Underwater positioning system based on multi-parameter fusion and sound channel multipath suppression

Abstract

The invention provides an underwater positioning system based on multi-parameter fusion and sound channel multipath suppression. The system comprises a multi-source parameter acquisition module, an environment perception modeling module, a sound channel multipath identification suppression module, a multi-parameter fusion observation construction module and a cooperative positioning calculation module. The multi-source parameter acquisition module is used for acquiring multi-dimensional parameters required by underwater target positioning; the environment sensing modeling module is used for acquiring underwater environment parameters and constructing a sound velocity propagation model; the sound channel multi-path identification suppression module is used for carrying out filtering processing identification on acoustic echo signals in the multi-dimensional parameters and suppressing multi-path components; the multi-parameter fusion observation construction module is used for carrying out modeling fusion on the suppressed acoustic measurement parameters and constructing a comprehensive observed quantity; the cooperative positioning resolving module is used for resolving and positioning the underwater target in combination with the comprehensive observed quantity; according to the invention, automatic discrimination and adaptive utilization of reliable observation can be realized in a complex underwater sound propagation environment.

Description

Technical Field

[0001] This invention relates to the field of underwater positioning and navigation technology, and in particular to an underwater positioning system based on multi-parameter fusion and acoustic multipath suppression. Background Technology

[0002] With the development of marine exploration, underwater operations and intelligent equipment, accurate positioning of underwater targets has become one of the key technologies in underwater communication, navigation and collaborative operations. Due to the severe attenuation of electromagnetic waves in the underwater environment, underwater acoustic signals have become the main technical means to realize underwater information perception and positioning. However, the underwater acoustic propagation process is easily affected by changes in the sea surface, seabed and water structure, resulting in multiple propagation paths for the acoustic signal during propagation, which makes the received signal exhibit obvious time delay and structural complexity.

[0003] In practical applications, underwater acoustic positioning systems often rely on information such as propagation time and spatial geometric relationships to calculate the target location. However, complex propagation environments can significantly reduce the reliability of observation information, thereby affecting positioning accuracy and stability. Especially in application scenarios such as shallow seas, multiple obstacles, or frequent environmental changes, traditional positioning methods based on single observations or fixed weights are difficult to balance positioning accuracy and system robustness, and are easily affected by abnormal observations or unstable propagation conditions.

[0004] Therefore, how to effectively distinguish different propagation characteristics under complex underwater acoustic propagation conditions and make reasonable use of the reliability of observation information in the positioning calculation process has become a technical problem that urgently needs to be solved to improve the performance of underwater positioning systems.

[0005] A review of publicly available technical solutions reveals that CN110703206A proposes an integrated underwater UUV communication and positioning system, comprising: one or more beacons and a positioning module mounted on the underwater UUV; the beacon includes: a chassis, a first depth sensor, an underwater acoustic communication transducer A, and an underwater acoustic communication transducer deployment and recovery device; the positioning module mounted on the underwater UUV includes: an underwater acoustic communication transducer B and a communication and positioning module. This invention achieves integrated underwater acoustic communication and positioning through software processing without increasing the hardware scale of underwater UUVs. Furthermore, by employing direct-sequence spread spectrum (DSS) signals as the signal form for underwater acoustic positioning and ranging, and high-speed OFDM communication signals as the signal form for transmitting (compressed) video data, the integrated communication and positioning design improves the usability of video data while ensuring its transmission. However, this scheme primarily focuses on the integrated implementation of communication and positioning functions, without specifically modeling and suppressing multipath interference in complex underwater acoustic propagation environments, nor introducing a positioning solution mechanism based on observation reliability or propagation consistency. Therefore, positioning stability and accuracy are limited in scenarios with significant multipath effects or large fluctuations in observation quality. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of current systems by proposing an underwater positioning system based on multi-parameter fusion and acoustic multipath suppression.

