Underwater sound MISO-OFDM low sidelobe transmitting beam forming method based on main path angle, storage medium and equipment

Abstract

The invention belongs to the technical field of underwater acoustic communication, and particularly relates to an underwater acoustic MISO-OFDM low-sidelobe transmitting beam forming method, a storage medium and equipment based on a main path angle, in particular to an underwater acoustic MISO-OFDM low-sidelobe transmitting beam forming method based on a main path angle, and a storage medium. The invention aims to solve the problems that the existing beamforming technology depends on real-time channel state information, and the feedback delay of an underwater acoustic channel is far greater than the channel coherence time due to low sound velocity, so that the performance of the traditional method is sharply reduced; the existing beamforming technology often neglects the problem of sidelobe suppression, resulting in energy leakage and introduction of multipath interference, which affects communication reliability. The method comprises the following steps of: 1, acquiring signal incidence azimuth angles of a target reaching different paths of a receiving array after being transmitted through a multi-path channel by adopting DOA (Direction of Arrival) estimation; 2, obtaining the main path direction of the transmitting array when the main lobe gain is maximum and the side lobe suppression achieves an expected target, and obtaining the beam forming weight;

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater acoustic communication, and specifically relates to an underwater acoustic MISO-OFDM low sidelobe transmission beamforming method, storage medium and device, which are applicable to an underwater multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) communication system. The aim is to improve the system performance by optimizing the beamforming algorithm to reduce the sidelobes of the transmission beam. Background Art

[0002] Underwater acoustic communication has important applications in fields such as ocean exploration and environmental monitoring. However, the underwater acoustic channel has characteristics such as significant multipath effects, long feedback delays, and complex noise. Traditional beamforming techniques rely on real-time channel state information (CSI), and due to the low sound speed in the underwater acoustic channel, the feedback delay is much greater than the channel coherence time, resulting in a sharp decline in the performance of traditional methods. In addition, existing beamforming techniques often ignore the problem of sidelobe suppression, leading to energy leakage and introducing multipath interference, which affects the communication reliability.

[0003] Existing technologies such as Chinese Patent CN119675724A propose to suppress sidelobes by jointly optimizing communication and sensing metrics. However, it focuses on the air target detection scenario, is not designed for the long feedback delay characteristics of the underwater acoustic channel, and does not solve the joint optimization problem of the main path angle stability and low sidelobes. Another patent CN119598704A improves the main lobe gain through the joint optimization of metasurfaces and arrays. However, it relies on complex optimization algorithms, is difficult to adapt to the real-time requirements of the underwater acoustic channel, and does not involve broadband low sidelobe beamforming design. Therefore, there is an urgent need for a transmission beamforming technology that adapts to the long feedback delay of the underwater acoustic channel and takes into account both low sidelobe suppression and high directivity of the main path. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems that existing beamforming techniques rely on real-time channel state information (CSI), and due to the low sound speed in the underwater acoustic channel, the feedback delay is much greater than the channel coherence time, resulting in a sharp decline in the performance of traditional methods. In addition, existing beamforming techniques often ignore the problem of sidelobe suppression, leading to energy leakage and introducing multipath interference, which affects the communication reliability. Therefore, a method, storage medium and device for underwater acoustic MISO-OFDM low sidelobe transmission beamforming based on the main path angle are proposed.

[0005] The specific process of the underwater acoustic MISO-OFDM low sidelobe transmission beamforming method based on the main path angle is as follows: Step 1: Use DOA estimation to obtain the signal incident azimuth angles of different paths that reach the receiving array after the target is transmitted through the multipath channel ; Step 2: Obtain the main path direction of the transmitting array when the main lobe gain is maximized and the sidelobe suppression reaches the expected target , and obtain the beamforming weight W.

[0006] Preferably, in the first step, the azimuth angle at which the target arrives at the receiving array after being transmitted through the multipath channel is obtained by DOA estimation ; The specific process is as follows: Step 1-1: Construct a DOA estimation model under far-field conditions; Step 1-2: Under far-field conditions, obtain the azimuth angle at which the target arrives at the receiving array after being transmitted through the multipath channel .

