Underwater positioning method and positioning system based on TF-GSC acoustic signal enhancement

Abstract

The invention relates to the technical field of underwater azimuth detection and positioning, in particular to an underwater positioning method and positioning system based on TF-GSC acoustic signal enhancement. Comprising the following steps that a plurality of underwater transducer arrays are arranged on the periphery of an area where a target object is located, and reference sound signals are periodically and actively emitted; the target object receives the emission signal, preliminary spherical positioning is carried out according to the time difference of arrival, and rough position estimation of the target object is obtained; each underwater transducer array performs STFT decomposition on an echo signal of a target object, time-frequency units are divided on a time-frequency two-dimensional plane, and a TF-GSC algorithm is executed on each time-frequency unit; the SNR of the enhanced signal is used as feedback, the array beam direction and weight are dynamically adjusted, and the receiving effect is further optimized; and carrying out global convergence judgment by utilizing a cross-iteration period. According to the method, the target extraction capability and the interference suppression effect of underwater positioning can be improved, so that the accuracy of underwater positioning is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater orientation detection and positioning, in particular to an underwater positioning method and system based on TF-GSC sound signal enhancement. BACKGROUND

[0002] Underwater acoustic positioning is a key technology for target positioning by taking advantage of the characteristics of stable propagation speed, strong penetration and anti-electromagnetic interference of sound waves in water, and has three core advantages of high-precision potential, long-distance adaptability and environmental robustness. It is the only positioning method that can balance accuracy and range in underwater environment and cannot be replaced by GPS and other land technologies. Therefore, it has become an indispensable technical support in many fields: in the field of ocean resource development, it can achieve centimeter-level inspection of underwater robots on oil and gas pipelines, dynamic navigation of deep-sea mining equipment, and solve the problem of precision control of equipment operation in complex water areas; in the field of ocean science and environmental protection, it can locate deep-sea underwater observation stations and track the migration trajectory of marine organisms; in the field of underwater engineering construction, it can calibrate underwater pipe docking and solve the problem of positioning deviation and water noise interference through sound signal enhancement technology, etc.

[0003] Underwater acoustic positioning is usually affected by multipath effect, marine environmental interference and signal attenuation. The traditional methods based on time difference of arrival, ultra-short baseline positioning, etc. have a significant decline in positioning accuracy under low signal-to-noise ratio conditions. Although array beamforming technology can enhance the signal, the traditional minimum variance distortionless response or fixed weight algorithm is difficult to adapt to wideband non-stationary signals and dynamic interference sources.

[0004] Current underwater acoustic positioning technology is developing towards high precision, intelligence and modularity. Although multi-sensor fusion can improve robustness, it is still limited by the anti-interference ability of the core acoustic signal. The three types of baseline systems in the industry all have significant defects: long baseline positioning system (LBL) has centimeter-level accuracy, but it needs to deploy a hundreds of meters to kilometers of seabed transponder array, which is complex, high cost and dependent on pre-set operation area, and is also prone to shallow sea error increase caused by seabed terrain obstruction; short baseline positioning system (SBL) is less difficult to install than LBL, but it needs a baseline length of more than 40m for deep water operation to ensure accuracy, which is high in cost for ship body modification; ultra-short baseline positioning (USBL) system becomes the mainstream of civil use due to its simple system and low cost, but it is greatly affected by sound velocity gradient, requires strict installation and calibration, and the signal is easily submerged in low signal-to-noise ratio environment, which cannot be stably positioned. SUMMARY

[0005] The present application aims to overcome the above-mentioned defects in the prior art and proposes an underwater positioning method and system based on TF-GSC sound signal enhancement to realize efficient enhancement and interference suppression of wideband non-stationary signals in complex environments, improve the target extraction capability and interference suppression effect of underwater positioning, and improve the accuracy of underwater positioning.

[0006] The technical scheme of the present application is: an underwater positioning method based on TF-GSC sound signal enhancement, comprising the following steps: S1. A plurality of underwater transducer arrays are arranged around the area where the target object is located, and periodically emit reference sound signals; S2. The target object receives the emitted signals and performs preliminary spherical positioning according to the time difference of arrival to obtain a rough position estimate of the target object; S3. Each underwater transducer array performs STFT decomposition on the echo signal of the target object, divides the time-frequency units on the "time-frequency" two-dimensional plane, and performs TF-GSC algorithm on each time-frequency unit to realize target signal enhancement and interference suppression; S4. The SNR of the enhanced signal is used as feedback to dynamically adjust the array beam direction and weight to further optimize the reception effect; S5. The global convergence is judged by using cross-iteration period.

[0007] In the present application, in the spherical positioning process of step S2, the distance from each acoustic array element to the target object is calculated by the time delay experienced by the sound wave from the acoustic array element to the target object, and each distance value defines a spherical surface centered on the acoustic array element. When at least three spherical surfaces in three-dimensional space intersect, the intersection point is the specific position of the uniquely determined target object: , Wherein, represents one of the acoustic array elements, represents the position coordinates of the acoustic array element, represents the speed of sound, represents the sound propagation time; For a target with unknown depth, when the measured slant range between the target and the acoustic array element has no error, the four spherical surfaces can determine a unique intersection point, and the four equations of the four-element array have the following form: , , , , After arranging the four equations of the four-element array, the following linear equations are obtained: , Let: , , , get The unique solution of , wherein P represents the three-dimensional spatial coordinate vector of the underwater target to be positioned, including the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the target, and is the core result of the final solution of spherical positioning.

