Time domain trigger detection method and device of underwater acoustic communication signal classification and identification module

Abstract

The invention provides a time domain trigger detection method and device for an underwater acoustic communication signal classification and identification module. The method comprises the following steps: acquiring a hydrophone receiving signal; constructing a reference channel and a detection channel; the reference channel is used for collecting marine environment noise and dynamically updating a time domain detection triggering threshold value; the detection channel is used for collecting actual underwater acoustic signals; acquiring a current time domain detection trigger threshold value, continuously scanning the detection channel data and judging whether the signal amplitude of each signal sampling point is greater than the current time domain detection trigger threshold value or not; and if n2 signal sampling points in the continuous n1 signal sampling points are greater than a threshold value, considering that a trigger condition is achieved, and storing data with a set length as input of the underwater acoustic communication signal classification and identification module. The method is an effective underwater acoustic communication signal detection algorithm suitable for an underwater acoustic communication signal classification and identification module, the calculation amount is small, the signal detection efficiency is improved, and the method has good practicability.

Description

Technical Field

[0001] The present invention relates to the technical field of signal detection, and in particular, to a time-domain trigger detection method and device for an underwater acoustic communication signal classification and recognition module. Background Art

[0002] The main detection objects of an underwater snorkel buoy include underwater acoustic communication signals, active sonar signals, and target radiated noise signals, and the modulation modes of underwater acoustic communication signals need to be classified and recognized. The classification and recognition algorithm of underwater acoustic communication signals is complex and computationally intensive. Therefore, before classifying and recognizing underwater acoustic communication signals, underwater acoustic communication signal detection is required to exclude noise and extract useful signals.

[0003] Underwater acoustic communication signals are non-cooperative signals (signals without a pre-set protocol or synchronization mechanism between the transmitter and the receiver), usually with a low signal-to-noise ratio (SNR), and their key parameters such as frequency band, pulse width, and period are unknown before reception. For snorkel buoy detection, the background noise in the marine environment it mainly faces can be approximated as white noise (uniformly distributed in the frequency domain).

[0004] In related underwater acoustic communication signal detection technologies, a commonly used method is to divide the entire detection frequency band into multiple sub-bands for energy estimation to achieve the exclusion of noise signals and the detection and extraction of useful signals.

[0005] Although this method improves the sensitivity of signal detection to a certain extent, the model coverage rate of the frequency division processing method is low, the detection rate is low under low signal-to-noise ratio conditions, and the number of frequency band divisions and the data time length for one FFT may affect the detection results. In addition, this method has a high complexity and requires a large amount of computing resources, resulting in an increase in device power consumption. Summary of the Invention

[0006] The present invention provides a time-domain trigger detection method and device for an underwater acoustic communication signal classification and recognition module, which solves the problem of how to efficiently detect underwater acoustic communication signals.

[0007] To achieve the above object, the present application adopts the following technical solutions: In a first aspect, a time-domain trigger detection method for an underwater acoustic communication signal classification and recognition module is provided, including: Obtain the signal received by the hydrophone; Construct a reference channel and a detection channel; the reference channel is used to collect the marine environmental noise and dynamically update the time-domain detection trigger threshold; the detection channel is used to collect actual underwater acoustic signals; Obtain the current time-domain detection trigger threshold, continuously scan the data of the detection channel, and determine whether the signal amplitude of each signal sampling point is greater than the current time-domain detection trigger threshold; if among n1 consecutive signal sampling points, n2 signal sampling points are greater than the current time-domain detection trigger threshold, it is considered that the trigger condition is met, and data of a set length is stored as the input of the underwater acoustic communication signal classification and recognition module; where n1 and n2 are preset initialization trigger parameters.

[0008] In the first possible implementation manner of the first aspect, it further includes: Output the obtained hydrophone received signal to the reference channel, perform low-pass filtering on the reference channel through a first filter with a first set cut-off frequency, then perform secondary amplification and A / D conversion to obtain a digital signal; At the same time, output the obtained hydrophone received signal to the detection channel, perform wide-band filtering on the detection channel through a second filter with a second set cut-off frequency, then perform secondary amplification and A / D conversion to obtain a digital signal.

