probability, and the summation yields the single-base target confirmation probability P. t ';

[0012] Step Six: Cumulative Detection Probability Calculation: The confirmation probabilities of each array to the target are combined to obtain the instantaneous confirmation probability of the cluster. The evaluation method for the cumulative detection probability (CDP) is as follows:

[0013] CDP(t)=1-Π(1-P t ');

[0014] Step 7: Compare the maximum cumulative detection probability with the threshold: If the maximum cumulative detection probability is >0.95, the number of times the target was detected is incremented by 1; otherwise, the number of times the target was not detected is incremented by 1.

[0015] Step 8: Determine if the number of Monte Carlo simulations has reached the set value. If it has not reached the set value, return to Step 2. If it has reached the set value, proceed to Step 9.

[0016] Step 9: Calculate the success rate of target detection using the following formula:

[0017]

[0018] Compared with existing technologies, the beneficial effects of this invention are: This multi-base detection effectiveness assessment method calculates propagation loss based on actual marine environmental data and acoustic propagation characteristics, resulting in lower propagation loss errors compared to empirical formula methods, and it can reflect the impact of specific sea area conditions on detection effectiveness. This algorithm employs Monte Carlo simulation and cumulative detection probability to simulate changes in target intensity and detection probability caused by changes in target motion patterns, thereby increasing the reliability of the multi-base detection effectiveness assessment results. Attached Figure Description

[0019] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0020] Figure 1 This is a calculation diagram of the present invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives, and effects of this invention readily understandable, the invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the relevant components of the invention.

[0022] According to the technical solution of the present invention, without changing the essential spirit of the present invention, those skilled in the art can propose various interchangeable structural methods and implementations. Therefore, the following detailed embodiments and accompanying drawings are merely exemplary descriptions of the technical solution of the present invention, and should not be regarded as the entirety of the present invention or as a limitation or restriction of the technical solution of the present invention.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0024] like Figure 1 As shown, a method for evaluating the effectiveness of multi-base detection based on marine environmental data includes the following steps:

[0025] Step 1: Array Parameter Settings: Set the deployment parameters for the multi-base sonar array, including the total number of sonars, the latitude and longitude of each sonar, the active and passive operating status and operating parameters of each sonar.

[0026] Step 2: Model the target trajectory: Assume the target maintains uniform linear motion during its movement. Its initial parameters include initial position, initial velocity, and initial heading. Detect whether the target's linear trajectory intersects with the distribution area of ​​the multi-base detection nodes. If it does, accept the three randomly generated parameters; otherwise, regenerate a new set of sampled parameters.

[0027] The algorithmic logic steps for modeling the target trajectory are as follows:

[0028] 1) Set the node_para parameter for the polygon node in the multi-base detection and control area;

[0029] 2) Set the parameters for target generation: The initial target speed V distribution follows the pattern V ~ U(V min V max The initial positions X and Y of the target are distributed according to X ~ U(X). min ,X max ), Y~U(Y min ,Y max The initial heading θ of the target follows the distribution of θ ~ U(θ). min ,θ max );

[0030] 3) Based on the distribution parameters in 2), randomly generate three target motion parameters: initial position X, Y, initial velocity V, and initial heading θ, and construct the target initial state vector [X...]. 0 ,Y 0 ,V cos(θ),V sin(θ)];

[0031] 4) Determine whether there is an intersection between the target's linear trajectory and the polygon node parameter node_para of the controlled area. If there is, jump to 3); otherwise, accept the randomly generated target motion parameters.

[0032] 5) Target trajectory modeling: The iterative relationship between the target state vector in frame (k+1) and the target state vector in frame k is shown in the following formula, T s This represents the interval between each frame.

[0033]

[0034] Step 3: Propagation Loss Calculation: The propagation loss calculation is based on actual marine environmental data, the location of the sonar and target, and the sonar operating parameters, and is performed using the publicly available BELLHOP acoustic propagation calculation model. The marine environmental data includes hydrological data: water temperature, salinity, and depth; seabed sediment data; and seabed topography data. The calculated propagation loss includes the propagation loss TL between the transmitting sonar and the target. t and the propagation loss TL between the receiving sonar and the target. r .

