Detection efficiency evaluation method and device for continuous spectrum of radiation noise of underwater vehicle

Abstract

The invention belongs to the technical field of underwater sound detection, and discloses a detection efficiency evaluation method and device for a radiation noise continuous spectrum of an underwater vehicle. The method comprises the following steps: S1, modeling an underwater vehicle radiation noise continuous spectrum signal, and constructing a marine acoustic channel model library and an array signal processing algorithm library; s2, designing detection statistics of an underwater target, and setting a detection threshold value to detect and identify the continuous spectrum of the radiation noise of the underwater vehicle; and S3, by changing the parameters or algorithms in S1 and S2, evaluating the detection efficiency under the combination of distribution modes, channel propagation modes and array signal processing algorithms of different continuous spectrums. According to the method, the detection efficiency of the radiation noise continuous spectrum of the underwater vehicle can be evaluated, and guidance can be provided for stealth performance optimization of the underwater vehicle.

Description

Technical Field

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

[0002] With the rapid development of underwater unmanned vehicles, it is very important to understand the main noise sources of underwater unmanned vehicles for detecting, identifying, and designing quieter underwater unmanned vehicles. The three main noise sources of underwater unmanned vehicles are mechanical noise generated by the vibration of various mechanical devices inside the underwater unmanned vehicle, especially the vibration of power machinery; propeller noise generated by the rotation of the propeller in water, including propeller cavitation noise and propeller rotation noise; and hydrodynamic noise generated by the interaction between the hull of the underwater unmanned vehicle and the water body when the underwater unmanned vehicle is navigating in water. The radiated noise spectrum of an underwater unmanned vehicle includes a continuous spectrum, a line spectrum, and a modulation spectrum.

[0003] The development of underwater acoustics is inseparable from the research on the physical mechanism of ocean sound propagation. The underwater acoustic propagation characteristics play an important role in the research process of the detection ability of underwater acoustic equipment. The convergence zone detection mode based on the deep-sea convergence zone has become an important way for sonar to detect distant targets. The research on the reflection of sound waves by the seabed has promoted the development of the sonar seabed bounce mode. Currently, the research hotspots of underwater acoustic propagation characteristics include stable ocean channels, deep-sea multi-path arrival structures, reliable sound paths, etc.

[0004] A sonar system uses the sound 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 to achieve functions such as target detection, obtaining target parameters, and species identification. The key technologies in underwater acoustic array signal processing technology are azimuth estimation and beamforming.

[0005] The detection performance of the continuous spectrum of the radiated noise of an underwater vehicle is restricted or affected by many factors such as the multi-path effect of the ocean channel, environmental noise, and array processing algorithms. However, there is little research in this field on the detection efficiency of the continuous spectrum under the combination of the above-mentioned multiple factors, and it is difficult to provide guidance for optimizing the stealth performance of underwater vehicles. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and device for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle, so as to evaluate the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle, and further provide guidance for optimizing the stealth performance of underwater vehicles.

[0007] The present invention provides a method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle, including the following steps: S1. Model the continuous spectrum signal of the underwater vehicle's radiated noise, and construct an ocean acoustic channel model library and an array signal processing algorithm library; S2. Design a detection statistic for the underwater target and set a detection threshold to detect and identify the continuous spectrum of the underwater vehicle's radiated noise; S3. By changing the parameters or algorithms in S1 and S2, evaluate the detection efficiency under different combinations of continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithms.

[0008] Preferably, in step S1, the three-parameter method is used to simulate the continuous spectrum signal, and the continuous spectrum signal is expressed as:

[0009] where, represents the continuous spectrum signal, represents the signal energy; is the curve parameter, which rotates the curve, when >0, the curve rotates clockwise, when <0, the curve rotates counterclockwise; represents the shape parameter of the spectral peak, which determines the sharpness and height of the curve peak; represents the position parameter of the spectral peak, which determines the position of the curve peak on the frequency axis.

[0010] Preferably, the autocorrelation function of the continuous spectrum signal is expressed as:

[0011] where, represents the autocorrelation function.

