Deeply coupled underwater acoustic communication positioning equipment and cooperative regulation method in uncertain marine environment

Abstract

The present application relates to the technical field of underwater acoustic communication positioning, in particular to an uncertain ocean environment deep-coupling underwater acoustic communication positioning equipment and a cooperative regulation method, comprising a water surface unit and a plurality of underwater units; the water surface unit and the underwater units are both configured with a deep-coupling communication positioning integrated algorithm module, comprising a communication positioning waveform integrated sub-module based on a superimposed identification code, used for generating a transmission signal frame containing linear superposition of user identification codes and communication data; a multi-user detection and data detection sub-module based on MMSE, used for user detection and data joint estimation of the plurality of underwater units under the MMSE framework. The user identification codes and the communication data are linearly superimposed through the superimposed identification code scheme, so that the same transmission signal carries the positioning and communication functions at the same time, realizing deep coupling of communication and positioning at the signal level and the algorithm level, and fundamentally solving the problem of mutual conflict of communication and positioning functions.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic communication and positioning technology, specifically to underwater acoustic communication and positioning equipment and collaborative control methods for deep coupling in uncertain marine environments. Background Technology

[0002] Underwater acoustic communication and positioning systems are essential technologies for marine scientific research, marine resource exploration, and deep-sea space station construction, ensuring the positioning, navigation, and communication functions of underwater vehicles. Currently, to achieve underwater acoustic communication and positioning, two separate sets of equipment are typically purchased, increasing the costs of equipment purchase, installation, and related operation and maintenance. While integrated communication and positioning products have emerged in recent years, these products merely integrate communication and positioning functions onto a single hardware platform, utilizing different time slots for communication and positioning. Essentially, this is simple time-division multiplexing of communication and positioning functions and does not achieve true integration.

[0003] For example, patent application CN116684004B discloses a logic control method for integrated underwater acoustic modem communication and positioning. The transmitting end main control module reads data from the positioning module, packages it, and then sequentially passes it along with the data packet through the communication module, power amplifier, and transducer to convert it into an underwater acoustic signal for output. However, this method still uses time-division multiplexing to switch between communication and positioning functions. Excessive communication time slots can reduce positioning efficiency, and in multi-target positioning scenarios, the communication signal can become a strong source of interference with the positioning signal.

[0004] Patent application CN115941060B discloses an integrated acoustic positioning and communication method for underwater multi-platform sensors, including three working modes: integrated positioning and communication mode, standalone positioning mode, and standalone communication mode. However, this method requires switching between different working modes, and still falls under the category of time-division multiplexing, failing to achieve simultaneous parallel processing of communication and positioning. Summary of the Invention

[0005] In order to overcome the deficiencies in the prior art, the purpose of this invention is to provide a deep-coupled underwater acoustic communication and positioning equipment and a collaborative control method for uncertain marine environments, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, on the one hand, the present invention provides a deep-coupled underwater acoustic communication and positioning device for uncertain marine environments, including a surface unit and at least one underwater unit; the surface unit includes a transducer array consisting of an ultra-short baseline array composed of N receiving hydrophones and at least one transmitting transducer, wherein the receiving hydrophones are used to receive acoustic signals transmitted by the underwater unit, and the transmitting transducer is used to transmit integrated communication and positioning waveform signals. The first signal processing unit is electrically connected to the transducer array and is used to perform acoustic signal preprocessing, signal detection, positioning calculation, communication signal processing and data transmission. The underwater unit includes a transceiver transducer and a second signal processing unit. The transceiver transducer is used for transmitting and receiving acoustic signals, and the signal processing unit is used for preprocessing analog received signals, processing communication signals, and transmitting positioning signals. Both the first signal processing unit of the surface unit and the second signal processing unit of the underwater unit are equipped with a deeply coupled communication and positioning integrated algorithm module. The deeply coupled communication and positioning integrated algorithm module includes a communication and positioning waveform integrated submodule based on superimposed identification code, which is used to generate a transmission signal frame containing a user identification code and communication data linearly superimposed. The user identification code serves as a positioning signal, a user detection signal, a frame synchronization signal, a channel estimation signal, and a Doppler estimation and compensation signal. The multi-user detection and data detection submodule based on the minimum mean square error criterion is used to perform user detection and joint data estimation for multiple underwater units under the framework of the minimum mean square error criterion. An environment-adaptive real-time compensation submodule for acoustic ray bending is used to adaptively optimize the layering based on the gradient characteristics of the current sea area's acoustic velocity profile, and to correct the sound wave propagation distance based on ray tracing technology. The channel equalization module is used to perform real-time tracking and estimation of rapidly changing underwater channels based on the superimposed identification code scheme, and to achieve channel equalization.

[0007] As a further improvement to this technical solution, the surface unit also includes a synchronizer for time synchronization between the surface unit and the underwater unit; the number of receiving hydrophones in the transducer array is N=5, forming a five-element ultra-short baseline array.

[0008] As a further improvement to this technical solution, the first signal processing unit includes an acoustic signal preprocessing unit, an acoustic signal transmitting unit, and a digital signal processing unit; the acoustic signal preprocessing unit is used to amplify and filter the received signal; the acoustic signal transmitting unit is used to amplify the transmitting excitation signal and load it onto the transmitting transducer; the digital signal processing unit is used to perform digital signal processing, control management, and external communication.

