Underwater acoustic signal identification and positioning method, device, medium and computer equipment

Abstract

The present invention provides a method, device, medium and computer equipment for underwater acoustic signal identification and positioning. The positioning method includes: receiving underwater acoustic signals using an underwater acoustic transducer array; preprocessing the underwater acoustic signals using a programmable logic end based on the category of the target underwater acoustic signals to obtain a preprocessing result; judging whether the preprocessing result contains a target underwater acoustic signal of a corresponding category using the programmable logic end or a processing system end; when the underwater acoustic signal contains the target underwater acoustic signal of the category, locating the sound source corresponding to the underwater acoustic signal using the processing system end to generate position data of the sound source; adopting the positioning method of the present application, by arranging an embedded computing device in an electronic cabin, online identification and positioning of underwater acoustic signals can be realized, without the need to salvage the underwater acoustic signal identification and positioning device ashore for offline identification, and having higher efficiency than traditional identification and positioning methods.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater acoustic signal detection, and more specifically, to an underwater acoustic signal recognition and positioning method, device, medium and computer equipment. Background Art

[0002] Measuring ship radiated noise in waterways, monitoring marine environmental information, and providing early warning of underwater targets in ports and shore-based areas are all areas of widespread application in underwater acoustic detection and monitoring systems. Ship radiated noise includes propeller noise, hydrodynamic noise, and mechanical noise. Currently, the impact of ship radiated noise warrants attention, whether for reducing marine environmental noise, minimizing interference with marine life, or improving the cabin environment. The most effective means of achieving these applications is through underwater acoustic detection. Current unmanned autonomous underwater platforms (buoys / submersibles) mostly operate in an offline acquisition mode, meaning the ship noise test system collects and stores signal data underwater. After testing, the device is brought to the surface and the data is exported for processing. This results in poor real-time performance and is inconvenient to operate. Traditional testing methods are insufficient for real-time noise assessment of ships in ports or waterways. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an underwater acoustic signal recognition and positioning method, device, medium and computer equipment to overcome the above shortcomings.

[0004] The above technical objectives of the present invention are achieved through the following technical solutions: First, a method for underwater acoustic signal recognition and positioning is applied to an embedded underwater acoustic detection platform based on a SOC architecture; the underwater acoustic detection platform specifically includes: a programmable logic terminal using an FPGA architecture, a processing system terminal using an ARM architecture, and an underwater acoustic transducer array, wherein the programmable logic terminal, the processing system terminal, and the underwater acoustic transducer array are communicatively connected to each other;

[0005] The method comprises:

[0006] Receiving underwater acoustic signals using the underwater acoustic transducer array;

[0007] Based on the category of the target underwater acoustic signal, preprocess the underwater acoustic signal using the programmable logic end to obtain a preprocessing result; and determine whether the preprocessing result includes a target underwater acoustic signal of a corresponding category using the programmable logic end or the processing system end; wherein the target underwater acoustic signal includes: a Category I underwater acoustic signal, a Category II underwater acoustic signal, and a Category III underwater acoustic signal;

[0008] When the underwater acoustic signal contains a target underwater acoustic signal of this category, the processing system is used to locate the sound source corresponding to the underwater acoustic signal to generate position data of the sound source.

[0009] In one embodiment, when the category of the target underwater acoustic signal is a type I underwater acoustic signal, the underwater acoustic transducer array is specifically a horizontal linear array;

[0010] When the category of the target underwater acoustic signal is a Class II underwater acoustic signal, the underwater acoustic transducer array is specifically a horizontal circular array;

[0011] When the category of the target underwater acoustic signal is a Class III underwater acoustic signal, the underwater acoustic transducer array is specifically a vertical plane array.

[0012] In one embodiment, when the category of the target underwater acoustic signal is a type of underwater acoustic signal, the processing system is used to determine whether the preprocessing result includes a target underwater acoustic signal of a corresponding category;

[0013] When the category of the target underwater acoustic signal is a category II underwater acoustic signal, determining by the processing system whether the preprocessing result includes a target underwater acoustic signal of a corresponding category;

[0014] When the category of the target underwater acoustic signal is a category three underwater acoustic signal, the programmable logic terminal is used to determine whether the preprocessing result includes a target underwater acoustic signal of a corresponding category.

[0015] In one embodiment, when the target underwater acoustic signal is a type I underwater acoustic signal, preprocessing the underwater acoustic signal using the programmable logic end to obtain a preprocessing result specifically includes: performing a fast Fourier transform on the underwater acoustic signal using the programmable logic end to generate a frequency domain underwater acoustic signal; and calculating corresponding power spectrum data based on the frequency domain underwater acoustic signal;

[0016] When the category of the target underwater acoustic signal is a Class II underwater acoustic signal, the preprocessing of the underwater acoustic signal by the programmable logic end to obtain a preprocessing result specifically includes: performing a fractional Fourier transform on the underwater acoustic signal by the programmable logic end to generate a discrete sample signal in the fractional Fourier domain; and calculating an energy-weighted sum corresponding to the discrete sample signal;

[0017] When the category of the target underwater acoustic signal is a Class III underwater acoustic signal, the programmable logic end is used to preprocess the underwater acoustic signal to obtain a preprocessing result, specifically including: using a pre-constructed matched filter to perform a convolution operation on the underwater acoustic signal to generate a matched filtering result.

