An autonomous underwater vehicle positioning method and system based on a relay forwarding mechanism

Abstract

The application relates to the technical field of underwater robot research, and provides an autonomous underwater robot positioning method and system based on a relay forwarding mechanism. The autonomous underwater robot positioning method based on the relay forwarding mechanism comprises the following steps: firstly, a kinematic model of an AUV and a measurement mapping model of multiple beacon nodes are uniformly modeled as a kind of asynchronous multi-rate sampling nonlinear system. On this basis, the resolution constraint of a data acquisition sensor of the beacon node and the bandwidth limitation of an underwater acoustic channel are fully considered, and a relevant pair quantizer is used to compress and process a transmission signal. A three-end relay transmission model is introduced to describe the cross-medium transmission process of data from the AUV to the buoy and then to the surface base station. Further, in combination with advanced mathematical tools such as the matrix maximum principle, graph theory topological modeling and geometric projection optimization, an on-line iterative high-efficiency positioning algorithm is designed, and high-precision and high-reliability estimation of the position of the AUV in a complex underwater acoustic environment is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater robot research, and particularly relates to an autonomous underwater vehicle positioning method and system based on a relay forwarding mechanism. BACKGROUND

[0002] An autonomous underwater vehicle (AUV) is the core equipment for ocean exploration, underwater operation and environmental monitoring, and its premise for completing the mission is to have accurate and reliable positioning and navigation capabilities. At present, using an unmanned surface vehicle (USV) or a buoy as a mobile beacon node and forming a cooperative navigation system with the AUV is an effective solution. The water surface beacon node can obtain its high-precision position information through a global navigation satellite system (GNSS), and use underwater acoustic communication to measure the distance and exchange information with the underwater AUV, thereby providing positioning correction for the AUV. In such a system, the AUVs share their states through an underwater acoustic communication network, further expanding the navigation range, improving the positioning accuracy and increasing the system redundancy.

[0003] However, this technology faces severe challenges in the actual underwater environment. First, the underwater acoustic channel is a typical attenuation channel with narrow bandwidth, large delay, high bit error rate, unstable link and other characteristics. The multipath effect, time-varying characteristics and inevitable communication packet loss of the signal cause the ranging information and state data between the AUV and the beacon to have missing, delay or serious errors, which directly destroys the “ideal communication” assumption basis on which traditional positioning algorithms rely, causing the positioning filtering algorithm to diverge and the system reliability to decrease. Second, to solve the problem of limited computing power of the AUV, high-precision positioning algorithms usually rely on remote estimators for calculation and use a relay forwarding mechanism, that is, the information received by a beacon node can be forwarded to a remote estimator for positioning calculation. However, this mechanism also introduces new challenges: different beacon nodes (such as USVs, buoys, etc.) have differences in performance and sensor resolution. When a beacon node carrying a low-precision sensor obtains positioning information with deviations and forwards it to a remote estimator, the observation error will spread to the entire network through the relay link, causing cross propagation and amplification of errors and seriously reducing the positioning accuracy of the AUV. Existing positioning methods do not fully consider this negative impact caused by the heterogeneity of sensor resolution under the relay forwarding mechanism, and lack effective error suppression and isolation mechanisms. Therefore, the existing technology cannot effectively suppress the error propagation problem caused by the difference in sensor resolution in an unreliable attenuation channel environment (especially when using the relay forwarding mode), and cannot meet the demand of the AUV for high-precision and high-reliability positioning. SUMMARY

[0004] In order to solve the technical problems in the above background art, the present application provides an autonomous underwater vehicle positioning method and system based on a relay forwarding mechanism, which comprehensively considers multiple factors such as multi-rate sampling, sensor resolution difference and attenuated channel, establishes a system model, and is closer to the actual underwater positioning environment; on this basis, relying on a relay strategy, the system model is processed through an iterative state equation and a signal reconstruction method, and multi-rate sampling information containing delay is uniformly converted into single-rate sampling information. Based on the processed model, a positioning method that can be iterated online is designed by combining mathematical tools such as matrix theory, thereby improving the positioning accuracy and reliability of the AUV position.

[0005] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0006] The first aspect of the present application provides an autonomous underwater vehicle positioning method based on a relay forwarding mechanism.

[0007] An autonomous underwater vehicle positioning method based on a relay forwarding mechanism comprises:

[0008] A discrete error compensation term and a process noise disturbance term are introduced, the position coordinates of the AUV in the three-dimensional space are taken as the state vector to be estimated of the system, and an AUV kinematic model is constructed;

[0009] The time of the underwater acoustic signal reaching the beacon node sensor is obtained, the relative distance between the beacon node sensor and the AUV is estimated in combination with the AUV kinematic model, a measurement mapping model is constructed, and based on the measurement mapping model, the physical resolution factor of the beacon node sensor is considered to establish a nonlinear mapping relationship between the actual measurement signal and the ideal signal, and a relevant pair quantizer is designed to quantize the actual measurement signal;

[0010] The present application establishes a nonlinear mapping relationship between the actual collected data and the ideal signal by pre-setting the physical resolution factor of the beacon node sensor, so as to reflect the errors and distortions existing in the actual measurement; in order to adapt to the characteristics of limited bandwidth of underwater acoustic communication, based on the embedded relevant pair quantizer, the original signal is processed by layered quantization, the data amount is effectively compressed under the premise of ensuring that the key information is not lost, the signal transmission efficiency is improved, and standardized, low-bandwidth demand preprocessed data is provided for the subsequent relay transmission and positioning algorithm module.

