Anti-interference underwater acoustic positioning system and method

Abstract

The invention relates to an anti-interference underwater acoustic positioning system and method, and the system comprises a fixed reference network which comprises at least three fixedly-arranged acoustic transducers and is used for transmitting multi-frequency acoustic signals; the mobile positioning target comprises at least one movable acoustic transducer and is used for receiving the multi-frequency acoustic signals transmitted by the fixed reference network; the central processing module is respectively in communication connection with the acoustic transducers in the fixed reference network and the mobile positioning target and is used for controlling each acoustic transducer; adaptive cascade filtering processing is carried out on the multi-frequency acoustic signals received by the mobile positioning target; calculating a signal arrival time difference between the movable acoustic transducer and each fixedly arranged acoustic transducer based on the filtered signal; and based on the signal arrival time difference, three-dimensional position information of the mobile positioning target is obtained through calculation. Compared with the prior art, high-precision three-dimensional position information can be stably output in a dynamic impurity interference environment.

Description

Technical Field

[0001] This application relates to the field of underwater acoustic positioning technology, and in particular to an anti-interference underwater acoustic positioning system and method. Background Technology

[0002] Underwater acoustic positioning is currently the most important technique for underwater target location. Its basic principle is to calculate distance and azimuth by measuring the propagation time or phase difference of sound waves in water. Existing baseline positioning systems include: Long Baseline Positioning (LBL): Multiple transponder arrays are deployed on the seabed, with baseline lengths ranging from hundreds to thousands of meters. The target's position is calculated by measuring the sound wave propagation time between the target and each transponder. LBL systems offer high positioning accuracy (meter-level or even sub-meter-level) and wide coverage. Short Baseline Positioning (SBL): A receiving array consisting of multiple hydrophones is installed on the bottom of the ship or platform, with a baseline length ranging from a few meters to tens of meters. The target's bearing and distance are calculated by measuring the time delay difference of sound waves reaching different hydrophones. SBL systems offer relatively flexible deployment. Ultra-Short Baseline Positioning (USBL): Multiple hydrophones are integrated into a compact acoustic probe, with a baseline length of only a few centimeters. Positioning is achieved by measuring the phase difference of sound waves reaching each hydrophone. USBL systems are easy to install and suitable for various platforms.

[0003] Existing baseline positioning systems face challenges in target localization within the unique underwater environment of net cage cleaning. Impurities within the net cages constitute a complex source of acoustic interference, primarily including: debris generated by the friction between the cleaning brush and the netting, microbubbles produced by high-pressure water guns used for fish repelling or cleaning, and inherent suspended particles in the water. These impurities affect acoustic signals in multiple ways, and existing baseline positioning systems suffer from the following drawbacks in this environment: Long baseline positioning (LBL): Deployment, calibration, and retrieval are extremely complex, time-consuming, and costly; for localized and enclosed applications such as cage cleaning, it is overkill and impractical; in addition, reflected signals from the seabed and interference from impurities will further degrade positioning performance. Short baseline positioning (SBL): Its accuracy is heavily dependent on the installation and calibration accuracy of the array, and near the cage, platform swaying, water fluctuations, and dense impurities will introduce significant errors in the time delay measurement of the small array. Ultra-short baseline positioning (USBL): Its positioning accuracy is relatively low, especially sensitive to the depth and roll and pitch attitude of the target; more importantly, its array size is small, its spatial diversity gain is weak, and its anti-multipath and anti-interference capabilities are inherently insufficient, resulting in severe performance degradation in impurity-dense cage environments. Summary of the Invention