[0018] In one embodiment, when the category of the target underwater acoustic signal is a type of underwater acoustic signal, determining by the processing system whether the preprocessing result includes a target underwater acoustic signal of a corresponding category specifically includes:

[0019] Comparing the power spectrum data with a preset first threshold value using the processing system end, and determining that the underwater acoustic signal contains a type of underwater acoustic signal when the power spectrum data is greater than the first threshold value;

[0020] When the category of the target underwater acoustic signal is a category II underwater acoustic signal, the determining by the processing system whether the preprocessing result includes a target underwater acoustic signal of a corresponding category specifically includes:

[0021] comparing the energy weighted sum with a preset second threshold value, and when the energy weighted sum is greater than the second threshold value, determining that the underwater acoustic signal contains a second type of underwater acoustic signal;

[0022] When the category of the target underwater acoustic signal is a Category 3 underwater acoustic signal, the determining, by the programmable logic end, whether the preprocessing result includes a target underwater acoustic signal of a corresponding category specifically includes:

[0023] The first time period Comparing the matched filtering result within with a preset third threshold value;

[0024] When the first time period When the maximum value of the matched filtering result within the second time period is less than the third threshold value, the second time period The matched filtering result within the period is compared with the preset fourth threshold value; when the second period When the maximum value of the matched filtering results within is greater than the fourth threshold value, it is determined that the underwater acoustic signal contains three types of underwater acoustic signals.

[0025] In one embodiment, performing a convolution operation on the three types of underwater acoustic signals using a pre-constructed matched filter to generate a matched filtering result specifically includes:

[0026] Using the programmable logic terminal to perform fast Fourier transform on the matched filter and the underwater acoustic signal respectively, so as to convert the matched filter and the underwater acoustic signal into a frequency domain;

[0027] After multiplying the matched filter converted to the frequency domain and the underwater acoustic signal, the product is subjected to inverse fast Fourier transform to generate a matched filtering result.

[0028] In one embodiment, the first threshold value is specifically:

[0029] ;

[0030] in, is the variance of the environmental noise data; is the false alarm probability of detection; is the number of discrete sampling points of the underwater acoustic signal;Q ( x ) function is the right tail function of the standard normal distribution, and the specific expression is:

[0031] .

[0032] In the second aspect, an underwater acoustic signal recognition and positioning device is applied to an embedded underwater acoustic detection platform based on a SOC architecture; the underwater acoustic detection platform specifically includes: a programmable logic end using an FPGA architecture, a processing system end using an ARM architecture, and an underwater acoustic transducer array, wherein the programmable logic end, the processing system end, and the underwater acoustic transducer array are communicatively connected to each other; the device includes:

[0033] A receiving component, configured to receive underwater acoustic signals using the underwater acoustic transducer array;

[0034] an identification component configured to pre-process the underwater acoustic signal using the programmable logic terminal based on the category of the target underwater acoustic signal, and determine whether the underwater acoustic signal contains a target underwater acoustic signal of a corresponding category based on the pre-processing result; wherein the target underwater acoustic signal includes: Category I underwater acoustic signal, Category II underwater acoustic signal, and Category III underwater acoustic signal;

[0035] The positioning component is used to locate the sound source corresponding to the underwater acoustic signal using the processing system end when the underwater acoustic signal contains the target underwater acoustic signal of this category, and generate the position data of the sound source.

[0036] In a third aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method steps described in the first aspect.

[0037] In a fourth aspect, a computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0038] In summary, the present invention has the following beneficial effects: the present invention provides a method for identifying and positioning underwater acoustic signals. The method of the present invention can realize online detection and positioning of underwater acoustic signals, which is more efficient than the traditional underwater acoustic signal identification and positioning method of first collecting and then salvaging and calculating. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the underwater acoustic signal recognition and positioning method of the present invention;

[0040] Figure 2 This is a structural diagram of an underwater acoustic signal recognition and positioning device according to an embodiment of the present invention;

[0041] Figure 3This is a diagram of the internal structure of a computer device according to an embodiment of the present invention;

[0042] Figure 4 Schematic diagram of the SOC structure in the first embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of ship radiated noise testing in Example 2 of the present invention;

[0044] Figure 6 This is a schematic diagram of ship noise power spectrum detection in Example 2 of the present invention;

[0045] Figure 7 This is a schematic diagram of linear array delay difference direction finding in embodiment 2 of the present invention;

[0046] Figure 8 This is a schematic diagram of underwater monitoring and early warning in Example 3 of the present invention;

[0047] Figure 9 This is the simulation result of FRFT spectrum detection and estimation in Example 3 of the present invention;

[0048] Figure 10 This is the simulation result of circular array MUSIC azimuth estimation in Example 3 of the present invention;

[0049] Figure 11 This is a schematic diagram of the three-dimensional spatial positioning of the underwater robot in the fourth embodiment of the present invention;

[0050] Figure 12 This is the simulation result of the matched filtering detection of the specific signal in the fourth embodiment of the present invention;

[0051] Figure 13 This is the simulation result of the azimuth estimation using the inner product response method in the fourth embodiment of the present invention;

[0052] In the figure: 1. Receiving component; 2. Identification component; 3. Positioning component; 4. Power management module; Data acquisition module 5, Signal processing module 6, System control module 7, Data storage module 8. DETAILED DESCRIPTION

[0053] To make the objectives, features, and advantages of the present invention more readily apparent, the following detailed description of the present invention is provided with reference to the accompanying drawings. The accompanying drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein.

