Target tracking methods, devices, electronic equipment, and media for underwater wireless sensor networks under measurement time delay
By constructing an underwater measurement delay model and designing an augmented state estimator for delay compensation, and extending it to a distributed augmented state estimator, the problem of measurement delay in underwater wireless sensor networks is solved, improving the accuracy and stability of target tracking.
Patent Information
- Application Number
- CN202410996127.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-24
- Publication Date
- 2025-10-31
- Estimated Expiration
- 2044-07-24
AI Technical Summary
Underwater wireless sensor networks suffer from severe measurement latency issues in target tracking, resulting in the inability to obtain real-time information and information asynchronicity, which affects the accuracy and real-time performance of target tracking. Existing technologies have failed to effectively solve this problem.
An underwater measurement delay model is constructed, an augmented state estimator is designed for delay compensation, and it is extended to a distributed augmented state estimator, which is suitable for underwater wireless sensor networks and can perform target tracking through distributed deployment and autonomous sensing.
It improves the accuracy and stability of underwater target tracking, reduces tracking errors, and enhances the target tracking performance of underwater wireless sensor networks.
Smart Images
Figure CN119012133B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of target tracking based on underwater wireless sensor networks, and more particularly to a target tracking method, apparatus, electronic device, and medium for underwater wireless sensor networks with measured time delay. Background Technology
[0002] Underwater target tracking technology, as an important research direction in marine science and technology, has significant practical importance and application value in improving the efficiency of marine resource utilization, protecting the marine environment, promoting marine scientific research, and ensuring maritime safety. With the rapid development and increasing maturity of wireless sensor network technology, underwater wireless sensor networks are widely used in the field of underwater target tracking. Within the monitoring area, underwater wireless sensor networks utilize underwater wireless sensor nodes to perceive targets and obtain information such as the morphology and dynamics of underwater targets based on localized signal processing and information fusion technologies. Therefore, they have advantages such as self-organizing structure, strong fault tolerance, strong concealment, and rapid deployment.
[0003] However, due to factors such as slow underwater acoustic propagation and underwater multipath effects, underwater wireless sensor nodes will face severe time delay problems when acquiring raw measurements of the target, specifically manifested in:
[0004] 1. Underwater wireless sensor nodes cannot obtain real-time information about underwater targets;
[0005] 2. Information between underwater wireless sensor nodes is asynchronous. These two points will cause the underwater wireless sensor network to be unable to update target information in a timely manner, thus affecting the accuracy and real-time performance of target tracking.
[0006] Currently, the existing technology has at least the following problems:
[0007] (1) Underwater measurement delay will seriously affect the measurement real-time performance and information synchronization of underwater wireless sensor nodes. At present, no relevant research has fully considered and effectively solved the measurement delay problem in target tracking based on underwater wireless sensor networks.
[0008] (2) Current research on underwater target tracking technology based on underwater wireless sensor networks is mainly limited to the design of centralized state estimators, which has high computational complexity and is prone to forming communication bottlenecks, while ignoring the characteristics of distributed deployment and autonomous perception of underwater wireless sensor networks.
[0009] Therefore, fully considering and effectively addressing the measurement delay issue underwater plays a crucial role in improving the accuracy and stability of underwater target tracking based on underwater wireless sensor networks. Summary of the Invention
[0010] The purpose of this application is to provide a target tracking method, apparatus, electronic device, and medium for underwater wireless sensor networks under measurement delay, so as to solve the measurement delay problem caused by the slow underwater acoustic propagation in target tracking based on underwater wireless sensor networks, and improve the accuracy and stability of underwater target tracking methods.
[0011] According to a first aspect of the embodiments of this application, a target tracking method based on an underwater wireless sensor network is provided under measured time delay, comprising:
[0012] An underwater measurement time delay model is constructed based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay.
[0013] Based on the underwater measurement time delay model, an augmented state estimator is designed to compensate for the time delay caused by underwater acoustic propagation.
[0014] The augmented state estimator is decoupled and extended into a distributed augmented state estimator suitable for underwater wireless sensor networks;
[0015] The underwater tracking target's morphological characteristics, dynamic parameters, initial state, and reference trajectory are defined, and an underwater wireless sensor network is deployed to perform target tracking using the distributed augmented state estimator.
