Acoustic imaging sonar sensing and calculation integrated technology for underwater fish school position and aquatic plant height perception

Abstract

The invention discloses an acoustic imaging sonar communication calculation integration technology for underwater fish school position and aquatic plant height perception. According to the technology, underwater target detection and overwater information interaction are realized by integrating detection sonar and communication sonar. An integrated waveform transmitting model, a receiving model, a minimum variance distortionless corresponding algorithm and a CFAR detection system of an acoustic image based on an OFDM signal are established to solve the problems that the acoustic image obtained after imaging still shows low resolution, low signal-to-noise ratio, non-uniform acoustic transmission, serious sidelobe interference and the like. Simulation results show that by means of the minimum variance distortionless corresponding algorithm and the CFAR detection system of the acoustic image, the perception accuracy can reach up to 91.4%, and the environmental adaptability is improved.

Description

Technical Field

[0001] The present invention relates to an integrated acoustic imaging sonar synaesthesia and computing technology for sensing the position of underwater fish schools and the height of aquatic plants, and belongs to the intersection of communication and artificial intelligence technologies. Background Art

[0002] Integrated Sensing and Communication (ISAC) is a new technology that integrates wireless communication and target perception. Due to its high integration and high cost-effectiveness, it can effectively reduce hardware costs. Detection sonar and communication sonar can respectively realize underwater target detection and water information interaction. The two systems are independent of each other, which brings many problems such as large space occupation and high resource consumption. The two systems have extremely high similarities in working principles and system structures, providing a prerequisite for their integration. The integrated sensing technology can integrate the two systems, so that the main sonar has both perception and communication functions. Compared with traditional communication modules, its communication performance is not affected by the strength of the network; compared with traditional survey equipment, the use of acoustic imaging technology can effectively avoid the accuracy deviation of visual data. This technology can provide more flexible, convenient and faster computing services.

[0003] Since radio waves and light waves are severely attenuated and have limited propagation distance in underwater environments, sound waves have become the main means of underwater perception. A series of activities such as obstacle avoidance and navigation, topographic mapping, and search and salvage of sunken objects are carried out around the complex underwater environment. The underwater acoustic imaging system processes echoes that carry underwater information to form images. During the imaging process, the underwater scene is "illuminated" by the sound pulse, and the echo signal is received by the acoustic transducer array and processed to form the original acoustic image. After drawing on new achievements in other disciplines and absorbing various new technologies and processes, the underwater acoustic imaging system has also been continuously improved and continuously improved. However, due to the existence of various restrictive conditions and interference factors, the acoustic image obtained after imaging will still show low resolution, low signal-to-noise ratio, uneven acoustic transmission, severe sidelobe interference, radiation and geometric distortion, etc., making it difficult to directly carry out various subsequent applications.

[0004] To solve the above problems, the paper "Tracking and classification of multiple objects in multibeam sector scan sonar image sequences" systematically studies the recognition and tracking of multiple submerged targets in sonar image sequences, applies frequency domain filters to segment dynamic and static targets, uses optical flow to estimate the target's direction of motion, uses inter-frame optical flow value matching to achieve target tracking, and uses linear discriminant functions to implement supervised classification of targets. The paper "Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios" studies a method to assist underwater human-robot interaction platforms in long-distance detection and tracking of submerged targets, using hidden Markov models as secondary filters to distinguish real submerged targets from false alarms. The paper "SCUBAdiver detection and classification in active and passive sonars-Aunified approach" proposed an adaptive double-frame difference method based on image binarization and region growing method. By combining the double-frame difference result with the binary image of the current frame and the region growing method, the influence of "double shadow" and "hole" was eliminated in time. Experiments have proved that the algorithm can detect real aquatic biological targets in underwater low signal-to-noise ratio environments. However, the design of synaesthesia integration was not used in the above work. Summary of the invention

[0005] The purpose of the present invention is to solve the problems that the acoustic image obtained after imaging still presents low resolution, low signal-to-noise ratio, uneven acoustic transmission, and serious sidelobe interference. An acoustic imaging sonar inter-sensing and computing integrated technology for underwater perception is proposed. This technology integrates the multi-carrier characteristics of the waveform and provides a target detection method based on the generalized likelihood ratio test. In addition, a method for optimizing the waveform of the next working cycle based on the test results of the echo signal is also provided. This method reallocates the transmission power of each subcarrier in the next cycle of the integrated waveform, so that the energy is concentrated on the channel and target-sensitive subcarriers, thereby enhancing environmental adaptability and improving detection performance.