[0054] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0055] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0057] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0058] The above description is merely a specific embodiment of the present application. Any person skilled in the art may easily conceive of variations or substitutions within the technical scope disclosed in this application, and such variations or substitutions shall be within the scope of protection of this application. The scope of protection of this application shall be subject to the scope of protection of the claims.

[0059] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0060] Example 1

[0061] Measuring ship radiated noise in waterways, monitoring marine environmental information, and providing early warning of underwater targets in ports and shore-based areas are all areas of widespread application in underwater acoustic detection and monitoring systems. The most effective means of achieving these applications is through underwater acoustic detection. Currently, most unmanned autonomous underwater platforms (buoys / submersibles) operate in an offline data acquisition mode. This means that after presetting operating parameters, the system performs simple data acquisition and storage, and only after recovery does a host computer complete the relatively complex detection algorithms. This operating method is inconvenient and lacks real-time performance, making it difficult to efficiently complete underwater measurement and monitoring tasks.

[0062] With the development of integrated circuits and embedded technology, underwater acoustic detection technology and signal processing algorithms based on embedded platforms have received extensive attention and research. Because underwater acoustic detection systems need to operate underwater for long periods of time, processing chips are usually required to have low power consumption and high computing speed.

[0063] This embodiment provides a SOC, including: a programmable logic (PL) side and a processing system (PS) side, wherein the PL side adopts an FPGA architecture and the PS side adopts an ARM architecture processor;

[0064] The SOC specifically includes: a power management module 4, a data acquisition module 5, a signal processing module 6, a system control module 7, and a data storage module 8;

[0065] The power management module 4 is used to supply power to the entire system to ensure the normal operation of other modules and has circuit protection functions such as preventing overcurrent, overvoltage, and overtemperature;

[0066] Data acquisition module 5 is used to collect underwater acoustic signals and noise data, and realize the conversion of data format from analog signals to digital signals. Its function is completed by the ADC chip, and the acquisition time series is controlled by the PL terminal;

[0067] Signal processing module 6 is used to implement data collection preprocessing, signal detection algorithm, target positioning algorithm, etc., and its functions are completed by the PL end and the PS end;

[0068] System control module 7, used to communicate with the external host computer, download instructions and parameters, and control other modules to achieve corresponding functions, which is completed by the PS end;

[0069] The data storage module 8 is used to store the original collected data, algorithm processing results and system work logs, etc. The storage address allocation is completed by the PS end;

[0070] Among them, the programmable logic end is mainly used to preprocess the underwater acoustic signal to obtain the preprocessing result. In some scenarios, the programmable logic end can also identify and judge the preprocessing result to determine whether the underwater acoustic signal contains the target signal.

[0071] Alternatively, the programmable logic end transmits the preprocessing result to the processing system end for identification to determine whether the underwater acoustic signal contains the target underwater acoustic signal.

[0072] When the underwater acoustic signal contains a target signal, the processing system will also locate the sound source target based on the underwater acoustic signal received by the underwater acoustic transducer array.

[0073] The pre-processing of underwater acoustic signals mainly includes bandpass filtering and Fourier transform. When the FPGA architecture is used on the PL side, the specific construction method is as follows:

[0074] Bandpass filtering uses a digital finite impulse response (FIR) filter. This is achieved by selecting the IP directory in the established Vivado project, opening the IP core library, and selecting the FIR IP core. In the configuration wizard, select Band Pass Filter as the filter type. Set the filter order and coefficients in the configuration window. Finally, use the IP core in the design, connect it, and instantiate it to ensure that the input and output ports can be correctly connected to other modules.

[0075] The Fast Fourier Transform (FFT) converts input data from the time domain to the frequency domain. This is achieved as follows: The PS control module reads information from the host computer, configures the channel, and writes the FFT point count to the PL, initializing the FFT core. The PL waits for the FIR filter to signal completion, upon which it initiates the FFT. It then reads the filtered signal from RAM (random access memory) and converts it into specific AXI_Stream data for input to the FFT IP core. After processing, the FFT core converts the output data back into AXI_Stream data and stores it in RAM. It then sends a conversion completion signal, awaiting further processing by subsequent modules. The FFT preprocessing is a complex FFT. When buffering input and output, both the real and imaginary parts of the data are stored. The data format is binary, and the sample points at the same time are arranged as follows: channel 1 real part, channel 1 imaginary part, channel 2 real part, channel 2 imaginary part, ..., channel M real part, channel M imaginary part.