[0009] Based on the first possible implementation manner of the first aspect, in the second possible implementation manner of the first aspect, the cut-off frequency of the first filter is 200 Hz; The cut-off frequency of the second filter is 40 kHz; The sampling rate of the reference channel is 500 Hz; The sampling rate of the detection channel is 100 kHz.

[0010] In the third possible implementation manner of the first aspect, the obtaining of the current time-domain detection trigger threshold includes: Obtain the time-domain data of the reference channel noise signal within a set time period, extract its maximum value, and multiply the maximum value by a preset coefficient as the time-domain detection trigger threshold.

[0011] Based on the third possible implementation manner of the first aspect, in the fourth possible implementation manner of the first aspect, use the following formula to calculate the time-domain detection trigger threshold Threshold1: Threshold1 = Vnoisemax * para1, where Vnoisemax is the extracted maximum value, and para1 is a preset initialization trigger parameter.

[0012] In the fifth possible implementation of the first aspect, continuously scan and detect the channel data, and determine whether the signal amplitude of each signal sampling point is greater than the current time-domain detection trigger threshold; if among n1 consecutive signal sampling points, n2 signal sampling points are greater than the current time-domain detection trigger threshold, it is considered that the trigger condition is met, and data of a set length is stored as the input of the underwater acoustic communication signal classification and recognition module, specifically including: Initialize the trigger parameters, including: coefficient para1; trigger condition parameters n1, n2; the length of data stored in one trigger length1; the scanning data FIFO queue data1; the FIFO queue record1 for recording the signal positions greater than the time-domain detection trigger threshold; Obtain the time-domain data of the reference channel for 1 s, and obtain its corresponding time-domain detection trigger threshold; If the signal amplitude of the signal sampling point is greater than the time-domain detection trigger threshold, store the signal sampling point position x1 into record1; Query n2 sampling point positions x2 greater than the time-domain detection trigger threshold forward from record1; Calculate the position difference diff of n2 records in record1: diff = x1 - x2; Judge whether diff is less than n1; if so, it is considered that the trigger condition is met; otherwise, continue to scan the next data point; If the trigger condition is met, judge whether the trigger storage process is in progress; if not, continuously store data of length length1 starting from position x2; if so, continue to store data of length length1 starting from the current position x1.

[0013] In the second aspect, a time-domain trigger detection device for an underwater acoustic communication signal classification and recognition module is provided, including a processor for implementing the time-domain trigger detection method of the underwater acoustic communication signal classification and recognition module as described in the first aspect; The processor includes a storage module and is an embedded low-power processor; The storage module is used to store data of a set length as the input of the underwater acoustic communication signal classification and recognition module when the trigger condition is met.

[0014] The time-domain trigger detection method of the underwater acoustic communication signal classification and recognition module of the present invention has the following advantages: This application designs a useful signal time-domain triggering method with low computational complexity and high reliability, which is an effective underwater acoustic communication signal detection algorithm applicable before the underwater acoustic communication signal classification and recognition module. This algorithm model does not involve FFT calculation and is independent of parameter information such as the pulse width, frequency band, amplitude, and pulse interval of the signal, overcoming the problems of low model coverage rate, complex method, large computational resources required, and high device power consumption caused by the unknown parameter information such as the pulse width, frequency band, amplitude, and pulse interval of non-cooperative signals in the frequency division processing method. Therefore, this algorithm is very suitable for the application scenario of useful signal detection before the classification and recognition module of underwater acoustic communication signals, which are non-cooperative signals. Due to its low computational complexity and high reliability, it has practicality. Description of the Drawings

