A high-precision short baseline signal and data processing method carried on an offshore self-elevating platform

Abstract

The application relates to the technical field of underwater acoustic positioning, in particular to a high-precision short-baseline signal and data processing method carried on a near-sea self-elevating platform, which comprises the following steps: a mu s level unified time stamp system is constructed based on PPS second pulses and NTP protocols of GPS / BD, and multi-hydrophone channel synchronous acquisition is completed; effective signal identification and high-precision time delay estimation are realized by adopting COSTAS frequency hopping copy correlation detection and correlation peak interpolation; a three-dimensional rotation matrix is constructed to complete platform attitude compensation, and sound ray tracks are corrected in combination with real-time sound velocity profiles; target three-dimensional coordinates are solved through a spherical intersection equation, and dynamic tracking and multi-source data fusion are completed by adopting an unscented Kalman filter and a federal filter. The application does not need to reform the platform structure, the shallow water positioning precision reaches the 0.1 m level, at least 8 targets can be synchronously tracked, the positioning reliability reaches 99.9%, and the application is suitable for near-sea self-elevating platform underwater high-precision positioning operation.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic positioning technology, specifically to a high-precision short baseline signal and data processing method mounted on a near-shore jack-up platform. Background Technology

[0002] Shipborne short baseline positioning (SBS) is an underwater target tracking technology based on underwater acoustic positioning principles. Its core lies in utilizing multiple hydrophones (called an array) deployed on the bottom or sides of the ship to receive acoustic signals emitted by underwater targets (such as transponders, moorings, and AUVs). Through signal processing and geometric calculations, the relative position of the target with respect to the ship (usually three-dimensional coordinates: horizontal distance, depth, and relative bearing) is calculated. It is suitable for scenarios such as marine resource exploration, underwater target tracking, and submersible navigation. In the shipborne SBS, the signal processing unit is the "brain" of the entire system, undertaking key functions such as acquiring, preprocessing, extracting features, matching, ranging, and calculating the positioning of the raw acoustic signals. It is the core module that determines the system's accuracy, reliability, and real-time performance. The data processing unit achieves high-precision positioning of underwater targets through a closed-loop link of "acoustic measurement - auxiliary correction - coordinate calculation - dynamic output."

[0003] Current technologies mostly achieve high real-time processing through FPGA+DSP heterogeneous architecture, combined with multi-sensor fusion algorithms and flexible array deployment, forming a positioning capability covering shallow water to deep sea. International mainstream systems lead in accuracy and reliability. For example, the French iXblue system features strong compatibility, employing separate installation of acoustic transducer arrays and attitude sensors, compensating for installation deviations through a figure-eight calibration scheme, and achieving a positioning accuracy of 0.2% slant range when used in conjunction with PHINS inertial navigation, supporting operations at depths of 7000m. The Norwegian Kongsberg HiPAP series uses Cymbal spread spectrum technology for ROV dynamic positioning in deep-sea energy development, improving positioning accuracy by 30%, with a slant range error of <0.05%, and an operating range of 10000m. Domestic solutions (Harbin Engineering University system) focus on low cost and adaptability, innovating a double-cross orthogonal linear array design and using correlation peak interpolation delay estimation to improve positioning accuracy. It achieves decoupling of the signal processing layer and display layer, supporting a modular architecture for multi-task parallel processing.

[0004] Traditional shipborne short baseline positioning systems have the following drawbacks: (1) The positioning unit needs to be rigidly installed, which requires modification of the ship's structure and limits its application range; (2) The signal processing algorithm has low complexity and is difficult to meet the needs of multi-target synchronous tracking; (3) The data processing unit lacks an intelligent correction mechanism, and the positioning accuracy is significantly affected by the ship's attitude.

[0005] Existing technologies lack reference materials in areas such as noise suppression, dynamic tracking, environmental adaptation, algorithm and data processing, and standardization, resulting in significant shortcomings. It is necessary to overcome these bottlenecks through collaborative optimization of algorithms, hardware, and protocols to meet the high-precision requirements of emerging scenarios such as deep-sea exploration and intelligent ships. Summary of the Invention

[0006] The purpose of this invention is to provide a high-precision short baseline signal and data processing method for use on a near-shore jack-up platform, in order to solve the problems mentioned in the background art, such as the need for rigid installation of traditional short baseline positioning systems, insufficient synchronization accuracy, susceptibility to interference from platform attitude and marine environment, and weak multi-target tracking capability.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a high-precision short baseline signal and data processing method mounted on a near-shore jack-up platform, comprising the following steps: S1 Synchronous Acquisition and Control: Based on GPS / BD dual-mode positioning, PPS second pulse and NTP protocol are used to construct a unified timestamp system. Through RS485 / RS232 hybrid communication architecture, μs-level synchronous trigger signals are sent to the soft-suspended multi-element hydrophone array deployed on the near-shore self-elevating platform. The underwater target response acoustic signals received by each hydrophone channel are acquired synchronously. The roll / pitch / heave attitude data of the self-elevating platform, water body CTD sound velocity profile data and platform geodetic reference coordinate data are also acquired synchronously. S2 multi-channel acoustic signal preprocessing: The raw acoustic signals acquired by each channel are sequentially processed by input matching, FIR bandpass filtering, gain control and envelope detection to obtain the preprocessed baseband signal; S3 Target Signal Detection and Delay Estimation: COSTAS frequency hopping replica correlation detection is used to identify valid target signals from the preprocessed baseband signal. Target depth information is extracted by dual-pulse delay difference coding. High-precision delay estimation of multi-channel signals is achieved by correlation peak interpolation. S4 Attitude and Environmental Error Correction: Based on the collected platform attitude data, a coordinate rotation correction matrix is ​​constructed to compensate for the roll, pitch, and heave of the basic coordinates of the hydrophone array. Combined with real-time sound velocity profile data, the sound ray propagation trajectory is corrected. S5 Target 3D Positioning Solution: Based on the multi-channel time delay estimation results, a spherical intersection positioning equation is constructed. The least squares method is used to solve the target's 3D coordinates relative to the platform. Combined with the platform's geodetic reference coordinates, the relative coordinates are converted to absolute coordinates in the northeast-east-sky direction, and the target's 3D positioning result is output.

[0008] Preferably, the method for constructing the unified timestamp system in step S1 is as follows: using the rising edge of the PPS second pulse output by GPS / BD as the time reference, and combining it with the NTP protocol to complete the clock synchronization of each acquisition node, with the synchronization error satisfying: ; in, For synchronization error, To collect the local clock time of the node, The reference time is GPS / BD; the synchronous trigger signal for each hydrophone channel is generated by FPGA based on the frequency division of the temperature-controlled crystal oscillator clock, and the trigger edge deviation is no more than 1μs.

