Target detection method, underwater vehicle radiation noise detection method and device

Abstract

The invention belongs to the technical field of underwater sound detection, and discloses a target detection method, and an underwater vehicle radiation noise detection method and device. According to the target detection method provided by the invention, an energy detection algorithm is adopted to carry out target detection, a beam forming weight vector is coupled with test statistics, and a calculation formula of a false alarm probability and a detection probability in the energy detection algorithm is constructed. On the basis, the underwater vehicle radiation noise detection method and the corresponding device are provided, firstly, an underwater vehicle radiation noise signal and ocean sound channel model library is constructed, then the underwater vehicle radiation noise signal is orientated based on a selected beam forming method, a target orientation estimation result is obtained, and the underwater vehicle radiation noise detection method based on the target orientation estimation result and the ocean sound channel model library based on the target orientation estimation result are obtained. And then performing beam forming according to a target azimuth estimation result, determining a beam forming weight vector, and finally performing target detection. According to the method, the influence of the beam forming weight vector and the beam forming method on target detection can be evaluated, and radiation noise detection of the underwater vehicle can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater acoustic detection, and more specifically, relates to a target detection method, an underwater vehicle radiated noise detection method and device. Background Art

[0002] The sonar system utilizes acoustic signals reflected or radiated by underwater targets and propagated through the ocean medium, and processes and analyzes the received signals through various signal processing means, so as to achieve functions such as target detection, obtaining target parameters, and species identification. Among them, the beamforming technology enhances the target direction signal, suppresses interference and noise by weighting the signals received by the array elements, thereby improving the signal-to-noise ratio. Target detection is usually based on statistical decision theory, and judges whether a target exists by calculating the test statistic and comparing it with a threshold, and its performance is measured by indicators such as detection probability and false alarm probability.

[0003] The prior art usually independently optimizes beamforming and processes target detection, lacking a systematic method to quantitatively establish and describe the coupling relationship between the beamforming weight vector and the test statistic. Furthermore, it is impossible to evaluate the specific impact of different beamforming strategies on the detection efficiency, and it is also not conducive to reverse-optimizing the beamformer design according to specific detection performance requirements, especially in complex marine environments and application scenarios for specific targets.

[0004] In addition, in the marine environment, the radiated signals of targets such as underwater vehicles contain a large number of line spectra and continuous spectra. These frequency components have strong energy and low frequencies, and can propagate over long distances. Therefore, they are usually used as the radiated acoustic signals to be detected. That is, narrowband signal detection and broadband signal detection are important components in the field of marine environment target detection.

[0005] The detection performance of the radiated noise of an underwater vehicle is restricted or affected by multiple factors such as target radiation characteristics, ocean channel propagation, and array processing algorithms. However, there is little research in this field on the detection of the radiated noise of an underwater vehicle under the combination of the above multiple factors, and it is difficult to provide guidance for optimizing the stealth performance of the underwater vehicle. Summary of the Invention

[0006] The present invention provides a target detection method, an underwater vehicle radiated noise detection method and device, to solve the problem that in the prior art, the coupling relationship between the test statistic and the beamforming weight vector in underwater acoustic detection is not explored or correlated, and the impact of the beamforming weight vector and the beamforming method on target detection cannot be evaluated.

[0007] In a first aspect, the present invention provides a target detection method, which uses an energy detection algorithm for target detection, couples the beamforming weight vector with the test statistic, and constructs calculation formulas for the false alarm probability and the detection probability in the energy detection algorithm; The false alarm probability is expressed as follows:

[0008] The detection probability is expressed as follows:

[0009] Wherein, is the false alarm probability, is the detection probability, is the test statistic, is the detection threshold, is the hypothesis representing that there is only noise and clutter signal in the array received signal, is the hypothesis representing that the array received signal contains the target signal, is the number of independent samplings, is the signal power, is the noise power, is the beamforming weight vector, is the array manifold vector, H represents the conjugate transpose, represents the standard Gaussian right tail function.

[0010] Preferably, the detection threshold is expressed as follows:

[0011] Wherein, is the inverse function of.

[0012] Preferably, the test statistic is expressed as:

[0013] Wherein, is the array received signal obtained by the m th sampling; The criterion for judging whether there is a target is as follows:

[0014] If the obtained test statistic is greater than or equal to the detection threshold, it is determined that there is a target; otherwise, it is determined that there is no target.