[0009] On the other hand, the present invention provides a method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments, comprising the following steps: S1. Assign a unique user identification code to each underwater unit, and construct the transmission signal of the k-th user as a linear composite signal of user identification code and communication data; when the user only has positioning needs, only the user identification code is transmitted without superimposed communication data; when the user has both communication and positioning needs, the superimposed signal of user identification code and communication data is transmitted simultaneously. S2. The surface unit receives composite signals transmitted by each underwater unit through a transducer array; under the minimum mean square error criterion, the received signals are decoupled for multiple users, separating the signal components of each user from the mixed received signals; channel estimation is performed using the user identification codes of each user, and the unknown noise variance and the number of active users are iteratively estimated based on the expectation-maximization algorithm; nonlinear estimation of data symbols is performed on the separated user signals to recover the communication data of each user; simultaneously, the relative distance and azimuth of each underwater unit and the surface unit are calculated based on the arrival delay of each user identification code, realizing simultaneous multi-user positioning; S3. Obtain the sound speed profile data of the current sea area; determine the optimal layering parameters using an adaptive layering algorithm based on the gradient characteristics of the sound speed profile; track the sound wave propagation path using ray tracing technology based on the determined optimal layering parameters, and calculate the sound ray bending correction amount; use the sound ray bending correction amount to compensate for the relative distance estimation result in step S2, and obtain the corrected positioning result. S4. Maintain the linear superposition of the identification sequence and the symbol sequence in the transmitted signal so that the identification sequence and the symbol sequence experience the same channel response; construct a channel correlation model between adjacent short blocks, divide the rapidly changing channel into multiple short blocks for connection processing; adopt a Gaussian likelihood-based channel estimation algorithm, use the channel information of multiple short blocks for lossless fusion, and estimate the channel of each short block; use a generalized approximate message passing algorithm for multiple iterative calculations to achieve channel equalization; S5. The user positioning results obtained in step S2 are fused with the communication data and output to achieve parallel processing of communication and positioning functions; when the positioning accuracy is lower than the preset threshold, it is corrected by sound ray bending compensation in step S3; when the communication bit error rate is higher than the preset threshold, it is optimized by channel equalization in step S4.

[0010] As a further improvement to this technical solution, in step S1, the modulation method of the transmitted signal adopts single-carrier transmission or multi-carrier transmission; the user identification code is selected from the Gold sequence or the orthogonal sequence family.

[0011] As a further improvement to this technical solution, in step S2, the minimum mean square error criterion framework includes a decoupling module, a nonlinear estimation module, and a parameter iterative estimation module; the decoupling module uses the correlation matrix of the user identification code to perform a linear transformation on the received signal to eliminate signal interference from other users; the nonlinear estimation module performs minimum mean square error estimation on the data symbols based on the prior information of the modulation constellation; and the parameter iterative estimation module iteratively updates the noise variance and the number of active users based on the expectation-maximization algorithm.

[0012] As a further improvement to this technical solution, in step S3, the adaptive layering algorithm dynamically adjusts the layering density according to the local gradient change rate of the sound velocity profile, using dense layering in regions with drastic gradient changes and sparse layering in regions with gentle gradient changes.

[0013] As a further improvement to this technical solution, in step S4, both the channel estimation and channel equalization steps are implemented using full FFT calculation.

[0014] As a further improvement to this technical solution, the collaborative control method also includes a multi-rate adaptive communication step: based on the current channel conditions, an appropriate coding rate is selected from 1 / 2, 1 / 4, 1 / 8, 1 / 16 and 1 / 16 concatenated coding rates for underwater acoustic communication.

[0015] As a further improvement to this technical solution, the coordinated control method also includes a Doppler estimation and compensation step: based on the multi-branch autocorrelation algorithm, multi-branch autocorrelation operation is performed using the user identification code of each underwater unit to achieve frame synchronization and Doppler estimation, and acceleration information is estimated to obtain Doppler estimation gain.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This deep-coupled underwater acoustic communication and positioning equipment and its collaborative control method for uncertain marine environments linearly superimposes user identification codes and communication data through a superimposed identification code scheme, enabling the same transmitted signal to simultaneously carry positioning and communication functions. This achieves deep coupling of communication and positioning at both the signal and algorithm levels, rather than the simple time-division multiplexing of existing technologies. It fundamentally solves the problem of conflict between communication and positioning functions, and improves the system's timeliness and spectrum utilization.

[0017] 2. The deep-coupled underwater acoustic communication positioning equipment and collaborative control method for uncertain marine environments is based on the minimum mean square error criterion framework. It uses the unique identification codes of different users to achieve decoupling and joint detection of multi-user signals. It can achieve simultaneous access of multiple users without complex handshake authorization protocols and is suitable for underwater vehicle cluster operation scenarios.

[0018] 3. The deep-coupled underwater acoustic communication positioning equipment and collaborative control method for uncertain marine environments adaptively optimizes the layering based on the gradient characteristics of the sound velocity profile, significantly reducing computational complexity while ensuring the accuracy of sound ray bending correction; at the same time, it predicts changes in the sound velocity profile through an adaptive algorithm, reducing positioning errors caused by local slow changes in the sound velocity profile. Attached Figure Description

[0019] The accompanying drawings described herein are for illustrative purposes only.