[0011] The quantized actual measurement signal is transmitted through a relay model to obtain a first AUV measurement signal transmitted by the quantizer through the relay forwarder to the fusion center and a second AUV measurement signal transmitted by the quantizer directly to the fusion center; the relay model comprises a quantizer, a relay forwarder and a fusion center;

[0012] The first and second AUV measurement signals are spatiotemporally fused. Based on the principle of matrix maxima, the optimal positioning gain matrix is ​​adaptively calculated. Through multiple iterative updates and covariance optimization, the AUV positioning result is obtained.

[0013] Furthermore, the method involves introducing a discretization error compensation term and a process noise disturbance term, using the AUV's position coordinates in three-dimensional space as the system's state vector to be estimated, and constructing an AUV kinematic model; the method includes:

[0014] Based on the rigid body kinematics and fluid dynamics principles of AUVs, a six-degree-of-freedom continuous-time kinematic model of the target AUV is established in a fixed Earth coordinate system. The continuous-time model is converted into a discrete-time kinematic model using the Euler method or the Runge-Kutta numerical integration method, while keeping the sampling period synchronized with the system clock. The nonlinear terms in the discrete-time kinematic model are processed by linearization techniques, while retaining higher-order terms as nonlinear perturbations, thus establishing an AUV kinematic model with discretization error compensation terms and process noise perturbation terms.

[0015] To address the motion characteristics of AUVs in complex underwater environments, this invention employs a discretized nonlinear dynamic model based on multi-rate sampling for system description. In this model, the AUV's position coordinates in three-dimensional space are... As the state vector to be estimated for the system, it comprehensively characterizes its spatial motion state. This modeling method fully considers the uncertain disturbances and multi-source interference that may exist in the underwater environment, and expresses the system evolution process in discrete-time state-space form, thus providing accurate and reliable dynamic constraints for subsequent positioning algorithms.

[0016] Furthermore, the related logarithmic quantizer is represented by the following formula:

[0017]

[0018] in, For quantizers; For actual collected data The One element; This represents the product of the quantization density and the scalar factor. These are the quantizer parameters; For quantization density.

[0019] Furthermore, the establishment of the nonlinear mapping relationship between the actual measured signal and the ideal signal is expressed by the following formula:

[0020]

[0021] in, It's the resolution. The One element; and These are the actual collected data. and ideal signal The One element; Indicates rounding up; This indicates rounding down to the nearest integer.

[0022] Furthermore, the relay repeater is used to amplify the quantized actual measurement signal with low complexity. The amplification gain is associated with the instantaneous channel state information of the uplink channel to compensate for path loss and fading.

[0023] Furthermore, the method involves performing spatiotemporal fusion processing on the first and second AUV measurement signals, adaptively calculating the optimal positioning gain matrix based on the matrix maxima principle, and obtaining the AUV positioning result through multiple iterative updates and covariance optimization; the method includes:

[0024] when At that time, according to the principle of matrix maxima, the first gain is located. Based on the first gain and the first AUV measurement signal, the positioning estimation of the initial segment AUV is performed to obtain the state estimation and covariance information of the initial segment.

[0025] when At that time, based on the principle of matrix maxima, the second gain is located; based on the second gain and the second AUV measurement signal, the location estimation of the intermediate AUV is performed to obtain the state estimation and covariance information of the intermediate segment;

[0026] when At that time, the state estimation and covariance information of the intermediate segment are used for online iteration to obtain the AUV positioning result.

[0027] A second aspect of the present invention provides an autonomous underwater robot positioning system based on a relay mechanism.

[0028] An autonomous underwater robot positioning system based on a relay mechanism includes:

[0029] The modeling module is used to introduce discretization error compensation terms and process noise disturbance terms, and to construct the AUV kinematic model by taking the position coordinates of the AUV in three-dimensional space as the state vector to be estimated of the system.

[0030] The sensing module is used to acquire the time when the underwater acoustic signal arrives at the beacon node sensor. Combined with the AUV kinematic model, it estimates the relative distance between the beacon node sensor and the AUV and constructs a measurement mapping model. Based on the measurement mapping model, considering the physical resolution factor of the beacon node sensor, it establishes a nonlinear mapping relationship between the actual measurement signal and the ideal signal and designs a correlation logarithm quantizer to quantize the actual measurement signal.

[0031] The relay module is used to transmit the quantized actual measurement signal through the relay model to obtain a first AUV measurement signal transmitted from the quantizer to the fusion center via the relay repeater and a second AUV measurement signal transmitted directly from the quantizer to the fusion center; the relay model includes a quantizer, a relay repeater, and a fusion center;

[0032] The positioning module is used to perform spatiotemporal fusion processing on the first AUV measurement signal and the second AUV measurement signal. Based on the principle of matrix maxima, it adaptively calculates the optimal positioning gain matrix and obtains the AUV positioning result through multiple iterative updates and covariance optimization.