[0016] Furthermore, an underwater measurement time delay model is established, including:
[0017] The time delay caused by the slow propagation of underwater sound is calculated using the following expression:
[0018]
[0019] Where N represents the number of underwater wireless sensor nodes, and i represents the i-th sensor node. This represents the measurement delay of the i-th sensor node at time k. Let c and T represent the Euclidean distance between the i-th sensor node and the tracked target. s These represent the sampling intervals for underwater sound speed and underwater wireless sensor networks, respectively. This indicates rounding down to the nearest integer.
[0020] Based on equation (24), the actual arrival time of the sensor node at time k is obtained as follows:
[0021]
[0022] in This indicates the time when the measurement actually reaches the i-th sensor node at time k.
[0023] Based on equation (25), the sensor measurements under time delay are obtained, as shown in the following expression:
[0024]
[0025] Where x k To track the state variables of the target at time k, and Let be the measurement and measurement noise at time kt for the i-th sensor node, respectively. h represents the time delay measurement at time k of the i-th sensor node. i (·) represents the measurement function of the i-th sensor node, t d The maximum acceptable latency is set. The value is Bernoulli. When the value is 1, it means that the measurement at time kt arrives at sensor node i at time k; otherwise, the value is 0, which means that sensor node i did not receive the measurement at time kt at time k.
[0026] Furthermore, based on the underwater measurement time delay model, an augmented state estimator is designed to compensate for the time delay caused by underwater acoustic propagation, including:
[0027] The target state model is established as follows:
[0028] x k+1 =F k x k +w k (29)
[0029] Where x k+1 F k and w k Let $\mathbf{k}$ represent the state variables of the target at time $k+1$, the state transition matrix at time $k$, and the process noise at time $k$, respectively.
[0030] The augmented state model is constructed as follows:
[0031]
[0032] Where X k+1 X k , and Let represent the augmented state variables of the tracking target at time k+1, the augmented state variables at time k, the augmented state transition function at time k, and the augmented process noise at time k, respectively. and Let be the augmented measurement function and augmented measurement noise of the i-th sensor node at time k, respectively. T This indicates the matrix transpose.
[0033] Based on the augmented state model described by equations (30)-(34), for any time k, the desired target state estimate can be obtained by estimating the augmented state. An initial augmented state estimate is set. and augmented error covariance matrix The augmented state estimator is represented by the following equations (35)-(41).
[0034] At time k, for the i-th sensor node, the augmented state estimate is first sampled. Augmented state predictions are obtained using equations (35)-(36). and the prediction augmentation error covariance matrix
[0035]
[0036]
[0037] Where n d and s represent the number of augmented state variables and the s-th sampling point, respectively. and α s They represent the s-th information about The sampling and the weight of the s-th sampling point, Q k Let be the covariance matrix of the process noise at time k.
[0038] Secondly, sampling augmentation state prediction Calculate augmentation measurement predictions using equations (37)-(39) Augmented measurement prediction error covariance matrix And augmented state measurement prediction error covariance matrix
[0039]
[0040] in This indicates the s-th element about... Sampling, Let be the covariance matrix of the measurement noise at time k+1-t.
[0041] Finally, at time k+1, sensor node i obtains the time delay measurement. Combining equations (40) and (41) yields the augmented state estimate. and augmented error covariance matrix
[0042]
[0043] in(·) -1 This represents the inverse of a matrix.
[0044] Furthermore, the augmented state estimator is decoupled and extended to a distributed augmented state estimator suitable for underwater wireless sensor networks, including:
[0045] Using Woodbury's identity, equations (40)-(41) can be transformed into the decoupling forms described by equations (42)-(44):
[0046]
[0047] Based on equations (42)-(44), the distributed augmented state estimator is represented by the following equations (45)-(46):
[0048]
[0049] Where j and r represent the j-th and r-th sensor nodes, respectively, N i and N j Let represent the sets of wireless sensor nodes that communicate with the i-th sensor node and the j-th sensor node, respectively. This represents the information fusion weight between sensor nodes i and j at time k+1.
[0050] According to a second aspect of the embodiments of this application, a target tracking device for an underwater wireless sensor network under measurement delay is provided, comprising:
[0051] The model building module is used to construct an underwater measurement time delay model based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay.
[0052] The design module is used to design an augmented state estimator based on the underwater measurement time delay model. The augmented state estimator is used to compensate for the time delay caused by underwater acoustic propagation.