[0006] The technical solution adopted by the present invention to solve the technical problem is: an integrated acoustic imaging sonar synaesthesia and computing technology for sensing the position of underwater fish schools and the height of aquatic plants, the method comprising the following steps:

[0007] Step 1: Establish an integrated waveform transmission model based on OFDM signal.

[0008] Step 1-1: Create a transmission weight coefficient matrix C = [C0, C1, C L-1 ] is used as the cost function variable to solve the optimization problem. The expression of the sidelobe transmission power is given. For OFDM subcarriers, the formula should be written as:

[0009]

[0010] In order to minimize the side lobes of the transmitted beam, the P corresponding to each subcarrier is sl (f l ) is the smallest, then the cost function expression is:

[0011]

[0012] In the process of underwater acoustic communication, it is often necessary to adjust the transmission power of the subcarrier at any time according to the underwater environment. Therefore, the power control factor is introduced into the optimization problem, and the optimization problem is expressed as:

[0013]

[0014] The integrated waveform transmission strategy can also be extended to multiple communication user scenarios, where the number of communication users should be less than the number of platform transmitting array elements, otherwise the problem is unsolvable. The waveform design optimization problem expression for multiple users is given:

[0015]

[0016] Step 1-2: Establish an integrated platform to embed information into an integrated waveform to realize communication functions. First, the system divides the data to be transmitted into multiple data segments. One OFDM symbol period transmits the data of one data segment. Each data includes M×Lbit data. The Mbit data is regarded as a signal symbol and averaged to the corresponding subcarrier. ρ is used as the transmission power factor. ψ has the phase freedom of constellation mapping, so the constellation mapping dictionary using MPSK modulation is given:

[0017] Ω={ω(m),m=1,2,···,2 M -1}

[0018] After the base conversion, each Mbit of data finds the corresponding constellation point in the constellation dictionary Ω according to its converted value m, which can be mapped to a symbol vector The transmission weight vector C of each subcarrier can be obtained l Finally, the transmission beam of the current pulse period can be synthesized to realize the transmission of the integrated waveform. When the integrated waveform is transmitted to the far field, different requirements are placed on the signal structure to form the detection pulse and the downlink communication signal.

[0019] Step 1-3: Establish an integrated transmission waveform system. Divide the transmitted serial data into multiple data segments, each data segment contains M×L bits of data, and allocate and embed each data segment in sequence in each symbol period. In each symbol period, the base conversion is matched with the constellation mapping dictionary one by one, and then embedded into each subcarrier. After obtaining the mapping symbol vector Determine the main lobe direction θ d and the communication side lobe direction θ c , and determine whether the detection direction signal in the current pulse period is after the pulse interval, obtain the transmission weight coefficient vector of all subcarriers, construct an integrated waveform with OFDM subcarriers as the base signal, and improve the optimization problem, and finally obtain the weight coefficient matrix C.

[0020] According to the above transmission scheme, the subcarrier term U(t) in the OFDM subcarrier is converted into [U1(t),U2(t),...,U L (t)] T By weighted addition of the emission weight coefficient matrix, an integrated waveform can be obtained Corresponding to the transmit signal of each array element respectively.

[0021] Step 2: Propose an integrated receive signal model.