[0007] Preferably, in Step 1-1, a DOA estimation model under far-field conditions is constructed; the specific process is as follows: Step 1-1-1: Assume that there are receiving array elements linearly arranged at equal intervals to form a receiving array. Let the distance between the target and the first array element be , and the signal incident azimuth angles of different paths of the signal emitted by the target arriving at the receiving array be , , , is the signal incident azimuth angle of the first path of the signal emitted by the target arriving at the receiving array, is the signal incident azimuth angle of the second path of the signal emitted by the target arriving at the receiving array, is the signal incident azimuth angle of the th path of the signal emitted by the target arriving at the receiving array; Step 1-1-2: Based on the distance between the target and the first array element, the direction , the spacing , and the receiving array element , obtain the distance between the target and the th receiving array element; It is expressed as: (1) where is the distance between the target and the th receiving array element, ; Step 1-1-3: Based on the distance between the target and the th receiving array element and the distance between the target and the first array element, calculate the distance difference ; It is expressed as: (2) where is the distance difference between ; Step 1-1-4: Based on Distance difference from and the distance of the target from the first array element , calculate the time difference of the signals received by the -th receiving array element and the first array element; expressed as: The time difference of the signals received by the -th receiving array element and the first array element (3) where is the speed of sound; Step 1-15: Perform Taylor expansion on Equation (3) to obtain: (4) Step 1-16: Based on Equation (4) obtain the phase difference of the signals received by the -th receiving array element and the first array element ; Expressed as: (5) where is the signal frequency, where , is the signal wavelength; When it satisfies: , in Equation (5) can be ignored, and at this time it is said to satisfy the far-field condition; That is, the phase difference of the signals received by the -th array element and the first array element is: (6).

[0008] Preferably, in Step 1-2, the azimuth angle of the target after being transmitted through the multipath channel and arriving at the receiving array is obtained under the far-field condition; The specific process is as follows: Step 1-21: Reconstruct the expressions of the receiving array and the transmitting array in matrix form under the far-field condition; the specific process is as follows: In the continuous time domain, assuming there are M array elements and P target echoes, the signals received by each array element are: (7) where is the received signal at time, is the spatial steering vector, is the transmitted signal at time, is the noise at time, is time; Step 122: Discretize Equation (7) to obtain (8) That is: ; ; ; (9) where is the discretized received signal of the array element for the received signal at time , is the spatial steering vector, is the discretized transmitted signal for the transmitted signal at time , is the discretized noise signal for the noise at time , is the value after discretizing time , is the total number of signal samples in discrete time; is the received signal of the first array element in the time - series received signal , is the received signal of the second array element in the time - series received signal , is the received signal of the th array element in the time - series received signal ; is the transmitted signal vector of the first path in the time - series transmitted signal , is the transmitted signal vector of the second path in the time - series transmitted signal , is the transmitted signal vector of the th path in the time - series transmitted signal ; is the noise received by the first array element in the time - series noise , is the noise received by the second array element in the time - series noise , is Time series noise the noise received by the th array element; is the imaginary unit, ; is the signal incident azimuth angle of the first path, is the signal incident azimuth angle of the second path, is the signal incident azimuth angle of the th path; Steps one, two, and three: Define the weight vector matrix of the array elements ; The array output of the received signal is ; The expression is: (10) where, is the array output; , the superscript H represents the conjugate transpose calculation; is the weight vector matrix the weight vector corresponding to the first array element in, is the weight vector matrix the weight vector corresponding to the second array element in, is the weight vector matrix the weight vector corresponding to the mth array element in, is the weight vector matrix in the th array element corresponding weight vector; is conjugate, is conjugate, is conjugate, is conjugate; is the signal received by the th array element; Array output power is: (11) where, is autocorrelation matrix ; represents the expectation in the statistical sense; Array output Power is a function of the weight vector matrix; expressed as: (12) Meanwhile, the autocorrelation matrix has the following properties: (13) Considering the noise as Gaussian white noise, we have: (14) (15) Since the autocorrelation function of the noise signal is real-valued, it is found that: (16) where, is the covariance matrix of the received signal ; is the Gaussian white noise power; is the identity matrix; represents the conjugate transpose calculation.

[0009] Preferably, the main path direction of the transmitting array when the main lobe gain is maximized and the sidelobe suppression reaches the expected goal is obtained in the second step , and the beamforming weight W is obtained; the specific process is as follows: Step 2-1: Define the array steering vector as ; where, is the total number of transmitting array elements, takes positive integer values; is for transpose; The transmitting beam response is ; where, is the beamforming vector of the th transmitting array element; To maximize the transmitting beam response of the transmitting array in the desired angle, i.e., the main path direction of the transmitting array , while suppressing the transmitting beam responses at other angles, the optimization problem is set as: (17) where, is the main path direction of the transmitting array; is the transmitting beam response;

[0010] Step 2-2: Let be the total power of the transmitting array, and Convert it into an optimization constraint problem and introduce auxiliary variables , transform the optimization constraint problem, and solve the transformed optimization constraint problem to obtain the main path direction of the transmitting array .