[0007] The present invention adopts the following technical solution:

[0008] An underwater positioning system based on multi-parameter fusion and acoustic multipath suppression is disclosed. The system includes a multi-source parameter acquisition module, an environmental perception and modeling module, an acoustic multipath identification and suppression module, a multi-parameter fusion observation construction module, and a cooperative positioning and calculation module. The multi-source parameter acquisition module is used to acquire multi-dimensional parameters required for underwater target positioning. The environmental perception and modeling module is used to acquire underwater environmental parameters and construct a sound speed propagation model. The acoustic multipath identification and suppression module is used to filter and identify and suppress multipath components in the acoustic echo signals of the multi-dimensional parameters. The multi-parameter fusion observation construction module is used to model and fuse the suppressed acoustic measurement parameters to construct a comprehensive observation. The cooperative positioning and calculation module is used to calculate and locate the underwater target by combining the comprehensive observation.

[0009] The multi-source parameter acquisition module includes multiple hydrophone nodes distributed in the underwater space to achieve collaborative acquisition of multi-dimensional parameters. Each hydrophone node includes an acoustic signal transceiver unit, a time synchronization unit, and an attitude and depth acquisition unit. The acoustic signal transceiver unit is used to transmit positioning acoustic signals and receive acoustic echo signals from underwater targets or other hydrophone nodes. The time synchronization unit is used to synchronize and calibrate the internal clock of each hydrophone node. The attitude and depth acquisition unit is used to acquire the attitude and depth information of the hydrophone node itself.

[0010] Furthermore, the environmental perception modeling module includes an environmental parameter acquisition unit and a sound velocity profile construction unit; the environmental parameter acquisition unit is used to acquire environmental parameters of the underwater environment, including temperature, salinity, and depth; the sound velocity profile construction unit constructs an underwater sound velocity propagation model based on the underwater environmental parameters.

[0011] Furthermore, the multipath recognition and suppression module includes a feature extraction unit, a candidate propagation path modeling unit, a multipath consistency discrimination unit, and a confidence evaluation unit. The feature extraction unit is used to filter and demodulate the acoustic echo signal acquired by the acoustic signal transceiver unit, converting the continuous acoustic echo signal into a discrete signal vector representation. The candidate propagation path modeling unit is used to construct a set of candidate propagation paths describing the possible propagation modes of the acoustic signal based on the prior spatial relationship between the hydrophone node and the underwater target and the underwater sound speed propagation model, and to represent the propagation path set as a computable path dictionary structure. The multipath consistency discrimination unit is used to represent the signal vector acquired by the feature extraction unit as a superposition of multiple propagation paths under the constraints of the candidate propagation path set, and to solve for the optimal propagation structure estimation result that conforms to the direct propagation characteristics. The confidence evaluation unit is used to evaluate the reliability of the propagation paths that conform to the direct propagation characteristics in the optimal propagation structure estimation result and output the direct propagation confidence.

[0012] Furthermore, the multi-parameter fusion observation construction module includes an observation modeling unit and a confidence-driven weight construction unit. The observation modeling unit is used to convert the direct propagation delay parameter into the observation expression form required for positioning calculation, and to perform unified modeling with other observations obtained in the system. The confidence-driven weight construction unit is used to adaptively adjust the weight of the corresponding observation in the integrated observation based on the propagation confidence output by the channel multipath recognition and suppression module, so that the observation weight changes dynamically with the propagation reliability.

[0013] Furthermore, the collaborative positioning solution module includes a state prediction unit and a positioning solution unit; the state prediction unit is used to predict the current position state of the underwater target based on historical positioning results and motion models, as a prior constraint for positioning solution; the positioning solution unit is used to perform weighted solution of the position state of the underwater target under the prior constraint, combined with the observations and their weight information output by the multi-parameter fusion observation construction module.