[0020] Figure 1 This is a schematic diagram of the underwater layout of the present invention; Figure 2 This is a schematic diagram of the k-th user communication and positioning integrated data frame structure of the present invention; Figure 3 This is a schematic diagram illustrating the principle of the combined multi-user detection and data detection of the present invention. Figure 4 This is a schematic diagram of the research technology route of the present invention; Figure 5 This is a flowchart of the collaborative control process of the present invention. Detailed Implementation

[0021] Under the guidance of this invention, any possible variations of this invention by those skilled in the art should be considered to fall within the scope of this invention.

[0022] Please see Figures 1-5 As shown, the present invention provides a deep-coupled underwater acoustic communication and positioning device for uncertain marine environments, including a surface unit, at least one underwater unit, and a synchronizer; the surface unit includes a transducer array consisting of an ultra-short baseline array composed of N receiving hydrophones and at least one transmitting transducer, the receiving hydrophones being used to receive acoustic signals transmitted by the underwater unit, and the transmitting transducer being used to transmit integrated communication and positioning waveform signals.

[0023] The first signal processing unit is electrically connected to the transducer array and is used to complete acoustic signal preprocessing, signal detection, positioning calculation, communication signal processing and data transmission; the number of receiving hydrophones in the transducer array is N=5, forming a five-element ultra-short baseline array.

[0024] The first signal processing unit includes an acoustic signal preprocessing unit, an acoustic signal transmitting unit, and a digital signal processing unit. The acoustic signal preprocessing unit is used to amplify and filter the received signal. The acoustic signal transmitting unit is used to amplify the transmitting excitation signal and then load it onto the transmitting transducer. The digital signal processing unit is used to perform digital signal processing, control management, and external communication.

[0025] The underwater unit includes a transceiver transducer and a second signal processing unit. The transceiver transducer is used for transmitting and receiving acoustic signals, and the signal processing unit is used for preprocessing analog received signals, processing communication signals, and transmitting positioning signals.

[0026] Synchronizers are used to achieve time synchronization between surface units and underwater units.

[0027] Specifically, the first signal processing unit of the surface unit and the second signal processing unit of the underwater unit are both equipped with a deeply coupled communication and positioning integrated algorithm module. The deeply coupled communication and positioning integrated algorithm module includes a communication and positioning waveform integrated sub-module based on superimposed identification code, which is used to generate a transmission signal frame containing a linear superposition of user identification code and communication data. The user identification code serves as a positioning signal, user detection signal, frame synchronization signal, channel estimation signal, and Doppler estimation and compensation signal. The multi-user detection and data detection submodule based on the minimum mean square error criterion is used to perform user detection and joint data estimation for multiple underwater units under the framework of the minimum mean square error criterion. An environment-adaptive real-time compensation submodule for acoustic ray bending is used to adaptively optimize the layering based on the gradient characteristics of the current sea area's acoustic velocity profile, and to correct the sound wave propagation distance based on ray tracing technology. The channel equalization module is used to perform real-time tracking and estimation of rapidly changing underwater channels based on the superimposed identification code scheme, and to achieve channel equalization.

[0028] The present invention provides a method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments. This method employs the coordinated control of such equipment and includes the following steps: S1. Assign a unique user identification code to each underwater unit, and construct the transmitted signal of the k-th user as a linear composite signal of the user identification code and communication data; when the user only has positioning needs, only the user identification code is transmitted without superimposed communication data; when the user has both communication and positioning needs, the superimposed signal of the user identification code and communication data is transmitted simultaneously; in step S1, the modulation method of the transmitted signal adopts single-carrier transmission or multi-carrier transmission; the user identification code is selected from the Gold sequence or the orthogonal sequence family.

[0029] S2. The surface unit receives composite signals transmitted by each underwater unit through a transducer array; under the minimum mean square error criterion, the received signals are decoupled for multiple users, separating the signal components of each user from the mixed received signals; channel estimation is performed using the user identification codes of each user, and the unknown noise variance and the number of active users are iteratively estimated based on the expectation-maximization algorithm; nonlinear estimation of data symbols is performed on the separated user signals to recover the communication data of each user; simultaneously, the relative distance and azimuth of each underwater unit and the surface unit are calculated based on the arrival delay of each user identification code, realizing simultaneous multi-user positioning; In step S2, the minimum mean square error criterion framework includes a decoupling module, a nonlinear estimation module, and a parameter iterative estimation module. The decoupling module uses the correlation matrix of the user identification code to perform a linear transformation on the received signal to eliminate signal interference from other users. The nonlinear estimation module performs minimum mean square error estimation on the data symbols based on the prior information of the modulation constellation. The parameter iterative estimation module iteratively updates the noise variance and the number of active users based on the expectation-maximization algorithm.

[0030] S3. Obtain the sound speed profile data of the current sea area; determine the optimal layering parameters using an adaptive layering algorithm based on the gradient characteristics of the sound speed profile; track the sound wave propagation path using ray tracing technology based on the determined optimal layering parameters, and calculate the sound ray bending correction amount; use the sound ray bending correction amount to compensate for the relative distance estimation result in step S2, and obtain the corrected positioning result; in step S3, the adaptive layering algorithm dynamically adjusts the layering density according to the local gradient change rate of the sound speed profile, using dense layering in areas with drastic gradient changes and sparse layering in areas with gentle gradient changes.