[0015] In a second aspect, the present invention provides an underwater vehicle radiated noise detection method, including the following steps: Construct an underwater vehicle radiated noise signal and construct an ocean acoustic channel model library; Based on the selected beamforming method, direct the underwater vehicle radiated noise signal to obtain a target azimuth estimation result; Perform beamforming according to the target azimuth estimation result to determine the beamforming weight vector; Perform target detection using the above-mentioned target detection method.

[0016] Preferably, the radiated noise signal of the underwater vehicle includes a broadband continuous spectrum and a narrowband line spectrum; configure according to the demand information, and simulate and synthesize the radiated noise signal of the underwater vehicle; the demand information includes the continuous spectrum mode, line spectrum distribution structure and modulation relationship of the underwater vehicle.

[0017] Preferably, the physical models in the ocean acoustic channel model library include convergence zone models, bottom bounce models and surface waveguide models, and the signal and calculation models in the ocean acoustic channel model library include low-frequency underwater acoustic channel modulation models, underwater acoustic channel transfer functions and channel response waveform solvers.

[0018] Preferably, by changing the beamforming weight vector, evaluate the influence of different beamforming weight vectors on the detection efficiency of the radiated noise of the underwater vehicle.

[0019] Preferably, by changing the beamforming method, evaluate the influence of different beamforming methods on the detection efficiency of the radiated noise of the underwater vehicle.

[0020] In a third aspect, the present invention provides a radiated noise detection device for an underwater vehicle, including: A signal and model construction module for constructing a radiated noise signal of the underwater vehicle and an ocean acoustic channel model library; An azimuth estimation module for orienting the radiated noise signal of the underwater vehicle based on the selected beamforming method to obtain a target azimuth estimation result; A beamforming module for performing beamforming according to the target azimuth estimation result to determine a beamforming weight vector; A target detection module for performing target detection using an energy detection algorithm; The radiated noise detection device for the underwater vehicle is used to execute the steps in the above-mentioned radiated noise detection method for the underwater vehicle.

[0021] Preferably, the radiated noise detection device for the underwater vehicle further includes: An efficiency evaluation module for evaluating the influence of different beamforming weight vectors on the detection efficiency of the radiated noise of the underwater vehicle by changing the beamforming weight vector, and for evaluating the influence of different beamforming methods on the detection efficiency of the radiated noise of the underwater vehicle by changing the beamforming method.

[0022] One or more technical solutions provided in the present invention have at least the following technical effects or advantages: (1) The present invention first provides a target detection method, which uses an energy detection algorithm for target detection, couples the beamforming weight vector with the test statistic, and constructs calculation formulas for the false alarm probability and detection probability in the energy detection algorithm. That is, the present invention combines both the false alarm probability and the detection probability in the energy detection algorithm with the beamforming weight vector, establishes a coupling formula between the beamforming weight vector and the test statistic. Considering that the beamforming weight vector is an important parameter for improving the signal-to-noise ratio, the present invention combines it with the test statistic, which can be used to analyze and evaluate the influence of the beamforming weight vector and the beamforming method on target detection, and can provide a reference for underwater target detection research.

[0023] (2) The present invention also provides an underwater vehicle radiated noise detection method and a corresponding device. The present invention constructs a model library of underwater vehicle radiated noise signals and ocean acoustic channels; based on the selected beamforming method, the underwater vehicle radiated noise signal is oriented to obtain the target azimuth estimation result; beamforming is performed according to the target azimuth estimation result to determine the beamforming weight vector; the above energy detection algorithm (coupling the beamforming weight vector with the test statistic) is used for target detection. That is, the present invention can realize the detection of underwater vehicle radiated noise, proposes a new detection scheme different from the prior art, and can further analyze and evaluate the influence of the beamforming weight vector and the detection method on the detection efficiency, and can provide guidance for optimizing the stealth performance of underwater vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flowchart of an underwater vehicle radiated noise detection method provided in Embodiment 2 of the present invention; Figure 2 It is a simulation result diagram of 7Hz energy detection; Figure 3 It is a simulation result diagram of st1 - energy detection. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0026] Embodiment 1: Embodiment 1 provides a target detection method, which uses an energy detection algorithm for target detection, couples the beamforming weight vector with the test statistic, and constructs calculation formulas for the false alarm probability and detection probability in the energy detection algorithm.

[0027] Among them, the false alarm probability is expressed as follows:

[0028] The detection probability is expressed as follows:

[0029] In the formula, is the false alarm probability, is the detection probability, is the test statistic, is the detection threshold, assumes that the received signal of the array only contains noise and clutter signals, assumes that the received signal of the array contains target signals, is the number of independent samplings, is the signal power, is the noise power, is the beamforming weight vector, is the array manifold vector, H represents the conjugate transpose, represents the standard Gaussian right tail function.

