A method for selecting a transmitting waveform for target wake field disturbance detection

Abstract

The application relates to the technical field of active sonar detection, in particular to a transmission waveform selection method for target wake field disturbance detection, which comprises the following steps: applying uniform engineering constraints to selected engineering realizable waveforms to construct a candidate transmission waveform set of a detection system; equivalent the influence of target wake field disturbance on sound propagation to a time delay perturbation process of multi-path propagation, and establishing a target wake field disturbance sensitivity model accordingly; using the target wake field disturbance sensitivity model, calculating the sensitivity indexes of each candidate transmission waveform in the candidate transmission waveform set and sorting, taking the candidate transmission waveform with the largest sensitivity index as the optimal transmission waveform most suitable for target wake field detection; instructing the transmission end of the detection system to transmit a sound wave signal based on the optimal transmission waveform. The method can improve the observability of weak propagation time-varying characteristics caused by wake disturbance, and make the selection of the transmission waveform have stronger explainability and scene adaptation ability.

Description

A method for selecting the transmission waveform for detecting target wake field disturbances Technical Field

[0001] The embodiments of this application relate to the field of active sonar detection technology, and in particular to a method for selecting transmission waveforms for detecting target wake field disturbances. Background Technology

[0002] In the field of active sonar detection technology, traditional target detection mainly relies on the intensity, time delay, and Doppler characteristics of the target's scattered echo. Therefore, engineering projects widely employ CW (Continuous Wave), LFM (Linear Frequency Modulation), and various phase / frequency coded waveforms, with design criteria typically focusing on indicators such as pulse compression gain, range / velocity resolution, and sidelobe suppression capability. However, when the target is a low-observable ship or exists in complex marine environments with strong reverberation and low signal-to-interference ratios, the target's echo is often severely submerged, rendering conventional detection methods ineffective.

[0003] Recent studies have revealed that high-speed targets generate a persistent wake field behind them. This wake field, composed of bubbles, temperature gradients, and turbulent vortices, significantly disturbs the local sound velocity profile, thereby altering the propagation characteristics of sound waves along the transmit and receive paths. Especially in a separate transmit and receive sonar system, although the wake no longer produces strong scattered echoes after the target crosses the transmit-receive line area, it can still induce an observable "acoustic scintillation" effect at the receiver by modulating the time delay structure, amplitude correlation, and phase stability of multipath sound propagation. This phenomenon provides a new approach for the indirect detection of weak or stealthy targets.

[0004] However, existing transmit waveform selection methods have not been optimized for the unique physical mechanism of wake disturbances. On the one hand, CW signals have narrow bandwidth and poor delay resolution, making it difficult to capture microsecond-level multipath delay disturbances. On the other hand, while conventional LFM or coded signals possess good pulse compression performance, their response characteristics to the mapping relationship between "delay perturbations" and "changes in received statistics" lack systematic modeling. This results in a lack of theoretical basis and effective quantitative evaluation standards for waveform selection in wake detection scenarios. More importantly, wake disturbances are essentially weak, slowly varying, and non-stationary random processes. The resulting changes in the acoustic field are much smaller than those of the target body echo. If the selected transmit waveform is insensitive to small changes in delay, such weak disturbances are easily masked by system noise or environmental fluctuations and cannot be effectively extracted.

[0005] Currently, no research team has proposed a method for selecting transmission waveforms for detecting wake field disturbances, nor is there a unified sensitivity evaluation model to guide waveform design. Therefore, it is urgent to establish a theoretical framework that can characterize the response capability of transmission waveforms to wake-induced time-delay disturbances, and based on this, to achieve the scientific screening and optimized configuration of candidate transmission waveforms, thereby improving the observability and detectability of wake field disturbances and expanding the detection capabilities of active sonar in complex underwater combat environments. Summary of the Invention

[0006] To address the aforementioned technical issues, embodiments of this application propose a method for selecting transmitted waveforms for detecting target wake field disturbances. By establishing a sensitivity model based on "acoustic propagation delay perturbation, autocorrelation of transmitted waveform derivative, and changes in received statistical characteristics," the method quantifies and ranks the sensitivity of various given transmitted waveforms, selecting the most suitable transmitted waveform for wake field disturbance detection.