[0022] Step 2-1: First, assume that the integrated platform and the communication node both use array reception when receiving signals, and both receiving arrays are half-wave spacing ULA. The number of receiving array elements of the integrated platform is M r , the expression of array element spacing is:

[0023]

[0024] At one of the communication nodes, ignoring the echo signal, the received signal can be expressed as:

[0025]

[0026] Considering that the communication node may receive downlink communication signals and peer communication signals arriving from different directions at the same time when it is working normally, if there is only one communication node in the whole system, the model can be simplified as follows: c The item (t) can be set to 0.

[0027] Step 2-2: In speech signal processing, for a dual-channel microphone array, the core idea of ​​beamforming is that for the far-field situation (satisfying the plane wave condition), when the beam is incident from the 0° direction, the starting positions of the signals collected by the two microphones coincide, and there is no time delay.

[0028] When processing the communication signal transmitted by the integrated platform, the communication signal of another communication node is regarded as interference, and the received signal expression is rewritten as:

[0029] y c (t) = h ic a c (β c )T(θ c ,t)+h cc a c (β i )S c (t)+Z c (t)

[0030] Represents signal y c The frequency point f in the frequency band j The frequency component at the channel coefficient h ic and h cc It varies with frequency and can be expressed as a function. Its acceptance steering vector is expressed as:

[0031]

[0032] Obviously, only the steering vector in the 0° direction is constant and independent of frequency. Therefore, referring to the method of speech signal processing in microphone array, the signal incident from the non-0° direction is artificially moved to the 0° direction to eliminate the phase offset of the receiving steering vector caused by the change of signal frequency. Therefore, there is:

[0033]

[0034] The expected signal component T(θ c , f j ) has been moved to the optimal direction. After the alignment transformation, it can be simply deduced that:

[0035]

[0036] In the formula, f min The lower limit frequency of the signal bandwidth can be taken as f min =f0, and the upper limit frequency of the signal bandwidth, that is, the maximum frequency of the signal mentioned above, is f max =f0+(L-1)Δf. Therefore, the corresponding The boundary value of the interference angle range after alignment is β i.min and β i,max The above formula can be used to solve the problem, and the interference signal is dispersed in an angle range.

[0037] For the signal processed by the alignment matrix transformation, a spatial domain filtering algorithm, namely a beamforming algorithm, can be used to obtain the desired signal and suppress the interference signal and the noise signal.

[0038] Step 3: Establish the minimum variance distortion-free response algorithm

[0039] Step 3-1: The minimum variance distortion-free response algorithm can achieve distortion-free output of signals in the target direction while suppressing interference signals and noise in other directions as much as possible, that is, the average power of the beam output is minimized.

[0040] The optimization problem corresponding to the OFDM beamforming weight vector can be expressed as:

[0041]

[0042] wxya H a c (β)=1

[0043] The cost function is:

[0044] J(W)=W H R yy W+μ(1-W H a c (β))

[0045] Derivative the cost function, and the result is 0. We can get:

[0046]

[0047] Where μ is the proportionality constant, we can solve:

[0048]

[0049] From the above conclusions, it can be concluded that the optimal beamforming weight vector of the MVDR algorithm can be expressed as:

[0050]

[0051] In the ISAC system, whether it is the downlink communication signal transmitted by the integrated platform or the OFDM communication signal transmitted by a single array element of the communication node, from the perspective of signal processing at the receiving end, they are all OFDM communication signals. Therefore, at the communication node, the downlink communication signal r obtained by the separation and extraction algorithm is c (t), which can be regarded as a traditional underwater OFDM communication signal for subsequent processing such as demodulation and decoding.

[0052] Step 4: Establish the CFAR detection system of acoustic image:

[0053] Step 4-1: Automatically detect ROI from the acoustic image. The basic strategy is to determine whether each pixel in the acoustic image belongs to a background noise signal or a potential target signal based on a detection threshold. The determination of the detection threshold depends on the average background noise power, and the time-varying nature of the underwater scene causes the average background noise power to change continuously. CFAR detection technology is usually used to adaptively set the detection threshold based on the statistical characteristics of the background clutter around the target.

