Passive underwater cooperative positioning system and method

Abstract

According to the passive underwater cooperative positioning system and method provided by the invention, an intelligent signal processing and analysis module in the system receives a target underwater sound backscattering signal and a target environment parameter output by an environment adaptive feedback module, and then a path identification result and a positioning result are obtained by combining an identification model; obtaining a sound velocity correction value and a propagation time correction value based on the target environment parameters and a sound velocity correction model so as to obtain the moving speed of the target object; the cooperative positioning calculation module predicts the positioning information of the next moment based on the historical positioning information and the positioning information of the current moment; the environment self-adaptive feedback module receives the underwater sound backscattering signals generated by the multi-mode signal acquisition module and the acquired environment parameters, and transmits the underwater sound backscattering signals and the acquired environment parameters to the intelligent signal processing and analysis module as target underwater sound backscattering signals and target environment parameters; and the dynamic adjustment module is also used for dynamically adjusting the state parameters of the underwater acoustic backscattering signals and the environmental parameters so as to obtain new target underwater acoustic backscattering signals and target environmental parameters.

Description

Technical Field

[0001] The present invention relates to the field of underwater Internet of Things positioning technology, and in particular to a passive underwater collaborative positioning system and method. Background Art

[0002] Traditional underwater positioning technologies primarily rely on beacons or buoys that actively transmit acoustic signals, performing triangulation by measuring signal propagation delays. However, these technologies have significant limitations: First, nodes must periodically transmit acoustic signals to achieve positioning, resulting in high energy consumption. Battery-powered nodes struggle to support long-term deployments. Second, acoustic wave propagation paths are complex in underwater environments, with signals repeatedly reflecting between the seawater and interfaces (such as the sea surface and seabed), creating a strong multipath effect that accumulates delay estimation errors. This is especially true in shallow waters, where the length of the reflected path is similar to the direct path, further exacerbating inter-symbol interference and severely reducing positioning accuracy. Furthermore, existing solutions typically employ fixed signal parameters (such as bit rate and bandwidth), which cannot accommodate the conflicting demands of low-speed, high-precision positioning and high-speed mobile target tracking, making them difficult to adapt to dynamic underwater scenarios.

[0003] In recent years, the emergence of underwater acoustic backscatter technology has provided a new approach for low-power underwater communications. This technology uses acoustic signals reflected from the environment to transmit data back, eliminating the need for nodes to actively generate signals and significantly reducing energy consumption. However, existing backscatter positioning solutions are mostly designed for radio frequency environments and fail to effectively address the unique challenges of underwater acoustic channels. For example, battery-free nodes rely on ambient acoustic energy to wake up and operate. However, the random delay introduced by the energy collection process can lead to time-domain synchronization deviations, which are difficult to eliminate using traditional delay estimation methods. Furthermore, underwater acoustic waves propagate at a slow speed (approximately 1500 m / s), and even slight differences in adjacent reflection paths can cause significant inter-symbol interference. Existing solutions often use fixed frequency hopping sequences, resulting in rigid spectrum resource allocation, low bandwidth utilization, and an inability to dynamically adapt to channel characteristics. Furthermore, existing systems often rely on a single reader for positioning and lack multi-node coordination mechanisms. In complex multipath scenarios or with rapidly moving targets, the limitations of single-point measurement make positioning robust, making it difficult to meet practical application requirements. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first object of the present invention is to propose a passive underwater collaborative positioning system to improve the accuracy of underwater positioning.

[0006] The second object of the present invention is to provide a passive underwater collaborative positioning method.

[0007] To achieve the above-mentioned objectives, the first aspect of the present invention proposes a passive underwater collaborative positioning system, comprising a multimodal signal acquisition module, an intelligent signal processing and analysis module, a collaborative positioning solution module, and an environment adaptive feedback module;

[0008] The multimodal signal acquisition module includes an underwater acoustic backscatter sensor and an environmental perception sensor, which are used to generate underwater acoustic backscatter signals and collect underwater environmental parameters;

[0009] The intelligent signal processing and analysis module is configured to receive the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, input the target underwater acoustic backscatter signal into a recognition model to obtain a path recognition result and a positioning result, and obtain a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, thereby obtaining a moving speed of the target object;

[0010] The collaborative positioning solution module is used to predict the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment, wherein the positioning information includes the positioning result and the moving speed of the target object;

[0011] The environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal generated by the multimodal signal acquisition module and the collected environmental parameters, and transfer them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters; it is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module, and dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters if the positioning error exceeds the set positioning error threshold to obtain new target underwater acoustic backscatter signal and target environmental parameters.

[0012] In the passive underwater collaborative positioning system provided by the first aspect of the present invention, the environmental adaptive feedback module is also used to dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters if the change of the environmental parameters meets the requirements, so as to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0013] In the passive underwater collaborative positioning system provided by the first aspect of the present invention, the state parameters include signal transmission bit rate, signal acquisition frequency and processing speed.

[0014] In the passive underwater collaborative positioning system provided by the first aspect of the present invention, the environmental parameters include temperature, salinity and pressure.

[0015] In the passive underwater collaborative positioning system provided by the first aspect of the present invention, the recognition model adopts a convolutional neural network model.