[0007] To achieve the above objectives, embodiments of this application propose a method for selecting transmitted waveforms for detecting target wake field disturbances. The method includes the following steps: applying consistent engineering constraints to all selected engineering realizable waveforms to construct a candidate transmitted waveform set for the detection system; wherein, the engineering constraints are used to ensure the comparability between different engineering realizable waveforms; equating the impact of target wake field disturbances on sound propagation to a time-delay perturbation process of multipath propagation, and establishing a target wake field disturbance sensitivity model accordingly; using the target wake field disturbance sensitivity model, calculating and sorting the sensitivity index of each candidate transmitted waveform in the candidate transmitted waveform set, and selecting the candidate transmitted waveform with the highest sensitivity index as the optimal transmitted waveform most suitable for target wake field detection; instructing the transmitting end of the detection system to transmit sound wave signals based on the optimal transmitted waveform.

[0008] To achieve the above objectives, embodiments of this application also propose a transmit waveform selection system for detecting target wake field disturbances. The system includes: a candidate transmit waveform set construction module, used to apply consistent engineering constraints to all selected engineering-realizable waveforms to construct a candidate transmit waveform set for the detection system; wherein the engineering constraints are used to ensure the comparability between different engineering-realizable waveforms; a target wake field disturbance sensitivity model construction module, used to equate the impact of target wake field disturbances on sound propagation to a time-delay perturbation process of multipath propagation, and establish a target wake field disturbance sensitivity model accordingly; a sensitivity index calculation module, used to calculate and sort the sensitivity index of each candidate transmit waveform in the candidate transmit waveform set using the target wake field disturbance sensitivity model, and select the candidate transmit waveform with the highest sensitivity index as the optimal transmit waveform most suitable for target wake field detection; and an application module, used to instruct the transmitting end of the detection system to transmit sound wave signals based on the optimal transmit waveform.

[0009] To achieve the above objectives, embodiments of this application also propose an electronic device, including: a processor and a memory storing a program, the program including instructions executable by the processor, the processor being configured to, when executing the instructions, enable the electronic device to implement a transmission waveform selection method for detecting target wake field disturbances as described above.

[0010] To achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program that, when executed by a processor, enables a transmission waveform selection method for target wake field disturbance detection as described above.

[0011] In some optional embodiments, the step of applying consistent engineering constraints to all selected engineering-achievable waveforms to construct a candidate transmit waveform set for the detection system includes:

[0012] Engineering realizeable waveforms, including CW and LFM with different bandwidths and pulse widths, are selected. Consistent engineering constraints are applied to all selected engineering realizeable waveforms to obtain candidate transmit waveforms.

[0013] Based on all candidate transmission waveforms, construct a candidate transmission waveform set for the detection system. ;

[0014] Candidate transmit waveform set Represented as:

[0015] ;

[0016] ;

[0017] in, This represents the total number of candidate transmitted waveforms. For the first One candidate transmit waveform, For the first The parameter set corresponding to each candidate transmitted waveform. It is a time variable.

[0018] In some optional embodiments, the step of equating the impact of the target wake field disturbance on sound propagation to a time-delay perturbation process of multipath propagation, and establishing a target wake field disturbance sensitivity model accordingly, includes:

[0019] The impact of the target wake field disturbance on sound propagation is equivalent to a time-delay perturbation process of multipath propagation. The covariance of the time-delay perturbation of the effective path pairs is used to describe the time-varying statistical intensity of the target wake field disturbance.

[0020] Based on the autocorrelation function of the first derivative of the candidate transmission waveform, a sensitive kernel for the change in effective path delay difference is defined;

[0021] Based on the time-varying statistical intensity of the target wake field disturbance, the sensitive kernel of the effective path delay difference change, and the weights of each effective path pair, the autocorrelation of the transmitted signal of the target wake field disturbance is calculated.

[0022] Based on the degree of fluctuation of the autocorrelation quantity of the transmitted signal under target wake field disturbance over multiple cycles, a target wake field disturbance sensitivity evaluation function is established as a target wake field disturbance sensitivity model.

[0023] In some optional embodiments, the step of using the covariance of the time delay perturbations of effective path pairs to describe the time-varying statistical strength of the target wake field disturbance includes:

[0024] Let the effective path index be , , The periodic index is the total number of valid paths. , No. The effective path is at the _th ... The time delay perturbation for each cycle is ;

[0025] Calculate the first using the following formula. The effective path and the first The covariance of the time delay perturbations of each effective path is used to describe the time-varying statistical strength of the target wake field perturbation:

[0026] ;

[0027] in, This indicates the search for the expected value. Indicates the first One cycle, For the first The effective path is at the _th ... The time delay perturbation of each cycle, , , For the first The effective path and the first The covariance of the time delay perturbation of each effective path is the time-varying statistical intensity of the target wake field perturbation.