[0033] A third aspect of the present invention provides a computer device comprising:

[0034] A processor, adapted to execute computer programs;

[0035] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the autonomous underwater robot localization method based on a relay mechanism as described in the first aspect above.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute steps in the autonomous underwater robot localization method based on a relay mechanism as described in the first aspect above.

[0037] The fifth aspect of the present invention provides a computer program product or computer program.

[0038] This invention provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the autonomous underwater robot localization method based on a relay mechanism as described in the first aspect above.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] To address the problems of significant acoustic channel attenuation, large differences in beacon node sensor resolution, and limited data transmission bandwidth in current underwater positioning technologies, this invention proposes an autonomous underwater robot (AUV) positioning method and system based on a relay mechanism under conditions of attenuated channels and beacon node resolution differences. First, the AUV kinematic model and the beacon node measurement mapping model are constructed as an asynchronous multi-rate sampling nonlinear system, which better reflects the operational scenarios under actual heterogeneous sensing architectures. Based on this, considering the resolution constraints of the beacon node data acquisition sensors and the bandwidth limitations of the acoustic channel, a correlation logarithmizer is used to compress the transmitted signal, significantly improving the utilization efficiency of the limited bandwidth acoustic channel. By introducing a three-terminal relay transmission model to characterize the cross-medium transmission process from the AUV to the buoy and then to the surface base station, the problem of decreased positioning accuracy and limited real-time performance caused by inconsistent buoy sensor resolution and quantization noise is effectively solved. Finally, combining advanced mathematical tools such as the matrix maxima principle, graph theory topology modeling, and geometric projection optimization, a high-efficiency positioning algorithm capable of online iteration is designed, achieving high-precision and high-reliability estimation of the AUV's position in complex underwater acoustic environments. Attached Figure Description

[0041] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0042] Figure 1 This is a flowchart illustrating an autonomous underwater robot localization method based on a relay forwarding mechanism, as shown in an embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of an AUV positioning scenario based on a relay mechanism, as shown in an embodiment of the present invention.

[0044] Figure 3 The AUV shown in the embodiments of the present invention is in x Positioning effect diagram in the axial direction;

[0045] Figure 4 The AUV shown in the embodiments of the present invention is in y Positioning effect diagram in the axial direction;

[0046] Figure 5 The AUV shown in the embodiments of the present invention is in x Positioning mean square error diagram in the axial direction;

[0047] Figure 6 The AUV shown in the embodiments of the present invention is in y Positioning mean square error diagram in the axial direction;

[0048] Figure 7This is a structural diagram of an autonomous underwater robot positioning system based on a relay forwarding mechanism, as shown in an embodiment of the present invention.

[0049] Figure 8 This is a structural diagram of a computer device shown in an embodiment of the present invention. Detailed Implementation

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0052] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0053] Figure 1 This is a flowchart illustrating an autonomous underwater robot localization method based on a relay forwarding mechanism, as shown in an embodiment of the present invention; see reference. Figure 1 The method includes:

[0054] By introducing discretization error compensation terms and process noise disturbance terms, and taking the position coordinates of the AUV in three-dimensional space as the state vector to be estimated of the system, a kinematic model of the AUV is constructed.

[0055] The time it takes for the underwater acoustic signal to reach the beacon node sensor is obtained. Combined with the AUV kinematic model, the relative distance between the beacon node sensor and the AUV is estimated, and a measurement mapping model is constructed. Based on the measurement mapping model, considering the physical resolution factor of the beacon node sensor, a nonlinear mapping relationship between the actual measurement signal and the ideal signal is established, and a correlation logarithmic quantizer is designed to quantize the actual measurement signal.

[0056] The quantized actual measurement signal is transmitted through a relay model to obtain a first AUV measurement signal transmitted from the quantizer to the fusion center via a relay repeater and a second AUV measurement signal transmitted directly from the quantizer to the fusion center; the relay model includes a quantizer, a relay repeater, and a fusion center;

[0057] The first and second AUV measurement signals are spatiotemporally fused. Based on the principle of matrix maxima, the optimal positioning gain matrix is ​​adaptively calculated. Through multiple iterative updates and covariance optimization, the AUV positioning result is obtained.

[0058] To address the problems of significant underwater acoustic channel attenuation, large differences in sensor resolution at beacon nodes, and limited data transmission bandwidth in current underwater positioning technologies, this invention proposes an AUV positioning method based on a multi-surface beacon node relay mechanism. This method first unifies the kinematic model of the AUV and the measurement mapping model of multiple beacon nodes into a class of asynchronous multi-rate sampling nonlinear systems. Based on this, it fully considers the resolution constraints of the beacon node data acquisition sensors and the bandwidth limitations of the underwater acoustic channel, and employs a correlation logarithmizer to compress the transmitted signal. A three-terminal relay transmission model is introduced to characterize the cross-medium transmission process of data from the AUV to the buoy and then to the surface base station. Furthermore, combining advanced mathematical tools such as the matrix maxima principle, graph theory topology modeling, and geometric projection optimization, a high-efficiency positioning algorithm capable of online iteration is designed, achieving high-precision and high-reliability estimation of AUV positions in complex underwater acoustic environments.