[0009] Preferably, the gain control in step S2 adopts a combination of analog PGC gain adjustment and DSP digital normalized gain control, wherein the calculation method for digital normalized gain control is as follows: ; in, To normalize the output signal, The target gain coefficient is set. Given the input digital signal sequence, The absolute value of the peak value of the input signal sequence; the gain coefficient is updated in real time according to the signal amplitude during each measurement cycle.

[0010] Preferably, the method for COSTAS frequency hopping replica correlation detection in step S3 is as follows: A sliding cross-correlation operation is performed between the preprocessed baseband signal and the locally pre-stored COSTAS frequency hopping replica signal. The cross-correlation function is: ; in, For delay The cross-correlation value, It is a baseband signal sequence. For the conjugate sequence of the replica signal, The sequence length is defined as follows: when the cross-correlation peak exceeds the preset detection threshold, it is determined to be a valid target signal; the signal is processed by a CNN-LSTM network to identify and filter out multipath interference signals reflected from the sea surface and seabed.

[0011] Preferably, the calculation method for extracting target depth information using dual-pulse delay difference encoding in step S3 is as follows: ; in, For target depth, The average speed of sound in water. The difference in reception delay for the dual-pulse signal. The elevation angle of the incident sound ray; the correlation peak interpolation method uses quadratic polynomial interpolation, and the time delay estimation accuracy reaches within 1 / 100 of the sampling interval.

[0012] Preferably, the method for constructing the coordinate rotation correction matrix in step S4 is as follows: based on the platform roll angle. Pitch angle Bow angle Construct a three-dimensional rotation matrix: ; ; In the formula, For the platform's roll angle, For the platform's pitch angle, For the platform's bow angle, For the roll rotation matrix, For the pitch and rotation matrix, Here is the heading rotation matrix; the target's absolute north-east sky coordinates. ,in The target is in relative rectangular coordinates. The GPS / BD absolute coordinates of the self-elevating platform are used to transform the carrier coordinate system coordinates of the hydrophone element through a rotation matrix to obtain the element coordinates in the geographic coordinate system, thus completing the compensation for platform attitude disturbances.

[0013] Preferably, the method for correcting the sound ray propagation trajectory by combining real-time sound velocity profile data in step S4 is as follows: based on the sound velocity profile acquired by the CTD sensor. A constant gradient ray tracking model is used to calculate the horizontal distance and propagation delay of ray propagation, with the ray curvature satisfying: ; in, For the curvature of the vocal tract, The vertical gradient of the speed of sound. The initial glancing angle of the sound ray is given; the sound path value in the spherical intersection localization equation is corrected based on the sound ray tracking results.

[0014] Preferably, step S5 is followed by a target dynamic tracking and filtering step, specifically: S51 State Vector Construction: Using the target's three-dimensional coordinates and three-dimensional velocity as state variables, construct the state equations and observation equations of the discrete-time system; S52UT Transform Sampling: Based on the current state estimate and covariance matrix, 2n+1 Sigma points are obtained through unscented transformation UT sampling, where n is the dimension of the state vector. S53 State Prediction Propagation: Substitute each Sigma point into the state equation to calculate the state prediction Sigma point for the next time step, and solve for the state prediction value and prediction covariance matrix based on the prediction Sigma point. S54 observation update calculation: Substitute the predicted Sigma point into the observation equation to obtain the observed prediction value, calculate the Kalman gain, update the state vector and covariance matrix in combination with the current positioning observation value, and output the target optimal state estimate; S55 multi-source data fusion: It adopts federated Kalman filtering to construct local filters for GPS / BD positioning, attitude measurement, and acoustic measurement. After each local filter completes its filtering update independently, the results are input into the main filter for fusion, and the global optimal state estimate is output.

[0015] Preferably, step S3 specifically includes a multi-target synchronization signal processing step: S31 Frequency Hopping Pattern Allocation: A unique COSTAS frequency hopping pattern is pre-assigned to each underwater target, and the target address is encoded using a combination of frequency division multiplexing and code division multiplexing. S32 Parallel Correlation Detection: Each target is assigned an independent parallel processing channel, and the preprocessed baseband signal is subjected to sliding cross-correlation with the local frequency hopping copy signal of the corresponding target to complete the synchronous detection of multiple target signals; S33 Target Delay Estimation: For each detected target's valid signal, high-precision delay estimation of multiple hydrophone channels is completed using correlation peak interpolation. S34 Parallel Localization and Solving: An independent localization and solving thread is assigned to each target, and the three-dimensional coordinates and trajectory updates of each target are completed synchronously based on the corresponding time delay estimation results.

[0016] As a preferred option, the system also includes self-testing and fault diagnosis steps, specifically: S01 Power-on self-test: After the system starts up, it generates a standard analog acoustic signal through the built-in signal simulator and injects it into each signal processing channel to complete the self-test of channel delay consistency, gain accuracy, and delay estimation accuracy. S02 Operation Status Monitoring: During system operation, the signal amplitude, signal-to-noise ratio, and primitive coordinate offset parameters of each hydrophone channel are collected in real time to monitor the system operation status. S03 Fault Identification and Isolation: When abnormal channel signal gain or element aperture offset fault is detected, the faulty channel is automatically identified and the signal of the faulty channel is isolated. S04 System Reconstruction and Calibration: Based on the normally functioning hydrophone channels, the array is reconstructed, the positioning solution model parameters are updated, and remote calibration commands are supported to complete the system parameter calibration.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention addresses the technical shortcomings of existing short baseline positioning systems, such as insufficient synchronous acquisition accuracy, weak multi-target resolution, and limited time delay estimation accuracy in the signal processing stage. It constructs a μs-level unified timestamp system based on GPS / BDPPS second pulses and NTP protocols, and achieves synchronous acquisition of multi-element hydrophones through an RS485 / RS232 hybrid communication architecture, reducing the time synchronization error of multi-channel signals at the source. A signal recognition scheme combining COSTAS frequency hopping signaling with replica correlation detection, along with a CNN-LSTM multipath suppression algorithm, effectively improves the detection probability and anti-interference capability of effective signals in complex nearshore environments, solving the problem of insufficient multi-target synchronous tracking capability in traditional solutions. Simultaneously, through a time delay estimation method using quadratic polynomial correlation peak interpolation, the time delay estimation accuracy is improved to within 1 / 100 of the sampling interval, providing a precise input foundation for subsequent high-precision positioning calculations and breaking through the bottleneck of positioning accuracy in shallow water scenarios of traditional short baseline systems.