[0028] In some optional embodiments, the autocorrelation function based on the first derivative of the candidate transmitted waveform defines the sensitive kernel for the effective path delay difference variation, and is implemented by the following formula:

[0029] ;

[0030] in, Indicates time delay. For the first The first derivative of each candidate transmitted waveform For the first The autocorrelation function of the first derivative of each candidate transmitted waveform is the sensitive kernel for the change in effective path delay difference.

[0031] In some optional embodiments, the calculation of the transmitted signal autocorrelation of the target wake field disturbance based on the time-varying statistical intensity of the target wake field disturbance, the sensitive kernel of the effective path delay difference change, and the weights of each effective path pair includes:

[0032] Assuming no disturbance, the first... The effective path and the first The arrival time difference of each effective path is ;

[0033] The weights of each effective path pair are determined based on path energy, arrival range, and engineering settings.

[0034] The autocorrelation of the transmitted signal of the target wake field disturbance can be calculated using the following formula:

[0035] ;

[0036] in, For the first The effective path and the first The weights of valid path pairs consisting of 1 valid path. For the target wake field disturbance in the first The autocorrelation quantity under each candidate emission waveform.

[0037] In some optional embodiments, the sensitivity evaluation function for the target wake field disturbance is established by assessing the severity of fluctuations in the autocorrelation quantity of the transmitted signal based on the target wake field disturbance over multiple periods, and is implemented through the following formula:

[0038] ;

[0039] in, The total number of cycles, for The target wake field disturbance within the period is in the first... The average value of the autocorrelation quantity under each candidate emission waveform. For the first Sensitivity index of candidate transmit waveforms.

[0040] This application proposes a method for selecting transmitted waveforms for detecting target wake field disturbances, effectively solving the problem that traditional transmitted waveform selection methods lack quantitative evaluation and selection criteria when detecting target wake field disturbances. First, this application constructs a set of candidate transmitted waveforms for the detection system. During this construction process, consistent engineering constraints are applied to all selected engineering-realizable waveforms, ensuring comparability between different engineering-realizable waveforms. Next, the impact of target wake field disturbances on acoustic propagation is equated to a time-delay perturbation process of multipath propagation. A target wake field disturbance sensitivity model is established based on the "time delay offset covariance" and "autocorrelation of the transmitted signal derivative." Then, using the target wake field disturbance sensitivity model, the sensitivity index of each candidate transmitted waveform in the candidate transmitted waveform set is calculated and ranked. The candidate transmitted waveform with the highest sensitivity index is selected as the optimal transmitted waveform for target wake field detection. This process achieves quantitative evaluation and ranking of the acoustic detection sensitivity of multiple candidate transmitted waveforms for target wake fields. Finally, the transmitting end of the instruction detection system transmits acoustic signals based on the optimal transmission waveform, thereby improving the observability of the weak propagation time-varying characteristics caused by the target wake field disturbance, and making the selection of the transmission waveform more interpretable and adaptable to different scenarios. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies of this application will be briefly introduced below. The following drawings are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings described herein are only used to explain this application and are not intended to limit this application.

[0042] Figure 1 is a flowchart of a transmission waveform selection method for detecting target wake field disturbances provided in one embodiment of this application;

[0043] Figure 2 is a detailed schematic diagram of a transmission waveform selection method for detecting target wake field disturbances provided in one embodiment of this application;

[0044] Figure 3 is a schematic diagram of a multiplex channel response provided in one embodiment of this application;

[0045] Figure 4 is a schematic diagram of the standard deviation of multipath delay disturbance provided in one embodiment of this application;

[0046] Figure 5 is a schematic diagram of a candidate transmission waveform provided in one embodiment of this application;

[0047] Figure 6 is a schematic diagram of the autocorrelation of the derivative of the transmitted signal provided in one embodiment of this application;

[0048] Figure 7 is a comparison chart of the fluctuation of autocorrelation quantities provided in one embodiment of this application;

[0049] Figure 8 is a schematic diagram of a transmission waveform selection system for detecting target wake field disturbances provided in another embodiment of this application;

[0050] Figure 9 is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. Those skilled in the art will understand that many technical details have been presented in the embodiments of this application to facilitate better understanding. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this application. The following embodiments can be combined with and referenced by each other without contradiction.