[0011] Preferably, in step two, set as the total power of the transmitting array, and convert into an optimization constraint problem, introduce auxiliary variables , transform the optimization constraint problem, and solve the transformed optimization constraint problem to obtain the main path direction of the transmitting array and obtain the beamforming weight W; the specific process is as follows: Step two one: Set as the total power of the transmitting array, and convert into an optimization constraint problem, expressed as (18) wherein, is to make value minimum, is the beamforming weight matrix, is the matrix trace operation; is corresponding steering vector, is the left half-power point angle, is the right half-power point angle; is corresponding steering vector, is corresponding steering vector, is corresponding steering vector; Step two two: Introduce auxiliary variables , transform the optimization constraint problem in equation (18) to obtain the transformed optimization constraint problem; Step two three: Solve the transformed optimization constraint problem in step two two to obtain the main path direction of the transmitting array and obtain the beamforming weight W.

[0012] Preferably, in step two two, introduce auxiliary variables , transform the optimization constraint problem in equation (18) to obtain the transformed optimization constraint problem; expressed as: (19) represents finding the rank of the matrix.

[0013] Preferably, in step 223, the optimized constraint problem after the transformation in step 222 is solved to obtain the main path direction of the transmitting array and the beamforming weight W is obtained; the specific process is as follows: Perform semidefinite relaxation processing on Equation (19) to obtain the main path direction of the transmitting array and the value of the auxiliary variable ; If the obtained satisfies , then perform eigenvalue decomposition on to obtain ; If the obtained does not satisfy , then perform Gaussian randomization on to generate O groups of candidate solutions, search for a suboptimal solution with a rank equal to 1 among the O groups of candidate solutions, and perform eigenvalue decomposition on the suboptimal solution to obtain .

[0014] A storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement an underwater acoustic MISO-OFDM low sidelobe transmitting beamforming method based on the main path angle.

[0015] An underwater acoustic MISO-OFDM low sidelobe transmitting beamforming device based on the main path angle, the device includes a processor and a memory, and the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement an underwater acoustic MISO-OFDM low sidelobe transmitting beamforming method based on the main path angle.

[0016] The beneficial effects of the present invention are: Considering the traditional underwater acoustic multiple-input single-output orthogonal frequency division multiplexing (MISO-OFDM) communication system, a beam discrimination method based on channel state information is adopted. The time for one communication including uplink feedback and downlink is about several seconds or even more than ten seconds. For a complex time-varying underwater acoustic channel, the channel state information (CSI) received by the array from the receiving unit at this time is already "outdated". Using the CSI for transmit beamforming at this time will result in a large deviation, leading to poor beam directivity. Although there is relative movement between the transmit array and the receive array, which will cause Doppler spread, the angle of the direct sound is almost unchanged. Utilizing this feature, the present invention proposes an underwater acoustic OFDM transmit beamforming method based on the main path angle. The direct sound can be regarded as the main path of the channel. The signal transmitter estimates the arrival angle of the main path through the information fed back by the receiver, and forms a transmit beam pointing to this angle through digital precoding. The invention mainly aims at the poor beamforming effect caused by the channel time-variability in the underwater acoustic MISO-OFDM communication system, and proposes a robust transmit beam optimization method based on the stability of the main path angle.

[0017] 1. The present invention utilizes the characteristic that the angle of the underwater acoustic main path changes slowly, estimates the angle through the uplink signal, avoids the dependence of transmit beamforming on real-time channel feedback, eliminates the influence of inaccurate CSI on transmit beamforming, and realizes stable transmit beamforming for the main path angle.