[0012] Preferably, in step S1, 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.

[0013] Preferably, the influencing factors of the underwater acoustic channel transfer function include signal frequency, sound speed profile, water depth, bottom sediment properties, relative position of the sound source and the array, sound propagation loss, multi-path effect, and underwater noise; Among them, the sound propagation loss is a function of the propagation distance and the signal frequency , and is approximately expressed as:

[0014] Expressing the above formula in decibels, the sound propagation loss is as follows:

[0015] Wherein, is the sound propagation loss, is the functional expression of the sound propagation loss, is the expansion factor, is the sound wave propagation distance, is the seawater absorption loss coefficient; The waveform emitted from the sound source After multi-path propagation, the received waveform reaching the receiving end is expressed as follows:

[0016] Wherein, is the received waveform reaching the receiving end, is the total number of sound wave propagation paths, is the signal amplitude value of the sound wave reaching the receiving point along the i th propagation path, is the signal propagation time delay of the sound wave reaching the receiving point along the i th propagation path; The underwater noise includes ocean ambient noise and self-noise, and the total noise level is expressed as:

[0017] Wherein, is the total noise level, is the ocean ambient noise level, is the self-noise level, is the working frequency bandwidth.

[0018] Preferably, in step S1, the array signal processing algorithm library includes multiple methods such as conventional beamforming, adaptive beamforming, matched field linear processor, and matched field adaptive processor, and evaluates the array gain and directivity index of different methods.

[0019] Preferably, in step S2, the detection statistic is expressed as:

[0020] When detecting and identifying the continuous spectrum of the radiated noise of an underwater vehicle, the criterion for judging whether there is a target is as follows:

[0021] Wherein, is the detection statistic, is the number of independent samplings, represents the hypothesis that the array received signal contains a target signal, It is assumed that the received signals of the representative array only contain noise and clutter signals. is the m array received signal obtained by the th subsampling, and is the detection threshold. If the obtained detection 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.

[0022] Preferably, in step S2, an energy detector is used for detection and identification, and the variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities is verified through simulation.

[0023] Preferably, step S3 further includes: optimizing the distribution of the continuous spectrum of the radiated noise of the underwater vehicle based on the evaluation result.

[0024] On the other hand, the present invention provides a detection efficiency evaluation device for the continuous spectrum of the radiated noise of an underwater vehicle, including: A model and algorithm library construction module, which is used to model the continuous spectrum signal of the radiated noise of the underwater vehicle, and construct an ocean acoustic channel model library and an array signal processing algorithm library; A detection and identification module, which is used to design a detection statistic for an underwater target and set a detection threshold to detect and identify the continuous spectrum of the radiated noise of the underwater vehicle; An evaluation module, which is used to evaluate the detection efficiency under different combinations of continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithms; The detection efficiency evaluation device for the continuous spectrum of the radiated noise of the underwater vehicle is used to execute the steps in the above-mentioned detection efficiency evaluation method for the continuous spectrum of the radiated noise of the underwater vehicle.

[0025] One or more technical solutions provided in the present invention have at least the following technical effects or advantages: (1) The present invention mainly includes the following steps: S1. Model the continuous spectrum signal of the radiated noise of the underwater vehicle, and construct an ocean acoustic channel model library and an array signal processing algorithm library; S2. Design a detection statistic for an underwater target and set a detection threshold to detect and identify the continuous spectrum of the radiated noise of the underwater vehicle; S3. By changing the parameters or algorithms in S1 and S2, evaluate the detection efficiency under different combinations of continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithms. The present invention can evaluate the detection efficiency under different combinations of continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithms, and based on the evaluation result, the distribution of the continuous spectrum of the radiated noise of the underwater vehicle can be optimized, that is, the present invention can also provide guidance for optimizing the stealth performance of the underwater vehicle.