[0059] The implementation process of the autonomous underwater robot localization method based on the relay forwarding mechanism described in this embodiment is described in detail below. The method includes the following steps:

[0060] Step 1: System Modeling: Kinematic Model of AUV.

[0061] The position coordinates of the AUV in three-dimensional space are used as the state vector to be estimated for the system. A class of multi-rate sampling nonlinear discrete systems is used to characterize the state information of the AUV.

[0062]

[0063] in, yes Location information of the AUV at any given time. For zero-mean process noise, the covariance matrices are respectively . and Here is the state transition matrix. Zero-mean white noise, random nonlinear function Used to describe discretization error and modeling uncertainty, satisfying It also has the following statistical properties:

[0064]

[0065]

[0066]

[0067] in, Represents the mathematical expectation. and Represents the sampling sequence. It is a known integer. and It is a known positive semi-definite matrix with appropriate dimensions. The sampling rate of the preset kinematic model is... To achieve high-precision positioning, beacon nodes require a faster sampling rate. This invention presupposes that the sampling rate of the beacon nodes is an integer multiple of the state sampling rate, i.e. ,in The sampling time for the beacon node. The integer is positive. Mapping the above kinematic model to a time scale of... Discrete-time nonlinear model:

[0068]

[0069] in, express Location information of the AUV at the sampling time; and This represents the state transition matrix after model transformation; This represents the matrix after model transformation. This represents the stochastic nonlinear function after model transformation; This represents zero-mean white noise after model transformation; This represents the zero-mean process noise after model transformation.

[0070] The mapping relationship of the model parameters is as follows:

[0071]

[0072]

[0073] in, This refers to the diag function; express The product of the state transition matrices at each sampling time; This represents the parameter matrix after model transformation; express State transition matrix at sampling time;

[0074] The mapping relationship between process noise and random nonlinear disturbance term is as follows:

[0075]

[0076]

[0077] in, This indicates taking the column vector.

[0078] Based on the characteristics of actual systems, this invention uses a nonlinear system with asynchronous multi-rate sampling to describe the kinematic behavior of an AUV, and maps this kinematic model to the same time scale as the beacon node measurement mapping model. The model uses the AUV's position in three-dimensional space as key state variables, and accurately describes the dynamic behavior and uncertainties of the AUV in complex underwater environments by introducing discretization error compensation terms and process noise disturbance terms.

[0079] Step 2, Perception Modeling: Mapping and Quantization of Data Collected by Beacon Nodes.

[0080] (1) Mapping relationship between actual acquired data and ideal signal:

[0081] Through linearization processing techniques, the first Each beacon node is The ideal observation output at time t is modeled as follows:

[0082]

[0083] in, Indicates the first Each beacon node is Ideal observation output at any given time; Used to characterize measurement noise and linearization error, its mean is zero and its variance is... , This is a measurement matrix. To accurately characterize the measurement errors and signal distortion caused by the accuracy limitations of beacon node sensors, this invention presets the first... The physical resolution factor of each beacon node sensor is:

[0084]

[0085] in, It's the resolution. The Each element. Based on the resolution factor, a nonlinear mapping relationship is established between the actual acquired data and the ideal signal:

[0086]

[0087] in, and These are the actual collected data. and ideal signal The One element, Indicates rounding up. This indicates rounding down to the nearest integer.

[0088] (2) Related logarithmic quantizer design:

[0089] To adapt to the bandwidth limitations of underwater acoustic communication, layered quantization processing is required before the actual measurement signal is transmitted through the underwater acoustic channel to reduce data transmission volume and improve communication reliability. This invention employs the following quantized measurement mapping model:

[0090]

[0091] in, This indicates the quantified measurement; For a quantizer, its quantization levels are:

[0092]

[0093] in, A set representing quantization levels; This represents the product of the quantization density and the scalar factor. To quantize density, The scalar factor is greater than zero. This invention designs the following related logarithmic quantizer:

[0094]

[0095] in, These are the quantizer parameters. Therefore, the mapping relationship between the quantizer output and the actual measured signal is:

[0096]

[0097] in, For a preset scalar factor, satisfying Based on the above mapping relationship, the output signal of the quantizer is:

[0098]

[0099] in, , represents the matrix formed by the preset scalar factors in the mapping relationship; For actual output Compared to ideal output The difference between them.

[0100] This invention integrates the physical resolution limitations of beacon node measurement sensors and the quantization characteristics of related logarithmic quantizers to establish a nonlinear mapping relationship between actual acquired data and ideal signals, so as to accurately describe the distortion and error characteristics of signal transmission in underwater environments; and performs hierarchical quantization processing on the measured signals to reduce data redundancy and improve channel utilization.

[0101] Step 3: Relay transmission mechanism design: Relay transmission of quantized signals.

[0102] This invention transmits the quantized signal through an amplification-relay relay scheme, thereby effectively improving the utilization of limited bandwidth. The transmission mechanism design includes establishing a three-terminal collaborative relay model between the quantizer and the relay repeater, between the relay repeater and the fusion center, and between the quantizer and the fusion center, respectively modeling the signal transmission and transformation relationships in different links.

