Underwater TDOA positioning method based on iterative weighted least squares, storage medium and electronic equipment

Abstract

The embodiment of the invention provides an underwater TDOA (Time Difference Of Arrival) positioning method based on iterative weighted least squares, a storage medium and electronic equipment, and relates to the technical field of underwater measurement and control communication, and the method mainly comprises the four steps of sensor arrangement and TDOA calculation, initial value calculation through a least squares (LS) algorithm, iterative weighted least squares (IWLS) positioning and iterative updating. According to the technical scheme, the adaptive weight updating strategy for measuring the residual error reciprocal is introduced into the iterative weighted least square method to meet the characteristics of complex noise and strong multipath interference of the underwater environment, so that the positioning robustness and the positioning precision are improved, and the higher precision is achieved under the condition of smaller iteration times.

Description

Technical Field

[0001] This application relates to the field of underwater telemetry, control and communication technology, and more specifically, to an underwater TDOA positioning method, storage medium and electronic device based on iterative weighted least squares. Background Technology

[0002] Precise underwater target positioning has significant application value in civilian and military fields such as marine surveying, exploration, and communications. TDOA positioning technology, because it does not require synchronization of target signal transmission time, has wide applications in passive acoustic positioning systems. However, the complex marine environment and the influence of factors such as seawater temperature, salinity, and noise interference on signal propagation lead to significant measurement errors. Traditional least squares (LS)-based solution methods are easily affected by noise and cannot suppress abnormal measurements, impacting positioning accuracy. Weighted least squares (WLS) methods use fixed weights, making it difficult to dynamically adapt to complex underwater noise and suppress abnormal measurements. Furthermore, the lack of convergence guarantees makes them prone to getting trapped in local optima. Summary of the Invention

[0003] The embodiments of this application provide an underwater TDOA positioning method, storage medium, and electronic device based on iterative weighted least squares to solve the technical problems existing in the prior art.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the embodiments of this application, an underwater TDOA localization method based on iterative weighted least squares is provided, comprising: Among multiple sensors deployed underwater at known locations, select one sensor as a reference point and calculate the time difference matrix of the target signal arriving at different sensors; A system of linear equations is constructed using the measurement distance difference between the reference base station and other base stations, and the initial position estimate of the target is obtained by solving the least square closed-form solution of the system of linear equations. Based on the initial position estimate, perform k iterations to obtain the unknown position of the target and the distance from the target to each sensor; Construct the Jacobian matrix based on the distance and the time difference matrix; An adaptive weight update strategy based on the reciprocal of the TDOA measurement residual is introduced based on the Jacobian matrix; The target location is determined by iteratively updating the unknown target location based on the adaptive weight update strategy.

[0006] In some embodiments of this application, based on the foregoing scheme, selecting one sensor from multiple sensors at known locations deployed underwater as a reference point and calculating the time difference matrix of the target signal arriving at different sensors includes: Based on the arrival time of the target signal received by each sensor, an arrival time matrix is ​​constructed. ; Select the sensor corresponding to the minimum time in the matrix as the reference point, and calculate the time difference matrix of the target signal arriving at different sensors. .

[0007] In some embodiments of this application, based on the foregoing scheme, the step of performing k iterations based on the initial position estimate to obtain the unknown position of the target and the distance from the target to each sensor includes: After k iterations, the unknown position of the target is obtained from the initial position estimate. ; Obtain the known positions of each sensor ; The distances from the target to each sensor are calculated based on the unknown location of the target and the known locations of each sensor. : ; in, Indicates the number of sensors.

[0008] In some embodiments of this application, based on the foregoing scheme, constructing the Jacobian matrix based on the distance and the time difference matrix includes: Based on the time difference matrix, a nonlinear positioning equation is established: ; in, The speed of sound underwater. For the first The second iteration Predicted time difference values ​​for each sensor; By using Taylor expansion and linearization, the Jacobian matrix is ​​constructed. : .

[0009] In some embodiments of this application, based on the foregoing scheme, the adaptive weight update strategy based on the reciprocal of the TDOA measurement residual introduced based on the Jacobian matrix includes: The predicted time difference value is determined based on the nonlinear positioning equation. Calculate the weight matrix based on the predicted time difference and the Jacobian matrix; The weighted least squares update is calculated based on the predicted time difference, the Jacobian matrix, and the weight matrix.

[0010] In some embodiments of this application, based on the foregoing scheme, the calculation of the weight matrix based on the predicted time difference and the Jacobian matrix includes: Predicted values ​​based on time difference and Jacobi matrix Calculate the weight matrix : ; in, For the first The second iteration Time difference prediction values ​​of each sensor To prevent extremely small values ​​from being divided by zero, they are set to a fixed constant.

[0011] In some embodiments of this application, based on the foregoing scheme, the calculation of the weighted least squares update based on the predicted time difference, the Jacobian matrix, and the weight matrix includes: Predicted values ​​based on time difference Jacobian matrix and weight matrix The weighted least squares update amount is calculated. : .