[0018] 2. The present invention proposes a beam optimization algorithm for sidelobe suppression, which suppresses the sidelobe energy while ensuring the maximum beam response gain in the main path direction. This algorithm has a lower sidelobe level and is more advantageous for underwater acoustic multipath channels. Description of the Drawings

[0019] Figure 1 It is a processing block diagram for beamforming technology based on the arrival angle of the main path; Figure 2 It is a beam pattern of beamforming based on the arrival angle of the main path proposed by the present invention, conventional beamforming, and without beamforming processing; Figure 3 It is a comparison diagram of bit error rates of beamforming based on the arrival angle of the main path proposed by the present invention, conventional beamforming, and without beamforming processing; Figure 4 It is a schematic diagram of the application scenario of the present invention. Detailed Embodiments

[0020] Detailed Embodiment 1: The specific process of a low-sidelobe transmit beamforming method for underwater acoustic MISO-OFDM based on the main path angle in this embodiment is as follows: Step 1. Use DOA estimation to obtain the signal incident azimuth angles of different paths after the target is transmitted through the multipath channel and reaches the receiving array. ; Step 2. Obtain the main path direction of the transmitting array when the main lobe gain is maximized and the sidelobe suppression reaches the expected goal , and obtain the beamforming weight W.

[0021] Preferably, in Step 1, DOA estimation is used to obtain the azimuth angle of the target after being transmitted through the multipath channel and reaching the receiving array ; The specific process is as follows: Step 1-1. Construct a DOA estimation model (Equations (1)-(6)) under far-field conditions; Step 1-2. Under far-field conditions, obtain the azimuth angle of the target after being transmitted through the multipath channel and reaching the receiving array .

[0022] Preferably, in Step 1-1, construct a DOA estimation model (Equations (1)-(6)) under far-field conditions; The specific process is as follows:

[0023] Step 1-1-1. Assume that there are receiving array elements linearly arranged at equal intervals to form a receiving array. Let the distance between the target and the first array element be , and the signal incident azimuth angles of different paths of the signal emitted by the target and reaching the receiving array be , , , is the signal incident azimuth angle of the first path of the signal emitted by the target and reaching the receiving array, is the signal incident azimuth angle of the second path of the signal emitted by the target and reaching the receiving array, is the signal incident azimuth angle of the th path of the signal emitted by the target and reaching the receiving array; takes positive integer values; such as Figure 4 ;

[0024] Step 1-1-2. Based on the distance between the target and the first array element, the direction , the spacing , and the receiving array element , obtain the distance between the target and the th receiving array element; It is expressed as: (1) where, is the distance between the target and the th receiving array element, ; Step 113: Based on the target distance The distance between the receiving elements The distance from the target to the first array element , calculate the distance difference ; expressed as: (2) in, for and The distance difference Step 114: Based on and The distance difference The distance from the target to the first array element , calculate the The time difference between the signal received by the first receiving element and the signal received by the first receiving element ; is expressed as: (3) in, is the speed of sound; Step 115: Taylor expansion (second order) of equation (3) yields: (4) Step 116: Based on formula (4) Get the first The phase difference between the first receiving element and the first receiving element ; It is expressed as: (5) in, is the signal frequency, where , is the signal wavelength; When satisfied: When, in formula (5) can be ignored. At this time, the far-field condition is satisfied. Geometrically speaking, it is equivalent to the line connecting the first array element and the target being parallel to the first array element. The connection between each array element and the target; That is The phase difference between the first element and the one received by the first element for: (6) The subsequent DOA estimation is approximated according to the far-field conditions.

[0025] The other steps and parameters are the same as those in the first or second embodiment.