[0076] After preprocessing the underwater acoustic signal, the signal detection process begins. Each channel's buffer array is initialized according to different algorithm requirements, binary data is converted into Float values ​​(representing voltage values), and floating-point data is grouped for operations such as noise power estimation, adaptive threshold estimation, and threshold decision accumulation. When preset signal detection conditions are met, the system caches the data in a FIFO (First-in, First-out) for subsequent signal processing. The PS side of the SOC architecture in this invention first implements upper-level control of the entire embedded system, configures logic parameters on the PL side, and performs positioning algorithms such as azimuth estimation and target ranging on the signal data, while simultaneously interacting with the host computer for data and instructions. The PS adopts an embedded Linux system development model based on the ARM architecture. To achieve high-performance processing of complex signals, the Cortex-A series chips were selected, which support Linux multitasking and hardware acceleration. High-speed Ethernet and USB 3.0 were selected as peripheral interfaces. The PS Linux system's boot loader (BootLoader) uses the U-Boot solution. Setup begins with initializing the DDR (Double Data Rate), configuring parameters such as clock frequency and system latency, and optimizing these parameters based on chip characteristics and hardware information. The Linux kernel is then loaded, and memory area is reserved for the signal processing algorithm. The signal processing algorithm within the PS is compiled in C++ assembly language. Global variables and callback functions are used to quickly execute steps such as data indexing and mathematical operations. Loop statements are reduced, and the main function structure is streamlined to improve the efficiency of the PS algorithm.

[0077] Furthermore, this embodiment also provides a method for underwater acoustic signal detection and positioning using SOC, such as Figure 1 As shown, the detection and positioning method includes:

[0078] S1. Receive underwater acoustic signals using an underwater acoustic transducer array;

[0079] S2. Based on the category of the target underwater acoustic signal, preprocess the underwater acoustic signal using the programmable logic end to obtain a preprocessing result; and determine, using the programmable logic end or the processing system end, whether the preprocessing result contains a target underwater acoustic signal of a corresponding category; wherein the target underwater acoustic signal includes: a Category I underwater acoustic signal, a Category II underwater acoustic signal, and a Category III underwater acoustic signal;

[0080] S3. When the underwater acoustic signal contains the target underwater acoustic signal of this category, the processing system is used to locate the sound source corresponding to the underwater acoustic signal to generate position data of the sound source.

[0081] Example 2

[0082] In one embodiment, when the target underwater acoustic signal is a type of underwater acoustic signal, a linear array of transducers is used as the receiving end of the sonar system. The schematic diagram of this embodiment is shown in FIG. Figure 5 As shown. One type of underwater acoustic signal is mainly for ship radiation noise, submarine noise, underwater robot propulsion noise, etc. Ship radiation noise includes propeller noise, hydrodynamic noise, mechanical noise, etc. At present, whether it is from the aspects of reducing marine environmental noise, reducing interference with marine life, and improving cabin environment, the impact of ship radiation noise should be taken seriously. For the radiation noise test of large ships at sea, large-aperture linear arrays are usually used. Considering that large areas of sea are shallow seas, the vertical arrangement of the transducer array may cause large interface sound reflection interference, so the horizontal linear array arrangement is relatively more suitable. Ship radiation noise is generally composed of multiple line spectra and broadband continuous spectra, and the frequencies are mostly concentrated in the low frequency band below 1000Hz. In the test, the first thing to solve is the target signal detection problem, and the positioning must be carried out at different positions of the ship. The existing ship noise test system mostly only collects and stores signal data underwater. After the test is completed, the equipment is taken out of the water and the data is exported for processing. The real-time performance is poor and the operation is inconvenient. When it is necessary to conduct real-time noise assessment of ships in ports or waterways, traditional test methods are difficult to meet the requirements. Figure 5 As shown, the present invention integrates an FPGA+ARM processing system and working modules within an electronic cabin. By placing the electronic cabin and transducer array in a fixed position, it can detect and locate radiated noise from passing ships, while simultaneously storing the collected data and processing results, achieving the goal of real-time waterway monitoring. The specific signal processing method steps for ship radiated noise detection are as follows:

[0083] When the target signal is a type of underwater acoustic signal, the frequency domain energy detection method is used for signal detection. The specific algorithm principles include:

[0084] Step 11: First, convert the analog underwater acoustic signal collected by the underwater acoustic transducer into a discrete digital underwater acoustic signal. Perform FFT processing directly on the PL side to obtain frequency domain data , where the number of FFT points is the signal length or an integer multiple of 1024;

[0085] Step 12: Use PL to analyze frequency domain data Take the amplitude and perform square operation, then average it according to the signal length to obtain the power spectrum data of different frequency points of the collected signal ;Transmit the power spectrum data to the PS end to detect the target underwater acoustic signal;

[0086] Step 13: Figure 6 As shown, the PS end is used to compare the signal power spectrum With the first threshold , exceeds the first threshold The judgment is that there is a type of underwater acoustic signal at this frequency point, and the expression is as follows:

[0087] ;

[0088] Among them, the first threshold value is designed according to the constant false alarm probability criterion ,include:

[0089] ;