[0004] Therefore, it is necessary to address the aforementioned technical problems of lack of anti-interference capability at the signal level, insufficient coverage and uneven accuracy at the system architecture level, and poor dynamic robustness at the algorithm level by providing an anti-interference underwater acoustic positioning system and method that can stably output high-precision three-dimensional position information under dynamic impurity interference environment.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: In a first aspect, the present invention provides an anti-interference underwater acoustic positioning system, comprising: A fixed reference network, comprising at least three fixedly positioned acoustic transducers, is used to transmit multi-frequency acoustic signals; The mobile positioning target includes at least one movable acoustic transducer for receiving multi-frequency acoustic signals emitted by the fixed reference network. The central processing module is communicatively connected to the fixed reference network and the acoustic transducers in the mobile positioning target, respectively, and is used to control each of the acoustic transducers; to perform adaptive cascade filtering on the multi-frequency acoustic signals received by the mobile positioning target; to calculate the signal arrival time difference between the movable acoustic transducers and each fixed acoustic transducer based on the filtered signal; and to calculate the three-dimensional position information of the mobile positioning target based on the signal arrival time difference.

[0006] Secondly, the present invention also provides an anti-interference underwater acoustic positioning method, utilizing an anti-interference underwater acoustic positioning system as described above, comprising: S1: The central processing module controls each acoustic transducer in the fixed reference network to emit multi-frequency acoustic signals. S2: The central processing module controls the acoustic transducer in the mobile positioning target to receive multi-frequency acoustic signals transmitted by the fixed reference network; S3: The central processing module performs adaptive cascaded filtering on the multi-frequency acoustic signals received by the mobile positioning target; S4: Calculate the signal arrival time difference between the movable acoustic transducer and each fixed acoustic transducer based on the filtered signal; S5: Based on the time difference of arrival of the signal, the three-dimensional position information of the mobile positioning target is calculated.

[0007] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention designs a cooperative positioning system consisting of multiple acoustic transducers fixed to a water surface platform and arranged around a net cage, and a mobile acoustic transducer mounted on a cleaning robot. This system fundamentally improves coverage and reliability through spatial diversity and redundancy design. By emitting a combination of low-frequency and high-frequency signals with different anti-interference characteristics and designing an adaptive filtering algorithm capable of dynamically estimating and filtering out impurity noise, the signal-to-noise ratio and ranging accuracy are improved at the signal level. Combining the above system architecture and signal processing results, and utilizing the Time Difference of Origin (TDOA) principle and multi-reference data fusion technology, a complete solution is designed that can stably output high-precision three-dimensional position information under dynamic impurity interference environments. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the structure of an anti-interference underwater acoustic positioning system in some embodiments of this application; Figure 2 This is a schematic diagram of the TDOA positioning principle of an anti-interference underwater acoustic positioning system in some embodiments of this application; Figure 3 This is a flowchart illustrating an anti-interference underwater acoustic positioning method in some embodiments of this application. Detailed Implementation

[0009] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the description of embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of units is not necessarily limited to those units, but may include other units not explicitly listed or inherent to those processes, methods, products, or apparatuses. The term "determine" broadly covers a wide variety of actions, including acquiring, calculating, processing, deriving, investigating, searching (e.g., searching in a table, database, or other data structure), probing, and similar actions; it may also include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), and similar actions; it may also include generating, creating, establishing, and similar actions; and parsing, selecting, choosing, and similar actions, etc. Definitions of other terms will be given in the following description.

[0010] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. Furthermore, in the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if there is transmission of electrical signals or data between the connected objects.

[0011] It should be emphasized that the acquisition, transmission, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0012] In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0013] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0014] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Example 1 This embodiment provides an anti-interference underwater acoustic positioning system, see reference. Figure 1 ,include: A fixed reference network, comprising at least three fixedly positioned acoustic transducers, is used to transmit multi-frequency acoustic signals; The mobile positioning target includes at least one movable acoustic transducer for receiving multi-frequency acoustic signals emitted by the fixed reference network. The central processing module is communicatively connected to the fixed reference network and the acoustic transducers in the mobile positioning target, respectively, and is used to control each of the acoustic transducers; to perform adaptive cascade filtering on the multi-frequency acoustic signals received by the mobile positioning target; to calculate the signal arrival time difference between the movable acoustic transducers and each fixed acoustic transducer based on the filtered signal; and to calculate the three-dimensional position information of the mobile positioning target based on the signal arrival time difference.