[0054] Through a set of samples X = (x1, x2, · · ·, x N ) is used to represent the acoustic image unit, where the maximum likelihood estimate of the shape parameter k and the size parameter λ is:

[0055]

[0056] Considering that it is too laborious to perform iterative calculations on each test point pixel unit, the average background noise power μ can be obtained by the maximum likelihood estimation of the size parameter λ, and the false alarm probability can be obtained as:

[0057]

[0058] Beneficial effects:

[0059] 1. The present invention proposes an integrated acoustic imaging sonar synaesthesia technology for underwater fish position and water grass height perception, which includes designing an integrated waveform and providing a transmitting and receiving model of the integrated waveform. By jointly optimizing the resolution, signal-to-noise ratio, uniform acoustic transmission power, sidelobe interference and other indicators, the system delay and energy consumption are minimized.

[0060] 2. The present invention uses underwater fish and water plants as the target of infiltration threat to carry out experimental research on target perception. In order to simulate the various situations that may be encountered in actual acoustic area alert, three groups of experiments with various movement modes of infiltration targets under different fields of view, namely fixed-point downward viewing, fixed-point horizontal viewing and moving side viewing, were designed to verify the effectiveness of target identification under the underwater target perception framework. The simulation results show that the perception accuracy is as high as 91.4%.

[0061] 3. The integrated synaesthesia system of the present invention can obtain the optimal weight according to the echo signal of the previous working cycle, and concentrate the energy of the optimized integrated waveform on the subcarrier with the highest adaptability to channel selective fading and target scattering coefficient, so that the OFDM empty carrier detection pulse is degenerated into a single carrier pulse that is most sensitive to the target, thereby improving environmental adaptability. Simulation results show that in the process of forming the far-field signal simulated at the receiving end under 14 different environments, the consistency between the detection pulse and the downlink communication signal is as high as 90%. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1It is the transmission signal beam diagram in the transmission model of the present invention.

[0063] Figure 2 This is a graph of the integrated waveform perception performance before and after the optimization scheme of the present invention.

[0064] Figure 3 This is a detection performance diagram of the present invention under a multipath channel.

[0065] Figure 4 It is a detection performance diagram of energy detection of the present invention. DETAILED DESCRIPTION

[0066] The following figure, in conjunction with the accompanying drawings, further illustrates the invention in detail.

[0067] Embodiment 1

[0068] like Figure 1 As shown, the present invention provides an acoustic imaging sonar synaesthesia and computing integrated technology for underwater perception, the method comprising the following steps:

[0069] Step 1: Establish an integrated waveform transmission model based on OFDM signal.

[0070] Step 1-1: Create a transmission weight coefficient matrix C = [C0, C1, C L-1 ] is used as the cost function variable to solve the optimization problem. The expression of the sidelobe transmission power is given. For OFDM subcarriers, the formula should be written as:

[0071]

[0072] In order to minimize the side lobes of the transmitted beam, the P corresponding to each subcarrier is sl (f l ) is the smallest, then the cost function expression is:

[0073]

[0074] In the process of underwater acoustic communication, it is often necessary to adjust the transmission power of the subcarrier at any time according to the underwater environment. Therefore, the power control factor is introduced into the optimization problem, and the optimization problem is expressed as:

[0075]

[0076] The integrated waveform transmission strategy can also be extended to multiple communication user scenarios, where the number of communication users should be less than the number of platform transmitting array elements, otherwise the problem is unsolvable. The waveform design optimization problem expression for multiple users is given:

[0077]

[0078] Step 1-2: Establish an integrated platform to embed information into an integrated waveform to realize communication functions. First, the system divides the data to be transmitted into multiple data segments. One OFDM symbol period transmits the data of one data segment. Each data includes M×Lbit data. The Mbit data is regarded as a signal symbol and averaged to the corresponding subcarrier. ρ is used as the transmission power factor. ψ has the phase freedom of constellation mapping, so the constellation mapping dictionary using MPSK modulation is given:

[0079] Ω={ω(m),m=1,2,···,2 M -1}

[0080] After the base conversion, each Mbit of data finds the corresponding constellation point in the constellation dictionary Ω according to its converted value m, which can be mapped to a symbol vector The transmission weight vector C of each subcarrier can be obtained l Finally, the transmission beam of the current pulse period can be synthesized to realize the transmission of the integrated waveform. When the integrated waveform is transmitted to the far field, different requirements are placed on the signal structure to form the detection pulse and the downlink communication signal.