[0030] The detection threshold is expressed as follows:

[0031] In the formula, is the inverse function of.

[0032] The test statistic is expressed as:

[0033] In the formula, is the m array received signal obtained from the Criterion for judging whether there is a target is as follows:

[0034] If the obtained test statistic is greater than or equal to the detection threshold, it is determined that there is a target; otherwise, it is determined that there is no target.

[0035] In summary, in Embodiment 1, the target detection parameters such as the detection probability and false alarm probability are combined with the beamforming weight vector to establish a coupling formula between the beamforming weight vector and the test statistic. The beamforming weight vector is an important parameter for improving the signal-to-noise ratio. Combining it with the test statistic can be used to analyze and evaluate the influence of the beamforming method and beamforming weight vector on target detection, and can provide a reference for the research of underwater target detection.

[0036] Based on the target detection method provided in Embodiment 1, the present invention further proposes an underwater vehicle radiated noise detection method and a corresponding device, which will be described below with Embodiment 2 and Embodiment 3.

[0037] Embodiment 2: Embodiment 2 provides a method for detecting the radiated noise of an underwater vehicle. Refer to Figure 1 , which includes the following steps: S1. Construct the radiated noise signal of the underwater vehicle and construct a marine acoustic channel model library; S2. Based on the selected beamforming method, direct the radiated noise signal of the underwater vehicle to obtain the target azimuth estimation result; S3. Perform beamforming according to the target azimuth estimation result to determine the beamforming weight vector; S4. Use the target detection method described in Embodiment 1 to perform target detection.

[0038] Among them, the radiated noise signal of the underwater vehicle includes a broadband continuous spectrum and a narrowband line spectrum; it is configured according to the demand information, and the radiated noise signal of the underwater vehicle is simulated and synthesized; the demand information includes the continuous spectrum mode of the underwater vehicle, the line spectrum distribution structure, and the modulation relationship.

[0039] Among them, the physical models in the marine acoustic channel model library include a convergence zone model, a bottom bounce model, and a surface waveguide model, and the signal and calculation models in the marine acoustic channel model library include a low-frequency underwater acoustic channel modulation model, an underwater acoustic channel transfer function, and a channel response waveform solver.

[0040] In addition, Embodiment 2 can also evaluate the influence of different beamforming weight vectors on the detection efficiency of the radiated noise of the underwater vehicle by changing the beamforming weight vector. Embodiment 2 can also evaluate the influence of different beamforming methods on the detection efficiency of the radiated noise of the underwater vehicle by changing the beamforming method.

[0041] The following further describes Embodiment 2.

[0042] Step S1 analyzes the characteristic parameters of the low-frequency radiated noise of the underwater vehicle, studies the radiated noise modeling method of the underwater vehicle, and establishes a radiated noise model of the underwater vehicle. It includes description algorithms for mechanical noise, propeller noise, and hydrodynamic noise. It has a typical continuous spectrum noise model, a low-frequency line spectrum noise model for components such as propellers, rotating shafts, and motors, a line spectrum modulation relationship model, and a coupling parameterization model for continuous spectrum and line spectrum. It is flexibly configured and called according to the requirements such as the continuous spectrum mode, line spectrum distribution structure, and modulation relationship of the selected underwater vehicle, and the radiated noise signal of the underwater vehicle is simulated and synthesized.

[0043] Time-domain signal of underwater target radiated noise can be expressed as:

[0044] In the formula, is the time-domain signal of the underwater target radiated noise, is the hydrodynamic noise signal, is the propeller noise signal, is the mechanical noise signal.

[0045] Its spectrum can be expressed as:

[0046] In the formula, represents the stationary continuous spectrum component originating from the dynamics of the fluid, represents the modulated spectrum component generated by the propeller noise, represents the narrowband line spectrum signal generated by mechanical vibration. In the underwater acoustic target radiation noise signal, narrowband line spectrum signals of multiple frequencies are often included.

[0047] For example, the present invention can use the three-parameter method to simulate the continuous spectrum signal, and the continuous spectrum signal is expressed as:

[0048] In the formula, represents the continuous spectrum signal, represents the signal energy; is the curve parameter, which rotates the curve, when >0, the curve rotates clockwise, that is, the low-frequency segment of the curve is elevated and the high-frequency segment is depressed; when <0, the curve rotates counterclockwise, that is, the low-frequency segment of the curve is depressed and the high-frequency segment is elevated; represents the shape parameter of the spectral peak, which determines the sharpness and height of the curve peak. For the same , if it is large, the peak is low and flat, if it is small, the peak is high and steep; represents the position parameter of the spectral peak, which determines the position of the curve peak on the frequency axis.