[0015] Figure 1 Schematic diagram of the sub-band division principle of a frequency division method provided by an embodiment of this application; Figure 2 Hardware platform block diagram of a time-domain triggering detection method for an underwater acoustic communication signal classification and recognition module provided by an embodiment of this application; Figure 3 Schematic flowchart of a time-domain triggering detection method for an underwater acoustic communication signal classification and recognition module provided by an embodiment of this application; Figure 4 Schematic flowchart of a threshold update calculation and data detection and storage process provided by an embodiment of this application; Figure 5 Schematic diagram of a simulation input signal provided by an embodiment of this application; Figure 6 Schematic of the simulation results provided by an embodiment of this application Figure One ; Figure 7 Schematic of the simulation results provided by an embodiment of this application Figure Two ; Figure 8 Schematic of the simulation results provided by an embodiment of this application Figure Three ; Figure 9 Schematic of the simulation results provided by an embodiment of this application Figure Four ; Figure 10 Schematic of the simulation results provided by an embodiment of this application Figure Five ; Figure 11 Schematic of the simulation results provided by an embodiment of this application Figure Six ; Figure 12 Schematic of the simulation results provided by an embodiment of this application Figure Seven ; Figure 13Schematic diagram of the simulation results provided by the embodiments of the present application Figure Eight ; Figure 14 Schematic diagram of the simulation results provided by the embodiments of the present application Figure Nine ; Figure 15 Schematic diagram of the simulation results provided by the embodiments of the present application Figure Ten 。 Detailed implementation manners

[0016] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of the present application are clearly described. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0017] The terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally represents an "or" relationship between the associated objects in front and behind.

[0018] In the present application, the description of the method flow in the specification and the steps in the flowchart in the drawings of the present invention do not have to be strictly executed according to the step numbers. The method steps can change the execution order. Moreover, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0019] The following will be a detailed description of the time-domain trigger detection method and device for the underwater acoustic communication signal classification and recognition module provided by the embodiments of the present application in conjunction with the accompanying drawings and preferred embodiments as follows.

[0020] First, a detailed description will be given of the application scenario of the time-domain trigger detection method for the underwater acoustic communication signal classification and recognition module in the embodiments of the present application.

[0021] In the related art, since underwater acoustic communication signals are non-cooperative signals with low signal-to-noise ratio and unknown frequency bands, and the ocean ambient noise can basically be regarded as white noise with a relatively uniform frequency-domain distribution, underwater acoustic communication signal detection can be carried out by means of frequency-domain segmented energy estimation. First, the entire frequency band covered by the device is divided into multiple small frequency bands, the frequency-domain energy within each small frequency band is calculated respectively, and then the determination of useful signals is carried out. One method is to compare the energy within each frequency band and set a threshold. If there are several signals with obvious energy fluctuations, they are determined as useful signals; another method is to dynamically calculate the noise energy threshold, and compare the frequency-domain energy at different time periods within each frequency band with the noise energy threshold. If there are several signals with obvious energy greater than the noise energy threshold, they are determined as useful signals.

[0022] 1) Sub-band division Since the signal-to-noise ratio of underwater acoustic communication signals is low, and the frequency band and pulse width are unknown, while the working frequency band of the sonar receiving array for signal detection is relatively wide, it is difficult to see the energy fluctuation when a signal arrives from the perspective of the entire frequency band. Therefore, energy estimation needs to be carried out in sub-bands, and the entire detection frequency band is divided into multiple sub-bands for simultaneous energy estimation.

[0023] If the signal pulse width is small and the signal-to-noise ratio is low, the frequency band range may span the division frequency points of the sub-bands. Therefore, partial overlap is required between adjacent sub-bands to ensure as much as possible that the energy estimation of underwater acoustic communication signals in all frequency bands is consistent with the signal energy. See Figure 1 .

[0024] 2) Signal extraction and endpoint detection There are two methods for signal detection based on multiple sub-bands. One is the time-domain filtering sub-band extraction method: Design filters according to the sub-bands. After the input signal passes through each filter, multiple sub-band time-domain signals are formed. Calculate the energy of each sub-band, compare the energy difference of multiple sub-bands with the threshold to see if there is an obvious energy fluctuation, or compare the sum of the energies of multiple sub-bands with the noise energy threshold to determine whether it is a useful signal.

[0025] Another method is the frequency-domain sub-band energy calculation method: Calculate the energy of each sub-band in the frequency domain, compare the energy difference of multiple sub-bands with the threshold to see if there is an obvious energy fluctuation, or compare the sum of the energies of multiple sub-bands with the noise energy threshold to determine whether it is a useful signal.