[0008] The specific implementation process of step S3 is as described below: S3.1, time-frequency transformation; S3.2, reference beam construction; S3.3, interference subspace modeling; S3.4, sidelobe suppression and weight optimization.

[0009] In step S3.1, a window function is added to the target echo signal received by the underwater transducer array, and a short-time Fourier transform is performed to extract the complex spectrum coefficient corresponding to each time-frequency unit, realizing the joint representation of the signal in time-frequency-space; The window function type, window length sampling points, sampling rate are selected, and frame shift setting is performed. For a signal of length N , the STFT calculation formula of the first frame is: , wherein represents the complex spectrum coefficient of the time-frequency unit; L represents the window length; R represents the frame shift; represents the window function; represents the frequency index; represents the imaginary unit; represents the time-domain discrete sequence of the target echo signal obtained by the underwater transducer array, which is the original input signal of STFT decomposition.

[0010] Through short-time Fourier transform, the target echo signal is converted into a multi-dimensional complex spectrum graph, which constitutes a three-dimensional tensor input for subsequent beam processing.

[0011] In step S3.2, let the coarse positioning result be the coordinate of the target in space , and the coordinate of the underwater transducer array be , then the direction vector of the target relative to the underwater transducer array can be calculated through the spatial geometric relationship; , wherein represents the azimuth and elevation that the reference beam needs to point to; d represents the straight-line distance between the target and the underwater transducer array; The echo signal of the target received by the underwater transducer array is divided into several time-frequency units after STFT decomposition, each unit corresponding to a specific time segment and frequency component, and the target direction is estimated for each time-frequency unit The reference beam is constructed separately, and the reference beam vector The calculation formula is: , Wherein, M represents the number of array elements; H represents the conjugate transpose; , represents the array manifold vector of the target direction; In order to meet the distortionless constraint in the above formula, the reference beam vector adopts the minimum norm solution, , Wherein, represents the array manifold vector of the target direction; represents the inner product of the array manifold vector.

[0012] The specific implementation process of step S3.3 is: S3.3.1, extract the multi-channel signal of the time-frequency unit, after completing the short-time Fourier transform, the multi-channel signal of each time-frequency unit is expressed as: , Wherein, M represents the number of acoustic elements in the underwater transducer array; represents the complex spectrum coefficient of the th acoustic element in the time-frequency unit; T represents the transpose; For the th time-frequency unit, the estimation formula of the covariance matrix is: , Wherein, is the mathematical expectation operator, and the average of multiple observation values of the same time-frequency unit is taken; H represents the conjugate transpose; According to the signal model, the covariance matrix is decomposed as: , Wherein, represents the covariance component of the target signal; represents the target signal power; represents the covariance matrix of the interference plus noise; S3.3.2, construct the interference subspace by eigenvalue decomposition, and the eigenvalue decomposition formula is: , Wherein, , represents the covariance matrix of the interference plus noise; The eigenvector matrix representing the interference subspace is calculated as follows: , where K represents the number of interference sources, the eigenvectors span the space where the interference signals are located, and contain the direction information of all interference signals. represents a diagonal matrix. The eigenvector matrix The column vectors of the eigenvector matrix span the interference subspace, which is represented as: , This subspace contains all possible directions of the interference signals, and provides a spatial reference for the orthogonal separation of the target signal and the interference signal in the next step.

[0013] In step S3.4, the generalized sidelobe suppression principle is used to calculate the optimal value of the weight vector under the constraint condition, so that the gain of the beam in the target direction is maximized while suppressing the interference signals in other directions; The calculation of the optimal value of the weight vector is performed by solving a constrained least squares problem, i.e. , with the constraint condition being: , where represents the target steering vector, describing the spatial phase characteristics of frequency k and target direction , corresponding to the array manifold, satisfying ; represents the norm of the weight vector, which is equivalent to suppressing the sidelobe energy, and its calculation formula is: ; In the process of sidelobe suppression and weight optimization, the optimal beam weight vector can be solved by minimizing the projection energy of the interference subspace, i.e. ; By using the spatial filtering method of orthogonal projection, the target signal space is mapped to the orthogonal direction of the interference subspace, so that the non-target signal is suppressed while ensuring the distortionless output of the target signal. Finally, after the TF-GSC processing, the output enhanced signal is: .

[0014] In step S4, an iteration cycle includes several rounds of iteration process. After each round of iteration, the SNR value of the enhanced signal is calculated. Let the target direction beam output power be , and the average power of the interference direction be , then: .