[0081] Step 1-3: Establish an integrated transmission waveform system. Divide the transmitted serial data into multiple data segments, each data segment contains M×L bits of data, and allocate and embed each data segment in sequence in each symbol period. In each symbol period, the base conversion is matched with the constellation mapping dictionary one by one, and then embedded into each subcarrier. After obtaining the mapping symbol vector Determine the main lobe direction θ d and the communication side lobe direction θ c , and determine whether the detection direction signal in the current pulse period is after the pulse interval, obtain the transmission weight coefficient vector of all subcarriers, construct an integrated waveform with OFDM subcarriers as the base signal, and improve the optimization problem, and finally obtain the weight coefficient matrix C.

[0082] According to the above transmission scheme, the subcarrier term U(t) in the OFDM subcarrier is converted into [U1(t),U2(t),...,U L (t)] T By weighted addition of the emission weight coefficient matrix, an integrated waveform can be obtained Corresponding to the transmit signal of each array element respectively.

[0083] Step 2: Propose an integrated receive signal model.

[0084] Step 2-1: First, assume that the integrated platform and the communication node both use array reception when receiving signals, and both receiving arrays are half-wave spacing ULA. The number of receiving array elements of the integrated platform is M r, the expression of array element spacing is:

[0085]

[0086] At one of the communication nodes, ignoring the echo signal, the received signal can be expressed as:

[0087]

[0088] Considering that the communication node may receive downlink communication signals and peer communication signals arriving from different directions at the same time when it is working normally, if there is only one communication node in the whole system, the model can be simplified as follows: c The item (t) can be set to 0.

[0089] Step 2-2: In speech signal processing, for a dual-channel microphone array, the core idea of ​​beamforming is that for the far-field situation (satisfying the plane wave condition), when the beam is incident from the 0° direction, the starting positions of the signals collected by the two microphones coincide, and there is no time delay.

[0090] When processing the communication signal transmitted by the integrated platform, the communication signal of another communication node is regarded as interference, and the received signal expression is rewritten as:

[0091] y c (t) = h ic a c (β c )T(θ c ,t)+h cc a c (β i )S c (t)+Z c (t)

[0092] Represents signal y c The frequency point f in the frequency band j The frequency component at the channel coefficient h ic and h cc It varies with frequency and can be expressed as a function. Its acceptance steering vector is expressed as:

[0093]

[0094] Obviously, only the steering vector in the 0° direction is constant and independent of frequency. Therefore, referring to the method of speech signal processing in microphone array, the signal incident from the non-0° direction is artificially moved to the 0° direction to eliminate the phase offset of the receiving steering vector caused by the change of signal frequency. Therefore, there is:

[0095]

[0096] The expected signal component T(θ c , f j ) has been moved to the optimal direction. After the alignment transformation, it can be simply deduced that:

[0097]

[0098] In the formula, f min The lower limit frequency of the signal bandwidth can be taken as f min =f0, and the upper limit frequency of the signal bandwidth, that is, the maximum frequency of the signal mentioned above, is f max =f0+(L-1)Δf. Therefore, the corresponding β can be obtained at each frequency point. i 1 ∈[β i,min ,β i.max ], where the boundary value of the interference angle range after alignment is β i.min and β i,max The above formula can be used to solve the problem, and the interference signal is dispersed in an angle range.

[0099] For the signal processed by the alignment matrix transformation, a spatial domain filtering algorithm, namely a beamforming algorithm, can be used to obtain the desired signal and suppress the interference signal and the noise signal.