[0014] Furthermore, the multipath consistency discrimination unit solves for the propagation structure using the following optimization objective function:

[0015] ;

[0016] in, The obtained optimal propagation structure estimation results are used to characterize the contribution magnitude of each candidate propagation path to the received signal; The signal vector obtained by the feature extraction unit. The candidate propagation path dictionary matrix; This is the set of complex amplitude coefficients corresponding to the candidate propagation paths, used to characterize the contribution of each candidate propagation path to the received signal; is a sparse regularization coefficient used to limit the number of effective propagation paths; The number of candidate propagation paths; This is the gating penalty coefficient, used to enhance the guiding role of direct propagation priors in the solution process; The direct propagation delay prediction value obtained a priori; For the first The amplitude coefficients corresponding to each candidate propagation path; For the first The gating weight function of the candidate propagation path satisfies:

[0017] ;

[0018] in, For the first The propagation delay parameters corresponding to the candidate propagation paths, This is the gating width parameter, used to reflect the uncertainty of propagation delay prediction.

[0019] The beneficial effects achieved by this invention are:

[0020] This invention introduces a collaborative positioning mechanism driven by propagation structure discrimination and confidence level during underwater positioning. First, it stably identifies valid observations that conform to direct propagation characteristics under complex underwater acoustic propagation conditions. Then, in the positioning calculation stage, it adaptively weights the observation information based on propagation reliability, thereby effectively suppressing the interference of multipath propagation on the positioning results. Through the coordinated cooperation between the front-end propagation characteristic discrimination and the back-end positioning calculation process, this invention can maintain the stability and continuity of positioning results in environments with significant multipath effects and large fluctuations in observation quality, thereby improving the robustness and overall accuracy of underwater target positioning. Attached Figure Description

[0021] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0022] Figure 1 This is a schematic diagram of the overall modules of the present invention.

[0023] Figure 2 This is a schematic diagram of the working process of the multipath recognition and suppression module of the present invention.

[0024] Figure 3 This is a schematic diagram comparing the positioning error of the present invention and the traditional solution over time.

[0025] Figure 4 This diagram illustrates the comparison between the present invention's solution and the traditional solution in terms of average positioning error. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to its embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. Other systems, methods, and / or features of this embodiment will become apparent to those skilled in the art after reviewing the following detailed description. It is intended that all such additional systems, methods, features, and advantages are included within this specification, are included within the scope of the present invention, and are protected by the appended claims. Further features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0027] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0028] Example 1: As Figure 1 , Figure 2 As shown in the figure, this embodiment provides an underwater positioning system based on multi-parameter fusion and acoustic multipath suppression. The system includes a multi-source parameter acquisition module, an environmental perception modeling module, an acoustic multipath identification and suppression module, a multi-parameter fusion observation construction module, and a cooperative positioning solution module. The multi-source parameter acquisition module is used to acquire multi-dimensional parameters required for underwater target positioning. The environmental perception modeling module is used to acquire underwater environmental parameters and construct a sound speed propagation model. The acoustic multipath identification and suppression module is used to filter and identify and suppress multipath components in the acoustic echo signals of the multi-dimensional parameters. The multi-parameter fusion observation construction module is used to model and fuse the suppressed acoustic measurement parameters to construct a comprehensive observation. The cooperative positioning solution module is used to solve and locate the underwater target by combining the comprehensive observation.

[0029] The multi-source parameter acquisition module includes multiple hydrophone nodes distributed in the underwater space to achieve collaborative acquisition of multi-dimensional parameters. Each hydrophone node includes an acoustic signal transceiver unit, a time synchronization unit, and an attitude and depth acquisition unit. The acoustic signal transceiver unit is used to transmit positioning acoustic signals and receive acoustic echo signals from underwater targets or other hydrophone nodes. The time synchronization unit is used to synchronize and calibrate the internal clock of each hydrophone node. The attitude and depth acquisition unit is used to acquire the attitude and depth information of the hydrophone node itself.

[0030] The environmental perception modeling module includes an environmental parameter acquisition unit and a sound velocity profile construction unit; the environmental parameter acquisition unit is used to acquire environmental parameters of the underwater environment, including temperature, salinity and depth; the sound velocity profile construction unit constructs an underwater sound velocity propagation model based on the underwater environmental parameters.