[0090] in is the false alarm probability of detection, is the variance of the environmental noise data, The function is the right tail function of the standard normal distribution, and the expression is:

[0091] ;

[0092] Step 14: When a type of underwater acoustic signal is detected in the underwater acoustic signal, use the PS end to collect data based on all channels of the linear array Calculate its non-jump phase ;

[0093] Step 15: Use the PS terminal to calculate the phase difference between the last array element and the first array element , and then according to the phase difference and the threshold frequency point , test the speed of sound in water Calculate the acoustic path difference between the sound wave and the first array element and the last array element:

[0094] ;

[0095] Step 16: Figure 7 As shown, the PS end is used according to the aperture size of the linear array and path difference The geometric relationship between the target sound source azimuth is calculated ,

[0096] ;

[0097] In this embodiment, the float and weight are used to fix the position of the transducer linear array, and the indicator light is used to mark the position of the electronic cabin. When there is sufficient power supply, the working module in the cabin can work underwater for a long time and independently complete data collection, detection, positioning, storage and other functions. The system working parameters are set by the host computer through the transmission cable.

[0098] Example 3

[0099] In one embodiment, when the target underwater acoustic signal is a Class II underwater acoustic signal, a circular array of underwater acoustic transducers is used as a receiving end of the sonar system. Figure 8 As shown; the second type of underwater acoustic signals mainly include underwater vehicle monitoring and early warning and observation and tracking of marine cetaceans. Underwater vehicle monitoring and early warning are mainly used in underwater protection of important ports and islands. When an unknown underwater vehicle sends a communication signal, pulse interception and target positioning are performed, and the detection results are transmitted to the shore-based workstation; the key to observing and tracking marine cetaceans is to detect their biological communication signals (click sound signals) and then locate the target position. The characteristics of the above-mentioned signals to be tested are that the specific parameters are unknown, the potential frequency band is relatively wide (5kHz-150kHz), and there is no prior target position information in the detection. In order to ensure the same positioning accuracy in all directions, a transducer circular array is selected as the receiving end of the sonar system in this embodiment. As shown Figure 5 As shown, the present invention integrates an FPGA+ARM processing system and working modules within the electronics cabin, simultaneously providing signal acquisition, detection, positioning, and storage capabilities. It is easy to operate and deploy, enabling long-term, real-time monitoring of uncertain underwater targets. A surface indicator light connected to the electronics cabin changes color based on detection results, conveying information to shore-based or shipboard personnel. Underwater detection of unknown FM pulses utilizes an energy detection method based on the fractional Fourier transform (FRFT). The FRFT method focuses the energy of the FM signal through time-frequency rotation and can estimate the center frequency and FM slope. The specific signal processing steps are as follows:

[0100] Step 21: To achieve the integration of the embedded end, the algorithm needs to be improved for practical use. The PL end uses the fractional Fourier transform calculation method to pre-process the signal. First, the collected data Perform dimension normalization to obtain a new sampling sequence , is the sequence length;

[0101] Step 22: New Sequence Perform discrete Chirp multiplication to obtain a phase modulation sequence

[0102] ;

[0103] in, is the rotation angle, also known as the transformation order;

[0104] Step 23: Sequence Perform FFT to generate discrete sample signals in the fractional Fourier domain

[0105] ;

[0106] The above fractional Fourier transform process is completed at the PL side;

[0107] Step 24: Use the PS side to Take the square of the amplitude and construct the following expression to compare with the second threshold for comparison

[0108] ;

[0109] where, The value is calculated using the constant false alarm rate criterion (same as in Embodiment 2), is a real number greater than 0 and can be adjusted according to different working conditions. When the result of the expression is greater than the threshold, it is determined that there is a target signal, and the center frequency and frequency modulation slope are calculated based on the maximum peak value. The expression is as follows:

[0110] Center frequency: ;

[0111] Frequency modulation slope: ;

[0112] In the above formula, is the order at the spectral peak, is the frequency at the spectral peak, is the dimension normalization factor, is the sampling rate.

[0113] As Figure 9 shown, it is the simulation result of FRFT spectrum detection and estimation. It can be seen that for a signal with a pulse width of 35 ms, the estimated signal center frequency at the maximum spectral peak is 23.9 kHz, and the estimated frequency modulation slope is 139.5 kHz / s. Then, based on the estimated signal parameters, the following positioning algorithm is further carried out using the PS side.

[0114] Since the azimuth range of the target sound source is usually unknown, to ensure a high positioning resolution and spatial spectrum dynamic range, the multiple signal subspace classification (MUSIC) algorithm is used to achieve azimuth estimation, and the algorithm process is improved to adapt to the embedded system. The specific process is as follows;

[0115] Step 25: When it is detected that there is a target signal, divide the array acquisition data into and , where D < M - D. Similarly, divide the array manifold with the th row as the boundary into and , is the number of target sound sources, which depends on the test working conditions in the unknown case, The specific expression is as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] in, Estimate the center frequency using the FRFT method, is the radius of the circular array; where the array flow pattern It is a core concept in array signal processing, which is used to describe the response pattern of the array to the underwater acoustic signal coming from a certain direction in space. Indicates the The steering vector of each element, The first discrete scanning angles, For the The angle of element No. relative to element No. 1.