[0012] In some embodiments of this application, based on the foregoing scheme, the step of iteratively updating the unknown target location based on the adaptive weight update strategy to determine the target location includes: The unknown position of the target is iteratively updated using the following formula: ; when When the value is less than the set threshold, stop the iteration and... The value is used as the target location.

[0013] According to a second aspect of the embodiments of this application, a computer-readable storage medium is provided, the storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method as described in the first aspect.

[0014] According to a third aspect of the embodiments of this application, an electronic device is provided, including: a memory and a processor; The memory is used to store computer instructions; The processor is configured to invoke computer instructions stored in the memory, causing the electronic device to execute the method described in the first aspect.

[0015] The technical solution of this application introduces an adaptive weight update strategy based on the inverse of the measurement residual into the iterative weighted least squares method to meet the characteristics of complex underwater environmental noise and strong multipath interference, thereby improving the robustness and accuracy of positioning and achieving high accuracy with a small number of iterations.

[0016] 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

[0017] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 A flowchart illustrating an underwater TDOA positioning method based on iterative weighted least squares according to an embodiment of this application is shown. Figure 2 A schematic diagram of an underwater detection network consisting of five underwater sensors according to one embodiment of this application is shown. Figure 3 A block diagram of an electronic device according to one embodiment of this application is shown; Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0019] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0020] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0021] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0022] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0025] The following detailed description of some embodiments of this application will be provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0026] See Figure 1 The diagram shows a flowchart of an underwater TDOA positioning method based on iterative weighted least squares according to an embodiment of this application.

[0027] like Figure 1 As shown, an underwater TDOA localization method based on iterative weighted least squares is demonstrated, specifically including steps S100 to S600.

[0028] refer to Figure 1 In step S100, select one sensor from multiple sensors at known locations deployed underwater as a reference point, and calculate the time difference matrix of the target signal arriving at different sensors.

[0029] It should be noted that each sensor records the time of arrival (TOA) of the target signal when it receives the signal, in order to calculate the time difference matrix.

[0030] In some feasible embodiments, based on the foregoing scheme, selecting one sensor from multiple sensors at known locations deployed underwater as a reference point and calculating the time difference matrix of the target signal arriving at different sensors includes: Based on the arrival time of the target signal received by each sensor, an arrival time matrix is ​​constructed. ; Select the sensor corresponding to the minimum time in the matrix as the reference point, and calculate the time difference matrix of the target signal arriving at different sensors. .

[0031] An example formula for calculating the time difference matrix: ; in, For the first i The measured time for each sensor to receive the target signal. The measured reception time of the reference sensor.

[0032] Continue to refer to Figure 1 In step S200, a system of linear equations is constructed using the measurement distance difference between the reference base station and other base stations, and the initial position estimate of the target is obtained by solving the least squares closed-form solution of the system of linear equations.

[0033] For example, the constructed system of linear equations can take the form of: The design matrix A consists of the base station coordinate differences, and the constant term b contains a combination of the squared distance difference and quadratic terms of the base station coordinates. Then, the least-squares closed-form solution of the linear equation system is obtained. Obtaining the initial position estimate of the target provides a robust initial iteration starting point for the subsequent Iterative Weighted Algorithm (IWLS).

[0034] Continue to refer to Figure 1 Step S300: Based on the initial position estimate, perform k iterations to obtain the unknown position of the target and the distance from the target to each sensor.

[0035] In some feasible embodiments, based on the foregoing scheme, the step of performing k iterations based on the initial position estimate to obtain the unknown position of the target and the distance from the target to each sensor includes: After k iterations, the unknown position of the target is obtained from the initial position estimate. ; Obtain the known positions of each sensor ; The distances from the target to each sensor are calculated based on the unknown location of the target and the known locations of each sensor. : ; in, Indicates the number of sensors.

[0036] Continue to refer to Figure 1 Step S400: Construct a Jacobian matrix based on the distance and the time difference matrix.

[0037] In some feasible embodiments, based on the foregoing scheme, constructing the Jacobian matrix based on the distance and the time difference matrix includes: Based on the time difference matrix, a nonlinear positioning equation is established: ; in, The speed of sound underwater. For the first The second iteration Predicted time difference values ​​for each sensor; By using Taylor expansion and linearization, the Jacobian matrix is ​​constructed. : .

[0038] Continue to refer to Figure 1 Step S500 introduces an adaptive weight update strategy based on the reciprocal of the TDOA measurement residual, based on the Jacobian matrix.

[0039] It should be noted that by introducing an adaptive weight update strategy based on the reciprocal of the TDOA measurement residual, smaller weights are assigned to TDOA with larger errors.

[0040] In some feasible embodiments, based on the foregoing scheme, the adaptive weight update strategy based on the reciprocal of the TDOA measurement residual introduced based on the Jacobian matrix includes: The predicted time difference value is determined based on the nonlinear positioning equation. Calculate the weight matrix based on the predicted time difference and the Jacobian matrix; The weighted least squares update is calculated based on the predicted time difference, the Jacobian matrix, and the weight matrix.