[0103] (1) Quantizer-relay transponder transmission mechanism:

[0104] Based on the amplification-forwarding relay scheme, the transmission mapping model of the quantizer-relay repeater in this invention is as follows:

[0105]

[0106] in, This indicates the signal received by the relay repeater. It is the known average signal energy. This describes packet loss during transmission, and it follows a Bernoulli distribution, satisfying the following condition: , Let be a known scalar, representing the probability of signal packet loss. The mean is zero and the covariance is Additive noise.

[0107] (2) Relay repeater-fusion center transmission mechanism:

[0108] The relay repeater further amplifies and forwards the received signal. The transmission mapping model used in this invention is as follows:

[0109]

[0110] in, This indicates the signal received by the fusion center. It is the forwarding amplification factor. This describes packet loss during transmission, and it follows a Bernoulli distribution, satisfying the following condition: . The mean is zero and the covariance is Additive noise.

[0111] (3) Quantizer-fusion center transmission mechanism:

[0112] For the transmission of signals via direct transmission links, the transmission mapping model adopted in this project is as follows:

[0113]

[0114] in, For the fusion center to receive signals from the quantizer, This is the delay signal for the quantizer. It is the known average signal energy. The mean is zero and the covariance is Additive noise. Denotes the random delay of a direct transmission link, which is in a finite set. The value is taken from the middle. Therefore, after passing through the direct transmission link, the signal that the fusion center may receive is:

[0115]

[0116] in, This indicates the signals that the fusion center may receive. Let d represent the signal with a time delay of l from the fusion unit, and d represent the maximum value of the random delay.

[0117]

[0118] binary random variable When the value is 1, it indicates that the fusion center has received information about... The AUV's status information at any given time is zero if no data was received, and its probability distribution follows... , The probability is known.

[0119] In summary, based on the three-terminal cooperative relay model using the amplification-forwarding relay transmission scheme, the measurement signal regarding the AUV received by the fusion center is as follows:

[0120]

[0121] in, express The signal received by the sampling fusion center at the sampling time; This indicates the measurement signal ultimately received by the fusion center.

[0122] This invention first transmits the quantized signal through an amplification-relay relay scheme. Based on this, three-terminal collaborative relay models are sequentially established between the quantizer and the relay transceiver, between the relay transceiver and the fusion center, and between the quantizer and the fusion center, modeling the signal transmission and transformation relationships in different links. Finally, at the receiving end, based on signal replicas from multi-path transmission and a fusion strategy, highly reliable recovery and reconstruction of the original signal is achieved. This mechanism effectively addresses the characteristics of high attenuation and high noise in underwater acoustic channels, improving the system's communication quality and the integrity of positioning data.

[0123] Step 4, Localization Method: Three-stage online iterative algorithm.

[0124] Based on the measurement signals received by the fusion center, this invention designs the following three-stage online iterative algorithm:

[0125] (1) Let The index of the current sampling time, when Initial segment AUV positioning estimation for:

[0126]

[0127] The definitions of each parameter are as follows:

[0128]

[0129] in, Represents the set of measurements for all nodes; Indicates a mathematical change; and For positioning gain. As a preset factor, This indicates that beacon node i and node j have no information exchange. This indicates that there is information exchange between beacon nodes i and j, with a weighting coefficient of . The positioning error and its covariance matrix are defined as follows: , The initial value is ;in, Indicates positioning error; T indicates transpose; This indicates the state of the target AUV.

[0130] According to the principle of matrix maxima, the positioning gain is:

[0131]

[0132] The definitions of each parameter are as follows:

[0133] ,

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] in, This is a block diagonal expansion matrix with diagonal elements of 1. The diagonal matrix is ​​the information matrix representing the packet loss probability. This is the expansion matrix of the measured signal parameter matrix. This is the column matrix of all measurement expansion matrices. The dimension is identity matrix The sum of the positioning error covariance matrix and the state covariance matrix. This is an expansion matrix of the topological information matrix. For topological information matrix, The transformation matrix is ​​the state covariance matrix. This is the difference matrix between the state matrices. The difference matrix is ​​the state covariance matrix. This represents the trace of the matrix. The end value of the initial segment provides the initial value for the iterative calculation of the middle segment, i.e. .

[0142] (2) When The positioning estimate for the mid-section AUV is:

[0143]

[0144] In the formula, This indicates the output signal.

[0145] in,

[0146]

[0147] in, Represents the set of all measured signals. Represents the information interaction topology matrix; and The positioning gain is defined as follows. The positioning error and its covariance matrix are defined as follows: , .

[0148] According to the principle of matrix maxima, the positioning gain is:

[0149]

[0150] in,

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] in, This is a block diagonal expansion matrix with diagonal elements of 1. The diagonal matrix is ​​the information matrix representing the packet loss probability. This is the expansion matrix of the measured signal parameter matrix. This is the column matrix of all measurement expansion matrices; The sum of the positioning error covariance matrix and the state covariance matrix. This is an expansion matrix of the topological information matrix. For topological information matrix, The transformation matrix is ​​the state covariance matrix. This is the difference matrix between the state matrices; The difference matrix is ​​the state covariance matrix. Indicates the number of matrix blocks.