[0031] S4. Maintain the linear superposition of the identification sequence and the symbol sequence in the transmitted signal so that the identification sequence and the symbol sequence experience the same channel response; construct a channel correlation model between adjacent short blocks, divide the rapidly changing channel into multiple short blocks for connection processing; adopt a Gaussian likelihood-based channel estimation algorithm, use the channel information of multiple short blocks for lossless fusion, and estimate the channel of each short block; use a generalized approximate message passing algorithm for multiple iterative calculations to achieve channel equalization; S5. The user positioning results obtained in step S2 are fused with the communication data and output to achieve parallel processing of communication and positioning functions; when the positioning accuracy is lower than the preset threshold, it is corrected by sound ray bending compensation in step S3; when the communication bit error rate is higher than the preset threshold, it is optimized by channel equalization in step S4.

[0032] The coordinated control method also includes a multi-rate adaptive communication step: based on the current channel conditions, an appropriate coding rate is selected from 1 / 2, 1 / 4, 1 / 8, 1 / 16 and 1 / 16 concatenated coding rates for underwater acoustic communication.

[0033] The coordinated control method also includes a Doppler estimation and compensation step: based on the multi-branch autocorrelation algorithm, multi-branch autocorrelation operation is performed using the user identification code of each underwater unit to achieve frame synchronization and Doppler estimation, and acceleration information is estimated to obtain Doppler estimation gain.

[0034] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to illustrate the present invention and should not be regarded as limiting the scope of the present invention.

[0035] like Figure 4 As shown, the project will be implemented in three phases: completing the integrated waveform design for communication and positioning, developing high-precision positioning technology based on the integrated waveform, and developing remote communication technology. Simulation and experimental verification studies of the individual technologies and the integrated technology will also be conducted. Based on the theoretical, simulation, and experimental results of the integrated communication and positioning algorithm, a corresponding real-time implementation hardware platform was developed. Extensive experimental verification improved the performance of the integrated communication and positioning equipment. Develop demonstration cases to promote the industrialization of integrated communication and positioning equipment. Ultimately, achieve all agreed-upon project targets.

[0036] Example 1: Integrated waveform generation for communication and positioning, and joint detection by multiple users; This embodiment details the integrated waveform generation method for communication and positioning based on superimposed identification codes and the multi-user joint detection method based on the minimum mean square error criterion of the present invention.

[0037] 1.1 Integrated communication and positioning data frame structure; like Figure 2 As shown, taking the k-th user as an example, its transmitted signal is a linear composite of the k-th user identification code and communication data. The user identification code can be a Gold sequence and has multiple uses: as a location signal for the k-th user; for user detection of the k-th user; for frame synchronization; for channel estimation; and for Doppler estimation and compensation.

[0038] In practical applications, each user is assigned a unique identification code when equipped with the integrated communication and positioning system. The overlay ratio of the user identification code and communication data in the transmitted signal can be dynamically adjusted according to actual needs. When a user only requires positioning, only the user identification code needs to be transmitted without overlaying communication data.

[0039] The modulation method of the transmitted signal can be selected as single-carrier transmission or multi-carrier transmission (such as OFDM) according to actual needs. In shallow water and short-distance scenarios, single-carrier transmission can be used to reduce the peak-to-average power ratio; in deep water and long-distance scenarios, multi-carrier transmission can be used to improve spectral efficiency.

[0040] 1.2 - Joint multi-user detection and data detection based on the minimum mean square error criterion; like Figure 3 As shown, this embodiment uses the minimum mean square error criterion algorithm to jointly estimate user activities and user data.

[0041] Assume the system has a total of There are [number] potential users, and the actual number of active users is [number]. ( ≤ The received signal can be represented as: ; in, For the first Channel response for each user For the first The user's identification code (known) For the first Communication data of individual users (unknown). It is noise.

[0042] Under the minimum mean square error criterion framework, the joint detection process includes three modules: Module A - Decoupling Module: Utilizing the orthogonality between user identification codes, this module decouples the received multi-user mixed signal, separating the signal components of each user from the mixed signal. Specifically, based on the minimum mean square error (MMSE) criterion, a linear transformation matrix is ​​constructed using the user identification codes to filter the received signal. This process suppresses interference from other users while also considering noise effects, thereby obtaining optimized separation estimates for each user's signal.

[0043] Module B - Nonlinear Estimation Module: Performs nonlinear estimation of data symbols for each decoupled user signal. Since communication data symbols are taken from a limited set of modulation constellations, such as BPSK and QPSK, a nonlinear estimator, such as a minimum mean square error estimator, can fully utilize this prior information to improve estimation accuracy.

[0044] Module C - Parameter Iterative Estimation Module: This module uses the Expectation-Maximization (EM) algorithm to iteratively estimate the unknown noise variance and the number of active users. In each iteration, the posterior probabilities of user activity and data are first calculated based on the current parameter estimates, and then the estimates of noise variance and the number of active users are updated based on these posterior probabilities.

[0045] Through iterative calculations of the above three modules, we can simultaneously obtain: communication data of each user; channel response of each user, used for location calculation; and system noise level estimation.

[0046] 1.3 Performance Verification; In a typical multi-user scenario =10, With a signal-to-noise ratio (SNR) of 4 and a signal-to-noise ratio (SNR) of 5dB, the method described in this embodiment achieves a user detection success rate of over 95% and a bit error rate of less than 10%. -3 The order of magnitude is small, and the number of iterations converges no more than 10, which meets the requirements for real-time processing.

[0047] Example 2: Adaptive compensation method for acoustic ray bending; This embodiment details the environmentally adaptive real-time compensation method for acoustic ray bending in this invention.