[0052] One embodiment of this application proposes a method for selecting a transmission waveform for detecting target wake field disturbances. The implementation details of the method for selecting a transmission waveform for detecting target wake field disturbances proposed in this embodiment are described below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0053] The specific flowchart of the transmission waveform selection method for target wake field disturbance detection proposed in this embodiment is shown in Figure 1, and its visualization details are shown in Figure 2. The method includes:

[0054] Step 11: Apply consistent engineering constraints to all selected engineering-realizable waveforms to construct a candidate transmit waveform set for the detection system. The engineering constraints are used to ensure the comparability between different engineering-realizable waveforms.

[0055] In practical implementation, the prerequisite for selecting the transmission waveform is to construct a candidate transmission waveform set containing a sufficient number of candidate transmission waveforms. We can select several engineering-realizable waveforms. In order to ensure the comparability between different engineering-realizable waveforms, we need to apply consistent engineering constraints to all selected engineering-realizable waveforms, thereby constructing a candidate transmission waveform set for the detection system (sonar system).

[0056] In one example, we can select engineered realizable waveforms, including CW and LFM waveforms with different bandwidths and pulse widths. We then apply consistent engineering constraints (such as frequency band constraints and pulse width constraints) to all selected engineered realizable waveforms to obtain candidate transmit waveforms. Finally, we combine all the candidate transmit waveforms into a set to construct the candidate transmit waveform set for the detection system. .

[0057] In one example, the candidate transmit waveform set Represented as:

[0058] ;

[0059] ;

[0060] in, This represents the total number of candidate transmitted waveforms. For the first One candidate transmit waveform, For the first The set of parameters corresponding to each candidate transmitted waveform (including center frequency, bandwidth, pulse width, frequency modulation, etc.). It is a time variable.

[0061] Step 12: The impact of the target wake field disturbance on sound propagation is equivalent to a time-delay perturbation process of multipath propagation, and a target wake field disturbance sensitivity model is established accordingly.

[0062] In the specific implementation, after constructing the candidate emission waveform set, it is necessary to equate the impact of the target wake field disturbance on sound propagation to a time delay perturbation process of multipath propagation, and establish a target wake field disturbance sensitivity model accordingly.

[0063] In one example, as shown in Figure 2, the establishment of the target wake field disturbance sensitivity model is divided into four stages. First, the impact of the target wake field disturbance on sound propagation is equivalent to a time-delay perturbation process of multipath propagation. The covariance of the time-delay perturbation of the effective path pairs is used to describe the time-varying statistical intensity of the target wake field disturbance. The meaning of the time-varying statistical intensity of the target wake field disturbance here is consistent with that of "time delay offset covariance" and can be used as one of the inputs to the target wake field disturbance sensitivity model. Next, based on the autocorrelation function of the first derivative of the candidate transmitted waveform, a sensitive kernel for the change of effective path time delay difference is defined. Then, based on the time-varying statistical intensity of the target wake field disturbance, the sensitive kernel for the change of effective path time delay difference, and the weights of each effective path pair, the autocorrelation quantity of the transmitted signal of the target wake field disturbance is calculated. Finally, based on the degree of fluctuation of the autocorrelation quantity of the transmitted signal of the target wake field disturbance over multiple periods, a target wake field disturbance sensitivity evaluation function is established as the target wake field disturbance sensitivity model.

[0064] In one example, let the effective path index be... , , The periodic index is the total number of valid paths. , No. The effective path is at the _th ... The time delay perturbation for each cycle is Based on this, we can calculate the th using the following formula. The effective path and the first The covariance of the time delay perturbations of two effective paths (forming a pair of effective paths) is used to describe the time-varying statistical strength of the target wake field perturbation:

[0065] ;

[0066] in, This indicates the search for the expected value. Indicates the first One cycle, For the first The effective path is at the _th ... The time delay perturbation of each cycle, , , For the first The effective path and the first The covariance of the time delay perturbation of each effective path is the time-varying statistical intensity of the target wake field perturbation.

[0067] In one example, the sensitive kernel for the effective path delay difference variation is defined based on the autocorrelation function of the first derivative of the candidate transmitted waveform, and can be implemented by the following formula:

[0068] ;

[0069] in, Indicates time delay. For the first The first derivative of each candidate transmitted waveform For the first The autocorrelation function of the first derivative of each candidate transmitted waveform is the sensitive kernel for changes in effective path delay difference. When the pulse width is constant, a wider bandwidth results in... The sharper the lobe, the greater the change in value for small time differences. If the time difference exceeds the width of the main lobe... It will then oscillate sharply and decrease.