[0015] , the square sum of the beam output signals of all directions in the interference subspace is calculated and averaged to obtain , the square sum of the beam output signals of all directions in the interference subspace is calculated and averaged to obtain If , it indicates that the current beam direction or weight configuration has sufficiently suppressed the interference, and the next step of processing can be directly performed; When , the current beam direction or weight configuration fails to sufficiently suppress the interference, the array adaptive control is performed to adjust the target direction, beam width and weight vector of each array beamformer, and the SNR value is recalculated until the SNR value obtained by the current iteration calculation is , the improvement rate of the SNR value obtained by the current iteration calculation is greater than 1% than the SNR value obtained by the last iteration calculation.

[0016] In step S5, if the positioning result SNR improvement rate obtained by two consecutive iteration periods is <1%, it indicates that the obtained positioning result has tended to be stable, the iteration process is determined to be converged, and the positioning result is outputted; If it has not converged, steps S3 to S4 are repeated until the positioning result tends to be stable and is outputted.

[0017] The application also includes a positioning system for implementing the above-mentioned underwater positioning method, which comprises: A plurality of underwater transducer array modules for transmitting signals and receiving signals feedbacked by the target object; A spherical positioning module for coarse positioning of the target object; A short-time Fourier transform module for time-frequency decomposition of the target object echo signal received by the underwater transducer array to obtain time-frequency unit complex spectrum coefficients; A reference beam construction module for forming an undistorted response channel in the target direction; An interference subspace modeling module for estimating and separating the interference signals of non-target directions; A sidelobe suppression and weight optimization module for realizing target enhancement and interference suppression.

[0018] The application has the following beneficial effects: ​(1) The TF-GSC beamforming algorithm is introduced into the underwater three-dimensional positioning method, by combining time-frequency analysis with generalized sidelobe suppression, the sidelobe energy of the interference direction is reduced by about 20 dB under the condition of the same main lobe gain, the output signal-to-noise ratio is improved by about 3-4 dB under the condition of low signal-to-noise ratio, which is significantly better than the traditional MVDR and DSB beamforming method, effectively enhances the fidelity of wideband, non-stationary target signal, and significantly suppresses the multipath and interference signal; the scheme can maintain high positioning accuracy under the condition of low signal-to-noise ratio and strong interference sea area, and improve the object direction detection and positioning ability of the system in complex marine acoustic environment; (2) An adaptive beam control mechanism based on signal-to-noise ratio feedback is adopted, the beam direction and array weight parameters can be automatically adjusted according to the real-time calculated signal-to-noise ratio, and the dynamic response to target motion state and marine environment change is realized; compared with the traditional fixed parameter beam forming, the mechanism significantly improves the robustness and dynamic tracking ability of the positioning system, and can still maintain stable output when the target direction changes or the environmental noise suddenly changes; (3) An active-passive hybrid working mechanism and energy consumption control strategy are proposed, which automatically switches the working mode according to whether the target has self-sound ability: when the target actively sounds, the system enters the passive listening mode, thereby greatly reducing the energy consumption; when the target is silent or the echo is weak, the active sound source is automatically activated to realize continuous positioning. The strategy effectively prolongs the endurance time of underwater robots and other low-energy platforms while ensuring high-precision positioning performance, meeting the application requirements of long-term tasks and unmanned deployment.

[0019] In summary, the TF-GSC technology is used to suppress water noise and multipath interference in the present application, which solves the positioning failure problem in the USBL / LBL noise environment; without laying out the seabed transponder group (LBL) or long baseline transducer array (SBL), the system complexity and deployment cost are reduced; with the help of time-frequency domain signal processing optimization, the influence of sound velocity gradient and ship attitude change on the positioning result is reduced, and the positioning stability in deep water area and dynamic operation scene is improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of the method described in the present application; Figure 2 is a flowchart of step S3 in the present application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0022] In the following description specific details are set forth in order to provide a thorough understanding of the application. However, the application can be practiced without the specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the application. Accordingly, the application is not limited to the specific embodiments disclosed below.

[0023] An underwater positioning method based on TF-GSC acoustic signal enhancement described in the present application comprises the following steps, and the flow chart of the method is shown in Figure 1

[0024] The first step is multi-array layout and active emission: multiple underwater transducer arrays are laid out around the target area, and reference acoustic signals are periodically actively emitted.

[0025] The target object can include underwater robots, biological tracking beacons, side-scan sonars, towed robots, device carriers, etc. At least three groups of underwater transducer arrays are laid out around the target area, each group of arrays including 8 to 16 acoustic elements, which are laid out in different directions of the target area in an equidistant line array structure, forming a spatial geometric constraint condition to improve the TDOA positioning accuracy and the direction resolution of beamforming. The spacing between the elements is designed to be half of the wavelength of the main frequency of the received signal , so as to suppress spatial aliasing and ensure that the array has sufficient beam focusing ability.

[0026] The array layout strategy adopts a "non-coplanar-asymmetric" multi-base station topology, which ensures good geometric precision factor when performing spherical intersection positioning and can effectively eliminate positioning blind areas. In actual deployment, the distance between each array group is planned according to the target activity area range and the water depth of the sea area, which not only ensures signal coverage, but also avoids the problem of excessive path length of sound propagation, which causes multipath overlap and signal attenuation.