[0048] 2.1 Analysis of the problem of vocal tract bending; In water, the speed of sound is non-uniform, influenced by factors such as temperature, salinity, and depth, exhibiting a gradient distribution with depth. When sound waves propagate in a non-uniform medium, according to Snell's law, the propagation direction deflects with the change in sound speed, causing the sound ray to bend. In practical positioning systems, relying on average sound speed or plane wave incidence assumptions for distance estimation can lead to significant distance estimation errors.

[0049] Specifically, the positioning errors caused by sound ray bending mainly include: sound ray bending error caused by non-uniform sound velocity distribution; time-varying error caused by the change of sound velocity profile over time; and Doppler effect error caused by high-speed relative motion between the positioning system and the target.

[0050] 2.2 Adaptive Layered Vocal Correction Method; The adaptive compensation method for acoustic ray bending in this embodiment includes the following steps: Step S31: Obtain sound velocity profile data; Acquire sound velocity profile data for the current sea area using a temperature, salinity, and depth (CTD) meter; that is, a discrete sequence of sampling points showing the variation of sound velocity with depth. , },in For the speed of sound, For depth.

[0051] Step S32: Analysis of sound velocity profile gradient characteristics; Calculate the gradient of the sound velocity profile = c / z and its rate of change = c / Regions with drastic gradient changes, such as the thermocline which significantly affects the curvature of sound ray, require fine-grained layering; regions with gentle gradient changes can be appropriately coarsened in layering.

[0052] Step S33: Adaptive optimal stratification; Based on the gradient characteristic analysis results, the optimal stratification parameters are determined. Specifically: at the gradient rate of change | For regions where the thickness is greater than the first threshold, dense layering is adopted, with a layer thickness Δz ≤ 1m; at the gradient rate of change | For regions where | is less than the second threshold and the second threshold is less than or equal to the first threshold, sparse layering is adopted with a layer thickness Δz ≥ 5m. In the region where the gradient change rate is between the first and second thresholds, a medium-density stratification is adopted, where 1m < Δz < 5m.

[0053] By employing this adaptive layering strategy, the total number of layers can be significantly less than that of dense uniform layering techniques, while ensuring the accuracy of sound ray bending correction. This greatly reduces the computational power required for real-time implementation.

[0054] Step S34: Voice tracking and distance correction; Based on the determined optimal layering parameters and constant gradient ray tracking model, ray tracing technology is used to track the sound wave propagation path. Within each layer, assuming that the sound speed varies linearly with depth, the propagation direction and propagation time of the sound ray in that layer are calculated according to Snell's law, and the total propagation time and horizontal propagation distance are obtained by accumulating them layer by layer.

[0055] The horizontal propagation distance calculated by ray tracing is compared with the straight-line distance estimated based on the average sound speed to obtain the ray curvature correction amount ΔR = R_straight_line - R_ray. This correction amount is then used to compensate for the original positioning results.

[0056] Step S305: Update sound velocity profile prediction; Considering that the sound velocity profile changes slowly over time, this embodiment also employs an adaptive algorithm to predict the changes in the sound velocity profile. Specifically, based on historical sound velocity profile data sequences, an autoregressive model or Kalman filter method is used to predict the sound velocity profile at the current moment, and the predicted profile is used to replace the measured profile for sound ray correction, thereby reducing the problem of increased positioning error caused by local slow changes in the sound velocity profile.

[0057] 2.3 Performance Verification In a typical deep-water scenario with a water depth of 1000m and a horizontal distance of 2.5km, the adaptive layered acoustic correction method of this embodiment achieves acoustic curvature correction accuracy comparable to that of dense and uniform layering with 100 layers, while reducing computational complexity by approximately 60%. The positioning accuracy can reach the technical specification of a distance error not exceeding 0.45% of the straight-line distance.

[0058] Example 3: Channel equalization method; This embodiment details the real-time extreme tracking and accurate estimation method for rapidly changing channels in this invention.

[0059] 3.1 Characteristics and Challenges of Underwater Acoustic Channels; Underwater acoustic communication systems often operate under conditions of low signal-to-noise ratio, severe multipath interference, and high dynamic motion. The relative speeds of underwater moving platforms (such as AUVs and UUVs) can reach 6 knots or even higher, leading to severe time-varying Doppler effects and multipath interference, significantly degrading the performance of underwater acoustic communication. Traditional channel estimation algorithms struggle to achieve accurate channel tracking and equalization under rapidly changing channel conditions.

[0060] 3.2-A fast-changing channel tracking method based on superimposed identification codes; The channel equalization method in this embodiment includes the following steps: Step S41: Design of superimposed identification code signal; At the transmitting end, the identification sequence and the symbol sequence are linearly superimposed. Let the... The transmitted signals of each user are: ; in, Indicates the first The complex envelope of the baseband transmitted signal of a user at a given time. This signal simultaneously contains positioning / identification information and communication information; Indicates the first The user identification sequence, also known as the user identification code, such as the Gold sequence, is completely known to the surface units at the receiving end. Its main functions are: serving as a positioning reference signal; for multi-user detection; and for channel estimation and Doppler estimation. This sequence typically exhibits good autocorrelation and orthogonality. Indicates the first A sequence of communication data symbols to be transmitted by a user. This sequence is unknown to the receiving end and needs to be recovered through demodulation and decoding. Indicates the first Power allocation factor for each user (0 < <1); and These are used to control the transmission power of the identification sequence and the communication data sequence, respectively. In a practical system, It can be dynamically adjusted according to channel conditions: when the channel changes drastically, the increase can be appropriately increased. To improve channel estimation accuracy; when channel conditions are good, the cost can be reduced. This frees up more power for data transmission.