[0070] In one example, when calculating the autocorrelation of the transmitted signal in the target wake field disturbance, assume that the first... The effective path and the first The arrival time difference of each effective path is , The value can be obtained through an acoustic propagation model or estimated from measured arrival structure / pulse compression peaks. Then, based on path energy, arrival amplitude, and engineering settings, the weights of each effective path pair are determined. At this point, the time-varying statistical intensity of the target wake field disturbance, the sensitive kernel for the effective path delay difference variation, and the path weights corresponding to each effective path are all clearly defined. We can calculate the autocorrelation of the transmitted signal of the target wake field disturbance using the following formula:

[0071] ;

[0072] in, For the first The effective path and the first The weights of valid path pairs consisting of 1 valid path. For the target wake field disturbance in the first The autocorrelation quantity under each candidate emission waveform.

[0073] The autocorrelation is the core of the target wake field disturbance sensitivity model. We establish the target wake field disturbance sensitivity evaluation function based on the drastic fluctuation of the transmitted signal autocorrelation over multiple cycles using the following formula, which serves as the target wake field disturbance sensitivity model:

[0074] ;

[0075] in, The total number of cycles, for The target wake field disturbance within the period is in the first... The average value of the autocorrelation quantity under each candidate emission waveform. For the first Sensitivity index of candidate transmit waveforms.

[0076] Understandable The larger the value, the more likely it is to be the first. The autocorrelation of a candidate transmitted waveform under the disturbance of the target wake field is extremely unstable, and such disturbance changes are more easily detected by the detection system (sonar system).

[0077] Step 13: Using the target wake field disturbance sensitivity model, calculate the sensitivity index of each candidate transmission waveform in the candidate transmission waveform set and sort them. Select the candidate transmission waveform with the largest sensitivity index as the optimal transmission waveform most suitable for target wake field detection.

[0078] In practice, after constructing the target wake field disturbance sensitivity model, the sensitivity index of each candidate transmitted waveform in the candidate transmitted waveform set can be calculated and sorted using the target wake field disturbance sensitivity model. The candidate transmitted waveform with the largest sensitivity index is then selected as the optimal transmitted waveform most suitable for target wake field detection.

[0079] In one example, we use the target wake field disturbance sensitivity evaluation function to calculate the sensitivity index of each candidate emission waveform and find the maximum value of the sensitivity index of each candidate emission waveform (i.e., ),Will The corresponding candidate emission waveform is selected as the optimal emission waveform for target wake field detection.

[0080] Step 14: Instruct the transmitter of the detection system to transmit an acoustic signal based on the optimal transmission waveform.

[0081] In practical implementation, after determining the optimal transmission waveform, the transmitting end of the detection system can be instructed to transmit acoustic signals based on the optimal transmission waveform, thereby expanding the detection capability of the detection system (active sonar) in complex underwater combat environments.

[0082] This embodiment proposes a method for selecting transmitted waveforms for detecting target wake field disturbances, effectively solving the problem that traditional transmitted waveform selection methods lack quantitative evaluation and selection criteria when detecting target wake field disturbances. This embodiment first constructs a set of candidate transmitted waveforms for the detection system. During the construction process, consistent engineering constraints are applied to all selected engineering-realizable waveforms to ensure comparability between different engineering-realizable waveforms. Next, the impact of target wake field disturbances on sound propagation is equated to a time-delay perturbation process of multipath propagation. A target wake field disturbance sensitivity model is established based on the "time delay offset covariance" and "autocorrelation of the transmitted signal derivative". Then, using the target wake field disturbance sensitivity model, the sensitivity index of each candidate transmitted waveform in the candidate transmitted waveform set is calculated and ranked. The candidate transmitted waveform with the highest sensitivity index is selected as the optimal transmitted waveform for target wake field detection. This process achieves a scientific quantitative evaluation and ranking of the acoustic detection sensitivity of multiple candidate transmitted waveforms for target wake fields. Finally, the transmitting end of the instruction detection system (sonar system) transmits acoustic signals based on the optimal transmission waveform, thereby improving the observability of the weak propagation time-varying characteristics caused by the target wake field disturbance, and making the selection of the transmission waveform more interpretable and adaptable to the scene.