[0120] Step 26: Calculate the autocorrelation matrix of the array acquisition data , and perform standard orthogonalization on its column vector, and then construct the signal subspace matrix U with its column vector;

[0121] Step 27: Construct a spatial spectrum function, including:

[0122]

[0123] And perform spectrum peak search, the energy is the maximum The value is the target signal direction of arrival estimation result. Steps 24-27 are all completed on the PS side.

[0124] like Figure 10 Figure 2 shows the simulation results of the MUSIC algorithm azimuth estimation based on a circular array. The simulation conditions are two sound sources incident at angles of -10° and 30°. The above MUSIC algorithm is used to perform azimuth estimation under signal-to-noise ratios of -10dB, 0dB, and 10dB, respectively. It can be seen that as the signal-to-noise ratio decreases, the spatial spectrum dynamic range decreases, but the positions of the two sound sources can be located in all three cases. The accuracy is higher at 0dB and 10dB, and the error is around 1.5° at -10dB. This shows that the above simplified MUSIC algorithm can achieve good positioning results under different ambient noise levels.

[0125] In this embodiment, the MUSIC algorithm can effectively improve the azimuth estimation accuracy of the circular array; in specific applications, the electronic cabin and the circular transducer array can be deployed in the form of buoys or suspended from a platform, which can meet the requirements of real-time monitoring and is also more convenient to operate and recover.

[0126] Example 4

[0127] In one embodiment, when the target underwater acoustic signal is a Class III underwater acoustic signal, such as Figure 11 As shown, a planar array of transducers is used as the receiving end of the sonar system; the three types of underwater acoustic signals are mainly used for underwater robot positioning. In practical applications, whether it is laying submarine cables and pipelines or repairing bridges and dams, the application of underwater robots will greatly improve work efficiency and reduce work costs. At present, one of the key issues faced by underwater robots in engineering applications is the realization of real-time and high-precision underwater positioning. The usual practice is to install an acoustic beacon on the operating robot and send specific pulses (with a known period) at regular intervals, and then use a sonar transducer array to receive the signal pulses and perform positioning externally. The present invention integrates signal acquisition, detection, target positioning, data storage and transmission into an embedded system, and achieves the purpose of real-time and high-precision positioning by efficiently processing multi-channel array acquisition data. Taking into account the needs of three-dimensional positioning and tracking, a planar array is used as the receiving end of the sonar system in the embodiment, and the specific processing method steps for the target signal being a specific frequency-modulated signal are as follows:

[0128] Step 31: First, use the PL terminal to pre-process the signal, specifically including: using the matched filtering method to detect the signal, storing the signal waveform coefficients of the known parameters in the PL terminal in advance, and constructing a matched filter , and then and collect data Perform convolution operation to obtain matched filtering results ; To implement convolution operation in FPGA is to and Perform FFT transformation separately and then multiply, and then perform inverse FFT transformation;

[0129] Step 32: Set the adaptive third threshold , is the noise detection threshold before the signal arrives, and its size is related to the background noise spectrum. Assume that when the first time period within Maximum value is less than hour , enter the target signal detection state;

[0130] Specifically, the waveforms and periods of the three types of underwater acoustic signals to be tested are known. The mechanism of matched filtering is that the peak value will be very large when the target signal appears, and the peak value will be very small when matching the environmental noise. Although positioning is only for the FM signal within one period, so that the positioning accuracy and time accuracy are guaranteed, the signal emitted by the acoustic beacon installed on the underwater robot is usually not just a simple FM signal, but will also contain a coded signal for communication. The matching result of the coded signal will be greater than the noise threshold but less than the positioning signal detection threshold. The purpose of setting the noise threshold is to detect the time period when only environmental noise exists between the detection periods. In the subsequent new period, the FM signal used for positioning is the first to be detected, so that the positioning refresh rate can also be determined.

[0131] Step 33: Set the adaptive fourth threshold according to the expected signal amplitude After entering the target signal detection state, obtain the second time period within The maximum value of Compare, if the maximum value exceeds the fourth threshold , it is determined that there is a target signal; wherein, the second time period The number can be multiple and arranged in chronological order. After entering the target detection state, if the first and second time periods If no target signal is detected within 3 seconds, the target signal detection action will continue until the target signal is detected.

[0132] The third and fourth thresholds are both adaptive thresholds. When an underwater acoustic signal exceeding the fourth threshold is detected, the fourth threshold needs to be adjusted according to the maximum value corresponding to the underwater acoustic signal. Specifically, the following steps are performed: , , depending on different test conditions, Indicates The time point corresponding to the maximum sampling point in the time period.