[0031] The multipath recognition and suppression module includes a feature extraction unit, a candidate propagation path modeling unit, a multipath consistency discrimination unit, and a confidence evaluation unit. The feature extraction unit filters and demodulates the acoustic echo signal acquired by the acoustic signal transceiver unit, converting the continuous acoustic echo signal into a discrete signal vector representation. The candidate propagation path modeling unit constructs a set of candidate propagation paths describing the possible propagation modes of the acoustic signal based on the prior spatial relationship between the hydrophone node and the underwater target and the underwater sound speed propagation model, and represents the propagation path set as a computable path dictionary structure. The multipath consistency discrimination unit, under the constraint of the candidate propagation path set, represents the signal vector acquired by the feature extraction unit as a superposition of multiple propagation paths and solves for the optimal propagation structure estimation result that conforms to the direct propagation characteristics. The confidence evaluation unit evaluates the reliability of the propagation paths that conform to the direct propagation characteristics in the optimal propagation structure estimation result and outputs the direct propagation confidence score.

[0032] Furthermore, the candidate propagation path dictionary structure is represented as follows:

[0033] ;

[0034] in, The candidate propagation path dictionary matrix, For the first The path atoms corresponding to each candidate propagation path are used to represent the propagation parameters. Under constraints, the standardized signal response formed at the receiving end by the transmitted acoustic signal; The number of candidate propagation paths, For the first A set of propagation parameters corresponding to each candidate propagation path, the set of propagation parameters including a propagation delay parameter, used to characterize the propagation time characteristics of the candidate propagation path;

[0035] Furthermore, the signal vector extracted by the feature extraction unit is expressed as a superposition of multiple propagation paths in the following way:

[0036] ;

[0037] in, For signal vectors, The candidate propagation path dictionary matrix, This is the set of complex amplitude coefficients corresponding to candidate propagation paths, used to characterize the contribution of each candidate propagation path to the received signal, expressed in the form of: ,in For the first The amplitude coefficient of each candidate propagation path in the received signal is used to characterize the degree to which the candidate propagation path contributes to the energy of the received signal. For pre-established noise terms;

[0038] Furthermore, the multipath consistency discrimination unit solves for the propagation structure using the following optimization objective function:

[0039] ;

[0040] in, The obtained optimal propagation structure estimation results are used to characterize the contribution magnitude of each candidate propagation path to the received signal; is a sparse regularization coefficient used to limit the number of effective propagation paths; This is the gating penalty coefficient, used to enhance the guiding role of direct propagation priors in the solution process; The direct propagation delay prediction value obtained a priori; For the first The amplitude coefficients corresponding to each candidate propagation path; For the first The gating weight function of the candidate propagation path satisfies:

[0041] ;

[0042] in, For the first The propagation delay parameters corresponding to the candidate propagation paths, This is the gate width parameter, used to reflect the uncertainty of propagation delay prediction;

[0043] Furthermore, the confidence assessment unit quantifies the reliability of direct propagation based on the optimal propagation structure estimation results. Specifically, the confidence assessment unit first compares the consistency between the propagation delay parameters of each candidate propagation path and the prior direct propagation delay in the optimal propagation structure estimation results, and determines the candidate propagation path with the highest consistency between its propagation delay and the prior direct propagation delay as the direct propagation path. Subsequently, it extracts the propagation delay parameters and their amplitude coefficients corresponding to the direct propagation path as characterization parameters of the direct propagation path. Further, based on the amplitude coefficient distribution of each candidate propagation path in the optimal propagation structure estimation results, it calculates the energy proportion of direct propagation in the overall propagation structure, thereby obtaining the direct propagation confidence, which is calculated as follows:

[0044] ;

[0045] in, The confidence level of direct communication is used to characterize the degree to which direct communication dominates the overall communication structure. The amplitude coefficient corresponding to the direct propagation path in the optimal propagation structure estimation results. For the first The amplitude coefficients of the candidate propagation paths in the optimal propagation structure estimation results; These are preset constants used to ensure numerical stability;

[0046] This scheme constructs a candidate propagation path dictionary structure and introduces direct propagation prior constraints during the propagation structure estimation process, achieving stable identification of effective propagation structures under complex propagation conditions. Compared with traditional localization methods based on peak search or single propagation assumptions, this invention can accurately extract propagation parameters that conform to the characteristics of direct propagation even under significant multipath interference. At the same time, by quantitatively evaluating the dominance of direct propagation in the overall propagation structure, it outputs confidence information that can be used for subsequent multi-parameter fusion, improving the reliability and robustness of localization observations.