[0016] In the passive underwater collaborative positioning system provided by the first aspect of the present invention, the prediction of the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment includes: a tracking algorithm based on Kalman filtering, and the prediction of the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment.

[0017] In the passive underwater collaborative positioning system provided in the first aspect of the present invention, the environmental adaptive feedback module is also used to adjust the acquisition frequency of the underwater acoustic backscatter signal and the environmental parameters based on the range of the moving speed after receiving the moving speed of the target object output by the intelligent signal processing and analysis module, so as to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0018] To achieve the above-mentioned object, the second aspect of the present invention provides a passive underwater collaborative positioning method, which is applicable to the passive underwater collaborative positioning system provided in the first aspect. The method comprises:

[0019] Using the underwater acoustic backscatter sensor of the multimodal signal acquisition module to generate an underwater acoustic backscatter signal, and using the environment perception sensor of the multimodal signal acquisition module to collect underwater environmental parameters;

[0020] The intelligent signal processing and analysis module receives the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, inputs the target underwater acoustic backscatter signal into the recognition model to obtain a path recognition result and a positioning result, and obtains a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, thereby obtaining the moving speed of the target object;

[0021] The collaborative positioning solution module predicts the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment, wherein the positioning information includes the positioning result and the moving speed of the target object;

[0022] The environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal generated by the multimodal signal acquisition module and the collected environmental parameters, and transfer them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters; the environmental adaptive feedback module is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module. If the positioning error exceeds the set positioning error threshold, the state parameters of the underwater acoustic backscatter signal and the environmental parameters are dynamically adjusted to obtain new target underwater acoustic backscatter signal and target environmental parameters.

[0023] The passive underwater collaborative positioning method provided in the second aspect of the present invention also includes: if the change of the environmental parameters meets the requirements, the environmental adaptive feedback module is used to dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0024] The passive underwater collaborative positioning method provided in the second aspect of the present invention also includes: using an environmental adaptive feedback module to receive the moving speed of the target object output by the intelligent signal processing and analysis module; based on the range of the moving speed, using the environmental adaptive feedback module to adjust the acquisition frequency of the underwater acoustic backscatter signal and the environmental parameters to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0025] The passive underwater collaborative positioning system and method provided by the present invention include a multimodal signal acquisition module, an intelligent signal processing and analysis module, a collaborative positioning solution module, and an environmental adaptive feedback module. The multimodal signal acquisition module includes an underwater acoustic backscatter sensor and an environmental perception sensor, which are used to generate underwater acoustic backscatter signals and collect underwater environmental parameters. The intelligent signal processing and analysis module is used to receive the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, input the target underwater acoustic backscatter signal into a recognition model to obtain a path recognition result and a positioning result, and obtain a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, thereby obtaining the moving speed of the target object. The collaborative positioning solution module is used to predict the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment. The positioning information includes the positioning result and the moving speed of the target object; the environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal and the collected environmental parameters generated by the multimodal signal acquisition module, and transfer them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters; it is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module. If the positioning error exceeds the set positioning error threshold, the state parameters of the underwater acoustic backscatter signal and environmental parameters are dynamically adjusted to obtain a new target underwater acoustic backscatter signal and target environmental parameters. In this case, a multimodal signal acquisition module, an intelligent signal processing and analysis module, a collaborative positioning solution module and an environmental adaptive feedback module are integrated. The multimodal signal acquisition module integrates an underwater acoustic backscatter sensor and an environmental perception sensor to obtain a variety of rich underwater information of underwater acoustic backscatter signals and environmental parameters. Using this underwater information, the sound speed correction value and the propagation time correction value are obtained through the intelligent signal processing and analysis module, the collaborative positioning solution module and the environmental adaptive feedback module, and then the positioning information at the next moment is predicted, thereby improving the accuracy of underwater positioning. In addition, the dynamic adjustment of state parameters is realized, which can greatly improve the versatility and reliability of the system.

[0026] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A basic positioning scene graph provided by an embodiment of the present invention;

[0029] Figure 2A block diagram of a passive underwater collaborative positioning system provided by an embodiment of the present invention;

[0030] Figure 3 This is a flow chart of the passive underwater collaborative positioning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0032] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0033] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. It should also be understood that the term "and / or" used in the present invention refers to and includes any or all possible combinations of one or more associated listed items.

[0034] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0035] The present invention provides a passive underwater collaborative positioning system and method to improve the accuracy of underwater positioning.

[0036] Figure 1This is the basic positioning scene graph provided by the embodiment of the present invention. Figure 1 As shown, multiple backscatter nodes (i.e., underwater acoustic backscatter sensors) are arranged underwater. Each backscatter node is responsible for receiving acoustic signals in the environment and reflecting underwater acoustic backscatter signals. The target object to be positioned is, for example, an underwater vehicle. The underwater vehicle is an integrated transceiver. The underwater vehicle can send acoustic signals into the water. The underwater vehicle is equipped with an environmental perception sensor, an intelligent signal processing and analysis module, a collaborative positioning solution module, and an environmental adaptive feedback module. The underwater vehicle receives the underwater acoustic backscatter signals reflected by each backscatter node. After receiving the underwater acoustic backscatter signals reflected by each backscatter node, the passive underwater collaborative positioning method of the present invention is implemented.