[0049] In step S2, for the typical sound propagation mode in the ocean environment, an ocean acoustic channel model is constructed by using the acoustic field software. The off-line simulation calculation method can be adopted to calculate the sound propagation modes of different frequency components in the typical ocean environment and pre-store them in the channel model library. According to the relative position scheme of the radiation noise source and the sonar array, the modulation model of the underwater vehicle radiation noise in the deep-sea acoustic channel can be obtained.

[0050] Specifically, the physical models in the ocean acoustic channel model library include the convergence zone model, the bottom bounce model and the surface waveguide model, and the signal and calculation models in the ocean acoustic channel model library include the low-frequency underwater acoustic channel modulation model, the underwater acoustic channel transfer function and the channel response waveform solver.

[0051] Among them, the influencing factors of the underwater acoustic channel transfer function include signal frequency, sound speed profile, water depth, bottom sediment characteristics, relative position of the sound source and the array, sound propagation loss, multi-path effect, and underwater noise.

[0052] In step S3, for the simulation signal orientation, conventional beamforming technology (CBF, Conventional Beamforming), adaptive beamforming technology, etc. can be used to estimate the target azimuth, providing a reference for determining the optimal weight vector of beamforming.

[0053] Taking the conventional beamforming technology as an example, the idea of conventional beamforming is to perform phase compensation on the signals received by each array element, so as to achieve the beam output of in-phase superposition, and its weighting vector For a uniform linear array, the weighting vector is:

[0054] In the formula, is the array manifold vector, is the angular frequency of the signal, d is the element spacing, c is the sound speed, is the angle between the target direction and the array axis, N is the number of array elements.

[0055] Therefore, the output of conventional beamforming is:

[0056] In the formula, is the array received signal, is the beamforming weighting vector, and H represents the conjugate transpose.

[0057] The power spectrum of the conventional beamforming output signal can be expressed as:

[0058] The array output covariance matrix can be estimated through the sampling covariance matrix obtained.

[0059]

[0060] At this time, the spatial azimuth spectrum estimated by conventional beamforming can be calculated by the following formula:

[0061] For a linear array, the observation space .

[0062] Adaptive beamforming technology is a data-driven beamforming technology that can adaptively generate notches in the direction of interference, thereby improving the signal-to-interference-plus-noise ratio (SINR) of the beam output. An effective measure to improve the interference suppression ability of a beamformer is to adopt adaptive beamforming technology, among which the most typical is the minimum variance distortionless response (MVDR) beamforming technology, which has good azimuth resolution ability and strong interference suppression ability.

[0063] The design principle of the MVDR beamformer is to minimize the beam output power under the condition of keeping the signal in the azimuth of interest undistorted. Assuming the complex weight vector is , then the beam output sequence can be expressed as:

[0064] Then the beam output power is:

[0065] In the formula, .

[0066] Therefore, according to the design principle of the standard MVDR beamformer, we can get:

[0067] In the formula, is the incident azimuth of the signal of interest.

[0068] Usually, the Lagrange multiplier technique is used to solve the constrained optimization problem in the above formula, and the cost function is constructed as:

[0069] In the formula, is the Lagrange multiplier.

[0070] Differentiate and set it to zero, we get:

[0071] Substitute the above formula into the equality constraint condition of the constrained problem, and the weight vector of the standard MVDR beamformer can be obtained as:

[0072] Scan the incident azimuth of the signal of interest in the observation space and calculate the beam output power, then the spatial azimuth spectrum can be obtained as:

[0073] Since the covariance matrix in practice is unknown, it can be estimated by the spatial correlation matrix of a block of data snapshots and is called the sampling covariance matrix. Using to replace , the beam weighting vector is obtained as:

[0074] At this time, the estimated value of the spatial azimuth spectrum can be calculated from the sampling covariance matrix as:

[0075] This beamforming method that adjusts the weighting vector using the received data samples is called the adaptive beamforming method. The method of implementing the MVDR beamformer by inverting the data sample covariance matrix is called the Sample Matrix Inversion (SMI) method.

[0076] Step S4 performs beamforming based on the target azimuth estimation result, and the beam output improves the signal-to-noise ratio and determines the weight vector.