[0018] 2. This invention addresses the technical shortcomings of existing short-baseline positioning systems, which are susceptible to the influence of platform attitude disturbances and changes in the underwater acoustic environment. It constructs a full-link error correction and positioning solution system. Based on platform roll, pitch, and bow data, a three-dimensional coordinate rotation correction matrix is ​​built, enabling real-time attitude compensation of hydrophone element coordinates. This controls the coordinate system correction error to within 0.2°, resolving the array aperture distortion problem caused by the swaying of self-elevating platforms. Combined with real-time CTD sound velocity profile data, a constant gradient ray tracking model is used to correct the sound ray propagation trajectory, eliminating the sound path calculation error caused by near-shore sound velocity gradients. Simultaneously, a target dynamic tracking scheme based on unscented Kalman filtering is constructed. Combined with a multi-source data fusion mechanism using federated Kalman filtering, mutual verification and fusion optimization of acoustic measurement, positioning, and attitude multi-source data are achieved. While ensuring positioning accuracy, the system's positioning reliability is improved to 99.9%, solving the problem of positioning failure due to single sensor failure in traditional solutions.

[0019] 3. This invention addresses the technical shortcomings of traditional shipborne short baseline positioning systems, which require rigid installation, involve significant modifications to the hull structure, and have limited applicability. Adapting to the operational characteristics of near-shore jack-up platforms, it employs a flexible, non-rigid array installation scheme, eliminating the need for modifications to the platform's main structure. This significantly reduces deployment difficulty and modification costs, expanding the applicable scenarios for short baseline positioning systems. Through a modular signal and data processing architecture, coupled with a multi-channel parallel processing mechanism, it achieves simultaneous tracking and parallel calculation of no fewer than eight underwater targets, with tracking latency controlled within 100ms, meeting the real-time requirements of near-shore multi-target operations. Simultaneously, a full-process system self-checking and fault diagnosis mechanism is constructed, which can automatically identify faults such as channel anomalies and array offsets, complete fault isolation and system reconstruction, and support remote parameter calibration. This greatly improves the system's environmental adaptability, fault tolerance, and ease of maintenance, providing a complete and engineerable solution for high-precision underwater positioning in near-shore jack-up platform scenarios. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are explained in detail together with the embodiments of the invention, but do not constitute a limitation thereof.

[0021] Figure 1 To illustrate the flowchart of the high-precision short baseline signal and data processing method of the present invention; Figure 2 This is a block diagram of the system components of the present invention; Figure 3 This is a detailed block diagram of the synchronous acquisition and preprocessing of the present invention; Figure 4 This is a detailed block diagram of the detection, correction, and positioning calculation of the present invention; Figure 5 This is a detailed block diagram of the dynamic tracking, multi-target processing, and self-diagnosis fault diagnosis of the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figures 1-5As shown, this high-precision short-baseline signal and data processing method, mounted on a near-shore jack-up platform, is applied to a short-baseline underwater acoustic positioning system mounted on the platform. The system includes a flexible-suspended multi-element hydrophone array, a signal processing unit, a data processing unit, an attitude measurement unit, a GPS / BD dual-mode positioning unit, a CTD sensor unit, and a display and control unit. The flexible-suspended multi-element hydrophone array is deployed at the bottom of the near-shore jack-up platform using a non-rigid flexible-suspended installation method, requiring no modification to the platform's main structure. The hydrophone array adopts a four-element cross-orthogonal array with element spacing ranging from 0.8m to 2m, adapting to the aperture requirements of near-shore shallow water positioning scenarios. The signal processing unit uses an FPGA+DSP heterogeneous architecture. The FPGA performs multi-channel synchronous acquisition and signal preprocessing, while the DSP performs signal detection, time delay estimation, and positioning calculations. The attitude measurement unit uses a three-axis electronic compass and an inertial measurement unit to acquire the roll, pitch, heading, and heave angles of the jack-up platform in real time. The GPS / BD dual-mode positioning unit outputs the geodetic reference coordinates and PPS second pulse signal of the platform. The CTD sensor unit collects water temperature, salinity, and depth data in real time and calculates the real-time sound velocity profile. The display and control unit completes human-computer interaction, parameter configuration, positioning result display, and data storage.

[0024] Synchronous acquisition and control is based on a unified timestamp system constructed using PPS second pulses and NTP protocol for GPS / BD dual-mode positioning. Through an RS485 / RS232 hybrid communication architecture, μs-level synchronous trigger signals are sent to the soft-dip multi-element hydrophone array. The underwater target response acoustic signals received by each hydrophone channel are acquired synchronously. The roll, pitch, heave attitude data of the self-elevating platform, the water body CTD sound velocity profile data, and the platform geodetic reference coordinate data are also acquired synchronously.

[0025] The unified timestamp system is constructed using the rising edge of the PPS second pulse output by GPS / BD as the time reference, and combines the NTP protocol to complete the clock synchronization of each acquisition node. The synchronization error satisfies the formula. ; in For synchronization error, To collect the local clock time of the node, The reference time is GPS / BD. The synchronization trigger signal for each hydrophone channel is generated by the FPGA based on a frequency division of a temperature-controlled crystal oscillator clock, with a trigger edge deviation of no more than 1 μs. The frequency stability of the temperature-controlled crystal oscillator is no less than 1 ppm, providing the FPGA with a 100 MHz reference clock signal. The FPGA generates multiple synchronization trigger signals based on the reference clock, with a phase deviation of no more than one clock cycle for each trigger signal, corresponding to a time deviation of 10 ns, meeting the requirements for μs-level synchronous acquisition.

[0026] During multi-channel synchronous acquisition, the start time of each sampling cycle is strictly aligned with the rising edge of the PPS second pulse, and the sampling rate is set to 100kHz to 1MHz to adapt to the bandwidth requirements of underwater acoustic signals. The synchronously acquired data includes acoustic signals from four hydrophones, attitude data from the attitude measurement unit, sound velocity profile data from the CTD sensor, and positioning and time data from GPS / BD. All data are marked with a uniform timestamp for subsequent time alignment and fusion processing.

[0027] Multi-channel acoustic signal preprocessing involves sequentially performing input matching, FIR bandpass filtering, gain adjustment, and envelope detection on the raw acoustic signals acquired from each channel to obtain the preprocessed baseband signal.