[0027] In the active mode, the array actively emits reference pulses at a set frequency, which is suitable for scenarios where the target does not have self-generated sound or initial positioning. If a target self-generated sound signal is detected within a specific time window, the system automatically switches to passive listening mode. In passive mode, the array turns off its own emission device and only retains the receiving and processing part. The switching conditions include energy mutation detection, spectrum correlation analysis, and signal classifier identification, ensuring that the probability of false switching is less than 1%. The underwater transducer array automatically and dynamically selects the mode according to the behavior characteristics of the target, maximizing the endurance time of the device.

[0028] The second step is target object received signal and TDOA estimation: the target object receives multiple emitted signals and performs preliminary spherical positioning based on the time difference of arrival of the signals to obtain a rough position estimate of the target object.

[0029] ​The target receives multiple transmitted signals, extracts the arrival time stamp of each pulse signal by using correlation detection method, calculates the time difference between any two arrays, performs preliminary spherical positioning according to the arrival time difference, and obtains a rough position estimation of the target. The position is used for subsequent beam reference direction construction.

[0030] Spherical positioning is a positioning method based on time delay measurement. Its core principle is to calculate the distance from the acoustic array to each target by the time delay experienced by the sound wave from the acoustic array to the target. Each distance value defines a spherical surface centered on the acoustic array, i.e. a geometric surface composed of all points satisfying the distance, indicating that the target may be located at any position on the spherical surface. When at least three nodes in three-dimensional space intersect, their intersection point is the specific position of the uniquely determined target: , wherein, represents one of the acoustic arrays, represents the position coordinates of the acoustic array, represents the sound velocity, represents the sound propagation time.

[0031] For a target with unknown depth, when the measured slant range between the target and the acoustic array has no error, the intersection of two spherical surfaces is a circle, the intersection of three spherical surfaces forms two points, and the fourth spherical surface can determine a unique point. Taking a four-element array as an example, the equation group has the following form: , , , , After arranging the four equations of the above four-element array, the following linear equation group is obtained: , Let: , , , Therefore, as long as the inverse matrix of exists, the unique solution of P can be obtained: , wherein, P represents the three-dimensional space coordinate vector of the underwater target to be positioned, including the x-axis coordinate, y-axis coordinate and z-axis coordinate of the target, and is the core result of the final solution of spherical positioning.

[0032] To enhance the stability and robustness of spherical positioning, the following can be further introduced: (1) Sound speed profile correction: considering the influence of sound speed gradient change on propagation time in actual sea area; (2) GDOP analysis: selecting array combination with good geometric distribution to reduce positioning accuracy degradation; (3) Data fusion processing: fusing TDOA results with other constraints (such as DOA) to improve accuracy; (4) Weighted least squares: assigning weights to different TDOA measurements to reduce the influence of outliers.

[0033] The spherical positioning result can provide direction for subsequent TF-GSC reference beam construction, can be used as the starting point for iterative positioning and SNR feedback optimization, and can also be used as the basis for positioning update when the target actively sounds.

[0034] Third step, time-frequency analysis and TF-GSC beamforming: each underwater transducer array performs STFT decomposition on the target echo signal, divides the minimum independent processing unit, i.e. time-frequency unit, on the "time-frequency" two-dimensional plane, and performs TF-GSC algorithm on each time-frequency unit to realize target echo signal enhancement and interference suppression. The specific steps include the following steps, as shown in Figure 2 .

[0035] First, time-frequency transform. Add a window function to the target echo signal received by the underwater transducer array and perform short-time Fourier transform (STFT), extract the complex spectrum coefficient corresponding to each time-frequency unit, and realize the joint representation of the signal in time-frequency-space.

[0036] Select window function type, window length sampling point, sampling rate, and perform frame shift setting: when performing STFT, the window function is selected as the Hanning window because the Hanning window has good balance characteristics in time domain and frequency domain. For a signal of length N , the STFT calculation formula of the first frame is: , wherein, represents the complex spectrum coefficient of the time-frequency unit; L represents the window length; R represents the frame shift; represents the window function; represents the frequency index; represents the imaginary unit, which is the symbol of complex number operation, and satisfies the mathematical relationship ; represents the time domain discrete sequence of the target echo signal obtained by the underwater transducer array, which is the original input signal of STFT decomposition.

[0037] The target echo signal is converted into a multi-dimensional complex spectrum by STFT calculation, forming a three-dimensional tensor input for subsequent beam processing.

[0038] In high-noise or strong-interference frequency bands, the window length can be adaptively increased to improve frequency domain resolution and ensure that the target signal energy is concentrated: by introducing STFT preprocessing before beamforming, the system can effectively improve time-domain spatial filtering to the level of time-frequency-space three-domain joint processing, enhancing the perception ability of non-stationary signals.

[0039] Second, reference beam construction: according to the coarse positioning result obtained in the second step, a reference beam signal pointing to the target direction is constructed at each time-frequency point, which is used as a distortionless response channel.

[0040] Each time-frequency unit is regarded as a local narrowband signal and is processed independently through the TF-GSC beamforming algorithm.