[0053] The extension module decouples the augmented state estimator and extends it into a distributed augmented state estimator suitable for underwater wireless sensor networks.
[0054] The target tracking module is used to set the morphological characteristics, dynamic parameters, initial state, and reference trajectory of the underwater target, and to deploy an underwater wireless sensor network to perform target tracking using the distributed augmented state estimator.
[0055] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising:
[0056] One or more processors;
[0057] Memory, used to store one or more programs;
[0058] When the one or more programs are executed by the one or more processors, the one or more processors implement the target tracking method of underwater wireless sensor networks under measurement delay described above.
[0059] According to a fourth aspect of the present application, a computer-readable storage medium is provided, on which computer instructions are stored, which, when executed by a processor, implement the steps of the target tracking method of the underwater wireless sensor network under the measurement delay described above.
[0060] The technical solutions provided by the embodiments of this application may include the following beneficial effects:
[0061] As can be seen from the above embodiments, this application addresses the measurement delay problem in target tracking based on underwater wireless sensor networks by considering the impact of delay on node measurements, constructing an augmented state estimator, and decoupling the augmented state estimator to extend it into a distributed state estimator suitable for underwater wireless sensor networks. This design compensates for the impact of delay caused by slow underwater acoustic propagation on the target tracking performance of underwater wireless sensor networks, thereby improving the accuracy and stability of underwater target tracking methods.
[0062] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0063] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0064] Figure 1 This is a flowchart illustrating a target tracking method based on an underwater wireless sensor network with measured time delay, according to an exemplary embodiment.
[0065] Figure 2 This is a schematic diagram illustrating the change in tracking error of an underwater wireless sensor network under conditions of uncompensated underwater measurement delay, according to an exemplary embodiment.
[0066] Figure 3 This is a schematic diagram illustrating the change in tracking error of an underwater wireless sensor network under conditions of compensating for underwater measurement delay, according to an exemplary embodiment.
[0067] Figure 4 This is a block diagram illustrating a target tracking device based on an underwater wireless sensor network for measuring time delay, according to an exemplary embodiment.
[0068] Figure 5 This is a schematic diagram of an electronic device according to an exemplary embodiment. Detailed Implementation
[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0070] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.
[0071] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."
[0072] Figure 1 This is a flowchart illustrating a target tracking method based on an underwater wireless sensor network with measured time delay, according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps:
[0073] S11: Construct an underwater measurement time delay model based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay;
[0074] S12: Based on the underwater measurement time delay model, design an augmented state estimator, which is used to compensate for the time delay caused by underwater acoustic propagation.
[0075] S13: Decouple the augmented state estimator and extend it into a distributed augmented state estimator suitable for underwater wireless sensor networks;
[0076] S14: Set the morphological characteristics, dynamic parameters, initial state, and reference trajectory of the underwater tracking target, and deploy an underwater wireless sensor network to track the target using the distributed augmented state estimator.
[0077] As can be seen from the above embodiments, this application addresses the measurement delay problem in target tracking based on underwater wireless sensor networks by considering the impact of delay on node measurements, constructing an augmented state estimator, and decoupling the augmented state estimator to extend it into a distributed state estimator suitable for underwater wireless sensor networks. This design compensates for the impact of delay caused by slow underwater acoustic propagation on the target tracking performance of underwater wireless sensor networks, thereby improving the accuracy and stability of underwater target tracking methods.
[0078] In the specific implementation of S11: an underwater measurement time delay model is constructed based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay.
[0079] Specifically, considering that existing underwater target tracking methods mainly rely on the arrival time and angle of sonar echoes to perceive distance and azimuth information about the target, the "transmit-receive" time difference is used to represent the measurement delay. This example calculates the delay caused by the slow propagation of underwater acoustic waves, as expressed below:
[0080]
[0081] Where N represents the number of underwater wireless sensor nodes, and i represents the i-th sensor node. This represents the measurement delay of the i-th sensor node at time k. Let c and T represent the Euclidean distance between the i-th sensor node and the tracked target. s These represent the sampling intervals for underwater sound speed and underwater wireless sensor networks, respectively. This indicates rounding down to the nearest integer.
[0082] The actual arrival time at the sensor node at time k is as follows:
[0083]
[0084] in This indicates the time when the measurement actually reaches the i-th sensor node at time k.