[0028] Input matching is achieved through an impedance matching circuit with a matching impedance of 50Ω, eliminating signal reflections from the hydrophone output cable and ensuring signal transmission integrity. The FIR bandpass filter uses a linear-phase FIR filter with a passband range of 8kHz to 16kHz and a stopband attenuation of no less than 60dB, filtering out out-of-band noise and interference signals.

[0029] Gain control employs a combination of analog PGC gain adjustment and DSP digital normalized gain control. The calculation method for digital normalized gain control is as follows: ; in To normalize the output signal, The target gain coefficient is set. Given the input digital signal sequence, This represents the absolute peak value of the input signal sequence. The gain coefficient is updated in real-time based on the signal amplitude during each measurement cycle to ensure that the output signal amplitude remains within the linear operating range of the ADC, thus avoiding decreased detection accuracy due to signal saturation or excessively low amplitude.

[0030] Envelope detection processing is implemented using Hilbert transform. The filtered and gain-controlled signal is subjected to Hilbert transform to obtain the analytical form of the signal, and the envelope amplitude of the signal is extracted for subsequent correlation detection and peak identification.

[0031] The target signal detection and time delay estimation uses COSTAS frequency hopping copy correlation detection to identify valid target signals from the preprocessed baseband signal, extracts target depth information through dual-pulse time delay difference coding, and completes high-precision time delay estimation of multi-channel signals using correlation peak interpolation.

[0032] The method for COSTAS frequency hopping replica correlation detection involves performing a sliding cross-correlation operation between the pre-processed baseband signal and the locally stored COSTAS frequency hopping replica signal. The cross-correlation function is as follows: ; in For delay The cross-correlation value, It is a baseband signal sequence. For the conjugate sequence of the replica signal, The sequence length is defined as follows. A valid target signal is determined when the cross-correlation peak exceeds a preset detection threshold. The detection threshold is set based on the Constant False Alarm Rate (CFAR) method, with the false alarm rate controlled within a certain range. Within this range. A CNN-LSTM network is used to process the signal, identifying and filtering out multipath interference signals reflected from the sea surface and seabed, thereby improving the detection probability of effective signals.

[0033] The calculation method for extracting target depth information using dual-pulse delay difference coding is as follows: ; in For target depth, The average speed of sound in water. The difference in reception delay for the dual-pulse signal. The elevation angle is the incident angle of the acoustic ray. The transmission interval of the dual-pulse signal is 10ms to 50ms, adjusted according to the expected depth range of the target.

[0034] The correlation peak interpolation method employs quadratic polynomial interpolation, performing quadratic polynomial fitting on three sampling points near the peak of the cross-correlation function to obtain the precise time delay value corresponding to the peak. The time delay estimation accuracy reaches within 1 / 100 of the sampling interval. When the sampling rate is 100kHz and the sampling interval is 10μs, the time delay estimation accuracy after interpolation can reach 0.1μs.

[0035] Attitude and environmental error correction is based on the coordinate rotation correction matrix constructed from the collected platform attitude data. Roll, pitch, and heave compensation are performed on the primitive coordinates of the hydrophone array, and the sound ray propagation trajectory is corrected by combining real-time sound velocity profile data.

[0036] The method for constructing the coordinate rotation correction matrix is ​​based on the platform roll angle. Pitch angle Bow angle Construct a three-dimensional rotation matrix ; in For the roll rotation matrix, For the pitch and rotation matrix, For heading rotation. For roll rotation. The expression is ; Pitch Rotation Matrix The expression is ; Heading rotation matrix The expression is ; The coordinates of the hydrophone element in the carrier coordinate system are transformed by a rotation matrix to obtain the element coordinates in the geographic coordinate system, thus compensating for platform attitude disturbances. The platform heave is collected by pressure sensors or inertial measurement units and directly compensates for the vertical coordinates of the element, eliminating the influence of vertical displacement of the platform on the positioning results.

[0037] The method for correcting the sound ray propagation trajectory by combining real-time sound velocity profile data is based on the sound velocity profile acquired by the CTD sensor. A constant gradient ray tracking model is used to calculate the horizontal distance and propagation delay of ray propagation, and the ray curvature satisfies... ; in For the curvature of the vocal tract, The vertical gradient of the speed of sound. The initial glancing angle of the sound ray is given. The sound path value in the spherical intersection positioning equation is corrected based on the sound ray tracking results to eliminate the influence of sound ray bending caused by the sound velocity gradient on the ranging accuracy.

[0038] The target's three-dimensional positioning solution is based on the multi-channel time delay estimation results to construct a spherical intersection positioning equation. The least squares method is used to solve the target's three-dimensional coordinates relative to the platform. Combined with the platform's geodetic reference coordinates, the relative coordinates are converted to absolute coordinates in the northeast-east-sky direction, and the target's three-dimensional positioning results are output.

[0039] The spherical intersection localization equation is constructed based on the acoustic path difference between each hydrophone element and the target. Let the target's coordinates in the carrier coordinate system be... , No. The coordinates of each hydrophone element are The speed of sound is , No. The time delay difference between each channel and the reference channel is Then the sound path difference satisfies ; in The coordinates of the reference primitive are used. After linearizing the above equations, the three-dimensional coordinates of the target in the carrier coordinate system are obtained by solving the least squares method.

[0040] By combining the geodetic reference coordinates of the platform, the target coordinates in the carrier coordinate system are converted into absolute coordinates in the northeast-east-sky coordinate system. During the conversion process, the aforementioned three-dimensional rotation matrix is ​​used to complete the alignment of the coordinate systems, and finally the absolute three-dimensional positioning result of the target is output.

[0041] The target dynamic tracking filter is executed after the target's 3D localization is solved, and specifically includes the following process.

[0042] State vector construction: Using the target's three-dimensional coordinates and three-dimensional velocity as state variables, the state equations and observation equations of the discrete-time system are constructed. Defined as ; in for The three-dimensional coordinates of the target at any given time. for The three-dimensional velocity of the target at any given time.

[0043] UT transformation sampling, based on the current state estimate and covariance matrix, obtains 2n+1 Sigma points through unscented UT transformation sampling, where n is the dimension of the state vector. Here, n=6, resulting in a total of 13 Sigma points.

[0044] State prediction propagation involves substituting each Sigma point into the state equation to calculate the state prediction Sigma point for the next time step, and then solving for the state prediction value and prediction covariance matrix based on the predicted Sigma points.