[0041] Spherical positioning calculates the approximate spatial coordinates of the target through the time difference of multiple underwater transducer arrays, which are used to deduce the azimuth (horizontal direction) and pitch angle (vertical direction) of the target relative to the underwater transducer array, i.e., the physical direction to which the reference beam needs to point.

[0042] Let the coarse positioning result be the coordinates of the target sound source in space , and the coordinates of the underwater transducer array be Then the direction vector of the target relative to the underwater transducer array can be calculated through spatial geometric relationships: , where represents the azimuth and pitch angle to which the reference beam needs to point; d is the straight-line distance between the target and the underwater transducer array, which is calculated from the coarse positioning result.

[0043] The target echo signal received by the underwater transducer array is divided into multiple time-frequency units after STFT decomposition, each corresponding to a specific time segment and frequency component. Due to the large differences in signal characteristics of underwater signals at different times and frequencies, a reference beam needs to be constructed for each time-frequency unit to adapt to its local stationary characteristics. The reference beam vector is the core of the "distortionless response channel" in the TF-GSC algorithm and needs to satisfy the "target direction distortionless response" constraint, with the formula being: , where is the reference beam weight vector of the time-frequency unit, M represents the number of array elements, and H is the conjugate transpose. , which represents the target direction, i.e., the spatial direction of the target acoustic source with respect to the underwater transducer array, and the frequency , the target azimuth , and the elevation . The "1" on the right side of the above equation indicates that the gain in the spatial direction of the target acoustic source with respect to the underwater transducer array is 1, i.e., no distortion constraint.

[0044] To meet the no distortion constraint in the above formula, the reference beam vector adopts the minimum norm solution, which ensures the minimum weight energy and suppresses noise, i.e., , where represents the inner product of the array manifold vector, and by normalizing the array manifold vector, it is ensured that the target signal, i.e., the signal along the spatial direction of the target acoustic source with respect to the underwater transducer array, is not attenuated or distorted in the reference beam channel, providing a reliable "reference signal" for subsequent interference suppression.

[0045] Third, interference subspace modeling: the remaining signals except for the spatial direction of the target acoustic source with respect to the underwater transducer array are constructed into an interference subspace, which can be obtained through the estimation of the multi-channel signal covariance matrix.

[0046] Through the analysis of the surrounding environment signals, the interference subspace contains all signal directions except for the spatial direction of the target acoustic source with respect to the underwater transducer array, including multipath effects, ocean ambient noise, dynamic interference sources, and other non-target signals, and is dedicated to separating and quantifying non-target signals.

[0047] Similar to the reference beam, the interference subspace is independently modeled in each time-frequency unit to adapt to the time-varying characteristics of underwater broadband non-stationary signals. When modeling, the multi-channel signals of the underwater transducer array are statistically analyzed, and the covariance matrix of the signals is calculated, based on which the spatial characteristics of the interference signals are extracted to construct a subspace containing all interference directions.

[0048] First, the multi-channel signals of the time-frequency unit are extracted, and after completing the short-time Fourier transform, the multi-channel signals of each time-frequency unit can be represented as: , where M represents the number of acoustic elements in the underwater transducer array; represents the complex spectral coefficient of the th acoustic element in the time-frequency unit; and T represents transposition.

[0049] The modeling basis of interference subspace is the covariance matrix of multi-channel signals, which reflects the correlation between the signals of acoustic elements in each underwater transducer array, including the joint estimation characteristics of targets, interference and noise.

[0050] For the first time-frequency unit, the estimation formula of the covariance matrix is: , where is the mathematical expectation operator, and the average of multiple sets of observation values for the same time-frequency unit; H represents the conjugate transpose, ensuring that the matrix is Hermitian, i.e., symmetric and diagonal with real numbers, which meets the characteristics of the covariance matrix.

[0051] The covariance matrix contains target signals, interference signals and noise, and needs to separate the components containing only interference signals and noise. According to the signal model, the covariance matrix can be decomposed as: , where represents the covariance component of the target signal; represents the target signal power; represents the covariance matrix of interference and noise, which is the basis of the interference subspace that needs to be extracted.

[0052] Next, the interference subspace is constructed by eigenvalue decomposition, and the eigenvalue decomposition formula is: , where , represents the covariance matrix of interference and noise. represents the eigenvector matrix of the interference subspace, which is composed of the eigenvectors corresponding to the first K largest eigenvalues of , and its calculation formula is: , where K represents the number of interference sources, and these eigenvectors span the space of interference signals, containing the direction information of all interference signals.

[0053] represents a diagonal matrix, and the diagonal elements are the first K largest eigenvalues of , arranged in descending order. The size of the eigenvalue reflects the power of the corresponding interference signal, and the greater the power, the stronger the interference in that direction.

[0054] In summary, the space spanned by the column vectors of the eigenvector matrix is the interference subspace, which can be represented as: , The subspace contains all possible directions of interference signals, providing a spatial reference for the next step of orthogonal separation of target signals and interference signals.