[0016] The central processing module is integrated on the main control computer or embedded processing platform (such as ARM+FPGA).

[0017] Compared to existing technologies, this invention designs a cooperative positioning system consisting of multiple acoustic transducers fixed to a water surface platform and arranged around a net cage, and a mobile acoustic transducer mounted on a cleaning robot. This system fundamentally improves the coverage and reliability of the system through spatial diversity and redundancy design. By emitting a combination of low-frequency and high-frequency signals with different anti-interference characteristics, and designing an adaptive filtering algorithm that can dynamically estimate and filter out impurity noise, the signal-to-noise ratio and ranging accuracy are improved at the signal level. Combining the above system architecture and signal processing results, and utilizing the time difference of location (TDOA) principle and multi-reference data fusion technology, a complete solution is designed that can stably output high-precision three-dimensional position information in a dynamic impurity interference environment.

[0018] In some preferred embodiments, the multi-frequency acoustic signal includes at least signal components of a first frequency and a second frequency; wherein the first frequency and the second frequency are different. Adaptive cascaded filtering processing is performed on the multi-frequency acoustic signals received by the mobile positioning target, including: The multi-frequency acoustic signal received by the mobile positioning target is separated by a matched filter bank to obtain signal components containing at least the first and second frequencies. Adaptive wavelet threshold denoising is performed on each of the signal components; wherein the wavelet threshold is adaptively adjusted based on the real-time estimated signal-to-noise ratio. An improved adaptive Kalman filter is used to perform secondary denoising on each signal component after adaptive wavelet threshold denoising; wherein, the observation noise covariance matrix of the improved adaptive Kalman filter is dynamically estimated based on the short-time energy and zero-crossing rate of each signal component after adaptive wavelet threshold denoising.

[0019] Compared to existing systems that use a single frequency signal, this system proactively addresses interference from complex underwater environments through a dual approach of multi-frequency signal fusion and adaptive cascaded filtering. It features an intelligent switching mechanism: in lightly contaminated environments, it relies on high-frequency signals for high-precision positioning; in heavily contaminated environments, the system automatically prioritizes low-frequency signals to maintain basic connectivity, while the adaptive filter enhances robustness. This fundamentally improves upon the current situation where existing systems experience drastic performance degradation or even failure in contaminated environments.

[0020] In some preferred embodiments, during the adaptive wavelet thresholding denoising process, the wavelet threshold is adaptively adjusted based on the real-time estimated signal-to-noise ratio, specifically including: Perform wavelet transform on each of the signal components to obtain the original wavelet coefficients; The wavelet threshold adaptively adjusted based on real-time estimated signal-to-noise ratio is expressed as follows:

[0021] in, Wavelet threshold; This is a baseline threshold related to the signal energy; The signal-to-noise ratio is estimated in real time; When the signal-to-noise ratio is low (strong interference from noise), the wavelet threshold... Automatically increase the volume to more aggressively filter out noise; conversely, decrease it to retain more signal details. The original wavelet system is processed based on adaptively adjusted wavelet thresholds and soft thresholding functions, as expressed below:

[0022] in, These are the denoised wavelet coefficients, i.e., the signal coefficients whose noise is suppressed after processing with the soft threshold function. The original wavelet coefficients are represented as the original coefficients decomposed from the received acoustic signal after wavelet transform, containing both effective signal and noise interference. The positive part operator is used; sign() is the sign function, whose processing logic is: when the absolute value of the coefficient is greater than the wavelet threshold, it is considered to mainly contain effective signals, and its amplitude is contracted; otherwise, it is regarded as noise and set to zero, so as to preserve the signal characteristics to the greatest extent while suppressing noise.

[0023] Impurities and noise are mostly manifested in the high-frequency detail coefficients. By applying an adaptive threshold related to the currently estimated signal-to-noise ratio to these detail coefficients, impulse noise and some scattering interference can be effectively suppressed.