[0061] The core advantage of this superimposed identification code scheme lies in: identifying sequences and symbol sequence They occupy exactly the same time and spectrum resources and are transmitted simultaneously in a linear superposition manner. Therefore, the underwater acoustic channel responses experienced by both during propagation are completely identical. This fundamentally overcomes the channel estimation mismatch problem caused by the different channel states experienced by the identification sequence and symbol sequence due to their transmission in different time slots in traditional time division multiplexing (TDM) identification schemes, especially under rapidly changing channels.

[0062] Step S42: Fast-changing channel segmentation and correlation modeling The continuous data stream is divided into multiple short blocks, each containing several symbol periods. A channel correlation model is constructed between adjacent short blocks, and the channel response between adjacent short blocks can be expressed as: ; in, For the first The channel response of a short block; Indicates the adjacent number +1 short block of channel response; This represents the channel time correlation coefficient between adjacent short blocks (0 < 0). ≤1); The closer to 1, the slower the channel changes and the stronger the correlation; conversely, The smaller the value, the faster the channel changes; For innovative noise, it represents the first The short block to the first Between +1 short blocks, the portion of the channel where uncorrelated changes occur, i.e., innovative noise or channel increment. This variable is typically modeled as having a mean of zero and a variance of... The complex Gaussian random variable is used to describe unpredictable channel variations caused by factors such as random multipath, turbulence, etc.

[0063] This modeling approach transforms the continuous changes in a rapidly changing channel into a recursive relationship between short blocks, providing a theoretical basis for subsequent channel information fusion.

[0064] Step S43: Accurate channel estimation based on Gaussian likelihood; A precise underwater acoustic channel estimation algorithm based on Gaussian likelihood (GL) is adopted. The core idea of ​​this algorithm is to construct the channel correlation between adjacent short blocks, efficiently and losslessly fuse the channel information of multiple short blocks, and use the information of the entire data block to estimate the channel response of each short block.

[0065] Specifically, the channel estimation problem for the entire data block is modeled as a maximum likelihood estimation problem: ; in, This represents the sequence of responses to all short channel blocks. The optimal estimate; it is the channel estimation result finally output by the algorithm; This represents the unknown variable to be estimated, which is the set of channel responses of all short blocks contained in the entire data frame; This represents the total number of short blocks divided within a complete data frame (or observation time window); This represents the complex envelope of the baseband received signal received by the receiver, corresponding to the i-th short block; Let represent the likelihood function; it describes the probability distribution of the received signal given the current short-block channel response . This probability density function depends on the statistical properties of the noise in the system. Under the assumption of Gaussian white noise, the likelihood function takes the form of a complex Gaussian distribution with as the variable. This represents the channel state transition probability density function. It describes the channel response in the preceding short block as... Under these conditions, the current short block channel response is transferred to The probability of transition. This transition probability strictly follows the first-order autoregressive model in step S42, i.e. Depend on Add a Gaussian increment get.

[0066] Due to the likelihood function and state transition probability All values ​​are Gaussian distributed, and the maximum likelihood estimation problem described above can be solved using efficient Kalman smoothing or Viterbi algorithms with closed-form solutions. In engineering implementation, all multiplication operations in this algorithm can be converted to the frequency domain, and batch computation can be performed using Fast Fourier Transform (FFT), reducing the total computational complexity to only a fraction of the standard value. ,in With a short block length, it can meet the requirements of real-time processing in underwater acoustic communication.

[0067] By solving the maximum likelihood estimation problem described above, this invention can use the energy of the entire data block to estimate the channel information of each short block. Compared with estimation algorithms that rely solely on a single identification sequence, it can achieve higher processing gain, thereby enabling accurate tracking of rapidly changing channels.

[0068] Step S44: Generalized approximate message passing channel equalization; Channel equalization is achieved using the underwater acoustic generalized approximate message passing (GAMP) algorithm. This algorithm preserves the form of discrete independent random variables of adjacent symbols and achieves efficient and reliable channel equalization by merging internal and external iterations and performing multiple iterations, utilizing coded redundancy information.

[0069] Specifically, in each iteration, the GAMP algorithm: transmits messages from the symbol node to the factor node; transmits messages from the factor node to the symbol node, i.e., the observation information of the channel; and calculates the posterior probability of the symbol based on the messages in both directions.

[0070] Through multiple iterations, typically 5-10 times, the GAMP algorithm can fully utilize the redundant information in the encoding, significantly improving the reliability of channel equalization. The channel equalization process is also entirely implemented using FFT calculations, making it compatible with current mainstream underwater acoustic communication development platforms.

[0071] Step S45: Multi-rate adaptive communication; The coding rate is adaptively selected based on the current channel conditions. This embodiment supports multiple coding rates, including 1 / 2, 1 / 4, 1 / 8, 1 / 16, and 1 / 16 concatenated coding rates. When channel conditions are good (high signal-to-noise ratio), a high coding rate of 1 / 2 is selected to improve the communication rate; when channel conditions are poor (low signal-to-noise ratio, strong multipath support), a low coding rate of 1 / 16 or 1 / 16 concatenated coding rates is selected to ensure communication reliability.