[0055] Fourth, sidelobe suppression and weight optimization: By minimizing the reference beam signal and projecting the energy of the interference subspace, the maximum suppression of interference components is achieved. The final beam output is the projection result of the target signal space in the orthogonal direction of the interference subspace, which suppresses non-target signals while maintaining the amplitude of the target signal.

[0056] By orthogonal projection of the interference subspace and the target signal space, i.e. the projection of the target signal space in the orthogonal direction of the interference subspace, the energy of the interference component can be maximally weakened, while the amplitude of the target signal is maintained.

[0057] Using the generalized sidelobe suppression principle, the optimal value of the weight vector under the constraint condition is calculated, so that the gain of the beam in the target direction is maximized while suppressing interference signals in other directions.

[0058] Specifically, the optimal value of the weight vector is calculated by solving a constrained least squares problem, i.e. , with the constraint condition being: , where is the target steering vector, describing the spatial phase characteristics of frequency k, target direction , corresponding to the array manifold, satisfying .

[0059] is the norm of the weight vector, equivalent to suppressing the sidelobe energy, and its calculation formula is: .

[0060] In the process of sidelobe suppression and weight optimization, the interference subspace provides the basis for the constraint condition, and by minimizing the projection energy of the interference subspace, i.e. , the optimal beam weight vector can be solved, making the beam gain maximum in the target direction and minimum in the interference direction.

[0061] Through the above orthogonal projection space filtering method, the target signal space is mapped to the orthogonal direction of the interference subspace, thereby suppressing non-target signals while ensuring distortionless output of the target signal. Finally, after TF-GSC processing, the output enhanced signal is: .

[0062] wherein, is the result obtained after the above STFT transformation, which is used as input at this time to achieve the enhancement of the target signal and the suppression of interference.

[0063] Fourth step, SNR feedback and array adaptive adjustment: using the SNR of the enhanced signal as feedback, dynamically adjusting the array beam direction and weight, and further optimizing the receiving effect.

[0064] Each iteration cycle includes several rounds of iteration. In one iteration cycle, after each round of iteration, the SNR value of the enhanced signal is calculated, using the ratio of the target direction beam output power to the average power of the interference direction. Let the target direction beam output power be , and the average power of the interference direction be , then: .

[0065] In actual calculation, the beam output signal in the target direction is squared and summed to obtain , and the beam output signal in all directions in the interference subspace is squared and averaged to obtain

[0066] If the calculated in the current round of iteration is not significantly improved compared to the calculated in the last round of iteration, it is determined that the current beam direction or weight configuration has not fully suppressed the interference. In this embodiment, the improvement rate is limited to 1%. When , it means that the current beam direction or weight configuration has fully suppressed the interference, and the next step can be directly processed.

[0067] When , the current beam direction or weight configuration has not fully suppressed the interference. Next, array adaptive control is performed to adjust the target direction, beam width and weight vector of each array beamformer, and the SNR value is recalculated until the calculated in the current round of iteration is improved by more than 1% compared to the calculated in the last round of iteration.

[0068] Fifth step, positioning iteration and convergence judgment.

[0069] The step is to use the judgment of global convergence across iteration cycles. If the SNR improvement rate of the positioning results obtained by two consecutive iteration cycles is less than 1%, it is indicated that the obtained positioning result has tended to be stable, and further iteration will not bring effective improvement. At this time, it is determined that the iteration process converges, and the positioning result is output.

[0070] If it has not converged, the third step and the fourth step are repeated, and the reference beam is reconstructed, the interference subspace is reconstructed, the TF-GSC beam forming is performed, the SNR is calculated, and the convergence is judged in turn, until the positioning result tends to be stable and is output. The above guarantees that the system can realize step-by-step convergence and high-stability target estimation output in a complex dynamic marine environment, and reduces the phenomenon of false positioning and false identification.

[0071] To verify the technical effect of the underwater positioning method based on the TF-GSC sound signal enhancement, simulation tests are performed on a MATLAB platform. The simulation adopts an eight-element uniform linear array structure, the element spacing is half of the wavelength, the array operating frequency is 20 kHz, and the sampling rate is 96 kHz. The target signal is a linear frequency modulation signal of 18-22 kHz, the target azimuth is 30°, the interference azimuth is , , and .

[0072] (1) Comparison of beam directions of the traditional MVDR algorithm and the method described in the application: Under the premise that the main lobe gain of the target direction 30° is consistent, the sidelobe energy of the interference direction -20° is significantly suppressed, and the sidelobe level is reduced by about 20 dB, indicating that the application can effectively suppress the interference signal in the spatial dimension and improve the resolution capability of the target signal.

[0073] (2) Energy distribution in the time-frequency domain before and after signal enhancement: The time-frequency spectrum energy of the original signal is relatively dispersed, and there is obvious wideband noise and interference energy. After the method described in the application is processed, the target signal energy is concentrated in the main frequency band area, the background noise energy is significantly weakened, and the time-frequency texture structure of the target signal is more clear, which reflects the ability of the application to jointly suppress noise and interference in the time-frequency domain.