[0024] In some preferred embodiments, the improved adaptive Kalman filter dynamically estimates the observation noise covariance matrix based on the short-time energy and zero-crossing rate of each signal component after adaptive wavelet threshold denoising, specifically including: If short-term abnormal energy fluctuations and abrupt changes in zero-crossing rate are detected in each signal component after adaptive wavelet threshold denoising, the observation noise covariance matrix is ​​automatically increased, as shown below:

[0025] in, The observation noise covariance matrix represents the uncertainty estimate of the system for the observations at time k; This represents the short-term energy of the current signal. The zero-crossing rate of the current signal; Base noise level; and These are the short-time energy and zero-crossing rate offsets of the current signal relative to the calm state, respectively. and These are the first weighting coefficient and the second weighting coefficient, respectively.

[0026] Improved It is no longer a fixed value; the system adjusts it based on the short-time energy of the received signal. and zero crossing rate Dynamically estimate features .

[0027] In this embodiment, the signal components after adaptive wavelet threshold denoising are... (at discrete time points) The signal is fed into an improved Kalman filter to further smooth the tracking and predict the signal state (such as the envelope or instantaneous phase). The update steps of the Kalman filter are as follows:

[0028]

[0029] in, The Kalman gain determines whether the system trusts the predictions or the observations more. The predicted state covariance matrix is ​​represented as... -1 time predicted At time t, the uncertainty of the state estimate is expressed; the larger this value, the higher the uncertainty of the model prediction. The observation matrix is ​​responsible for mapping the system state vector to the observation domain; The posterior estimate, or optimal estimate, of the state combines the observations at the current time. After that, what was obtained The optimal estimate of the system state at time t; The prior estimate, or prediction, of the state is based on The optimal estimate and system dynamics model at time -1, for Predicting the state at any given moment; For a moment The observed values, that is, the signal state input to the Kalman filter after wavelet denoising preprocessing; When the system detects an increase in impurity concentration (manifested as signal energy) Abnormal fluctuations, zero-crossing rate (Mutation), will automatically increase Based on the update steps of the Kalman filter described above, Enlargement will lead to This means the filter will reduce the sensitivity to current, interfering observations. The level of trust has shifted to relying more on the predictions of system dynamics models. This effectively smooths out outliers caused by impurities, significantly improving the tracking stability of the system in harsh environments.

[0030] In the adaptive filtering stage, particle filtering (PF) or deep learning methods can serve as alternatives to Kalman filtering. Particle filtering is particularly suitable for highly nonlinear system models, but its computational complexity is extremely high, posing a challenge to the real-time processing capabilities of embedded systems mounted on underwater robots. Deep learning methods require a large amount of real data for model training, but obtaining sufficient labeled data is difficult in this specific application scenario. Therefore, adaptive Kalman filtering currently represents the most balanced choice between real-time processing performance and filtering effectiveness.

[0031] In some preferred embodiments, the signal components of the first frequency and the second frequency are a low-frequency LFM pulse signal and a high-frequency LFM pulse signal, respectively.

[0032] In this embodiment, the system commands three fixed acoustic transducers T1, T2, and T3 to emit a pre-designed composite acoustic signal in a time-division or coded orthogonal manner according to a predetermined cycle. This signal consists of two linear frequency modulated (LFM) pulse signals of different frequencies: Low-frequency LFM pulse signals (f_center≈1kHz): These signals have low frequency, long wavelength, low attenuation in water, strong diffraction ability, low sensitivity to bubbles and small particles, and good penetration. However, their wavelength results in relatively low absolute ranging accuracy.

[0033] High-frequency LFM pulse signals (f_center≈10kHz): High frequency and short wavelength, theoretically capable of achieving higher ranging accuracy and resolution. However, in environments with impurities, they experience significant attenuation and are easily scattered.