[0026] (2) The present invention uses the three-parameter method to model the continuous spectrum signal of the underwater vehicle's radiated noise, which well simulates the continuous spectrum characteristics. Based on the power spectrum analysis, quantitative analysis can be carried out on the target radiated noise signal, key parameters can be extracted, which can provide a basis for noise signal classification, target recognition, etc. Based on its autocorrelation function, the time correlation and oscillation characteristics of the signal can also be studied. Description of the Drawings

[0027] Figure 1 It is a flowchart of a method for evaluating the detection efficiency of the continuous spectrum of the underwater vehicle's radiated noise provided in Embodiment 1 of the present invention; Figure 2 It is a decibel representation diagram of the continuous spectrum of the underwater vehicle; Figure 3 It is a simulation result diagram of st1 - energy detection; Figure 4 It is a simulation result diagram of st2 - energy detection; Figure 5 It is a simulation result diagram of st3 - energy detection. Specific Embodiments

[0028] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the drawings of the specification and specific embodiments.

[0029] Embodiment 1: Embodiment 1 provides a method for evaluating the detection efficiency of the continuous spectrum of the underwater vehicle's radiated noise. Refer to Figure 1 , including the following steps: S1. Model the continuous spectrum signal of the underwater vehicle's radiated noise, and construct an ocean acoustic channel model library and an array signal processing algorithm library; S2. Design a detection statistic for the underwater target and set a detection threshold to detect and identify the continuous spectrum of the underwater vehicle's radiated noise; S3. By changing the parameters or algorithms in S1 and S2, evaluate the detection efficiency under different continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithm combinations.

[0030] Specifically, in step S1 of the present invention, emphasis is placed on modeling the continuous spectrum signal of the underwater vehicle's radiated noise. In addition, the present invention as a whole also analyzes the characteristic parameters of the underwater vehicle's radiated noise, and an underwater vehicle radiated noise model library can be established.

[0031] The time-domain signal of the underwater target's radiated noise can be expressed as:

[0032] In the formula, is the time-domain signal of the underwater target's radiated noise, is the propeller noise signal, is the hydrodynamic noise signal, is the mechanical noise signal.

[0033] Its spectrum can be expressed as:

[0034] In the formula, represents the stationary continuous spectrum component originating from the dynamics of the fluid, represents the modulation spectrum component generated by the propeller noise, represents the narrowband line spectrum signal generated by mechanical vibration. Multiple frequency narrowband line spectrum signals are often included in the underwater acoustic target radiation noise signal.

[0035] Among them, the decibel representation of the continuous spectrum of the underwater vehicle can be referred to Figure 2 .

[0036] For underwater targets with different working environments and mechanical characteristics, the three-parameter model can well simulate its spectral characteristics under certain conditions. The present invention uses the three-parameter method to simulate the continuous spectrum signal, and the continuous spectrum signal is expressed as:

[0037] 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 raised 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 raised; 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.

[0038] Under the conditions of the three-parameter model, its autocorrelation function is equal to the Fourier transform of the power spectral density. That is, the autocorrelation function of the continuous spectrum signal is expressed as:

[0039] In the formula, represents the autocorrelation function.

[0040] Specifically, in step S1, 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.

[0041] The influencing factors of the underwater acoustic channel transfer function include signal frequency, sound speed profile, water depth, bottom sediment properties, relative position of the sound source and the array, sound propagation loss, multipath effect, and underwater noise.

[0042] Among them, the sound speed in seawater is a function of temperature, salinity, and pressure, and increases with the increase of any one of these three parameters, with the temperature having the most significant influence.

[0043] Among them, the sound propagation loss is the propagation distance and the signal frequency function, approximately expressed as:

[0044] Expressing the above formula in decibels (dB), the sound propagation loss is as follows:

[0045] In the formula, is the sound propagation loss, is the functional expression of the sound propagation loss, is the spreading factor, is the acoustic wave propagation distance, with the unit of m; is the signal frequency, with the unit of kHz ; is the seawater absorption loss coefficient.

[0046] The first term in the above formula is the spreading loss, and the second term is the absorption loss. Among them, the spreading factor n is related to the propagation conditions. When the propagation mode of the acoustic wave is spherical wave spreading, n = 2; when the propagation mode of the acoustic wave is cylindrical wave spreading, n = 1; in actual calculations, generally both cases need to be considered, so usually n = 1.5.