[0083] The steps described above are merely for clarity in describing the technical solution. In actual implementation, they can be combined into one step, or certain steps can be broken down into multiple steps, as long as they involve the same logical relationship, they are all within the scope of protection of this application. Any insignificant modifications or designs added to the algorithm or process, as long as they do not change the core of the algorithm or process, are also within the scope of protection of this application.

[0084] In one embodiment, to verify the effectiveness of the proposed method for selecting a transmission waveform for detecting target wake field disturbances, we conducted relevant simulation experiments.

[0085] First, we constructed a typical underwater acoustic multipath propagation simulation environment. Referring to Figure 3 (multipath channel response), this embodiment sets the underwater acoustic channel to include four intrinsic acoustic propagation paths. The relative arrival delays of each path are 1.00s, 1.01s, 1.05s, and 1.09s, respectively, and the corresponding path normalization amplitude weights are 1.0, 0.7, 0.4, and 0.1, respectively, simulating a typical multipath structure in a shallow sea environment where direct waves and multiple reflected waves coexist.

[0086] To address the wake disturbance effect generated after the target passes through the sound field, this experiment treats it as an equivalent random perturbation of the multipath delay. Referring to Figure 4 (standard deviation of multipath delay perturbation), the observation duration is set to 10 detection cycles (Ping), and a normally distributed random perturbation is superimposed on the base delays of the four paths mentioned above. Figure 4 shows the standard deviation distribution of the delay perturbation for each path, which are respectively... s、 s、 s、 This non-uniform perturbation intensity distribution is used to simulate the spatial non-uniformity of the wake and the differences in the degree of influence on different sound propagation paths.

[0087] To evaluate the differences in sensitivity of different waveforms to wake disturbances, we selected two typical active sonar transmission waveforms as comparison objects. Referring to Figure 5 (given transmission signal waveform), the upper waveform is a single-frequency pulse (CW), and the lower waveform is a linear frequency modulated pulse (LFM). The pulse width of both signals was set to 50 ms, and the sampling rate was set to 100 kHz. The CW signal has a single center frequency and a smooth phase change, while the LFM signal has a wider spectral bandwidth, providing higher time delay resolution.

[0088] According to the evaluation model (target wake field disturbance sensitivity model) proposed in this application, the sensitivity of the transmitted waveform to time delay disturbances is determined by the autocorrelation function of its first derivative. Referring to Figure 6 (autocorrelation of transmitted signal derivative), the time derivatives of the CW signal and LFM signal were calculated respectively. Since the frequency of the LFM signal changes rapidly with time, the amplitude and rate of change of its derivative signal are significantly higher than those of the CW signal. This "steep" derivative characteristic, after autocorrelation calculation, forms a sharper main lobe (i.e., a sensitivity kernel), meaning that even a small input time delay difference... At this time, the LFM signal will produce a larger decorrelation response, thus theoretically possessing a stronger perturbation amplification capability.

[0089] Substituting the dynamic time delay perturbation data of the 10 cycles generated by the above simulation into the sensitivity model of this invention, the autocorrelation quantity in each detection cycle was calculated, and its change trajectory over time was observed. Referring to Figure 7 (comparison of autocorrelation fluctuations), Figure 7 shows the degree of deviation of the sensitivity error energy of the two waveforms from the mean within 10 detection cycles. The autocorrelation value of the CW signal oscillates slightly around 0, with a limited fluctuation range (peak value of about 1000), indicating that the CW signal is relatively sluggish in response to time delay perturbations caused by the wake, and its received energy characteristics are relatively stable. The autocorrelation value of the LFM signal exhibits dramatic fluctuations, especially with a significant peak value (amplitude exceeding 2000) appearing near the third detection cycle, and the overall fluctuation standard deviation is significantly greater than that of the CW signal.

[0090] Based on the above results, the sensitivity index was calculated. The sensitivity index of CW was 538.28, and the sensitivity index of LFM was 848.02. The sensitivity index of LFM is 1.58 times that of CW.

[0091] In summary, the simulation results intuitively verify the effectiveness of the evaluation index proposed in this application. The sensitivity fluctuation index (i.e., the standard deviation of the curve) of the LFM signal is significantly higher than that of the CW signal, proving that under the same wake disturbance conditions, the broadband frequency-modulated waveform can excite a stronger acoustic field scintillation effect. Therefore, based on the technical solution of this application, it is determined that the LFM waveform has better detection performance for the target wake field than the CW waveform. This embodiment shows that this application can effectively transform complex time-varying wake field disturbances into a single quantifiable fluctuation index, providing clear data support for the waveform design and optimization of the detection system (active sonar).