[0034] Therefore, the strong penetrating power of low-frequency signals ensures effective connection and coarse positioning even in harsh environments, while high-frequency signals provide fine positioning when the signal-to-noise ratio allows. These two approaches complement each other, enhancing the system's robustness in environments with impurities.

[0035] Besides linear frequency modulation (LFM) signals, spread spectrum signals, such as pseudo-random sequences (e.g., m-sequences), are also a viable alternative. Spread spectrum signals inherently possess advantages in terms of anti-interference and low probability of intercept. However, their implementation requires a wider signal bandwidth, and in underwater acoustic channels with limited bandwidth resources, their flexibility may not be as good as that of LFM signals.

[0036] In some preferred embodiments, the signal arrival time difference between the movable acoustic transducer and each fixed acoustic transducer is calculated using the generalized cross-correlation method (GCC-PHAT) based on the filtered signal.

[0037] The GCC-PHAT algorithm is insensitive to amplitude changes and can effectively suppress multipath effects through phase weighting, making it particularly suitable for this application scenario.

[0038] In this embodiment, the movable acoustic transducer is calculated using the GCC-PHAT method. Acoustic transducers with fixed installations ( The signal arrival time difference between (=1,2,3) Specifically, it includes: Calculate each fixed acoustic transducer With mobile acoustic transducers Cross-power spectrum between received signals:

[0039] in, for The cross-power spectrum; For fixed transducers Frequency domain representation of the received signal; The frequency domain representation of the signal received by the mobile transducer; for The complex conjugate; Apply PHAT weights:

[0040] in, This is a PHAT weighting function used to emphasize phase information and suppress amplitude effects; Performing the inverse Fourier transform yields the generalized cross-correlation function:

[0041] in, For time delay variables; The generalized cross-correlation function represents the signal's time delay. The correlation below; Find the peak position of the cross-correlation function, which is the estimated signal arrival time difference:

[0042] in, ; .

[0043] Combining the high-precision GCC-PHAT time difference extraction algorithm and the robust iterative solution method, theoretical analysis and simulation verification show that its positioning accuracy (especially in the horizontal direction) can be improved by more than 25% compared with the traditional short baseline system under the same impurity concentration environment, and the output trajectory is smoother and more stable.

[0044] In some preferred embodiments, the distance difference between the movable acoustic transducer and each fixed acoustic transducer is calculated based on the signal arrival time difference; A set of nonlinear observation equations is constructed based on the distance difference; The nonlinear observation equations are solved iteratively using Taylor series expansion and least squares method to obtain the three-dimensional position information of the moving target.

[0045] In this embodiment, high-precision time difference measurements are obtained. Then, the distance difference can be calculated. ,in The speed of sound (which can be corrected in real time by temperature, salinity and depth sensors).

[0046] Each distance difference Define a and A hyperboloid of revolution with focal point centered. The intersection of three such hyperboloids in space constitutes a moving acoustic transducer. For the location, please refer to Figure 2 .

[0047] This invention employs a Taylor series expansion least squares iterative algorithm for position calculation. This algorithm has the advantages of fast convergence speed and high accuracy. Let... The coordinates are Fixed acoustic transducer The coordinates are Then the true distance to each fixed acoustic transducer for:

[0048] by Using the reference station, we obtain information about the distance difference. The nonlinear observation equations:

[0049]

[0050] because The system of equations can be written as:

[0051]

[0052] Let the initial estimate of the target location be... Expanding the above nonlinear equation at this point using a Taylor series and neglecting higher-order terms yields the linearized equation:

[0053] in, To observe the residual vector, superscript Represents the initial estimated position The calculated value; The position correction vector to be determined; The Jacobian matrix (observation matrix):

[0054] Its specific elements are: , The same applies to components.

[0055] Solve for the correction using the least squares method:

[0056] Then update the location estimate: Repeat this process iteratively until... If the value is less than a preset threshold, then the converged high-precision three-dimensional position information of the moving target is obtained. .