[0045] The observation update calculation substitutes the predicted Sigma point into the observation equation to obtain the observed prediction value, calculates the Kalman gain, and updates the state vector and covariance matrix by combining the current positioning observation value, outputting the optimal state estimate of the target.

[0046] Multi-source data fusion employs federated Kalman filtering, constructing local filters for GPS / BD positioning, attitude measurement, and acoustic measurement. After each local filter independently completes its filtering update, the results are input into the main filter for fusion, outputting a globally optimal state estimate.

[0047] Multi-target synchronization signal processing is performed during target signal detection and time delay estimation, and specifically includes the following processes.

[0048] Frequency hopping pattern allocation: A unique COSTAS frequency hopping pattern is pre-assigned to each underwater target, and target address encoding is completed by combining frequency division multiplexing and code division multiplexing.

[0049] Parallel correlation detection allocates an independent parallel processing channel to each target, performs sliding cross-correlation calculations on the preprocessed baseband signal and the local frequency-hopping copy signal of the corresponding target, and completes the synchronous detection of multi-target signals.

[0050] For each detected target's effective signal, high-precision time delay estimation of multiple hydrophone channels is achieved by using correlation peak interpolation.

[0051] Parallel localization and calculation is performed by assigning an independent localization and calculation thread to each target, and synchronously completing the 3D coordinate calculation and trajectory update of each target based on the corresponding time delay estimation results.

[0052] System self-checks and fault diagnosis are performed during system startup and operation, specifically including the following processes.

[0053] Upon power-on self-test, after the system starts up, it generates a standard analog acoustic signal through the built-in signal simulator and injects it into each signal processing channel to complete the self-test of channel delay consistency, gain accuracy, and delay estimation accuracy.

[0054] Operational status monitoring: During system operation, the signal amplitude, signal-to-noise ratio, and primitive coordinate offset parameters of each hydrophone channel are collected in real time to monitor the system's operational status.

[0055] Fault identification and isolation: When abnormal channel signal gain or element aperture offset fault is detected, the faulty channel is automatically identified and the signal of the faulty channel is isolated.

[0056] The system reconfiguration calibration completes the array reconfiguration based on the normally functioning hydrophone channels, updates the positioning solution model parameters, and supports remote issuance of calibration commands to complete system parameter calibration.

[0057] Example 1: Implementation of Hardware Architecture for Short Baseline Positioning System of Offshore Self-Jack-Up Platform This embodiment constructs a short-baseline underwater acoustic positioning system hardware platform mounted on a near-shore jack-up platform to implement the signal and data processing method described in this invention.

[0058] The positioning system hardware platform includes a flexible suspended four-element hydrophone array, a signal processing chassis, a display and control industrial computer, and auxiliary sensor units.

[0059] The flexible-suspended four-element hydrophone array adopts a cross-shaped orthogonal array, with four hydrophone elements positioned at the four ends of the cross-shaped array. The spacing between adjacent elements is 1.2m, and the center of the array is aligned with the central axis of the jack-up platform. The hydrophone elements are piezoelectric ceramic hydrophones, operating in the frequency band of 8kHz to 20kHz, with a receiving sensitivity of -190dB re 1V / μPa, a waterproof depth of 100m, and are suitable for near-shore shallow water operations. The hydrophone array is connected to the bottom of the jack-up platform via stainless steel cables, using a flexible suspension installation method. No drilling or welding modifications to the platform's steel structure are required, and installation and disassembly can be completed while the platform is docked.

[0060] The signal processing chassis features a waterproof and dustproof design and is installed in the deck equipment compartment of the self-elevating platform. The chassis integrates an FPGA core board, a DSP core board, a multi-channel AD acquisition module, a power supply module, and a communication interface module. The FPGA core board uses a Xilinx Artix-7 series FPGA, model XC7A35T, responsible for multi-channel synchronous acquisition control, signal preprocessing, and synchronous trigger signal generation. The DSP core board uses a TI TMS320C6748 floating-point DSP with a main frequency of 456MHz, responsible for signal detection, delay estimation, positioning calculation, and filtering. The multi-channel AD acquisition module uses an 8-channel synchronous sampling ADC, model AD7606, with a maximum sampling rate of 200kHz and a sampling accuracy of 16 bits, completing the synchronous acquisition of four hydrophone signals. The power supply module supports a wide voltage DC input from 9V to 36V, adapting to the platform's onboard power system, and outputs isolated power supplies to power various modules of the system. The communication interface module integrates two RS485 interfaces, two RS232 interfaces, and one gigabit Ethernet interface to complete communication with auxiliary sensors and industrial control computers.

[0061] The display and control industrial computer is a ruggedized industrial computer equipped with an Intel Core i5 processor, 8GB of RAM, a 512GB solid-state drive, and runs the Windows 10 operating system. It also has positioning system display and control software installed to complete human-computer interaction, parameter configuration, visualization of positioning results, data storage and playback.

[0062] The auxiliary sensor unit includes a GPS / BD dual-mode positioning module, a three-axis attitude measurement module, and a CTD sensor. The GPS / BD dual-mode positioning module, model UM220-IV N, outputs the platform's geodetic coordinates and PPS second pulse signal, with a positioning accuracy of 2.5m in the horizontal plane and 5m in elevation, and a PPS second pulse rise time accuracy of no more than 50ns. The three-axis attitude measurement module, model WT901C, outputs the platform's roll, pitch, and heading angles, with an angle measurement accuracy of 0.05° statically and 0.1° dynamically. The CTD sensor, model RBRconcerto3, has a sampling frequency of 1Hz, a measurement range of -5℃ to 35℃ temperature, 0 to 42 PSU salinity, and 0 to 100m depth, and outputs real-time sound velocity profile data of the water body.

[0063] The system modules are connected via communication interfaces. The GPS / BD dual-mode positioning module and the three-axis attitude measurement module are connected to the signal processing chassis via an RS232 interface, the CTD sensor is connected to the signal processing chassis via an RS485 interface, and the signal processing chassis is connected to the display and control industrial computer via a gigabit Ethernet interface. After the system is powered on, each module completes initialization, and the entire system is synchronized using the PPS second pulse of the GPS / BD sensor. The synchronization error does not exceed 10μs, meeting the synchronization acquisition requirements of the method of this invention.

[0064] Example 2: Implementation of High-Precision Positioning Processing for a Single Target This embodiment is based on the hardware system built in Embodiment 1, and uses the method described in this invention to achieve high-precision positioning of a single underwater transponder target. The specific implementation process and calculations are as follows.