[0072] 3.3 Performance Verification In a typical high-dynamic scenario, with a relative speed of 6 knots, a signal-to-noise ratio (SNR) of 0 dB, and a multipath delay spread of 50 ms, the bit error rate can be reduced to less than 10% using the fast-changing channel tracking and equalization method described in this embodiment. -4 The communication speed can reach 20-2400bps, and the maximum communication distance can reach 10km.

[0073] Example 4: Deeply Coupled Communication and Positioning Integrated Equipment; This embodiment details the hardware implementation of the deeply coupled communication and positioning integrated equipment of the present invention.

[0074] 4.1 System Overall Architecture; like Figure 1 As shown, the deep-coupled communication and positioning integrated equipment of this embodiment includes a surface unit and at least one underwater unit.

[0075] The water surface unit includes: Transducer array: Composed of a five-element ultra-short baseline array consisting of five receiving hydrophones for receiving acoustic signals transmitted by the underwater unit; and one transmitting transducer for transmitting integrated communication and positioning waveform signals. Unlike traditional three-element and four-element arrays, the ultra-short baseline positioning system constructed with the five-element array has stronger time delay error detection capability, which can improve the robustness of the entire positioning system in complex positioning scenarios.

[0076] The signal processing unit includes an acoustic signal preprocessing unit, an acoustic signal transmission unit, and a digital signal processing unit. The acoustic signal preprocessing unit amplifies and filters the output signal from the hydrophone; the signal is then bandpass filtered and amplified before being input to the A / D converter. The acoustic signal transmission unit first amplifies the transmission excitation signal and then loads it onto the transmitting transducer through a network matching circuit to complete the transmission. The digital signal processing unit handles the signal processing and control of the entire equipment, including digital signal processing, control management, and external communication.

[0077] Synchronizer: Used to achieve time synchronization between surface unit and underwater unit. The two work in synchronization mode, thereby improving the positioning accuracy and efficiency of the system.

[0078] The underwater unit includes a transceiver transducer for transmitting and receiving acoustic signals; and a signal processing unit for preprocessing analog received signals, processing communication signals, and transmitting positioning signals.

[0079] 4.2 - Deeply Coupled Integrated Algorithm Module; Both the signal processing units of the surface unit and the underwater unit are equipped with a deeply coupled communication and positioning integrated algorithm module; a multi-user detection and data detection submodule based on MMSE; and an environment-adaptive real-time acoustic ray bending compensation submodule.

[0080] 4.3 - Workflow; The workflow of the deeply coupled communication and positioning integrated equipment in this embodiment is as follows: Step S51: System initialization; The surface unit establishes time synchronization with each underwater unit through a synchronizer; each underwater unit is assigned a unique user identification code; the system sets integrated communication and positioning parameters according to operational requirements, such as power allocation factor and coding rate.

[0081] Step S52: Signal transmission; Each underwater unit generates an integrated communication and positioning waveform signal based on the assigned identification code and the communication data to be transmitted, and transmits it through a shared transceiver transducer.

[0082] Step S53: Signal reception and processing; The surface unit receives signals from each underwater unit through a five-element ultra-short baseline array. After acoustic signal preprocessing, the signals are sent to the digital signal processing unit. The digital signal processing unit runs a deeply coupled communication and positioning integrated algorithm module, which simultaneously performs multi-user detection, data demodulation, time delay estimation, and positioning calculation.

[0083] Step S54: Output the result; The surface unit outputs the positioning results (distance, azimuth, depth) and communication data of each underwater unit, realizing integrated communication and positioning coordination of the underwater cluster.

[0084] 4.4 - Industrial Application; The deeply coupled communication and positioning integrated equipment developed in this embodiment can be widely used in marine development and marine engineering fields such as underwater rescue, marine grain storage, coastal surveillance, underwater swarm operations, and deep-sea exploration. Field tests have verified that the positioning accuracy of this equipment is no higher than 0.45% of the straight-line distance, the directional deviation is better than 0.24 degrees, the positioning distance is no less than 2.5 km, and the positioning success rate is no less than 92%. The communication rate is 20-2400 bps, the maximum communication distance is 10 km, and reliable communication is possible under relative speed conditions of 6 knots.

[0085] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A deep-coupled underwater acoustic communication and positioning device for uncertain marine environments, comprising a surface unit and at least one underwater unit; the surface unit comprises a transducer array consisting of an ultra-short baseline array composed of N receiving hydrophones and at least one transmitting transducer, wherein the receiving hydrophones are used to receive acoustic signals transmitted by the underwater unit, and the transmitting transducer is used to transmit integrated communication and positioning waveform signals; The first signal processing unit is electrically connected to the transducer array and is used to perform acoustic signal preprocessing, signal detection, positioning calculation, communication signal processing and data transmission. The underwater unit includes a transceiver transducer and a second signal processing unit. The transceiver transducer is used for transmitting and receiving acoustic signals, and the signal processing unit is used for preprocessing analog received signals, processing communication signals, and transmitting positioning signals. Its features are: Both the first signal processing unit of the surface unit and the second signal processing unit of the underwater unit are equipped with a deeply coupled communication and positioning integrated algorithm module. The deeply coupled communication and positioning integrated algorithm module includes a communication and positioning waveform integrated submodule based on superimposed identification code, which is used to generate a transmission signal frame containing a user identification code and communication data linearly superimposed. The user identification code serves as a positioning signal, a user detection signal, a frame synchronization signal, a channel estimation signal, and a Doppler estimation and compensation signal. The multi-user detection and data detection submodule based on the minimum mean square error criterion is used to perform user detection and joint data estimation for multiple underwater units under the framework of the minimum mean square error criterion. An environment-adaptive real-time compensation submodule for acoustic ray bending is used to adaptively optimize the layering based on the gradient characteristics of the current sea area's acoustic velocity profile, and to correct the sound wave propagation distance based on ray tracing technology. The channel equalization module is used to perform real-time tracking and estimation of underwater fast-changing channels based on the superimposed identification code scheme, and to achieve channel equalization.