[0085] Based on this, and considering the multipath effect, multiple time delay measurements may arrive at the sensor node at the same time. Therefore, this example adopts the following time delay measurement model:
[0086]
[0087]
[0088] Where x k To track the state variables of the target at time k, and Let be the measurement and measurement noise at time kt for the i-th sensor node, respectively. h represents the time delay measurement at time k of the i-th sensor node. i (·) represents the measurement function of the i-th sensor node, t d The maximum acceptable latency is set. The value is Bernoulli. When the value is 1, it means that the measurement at time kt arrives at sensor node i at time k; otherwise, the value is 0, which means that sensor node i did not receive the measurement at time kt at time k.
[0089] In the specific implementation of S12: Based on the underwater measurement time delay model, an augmented state estimator is designed, which is used to compensate for the time delay caused by underwater acoustic propagation.
[0090] Specifically, according to the underwater measurement delay model, the target information sensed by nodes is asynchronous, therefore an augmented state estimator needs to be designed to compensate for the measurement delay. This example uses the following target state model:
[0091] x k+1 =F k x k +w k (52)
[0092] Where x k+1 F k and w k Let $\mathbf{k}$ represent the state variables of the target at time $k+1$, the state transition matrix at time $k$, and the process noise at time $k$, respectively.
[0093] Based on this, the following augmented state model is constructed:
[0094]
[0095]
[0096] Where X k+1 X k , and Let represent the augmented state variables of the tracking target at time k+1, the augmented state variables at time k, the augmented state transition function at time k, and the augmented process noise at time k, respectively. and Let be the augmented measurement function and augmented measurement noise of the i-th sensor node at time k, respectively. T This indicates the matrix transpose.
[0097] The augmented state model described by equations (53)-(57) transforms the problem of solving the target state estimation into the problem of solving the augmented state estimation, while eliminating the asynchronicity of information between various sensor nodes. Based on this, an initial augmented state estimation is set. and augmented error covariance matrix The augmented state estimator is represented by the following equations (58)-(67).
[0098] At time k, for the i-th sensor node, the augmented state estimate is first sampled using equation (58). The augmented state prediction is obtained by reusing equations (59)-(60). and the prediction augmentation error covariance matrix
[0099]
[0100] Where (θ) represents the θ-th column of the corresponding matrix, θ = 1, 2, ..., n. d and s represent the number of augmented state variables and the s-th sampling point, respectively. and Let be the augmented state estimate and the augmented error covariance matrix of the i-th sensor node at time k, respectively. and α s They represent the s-th information about The sampling and the weight of the s-th sampling point, Q k Let be the covariance matrix of the process noise at time k. In this example, the weights are selected as follows:
[0101]
[0102] Then, the augmented state prediction is sampled using equation (62). Reuse equations (63)-(65) to calculate augmented measurement predictions Augmented measurement prediction error covariance matrix And augmented state measurement prediction error covariance matrix
[0103]
[0104] in This indicates the s-th element about... Sampling, Let be the covariance matrix of the measurement noise at time k+1-t.
[0105] Finally, at time k+1, sensor node i obtains the time delay measurement. The augmented state estimate is obtained using equations (66)-(67). and augmented error covariance matrix
[0106]
[0107] in(·) -1 This represents the inverse of a matrix.
[0108] In the specific implementation of S13: the augmented state estimator is decoupled and extended into a distributed augmented state estimator suitable for underwater wireless sensor networks.
[0109] Specifically, due to the distributed deployment and autonomous sensing characteristics of underwater wireless sensor networks, the set of node measurement information acquired by underwater wireless sensor nodes is random, which makes it impossible to calculate the augmented state estimator gain in the actual system based on a centralized method. Therefore, it is necessary to decouple the augmented state estimator and extend it into a distributed augmented state estimator suitable for underwater wireless sensor networks.
[0110] Specifically, by applying the Woodbury identities in equations (66)-(67), the following decoupling forms can be obtained:
[0111]
[0112] Based on this, in order to ensure the accuracy and stability of underwater target tracking based on underwater wireless sensor networks, this example adopts a two-layer distributed fusion method, that is, first fusing the measurement information between sensor nodes to obtain local estimates, and then fusing the local estimates obtained by each sensor node to obtain global estimates. The distributed augmented state estimator is represented by the following equations (71)-(72):
[0113]
[0114] Where j and r represent the j-th and r-th sensor nodes, respectively, N i and N j Let represent the sets of wireless sensor nodes that communicate with the i-th sensor node and the j-th sensor node, respectively. This represents the information fusion weight between sensor nodes i and j at time k+1.