[0047] Example 2: This example should be understood as including at least all the features of any of the foregoing examples, and further improving upon them;

[0048] This embodiment provides an underwater positioning system based on multi-parameter fusion and acoustic multipath suppression. The system includes a multi-source parameter acquisition module, an environmental perception and modeling module, an acoustic multipath identification and suppression module, a multi-parameter fusion observation construction module, and a collaborative positioning and calculation module. The multi-source parameter acquisition module is used to acquire multi-dimensional parameters required for underwater target positioning. The environmental perception and modeling module is used to acquire underwater environmental parameters and construct a sound speed propagation model. The acoustic multipath identification and suppression module is used to filter and identify and suppress multipath components in the acoustic echo signals of the multi-dimensional parameters. The multi-parameter fusion observation construction module is used to model and fuse the suppressed acoustic measurement parameters to construct a comprehensive observation. The collaborative positioning and calculation module is used to calculate and locate the underwater target by combining the comprehensive observation.

[0049] Furthermore, the multi-parameter fusion observation construction module includes an observation modeling unit and a confidence-driven weight construction unit. The observation modeling unit is used to convert the direct propagation delay parameters into the observation expression form required for positioning calculation, and to perform unified modeling with other observations acquired in the system. The confidence-driven weight construction unit is used to adaptively adjust the weight of the corresponding observation in the integrated observation based on the propagation confidence output by the channel multipath recognition and suppression module, so that the observation weight changes dynamically with the propagation reliability. For the first... Observations acquired by each hydrophone node Its corresponding weight is defined as:

[0050] ;

[0051] in, The output of the channel multipath recognition and suppression module is the first Direct propagation confidence of each hydrophone node;

[0052] Specifically, the observation is the equivalent propagation distance observation obtained by converting the direct propagation delay parameters extracted by the channel multipath identification and suppression module into an equivalent sound speed through a sound speed propagation model;

[0053] Furthermore, the comprehensive observation output by the multi-parameter fusion observation construction module can be expressed as:

[0054] ;

[0055] in, For comprehensive observation; This represents the total number of hydrophone nodes.

[0056] Furthermore, the collaborative positioning and calculation module includes a state prediction unit and a positioning and calculation unit. The state prediction unit is used to predict the current position and state of the underwater target based on historical positioning results and a motion model, serving as a priori constraint for the positioning and calculation. The positioning and calculation unit is used, under the priori constraint, to perform a weighted calculation of the position and state of the underwater target by combining the observations and their weight information output by the multi-parameter fusion observation construction module. The specific calculation process can be expressed as follows:

[0057] ;

[0058] in, To calculate the location of the underwater target; For the positional variables during the solution process; Represents all possible position variables In the expression, find the expression that minimizes the function within the parentheses. ; For the first The observation model corresponding to each hydrophone node is a functional relationship established based on the underwater acoustic propagation mechanism, which is used to map the spatial position state of the underwater target to the corresponding observation and prediction value.

[0059] This invention unifies the modeling of observation information from different sources within a multi-parameter fusion observation construction module and introduces propagation confidence as an observation weight, enabling the positioning solution process to fully reflect the reliability of each observation under the current propagation conditions. Furthermore, the collaborative positioning solution module solves for the underwater target position under weighted observation constraints, effectively reducing the interference of unreliable observations on the positioning results. Compared to traditional positioning methods that equally weight various observations, this invention can improve the stability and robustness of positioning results in complex underwater acoustic propagation environments, thereby enhancing overall positioning accuracy.

[0060] Example 3: This example should be understood as including at least all the features of any of the foregoing examples, and further improving upon them;

[0061] Based on Examples 1 and 2, this embodiment further refines the engineering deployment method, multi-source parameter acquisition configuration parameters, and collaborative positioning solution process of the underwater positioning system based on multi-parameter fusion and channel multipath suppression in real and complex underwater acoustic environments, so as to improve the real-time positioning accuracy, continuity, and robustness of the system under conditions of strong multipath propagation, dynamic environmental changes, and fluctuations in observation quality.