[0026] a) Signal extraction and endpoint detection by time-domain filtering method After operations such as frequency division, bandwidth rough estimation, and frequency-domain filtering are completed, the approximate frequency band range can be obtained. Design a band-pass filter according to the approximate frequency band range, input the upper and lower cut-off frequencies, the filter order, and the window function (Hamming window, Chebyshev window, etc.) to obtain the corresponding filter impulse response.

[0027] Filter the original signal using the obtained filter to reduce the interference of out-of-band noise and improve the signal-to-noise ratio. Compared with directly sorting the original signal, sorting the filtered signal can effectively improve the sorting reliability.

[0028] Perform short-time Fourier transform on the preprocessed time-domain signal, and use the short-time spectrum to achieve signal endpoint detection and extraction of useful signals. The definition formula of the short-time Fourier transform is (1) where is the signal to be analyzed, is the window function.

[0029] Perform short-time Fourier transform detection on the input processed signal. The output detection statistic is further used to obtain the variation curve of the detection statistic with time. After performing short-time Fourier transform on the signal in each sliding window, optimize and accumulate the detection statistics within the time of each window function, and use the smoothed result as the final statistic.

[0030] (2) where, represents the number of sliding windows, is the number of accumulations, represents the statistical value obtained by short-time Fourier transform detection, that is, the statistical peak value within each sliding window.

[0031] Determine the final detection threshold based on the detection statistic obtained by smoothing, compare the sizes of the detection statistics in different time windows with the threshold, and perform signal endpoint detection. Take the left endpoint of the time window function when the detection statistic is greater than the threshold for the first time as the starting position of the signal, and take the right endpoint of the time window when the detection statistic is less than the threshold for the first time as the termination position of the signal.

[0032] b) Frequency-domain energy detection method The input signal is calculated by fft. According to the sub-band division, extract the sub-band energy, and at the same time dynamically calculate the noise energy threshold. Along the time axis, compare the sizes of the sub-band energy and the noise energy to perform signal endpoint detection. Take the left endpoint of the time axis when it is greater than the threshold for the first time as the starting position of the signal, and take the right endpoint of the time axis when the detection statistic is less than the threshold for the first time as the termination position of the signal.

[0033] Based on this, in the case of unknown signal characteristics, low signal-to-noise ratio, and complex ocean environment noise, etc., when using the above frequency division method for detecting useful signals in underwater acoustic communication, the number of frequency band divisions and the data time length for one FFT may affect the detection results, and there are limitations such as low model coverage rate, low detection efficiency, and large resource consumption.

[0034] Therefore, the embodiments of the present application provide a time-domain trigger detection method and device, which is a time-domain trigger detection method and device applicable before the underwater acoustic communication signal classification and recognition module; this method has a small computational amount, a high detection rate, and good practicability, and can effectively improve the accuracy and efficiency of signal detection.

[0035] Please refer to Figure 3 The embodiments of the present application provide a time-domain trigger detection method for an underwater acoustic communication signal classification and recognition module, as Figure 3 shown, the recommended method of the embodiments of the present application includes: Step S1, obtain the signal received by the hydrophone.

[0036] In the specific implementation process, the underwater acoustic signal picked up by the hydrophone first passes through hydrophone impedance matching and first-stage amplification, and then enters the reference channel and the detection channel respectively.

[0037] Step S2, construct a reference channel and a detection channel; the reference channel is used to collect ocean ambient noise and dynamically update the time-domain detection trigger threshold; the detection channel is used to collect actual underwater acoustic signals.

[0038] Further, the obtained signal received by the hydrophone is output to the reference channel, and low-frequency filtering is performed through a first filter with a first set cut-off frequency in the reference channel, and then secondary amplification and A / D conversion are performed to obtain a digital signal.

[0039] At the same time, the obtained signal received by the hydrophone is output to the detection channel, and wide-frequency filtering is performed through a second filter with a second set cut-off frequency in the detection channel, and then secondary amplification and A / D conversion are performed to obtain a digital signal.