[0037] In a first embodiment, Figure 2 A block diagram of a passive underwater collaborative positioning system provided by an embodiment of the present invention.

[0038] like Figure 2 As shown in Figure 1, the passive underwater collaborative positioning system includes a multimodal signal acquisition module, an intelligent signal processing and analysis module, a collaborative positioning solution module, and an environmental adaptive feedback module. The intelligent signal processing and analysis module and the collaborative positioning solution module implement the calculation and processing functions.

[0039] In an embodiment of the invention, the multimodal signal acquisition module includes an underwater acoustic backscatter sensor and an environmental perception sensor. The underwater acoustic backscatter sensor is arranged underwater. The environmental perception sensor can be arranged on a target object. The target object is, for example, Figure 1 The underwater vehicle is used to generate underwater acoustic backscatter signals. The environmental sensing sensor is used to collect underwater environmental parameters, including temperature, salinity, and pressure.

[0040] Specifically, the multimodal signal acquisition module integrates an underwater acoustic backscatter sensor and an environmental perception sensor. The underwater acoustic backscatter sensor is responsible for receiving and reflecting sound wave signals in the environment, and the reflected sound wave signal is an underwater acoustic backscatter signal. Unlike the prior art, the underwater acoustic backscatter sensor of the present invention uses a wide-band piezoelectric material, which can more sensitively sense and reflect sound waves, broaden the signal bandwidth, and improve positioning resolution. The environmental perception sensor is used to monitor underwater parameters such as temperature, salinity, and pressure in real time. These parameters have an important influence on the propagation speed v of sound waves in water, which in turn affects the accuracy of positioning. These data will be used for subsequent positioning compensation and dynamic adjustment.

[0041] Among them, the use of broadband piezoelectric materials can more sensitively sense and reflect sound waves, broaden the signal bandwidth, and improve positioning resolution. This is because: based on the principle of piezoelectric effect, piezoelectric materials will generate electrical signals when subjected to sound wave pressure. The intensity of the generated electrical signal E and the pressure P satisfy the piezoelectric equation: E = d·P, where d is the piezoelectric constant. Different materials have different piezoelectric constants. Broadband piezoelectric materials have larger piezoelectric constants, which means that they can generate stronger electrical signals under the same sound wave pressure, thereby more sensitively sensing sound waves. In terms of reflecting sound waves, according to the principle of underwater acoustic reflection, the reflection coefficient is related to the acoustic impedance of the material. By optimizing the acoustic impedance matching, the reflection coefficient is better, and the sound waves can be reflected more efficiently. Thanks to the above characteristics, the signal bandwidth of the sensor is effectively widened. According to the Fourier transform principle, the signal bandwidth B is inversely proportional to the time resolution Δt, that is Broadened signal bandwidth means smaller time resolution, which in turn improves positioning resolution. For example, when the bandwidth of a traditional sensor is B1, the positioning resolution is Δx1. When using broadband piezoelectric materials, the bandwidth is increased to B2. According to c = B1 Δx1 = B2 Δx2 (where c is the speed of sound), the positioning resolution can be improved to Δx2, and Δx2 < Δx1.

[0042] In an embodiment of the invention, the intelligent signal processing and analysis module is configured to receive the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, input the target underwater acoustic backscatter signal into a recognition model to obtain path recognition and positioning results, and then, based on the target environmental parameters and a sound velocity correction model, obtain sound velocity correction values ​​and propagation time correction values, thereby determining the target object's movement speed. The path recognition result indicates whether the target underwater acoustic backscatter signal is a direct path or a multipath reflection path. The recognition model utilizes a convolutional neural network.

[0043] Specifically, the intelligent signal processing and analysis module incorporates a deep learning algorithm to construct a recognition model to extract features from the target underwater acoustic backscatter signal. This deep learning algorithm uses a convolutional neural network (CNN) model to automatically learn the characteristic patterns of signals in different environments, effectively separating direct path signals from multipath reflections, and overcoming the effects of multipath and inter-symbol interference on positioning.

[0044] Specifically, the present invention introduces a deep learning algorithm and, with the help of the powerful feature learning ability of a convolutional neural network (CNN), realizes efficient processing of the target underwater acoustic backscatter signal and improves the accuracy of positioning. The core components of CNN include convolutional layers, pooling layers and fully connected layers. When processing the target underwater acoustic backscatter signal, the signal is regarded as one-dimensional time series data and processed analogously to the sequence of pixels in an image. When constructing a CNN model for feature extraction of the target underwater acoustic backscatter signal, it is necessary to optimize it according to the characteristics of the signal. The input layer receives the preprocessed target underwater acoustic backscatter signal, and the preprocessing includes operations such as denoising and normalization to improve the signal quality and model training effect. Multiple convolutional layers are set in the model, and the size, step size and number of the convolution kernel are adjusted according to the characteristics of the signal. The pooling layer follows the convolution layer, and the pooling window size and step size are reasonably set to reduce the data dimension while maintaining the key features of the signal. The fully connected layer is set at the end of the model and outputs the corresponding results according to the requirements of the positioning task.