[0047] The waveform emitted from the sound source after multipath propagation, the received waveform

[0048] is expressed as follows: is the received waveform reaching the receiving end, is the total number of acoustic wave propagation paths, is the signal amplitude value when the acoustic wave reaches the receiving point along the i th propagation path, is the signal propagation delay when the acoustic wave reaches the receiving point along the i th propagation path.

[0049] The underwater noise includes marine environmental noise and self-noise, and the total noise level is expressed as:

[0050] wherein, is the total noise level, is the marine environmental noise level (spectral level), is the self-noise level (spectral level), is the working frequency bandwidth.

[0051] That is, the present invention constructs an ocean acoustic channel model library with a low-frequency underwater acoustic channel modulation model based on normal modes and a channel response waveform solver for typical acoustic propagation modes in the ocean environment, including a convergence zone model, a bottom bounce model, and a surface waveguide model. An offline simulation calculation method can be used to calculate the acoustic propagation modes of different frequency components in a 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, a modulation model database of the radiation noise of the underwater vehicle in the deep-sea acoustic channel can be obtained.

[0052] Specifically, in step S1, the array signal processing algorithm library includes various methods such as conventional beamforming, adaptive beamforming, matched field linear processor, and matched field adaptive processor, and evaluates the array gain and directivity index of different methods.

[0053] That is, the present invention integrates typical array signal processing technologies, including conventional beamforming technology (CBF), adaptive beamforming technology (MVDR), matched field linear processor, and matched field adaptive processor, evaluates the array gain and directivity index of different methods, and fuses the positioning algorithm by considering the influence of the acoustic channel to obtain the algorithm library of the detection system.

[0054] 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 to achieve an in-phase superposition beam output, and its weighting vector , for a uniform linear array, the weighting vector is:

[0055] wherein, is the array manifold vector, is the angular frequency of the signal, d is the array 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.

[0056] Therefore, the output of conventional array beamforming is:

[0057] where, is the array received signal, is the beamforming weight vector, and H represents the conjugate transpose.

[0058] The power spectrum of the output signal of conventional beamforming can be expressed as:

[0059] The array output covariance matrix can be estimated through the sampling covariance matrix obtained by estimation.

[0060]

[0061] At this time, the spatial azimuth spectrum estimated by conventional beamforming can be calculated by the following formula:

[0062] Assuming it is a uniform isotropic noise field, at this time:

[0063] where, is the array gain, is the directivity index, is the signal-to-noise ratio of the array output, is the signal-to-noise ratio of the array input.

[0064] The following explains step S2 of the present invention.

[0065] The basic theory of signal detection mainly refers to studying and determining concepts, methods, and performances of the best decision on the presence / absence of a target or the state of the target based on the underwater target radiated noise signal. Its mathematical basis is the hypothesis testing theory, also known as the statistical decision theory.

[0066] Underwater target detection is a typical binary hypothesis testing problem. It is necessary to construct a detection statistic from the echo sequence received by the resolution cell and set a detection threshold at the same time. If the detection 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.

[0067] In step S2, the detection statistic is expressed as:

[0068] When detecting and identifying the continuous spectrum of the radiated noise of an underwater vehicle, the criterion for judging whether there is a target is as follows:

[0069] where is the detection statistic, is the number of independent samplings, indicates the hypothesis that the received signal of the array contains the target signal, indicates the hypothesis that the received signal of the array only contains noise and clutter signals, is the m th received signal of the array obtained by sampling, is the detection threshold.

[0070] If the obtained detection 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.

[0071] The present invention can use an energy detector for detection and identification, that is, the present invention can determine the detection threshold according to the energy detector detection method, and use this threshold to judge whether the continuous spectrum of the radiated noise of the underwater vehicle is received, so as to realize the detection and identification of the radiated noise of the underwater vehicle. In addition, the present invention can also verify the variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities through simulation.