[0115] In the specific implementation of S14: the morphological characteristics, dynamic parameters, initial state and reference trajectory of the underwater tracking target are set, and an underwater wireless sensor network is deployed to track the target using the distributed augmented state estimator.
[0116] The distributed augmented state estimator obtained in S13 is then applied to target tracking based on an underwater wireless sensor network under measurement delay to verify the effectiveness of the present invention.
[0117] The target state is defined by the target's position and velocity in three-dimensional Cartesian coordinates, i.e. in Indicates the target position at time k. Let represent the target velocity at time k. The parameters of the state equation are:
[0118]
[0119] Where w k ~N(0,Q) represents w k It follows a Gaussian distribution with mean 0 and covariance Q.
[0120] Underwater wireless sensor node measurements are performed using pure distance measurements, i.e.
[0121]
[0122] in This indicates the position of node i. The parameter values for the node measurement equation are:
[0123]
[0124] The tracking scenario is set as a cubic region of 2000m×2000m×100m, in which N=20 underwater wireless sensor network nodes are evenly distributed. The communication distance between nodes is 1770m, the underwater speed of sound is c=1470m / s, and the sampling time is T. s =0.1s, maximum acceptable delay t d = 4 steps, with a total simulation step size of 1000 steps. In the simulation, the initial system state is set as x(0) = [-929, 16, -760, -55, 100, -2]. T The initial state of the state estimator is
[0125]
[0126] Among them 1 24 and I 30 These represent a 24×1 matrix with all elements equal to 1 and an identity matrix with a dimension of 30×30, respectively.
[0127] make Let represent the estimate of the target state by the i-th sensor node at time k. Target tracking accuracy is measured by the following two metrics, where ... Used to measure the positioning error at time k. Used to measure the velocity error at time k:
[0128]
[0129]
[0130] Figure 2 The image shows target tracking without compensating for underwater measurement delay. and It can be seen that the tracking error of the underwater wireless sensor network is relatively large at this time. Figure 3 The image shows target tracking under compensated underwater measurement time delay conditions. and It can be seen that the tracking error of the underwater wireless sensor network is stable within a small range at this time.
[0131] Corresponding to the aforementioned embodiments of the target tracking method based on underwater wireless sensor networks under measurement delay, this application also provides embodiments of a target tracking device based on underwater wireless sensor networks under measurement delay.
[0132] Figure 4 This is a block diagram illustrating a target tracking device based on an underwater wireless sensor network for measuring time delay, according to an exemplary embodiment. (Refer to...) Figure 4 The device includes:
[0133] The model building module is used to construct an underwater measurement time delay model based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay.
[0134] The design module is used to design an augmented state estimator based on the underwater measurement time delay model. The augmented state estimator is used to compensate for the time delay caused by underwater acoustic propagation.
[0135] The extension module decouples the augmented state estimator and extends it into a distributed augmented state estimator suitable for underwater wireless sensor networks.
[0136] The target tracking module is used to set the morphological characteristics, dynamic parameters, initial state, and reference trajectory of the underwater target, and to deploy an underwater wireless sensor network to perform target tracking using the distributed augmented state estimator.
[0137] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0138] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0139] Accordingly, this application also provides an electronic device, such as Figure 5 As shown, it includes: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the target tracking method based on underwater wireless sensor networks under measurement delay as described above.
[0140] Accordingly, this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the target tracking method based on underwater wireless sensor networks under multipath interference as described above.
[0141] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.
[0142] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.