[0045] In order for the CNN model to accurately learn the characteristics of the target's underwater acoustic backscatter signal, it requires training with a large amount of labeled data. This labeled data includes the signal's true path information (including direct paths and multipath reflection paths) and the corresponding accurate location information. The model input is the target's underwater acoustic backscatter signal, and the output is the predicted location information (also known as the positioning result), which is subsequently used for positioning tasks.

[0046] During the training process, a suitable loss function is used to measure the difference between the model prediction results and the actual labels. For classification tasks (determining the signal path type), the cross-extraction loss function is often used:

[0047]

[0048] Where n is the number of samples, y i is the true label (0 or 1) of the i-th sample, and in this invention refers to the i-th target underwater acoustic backscatter signal. i It is the probability predicted by the model. In the present invention, it refers to the probability that the underwater acoustic backscatter signal of the i-th target is a direct path or a multipath reflection path.

[0049] For positioning tasks (location information of target objects), the mean square error loss function is commonly used:

[0050]

[0051] Where y i It is the real location, and the present invention refers to the accurate location information in the tag. It is the value predicted by the model, and in the present invention, it refers to the predicted position information.

[0052] During training, we use Adam as the optimization algorithm to minimize the loss function by continuously adjusting the model parameters. During training, we use L2 regularization to prevent overfitting and improve the generalization ability of the model.

[0053] The CNN model constructed and trained as described above can automatically learn the characteristic patterns of underwater acoustic backscatter signals in different environments, effectively separate direct path signals and multipath reflection signals, thereby overcoming the influence of multipath effects and inter-symbol interference on positioning. Compared with traditional signal processing methods, it significantly improves positioning accuracy.

[0054] In an embodiment of the invention, the intelligent signal processing and analysis module is further used to obtain a sound speed correction value and a propagation time correction value based on target environment parameters and a sound speed correction model, and thus obtain the moving speed of the target object.

[0055] Specifically, in the underwater positioning process, in order to further improve the positioning accuracy, the present invention deeply integrates the time-frequency characteristics of the signal with the data collected by the environmental perception sensor, and realizes the precise correction of the signal propagation time by establishing a sound speed correction model, thereby more accurately determining the position of the target object.

[0056] Considering that the underwater sound speed is affected by multiple environmental parameters such as temperature T, salinity S, and pressure p, according to the empirical formula, the relationship between the sound speed v and temperature, salinity, and pressure in the sound speed correction model can be approximately expressed as:

[0057] v=v0+a1(T-T0)+a2(S-S0)+a3(p-p0)

[0058] In the formula, v0 is the reference sound speed (corresponding to the reference temperature T0, the reference salinity S0, and the reference pressure p0), and a1, a2, and a3 are the corresponding empirical coefficients. In order to better fit the specific underwater environment, multiple field measurements are carried out in the target waters to obtain the sound speed at different locations and the corresponding environmental parameters, and the coefficients in the formula are adjusted using optimization algorithms such as the least squares method. By adjusting the coefficients in the formula, the error function of the least squares method is minimized, thereby determining the empirical coefficients in the sound speed correction model to obtain the sound speed correction model for the corresponding area. The environmental perception sensor then transmits the temperature, salinity, and pressure data collected in real time to the intelligent signal processing and analysis module, and uses the formula of the above-mentioned sound speed correction model to obtain the sound speed correction value. As a result, the sound wave propagation time can be accurately compensated, thereby improving the positioning accuracy.

[0059] After obtaining the time-frequency characteristics of the signal and the accurate sound velocity value (i.e., the sound velocity correction value), a joint analysis is performed to optimize the delay estimation. Assuming that the propagation time of the signal in an ideal uniform medium is t0, the signal propagation time t can be preliminarily estimated based on the signal characteristics obtained by time-frequency analysis.est Due to the complexity of the underwater environment, the actual sound speed c real and the ideal speed of sound c ideal There is a difference, so the propagation time needs to be corrected according to the sound speed correction model.

[0060] Assume that the actual sound speed obtained from the sound speed correction model is c real , the ideal speed of sound is c ideal , then the correction of propagation time is: Then the propagation time correction value t corrected is: t corrected =t est +Δt1.

[0061] The intelligent signal processing and analysis module calculates the moving speed of the target object based on the moving distance of the target object within the propagation time correction value.

[0062] In an embodiment of the invention, a collaborative positioning solution module is configured to predict the next moment's positioning information based on historical positioning information and current moment's positioning information. The positioning information includes the positioning result and the target object's movement speed. Predicting the next moment's positioning information based on the historical positioning information and current moment's positioning information includes: using a Kalman filter-based tracking algorithm to predict the next moment's positioning information based on the historical positioning information and current moment's positioning information.

[0063] Specifically, for the target object, a tracking algorithm based on Kalman filtering is designed. By combining the historical position information of the target object with the current positioning information, the next position information of the target object is predicted, thus achieving real-time tracking of the moving target. The specific prediction process of the tracking algorithm based on Kalman filtering includes:

[0064] Assume that the prediction is based on the system state, which includes the positioning result (i.e., position information) and the moving speed of the target object. Assume that at discrete time k, the state vector X k Expressed as:

[0065]

[0066] Among them, x k and y k is the two-dimensional position coordinate of the target object at time k, and are the velocities of the target object in the x and y directions, respectively.