[0100] Step 3: Establish the minimum variance distortion-free response algorithm

[0101] Step 3-1: The minimum variance distortion-free response algorithm can achieve distortion-free output of signals in the target direction while suppressing interference signals and noise in other directions as much as possible, that is, the average power of the beam output is minimized.

[0102] The optimization problem corresponding to the OFDM beamforming weight vector can be expressed as:

[0103]

[0104] wxya H a c (β)=1

[0105] The cost function is:

[0106] J(W)=W H R yy W+μ(1-W H a c (β))

[0107] Derivative the cost function, and the result is 0. We can get:

[0108]

[0109] Where μ is the proportionality constant, we can solve:

[0110]

[0111] From the above conclusions, it can be concluded that the optimal beamforming weight vector of the MVDR algorithm can be expressed as:

[0112]

[0113] In the ISAC system, whether it is the downlink communication signal transmitted by the integrated platform or the OFDM communication signal transmitted by a single array element of the communication node, from the perspective of signal processing at the receiving end, they are all OFDM communication signals. Therefore, at the communication node, the downlink communication signal r obtained by the separation and extraction algorithm is c (t), which can be regarded as a traditional underwater OFDM communication signal for subsequent processing such as demodulation and decoding.

[0114] Step 4: Establish the CFAR detection system of acoustic image:

[0115] Step 4-1: Automatically detect ROI from the acoustic image. The basic strategy is to determine whether each pixel in the acoustic image belongs to a background noise signal or a potential target signal based on a detection threshold. The determination of the detection threshold depends on the average background noise power, and the time-varying nature of the underwater scene causes the average background noise power to change continuously. CFAR detection technology is usually used to adaptively set the detection threshold based on the statistical characteristics of the background clutter around the target.

[0116] Through a set of samples X = (x1, x2, · · ·, x N ) is used to represent the acoustic image unit, where the maximum likelihood estimate of the shape parameter k and the size parameter λ is:

[0117]

[0118]

[0119] Considering that it is too laborious to perform iterative calculations on each test point pixel unit, the average background noise power μ can be obtained by the maximum likelihood estimation of the size parameter λ, and the false alarm probability can be obtained as:

[0120]

[0121] The effects of the present invention are further described in detail below in conjunction with simulation experiments, specifically including:

[0122] 1. Simulation hardware conditions

[0123] The following simulation experiment is conducted on the simulation platform of Python 3.12.4 and TensorFlow 2.70. The computer CPU model is E5-2680 v4, the number is 6, the GPU model is NVIDIA Geforce RTX 3060, and the video memory is 12.6GB.

[0124] 2. Simulation system parameters

[0125] The detection signal used in the simulation has an integrated platform sampling rate of 150kHz, the OFDM subcarrier frequency interval as the base signal is 50Hz, the carrier frequency is 10kHz, and 3 detection pulses are emitted in one working cycle. From the characteristics of the OFDM signal, it can be seen that the detection pulse length is 20ms, and the pulse interval is set to 40ms.

[0126] 3. Simulation content

[0127] Figure 1 During the test, the transmitting transducer is first placed at position 1, and the signal corresponding to the first array element is transmitted. The received signal at this time is recorded using self-contained hydrophones 1 and 2. The transmitting transducer is moved to positions 2 to 8 in sequence, and the above operation is repeated while ensuring that the sound source level is the same each time. After superposition, the downlink communication signal formed by the virtual array in the far field can be obtained. The receiving data processing method of self-contained hydrophone 2 is similar to that of the detection pulse, and the DOA of the received signal is estimated to obtain the transmitting beam.

[0128] Figure 2 The condition for achieving optimal GLRT detection performance is that the multi-carrier OFDM signal is degraded into a single-carrier signal, and the optimal weight is obtained according to the echo signal of the previous working cycle. The optimized integrated waveform concentrates the energy on the subcarrier with the highest adaptability to channel selective fading and target scattering coefficient, making the OFDM empty carrier detection pulse degraded into the single-carrier pulse that is most sensitive to the target, thereby improving environmental adaptability.