[0057] In some preferred embodiments, the central processing module is further configured to: A Kalman filter based on a uniform or uniformly accelerated motion model smooths the three-dimensional position information of the moving target. This further improves the stability and real-time performance of the output trajectory.

[0058] In its implementation, this solution designs a collaborative positioning system consisting of three acoustic transducers fixedly installed on a surface platform (such as a floating bridge or workboat) and arranged around the net cage, and a mobile acoustic transducer mounted on a cleaning robot. This architecture, through its spatially ring-shaped deployment, provides a stable and fully covered positioning reference network for the net cage perimeter. Physically, this ensures that regardless of the robot's orientation within the net cage, it maintains a good geometric configuration with multiple reference points, laying the hardware foundation for omnidirectional, uniform, and high-precision positioning. This avoids the accuracy fluctuations caused by different orientations in traditional single-station deployments and eliminates the need for complex and cumbersome seabed deployment and calibration required by long-baseline systems, significantly reducing deployment time and costs. This solution has more relaxed requirements for the hardware platform, thus possessing good ease of implementation and widespread application potential.

[0059] Example 2 This embodiment further provides an anti-interference underwater acoustic positioning method based on Embodiment 1, utilizing an anti-interference underwater acoustic positioning system as described above. (See reference...) Figure 3 ,include: S1: The central processing module controls each acoustic transducer in the fixed reference network to emit multi-frequency acoustic signals. S2: The central processing module controls the acoustic transducer in the mobile positioning target to receive multi-frequency acoustic signals transmitted by the fixed reference network; S3: The central processing module performs adaptive cascaded filtering on the multi-frequency acoustic signals received by the mobile positioning target; S4: Calculate the signal arrival time difference between the movable acoustic transducer and each fixed acoustic transducer based on the filtered signal; S5: Based on the time difference of arrival of the signal, the three-dimensional position information of the mobile positioning target is calculated.

[0060] In some preferred embodiments, the method further includes: S6: The three-dimensional position information of the moving positioning target is smoothed by a Kalman filter based on a uniform or uniformly accelerated motion model.

[0061] This method innovatively combines low-frequency LFM signals with high-frequency LFM signals for underwater positioning. The low-frequency signal (center frequency approximately 1 kHz) ensures system connectivity in heavily contaminated environments due to its strong penetration and anti-attenuation capabilities; the high-frequency signal (center frequency approximately 10 kHz) provides higher ranging accuracy when channel conditions permit. Furthermore, a cascaded method of wavelet threshold denoising and dynamic adaptive Kalman filtering is proposed, which dynamically adjusts the filtering parameters based on the real-time estimated signal-to-noise ratio, effectively suppressing time-varying contaminant interference and significantly improving signal quality.

[0062] This method also constructs a complete localization solution process suitable for dynamic, cluttered environments. The process first utilizes the GCC-PHAT algorithm for high-precision time difference extraction, which is insensitive to amplitude variations and effectively suppresses multipath effects. Subsequently, a Taylor series expansion least squares iterative algorithm is employed to accurately solve the nonlinear localization equation, ensuring both accuracy and convergence speed. Finally, a Kalman filter based on a motion model is introduced to post-filter the calculated position sequence, further improving the smoothness and stability of the localization trajectory in dynamic environments.

[0063] It is understood that the method of this embodiment utilizes the system of Embodiment 1 described above, and the options in Embodiment 1 described above are also applicable to this embodiment, so they will not be described again here.

[0064] Example 3 This embodiment provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor, causing the processor to perform some or all of the steps of the method provided in Embodiment 2 of this application.

[0065] It is understood that the storage medium can be transient or non-transient. Exemplarily, the storage medium includes, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] By way of example, the processor may be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0067] By way of example, the read-only memory includes, but is not limited to, MASK ROM, PROM, EPROM, EEPROM, Flash, etc.

[0068] By way of example, the random access memory includes, but is not limited to, DRAM, SRAM, SDRAM, DDR SDRAM, etc.