[0067] The state transition equation of the system describes the change of the state of the target object from time k to time k+1. Assuming that the target object moves in a uniform straight line, the state transition equation is:

[0068] X k+1=F k X k +W k

[0069] Where, F k is the state transfer matrix, which is in the form of:

[0070]

[0071] Where Δt is the time interval; W k is the process noise vector, which represents the unmodeled factors and random interference in the system, and is usually assumed to obey a Gaussian distribution with a mean of zero, that is, W k ~N(0,Q k ), Q k is the process noise covariance matrix.

[0072] The observation equation describes the mapping relationship from sensor observation data to system state. In underwater positioning, the sensor measures the position information of the target object (which may be noisy). The observation equation is:

[0073] Z k =H k X k +V k

[0074] Where Z k is the observation vector, in two-dimensional positioning z x,k and z y,k is the position of the target object in the x and y directions measured by the sensor; H k is the observation matrix at time k. For the case where only the position is measured, V k is the observation noise vector, which is also assumed to obey a Gaussian distribution with a mean of zero, that is, V k ~N(0,R k ), R k is the observation noise covariance matrix at time k.

[0075] According to the state estimate at time k And the state transition equation, predict the state at time k+1:

[0076]

[0077] At the same time, the predicted state covariance matrix:

[0078]

[0079] Where, is the predicted value of the state at time k+1 based on the information at time k, P k+1|k is the corresponding predicted state covariance matrix, P k is the state covariance matrix at time k.

[0080] When the observation data Z at time k+1 is received k+1 Then, calculate the Kalman gain K k+1 :

[0081]

[0082] Where, It is H k+1 The transpose of H. k+1 is the observation matrix at time k+1. k+1 is the observation noise covariance matrix at time k+1.

[0083] Then, the predicted state is updated using the Kalman gain to obtain a more accurate state estimate:

[0084]

[0085] At the same time, update the state covariance matrix:

[0086] P k+1 =(IK k+1 H k+1 )P k+1|k

[0087] Where, is the estimated value of the system state at time k+1 (i.e., the position information at the next moment), and I is the unit matrix.

[0088] In an embodiment of the present invention, the environmental adaptive feedback module is configured to receive the underwater acoustic backscatter signal and collected environmental parameters generated by the multimodal signal acquisition module and transmit them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters. The module is also configured to, upon receiving the positioning result output by the intelligent signal processing and analysis module, calculate the positioning error based on the positioning result. If the positioning error exceeds a set positioning error threshold, the module dynamically adjusts the state parameters of the underwater acoustic backscatter signal and environmental parameters to obtain new target underwater acoustic backscatter signal and target environmental parameters. The state parameters include the signal transmission bit rate, signal acquisition frequency, and processing speed.

[0089] Positioning error is an important indicator to measure the performance of the positioning system. In a two-dimensional positioning scenario, let the real position coordinates of the target object be (x true ,y true ), the estimated position coordinates of the positioning system are (x est ,y est), the positioning error E is usually calculated using the Euclidean distance:

[0090]

[0091] In practical applications, since the true position is often unknown, the positioning error can be approximately calculated by taking the average of multiple measurements or comparing it with a known reference position.

[0092] In some embodiments, the environmental adaptive feedback module is further configured to dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters to obtain new target underwater acoustic backscatter signals and target environmental parameters if the change in the environmental parameters meets the requirements. The change in the environmental parameters meeting the requirements refers to a significant change in the environmental parameters, for example, a change in the environmental parameters exceeding a set change threshold, which can be considered a significant change.

[0093] The environmental sensing sensor collects parameters such as temperature T, salinity S, and pressure p, and sets reasonable thresholds to detect whether they have changed significantly. Taking temperature as an example, let the current temperature be T current , the temperature at the last moment is T prev , the temperature change threshold is ΔT th , when |T current -T prev |>ΔT th When the temperature changes significantly, it is determined that the temperature has changed significantly. The salinity and pressure change detection are similar, and the salinity change threshold ΔS is set respectively. th and pressure change threshold Δp th .

[0094] Specifically, the environmental adaptive feedback module forms a closed-loop control system. The passive underwater collaborative positioning system collects environmental data and positioning results in real time, calculates positioning errors and assesses environmental changes, and adjusts system parameters based on the assessment results. The adjusted system then performs positioning and environmental perception again, repeating this cycle.

[0095] For example, let the state parameter vector of the system be S = [R bit ,T s ,f s ,P speed ,…] T , the performance evaluation function is J(S), and the goal of feedback adjustment is to minimize J(S). For example, when the state parameter vector contains the signal transmission bit rate R bit , signal acquisition frequency f s and processing speed P speed When , the performance evaluation function J(S) can be expressed as Through continuous monitoring and adjustment, the system can keep J(S) minimized in different underwater environments and the motion state of the target object, so as to automatically optimize performance and maintain stable and accurate positioning effects.