[0159] (3) When That is, AUV in The location output at any given time can be obtained through online iteration of the intermediate segment. High-precision positioning results of AUV at any given time.

[0160] This invention employs a distributed fusion framework. Based on the received relay signals, each node first performs preliminary prediction of the AUV's state information to obtain preliminary state estimates and covariance information. Subsequently, according to the distributed fusion strategy, spatiotemporal fusion processing is performed on the information from the quantizer, relay transceiver, and fusion center. An adaptive positioning gain matrix is ​​designed based on the matrix maxima principle to effectively suppress the influence of measurement noise and communication errors. Through multiple iterative updates and covariance optimization, high-precision and robust positioning of the AUV is finally achieved.

[0161] Through steps one through four, the autonomous underwater robot localization method based on a relay forwarding mechanism proposed in this invention is summarized as follows:

[0162] (1) According to step one, the position of the AUV in three-dimensional space is taken as the key state variables. By introducing discretization error compensation term and process noise disturbance term, and based on the discretization nonlinear dynamic model of multi-rate sampling, the kinematic model of the AUV is accurately characterized.

[0163] (2) According to step two, establish the nonlinear mapping relationship between the actual measurement signal and the ideal signal, and perform hierarchical quantization processing on the actual measurement signal;

[0164] (3) Based on step three, establish three-terminal collaborative relay models between the quantizer and the relay repeater, between the relay repeater and the fusion center, and between the quantizer and the fusion center, respectively, and model the signal transmission and transformation relationship in different links;

[0165] (4) Based on step four, design an online iterative positioning algorithm and calculate the adaptive positioning gain matrix based on the principle of matrix maxima.

[0166] The specific implementation process is achieved using the simulation tool Matlab. The effectiveness of this invention can be further illustrated by the following experimental simulations. The target AUV of this invention, as a moving target in three-dimensional space, employs a multi-source information fusion strategy to acquire its pose information. High-precision real-time pose calculation is achieved through distributed elastic state estimation and an adaptive weighted fusion algorithm.

[0167] In this embodiment, a positioning system consisting of four surface buoy nodes and surface vessels is constructed. The communication topology of the surface buoy nodes and surface vessels adopts the following... Figure 2 The scene architecture shown; in this invention, the key parameters for the multi-rate sampling kinematic model are set as follows:

[0168]

[0169]

[0170] initial state Initial estimation error covariance matrix The perception model for beacon nodes is isomorphic, and the key parameters are set as follows:

[0171]

[0172] The covariances of process noise, observation noise, and channel noise are respectively , , The key parameters for sensor resolution are set as follows: , , , The key parameters of the quantizer are set to... , The average signal energy is , The initial point on the plane projected by the AUV is set to (10m, 10m).

[0173] Based on the calculations of the relay transmission mechanism-based positioning method proposed in this invention, the beacon node utilizes its sensing measurements of the AUV to... Figure 3 The image clearly shows the real-time positioning effect of the AUV in the x-axis direction. Figure 4 This shows the real-time positioning effect of the AUV in the y-axis direction. Figure 5 and Figure 6 The simulation results demonstrate that the positioning error of the relay-based positioning method varies over time, with positioning accuracy consistently around 0.5m. The simulation results show that the proposed relay-based positioning method exhibits high positioning accuracy and robustness.

[0174] This invention unifies the kinematic model of an AUV and the measurement mapping model of multiple beacon nodes into a class of asynchronous multi-rate sampling nonlinear systems to adapt to the asynchronous update characteristics of different sensors and data sources. It constructs a nonlinear mapping relationship between actual acquired data and ideal signals to accurately describe the distortion and error characteristics of signal transmission in the underwater environment. The original signal undergoes hierarchical quantization processing to reduce data redundancy and improve channel utilization. A three-terminal collaborative relay model is established between the quantizer and the relay transponder, between the relay transponder and the fusion center, and between the quantizer and the fusion center, modeling the signal transmission and transformation relationships in different links. Based on a distributed fusion strategy, spatiotemporal fusion processing is performed on the information from the three nodes—quantizer, relay transponder, and fusion center—and an adaptive positioning gain matrix is ​​designed based on the matrix maxima principle to effectively suppress the influence of measurement noise and communication errors. Through multiple iterative updates and covariance optimization, high-precision and robust positioning of the AUV is ultimately achieved.

[0175] The above combination Figure 1 The autonomous underwater robot positioning method based on a relay forwarding mechanism provided in the embodiments of the present invention has been described in detail. Next, the autonomous underwater robot positioning system based on a relay forwarding mechanism provided in the embodiments of the present invention will be described in conjunction with the accompanying drawings.

[0176] Figure 7 This is a schematic diagram of the structure of an autonomous underwater robot positioning system based on a relay forwarding mechanism, as shown in an embodiment of the present invention. Figure 7 The system described in this invention includes:

[0177] The modeling module is used to construct a state-space discretized nonlinear kinematic model of the AUV. This model uses the AUV's position in three-dimensional space as key state variables, and accurately describes the dynamic behavior and uncertainties of the AUV in complex underwater environments by introducing discretization error compensation terms and process noise disturbance terms. The model employs a multi-rate sampling strategy to adapt to the asynchronous update characteristics of different sensors and data sources.