[0026] Embodiment 4: The difference between this embodiment and any one of Embodiments 1 to 3 is that: in Step 12, the azimuth angle at which the target reaches the receiving array after being transmitted through the multipath channel is obtained under far-field conditions ; The specific process is as follows: Step 121: Reconstruct the expressions of the receiving array and the transmitting array in matrix form under far-field conditions; The specific process is as follows: In the continuous time domain, assuming there are M array elements and P target echoes (from P different directions respectively), the signals received by each array element are: (7) Where, is the received signal at time is the spatial steering vector, is the transmitted signal at time is the noise at time is the time; Step 122: Discretize Equation (7) to obtain (8) That is: ; ; ; (9) Where, is the discretized array element received signal of the received signal at time is the spatial steering vector, is the discretized transmitted signal of the transmitted signal at time is the discretized noise signal of the noise at time is the value after discretizing the time is the total number of signal samplings in the discrete time; is the received signal of the first array element in the time series received signal is the received signal of the second array element in the time series received signal is the time series received signal The received signal of the th array element; is the transmitted signal vector of the first path in the time-series transmitted signal, is the transmitted signal vector of the second path in the time-series transmitted signal, is the transmitted signal vector of the th path in the time-series transmitted signal; is the noise received by the first array element in the time-series noise, is the noise received by the second array element in the time-series noise, is the noise received by the th array element in the time-series noise; is the imaginary unit, ; is the signal incident azimuth angle of the first path, is the signal incident azimuth angle of the second path, is the signal incident azimuth angle of the th path; Steps One, Two, and Three: Define the weight vector matrix of the array elements ; For the received signal, define the weight vector of the array element. The weight vector is similar to a spatial filter. It determines the gain value of the signals received by each array element after weighting to obtain the output of the entire array. Through the filtering effect of the weight vector, it is expected to achieve greater gain for the signals received by the array at the target arrival angle, while attenuation occurs at other angles. This filtering effect actually carries the information of the target arrival angle, thereby realizing the DOA estimation effect. The array output of the received signal is ; The expression is: (10) where is the array output; , the superscript H represents the conjugate transpose calculation; is the weight vector corresponding to the first array element in the weight vector matrix is the weight vector corresponding to the second array element in the weight vector matrix is the weight vector corresponding to the m-th array element in the weight vector matrix is the weight vector corresponding to the -th array element in the weight vector matrix; is the conjugate of ; is the conjugate of ; is the conjugate of ; is the conjugate of ; is the signal received by the -th array element; The power of the array output is: (11) where is the autocorrelation matrix of ; ; represents the expectation in the statistical sense; The power of the array output is a function of the weight vector matrix; expressed as: (12) This function represents the power of the output signal of the entire system after the array generates a filtering effect through different weight vectors . If is regarded as a function related to , then the obtained power function presents the signal power distribution relationship at different direction angles, which produces the effect of DOA estimation.

[0027] Meanwhile, the autocorrelation matrix has the following properties: (13) Considering that the noise is Gaussian white noise, we have: (14) (15) Since the autocorrelation function of the noise signal is real-valued, it is found that:​​ (16) That is is a Hermitian matrix; Among them, is the received signal covariance matrix; is the Gaussian white noise power; is the identity matrix; represents conjugate transpose calculation.

[0028] Other steps and parameters are the same as those in any one of the specific embodiments one to three.

[0029] Specific embodiment five: The difference between this embodiment and any one of the specific embodiments one to four is that: in step two, when obtaining the main path direction of the transmitting array when the main lobe gain is maximized and the side lobe suppression reaches the expected goal (reflected in ), and the beamforming weight W is obtained; the specific process is as follows: In the communication process, multipath channels arriving at other angles generally pass through reflections on the water surface and the bottom of the water. During this process, the signal experiences a large fade, resulting in a slow change in the angle of the direct sound path and a rapid time-varying angle of the reflected path due to reflections on the water surface and the bottom of the water. For such secondary paths, the energy on these paths should be limited by suppressing the side lobe level of the transmitting beam, and finally most of the energy of the transmitting array is concentrated on the main path. Step 2-1. In order to maximize the main lobe gain and achieve the expected goal of side lobe suppression, the array steering vector is defined as

[0030] ; ; Among them, is the azimuth angle of the target after passing through the multipath channel and arriving at the receiving array after DOA estimation; is the total number of transmitting array elements, takes positive integers; is to find the transpose; The transmitting beam response is ; Among them, is the beamforming vector of the th transmitting array element; The purpose of the present invention is to maximize the transmitting beam response of the transmitting array at the desired angle, that is, the main path direction of the transmitting array, and at the same time suppress the transmitting beam response at other angles, and set the optimization problem as: (17) Among them, For the main path direction of the transmitting array (one of the remaining path directions obtained in Step 1 ); Is the transmitting beam response; Step 2: Set As the total power of the transmitting array, and convert Into an optimization constraint problem, introduce an auxiliary variable , transform the optimization constraint problem, and solve the transformed optimization constraint problem to obtain the main path direction of the transmitting array .

[0031] Other steps and parameters are the same as those in Embodiments 1 to 4.