[0096] In some embodiments, the environmental adaptive feedback module is also used to adjust the acquisition frequency of the underwater acoustic backscatter signal and environmental parameters based on the range of the moving speed after receiving the moving speed of the target object output by the intelligent signal processing and analysis module, so as to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0097] Specifically, when the target object moves faster, in order to more accurately track the position change of the target object, the signal acquisition frequency is increased. Assume that the speed of the target object (the speed is predicted by the collaborative positioning settlement module) is Preset speed threshold v th1 and v th2 (v th1 <v th2 ). When v est >v th2 When the target object is considered to be moving faster, the environment adaptive feedback module adjusts the signal acquisition frequency f to a higher frequency f high ; When v<v th1 When the target object is considered to be moving at a slower speed, the environment adaptive feedback module can appropriately reduce the signal acquisition frequency to f low , to reduce energy consumption; when v th1 ≤v≤v th2 When the environment adaptive feedback module maintains the current acquisition frequency f current Thus, it can better adapt to the motion state of the target object.

[0098] Specifically, the signal acquisition frequency f s The adjustment is based on the Nyquist sampling theorem. In order to accurately collect the signal of a fast-moving target (i.e., the target object), the sampling frequency should satisfy f s ≥2f max , where f max It is the highest frequency component of the target signal. When the target object moves faster, the frequency component of the signal changes due to the Doppler effect. max Increase, so it is necessary to increase the signal acquisition frequency f s . Assume the original acquisition frequency is f s0 , the adjusted acquisition frequency f s1 It can be adjusted according to the speed change ratio of the target object:

[0099]

[0100] Among them, v previs the target object's estimated velocity at the last moment. At the same time, in order to ensure that the high-speed collected data can be processed in time, the signal processing speed needs to be increased. This can be achieved by optimizing the algorithm execution efficiency, increasing computing resources (such as multi-core processor parallel computing), etc. Assuming that the original processing speed is P speed0 , the adjusted processing speed P speed1 Meet the demand for processing data volume, that is, P speed1 ≥f s1 D, D is the amount of data collected per unit time.

[0101] In some embodiments, the environment adaptive feedback module is further used to adjust the signal processing parameters according to the target object's motion direction and speed. For example, when performing time-frequency analysis, for a fast-moving target object, the window size and overlap rate of the time-frequency analysis can be adjusted to better capture the Doppler shift characteristics of the signal. Assume that the angle between the target object's motion direction and the x-axis is According to the values ​​of θ and v, the time-frequency analysis window size T is dynamically adjusted. window The overlap ratio r is used to optimize signal feature extraction and improve positioning accuracy. Through the above dynamic adjustment strategy, the system can effectively track underwater moving targets, adapt to the changes in the target object's motion state, and achieve accurate positioning and real-time tracking of the target object.

[0102] In some embodiments, when an increase in the multipath effect is detected, the environment adaptive feedback module can automatically reduce the bit rate of signal transmission, increase the symbol interval, and reduce inter-symbol interference. When the target object moves faster, the environment adaptive feedback module can increase the signal acquisition frequency and processing speed to ensure that the target object's position changes can be tracked in a timely manner. The strength of the multipath effect can be evaluated by analyzing the characteristics of the received signal. A common method is to calculate the signal's multipath resolution index M. Assuming that the correlation function of the received signal is R(τ), the multipath resolution index can be defined as:

[0103]

[0104] The larger the value of M, the more serious the multipath effect. When M exceeds the pre-set multipath effect threshold M th When , it is determined that the multipath effect is enhanced.

[0105] According to Shannon's theorem, the relationship between channel capacity C, signal bandwidth B, and signal-to-noise ratio SNR is C = Blog2(1+SNR). When the multipath effect increases, the signal is interfered with and the signal-to-noise ratio decreases. To ensure the reliability of communication, the bit rate R of signal transmission is reduced. bit . Assume the original bit rate is R bit0 , the adjusted bit rate R bit1 satisfy:

[0106]

[0107] Among them, SNR new and SNR old are the estimated values ​​of the signal-to-noise ratio before and after adjustment. At the same time, the symbol interval T is increased. s , to reduce inter-symbol interference. The relationship between symbol interval and bit rate is Adjusted symbol interval

[0108] The environmental adaptive feedback module is a key component of the positioning system of the present invention to achieve accurate and stable positioning. It intelligently adjusts system parameters through real-time analysis of environmental perception data and positioning results, ensuring that the positioning system always maintains optimal performance in complex and changing underwater environments.

[0109] The following is a method embodiment of the present invention. For details not disclosed in the method embodiment of the present invention, please refer to the system embodiment of the present invention. The method embodiment of the present invention provides a passive underwater collaborative positioning method. This passive underwater collaborative positioning method utilizes the passive underwater collaborative positioning system of the system embodiment described above.

[0110] Figure 3 This is a flow chart of the passive underwater collaborative positioning method provided by an embodiment of the present invention.

[0111] like Figure 3 As shown, the passive underwater collaborative positioning method includes:

[0112] Step S101, using the underwater acoustic backscatter sensor of the multimodal signal acquisition module to generate an underwater acoustic backscatter signal, and using the environment perception sensor of the multimodal signal acquisition module to collect underwater environmental parameters;

[0113] Step S102: The intelligent signal processing and analysis module receives the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, inputs the target underwater acoustic backscatter signal into the recognition model to obtain a path recognition result and a positioning result, and obtains a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, thereby obtaining the moving speed of the target object;

[0114] Step S103: The collaborative positioning solution module predicts the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment. The positioning information includes the positioning result and the moving speed of the target object.