[0069] In some examples, a computer program product is provided, which can be implemented by hardware, software, or a combination thereof. As a non-limiting example, the computer program product can be embodied in the storage medium, or it can be embodied in a software product, such as an SDK (Software Development Kit).

[0070] As a non-limiting example, a computer program product is provided, comprising a computer program or computer-executable instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and executes the computer-executable instructions, causing the electronic device to perform some or all of the steps of the method described in the embodiments of this application.

[0071] In some examples, a computer program is provided, including computer-readable code, wherein, when the computer-readable code is run in a computer device, a processor in the computer device performs some or all of the steps for implementing the method.

[0072] This embodiment also proposes an electronic device, including a memory and a processor. The memory stores at least one instruction, at least one program, code set, or instruction set. When the processor executes the at least one instruction, at least one program, code set, or instruction set, it implements some or all of the steps of the method described in Embodiment 2.

[0073] In some examples, a hardware entity of the electronic device is provided, including: a processor, a memory, and a communication interface; wherein the processor typically controls the overall operation of the electronic device; the communication interface is used to enable the electronic device to communicate with other terminals or servers via a network; the memory is configured to store instructions and applications executable by the processor, and may also cache data to be processed or already processed (including but not limited to image data, audio data, voice communication data, and video communication data) to be processed by the processor and various modules in the electronic device, and may be implemented using flash memory (FLASH), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or random access memory (RAM).

[0074] A processor may include one or more processing elements. Therefore, a processor may include one or more integrated circuits (ICs) configured to perform the functions of the processor. Furthermore, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, and other circuitry, etc.) configured to perform the functions of the processor.

[0075] Furthermore, data can be transferred between the processor, communication interface, and memory via a bus, which can include any number of interconnected buses and bridges, connecting various circuits of one or more processors and memories together.

[0076] It is understood that the options in Embodiment 2 above also apply to this embodiment, so they will not be described again here.

[0077] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this application. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0078] In different specific implementations, the methods or systems described in this application can be implemented in software, hardware, or a combination thereof. Furthermore, the order of the method steps can be changed, and various elements can be added, reordered, combined, omitted, or modified.

[0079] Obviously, the above embodiments of this application are merely examples for clearly illustrating this application, and are not intended to limit the implementation of this application, nor are they intended to limit this application. For those skilled in the art, other variations or modifications can be made based on the above description. The separate structural / functional modules or units can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. The structure and function of the separate components can be implemented as a combined structure or component. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of the claims of this application.

Claims

1. An anti-jamming underwater acoustic positioning system, characterized in that, The application relates to a three-dimensional positioning system, comprising: a fixed reference network comprising at least three fixedly arranged acoustic transducers for emitting multi-frequency acoustic signals; a mobile positioning target comprising at least one movable acoustic transducer for receiving the multi-frequency acoustic signals emitted by the fixed reference network; a central processing module in communication connection with the acoustic transducers in the fixed reference network and the mobile positioning target respectively, for controlling the acoustic transducers and performing adaptive cascade filtering on the multi-frequency acoustic signals received by the mobile positioning target; calculating the signal arrival time difference between the movable acoustic transducer and each fixedly arranged acoustic transducer based on the filtered signals, and calculating the three-dimensional position information of the mobile positioning target based on the signal arrival time difference.

2. The anti-jamming underwater acoustic positioning system according to claim 1, characterized in that, The multi-frequency acoustic signals at least comprise signal components of a first frequency and a second frequency, wherein the first frequency and the second frequency are different. The adaptive cascade filtering on the multi-frequency acoustic signals received by the mobile positioning target comprises: separating the multi-frequency acoustic signals received by the mobile positioning target to obtain at least the signal components of the first frequency and the second frequency; performing adaptive wavelet threshold denoising on each signal component, wherein the wavelet threshold is adaptively adjusted based on the real-time estimated signal-to-noise ratio; performing secondary denoising on each signal component after the adaptive wavelet threshold denoising by using an improved adaptive Kalman filter, wherein the observation noise covariance matrix of the improved adaptive Kalman filter is dynamically estimated based on the short-time energy and the zero-crossing rate of each signal component after the adaptive wavelet threshold denoising.