[0178] The sensing module integrates various sensors in the beacon node and, taking into account their physical resolution limitations and the quantization characteristics of related logarithmic quantizers, constructs a nonlinear mapping relationship between actual acquired data and ideal signals to accurately describe the distortion and error characteristics of signal transmission in the underwater environment. Furthermore, this module will perform hierarchical quantization processing on the raw signals to reduce data redundancy, improve channel utilization, and provide standardized, low-bandwidth preprocessed signals for subsequent relay transmission and positioning.

[0179] The relay module is used to implement a transmission scheme based on an amplification-forwarding relay mechanism. This module accurately models the signal amplification, forwarding, and transformation relationships in different transmission links by constructing a three-terminal collaborative relay model between the quantizer and the relay repeater, between the relay repeater and the fusion center, and between the quantizer and the fusion center.

[0180] The positioning module is used to build and execute an online iterative positioning algorithm. Based on a distributed fusion strategy, this module fuses information from three nodes—quantizer, relay repeater, and fusion center—to integrate multi-source heterogeneous data. Furthermore, based on graph theory and the principle of matrix maxima, it adaptively calculates the optimal positioning gain matrix and continuously improves the accuracy and stability of the positioning results through multiple iterations.

[0181] In some embodiments, the modeling module is specifically implemented by: using a multi-rate sampling system to acquire data from the AUV system; discretizing the continuous-time kinematic model of the AUV in the global coordinate system to construct a state-space model suitable for digital computation; introducing discretization error compensation terms and process noise disturbance terms into the model to accurately describe the system uncertainties caused by water flow disturbances, model simplification, and external disturbances in the underwater environment; and employing a multi-rate sampling strategy to match the asynchronous update characteristics of different sensors and data sources, thereby achieving multi-scale, high-fidelity modeling of the AUV's motion state and providing a reliable dynamic basis for subsequent modules.

[0182] In some embodiments, the specific implementation of the sensing module includes: integrating various sensors in the beacon node, and comprehensively considering their physical resolution limitations and the quantization characteristics of the relevant logarithmic quantizer, presetting the physical resolution factor of the beacon node sensors, and establishing a nonlinear mapping relationship between the actual collected data and the ideal signal to accurately characterize the measurement error and signal distortion caused by the sensor accuracy limitation; further, based on the embedded relevant logarithmic quantizer, performing hierarchical quantization processing on the original signal, effectively compressing the data volume while ensuring that key information is not lost, improving signal transmission efficiency, and providing standardized, low-bandwidth preprocessed data for subsequent relay transmission and positioning algorithm modules.

[0183] In some embodiments, the relay module is specifically implemented by executing a quantization-amplification-forwarding strategy through an embedded three-terminal cooperative relay model. The quantized signal from the beacon node is input into an embedded three-terminal cooperative relay model. The relay node amplifies the received signal from the beacon node with low complexity; its amplification gain is typically correlated with the instantaneous channel state information of the uplink channel to compensate for path loss and fading, while avoiding excessive noise amplification. The fusion center simultaneously receives the direct transmission link signal from the beacon node and the forwarded link signal from the relay node. These two signals undergo independent and different fading channel and noise effects, constituting spatial diversity for transmitting the same information.

[0184] In some embodiments, the specific implementation of the positioning module includes: adopting a hierarchical collaborative and centralized optimization hybrid architecture to fuse information from three nodes and integrate multi-source heterogeneous data; calculating the optimal positioning gain matrix based on graph theory and the principle of matrix maxima, and continuously improving the accuracy and stability of the positioning results through multiple iterations.

[0185] According to embodiments of the present invention, the autonomous underwater robot localization system based on a relay forwarding mechanism can correspond to the execution of the methods described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the autonomous underwater robot localization system based on the relay forwarding mechanism are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the code will not be elaborated here.

[0186] See Figure 8The diagram shows the structure of a computer device, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, communication interface, and computer-readable storage medium are connected via a bus or other means. The communication interface is used to receive and send data. The computer-readable storage medium can be stored in the computer device's memory. The computer-readable storage medium stores computer programs, including program instructions, and the processor executes the program instructions stored in the computer-readable storage medium. The processor (or CPU, Central Processing Unit) is the computing and control core of the computer device, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding steps in the embodiment of the autonomous underwater robot localization method based on a relay forwarding mechanism.

[0187] This embodiment provides a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the processing system of the computer device.

[0188] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM memory or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0189] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the above embodiment of the autonomous underwater robot localization method based on the relay forwarding mechanism.

[0190] This embodiment provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding steps in the above-described embodiment of the autonomous underwater robot localization method based on a relay mechanism.

[0191] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0192] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0193] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0194] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes ​ The steps of the function specified in one or more boxes.