[0129] Figure 3 The GLRT-based target detection algorithm was simulated in a multipath channel. The number of subcarriers of the detection pulse was set to 4, and the signal-to-noise ratio was changed from 0 to 50 dB. 500 independent experiments were performed. When other parameters of the detection pulse were the same, the target detection performance was improved by about 5 dB when the multipath effect was considered. The experiment showed that the performance was improved by 70%.

[0130] Figure 4 The performance of the target detection method based on energy detection is analyzed by Monte Carlo simulation. Assuming that the underwater acoustic channel is a single-path channel, the detection pulse contains 10 subcarriers, and the signal-to-noise ratio is changed from -15 to 10 dB. 3000 independent experiments are performed for different false alarm probabilities P.f = 0.01 and P f =0.1 changes with the signal-to-noise ratio. Experimental results show that if the detection pulse includes more subcarriers, the detection performance is improved by 88% under low signal-to-noise ratio conditions.

Claims

1. The technical solution adopted by the present invention to solve the technical problem is: an integrated acoustic imaging sonar synaesthesia and computing technology for sensing the position of underwater fish schools and the height of aquatic plants, the method comprising the following steps: Step 1: Establish an integrated waveform transmission model based on OFDM signal. Step 1-1: Create a transmission weight coefficient matrix C = [C0, C1, C L-1 ] is used as the cost function variable to solve the optimization problem. The expression of the sidelobe transmission power is given. For OFDM subcarriers, the formula should be written as: In order to minimize the side lobes of the transmitted beam, the P corresponding to each subcarrier is sl (f l ) is the smallest, then the cost function expression is: In the process of underwater acoustic communication, it is often necessary to adjust the transmission power of the subcarrier at any time according to the underwater environment. Therefore, the power control factor is introduced into the optimization problem, and the optimization problem is expressed as: The integrated waveform transmission strategy can also be extended to multiple communication user scenarios, where the number of communication users should be less than the number of platform transmitting array elements, otherwise the problem is unsolvable. The waveform design optimization problem expression for multiple users is given: Step 1-2: Establish an integrated platform to embed information into an integrated waveform to realize communication functions. First, the system divides the data to be transmitted into multiple data segments. One OFDM symbol period transmits the data of one data segment. Each data includes M×Lbit data. The Mbit data is regarded as a signal symbol and averaged to the corresponding subcarrier. ρ is used as the transmission power factor. ψ has the phase freedom of constellation mapping, so the constellation mapping dictionary using MPSK modulation is given: Ω={ω(m),m=1,2,···,2 M -1} After the base conversion, each Mbit of data finds the corresponding constellation point in the constellation dictionary Ω according to its converted value m, which can be mapped to a symbol vector The transmission weight vector C of each subcarrier can be obtained l Finally, the transmission beam of the current pulse period can be synthesized to realize the transmission of the integrated waveform. When the integrated waveform is transmitted to the far field, different requirements are placed on the signal structure to form the detection pulse and the downlink communication signal. Step 1-3: Establish an integrated transmission waveform system. Divide the transmitted serial data into multiple data segments, each data segment contains M×L bits of data, and allocate and embed each data segment in sequence in each symbol period. In each symbol period, the base conversion is matched with the constellation mapping dictionary one by one, and then embedded into each subcarrier. After obtaining the mapping symbol vector Determine the main lobe direction θ d and the communication side lobe direction θ c , and determine whether the detection direction signal in the current pulse period is after the pulse interval, obtain the transmission weight coefficient vector of all subcarriers, construct an integrated waveform with OFDM subcarriers as the base signal, and improve the optimization problem, and finally obtain the weight coefficient matrix C. According to the above transmission scheme, the subcarrier term U(t) in the OFDM subcarrier is converted into [U1(t),U2(t),...,U L (t)] T By weighted addition of the emission weight coefficient matrix, an integrated waveform can be obtained Corresponding to the transmit signal of each array element respectively. Step 2: Propose an integrated receive signal model. Step 2-1: First, assume that the integrated platform and the communication node both use array reception when receiving signals, and both receiving arrays are half-wave spacing ULA. The number of receiving