[0062] The multi-source parameter acquisition module described in this embodiment employs a spatial collaborative observation array consisting of no fewer than four hydrophone nodes. These nodes are distributed at intervals of 500 to 1500 meters to form a geometric structure covering the positioning area. Each hydrophone node integrates an acoustic signal transceiver unit, a time synchronization unit, and an attitude and depth acquisition unit. The acoustic signal transceiver unit preferably operates in the 8 kHz to 12 kHz frequency band, with a single transmitted pulse duration preferably between 50 and 100 milliseconds. The receiving end sampling rate is no less than 48 kHz to ensure the resolution capability of the multipath echo structure. The time synchronization unit uses a bidirectional timestamp calibration mechanism to control the clock alignment error between multiple nodes to the sub-millisecond level, thereby ensuring the consistency of cross-node propagation delay measurements. The attitude and depth acquisition unit outputs node depth, attitude angle, and short-term motion state in real time, providing prior constraint inputs for propagation path modeling and geometric consistency evaluation.

[0063] In this embodiment, the environmental perception modeling module employs a multi-layer temperature, salinity, and depth joint sampling method. The environmental parameter acquisition unit collects temperature, salinity, and pressure data at depth intervals of two to five meters and updates the sound velocity profile at a five-minute cycle. The sound velocity profile construction unit generates a real-time sound velocity propagation model based on environmental parameters and inputs the model into the candidate propagation path modeling unit. This allows the multipath dictionary to be dynamically adjusted according to environmental changes, thereby reducing systematic errors in propagation delay caused by changes in sound velocity.

[0064] In this embodiment, the multipath identification and suppression module employs a combined processing flow of "candidate path dictionary constraints + sparse solution of propagation structure + direct propagation confidence assessment". The feature extraction unit first performs adaptive bandpass filtering and envelope extraction on the original acoustic echo signal, improving the effective signal-to-noise ratio by an average of four to six dB. The candidate propagation path modeling unit generates a multipath propagation dictionary based on the hydrophone node geometry, real-time sound velocity profile, and prior knowledge of underwater target motion. The multipath consistency discrimination unit performs sparse structure solution on the signal under candidate path constraints, enabling stable separation of the direct propagation path from the superimposed multipath signal. The confidence assessment unit further calculates the energy proportion of direct propagation in the overall propagation structure, outputs the propagation confidence, and continuously updates it over time, thereby achieving adaptive suppression of multipath components and reliable propagation extraction.

[0065] In this embodiment, the multi-parameter fusion observation construction module adopts a confidence-driven weighted modeling method. The observation modeling unit converts the direct propagation delay parameter into an equivalent propagation distance observation through the sound speed model, and models it in a unified manner with the hydrophone node attitude and depth observations and motion state priors. The confidence-driven weight construction unit dynamically adjusts the observation weights according to the propagation confidence of each node, so that high-confidence observations dominate the positioning solution, while low-confidence observations automatically reduce their influence weight, thereby suppressing the interference of abnormal observations on the positioning results.

[0066] In this embodiment, the collaborative positioning and solving module adopts a "state prediction + weighted collaborative optimization" structure. The state prediction unit outputs the current position prior based on the historical positioning trajectory and the underwater target motion model. The positioning and solving unit integrates multi-node weighted observations under prior constraints to perform joint optimization and solve the problem, and completes real-time iteration in a period of less than 100 milliseconds, so that the system can continuously output the three-dimensional positioning results of the underwater target and maintain the smoothness and continuity of the trajectory.

[0067] like Figure 3 , Figure 4 As shown, in order to verify the effect of the underwater positioning system based on multi-parameter fusion and acoustic multipath suppression proposed in this invention on improving the positioning accuracy and continuous stability of underwater targets in complex underwater acoustic propagation environments, the following experimental verification scheme is provided, and the experimental process and experimental data results are explained.