[0065] The test scenario in this embodiment is a shallow nearshore water area with a water depth of 30m. The self-elevating platform has completed the hoisting and fixing, and the platform is in a stationary state with a roll angle of [missing information]. The pitch angle is 0.5°. The heading angle is 0.3°. The angle is 30°. The underwater transponder is deployed on the seabed at a depth of 28m, approximately 50m horizontally from the platform. The system sampling rate is set to 100kHz, and the sound velocity profile is obtained through CTD sensors, with the average sound velocity in the water being... It is 1520 m / s.

[0066] The first step is synchronous acquisition and control. The system uses the rising edge of the PPS second pulse from GPS / BD as the time reference to complete the time synchronization of the entire system, minimizing synchronization error. The time is 8μs, which meets the requirement of not exceeding 10μs. The FPGA generates four synchronous trigger signals with a trigger edge deviation of 0.8μs to control the four hydrophones to synchronously acquire the acoustic signals returned by the transponders, and synchronously acquire attitude data, CTD data and platform positioning data. All data are marked with a unified timestamp.

[0067] The second step is multi-channel acoustic signal preprocessing. The acquired raw acoustic signals are sequentially subjected to input matching, FIR bandpass filtering, gain control, and envelope detection. The FIR bandpass filter has a passband of 8kHz to 16kHz and a stopband attenuation of 60dB. In the digital normalized gain control, a target gain coefficient is set. The absolute value of the peak value of the input signal sequence is 1000. The value is 200. Substitute this into the gain control formula. ; The signal is normalized and its gain is adjusted to match the dynamic range of subsequent processing. A Hilbert transform is then performed on the gain-adjusted signal to extract its envelope, yielding the preprocessed baseband signal.

[0068] The third step is target signal detection and time delay estimation. The preprocessed baseband signal is subjected to sliding cross-correlation with a locally stored COSTAS frequency-hopping copy signal. The sequence length... The cross-correlation function is calculated to be 1024. When the cross-correlation peak value is 2500, exceeding the preset detection threshold of 1200, it is determined to be a valid target signal. A quadratic polynomial interpolation method is used to fit the cross-correlation peak value to obtain the precise time delay value of each channel. The reference channel is element 0 at the center of the array, and the time delay difference between element 1 and the reference channel is... The time delay difference between primitive 2 and the reference channel is 12.3 μs. The time delay difference between primitive 3 and the reference channel is -8.7 μs. It is 4.2 μs.

[0069] Dual-pulse delay difference encoding is used to extract target depth. The dual-pulse transmission interval is 20ms, and the reception delay difference is... The incident elevation angle of the sound ray is 36.84 μs. 30°, average speed of sound The velocity is 1520 m / s. Substitute this into the depth calculation formula. ; The calculated target depth is 32.4m, which is less than 0.5m from the actual depth, meeting the requirement that the depth measurement error is no more than 0.5%.

[0070] The fourth step is attitude and environmental error correction. This is based on the platform's roll angle. The pitch angle is 0.5°. The heading angle is 0.3°. Construct a 3D rotation matrix with a radius of 30°. Substitute the trigonometric function values ​​for each angle... , , , , , The roll rotation matrix is ​​calculated. Pitch-rotation matrix Heading rotation matrix Finally, a three-dimensional rotation matrix is ​​obtained. The carrier coordinates of the hydrophone primitives are transformed using a rotation matrix to obtain the primitive coordinates in the geographic coordinate system, thus completing attitude compensation. Based on the sound velocity profile measured by CTD, a constant gradient sound ray tracking model is used to correct the sound ray propagation trajectory, with the sound velocity vertical gradient... The initial glancing angle of the sound ray is 0.2 s^-1. Given 10°, substitute it into the formula for the curvature of the vocal tract. ; The sound path value is corrected based on the curvature of the sound ray to eliminate the effect of sound ray bending.

[0071] Step 5: Target 3D localization calculation. Based on the time delay difference of each channel, a spherical intersection localization equation is constructed. The least squares method is used to solve for the target's coordinates in the carrier coordinate system. By combining the platform's geodetic reference coordinates and rotation matrix, the carrier coordinates are converted into absolute coordinates in the NE-CE coordinate system, ultimately outputting the target's three-dimensional positioning result. The horizontal error between the positioning result and the target's actual coordinates is 0.08m, achieving a positioning accuracy of 0.1m in shallow water, and the slant distance error is 0.12%, meeting the accuracy requirement of no more than 0.2% slant distance.

[0072] Step 6: Target dynamic tracking filtering. Construct the target state vector. Using the target's three-dimensional coordinates and three-dimensional velocity as state variables, an unscented Kalman filter is employed for dynamic tracking. The sampling period is 0.1s, the state equation adopts a uniform motion model, and filtering iterations are completed based on the positioning results to output the optimal state estimate of the target. The tracking delay is 80ms, meeting the requirement of not exceeding 100ms.

[0073] Example 3: Implementation of Multi-Target Synchronous Tracking and System Self-Test This embodiment is based on the hardware system built in Embodiment 1. It uses the method described in this invention to complete the synchronous tracking of 8 underwater targets and the system self-check fault handling. The specific implementation process is as follows.

[0074] The test scenario in this embodiment was a shallow nearshore water area with a depth of 40m. The jack-up platform was secured with jacking piles and was in a low-amplitude swaying state, with a roll angle ranging from -2° to 2° and a pitch angle ranging from -1.5° to 1.5°. A total of 8 transponder targets were deployed underwater, each assigned a unique COSTAS frequency hopping pattern. Address encoding was completed using a combination of frequency division multiplexing and code division multiplexing. The 8 targets were distributed within a range of 50m to 200m around the platform, at depths ranging from 10m to 35m.

[0075] The first step is multi-target synchronization signal processing. Independent FPGA parallel processing channels are allocated to each of the eight targets, with each channel corresponding to a local COSTAS frequency-hopping replica signal of the target. The system synchronously acquires acoustic signals from four hydrophones, preprocesses them, and inputs them into the eight parallel processing channels to synchronously complete sliding cross-correlation detection. The cross-correlation peak values ​​of each channel exceed the detection threshold, completing the synchronization signal detection for all eight targets. For the valid signal of each target, multi-channel time delay estimation is performed using quadratic polynomial interpolation to obtain the time delay difference for each channel corresponding to each target. An independent DSP positioning and calculation thread is allocated to each target, synchronously completing the 3D coordinate calculation and trajectory update of the eight targets based on the corresponding time delay difference. The positioning and calculation cycle for each target is 0.1s, and the tracking delay is no greater than 100ms.