[0072] For example, the following simulation is carried out: Simulation 1: Set an 8-element linear array with an element spacing of 15 m. Simulate the signal st1 using a three-parameter model. The parameter settings of st1 are: 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. Using an energy detector, the variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities is as Figure 3 shown.

[0073] Simulation 2: Set an 8-element linear array with an element spacing of 15 m. Simulate the signal st2 using a three-parameter model. The parameter settings of st2 are: is 500 Hz, is 200 Hz, is 0. The fundamental frequency of the line spectrum is 29 Hz, and the number of harmonics is 4. Using an energy detector, the variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities is as Figure 4 shown.

[0074] Simulation 3: Set an 8-element linear array with an element spacing of 15 m. Simulate the signal st3 using a three-parameter model. The parameter settings of st3 are: is 700 Hz, is 200 Hz, is 0. The fundamental frequency of the line spectrum is 7 Hz, and the number of harmonics is 7. Using an energy detector, the variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities is as Figure 5 shown.

[0075] The above simulations show 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.

[0076] In step S3 of the present invention, it may further include: optimizing the distribution of the continuous spectrum of the radiated noise of the underwater vehicle based on the evaluation result.

[0077] The present invention determines the performance of the continuous spectrum of the radiated noise of the underwater vehicle that can be detected and recognized according to the detection threshold, and evaluates the distribution pattern of the sound source level in S1 by changing the parameters or methods in S1 to S2, providing guidance for optimizing the distribution of the continuous spectrum of the radiated noise of the underwater vehicle.

[0078] In summary, in Embodiment 1, the three-parameter method is used to model the continuous spectrum signal of the radiated noise of the underwater vehicle. According to the statistical model of the radiated noise signal of the underwater acoustic target, general quantitative analysis of the target radiated noise signal can be carried out through power spectrum analysis; based on the typical ocean acoustic channel, the propagation loss, the ocean ambient noise level, the directivity index, etc. are modeled, and for the typical acoustic propagation modes in the ocean environment, an ocean acoustic channel model library with a low-frequency underwater acoustic channel modulation model based on normal modes and a channel response waveform solver is constructed; by performing phase compensation on the received signals of each array element, the beam output of in-phase superposition is realized; the detection threshold can be obtained according to the signal detection method of the passive sonar equation, and referring to the sonar equation of the passive sonar system, the detection threshold is determined to realize the detection and recognition of the radiated noise of the underwater vehicle; the obtained detection threshold is used to judge whether the continuous spectrum of the radiated noise of the underwater vehicle is accepted, and from the perspective of sonar system detection, the detection efficiency under different radiated noise distribution modes, channel structures and array processing technology combinations in the typical sonar working mode is analyzed and evaluated, and dynamic adjustment or optimization guidance can also be carried out for the above methods.

[0079] Embodiment 2: Embodiment 2 provides a detection efficiency evaluation device for the continuous spectrum of the radiated noise of an underwater vehicle, including: A model and algorithm library construction module, which is used to model the continuous spectrum signal of the radiated noise of the underwater vehicle, and construct an ocean acoustic channel model library and an array signal processing algorithm library; A detection and recognition module, which is used to design the detection statistic of the underwater target and set the detection threshold to detect and recognize the continuous spectrum of the radiated noise of the underwater vehicle; An evaluation module, configured to evaluate the detection efficiency under combinations of distribution patterns of different continuous spectra, channel propagation patterns, and array signal processing algorithms; The provided detection efficiency evaluation device for the continuous spectrum of the underwater vehicle's radiated noise is used to execute the steps in the detection efficiency evaluation method for the continuous spectrum of the underwater vehicle's radiated noise as described in Embodiment 1.

[0080] Since the functions of the modules in the detection efficiency evaluation device provided in Embodiment 2 correspond to the steps in the detection efficiency evaluation method provided in Embodiment 1, therefore, reference can be made to the description in Embodiment 1 for understanding and details are not repeated here.