[0032] Embodiment 6: The difference between this embodiment and any one of Embodiments 1 to 5 is that in Step 2, set As the total power of the transmitting array, and convert Into an optimization constraint problem, introduce an auxiliary variable , transform the optimization constraint problem, and solve the transformed optimization constraint problem to obtain the main path direction of the transmitting array And obtain the beamforming weight W; The specific process is as follows: Step 2.1: Set As the total power of the transmitting array, and convert Into an optimization constraint problem, expressed as (18) Wherein, Is to make The value is minimized, Is the beamforming weight matrix, Is the matrix trace operation; Is The corresponding steering vector, Is the left half-power point angle (at The left point among the two points on the two curves corresponding to the main lobe peak down 3 dB), Is the right half-power point angle (at The right point among the two points on the two curves corresponding to the main lobe peak down 3 dB); Is The corresponding steering vector, Is The corresponding steering vector, Is The corresponding steering vector; Step 2.2: Since this problem is non-convex and difficult to solve directly, we can introduce an auxiliary variable , the optimization constraint problem of formula (18) is transformed to obtain the transformed optimization constraint problem of formula (19); Step Two Two Three: Solve the transformed optimization constraint problem of formula (19) in Step Two Two Two to obtain the main path direction of the transmitting array and obtain the beamforming weight W.

[0033] Other steps and parameters are the same as those in the first to fifth specific embodiments.

[0034] Specific Embodiment Seven: The difference between this embodiment and any one of the first to sixth specific embodiments is that: in Step Two Two Two, since this problem is non-convex and difficult to solve directly, we can introduce an auxiliary variable , the optimization constraint problem of formula (18) is transformed to obtain the transformed optimization constraint problem of formula (19); It is expressed as: (19) It represents finding the rank of a matrix.

[0035] Other steps and parameters are the same as those in any one of the first to sixth specific embodiments.

[0036] Specific Embodiment Eight: The difference between this embodiment and any one of the first to seventh specific embodiments is that: in Step Two Two Three, the transformed optimization constraint problem of formula (19) in Step Two Two Two is solved to obtain the main path direction of the transmitting array and obtain the beamforming weight W; the specific process is as follows: Perform semi-definite relaxation (SDR) processing on formula (19) to obtain the main path direction of the transmitting array and the value of the auxiliary variable ; If the obtained satisfies , then perform eigenvalue decomposition on to obtain ; If the obtained does not satisfy , then perform Gaussian randomization on to generate O groups of candidate solutions, find the sub-optimal solution with a rank equal to 1 among the O groups of candidate solutions, and perform eigenvalue decomposition on the sub-optimal solution to obtain ; Since The existence of this constraint results in the problem still being non-convex, and semi-definite relaxation (SDR) can be performed to transform the original non-convex problem into a semi-definite programming (SDP) problem. Since introducing auxiliary variables requires corresponding additional constraints on the auxiliary variables, using SDR to relax this constraint transforms the problem into a convex problem, and a convex optimization solver is used to solve it. If the obtained satisfies this condition, then eigenvalue decomposition is performed to obtain the corresponding which is the optimal solution of the original problem. If the obtained does not satisfy this condition, then the Gaussian randomization method needs to be used to perform Gaussian randomization on generate multiple groups of candidate solutions, find the sub-optimal solution with rank equal to 1 and perform eigenvalue decomposition to obtain , and select the sub-optimal solution that satisfies the sidelobe constraint and has the maximum main lobe gain.

[0037] Other steps and parameters are the same as those in any one of the specific embodiments one to seven.

[0038] Specific Embodiment Nine: This embodiment is a storage medium, and at least one instruction is stored in the storage medium. The at least one instruction is loaded and executed by a processor to implement the above-mentioned underwater acoustic MISO-OFDM low sidelobe transmitting beamforming method based on the main path angle.

[0039] It should be understood that any method described in the present invention can be provided as a computer program product, software, or computerized method, which may include a non-transitory machine-readable medium having instructions stored thereon. The instructions can be used to program a computer system or other electronic devices. The storage medium may include, but is not limited to, magnetic storage media, optical storage media; magneto-optical storage media includes: read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM), and flash memory layers; or other types of media suitable for storing electronic instructions.

[0040] Specific Embodiment Ten: This embodiment is an underwater acoustic MISO-OFDM low sidelobe transmitting beamforming device based on the main path angle. The device includes a processor and a memory. It should be understood that any device including a processor and a memory described in the present invention may further include other units and modules for display, interaction, processing, control, etc. through signals or instructions, and other functions; at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the above-mentioned underwater acoustic MISO-OFDM low sidelobe transmitting beamforming based on the main path angle.

[0041] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and deformations according to the present invention. However, these corresponding changes and deformations should all fall within the protection scope of the appended claims of the present invention.