[0077] For a linear array, the signals received by the hydrophones can be expressed in the frequency domain and the time domain as follows:

[0078] where and are the representations of the signals received by the hydrophones in the frequency domain and the time domain respectively, and are the representations of the signals radiated by the sound source in the frequency domain and the time domain respectively, and are the representations of the channel transfer functions from the sound source to each array element in the frequency domain and the time domain respectively, and are the representations of the noise received by the array in the frequency domain and the time domain respectively.

[0079] The signals received by the array can be written as:

[0080] where is the signal radiated by the sound source; is composed of the channel transfer functions from the sound source to each array element and is called the signal wavefront; is the noise received by the array.

[0081] The output of array beamforming is:

[0082] Define the ratio of the total energy received by all array elements to the total noise energy as the signal-to-noise ratio. Since this signal-to-noise ratio is defined using the total energy received by the array, it is called the array input signal-to-noise ratio.

[0083]

[0084] In the formula, is the array input signal-to-noise ratio, is the total energy of the sound source radiation signal received by the array, is the total noise energy received by the array.

[0085] The output signal-to-noise ratio of the array can be expressed as:

[0086] In the formula, is the array output signal-to-noise ratio, is the output of array beamforming, is the sound source radiation signal.

[0087] Assume it is a uniform isotropic noise field. At this time:

[0088] In the formula, is the array gain, is the directivity index.

[0089] In step S5, referring to the weak target detection scheme and statistical decision method of the sonar system, use energy detection to determine the detection threshold. Combine target detection formulas such as detection probability and false alarm probability with the weight vector to establish a coupling formula between the beamforming weight vector and the test statistic.

[0090] Array received signal:

[0091] Received signal energy of each array element:

[0092] Received noise energy of each array element:

[0093] In the formula, is the calculated energy.

[0094] Weight vector: , which is related to the beamforming method.

[0095] Beam output:

[0096] Beam output signal energy:

[0097] Beam output noise energy:

[0098] Detection threshold:

[0099] False alarm probability:

[0100] Detection probability:

[0101] Underwater target detection is a typical binary hypothesis testing problem. It is necessary to construct a test statistic from the echo sequence received by the resolution cell, and at the same time set a detection threshold. If the test statistic is greater than or equal to the set detection threshold, it is determined that there is a target in the resolution cell; otherwise, it is determined that there is no target in the resolution cell.

[0102] The test statistic is expressed as:

[0103] where is the array received signal obtained by the m th sampling; The criterion for judging whether there is a target is as follows:

[0104] If the obtained test statistic is greater than or equal to the detection threshold, it is determined that there is a target; otherwise, it is determined that there is no target.

[0105] The present invention can determine the detection threshold according to the energy detector detection method, and use this detection threshold to judge whether the radiation noise of the underwater vehicle is received, so as to realize the detection and identification of the radiation noise of the underwater vehicle.

[0106] In addition, the present invention can also verify the change of the detection probability with the signal-to-noise ratio under different false alarm probabilities through simulation. For example, the following simulation is carried out: Narrowband simulation: Set an 8-element linear array, the element spacing is 15m, and the signal frequency is 7Hz. Using an energy detector, the change of the detection probability with the signal-to-noise ratio under different false alarm probabilities is as Figure 2 shown.

[0107] Wideband simulation: Set an 8-element linear array with an element spacing of 15 m. Simulate the signal st1 using the three-parameter model. The parameters of st1 are set as follows: is 200 Hz, is 200 Hz, is 0. The fundamental frequency of the line spectrum is 12 Hz, and the number of harmonics is 5. An energy detector is used. The variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities is as Figure 3 shown.

[0108] The above simulation shows the relationship between the detection probability and the signal-to-noise ratio under different conditions, which can be used as a reference for subsequent research.

[0109] In summary, Example 2 can achieve the detection of the radiated noise of an underwater vehicle. By adjusting and changing the beamforming weight vector and / or the beamforming method, it is also possible to analyze and evaluate the influence of the beamforming weight vector and the detection method on the detection efficiency, and thus can provide guidance for optimizing the stealth performance of the underwater vehicle.

[0110] Example 3: Example 3 provides an underwater vehicle radiated noise detection device, including: A signal and model construction module for constructing a radiated noise signal model library of an underwater vehicle and an ocean acoustic channel model library; An azimuth estimation module for orienting the radiated noise signal of the underwater vehicle based on the selected beamforming method to obtain a target azimuth estimation result; A beamforming module for performing beamforming according to the target azimuth estimation result to determine a beamforming weight vector; A target detection module for performing target detection using an energy detection algorithm; The underwater vehicle radiated noise detection device is used to execute the steps in the underwater vehicle radiated noise detection method described in Example 2.