[0035] Step 4: Instantaneous Detection Probability Assessment of Multistatic Detection: The instantaneous detection probability assessment of multistatic detection is calculated based on the multistatic active sonar equations. The calculation process is as follows:

[0036] 1) Quality Factor Calculation

[0037] FOM = SL - (NL - DI) - DT + TS

[0038] Where SL is the active sonar transmitting source level, NL is the ambient noise level within the array's operating bandwidth, DI is the receiver directivity index, DT is the detection threshold, and TS is the target intensity. Parameters SL, NL, DI, and DT need to be preset, while parameter TS is calculated using the following algorithm:

[0039] 2) Target strength calculation

[0040] The target intensity calculation model includes four scattering mechanisms: 1. Backscattering from a cylinder, 2. Scattering from a hemispherical end cap, 3. Forward scattering from a cylinder, and 4. Elastic wave scattering from a cylinder.

[0041] Suppose the target is a finite cylinder of length L, radius r, and hemispherical end caps at the front and rear. The intensity of the backscattered radiation is σ. cbs for:

[0042]

[0043]

[0044] In the formula, j0 is the zeroth-order Bessel function; λ is the wavelength; k is the wavenumber; θ i θ r These are the incident angle and the exit angle, respectively.

[0045] The reflection intensity of the end cap is half that of the hemispherical end cap because the submarine is a gradually narrowing cylinder, and its end cap is even smaller. Therefore, the reflection intensity of the end cap is:

[0046]

[0047]

[0048] The forward scattering intensity is obtained by using a modified Babiné principle and a forced interchange method:

[0049]

[0050] The elastic wave scattering intensity is:

[0051] σ ew =P(θ) i )P(θ r )

[0052] if

[0053]

[0054] otherwise

[0055] P(θ=0)

[0056]

[0057]

[0058] In the formula: c is the speed of sound in water; c v υ is the transverse wave velocity inside the shell; υ is the conversion factor, with a representative value of 2; B is the effective factor, with a representative value of 0.2.

[0059] The target strength is:

[0060] TS=10lg(σ cbs +σ ecbs +σ fs +σ ew )

[0061] It is related to both the incident angle and the exit angle of the sound wave, and can reflect the change in target intensity caused by changes in the incident angle and exit angle due to the target's motion state.

[0062] The settings of the four parameters SL, NL, DI, and DT involve parameters such as the number of sonar elements and bandwidth. Their setting methods are generally accepted in the industry, so no special explanation is given here. However, regarding the calculation of parameter TS, this patent considers the influence of the incident and exit angles on TS in multistatic detection performance evaluation, and therefore adopts this TS calculation method. Traditional TS calculation methods often use the case where the incident angle equals the exit angle, which cannot meet the requirements of multistatic detection.

[0063] 3) Instantaneous detection probability calculation

[0064] Instantaneous detection probability P d The relationship with the signal margin SE is as follows:

[0065]

[0066] SE = FOM - (TL) t +TL r )

[0067] The standard deviation σ is usually between 8 and 9, and here we take σ = 8. SE represents the signal margin, which is calculated by combining the FOM value obtained in step 1) and the TL value obtained in step 3.

[0068] Step 5: The KOFN model states that a target is considered confirmed if it is detected at least K times out of N probes. The probability of target confirmation is calculated based on the permutation and combination relationship between N and K. The probability of each permutation and combination is calculated using the instantaneous detection probability, and the summation yields the single-base target confirmation probability P. t ':

[0069]

[0070] Step Six: Cumulative Detection Probability Calculation: The confirmation probabilities of each array to the target are combined to obtain the instantaneous confirmation probability of the cluster. The evaluation method for the cumulative detection probability (CDP) is as follows:

[0071] CDP(t)=1-Π(1-P t ')

[0072] Step 7: Compare the maximum cumulative detection probability with the threshold. If the maximum cumulative detection probability is >0.95, the number of times the target was detected is incremented by 1; otherwise, the number of times the target was not detected is incremented by 1.

[0073] Step 8: Determine if the number of Monte Carlo simulations has reached the set value. If it has not reached the set value, return to Step 2. If it has reached the set value, proceed to Step 9.

[0074] Step 9: Calculate the success rate of target detection using the following formula:

[0075]

[0076] This invention patent describes a multi-site detection effectiveness evaluation algorithm based on marine environmental data. This algorithm calculates propagation loss based on actual marine environmental data and sound propagation characteristics, reducing the error compared to propagation loss calculated using empirical formulas, and effectively reflects the impact of specific sea area conditions on detection effectiveness. The algorithm employs Monte Carlo simulation and cumulative detection probability to simulate changes in target intensity and detection probability caused by changes in target movement, thereby increasing the reliability of the multi-site detection effectiveness evaluation results.