2. The deep-coupled underwater acoustic communication and positioning equipment for uncertain marine environments according to claim 1, characterized in that: The surface unit also includes a synchronizer for time synchronization between the surface unit and the underwater unit; the number of hydrophones receiving the transducer array is N=5, forming a five-element ultra-short baseline array.

3. The deep-coupled underwater acoustic communication and positioning equipment for uncertain marine environments according to claim 1, characterized in that: The first signal processing unit includes an acoustic signal preprocessing unit, an acoustic signal transmitting unit, and a digital signal processing unit; the acoustic signal preprocessing unit is used to amplify and filter the received signal; the acoustic signal transmitting unit is used to amplify the transmitting excitation signal and then load it onto the transmitting transducer; the digital signal processing unit is used to perform digital signal processing, control management, and external communication.

4. A method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments, employing the deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments as described in claim 3, characterized in that... Includes the following steps: S1. Assign a unique user identification code to each underwater unit, and construct the transmission signal of the k-th user as a linear composite signal of user identification code and communication data; when the user only has positioning needs, only the user identification code is transmitted without superimposed communication data; when the user has both communication and positioning needs, the superimposed signal of user identification code and communication data is transmitted simultaneously. S2. The surface unit receives the composite signal transmitted by each underwater unit through the transducer array; under the minimum mean square error criterion framework, the received signal is decoupled by multiple users to separate the signal components of each user from the mixed received signal. Channel estimation is performed using each user's identification code, and the unknown noise variance and number of active users are iteratively estimated based on the expectation-maximization algorithm. Nonlinear estimation of data symbols is performed on the separated user signals to recover the communication data of each user. At the same time, the relative distance and orientation of each underwater unit and the surface unit are calculated based on the arrival delay of each user's identification code to achieve simultaneous positioning of multiple users. S3. Obtain the sound velocity profile data of the current sea area; determine the optimal layering parameters using an adaptive layering algorithm based on the gradient characteristics of the sound velocity profile; and use ray tracing technology to track the sound wave propagation path based on the determined optimal layering parameters, and calculate the sound ray bending correction amount. The relative distance estimation result in step S2 is compensated by the sound ray bending correction amount to obtain the corrected positioning result; S4. Maintain the linear superposition of the identification sequence and the symbol sequence in the transmitted signal so that the identification sequence and the symbol sequence experience the same channel response; construct a channel correlation model between adjacent short blocks, divide the rapidly changing channel into multiple short blocks for connection processing; adopt a Gaussian likelihood-based channel estimation algorithm, use the channel information of multiple short blocks for lossless fusion, and estimate the channel of each short block; use a generalized approximate message passing algorithm for multiple iterative calculations to achieve channel equalization; S5. The user positioning results obtained in step S2 are fused with the communication data and output to achieve parallel processing of communication and positioning functions; when the positioning accuracy is lower than the preset threshold, it is corrected by sound ray bending compensation in step S3; when the communication bit error rate is higher than the preset threshold, it is optimized by channel equalization in step S4.

5. The method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments according to claim 4, characterized in that: In step S1, the modulation method of the transmitted signal adopts single-carrier transmission or multi-carrier transmission; the user identification code is selected from the Gold sequence or the orthogonal sequence family.

6. The method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments according to claim 5, characterized in that: In step S2, the minimum mean square error criterion framework includes a decoupling module, a nonlinear estimation module, and a parameter iterative estimation module. The decoupling module uses the correlation matrix of the user identification code to perform a linear transformation on the received signal to eliminate signal interference from other users. The nonlinear estimation module performs minimum mean square error estimation on the data symbols based on the prior information of the modulation constellation. The parameter iterative estimation module iteratively updates the noise variance and the number of active users based on the expectation-maximization algorithm.

7. The method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments according to claim 6, characterized in that: In step S3, the adaptive layering algorithm dynamically adjusts the layering density according to the local gradient change rate of the sound velocity profile, using dense layering in regions with drastic gradient changes and sparse layering in regions with gentle gradient changes.

8. The method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments according to claim 7, characterized in that: In step S4, both channel estimation and channel equalization are implemented using full FFT calculation.

9. The method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments according to claim 8, characterized in that, The coordinated control method also includes a multi-rate adaptive communication step: based on the current channel conditions, an appropriate coding rate is selected from 1 / 2, 1 / 4, 1 / 8, 1 / 16 and 1 / 16 concatenated coding rates for underwater acoustic communication.

10. The method for coordinated control of deeply coupled underwater acoustic communication and positioning equipment in uncertain marine environments according to claim 9, characterized in that, The coordinated control method also includes a Doppler estimation and compensation step: based on the multi-branch autocorrelation algorithm, multi-branch autocorrelation operation is performed using the user identification code of each underwater unit to achieve frame synchronization and Doppler estimation, and acceleration information is estimated to obtain Doppler estimation gain.

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