[0111] In addition, the underwater vehicle radiated noise detection device may further include an efficiency evaluation module. The efficiency evaluation module is used to evaluate the influence of different beamforming weight vectors on the detection efficiency of the underwater vehicle radiated noise by changing the beamforming weight vector, and is also used to evaluate the influence of different beamforming methods on the detection efficiency of the underwater vehicle radiated noise by changing the beamforming method.

[0112] Since the functions of the modules in the detection device provided in Example 3 correspond to the steps in the detection method provided in Example 2, therefore, reference can be made to the description in Example 2 for understanding, and details will not be repeated here.

[0113] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the examples, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A target detection method, characterized in that, Target detection is performed using an energy detection algorithm. The beamforming weight vector is coupled with the test statistic to construct the calculation formulas for the false alarm probability and the detection probability in the energy detection algorithm; The false alarm probability is expressed as follows: The detection probability is expressed as follows: Wherein, is the false alarm probability, is the detection probability, is the test statistic, is the detection threshold, is the hypothesis representing that there is only noise and clutter signal in the array received signal, is the hypothesis representing that the array received signal contains the target signal, is the number of independent samplings, is the signal power, is the noise power, is the beamforming weight vector, is the array manifold vector, H represents the conjugate transpose, represents the standard Gaussian right tail function.

2. The object detection method according to claim 1, wherein The detection threshold is expressed as follows: In the formula, is the inverse function of.

3. The object detection method according to claim 1, wherein The test statistic is expressed as: In the formula, is the array received signal obtained by the m th sampling. The criterion for determining whether there is a target is as follows: If the obtained test statistic is greater than or equal to the detection threshold, it is determined that there is a target; otherwise, it is determined that there is no target.

4. A method for detecting the radiated noise of an underwater vehicle, characterized in that, It includes the following steps: Construct the radiated noise signal of the underwater vehicle and construct a library of ocean acoustic channel models; Based on the selected beamforming method, direct the radiated noise signal of the underwater vehicle to obtain the target azimuth estimation result; Perform beamforming according to the target azimuth estimation result to determine the beamforming weight vector; Use the target detection method described in any one of claims 1-3 to perform target detection.

5. The underwater vehicle radiated noise detection method according to claim 4, wherein The radiated noise signal of the underwater vehicle includes a broadband continuous spectrum and a narrowband line spectrum; Configure according to the demand information and simulate and synthesize the radiated noise signal of the underwater vehicle; the demand information includes the continuous spectrum mode, line spectrum distribution structure, and modulation relationship of the underwater vehicle.

6. The underwater vehicle radiation noise detection method according to claim 4, characterized in that, The physical models in the ocean acoustic channel model library include the convergence zone model, the seabed bounce model, and the surface waveguide model. The signal and calculation models in the ocean acoustic channel model library include the low-frequency underwater acoustic channel modulation model, the underwater acoustic channel transfer function, and the channel response waveform solver.

7. The underwater vehicle radiation noise detection method according to claim 4, characterized in that It also includes: By changing the beamforming weight vector, evaluate the influence of different beamforming weight vectors on the detection efficiency of the radiated noise of the underwater vehicle.

8. The underwater vehicle radiation noise detection method according to claim 4, characterized in that, It also includes: By changing the beamforming method, evaluate the influence of different beamforming methods on the detection efficiency of the radiated noise of the underwater vehicle.

9. An underwater vehicle radiation noise detection device, characterized in that, It includes: A signal and model construction module for constructing the radiated noise signal of the underwater vehicle and a library of ocean acoustic channel models; An azimuth estimation module for directing the radiated noise signal of the underwater vehicle based on the selected beamforming method to obtain the target azimuth estimation result; A beamforming module for performing beamforming according to the target azimuth estimation result to determine the beamforming weight vector; A target detection module for performing target detection using an energy detection algorithm; The underwater vehicle radiated noise detection device is used to execute the steps in the underwater vehicle radiated noise detection method described in claim 4.

10. The underwater vehicle radiation noise detection device according to claim 9, characterized in that, It also includes: An efficiency evaluation module for evaluating the influence of different beamforming weight vectors on the detection efficiency of the radiated noise of the underwater vehicle by changing the beamforming weight vector, and for evaluating the influence of different beamforming methods on the detection efficiency of the radiated noise of the underwater vehicle by changing the beamforming method.

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