[0115] In step S104, the environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal generated by the multimodal signal acquisition module and the collected environmental parameters, and transfers them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters. The environmental adaptive feedback module is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module. If the positioning error exceeds the set positioning error threshold, the state parameters of the underwater acoustic backscatter signal and the environmental parameters are dynamically adjusted to obtain new target underwater acoustic backscatter signal and target environmental parameters.

[0116] In some embodiments, the passive underwater collaborative positioning method further includes: if the change in environmental parameters meets the requirements, using the environmental adaptive feedback module to dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0117] In some embodiments, the passive underwater collaborative positioning method further includes: using an environmental adaptive feedback module to receive the moving speed of the target object output by the intelligent signal processing and analysis module; based on the range of the moving speed, using the environmental adaptive feedback module to adjust the acquisition frequency of the underwater acoustic backscatter signal and environmental parameters to obtain new target underwater acoustic backscatter signals and target environmental parameters.

[0118] It should be noted that the above explanation of the passive underwater collaborative positioning system embodiment is also applicable to the passive underwater collaborative positioning method of this embodiment, and will not be repeated here.

[0119] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0120] In the passive underwater collaborative positioning system and method of the embodiment of the present invention, the system includes a multimodal signal acquisition module, an intelligent signal processing and analysis module, a collaborative positioning solution module and an environmental adaptive feedback module; the multimodal signal acquisition module includes an underwater acoustic backscatter sensor and an environmental perception sensor, which are used to generate underwater acoustic backscatter signals and collect underwater environmental parameters; the intelligent signal processing and analysis module is used to receive the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, input the target underwater acoustic backscatter signal into the recognition model to obtain a path recognition result and a positioning result, obtain a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, and then obtain the moving speed of the target object. ; A collaborative positioning solution module is used to predict the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment. The positioning information includes the positioning result and the moving speed of the target object; an environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal and the collected environmental parameters generated by the multimodal signal acquisition module, and transfer them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters; it is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module. If the positioning error exceeds the set positioning error threshold, the state parameters of the underwater acoustic backscatter signal and the environmental parameters are dynamically adjusted to obtain a new target underwater acoustic backscatter signal and target environmental parameters. In this case, a multimodal signal acquisition module, an intelligent signal processing and analysis module, a collaborative positioning solution module and an environmental adaptive feedback module are integrated. The multimodal signal acquisition module integrates an underwater acoustic backscatter sensor and an environmental perception sensor to obtain a variety of rich underwater information of underwater acoustic backscatter signals and environmental parameters. Using this underwater information, the sound speed correction value and the propagation time correction value are obtained through the intelligent signal processing and analysis module, the collaborative positioning solution module and the environmental adaptive feedback module, and then the positioning information at the next moment is predicted, thereby improving the accuracy of underwater positioning. In addition, the dynamic adjustment of state parameters is realized, which can greatly improve the versatility and reliability of the system.

[0121] The present invention proposes an innovative passive underwater multi-modal collaborative positioning system, specifically a passive positioning system based on underwater acoustic backscattering and dynamic spectrum modulation, which is suitable for real-time tracking and high-precision positioning of underwater mobile targets with multi-node collaboration, and is particularly suitable for deep-sea resource exploration and marine life monitoring scenarios. Efficient and accurate underwater positioning is achieved. Specifically, 1) the positioning error problem caused by the energy collection delay of battery-free nodes is solved, and high-precision time delay estimation is achieved. 2) Spectrum utilization is optimized, and inter-symbol interference in multipath environments is effectively suppressed, thereby improving the performance of the positioning system in complex underwater channels. 3) Multi-node collaborative work is achieved, and the positioning accuracy and real-time tracking capability of mobile multi-targets are improved, thereby enhancing the robustness of the positioning system. 4) The positioning system is given environmental adaptability, so that it can automatically adjust the positioning strategy according to changes in the underwater environment to ensure the accuracy and stability of positioning.

[0122] This invention significantly improves positioning accuracy by integrating multimodal data and deep learning algorithms. The multimodal data acquisition module integrates underwater acoustic backscatter sensors and environmental perception sensors to acquire rich underwater information. The convolutional neural network employed automatically learns signal characteristics, effectively overcoming issues such as battery-free node wake-up delay, multipath effects, and inter-symbol interference, effectively improving positioning accuracy and meeting the demand for high-precision positioning of underwater targets.

[0123] In terms of environmental adaptability, the present invention features an adaptive feedback module that automatically adjusts positioning strategies based on dynamic changes in the underwater environment. Whether operating in deep or shallow waters, or in areas subject to significant temperature and salinity fluctuations, the system evaluates positioning performance in real time. When positioning errors exceed set thresholds or environmental parameters change significantly, it automatically adjusts system parameters, including the signal transmission bit rate, symbol interval, and signal acquisition frequency, to maintain stable positioning performance, significantly improving the system's versatility and reliability.