3. The anti-jamming underwater acoustic positioning system according to claim 2, characterized in that, During the adaptive wavelet threshold denoising, the wavelet threshold is adaptively adjusted based on the real-time estimated signal-to-noise ratio, and the specific process comprises: performing wavelet transform on each signal component to obtain original wavelet coefficients; adaptively adjusting the wavelet threshold based on the real-time estimated signal-to-noise ratio, which is expressed as: wherein, is a wavelet threshold; is a reference threshold related to the signal energy; is a real-time estimated signal-to-noise ratio; processing the original wavelet coefficients based on the adaptively adjusted wavelet threshold and a soft threshold function, which is expressed as: wherein is the denoised wavelet coefficient; is the original wavelet coefficient; is the take positive operator; sign() is the sign function.

4. The anti-jamming underwater acoustic positioning system according to claim 3, characterized in that, In the improved adaptive Kalman filter, the observation noise covariance matrix is dynamically estimated based on the short-time energy and the zero-crossing rate of each signal component after the adaptive wavelet threshold denoising, and the specific process comprises: if the short-time energy of each signal component after the adaptive wavelet threshold denoising is detected to abnormally fluctuate and the zero-crossing rate is detected to suddenly change, the observation noise covariance matrix is automatically increased, which is expressed as: wherein is the observed noise covariance matrix; is the short-term energy of the current signal; is the zero-crossing rate of the current signal; is the underlying noise level; and are the offsets of the short-term energy and the zero-crossing rate of the current signal, respectively, with respect to the quiet state; and are the first and second weighting coefficients, respectively.

5. The anti-jamming underwater acoustic positioning system according to claim 4, characterized in that, The signal components of the first frequency and the second frequency are low-frequency LFM pulse signals and high-frequency LFM pulse signals respectively.

6. The anti-jamming underwater acoustic positioning system according to claim 5, characterized in that, The signal arrival time difference between the movable acoustic transducer and each fixedly arranged acoustic transducer is calculated by using a generalized cross-correlation method based on the filtered signals.

7. The anti-jamming underwater acoustic positioning system according to claim 6, characterized in that, The distance difference between the movable acoustic transducer and each fixedly arranged acoustic transducer is calculated based on the signal arrival time difference. A nonlinear observation equation set is constructed based on the distance difference. The nonlinear observation equation set is iteratively solved by using a Taylor series expansion least square method, so that the three-dimensional position information of the mobile positioning target is obtained.

8. An anti-jamming underwater acoustic positioning system according to any one of claims 1-7, characterized in that, The central processing module is further used for: The Kalman filter based on the uniform motion model or the uniform acceleration motion model is used to smooth the three-dimensional position information of the mobile positioning target.

9. An anti-jamming underwater acoustic positioning method, using an anti-jamming underwater acoustic positioning system according to any one of claims 1-8, characterized in that, The method comprises the following steps: S1: controlling each acoustic transducer in the fixed reference network to emit a multi-frequency acoustic signal through the central processing module; S2: controlling the acoustic transducer in the mobile positioning target to receive the multi-frequency acoustic signal emitted by the fixed reference network through the central processing module; S3: performing adaptive cascade filtering processing on the multi-frequency acoustic signal received by the mobile positioning target through the central processing module; S4: calculating the signal arrival time difference between the movable acoustic transducer and each fixed acoustic transducer based on the filtered signal; S5: based on the signal arrival time difference, the three-dimensional position information of the mobile positioning target is solved.

10. The anti-jamming underwater acoustic positioning method according to claim 9, characterized in that, The method further comprises: S6: smoothing the three-dimensional position information of the mobile positioning target based on the Kalman filter of the uniform motion model or the uniform acceleration motion model.

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