array elements of the integrated platform is M r , the expression of array element spacing is: At one of the communication nodes, ignoring the echo signal, the received signal can be expressed as: Considering that the communication node may receive downlink communication signals and peer communication signals arriving from different directions at the same time when it is working normally, if there is only one communication node in the whole system, the model can be simplified as follows: c The item (t) can be set to 0. Step 2-2: In speech signal processing, for a dual-channel microphone array, the core idea of ​​beamforming is that for the far-field situation (satisfying the plane wave condition), when the beam is incident from the 0° direction, the starting positions of the signals collected by the two microphones coincide, and there is no time delay. When processing the communication signal transmitted by the integrated platform, the communication signal of another communication node is regarded as interference, and the received signal expression is rewritten as: y c (t)=h ic a c (β c )T(θ c ,t)+h cc a c (β i )S c (t)+Z c (t) Represents signal y c The frequency point f in the frequency band j The frequency component at the channel coefficient h ic and h cc It varies with frequency and can be expressed as a function. Its acceptance steering vector is expressed as: Obviously, only the steering vector in the 0° direction is constant and independent of frequency. Therefore, referring to the method of speech signal processing in microphone array, the signal incident from the non-0° direction is artificially moved to the 0° direction to eliminate the phase offset of the receiving steering vector caused by the change of signal frequency. Therefore, there is: The expected signal component T(θ c , f j ) has been moved to the optimal direction. After the alignment transformation, it can be simply deduced that: In the formula, f min The lower limit frequency of the signal bandwidth can be taken as f min =f0, and the upper limit frequency of the signal bandwidth, that is, the maximum frequency of the signal mentioned above, is f max =f0+(L-1)Δf. Therefore, the corresponding The boundary value of the interference angle range after alignment is β i.min and β i,max The above formula can be used to solve the problem, and the interference signal is dispersed in an angle range. For the signal processed by the alignment matrix transformation, a spatial domain filtering algorithm, namely a beamforming algorithm, can be used to obtain the desired signal and suppress the interference signal and the noise signal. Step 3: Establish the minimum variance distortion-free response algorithm Step 3-1: The minimum variance distortion-free response algorithm can achieve distortion-free output of signals in the target direction while suppressing interference signals and noise in other directions as much as possible, that is, the average power of the beam output is minimized. The optimization problem corresponding to the OFDM beamforming weight vector can be expressed as: s.t.W H a c (β)=1 The cost function is: J(W)=W H R yy W+μ(1-W H a c (β)) Derivative the cost function, and the result is 0. We can get: Where μ is the proportionality constant, we can solve: From the above conclusions, it can be concluded that the optimal beamforming weight vector of the MVDR algorithm can be expressed as: In the ISAC system, whether it is the downlink communication signal transmitted by the integrated platform or the OFDM communication signal transmitted by a single array element of the communication node, from the perspective of signal processing at the receiving end, they are all OFDM communication signals. Therefore, at the communication node, the downlink communication signal r obtained by the separation and extraction algorithm is c (t), which can be regarded as a traditional underwater OFDM communication signal for subsequent processing such as demodulation and decoding. Step 4: Establish the CFAR detection system of acoustic image: Step 4-1: Automatically detect ROI from the acoustic image. The basic strategy is to determine whether each pixel in the acoustic image belongs to a background noise signal or a potential target signal based on a detection threshold. The determination of the detection threshold depends on the average background noise power, and the time-varying nature of the underwater scene causes the average background noise power to change continuously. CFAR detection technology is usually used to adaptively set the detection threshold based on the statistical characteristics of the background clutter around the target. Through a set of samples X = (x1, x2, · · ·, x N ) is used to represent the acoustic image unit, where the maximum likelihood estimate of the shape parameter k and the size parameter λ is: Considering that it is too laborious to perform iterative calculations on each test point pixel unit, the average background noise power μ can be obtained by the maximum likelihood estimation of the size parameter λ, and the false alarm probability can be obtained as:

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