[0077] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the effectiveness of multi-site detection based on marine environmental data, characterized in that, Includes the following steps: Step 1: Array Parameter Settings: Set the deployment parameters for the multi-base sonar array, including the total number of sonars, the latitude and longitude of each sonar, the active and passive operating status and operating parameters of each sonar; Step 2: Model the target trajectory: Assume that the target maintains uniform linear motion during its movement. Its initial parameters include three parameters: initial position, initial velocity, and initial heading. Detect whether the target's linear motion trajectory intersects with the distribution area of ​​the multi-base detection nodes. If it does, accept the three randomly generated parameters; otherwise, regenerate a set of sampling parameters. Step 3: Calculate propagation loss: The propagation loss calculation is based on actual marine environmental data, the location of the sonar and the target, and the sonar operating parameters, and is performed using the publicly available BELLHOP acoustic propagation calculation model; Step 4: Instantaneous Detection Probability Assessment of Multistatic Detection: The instantaneous detection probability assessment of multistatic detection is calculated based on the multistatic active sonar equations. The calculation process is as follows: ① Calculation of quality factors in, For active sonar emission source level, This represents the ambient noise level within the array's operating bandwidth. The receiver directivity index is the receiver array. For detection threshold, For target strength; ② Target strength calculation The target intensity calculation model includes four scattering mechanisms:

1. Backscattering from a cylinder, 2. Scattering from a hemispherical end cap, 3. Forward scattering from a cylinder, and 4. Elastic wave scattering from a cylinder. Given a target that is a finite cylinder of length L, radius r, and hemispherical end caps at the front and rear, consider the intensity of the backscattering. for: In the formula, It is a zero-order Bessel function; Wavelength; Wave number; , These are the angle of incidence and the angle of exit, respectively. The end cap scattering intensity is: The forward scattering intensity is obtained by using a modified Babiné principle and a forced interchange method: The elastic wave scattering intensity is: ③ Instantaneous detection probability calculation Instantaneous detection probability With signal margin The relationship between them is as follows: Among them, standard deviation Take this place ; Step 5: Detect the target using the K-of-N model: The probability of target confirmation is calculated based on the permutation and combination relationship between N and K. The probability of each permutation and combination is calculated using the instantaneous detection probability, and the summation yields the single-base target confirmation probability. ； Step Six: Cumulative Detection Probability Calculation: Combine the confirmation probabilities of each array to obtain the instantaneous confirmation probability and cumulative detection probability of the cluster. The evaluation method is as follows: ； Step 7: Compare the maximum cumulative detection probability with the threshold: If the maximum cumulative detection probability is >0.95, the number of times the target was detected is incremented by 1; otherwise, the number of times the target was not detected is incremented by 1. Step 8: Determine if the number of Monte Carlo simulations has reached the set value. If it has not reached the set value, return to Step 2. If it has reached the set value, proceed to Step 9. Step 9: Calculate the success rate of target detection using the following formula: 。 2. The method for evaluating the effectiveness of multi-site detection based on marine environmental data according to claim 1, characterized in that: In step two, the algorithm logic steps are as follows: 1) Set the node_para parameter for the polygon node in the multi-base detection and control area; 2) Set the parameters for target generation: initial target speed Distribution follows Initial position of the target Distribution follows , Initial heading of the target Distribution follows ; 3) Randomly generate the initial target position based on the distribution parameters in 2). initial velocity Initial heading Three target motion parameters are used to construct the target initial state vector. ; 4) Determine whether there is an intersection between the target's linear trajectory and the polygon node parameter node_para of the controlled area. If there is, jump to 3); otherwise, accept the randomly generated target motion parameters. 5) Target trajectory modeling: The first The target state vector of the frame and the first The iterative relationship of the target state vector of the frame is shown in the following equation. This represents the interval between each frame; 。 3. The method for evaluating the effectiveness of multi-site detection based on marine environmental data according to claim 1, characterized in that: In step three, the marine environmental data includes hydrological data: water temperature, salinity, depth, seabed sediment data, and seabed topography data.

4. The method for evaluating the effectiveness of multi-site detection based on marine environmental data according to claim 1, characterized in that: In step three, the calculated propagation loss includes the propagation loss between the transmitted sonar and the target. and propagation loss between the receiving sonar and the target .

5. The method for evaluating the effectiveness of multi-site detection based on marine environmental data according to claim 1, characterized in that: In step four, if otherwise In the formula: The speed of sound in water; The transverse wave velocity inside the shell; These are conversion factors; As an effective factor; The target strength is: It is related to both the incident angle and the exit angle of the sound wave, and can reflect the change in target intensity caused by changes in the incident angle and exit angle due to the target's motion state.

Images