[0092] Another embodiment of this application proposes a transmission waveform selection system for target wake field disturbance detection. The details of this transmission waveform selection system are described below for ease of understanding and are not essential for implementing this solution. Figure 8 is a schematic diagram of the structure of the transmission waveform selection system for target wake field disturbance detection proposed in this embodiment, including: a candidate transmission waveform set construction module 21, a target wake field disturbance sensitivity model construction module 22, a sensitivity index calculation module 23, and an application module 24.

[0093] The candidate transmission waveform set construction module 21 is used to apply consistent engineering constraints to all selected engineering-realizable waveforms to construct a candidate transmission waveform set for the detection system; wherein, the engineering constraints are used to ensure the comparability between different engineering-realizable waveforms.

[0094] The target wake field disturbance sensitivity model construction module 22 is used to equate the impact of target wake field disturbance on sound propagation to a time-delayed perturbation process of multipath propagation, and establish a target wake field disturbance sensitivity model accordingly.

[0095] The sensitivity index calculation module 23 is used to calculate and sort the sensitivity index of each candidate transmission waveform in the candidate transmission waveform set using the target wake field disturbance sensitivity model, and select the candidate transmission waveform with the largest sensitivity index as the optimal transmission waveform most suitable for target wake field detection.

[0096] Application module 24 is used to instruct the transmitter of the detection system to transmit acoustic signals based on the optimal transmission waveform.

[0097] It is worth noting that all modules involved in this embodiment are logical modules. In practical applications, a logical module can be a physical module, a part of a physical module, or an organic combination of multiple physical modules. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce modules that are not closely related to solving the technical problems proposed in this application. However, this does not mean that other modules are absent from this embodiment.

[0098] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0099] Another embodiment of this application proposes an electronic device, as shown in FIG9, including: a processor 31 and a memory 32 storing a program, the program including instructions executable by the processor 31, the processor 31 being configured to, when executing the instructions, enable the electronic device to implement a transmission waveform selection method for detecting target wake field disturbances as described in the above method embodiment.

[0100] The memory and processor are connected via a bus, which includes any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0101] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0102] Another embodiment of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, enables a transmission waveform selection method for detecting target wake field disturbances as described in the above method embodiments.

[0103] That is, those skilled in the art will understand that all or part of the steps in the above method embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (such as a microcontroller, chip, etc.) or processor to execute all or part of the steps of the method described in the method embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0104] It will be understood by those skilled in the art that the above embodiments are specific implementations of this application, and various changes in form and detail can be made in practical applications without departing from the spirit and scope of this application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for selecting a transmission waveform for detecting target wake field disturbances, characterized in that, include: A consistent engineering constraint is applied to all selected engineering-realizable waveforms to construct a candidate transmitted waveform set for the detection system. The engineering constraint ensures comparability between different engineering-realizable waveforms. The impact of target wake field disturbance on sound propagation is equated to a time-delay perturbation process of multipath propagation, and a target wake field disturbance sensitivity model is established accordingly. Using the target wake field disturbance sensitivity model, the sensitivity index of each candidate transmitted waveform in the candidate transmitted waveform set is calculated and ranked. The candidate transmitted waveform with the highest sensitivity index is selected as the optimal transmitted waveform for target wake field detection. The transmitting end of the detection system is instructed to transmit acoustic signals based on the optimal transmitted waveform. The process of applying consistent engineering constraints to all selected engineering-realizable waveforms to construct the candidate transmitted waveform set for the detection system includes: selecting engineering-realizable waveforms including CW and LFM waveforms with different bandwidths and pulse widths; applying consistent engineering constraints to all selected engineering-realizable waveforms to obtain each candidate transmitted waveform; and constructing the candidate transmitted waveform set for the detection system based on all candidate transmitted waveforms. Candidate transmit waveform set Represented as: ； ;in, This represents the total number of candidate transmitted waveforms. For the first One candidate transmit waveform, For the first The parameter set corresponding to each candidate transmitted waveform. The time variable is used as the basis for the following: The effect of the target wake field disturbance on sound propagation is equated to a time-delay perturbation process of multipath propagation, and a target wake field disturbance sensitivity model is established accordingly. This includes: equating the effect of the target wake field disturbance on sound propagation to a time-delay perturbation process of multipath propagation; using the covariance of the time-delay perturbation of effective path pairs to describe the time-varying statistical intensity of the target wake field disturbance; defining a sensitive kernel for the change in effective path time delay difference based on the autocorrelation function of the first derivative of the candidate transmitted waveform; calculating the autocorrelation quantity of the transmitted signal of the target wake field disturbance based on the time-varying statistical intensity of the target wake field disturbance, the sensitive kernel for the change in effective path time delay difference, and the weights of each effective path pair; and establishing a target wake field disturbance sensitivity evaluation function as the target wake field disturbance sensitivity model based on the degree of fluctuation of the transmitted signal autocorrelation quantity of the target wake field disturbance over multiple periods.