Claims

1. An underwater acoustic MISO-OFDM low sidelobe transmitting beamforming method based on the main path angle, characterized in that: The specific process of the method is as follows: Step 1: Use DOA estimation to obtain the signal incident azimuth angles of different paths that reach the receiving array after the target is transmitted through a multipath channel ; Step 2: Obtain the main path direction of the transmitting array when the main lobe gain is maximized and the sidelobe suppression reaches the expected target , and obtain the beamforming weight W.

2. The method for underwater acoustic MISO-OFDM low sidelobe transmission beamforming based on the main path angle according to claim 1, characterized in that: In the first step, DOA estimation is used to obtain the azimuth angle at which the target arrives at the receiving array after being transmitted through the multipath channel ; The specific process is as follows: Step 11: Construct a DOA estimation model under far-field conditions; Steps 1 and 2: Obtain the azimuth angle at which the target arrives at the receiving array after being transmitted through a multipath channel under far-field conditions .

3. The method for underwater acoustic MISO-OFDM low sidelobe transmitting beamforming based on the main path angle according to claim 2, wherein: In Step 11, the process of constructing a DOA estimation model under far-field conditions is as follows: Step 111. Assume that there are receiving array elements linearly arranged at equal intervals to form a receiving array. Let the distance between the target and the first array element be . The signal incident azimuth angles of the signals from different paths of the signal emitted by the target reaching the receiving array are , , , . is the signal incident azimuth angle of the first path of the signal emitted by the target reaching the receiving array, is the signal incident azimuth angle of the second path of the signal emitted by the target reaching the receiving array, is the signal incident azimuth angle of the th path of the signal emitted by the target reaching the receiving array; Step 112: Based on the distance, direction, spacing, and receiving array of the target from the first array element, obtain the distance of the target from the , direction , spacing , receiving array element , and obtain the distance of the target from the th receiving array element ; expressed as: (1) Among them, is the distance from the target to the th receiving array element, ; Step 113: Based on the distance to the th receiving array element and the distance from the target to the first array element , calculate the distance difference ; expressed as: ; (2) Among them, is the distance difference from ; Step 114, based on and distance difference and the distance from the target to the first array element , calculate the time difference between the signals received by the th receiving array element and the first array element; expressed as: (3) wherein, is the speed of sound; Step 115: Perform Taylor expansion on Equation (3) to obtain: (4) Step 116: Based on Equation (4) Obtain the phase difference between the received signal of the th receiving array element and the first array element; expressed as: (5) Among them, is the signal frequency, where , is the signal wavelength; When the following conditions are met: in Equation (5), can be ignored, and in this case, it is said that the far-field condition is satisfied; That is, the phase difference between the th array element and the first array element received is as follows: (6)。 4. The method for underwater acoustic MISO-OFDM low sidelobe transmitting beamforming based on the main path angle according to claim 3, characterized in that: In the first and second steps, the azimuth angle at which the target arrives at the receiving array after being transmitted through the multipath channel is obtained under far-field conditions ; The specific process is as follows: Step 121: Reconstruct the expressions of the receiving array and the transmitting array in matrix form under far-field conditions; the specific process is as follows: In the continuous time domain, assuming there are M array elements and P target echoes, the signals received by each array element are: (7) Among them, is the signal received at time the spatial domain steering vector, is the signal transmitted at time is the noise at time is time; Step 122: Discretize Equation (7) to obtain (8) That is: ； ； ； (9) Among them, is for the received signal at a moment after discretization of the received signal of array elements, is the spatial steering vector, is for the transmitted signal at a moment after discretization of the transmitted signal, is for the noise at a moment after discretization of the noise signal, is the value after discretization of time and is the total number of signal samplings in discrete time; is the received signal of the first array element in the time-series received signal, is the received signal of the second array element in the time-series received signal, is the received signal of the th array element in the time-series received signal;​ is the transmitted signal vector of the first path in the time series transmitted signal, is the transmitted signal vector of the second path in the time series transmitted signal, is the transmitted signal vector of the th path in the time series transmitted signal; is the noise received by the first array element in the time series noise, is the noise received by the second array element in the time series noise, is the noise received by the th array element in the time series noise; where \(i\) is the imaginary unit, ; is the signal incident azimuth angle of the first path, is the signal incident azimuth angle of the second path, is the signal incident azimuth angle of the Steps One, Two, and Three: Define the weight vector matrix of the array elements ; Received signal The array output of ; The expression is: (10) Among them, is the array output; , the superscript H represents the conjugate transpose calculation; is the weight vector corresponding to the first array element in the weight vector matrix is the weight vector corresponding to the second array element in the weight vector matrix is the weight vector corresponding to the m-th array element in the weight vector matrix is the weight vector corresponding to the -th array element in the weight vector matrix; is the conjugate of, is the conjugate of, is the conjugate of, is the conjugate of; is the signal received by the th array element; Array output Power is as follows: (11) Among them, is the autocorrelation matrix of ; Denote the statistical expectation; Array output power is a function of the weight vector matrix; expressed as: (12) Meanwhile, the autocorrelation matrix has the following properties: (13) Considering the noise as Gaussian white noise, we have: (14) (15) Since the autocorrelation function of the noise signal is real-valued, it is found that: (16) Among them, is the covariance matrix of the received signal ; is the power of Gaussian white noise; is the identity matrix; represents the conjugate transpose calculation.