[0068] In this experiment, the multi-parameter fusion and acoustic multipath suppression underwater positioning system described in Example 3 was deployed in a controlled test water area. The multi-source parameter acquisition module was installed on four hydrophone nodes to form a spatially distributed collaborative measurement array. The test area was designed with enhanced surface and seabed reflection conditions to create a significant multipath propagation environment. At the same time, an external high-precision reference positioning device was used to obtain the real trajectory of the underwater target as a reference. The system ran continuously for thirty minutes and recorded the acoustic signals, environmental parameter data and positioning calculation results of each hydrophone node at a sampling period of one second.

[0069] Three processing schemes were set up in the experiment while keeping the hardware and sampling conditions consistent. Traditional scheme 1 did not enable multipath suppression and multi-parameter fusion, and only used the earliest arrival path for localization calculation. Traditional scheme 2 enabled multi-parameter fusion processing but did not enable channel multipath suppression. The scheme of this invention simultaneously enables multi-parameter fusion modeling, environmental adaptive sound velocity correction, and channel multipath recognition and suppression processing. During operation, the system recorded the localization error time series in real time and used a sliding time window to statistically analyze the average localization error and the continuous effective localization success rate.

[0070] Experimental data shows that traditional scheme 1 exhibits significant fluctuations in positioning error under strong multipath propagation conditions, with an average positioning error of approximately seven to eight meters and noticeable jumps in the positioning trajectory. Traditional scheme 2, after introducing multi-parameter fusion, reduces the average positioning error to approximately four to five meters, but still experiences intermittent error drift during abrupt changes in multipath propagation. In contrast, the scheme of this invention, after fully implementing multipath identification suppression and multi-parameter collaborative fusion, further reduces the average positioning error to two to three meters, maintaining a continuous and stable positioning trajectory, with a continuous effective positioning success rate exceeding 98%. Therefore, the multi-parameter fusion and acoustic multipath suppression joint mechanism proposed in Embodiment 3 of this invention can significantly suppress ranging misjudgments under complex multipath propagation conditions, stably improving the accuracy and continuity of underwater target positioning, fully verifying the beneficial effects of this invention.

[0071] Furthermore, in this embodiment, the hydrophone node can be replaced with a buoy-type acoustic base station or an airborne acoustic base station to adapt to different mission scenarios; the multipath consistency discrimination method can also be replaced with a propagation structure estimation method based on a learning model to further improve the adaptability in unknown and complex propagation environments; the confidence-driven weight construction strategy can also be replaced with a dynamic Bayesian confidence evaluation method to enhance the system's robustness to sudden propagation anomalies.

[0072] This embodiment achieves high-precision, high-continuity, and high-reliability underwater positioning capabilities under complex underwater acoustic propagation and observation quality fluctuation conditions through the joint design of multi-source parameter collaborative acquisition, environmental adaptive sound velocity modeling, sound channel multipath structure identification and suppression, and confidence-driven fusion positioning solution. It has good engineering application value and promotion prospects.

[0073] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. An underwater positioning system based on multi-parameter fusion and acoustic multipath suppression, characterized in that, The system includes a multi-source parameter acquisition module, an environmental perception and modeling module, a sound channel multipath identification and suppression module, a multi-parameter fusion observation construction module, and a cooperative positioning and calculation module. The multi-source parameter acquisition module is used to acquire multi-dimensional parameters required for underwater target positioning. The environmental perception and modeling module is used to acquire underwater environmental parameters and construct a sound speed propagation model. The sound channel multipath identification and suppression module is used to filter and identify and suppress multipath components in the acoustic echo signals of the multi-dimensional parameters. The multi-parameter fusion observation construction module is used to model and fuse the suppressed acoustic measurement parameters to construct a comprehensive observation. The collaborative positioning and solving module is used to solve and locate underwater targets by combining comprehensive observations; The multi-source parameter acquisition module includes multiple hydrophone nodes distributed in the underwater space to achieve collaborative acquisition of multi-dimensional parameters. Each hydrophone node includes an acoustic signal transceiver unit, a time synchronization unit, and an attitude and depth acquisition unit. The acoustic signal transceiver unit is used to transmit positioning acoustic signals and receive acoustic echo signals from underwater targets or other hydrophone nodes. The time synchronization unit is used to synchronize and calibrate the internal clock of each hydrophone node. The attitude and depth acquisition unit is used to acquire the attitude and depth information of the hydrophone node itself.