[0195] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0196] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for localization of an autonomous underwater robot based on a relay mechanism, characterized in that, include: By introducing discretization error compensation terms and process noise disturbance terms, and taking the position coordinates of the AUV in three-dimensional space as the state vector to be estimated of the system, a kinematic model of the AUV is constructed. The time it takes for the underwater acoustic signal to reach the beacon node sensor is obtained. Combined with the AUV kinematic model, the relative distance between the beacon node sensor and the AUV is estimated, and a measurement mapping model is constructed. Based on the measurement mapping model, considering the physical resolution factor of the beacon node sensor, a nonlinear mapping relationship between the actual measurement signal and the ideal signal is established, and a correlation logarithmic quantizer is designed to quantize the actual measurement signal. The quantized actual measurement signal is transmitted through a relay model to obtain a first AUV measurement signal transmitted from the quantizer to the fusion center via a relay repeater and a second AUV measurement signal transmitted directly from the quantizer to the fusion center; the relay model includes a quantizer, a relay repeater, and a fusion center; The first and second AUV measurement signals are spatiotemporally fused. Based on the principle of matrix maxima, the optimal positioning gain matrix is ​​adaptively calculated. Through multiple iterative updates and covariance optimization, the AUV positioning result is obtained.

2. The autonomous underwater robot localization method based on a relay forwarding mechanism according to claim 1, characterized in that, The method involves introducing discretization error compensation terms and process noise disturbance terms, using the AUV's position coordinates in three-dimensional space as the system's state vector to be estimated, and constructing an AUV kinematic model; the method includes: Based on the rigid body kinematics and fluid dynamics principles of AUVs, a six-degree-of-freedom continuous-time kinematic model of the target AUV is established in a fixed Earth coordinate system. The continuous-time model is converted into a discrete-time kinematic model using the Euler method or the Runge-Kutta numerical integration method, while keeping the sampling period synchronized with the system clock. The nonlinear terms in the discrete-time kinematic model are processed by linearization techniques, while retaining higher-order terms as nonlinear perturbations, thus establishing an AUV kinematic model with discretization error compensation terms and process noise perturbation terms.

3. The autonomous underwater robot localization method based on a relay forwarding mechanism according to claim 1, characterized in that, The related logarithmic quantizer is represented by the following formula: in, For quantizers; For actual collected data The One element; This represents the product of the quantization density and the scalar factor. These are the quantizer parameters; For quantization density.

4. The autonomous underwater robot localization method based on a relay forwarding mechanism according to claim 1, characterized in that, The nonlinear mapping relationship between the actual measured signal and the ideal signal is established using the following formula: in, It's the resolution. The One element; and These are the actual collected data. and ideal signal The One element; Indicates rounding up; This indicates rounding down to the nearest integer.

5. The autonomous underwater robot localization method based on a relay forwarding mechanism according to claim 1, characterized in that, The relay repeater is used to amplify the quantized actual measurement signal with low complexity. The amplification gain is associated with the instantaneous channel state information of the uplink channel to compensate for path loss and fading.

6. The autonomous underwater robot localization method based on a relay forwarding mechanism according to claim 1, characterized in that, The method involves spatiotemporal fusion processing of the first and second AUV measurement signals, adaptively calculating the optimal positioning gain matrix based on the matrix maxima principle, and obtaining the AUV positioning result through multiple iterative updates and covariance optimization. The method includes: when At that time, according to the principle of matrix maxima, the first gain is located. Based on the first gain and the first AUV measurement signal, the positioning estimation of the initial segment AUV is performed to obtain the state estimation and covariance information of the initial segment. when At that time, based on the principle of matrix maxima, the second gain is located; based on the second gain and the second AUV measurement signal, the location estimation of the intermediate AUV is performed to obtain the state estimation and covariance information of the intermediate segment; when At that time, the state estimation and covariance information of the intermediate segment are used for online iteration to obtain the AUV positioning result.

7. An autonomous underwater robot positioning system based on a relay mechanism, characterized in that, include: The modeling module is used to introduce discretization error compensation terms and process noise disturbance terms, and to construct the AUV kinematic model by taking the position coordinates of the AUV in three-dimensional space as the state vector to be estimated of the system. The sensing module is used to acquire the time when the underwater acoustic signal arrives at the beacon node sensor. Combined with the AUV kinematic model, it estimates the relative distance between the beacon node sensor and the AUV and constructs a measurement mapping model. Based on the measurement mapping model, considering the physical resolution factor of the beacon node sensor, it establishes a nonlinear mapping relationship between the actual measurement signal and the ideal signal and designs a correlation logarithm quantizer to quantize the actual measurement signal. The relay module is used to transmit the quantized actual measurement signal through the relay model to obtain a first AUV measurement signal transmitted from the quantizer to the fusion center via the relay repeater and a second AUV measurement signal transmitted directly from the quantizer to the fusion center; the relay model includes a quantizer, a relay repeater, and a fusion center; The positioning module is used to perform spatiotemporal fusion processing on the first AUV measurement signal and the second AUV measurement signal. Based on the principle of matrix maxima, it adaptively calculates the optimal positioning gain matrix and obtains the AUV positioning result through multiple iterative updates and covariance optimization.

8. A computer device, characterized in that, A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the steps of the autonomous underwater robot localization method based on a relay forwarding mechanism as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and to execute the steps of the autonomous underwater robot localization method based on a relay mechanism as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the autonomous underwater robot localization method based on a relay forwarding mechanism as described in any one of claims 1-6.

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