[0133] The detection simulation of the matched filtering results of a specific signal is as follows: Figure 12 As shown in the figure, when the target signal appears in the time domain, the collected data completely matches the preset reference waveform coefficient, and a large peak will appear. It can be seen that the use of dual threshold detection can accurately find the matching peak moment, effectively avoid interference from other clutter, and reduce the probability of false alarms. Figure 12 The effective signal moment detected is the 25028th sampling point;

[0134] Step 34: Extraction The original data received by each array element at the moment , and transmit it to the PS end for target direction estimation;

[0135] Step 35: Use the inner product response method based on conventional beamforming to estimate the target direction. Construct the array flow pattern in advance based on the array's detection range, number of scanning beams, beam width, position of each array element, operating frequency, etc. ,in Indicates the Array elements, Indicates the three-dimensional beam angle of the scan, which can include both azimuth and elevation information;

[0136] Step 36: Flow the Array The discrete value of is stored in the PS end, and the corresponding value of each beam angle is Dimensional vectors are sequentially combined with arrays to collect data Do the inner product:

[0137] ;

[0138] Step 37: Perform the inner product operation on all the beam angles above and take the maximum value Corresponding beam angle That is the direction of the target sound source; the simulation of the direction estimation of the inner product response method is as follows Figure 13 As shown in , the algorithm can be generally understood as dividing a number of beams within the scanning range. A beam with the maximum amplitude will appear at the target azimuth. The azimuth of this beam is taken as the estimated target azimuth. The estimation accuracy is related to the beam width. Figure 13 The estimated result is 10°, which is consistent with the preset sound source direction.

[0139] In this embodiment, since the target carrier is known, a time synchronization device, such as a constant temperature crystal oscillator, can be deployed on the sonar receiving system and the acoustic beacon to be tested. Before the device is launched, the periodic pulse obtained by frequency division of the constant temperature crystal oscillator inside the acoustic beacon is connected to the sonar receiver (system control module), triggering the receiver to generate a synchronous periodic signal based on the local constant temperature crystal oscillator, completing the clock synchronization. When the acoustic beacon signal transmission time is known, the synchronization counter is controlled to count. The correlation method is used to calculate the delay of the received periodic pulse. , and combined with the sound speed of the test waters The distance of the acoustic beacon relative to the receiver can be calculated as , thereby achieving precise positioning control of target carriers, such as underwater robots.

[0140] Example 5

[0141] See also Figure 2 , an underwater acoustic signal recognition and positioning device, the underwater acoustic signal recognition and positioning device comprising:

[0142] A receiving component 1, configured to receive underwater acoustic signals using the underwater acoustic transducer array;

[0143] Identification component 2 is configured to pre-process the target underwater acoustic signal using the programmable logic end based on the category of the target underwater acoustic signal to obtain a pre-processing result; and determine whether the pre-processing result contains a target underwater acoustic signal of a corresponding category using the programmable logic end or the processing system end; wherein the target underwater acoustic signal includes: Category I underwater acoustic signal, Category II underwater acoustic signal, and Category III underwater acoustic signal;

[0144] The positioning component 3 is used to locate the sound source corresponding to the underwater acoustic signal using the processing system end when the underwater acoustic signal contains the target underwater acoustic signal of this category, and generate the position data of the sound source.

[0145] For the specific definition of the underwater acoustic signal recognition and positioning device, please refer to the definition of the underwater acoustic signal recognition and positioning method above, and will not be repeated here. The various modules in the above-mentioned underwater acoustic signal recognition and positioning device can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0146] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation of the present application scheme. The specific underwater acoustic signal recognition and positioning device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0147] Example 6

[0148] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the underwater acoustic signal recognition and positioning method as described in Example 1.

[0149] Example 7

[0150] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. When executed by the processor, the computer program implements the underwater acoustic signal recognition and positioning method.

[0151] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0153] S1. Receive an underwater acoustic signal using the underwater acoustic transducer array;

[0154] S2. Based on the category of the target underwater acoustic signal, preprocess the underwater acoustic signal using the programmable logic end to obtain a preprocessing result; and determine, using the programmable logic end or the processing system end, whether the preprocessing result contains a target underwater acoustic signal of a corresponding category; wherein the target underwater acoustic signal includes: a Category I underwater acoustic signal, a Category II underwater acoustic signal, and a Category III underwater acoustic signal;

[0155] S3. When the underwater acoustic signal contains a target underwater acoustic signal of this category, the processing system is used to locate the sound source corresponding to the underwater acoustic signal to generate position data of the sound source.