[0040] In some possible implementation manners, the difference between the reference channel and the detection channel lies in the cut-off frequency of the filter. The reference channel is mainly used to pick up ocean ambient noise and perform dynamic update calculation of the detection threshold. Since most of the underwater ambient noise is in the low-frequency band, in order to avoid mixing in useful signals and causing errors in threshold calculation, its low-frequency filtering cut-off frequency is relatively low; the detection channel is the actual underwater acoustic signal, and it is necessary to cover all communication frequency bands of existing underwater acoustic communication products as much as possible; according to the current research on underwater acoustic communication products, the coverage range of underwater acoustic communication signals is 400 Hz - 40 kHz. Therefore, the embodiments of the application select the cut-off frequency of the reference channel filter to be 200 Hz, the sampling rate to be 500 Hz, the cut-off frequency of the detection channel filter to be 40 kHz, and the sampling rate to be 100 kHz.

[0041] Step S3: Obtain the current time-domain detection trigger threshold, continuously scan the data of the detection channel, and determine whether the signal amplitude of each signal sampling point is greater than the current time-domain detection trigger threshold; if among n1 consecutive signal sampling points, n2 signal sampling points are greater than the current time-domain detection trigger threshold, it is considered that the trigger condition is met, and data of a set length is stored as the input of the underwater acoustic communication signal classification and recognition module; where n1 and n2 are preset initialization trigger parameters.

[0042] Specifically, obtaining the current time-domain detection trigger threshold includes: Obtain the time-domain data of the reference channel noise signal within a set time period, extract its maximum value, and multiply the maximum value by a preset coefficient as the time-domain detection trigger threshold.

[0043] Since the underwater acoustic signals in oceans and lakes are non-stationary signals, the time-domain detection trigger threshold needs to be updated dynamically in real time. We extract the maximum value (Nosiemax) of the noise signal within a period of time (t1), and multiply the maximum value by the coefficient (para) as the time-domain trigger threshold.

[0044] For example, obtain 1 second of data from the reference channel, calculate the maximum amplitude value Vnoisemax in this 1-second data, and calculate the time-domain detection trigger threshold Threshold1 using the following formula: Threshold1 = Vnoisemax * para1, where para1 is a preset initialization trigger parameter.

[0045] The above step of obtaining the current time-domain detection trigger threshold is a process of threshold update calculation. This step reads data from the reference data channel for real-time threshold calculation and update. The other steps of Step S3 are processes of data detection and storage. Further, see Figure 4 , which specifically includes the following steps: Step S301: Initialize the trigger parameters, including: coefficient para1; trigger condition parameters n1, n2; the length of data stored in one trigger length1; the FIFO queue data1 for scanning data; the FIFO queue record1 for recording the signal positions greater than the time-domain detection trigger threshold.

[0046] Step S302: Obtain the time-domain data of the 1s reference channel and obtain its corresponding time-domain detection trigger threshold.

[0047] Step S303: If the signal amplitude of the signal sampling point is greater than the time-domain detection trigger threshold, store the position x1 of this signal sampling point into record1.

[0048] Step S304: Query the positions x2 of the n2 sampling points greater than the time-domain detection trigger threshold from record1 in the forward direction; Step S305: Calculate the position difference diff between the positions with a difference of n2 records in record1: diff = x1 - x2; Step S306: Determine whether diff is less than n1; if so, it is considered that the trigger condition is met; otherwise, continue to scan the next data point.

[0049] Step S307: If the trigger condition is met, determine whether the storage process is being triggered; if not, continuously store data with a length of length1 starting from the position x2; if so, continue to store data with a length of length1 starting from the current position x1.

[0050] In the specific implementation process, the signal-to-noise ratio requirement for the underwater acoustic communication signal classification and recognition module to effectively recognize signals is greater than 6 dB. Signals with a signal-to-noise ratio lower than this value cannot be recognized even if they are input into the underwater acoustic communication signal module, that is, the effective input signal-to-noise ratio of the underwater acoustic communication signal classification and recognition module is greater than 6 dB.