5. The method for underwater acoustic MISO-OFDM low sidelobe transmitting beamforming based on the main path angle according to claim 4, characterized in that: Obtain the main path direction of the transmitting array when the main lobe gain is maximized and the sidelobe suppression achieves the expected goal in the second step , and obtain the beamforming weight W; the specific process is as follows: Step 2-1. Define the array steering vector as ; Among them, is the total number of transmitting array elements, and its value is a positive integer; is for transpose; The transmit beam response is ; Among them, is the beamforming vector of the th transmitting array element; To maximize the transmit beam response of the transmit array at the desired angle, i.e., the main path direction of the transmit array while suppressing the transmit beam responses at other angles, the optimization problem is set as follows: (17) Among them, is the main path direction of the transmitting array; is the transmitting beam response; Step 22. Set as the total power of the transmitting array, and transform into an optimization constraint problem. Introduce an auxiliary variable , transform the optimization constraint problem, and solve the transformed optimization constraint problem to obtain the main path direction of the transmitting array.

6. The method for underwater acoustic MISO-OFDM low sidelobe transmission beamforming based on the main path angle according to claim 5, wherein: In the second step is the total power of the transmitting array. Convert into an optimization constraint problem, introduce an auxiliary variable , transform the optimization constraint problem, solve the transformed optimization constraint problem to obtain the main path direction of the transmitting array and obtain the beamforming weight W. The specific process is as follows: Step 221. Set as the total power of the transmitting array, and convert into an optimization constraint problem, expressed as (18) Among them, To make the value minimum, is the beamforming weight matrix, is the matrix trace operation; is the corresponding steering vector, is the left half-power point angle, is the right half-power point angle; is the corresponding steering vector, is the corresponding steering vector, is the corresponding steering vector; Step Two Two Two: Introduce an auxiliary variable , transform the optimization constraint problem in Equation (18) to obtain the transformed optimization constraint problem; Step Two Two Three: Solve the optimized constraint problem transformed in Step Two Two Two to obtain the main path direction of the transmitting array and obtain the beamforming weight W.

7. The method for underwater acoustic MISO-OFDM low sidelobe transmission beamforming based on the main path angle according to claim 6, characterized in that: Introduce an auxiliary variable in the second step mentioned above , transform the optimization constraint problem in Equation (18) to obtain the transformed optimization constraint problem, which is expressed as: (19) Indicates finding the rank of a matrix.

8. The method for underwater acoustic MISO-OFDM low sidelobe transmitting beamforming based on the main path angle according to claim 7, wherein: In step 223, the optimized constraint problem after the transformation in step 222 is solved to obtain the main path direction of the transmitting array and the beamforming weight W is obtained; the specific process is as follows: Perform semi - definite relaxation on Equation (19) to obtain the main path direction of the transmitting array and the values of the auxiliary variables ; If the obtained satisfies , then perform eigenvalue decomposition on to obtain ; If the obtained does not satisfy , then the Gaussian randomization method is used to perform Gaussian randomization on , generate O groups of candidate solutions, find the sub-optimal solution with a rank equal to 1 among the O groups of candidate solutions, and perform eigenvalue decomposition on the sub-optimal solution to obtain .

9. A storage medium, characterized in that, At least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the underwater acoustic MISO-OFDM low sidelobe transmission beamforming method based on the main path angle as described in any one of Claims 1 to 8.

10. An underwater acoustic MISO-OFDM low sidelobe transmitting beamforming device based on the main path angle, characterized in that, The device includes a processor and a memory. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the underwater acoustic MISO-OFDM low sidelobe transmission beamforming method based on the main path angle as described in any one of Claims 1 to 8.

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