[0156] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0157] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An underwater acoustic signal recognition and positioning method, characterized in that: Applied to embedded underwater acoustic detection platform based on SOC architecture; The underwater acoustic detection platform specifically includes: a programmable logic end using an FPGA architecture, a processing system end using an ARM architecture, and an underwater acoustic transducer array, wherein the programmable logic end, the processing system end, and the underwater acoustic transducer array are communicatively connected to each other; The method comprises: When the target underwater acoustic signal is a type I underwater acoustic signal, the underwater acoustic signal is received by using a horizontal linear array underwater acoustic transducer array; when the target underwater acoustic signal is a type II underwater acoustic signal, the underwater acoustic signal is received by using a horizontal circular array underwater acoustic transducer array; when the target underwater acoustic signal is a type III underwater acoustic signal, the underwater acoustic signal is received by using a vertical plane array underwater acoustic transducer array; When the target underwater acoustic signal is a Class I underwater acoustic signal, the programmable logic end is used to perform a fast Fourier transform on the underwater acoustic signal to generate a frequency domain underwater acoustic signal, corresponding power spectrum data is calculated based on the frequency domain underwater acoustic signal, and the processing system end is used to compare the power spectrum data with a preset first threshold value. When the power spectrum data is greater than the first threshold value, it is determined that the underwater acoustic signal contains a Class I underwater acoustic signal; When the target underwater acoustic signal is a Class II underwater acoustic signal, the programmable logic end is used to perform a fractional Fourier transform on the underwater acoustic signal to generate a discrete sample signal in the fractional Fourier domain, an energy-weighted sum corresponding to the discrete sample signal is calculated, and the energy-weighted sum is compared with a preset second threshold value. When the energy-weighted sum is greater than the second threshold value, it is determined that the underwater acoustic signal contains a Class II underwater acoustic signal; When the target underwater acoustic signal is a Class III underwater acoustic signal, a convolution operation is performed on the underwater acoustic signal using a pre-constructed matched filter to generate a matched filtering result. The matched filtering result within the period is compared with the preset third threshold value; when the first time period When the maximum value of the matched filtering result within the second time period is less than the third threshold value, the second time period The matched filtering result within the period is compared with the preset fourth threshold value; when the second period When the maximum value of the matched filtering results within is greater than the fourth threshold value, it is determined that the underwater acoustic signal contains three types of underwater acoustic signals; When the underwater acoustic signal contains a target underwater acoustic signal of this category, the processing system is used to locate the sound source corresponding to the underwater acoustic signal to generate position data of the sound source.

2. The underwater acoustic signal recognition and positioning method according to claim 1, characterized in that: The method of performing a convolution operation on the underwater acoustic signal using a pre-constructed matched filter to generate a matched filtering result specifically includes: Using the programmable logic terminal to perform fast Fourier transform on the matched filter and the underwater acoustic signal respectively, so as to convert the matched filter and the underwater acoustic signal into a frequency domain; After multiplying the matched filter converted to the frequency domain and the underwater acoustic signal, the product is subjected to inverse fast Fourier transform to generate a matched filtering result.

3. The underwater acoustic signal recognition and positioning method according to claim 1, characterized in that: The first threshold value is specifically: ； in, is the variance of the environmental noise data; is the false alarm probability of detection; is the number of discrete sampling points of the underwater acoustic signal; Q ( x ) function is the right tail function of the standard normal distribution, and the specific expression is: 。 4. Underwater acoustic signal recognition and positioning device, characterized in that: Applied to embedded underwater acoustic detection platform based on SOC architecture; The underwater acoustic detection platform specifically includes: a programmable logic end using an FPGA architecture, a processing system end using an ARM architecture, and an underwater acoustic transducer array, wherein the programmable logic end, the processing system end, and the underwater acoustic transducer array are communicatively connected to each other; The device comprises: a receiving component for receiving the underwater acoustic signal using a horizontal linear array of underwater acoustic transducers when the target underwater acoustic signal is a Class I underwater acoustic signal; receiving the underwater acoustic signal using a horizontal circular array of underwater acoustic transducers when the target underwater acoustic signal is a Class II underwater acoustic signal; and receiving the underwater acoustic signal using a vertical planar array of underwater acoustic transducers when the target underwater acoustic signal is a Class III underwater acoustic signal; An identification component is used to, when the target water acoustic signal is a type I water acoustic signal, use the programmable logic end to perform fast Fourier transform on the type I water acoustic signal to generate a frequency domain water acoustic signal, calculate the corresponding power spectrum data based on the frequency domain water acoustic signal, use the processing system end to compare the power spectrum data with a preset first threshold value, and when the power spectrum data is greater than the first threshold value, determine that the water acoustic signal contains a type I water acoustic signal; when the target water acoustic signal is a type II water acoustic signal, use the programmable logic end to perform fractional Fourier transform on the water acoustic signal to generate a discrete sample signal in the fractional Fourier domain, calculate the energy weighted sum corresponding to the discrete sample signal, compare the energy weighted sum with a preset second threshold value, and when the energy weighted sum is greater than the second threshold value, determine that the water acoustic signal contains a type II water acoustic signal; when the target water acoustic signal is a type III water acoustic signal, use a pre-constructed matched filter to perform a convolution operation on the water acoustic signal to generate a matched filtering result, and convert the first time period into a fractional Fourier transform. The matched filtering result within the period is compared with the preset third threshold value; when the first time period When the maximum value of the matched filtering result within the second time period is less than the third threshold value, the second time period The matched filtering result within the period is compared with the preset fourth threshold value; when the second period When the maximum value of the matched filtering results within is greater than the fourth threshold value, it is determined that the underwater acoustic signal contains three types of underwater acoustic signals; The positioning component is used to locate the sound source corresponding to the underwater acoustic signal using the processing system end when the underwater acoustic signal contains the target underwater acoustic signal of this category, and generate the position data of the sound source.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the underwater acoustic signal recognition and positioning method according to any one of claims 1 to 3 is implemented.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the underwater acoustic signal recognition and positioning method as described in any one of claims 1 to 3 is implemented.

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