Claims
1. A target tracking method for an underwater wireless sensor network under measured time delay, characterized in that, include: An underwater measurement time delay model is constructed based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay. Based on the underwater measurement time delay model, an augmented state estimator is designed to compensate for the time delay caused by underwater acoustic propagation. The augmented state estimator is decoupled and extended into a distributed augmented state estimator suitable for underwater wireless sensor networks; Define the morphological characteristics, dynamic parameters, initial state, and reference trajectory of the underwater target, and deploy an underwater wireless sensor network to track the target using the distributed augmented state estimator; Specifically, based on the underwater measurement time delay model, an augmented state estimator is designed to compensate for the time delay caused by underwater acoustic propagation, including: The target state model is established as follows: x k+1 =F k x k +w k (1) Where x k+1 F k and w k These represent the state variables of the tracking target at time k+1, the state transition matrix at time k, and the process noise at time k, respectively. The augmented state model is constructed as follows: Where X k+1 X k , and Let represent the augmented state variables of the tracking target at time k+1, the augmented state variables at time k, the augmented state transition function at time k, and the augmented process noise at time k, respectively. and Let be the augmented measurement function and augmented measurement noise of the i-th sensor node at time k, respectively. T Indicates matrix transpose; Based on the augmented state model described by equations (7)-(11), for any time k, the desired target state estimate can be obtained by estimating the augmented state; an initial augmented state estimate is set. and augmented error covariance matrix The augmented state estimator is represented by the following equations (12)-(18); At time k, for the i-th sensor node, the augmented state estimate is first sampled. Augmented state predictions are obtained using equations (12)-(13). and the prediction augmentation error covariance matrix Where n d and s represent the number of augmented state variables and the s-th sampling point, respectively. and α s They represent the s-th information about The sampling and the weight of the s-th sampling point, Q k Let be the covariance matrix of the process noise at time k; Secondly, sampling augmentation state prediction Calculate augmentation measurement predictions using equations (14)-(16) Augmented measurement prediction error covariance matrix And augmented state measurement prediction error covariance matrix in Indicates the s-th information about Sampling, The covariance matrix of the measurement noise at time k+1-t; Finally, at time k+1, sensor node i obtains the time delay measurement. Combining equations (17) and (18) yields the augmented state estimate. and augmented error covariance matrix in(·) -1 This represents the inverse of a matrix.
2. The method according to claim 1, characterized in that, Establish an underwater measurement time delay model, including: The time delay caused by the slow propagation of underwater sound is calculated using the following expression: Where N represents the number of underwater wireless sensor nodes, and i represents the i-th sensor node. This represents the measurement delay of the i-th sensor node at time k. Let T represent the Euclidean distance between the i-th sensor node and the tracked target, c represent the underwater speed of sound, and T represent the speed of sound. s This indicates the sampling interval of the underwater wireless sensor network. Indicates rounding down; Based on equation (1), the actual arrival time of the sensor node at time k is obtained as follows: in This indicates the actual time when the measurement reaches the i-th sensor node at time k. Based on equation (2), the sensor measurements under time delay are obtained, as shown in the following expression: Where, x k To track the state variables of the target at time k, and Let be the measurement and measurement noise at time kt for the i-th sensor node, respectively. h represents the time delay measurement at time k of the i-th sensor node. i (·) represents the measurement function of the i-th sensor node, t d The maximum acceptable latency is set. The value is Bernoulli. When the value is 1, it means that the measurement at time kt arrives at sensor node i at time k; otherwise, the value is 0, which means that sensor node i did not receive the measurement at time kt at time k.
3. The method according to claim 1, characterized in that, The augmented state estimator is decoupled and extended into a distributed augmented state estimator suitable for underwater wireless sensor networks, including: Using Woodbury's identity, equations (17)-(18) can be transformed into the decoupling forms described by equations (19)-(21): Based on equations (19)-(21), the distributed augmented state estimator is represented by the following equations (22)-(23): Where j and r represent the j-th and r-th sensor nodes, respectively, and N i and N j Let represent the sets of wireless sensor nodes that communicate with the i-th sensor node and the j-th sensor node, respectively. This represents the information fusion weight between sensor nodes i and j at time k+1.
4. A target tracking device for an underwater wireless sensor network under measured time delay, characterized in that, The apparatus for implementing the method as described in any one of claims 1-3, the apparatus comprising: The model building module is used to construct an underwater measurement time delay model based on the time delay caused by underwater acoustic propagation and the sensor measurements under the time delay. The design module is used to design an augmented state estimator based on the underwater measurement time delay model. The augmented state estimator is used to compensate for the time delay caused by underwater acoustic propagation. The extension module decouples the augmented state estimator and extends it into a distributed augmented state estimator suitable for underwater wireless sensor networks. The target tracking module is used to set the morphological characteristics, dynamic parameters, initial state, and reference trajectory of the underwater target, and to deploy an underwater wireless sensor network to perform target tracking using the distributed augmented state estimator.
5. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-3.
6. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-3.