[0081] 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 method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle, characterized in that, It includes the following steps: S1. Model the continuous spectrum signal of the underwater vehicle's radiated noise, and construct an ocean acoustic channel model library and an array signal processing algorithm library; S2. Design the detection statistic of the underwater target and set the detection threshold to detect and identify the continuous spectrum of the underwater vehicle's radiated noise; S3. By changing the parameters or algorithms in S1 and S2, evaluate the detection efficiency under different continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithm combinations.

2. The method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle according to claim 1, wherein In step S1, use the three-parameter method to simulate the continuous spectrum signal, and the continuous spectrum signal is expressed as: 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, when <0, the curve rotates counterclockwise; represents the shape parameter of the spectral peak, which determines the sharpness and height of the curve peak; represents the position parameter of the spectral peak, which determines the position of the curve peak on the frequency axis.

3. The detection efficiency evaluation method for the continuous spectrum of the radiated noise of an underwater vehicle according to claim 2, characterized in that, The autocorrelation function of the continuous spectrum signal is expressed as: In the formula, represents the autocorrelation function.

4. The method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle according to claim 1, wherein, In step S1, 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.

5. The method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle according to claim 4, wherein 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, multipath effect, and underwater noise; wherein, the acoustic propagation loss is a function of the propagation distance and the signal frequency and is approximately expressed as: Express the above formula in decibels, and the sound propagation loss is as follows: Wherein, is the sound propagation loss, is the functional expression of the sound propagation loss, is the expansion factor, is the sound wave propagation distance, is the seawater absorption loss coefficient; The waveform emitted from the sound source After multi-path propagation, the received waveform reaching the receiving end Is expressed as follows: In the formula, is the received waveform arriving at the receiving end, is the total number of acoustic wave propagation paths, is the signal amplitude value of the acoustic wave arriving at the receiving point along the i th propagation path, is the signal propagation delay of the acoustic wave arriving at the receiving point along the i th propagation path; The underwater noise includes ocean ambient noise and self-noise, and the total noise level is expressed as: In the formula, is the total noise level, is the ocean ambient noise level, is the self-noise level, is the operating frequency bandwidth.

6. The method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle according to claim 1, characterized in that, In step S1, the array signal processing algorithm library includes various methods such as conventional beamforming, adaptive beamforming, matched field linear processor, and matched field adaptive processor, and evaluate the array gain and directivity index of different methods.

7. The method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle according to claim 1, characterized in that, In step S2, the detection statistic is expressed as: When detecting and identifying the continuous spectrum of the underwater vehicle's radiated noise, the criterion for judging whether there is a target is as follows: In the formula, is the detection statistic, is the number of independent samplings, indicates the hypothesis that the received signal of the array contains the target signal, indicates the hypothesis that the received signal of the array contains only noise and clutter signals, is the m received signal of the array obtained by the th sampling; If the obtained detection 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.

8. The method for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle according to claim 1, wherein, In step S2, use an energy detector for detection and identification, and verify through simulation the variation of the detection probability with the signal-to-noise ratio under different false alarm probabilities.

9. The method for evaluating the detection efficiency of the continuous spectrum of the underwater vehicle's radiated noise according to claim 1, characterized in that, Step S3 also includes: Based on the evaluation results, optimize the distribution of the continuous spectrum of the underwater vehicle's radiated noise.

10. An apparatus for evaluating the detection efficiency of the continuous spectrum of the radiated noise of an underwater vehicle, characterized in that, It includes: A model and algorithm library construction module, which is used to model the continuous spectrum signal of the underwater vehicle's radiated noise and construct an ocean acoustic channel model library and an array signal processing algorithm library; A detection and identification module, which is used to design the detection statistic of the underwater target and set the detection threshold to detect and identify the continuous spectrum of the underwater vehicle's radiated noise; An evaluation module, which is used to evaluate the detection efficiency under different continuous spectrum distribution patterns, channel propagation patterns, and array signal processing algorithm combinations; The detection efficiency evaluation device for the continuous spectrum of the underwater vehicle's radiated noise is used to execute the steps in the detection efficiency evaluation method for the continuous spectrum of the underwater vehicle's radiated noise according to any one of claims 1-9.

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