[0124] The system's tracking algorithm based on Kalman filtering combines the historical position information of the target object with the current positioning data to predict the next moment's position of the target object. It has the ability to perform high-precision positioning and real-time tracking of moving targets, greatly improving the application capability of the positioning system in complex scenarios. It is widely used in marine biological community monitoring, underwater robot formation operations and other fields.

[0125] Finally, based on the principle of underwater acoustic backscatter, the system nodes of the present invention are passive and do not need to actively transmit signals. They communicate and locate only by reflecting environmental sound waves, which reduces energy consumption, eliminates the need for frequent battery replacement, reduces maintenance costs, and reduces the impact on the marine environment. This makes the system suitable for long-term underwater monitoring tasks and has good sustainability.

[0126] The accompanying drawings illustrate schematic diagrams of the structures of the disclosed embodiments of the present invention. These figures are not drawn to scale; for the purpose of clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0127] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. The present invention is not limited here.

[0128] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A passive underwater collaborative positioning system, characterized in that: It includes multimodal signal acquisition module, intelligent signal processing and analysis module, collaborative positioning solution module and environment adaptive feedback module; The multimodal signal acquisition module includes an underwater acoustic backscatter sensor and an environmental perception sensor, which are used to generate underwater acoustic backscatter signals and collect underwater environmental parameters; The intelligent signal processing and analysis module is configured to receive the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, input the target underwater acoustic backscatter signal into a recognition model to obtain a path recognition result and a positioning result, and obtain a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, thereby obtaining a moving speed of the target object; The collaborative positioning solution module is used to predict the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment, wherein the positioning information includes the positioning result and the moving speed of the target object; The environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal generated by the multimodal signal acquisition module and the collected environmental parameters, and transfer them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters; it is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module, and dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters if the positioning error exceeds the set positioning error threshold to obtain new target underwater acoustic backscatter signal and target environmental parameters.

2. The passive underwater collaborative positioning system according to claim 1, characterized in that: The environmental adaptive feedback module is further used to dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters if the change of the environmental parameters meets the requirements, so as to obtain new target underwater acoustic backscatter signals and target environmental parameters.

3. The passive underwater collaborative positioning system according to claim 1, characterized in that: The state parameters include signal transmission bit rate, signal acquisition frequency and processing speed.

4. The passive underwater collaborative positioning system according to claim 1, characterized in that: The environmental parameters include temperature, salinity and pressure.

5. The passive underwater collaborative positioning system according to claim 1, characterized in that: The recognition model adopts a convolutional neural network model.

6. The passive underwater collaborative positioning system according to claim 1, characterized in that: The predicting of the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment includes: The tracking algorithm based on Kalman filtering predicts the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment.

7. The passive underwater collaborative positioning system according to claim 1, characterized in that: The environmental adaptive feedback module is also used to adjust the acquisition frequency of the underwater acoustic backscatter signal and the environmental parameters based on the range of the moving speed after receiving the moving speed of the target object output by the intelligent signal processing and analysis module, so as to obtain new target underwater acoustic backscatter signals and target environmental parameters.

8. A passive underwater collaborative positioning method, characterized in that: The passive underwater collaborative positioning system according to any one of claims 1 to 7, wherein the method comprises: Using the underwater acoustic backscatter sensor of the multimodal signal acquisition module to generate an underwater acoustic backscatter signal, and using the environment perception sensor of the multimodal signal acquisition module to collect underwater environmental parameters; The intelligent signal processing and analysis module receives the target underwater acoustic backscatter signal and target environmental parameters output by the environmental adaptive feedback module, inputs the target underwater acoustic backscatter signal into the recognition model to obtain a path recognition result and a positioning result, and obtains a sound speed correction value and a propagation time correction value based on the target environmental parameters and the sound speed correction model, thereby obtaining the moving speed of the target object; The collaborative positioning solution module predicts the positioning information at the next moment based on the historical positioning information and the positioning information at the current moment, wherein the positioning information includes the positioning result and the moving speed of the target object; The environmental adaptive feedback module is used to receive the underwater acoustic backscatter signal generated by the multimodal signal acquisition module and the collected environmental parameters, and transfer them to the intelligent signal processing and analysis module as the target underwater acoustic backscatter signal and target environmental parameters; the environmental adaptive feedback module is also used to calculate the positioning error based on the positioning result after receiving the positioning result output by the intelligent signal processing and analysis module. If the positioning error exceeds the set positioning error threshold, the state parameters of the underwater acoustic backscatter signal and the environmental parameters are dynamically adjusted to obtain new target underwater acoustic backscatter signal and target environmental parameters.

9. The passive underwater collaborative positioning method according to claim 8, characterized in that: Also includes: If the change of the environmental parameters meets the requirements, the environmental adaptive feedback module is used to dynamically adjust the state parameters of the underwater acoustic backscatter signal and the environmental parameters to obtain new target underwater acoustic backscatter signals and target environmental parameters.

10. The passive underwater collaborative positioning method according to claim 8, characterized in that: Also includes: Using the environment adaptive feedback module to receive the moving speed of the target object output by the intelligent signal processing and analysis module; Based on the range of the moving speed, the acquisition frequency of the underwater acoustic backscatter signal and the environmental parameters is adjusted by using the environmental adaptive feedback module to obtain new target underwater acoustic backscatter signals and target environmental parameters.

Images