[0051] There are various noise spikes in the ocean ambient noise. For a signal with a signal-to-noise ratio of 6 dB, the amplitude of the noise spikes is close to the maximum value of the effective signal.

[0052] However, compared with the signal, the noise spikes are relatively sparse and the interval between adjacent triggered noise spikes is relatively large. Therefore, in the embodiments of the present application, the trigger condition is that the absolute value of the amplitude of more than a certain number (n2) in a continuous certain number of sampling points (n1) is greater than the threshold. Continuously scan the data of the detection channel. If the trigger condition is reached, store data with a length of length1 as the input of the underwater acoustic communication signal classification and recognition module; if the current data storage process has been triggered, update the data storage length and continue to store data with a length of length1 from the current position.

[0053] See Figures 5 - 15 , the following provides the simulation results of the embodiments of the present application: Algorithm-related parameters: T1 = 1 s, para1 = 0.7, n1 = 30, n2 = 5.

[0054] Simulation data parameters: Data duration: 10 s, and there are three groups of effective trigger data per second of data, as Figures 6 - 15As shown, the signal-to-noise ratio of each group of trigger data is less than and close to 6 dB and there are noise spikes. The pulse width of each group of trigger data is 5 ms (a pulse train for 5 consecutive ms, or multiple pulse trains with a 50% duty cycle). The start times of the three groups of trigger data are 0.21 s, 0.41 s, and 0.61 s at the start of each second. The trigger data frequencies per second are (2 kHz, 4 kHz, 6 kHz), 3 kHz + (2 kHz, 4 kHz, 6 kHz), 2 * 3 kHz + (2 kHz, 4 kHz, 6 kHz)...... 9 * 3 kHz + (2 kHz, 4 kHz, 6 kHz). The sampling rate of the reference channel is 500 Hz, and the sampling rate of the data detection channel is 100 kHz.

[0055] The simulation results are shown in the figure. All three groups of data per second are triggered successfully, and the trigger time is consistent with the simulation setting time. The number of triggered 5-ms pulse trains at different frequencies is inconsistent because the number of sampling points of the signal greater than the trigger threshold under the 100-kHz sampling rate condition is inconsistent at different frequencies, which does not affect the trigger result. Therefore, the trigger detection rate of this method is relatively high.

[0056] For the hardware platform of the embodiment of the present application, see Figure 2 , the hardware platform includes two parts: a reference channel and a detection channel; the hydrophone picks up the underwater acoustic signal. After impedance matching and first-stage amplification of the hydrophone, it enters the reference channel and the detection channel respectively. The data of the two channels are filtered, second-stage amplified, and then converted into digital signals and enter the processor for subsequent processing. Among them, the processor includes a storage module and is a milliwatt-level low-power processor. For the application scenario of underwater acoustic signal processing, the present application especially realizes low-power design. For example, some milliwatt-level low-power embedded platforms can be used, which are suitable for long-term operation.

[0057] Based on the above scheme, the embodiment of the present application has the following advantages and effects: The present application designs a useful signal time-domain triggering method with small computational complexity and high reliability. This triggering method can be used as an effective underwater acoustic communication signal detection algorithm before the underwater acoustic communication signal classification and recognition module.

[0058] This algorithm model does not involve fft calculation and is independent of parameter information such as the pulse width, frequency band, amplitude, and pulse interval of the signal, overcoming the problems of low model coverage rate, complex method, large computational resources required, and high device power consumption caused by unknown parameter information such as the pulse width, frequency band, amplitude, and pulse interval of non-cooperative signals in the frequency division processing method. Therefore, this algorithm is very suitable for the application scenario of useful signal detection before the classification and recognition module of underwater acoustic communication signals, which are non-cooperative signals. Because of its small computational complexity and high reliability, it has practicality.

[0059] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including such element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0060] It can be understood that the embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. As is known to those skilled in the art, without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. Additionally, those of ordinary skill in the art, under the inspiration or teaching of the present application, can modify these features and embodiments to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of the present application belong to the scope protected by the present invention.