[0074] (3) Comparison of output signal-to-noise ratios of MVDR and the method described in the application under different input signal-to-noise ratios: When the input signal-to-noise ratios are , and The output signal-to-noise ratio of the traditional MVDR algorithm gradually improves with the increase of the input SNR, while the method described in the application achieves a higher output signal-to-noise ratio under the same conditions, with an average improvement of about 4-5 dB. The results show that the application can still achieve stable signal enhancement effect in a low signal-to-noise ratio environment. Further, the enhanced signal is input into the spherical positioning module for three-dimensional position estimation, and the positioning error is reduced by about 50% compared with the MVDR result, and the positioning error is less than 1 m in the low signal-to-noise ratio condition, and can still maintain sub-meter accuracy in the medium and high signal-to-noise ratio conditions, and the positioning accuracy can be stabilized to achieve centimeter-level level.

[0075] In summary, the simulation results fully verify the robustness and superiority of the method disclosed in the application under the conditions of low signal-to-noise ratio, strong interference and wideband non-stationary signal. The TF-GSC acoustic signal enhancement method proposed in the application can significantly improve the signal-to-noise ratio of the target signal, suppress multi-source interference, and effectively improve the accuracy and stability of the underwater positioning system, and has good engineering realizability and application prospect.

[0076] The application also includes a positioning system for implementing the above positioning method, comprising: a plurality of underwater transducer array modules for transmitting signals and receiving signals feedback by target objects; a spherical positioning module for coarse positioning of the target object; a short-time Fourier transform module for time-frequency decomposition of the target object echo signal received by the underwater transducer array to obtain time-frequency unit complex spectrum coefficients; a reference beam construction module for forming an undistorted response channel in the target direction; an interference subspace modeling module for estimating and separating the interference signals in non-target directions; a sidelobe suppression and weight optimization module for target enhancement and interference suppression.

[0077] The above describes in detail the underwater positioning method and positioning system based on the TF-GSC sound signal enhancement provided by the present application. The principles and implementation manners of the present application are described by using specific examples, and the above description of the examples is only used to help understand the method of the present application and the core idea thereof. It should be noted that, for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application. The above description of the disclosed examples enables those skilled in the art to implement or use the present application. Various modifications of the examples will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other examples without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the examples shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An underwater positioning method based on TF-GSC acoustic signal enhancement, characterized in that, Includes the following steps: S1. Deploy several underwater transducer arrays around the area where the target is located, and periodically and actively transmit reference acoustic signals; S2. The target receives the transmitted signal and performs preliminary spherical positioning based on the time difference of arrival to obtain a rough estimate of the target's position. S3. Each underwater transducer array performs STFT decomposition on the target echo signal, divides the time-frequency unit on the "time-frequency" two-dimensional plane, and executes the TF-GSC algorithm on each time-frequency unit to achieve target signal enhancement and interference suppression. S4. Using the SNR of the enhanced signal as feedback, dynamically adjust the array beam direction and weight to further optimize the reception effect; S5. Use cross-iteration cycles to determine global convergence.

2. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 1, characterized in that, In the spherical positioning process of step S2, the distance from each acoustic element to the target is calculated by the time delay experienced by the sound wave from the acoustic element to the target. Each distance value defines a sphere centered on the acoustic element. When at least three spheres in three-dimensional space intersect, their intersection point is the uniquely determined specific location of the target. ， in, This represents a specific acoustic array element. Represents the position coordinates of the acoustic array elements. Indicates the speed of sound. Indicates the sound propagation time; For a target of unknown depth, when there is no error in the slant distance between the measured target and the acoustic array elements, the four spheres can determine a unique intersection point, and the four equations of its four-element array can be expressed as follows: ， ， ， ， After rearranging the four equations of the quaternion matrix, we can obtain the following system of linear equations: ， make: ， ， ， get The only solution: ， Where P represents the three-dimensional spatial coordinate vector of the underwater target to be located, including the x-axis coordinate, y-axis coordinate, and z-axis coordinate of the target, and is the core result of the final solution for spherical positioning.

3. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 1, characterized in that, The specific implementation process of step S3 is as follows: S3.1, Time-Frequency Transformation; S3.2, Reference beam construction; S3.3, Modeling the interference subspace; S3.4, Sidelobe Suppression and Weight Optimization.

4. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 3, characterized in that, In step S3.1, A window function is added to the target echo signal received by the underwater transducer array and a short-time Fourier transform is performed to extract the complex spectral coefficients corresponding to each time-frequency unit, thereby realizing the joint representation of the signal in time-frequency-space. Select the window function type, window length, sampling points, and sampling rate, and set the frame shift for a signal of length N. , No. The formula for calculating the STFT of a frame is: ， in, Represents the complex spectral coefficients of the time-frequency unit; L represents the window length; R represents the frame shift; Indicates the window function; Indicates frequency index; Represents the imaginary unit; This represents the time-domain discrete sequence of the target echo signal acquired by the underwater transducer array, which is the original input signal for STFT decomposition. The target echo signal is converted into a multidimensional complex spectrum by short-time Fourier transform, forming a three-dimensional tensor input for subsequent beam processing.

5. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 3, characterized in that, In step S3.2, let the coarse positioning result be the coordinates of the target object in space. The coordinates of the underwater transducer array are Then the direction vector of the target object relative to the underwater transducer array can be calculated through spatial geometric relationships; ， in, d represents the azimuth and elevation angles that the reference beam needs to point to; d represents the straight-line distance between the target and the underwater transducer array. The target echo signal received by the underwater transducer array is decomposed by STFT and divided into several time-frequency units. Each unit corresponds to a specific time segment and frequency component. Construct a separate reference beam, and the reference beam vector. The calculation formula is: ， Where M represents the number of array elements; H represents the conjugate transpose; , representing the array manifold vector of the target direction; To satisfy the distortion-free constraint in the above formula, the reference beam vector adopts the minimum norm solution. ， in, The array manifold vector representing the target direction; This represents the inner product of the array manifold vectors.

6. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 3, characterized in that, The specific implementation process of step S3.3 is as follows: S3.3.1 Extract the multi-channel signals of the time-frequency unit. After completing the short-time Fourier transform, each time-frequency unit... The multi-channel signal is represented as: ， Where M represents the number of acoustic array elements in the underwater transducer array; Indicates the first The complex spectral coefficients of each acoustic array element in this time-frequency unit; T represents the transpose; For the Time-frequency unit, covariance matrix The estimation formula is: ， in, For the mathematical expectation operator, the average of multiple sets of observations in the same time-frequency unit is taken; H represents the conjugate transpose. According to the signal model, the covariance matrix is ​​decomposed as follows: ， in, Represents the covariance component of the target signal; Indicates the target signal power; Represents the covariance matrix of the interference plus noise; S3.3.2 Constructing the interference subspace through eigenvalue decomposition, the eigenvalue decomposition formula is as follows: ， in, , representing the covariance matrix of the interference plus noise; The eigenvector matrix representing the interference subspace is calculated using the following formula: ， Where K represents the number of interference sources, and these feature vectors span the space where the interference signal is located, containing the directional information of all interference signals; Represents a diagonal matrix; eigenvector matrix The space spanned by the column vectors is the interference subspace, denoted as: ， This subspace contains all possible directions of interference signals, providing a spatial reference for the next step of orthogonal separation of the target signal and interference signals.

7. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 3, characterized in that, In step S3.4, the principle of generalized sidelobe suppression is used to calculate the weight vector under the constraints. The optimal value ensures that the beam is aligned with the target direction. The gain is maximized on the upper direction, while suppressing interference signals from other directions; Weight vector The optimal value is calculated by solving a constrained least squares problem, i.e. The constraints are: ， in, This represents the target guidance vector, describing the frequency k and the target direction. The spatial phase characteristics, corresponding to the array manifold, satisfy... ; Represents the weight vector Norm, This is equivalent to suppressing sidelobe energy, and its calculation formula is: ； In the process of sidelobe suppression and weight optimization, the projected energy of the interference subspace is minimized, i.e. The optimal beam weight vector can be solved. By using orthogonal projection spatial filtering, the target signal space is mapped to the orthogonal direction of the interference subspace, thereby suppressing non-target signals while ensuring the distortion-free output of the target signal. Finally, after TF-GSC processing, the enhanced output signal is: 。 8. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 1, characterized in that, In step S4, after each iteration within an iteration cycle, the enhanced signal is... To calculate the SNR value, assume the target direction beam output power is... The average power in the direction of interference is ,but: ； It is obtained by performing a sum of squares operation on the beam output signal in the target direction. It is obtained by averaging the sum of squares of the beam output signals in all directions within the interference subspace; like If the current beam direction or weight configuration has sufficiently suppressed interference, then the next step of processing can be carried out directly. when If the current beam direction or weight configuration fails to adequately suppress interference, adaptive array control is performed to adjust the target direction, beamwidth, and weight vector of each array beamformer, and the SNR value is recalculated until the result obtained in the current iteration is obtained. Compared to the previous iteration calculation The improvement rate is greater than 1%.

9. The underwater positioning method based on TF-GSC acoustic signal enhancement according to claim 1, characterized in that, In step S5, if the SNR improvement rate of the positioning result obtained in two consecutive iteration cycles is <1%, it indicates that the positioning result has become stable, the iteration process is determined to have converged, and the positioning result is output. If convergence is not achieved, repeat steps S3 to S4 until the positioning result stabilizes and is output.

10. A positioning system for implementing the underwater positioning method based on TF-GSC acoustic signal enhancement as described in any one of claims 1-9, characterized in that, include: Multiple underwater transducer array modules are used to transmit signals and receive signals fed back from the target. Spherical positioning module for coarse positioning of the target object; The short-time Fourier transform module is used to perform time-frequency decomposition on the target echo signal received by the underwater transducer array to obtain the complex spectral coefficients of the time-frequency unit; Reference beamforming module for forming a distortion-free response channel in the target direction; The interference subspace modeling module is used to estimate and separate interference signals from non-target directions; The sidelobe suppression and weight optimization module is used to achieve target enhancement and interference suppression.

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