[0076] The second step is multi-source data fusion and dynamic tracking. Federated Kalman filtering is employed to construct three local filters for GPS / BD positioning, attitude measurement, and acoustic measurement. Each local filter independently updates its filtering, inputting the local state estimate and covariance into the main filter. The main filter then fuses the information and outputs the globally optimal state estimate for each target. Even when the attitude measurement unit experiences a short-term data interruption, the filtering results of the acoustic measurement local filter can still maintain stable target tracking, achieving a positioning reliability of 99.9%.

[0077] The third step is system self-test and fault diagnosis. Upon system power-on, a power-on self-test procedure is executed. The built-in signal simulator generates a standard analog acoustic signal with a frequency of 12kHz and an amplitude of 100mV, which is injected into the four signal processing channels. The system acquires and processes the analog signal, completing self-tests for channel delay consistency, gain accuracy, and delay estimation accuracy. The self-test results show that the delay difference between the four channels is less than 0.5μs, the amplitude error is less than 0.3dB, and the delay estimation accuracy is 0.1μs. The system self-test passes, and the system enters normal operation.

[0078] During system operation, the operating status of each channel is monitored in real time. When a cable becomes loose in hydrophone channel 1, causing the signal amplitude to drop to 10% of its normal value and the signal-to-noise ratio to fall below 6dB, the system automatically identifies channel 1 as the faulty channel and isolates its signal. Based on the remaining three normally functioning hydrophone channels (channels 2, 3, and 0), the system reconstructs the array, updates the parameters of the positioning solution model, and uses the three-element spherical intersection positioning equation to calculate the target coordinates, maintaining the system's normal positioning function. A calibration command is issued via a remote display and control terminal, and the system remotely calibrates the array parameters. After calibration, the positioning error is restored to within 0.15% of the normal slant range.

[0079] This invention addresses the technical shortcomings of existing short baseline positioning systems, such as insufficient synchronous acquisition accuracy, weak multi-target resolution, and limited time delay estimation accuracy in the signal processing stage. It constructs a μs-level unified timestamp system based on GPS / BDPPS second pulses and NTP protocols, and achieves synchronous acquisition of multi-element hydrophones through an RS485 / RS232 hybrid communication architecture, reducing time synchronization errors of multi-channel signals at the source. A signal recognition scheme combining COSTAS frequency hopping signaling with replica correlation detection, along with a CNN-LSTM multipath suppression algorithm, effectively improves the detection probability and anti-interference capability of effective signals in complex nearshore environments, solving the problem of insufficient multi-target synchronous tracking capability in traditional solutions. Simultaneously, through a time delay estimation method using quadratic polynomial correlation peak interpolation, the time delay estimation accuracy is improved to within 1 / 100 of the sampling interval, providing a precise input foundation for subsequent high-precision positioning calculations and breaking through the bottleneck of positioning accuracy in shallow water scenarios of traditional short baseline systems.

[0080] This invention addresses the technical shortcomings of existing short-baseline positioning systems, which are susceptible to the influence of platform attitude disturbances and changes in the underwater acoustic environment. It constructs a full-link error correction and positioning solution system. Based on platform roll, pitch, and heading data, a three-dimensional coordinate rotation correction matrix is ​​built, enabling real-time attitude compensation of hydrophone element coordinates. This keeps the coordinate system correction error within 0.2°, resolving the array aperture distortion problem caused by the swaying of self-elevating platforms. Combined with real-time CTD sound velocity profile data, a constant gradient ray tracking model is used to correct the sound ray propagation trajectory, eliminating the sound path calculation error caused by near-shore sound velocity gradients. Simultaneously, a target dynamic tracking scheme based on unscented Kalman filtering is constructed. Combined with a multi-source data fusion mechanism using federated Kalman filtering, mutual verification and fusion optimization of acoustic measurement, positioning, and attitude multi-source data are achieved. While ensuring positioning accuracy, the system's positioning reliability is improved to 99.9%, solving the problem of positioning failure due to single sensor failure in traditional solutions.

[0081] This invention addresses the technical shortcomings of traditional shipborne short baseline positioning systems, which require rigid installation, involve significant modifications to the hull structure, and have limited applicability. Adapted to the operational characteristics of near-shore jack-up platforms, it employs a flexible, non-rigid array installation scheme, eliminating the need for modifications to the platform's main structure. This significantly reduces deployment difficulty and modification costs, expanding the applicable scenarios for short baseline positioning systems. Through a modular signal and data processing architecture, coupled with a multi-channel parallel processing mechanism, it achieves simultaneous tracking and parallel calculation of no fewer than eight underwater targets, with tracking latency controlled within 100ms, meeting the real-time requirements of near-shore multi-target operations. Simultaneously, a full-process system self-checking and fault diagnosis mechanism is constructed, automatically identifying faults such as channel anomalies and array offsets, completing fault isolation and system reconstruction, and supporting remote parameter calibration. This greatly improves the system's environmental adaptability, fault tolerance, and ease of maintenance, providing a complete and engineerable solution for high-precision underwater positioning in near-shore jack-up platform scenarios.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-precision short baseline signal and data processing method mounted on a near-shore jack-up platform, characterized in that, Includes the following steps: S1 Synchronous Acquisition and Control: Based on GPS / BD dual-mode positioning, a unified timestamp system is constructed using PPS second pulse and NTP protocol. Through RS485 / RS232 hybrid communication architecture, μs-level synchronous trigger signals are sent to the soft-suspended multi-element hydrophone array deployed on the near-shore self-elevating platform. The underwater target response acoustic signals received by each hydrophone channel are acquired synchronously, as well as the roll / pitch / heave attitude data of the self-elevating platform, the water body CTD sound velocity profile data, and the platform geodetic reference coordinate data. S2 Multi-channel Acoustic Signal Preprocessing: The raw acoustic signals acquired from each channel are sequentially processed by input matching, FIR bandpass filtering, gain control and envelope detection to obtain the preprocessed baseband signal; S3 Target Signal Detection and Delay Estimation: COSTAS frequency hopping replica correlation detection is used to identify valid target signals from the preprocessed baseband signal. Target depth information is extracted through dual-pulse delay difference coding. High-precision delay estimation of multi-channel signals is achieved using correlation peak interpolation. S4 Attitude and Environmental Error Correction: Based on the collected platform attitude data, a coordinate rotation correction matrix is ​​constructed to compensate for the roll, pitch, and heave of the basic coordinates of the hydrophone array. Combined with real-time sound velocity profile data, the sound ray propagation trajectory is corrected. S5 Target 3D Positioning Solution: Based on the multi-channel time delay estimation results, a spherical intersection positioning equation is constructed. The least squares method is used to solve the target's 3D coordinates relative to the platform. Combined with the platform's geodetic reference coordinates, the relative coordinates are converted to absolute coordinates in the northeast-east-sky direction, and the target's 3D positioning results are output.

2. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, The method for constructing the unified timestamp system described in step S1 is as follows: using the rising edge of the PPS second pulse output by GPS / BD as the time reference, and combining it with the NTP protocol to complete the clock synchronization of each acquisition node, with the synchronization error satisfying: ； in, For synchronization error, To collect the local clock time of the node, The reference time is GPS / BD; the synchronous trigger signal for each hydrophone channel is generated by FPGA based on the frequency division of the temperature-controlled crystal oscillator clock, and the trigger edge deviation is no more than 1μs.

3. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, The gain control described in step S2 adopts a combination of analog PGC gain adjustment and DSP digital normalized gain control. The calculation method for digital normalized gain control is as follows: ； in, To normalize the output signal, The target gain coefficient is set. Given the input digital signal sequence, The absolute value of the peak value of the input signal sequence; the gain coefficient is updated in real time according to the signal amplitude during each measurement cycle.

4. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, The method for COSTAS frequency hopping replica correlation detection in step S3 is as follows: The preprocessed baseband signal is subjected to a sliding cross-correlation operation with the locally pre-stored COSTAS frequency hopping replica signal. The cross-correlation function is: ； in, For delay The cross-correlation value, It is a baseband signal sequence. For the conjugate sequence of the replica signal, The sequence length is defined as follows: when the cross-correlation peak exceeds the preset detection threshold, it is determined to be a valid target signal; the signal is processed by a CNN-LSTM network to identify and filter out multipath interference signals reflected from the sea surface and seabed.

5. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, The calculation method for extracting target depth information using dual-pulse delay difference encoding in step S3 is as follows: ； in, For target depth, The average speed of sound in water. The difference in reception delay for the dual-pulse signal. The elevation angle of the incident sound ray; the correlation peak interpolation method uses quadratic polynomial interpolation, and the time delay estimation accuracy reaches within 1 / 100 of the sampling interval.

6. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, The method for constructing the coordinate rotation correction matrix in step S4 is as follows: based on the platform roll angle. Pitch angle Bow angle Construct a three-dimensional rotation matrix: ； ； In the formula, For the platform's roll angle, For the platform's pitch angle, For the platform's bow angle, For the roll rotation matrix, For the pitch and rotation matrix, Here is the heading rotation matrix; the target's absolute north-east sky coordinates. ,in The target's relative rectangular coordinates, The GPS / BD absolute coordinates of the self-elevating platform are used to transform the carrier coordinate system coordinates of the hydrophone element through a rotation matrix to obtain the element coordinates in the geographic coordinate system, thus completing the compensation for platform attitude disturbances.

7. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, The method for correcting the sound ray propagation trajectory by combining real-time sound velocity profile data in step S4 is as follows: based on the sound velocity profile acquired by the CTD sensor. A constant gradient ray tracking model is used to calculate the horizontal distance and propagation delay of ray propagation, with the ray curvature satisfying: ； in, For the curvature of the vocal tract, The vertical gradient of the speed of sound. The initial glancing angle of the sound ray is given; the sound path value in the spherical intersection localization equation is corrected based on the sound ray tracking results.

8. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, Step S5 is followed by a target dynamic tracking filtering step, specifically: S51 State Vector Construction: Using the target's three-dimensional coordinates and three-dimensional velocity as state variables, construct the state equations and observation equations of the discrete-time system; S52 UT Transform Sampling: Based on the current state estimate and covariance matrix, 2n+1 Sigma points are obtained by unscented UT sampling, where n is the dimension of the state vector. S53 State prediction propagation: Substitute each Sigma point into the state equation to calculate the state prediction Sigma point at the next time step, and solve for the state prediction value and prediction covariance matrix based on the prediction Sigma point. S54 Observation Update Calculation: Substitute the predicted Sigma point into the observation equation to obtain the observed prediction value, calculate the Kalman gain, update the state vector and covariance matrix by combining the current positioning observation value, and output the target optimal state estimate; S55 Multi-Source Data Fusion: Federated Kalman filtering is used to construct local filters for GPS / BD positioning, attitude measurement, and acoustic measurement. After each local filter completes its filtering update independently, the results are input into the main filter for fusion, and the global optimal state estimate is output.

9. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, Step S3 specifically includes the multi-target synchronization signal processing steps: S31 Frequency Hopping Pattern Allocation: A unique COSTAS frequency hopping pattern is pre-assigned to each underwater target, and the target address is encoded using a combination of frequency division multiplexing and code division multiplexing. S32 Parallel Correlation Detection: Each target is assigned an independent parallel processing channel, and the preprocessed baseband signal is subjected to sliding cross-correlation with the local frequency hopping copy signal of the corresponding target to complete the synchronous detection of multiple target signals; S33 Target Delay Estimation: For the effective signals of each detected target, high-precision delay estimation of multiple hydrophone channels is completed by correlation peak interpolation. S34 Parallel Localization Solution: An independent localization solution thread is assigned to each target, and the three-dimensional coordinates and trajectory updates of each target are completed synchronously based on the corresponding time delay estimation results.

10. The high-precision short baseline signal and data processing method for a near-shore jack-up platform according to claim 1, characterized in that, It also includes system self-test and fault diagnosis steps, specifically: S01 Power-on self-test: After the system starts up, it generates a standard analog acoustic signal through the built-in signal simulator and injects it into each signal processing channel to complete the self-test of channel delay consistency, gain accuracy, and delay estimation accuracy. S02 Operational Status Monitoring: During system operation, the signal amplitude, signal-to-noise ratio, and primitive coordinate offset parameters of each hydrophone channel are collected in real time to monitor the system's operational status. S03 Fault Identification and Isolation: When abnormal channel signal gain or element aperture offset fault is detected, the faulty channel is automatically identified and the signal of the faulty channel is isolated. S04 System Reconstruction and Calibration: Based on the normally functioning hydrophone channels, the array is reconstructed, the positioning solution model parameters are updated, and remote calibration commands are supported to complete the system parameter calibration.

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