2. The underwater positioning system based on multi-parameter fusion and acoustic multipath suppression according to claim 1, characterized in that, The environmental perception modeling module includes an environmental parameter acquisition unit and a sound velocity profile construction unit. The environmental parameter acquisition unit is used to collect environmental parameters of the underwater environment, including temperature, salinity, and depth. The sound velocity profile construction unit constructs an underwater sound velocity propagation model based on the underwater environmental parameters.

3. The underwater positioning system based on multi-parameter fusion and acoustic multipath suppression according to claim 1, characterized in that, The multipath recognition and suppression module includes a feature extraction unit, a candidate propagation path modeling unit, a multipath consistency discrimination unit, and a confidence evaluation unit. The feature extraction unit filters and demodulates the acoustic echo signal acquired by the acoustic signal transceiver unit, converting the continuous acoustic echo signal into a discrete signal vector representation. The candidate propagation path modeling unit constructs a set of candidate propagation paths describing the possible propagation modes of the acoustic signal based on the prior spatial relationship between the hydrophone node and the underwater target and the underwater sound speed propagation model, and represents the propagation path set as a computable path dictionary structure. The multipath consistency discrimination unit, under the constraint of the candidate propagation path set, represents the signal vector acquired by the feature extraction unit as a superposition of multiple propagation paths and solves for the optimal propagation structure estimation result that conforms to the direct propagation characteristics. The confidence evaluation unit evaluates the reliability of the propagation paths that conform to the direct propagation characteristics in the optimal propagation structure estimation result and outputs the direct propagation confidence score.

4. The underwater positioning system based on multi-parameter fusion and acoustic multipath suppression according to claim 1, characterized in that, The multi-parameter fusion observation construction module includes an observation modeling unit and a confidence-driven weight construction unit. The observation modeling unit is used to convert the direct propagation delay parameters into the observation expression form required for positioning calculation, and to perform unified modeling with other observations obtained in the system. The confidence-driven weight construction unit is used to adaptively adjust the weight of the corresponding observation in the integrated observation based on the propagation confidence output by the channel multipath recognition and suppression module, so that the observation weight changes dynamically with the propagation reliability.

5. The underwater positioning system based on multi-parameter fusion and acoustic multipath suppression according to claim 1, characterized in that, The collaborative positioning and calculation module includes a state prediction unit and a positioning and calculation unit. The state prediction unit is used to predict the current position and state of the underwater target based on historical positioning results and motion models, as a prior constraint for positioning and calculation. The positioning and calculation unit is used to perform weighted calculation of the position and state of the underwater target under the prior constraint, combined with the observations and their weight information output by the multi-parameter fusion observation construction module.

6. The underwater positioning system based on multi-parameter fusion and acoustic multipath suppression according to claim 3, characterized in that, The multipath consistency discrimination unit solves for the propagation structure using the following optimization objective function: ； in, The obtained optimal propagation structure estimation results are used to characterize the contribution magnitude of each candidate propagation path to the received signal; The signal vector obtained by the feature extraction unit. The candidate propagation path dictionary matrix; This is the set of complex amplitude coefficients corresponding to the candidate propagation paths, used to characterize the contribution of each candidate propagation path to the received signal; is a sparse regularization coefficient used to limit the number of effective propagation paths; The number of candidate propagation paths; This is the gating penalty coefficient, used to enhance the guiding role of direct propagation priors in the solution process; The direct propagation delay prediction value obtained a priori; For the first The amplitude coefficients corresponding to each candidate propagation path; For the first The gating weight function of the candidate propagation path satisfies: ； in, For the first The propagation delay parameters corresponding to the candidate propagation paths, This is the gating width parameter, used to reflect the uncertainty of propagation delay prediction.

Images