2. The method for selecting a transmission waveform for detecting target wake field disturbances according to claim 1, characterized in that, The method of using the covariance of the time delay perturbations of effective path pairs to describe the time-varying statistical intensity of the target wake field disturbance includes: assuming the effective path index is... ， ， The periodic index is the total number of valid paths. , the The effective path is at the _th ... The time delay perturbation for each cycle is ; Calculate the first using the following formula The effective path and the first The covariance of the time delay perturbations of each effective path is used to describe the time-varying statistical strength of the target wake field perturbation: ;in, This indicates the search for the expected value. Indicates the first One cycle, For the first The effective path is at the _th ... The time delay perturbation of each cycle, ， ， For the first The effective path and the first The covariance of the time delay perturbation of each effective path is the time-varying statistical intensity of the target wake field perturbation.

3. The method for selecting a transmission waveform for detecting target wake field disturbances according to claim 2, characterized in that, The autocorrelation function based on the first derivative of the candidate transmission waveform defines a sensitive kernel for the effective path delay difference variation, which is implemented by the following formula: ;in, Indicates time delay. For the first The first derivative of each candidate transmitted waveform For the first The autocorrelation function of the first derivative of each candidate transmitted waveform is the sensitive kernel for the change in effective path delay difference.

4. The method for selecting a transmission waveform for detecting target wake field disturbances according to claim 3, characterized in that, The calculation of the autocorrelation of the transmitted signal of the target wake field disturbance based on the time-varying statistical intensity of the target wake field disturbance, the sensitive kernel of the effective path delay difference change, and the weights of each effective path pair includes: assuming the _th__ under the undisturbed background... The effective path and the first The arrival time difference of each effective path is Based on path energy, arrival amplitude, and engineering settings, determine the weights of each effective path pair; calculate the autocorrelation of the transmitted signal of the target wake field disturbance using the following formula: ;in, For the first The effective path and the first The weights of valid path pairs consisting of 1 valid path. For the target wake field disturbance in the first The autocorrelation quantity under each candidate emission waveform.

5. The method for selecting a transmission waveform for detecting target wake field disturbances according to claim 4, characterized in that, The sensitivity evaluation function for the target wake field disturbance is established by assessing the drastic fluctuations of the transmitted signal autocorrelation quantity over multiple periods based on the target wake field disturbance, and is implemented through the following formula: ;in, The total number of cycles, for The target wake field disturbance within the period is in the first... The average value of the autocorrelation quantity under each candidate emission waveform. For the first Sensitivity index of candidate transmit waveforms.

6. A transmit waveform selection system for detecting target wake field disturbances, used to implement the transmit waveform selection method for detecting target wake field disturbances as described in any one of claims 1 to 5, characterized in that, include: The candidate transmitted waveform set construction module applies consistent engineering constraints to all selected engineering-realizable waveforms to construct a candidate transmitted waveform set for the detection system. These engineering constraints ensure comparability between different engineering-realizable waveforms. The target wake field disturbance sensitivity model construction module equates the impact of target wake field disturbances on sound propagation to a time-delay perturbation process of multipath propagation, and establishes a target wake field disturbance sensitivity model accordingly. The sensitivity index calculation module uses the target wake field disturbance sensitivity model to calculate and sort the sensitivity indices of each candidate transmitted waveform in the candidate transmitted waveform set, selecting the candidate transmitted waveform with the highest sensitivity index as the optimal transmitted waveform for target wake field detection. The application module instructs the transmitting end of the detection system to transmit acoustic signals based on the optimal transmitted waveform.

7. An electronic device, characterized in that, include: A processor and a memory storing a program, the program including instructions executable by the processor, the processor being configured to, when executing the instructions, enable the electronic device to implement a transmit waveform selection method for detecting target wake field disturbances as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement a method for selecting a transmission waveform for detecting target wake field disturbances as described in any one of claims 1 to 5.

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