Claims

1. A time domain trigger detection method for an underwater acoustic communication signal classification and recognition module, characterized in that: include: Obtaining hydrophone receiving signals; Construct reference channel and detection channel; The reference channel is commonly used to collect ocean environmental noise and dynamically update the time domain detection trigger threshold; The detection channel is used to collect actual hydroacoustic signals; Obtain the current time domain detection trigger threshold, continuously scan the detection channel data and determine whether the signal amplitude of each signal sampling point is greater than the current time domain detection trigger threshold; If n2 of the n1 consecutive signal sampling points are greater than the current time domain detection trigger threshold, the trigger condition is considered to be met, and data of a set length is stored as input to the underwater acoustic communication signal classification and recognition module; wherein n1 and n2 are preset initialization trigger parameters.

2. The time domain trigger detection method of the underwater acoustic communication signal classification and recognition module according to claim 1 is characterized in that: Also includes: Outputting the acquired hydrophone receiving signal to the reference channel, performing low-frequency filtering on the reference channel through a first filter with a first set cutoff frequency, and then performing secondary amplification and A / D conversion to obtain a digital signal; At the same time, the acquired hydrophone receiving signal is output to the detection channel, and is subjected to broadband filtering through a second filter with a second set cutoff frequency in the detection channel, and then subjected to secondary amplification and A / D conversion to obtain a digital signal.

3. The time domain trigger detection method of the underwater acoustic communication signal classification and recognition module according to claim 2 is characterized in that: The cut-off frequency of the first filter is 200 Hz; The cutoff frequency of the second filter is 40kHz; The reference channel sampling rate is 500 Hz; The sampling rate of the detection channel is 100 kHz.

4. The time domain trigger detection method of the underwater acoustic communication signal classification and recognition module according to claim 1 is characterized in that: The obtaining of the current time domain detection trigger threshold comprises: The time domain data of the reference channel noise signal within a set time period is obtained, the maximum value thereof is extracted, and a preset coefficient is used to multiply the maximum value as a time domain detection trigger threshold.

5. The time domain trigger detection method of the underwater acoustic communication signal classification and recognition module according to claim 4 is characterized in that: Use the following formula to calculate the time domain detection trigger threshold Threshold1: Threshold1 = Vnoisemax * para1, Among them, Vnoisemax is the extracted maximum value, and para1 is the preset initialization trigger parameter.

6. The time domain trigger detection method of the underwater acoustic communication signal classification and recognition module according to claim 1 is characterized in that: The continuous scanning detects channel data and determines whether the signal amplitude of each signal sampling point is greater than the current time domain detection trigger threshold; If n2 of the consecutive n1 signal sampling points are greater than the current time domain detection trigger threshold, the trigger condition is considered to be met, and data of a set length is stored as input to the underwater acoustic communication signal classification and recognition module, specifically including: Initialize trigger parameters, including: coefficient para1; trigger condition parameters n1, n2; one-time trigger storage data length length1; scan data FIFO queue data1; signal position record FIFO queue record1 that is greater than the time domain detection trigger threshold; Obtain the time domain data of the reference channel of 1s, and obtain the corresponding time domain detection trigger threshold; If the signal amplitude of the signal sampling point is greater than the time domain detection trigger threshold, the signal sampling point position x1 is stored in record1; Query the n2 sampling points x2 that are greater than the time domain detection trigger threshold from record1; Calculate the position difference diff of n2 records in record1: diff = x1 - x2; Determine whether diff is less than n1; if so, it is considered that the trigger condition is met; otherwise, continue to scan the next data point; If the trigger condition is met, determine whether the storage process is being triggered; if not, continuously store data of length1 length starting from position x2; if yes, continue to store data of length1 length from the current position x1.

7. The time domain trigger detection device of the underwater acoustic communication signal classification and recognition module is characterized in that: A processor is included for implementing the time domain trigger detection method of the underwater acoustic communication signal classification and recognition module as described in any one of claims 1 to 6; The processor includes a storage module and is an embedded low-power processor; The storage module is used to store data of a set length as input to the underwater acoustic communication signal classification and recognition module when the trigger condition is met.

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