[0041] In some feasible embodiments, based on the foregoing scheme, the calculation of the weight matrix based on the predicted time difference and the Jacobian matrix includes: Predicted values ​​based on time difference and Jacobi matrix Calculate the weight matrix : ; in, For the first The second iteration Time difference prediction values ​​of each sensor To prevent extremely small values ​​from being divided by zero, they are set to a fixed constant.

[0042] In some feasible embodiments, based on the foregoing scheme, the calculation of the weighted least squares update based on the predicted time difference, the Jacobian matrix, and the weight matrix includes: Predicted values ​​based on time difference Jacobian matrix and weight matrix The weighted least squares update amount is calculated. : .

[0043] It should be noted that in this embodiment, the weight matrix is ​​dynamically adjusted by calculating the inverse of the residual in real time to suppress outliers.

[0044] Continue to refer to Figure 1 Step S600: Based on the adaptive weight update strategy, iteratively update the unknown target position to determine the target position.

[0045] In some feasible embodiments, based on the foregoing scheme, the step of iteratively updating the unknown target location based on the adaptive weight update strategy to determine the target location includes: The unknown position of the target is iteratively updated using the following formula: ; when When the value is less than the set threshold, stop the iteration and... The value is used as the target location.

[0046] In summary, this method has the following advantages: High robustness: A weighting matrix W is used to assign smaller weights to high-error TDOA, improving noise resistance. This avoids the large deviation problem caused by noise in the traditional LLS method. The adaptive weight update strategy based on the reciprocal of the TDOA measurement residual can suppress sudden noise and multipath interference.

[0047] High positioning accuracy: Through iterative optimization, the target position is continuously corrected, improving positioning accuracy and avoiding the problem of WLS iterative methods easily getting trapped in local optima.

[0048] Low computational complexity: Compared with methods such as Kalman filtering and particle filtering, IWLS has a moderate computational cost and is suitable for real-time underwater target localization.

[0049] Suitable for complex underwater environments: can be applied to scenarios such as marine exploration, port monitoring, and underwater communication and navigation.

[0050] Below is a specific application example of this method.

[0051] like Figure 2 As shown, an underwater detection network consisting of five underwater sensors locates a target. The location of each sensor is .

[0052] (1) Sensor placement and TDOA calculation: Each sensor receives the target signal and records its arrival time, resulting in an arrival time matrix:

[0053] Select the sensor corresponding to the minimum time in the matrix as the reference point;

[0054] If the calculation is The time difference matrix of the target signal arriving at different sensors is calculated as follows: .

[0055] (2) Calculate the initial value using the least squares (LS) algorithm: Using reference base station Construct a system of linear equations to compare the measured distance differences between the base stations and other base stations. First, construct the following system of equations. ; Subtracting equation 2 from each equation and simplifying, we get... ; make ; Then we obtain the linear equation Then, the least-squares closed-form solution of the linear equation system was obtained. The initial position estimate of the target is obtained as follows: .

[0056] (3) Iterative Weighted Least Squares (IWLS) localization: Proceed to the first The nth iteration. If the nth iteration... The position estimate of the unknown target in the next iteration is: Then the measured distance from the target to each sensor is expressed as:

[0057] Since distance can be converted into the speed of light, then we have

[0058] Construct the Jacobian matrix as follows

[0059] make ; Calculate the weight matrix:

[0060] Calculate the weighted least squares update: .

[0061] (4) Iterative update: make ,like If the value is less than the threshold, stop iterating; otherwise, continue updating. The initial... , The set threshold value is .

[0062] like Figure 3 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of the above-mentioned underwater TDOA positioning method based on iterative weighted least squares.

[0063] Since the electronic device described in this embodiment is used to implement the underwater TDOA positioning method based on iterative weighted least squares in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0064] In practice, when the computer program 311 is executed by the processor, it can implement any of the embodiments corresponding to the first aspect.

[0065] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0066] It should be noted that, Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0067] like Figure 4 As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage portion 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. Various programs and data required for system operation are also stored in RAM 403. The CPU 401, ROM 402, and RAM 403 are interconnected via bus 404. An Input / Output (I / O) interface 405 is also connected to bus 404.

[0068] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0069] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of this application.

[0070] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0072] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0073] In another aspect, this application also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the underwater TDOA positioning method based on iterative weighted least squares described in the above embodiments.

[0074] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the underwater TDOA positioning method based on iterative weighted least squares described in the above embodiments.

[0075] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0076] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0077] Other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the embodiments disclosed 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. It should be understood that this application is not limited to the precise structures 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. An underwater TDOA positioning method based on iterative weighted least squares, characterized in that, include: Among multiple sensors deployed underwater at known locations, select one sensor as a reference point and calculate the time difference matrix of the target signal arriving at different sensors; A system of linear equations is constructed using the measurement distance difference between the reference base station and other base stations, and the initial position estimate of the target is obtained by solving the least squares closed-form solution of the system of linear equations. Based on the initial position estimate, perform k iterations to obtain the unknown position of the target and the distance from the target to each sensor; Construct the Jacobian matrix based on the distance and the time difference matrix; An adaptive weight update strategy based on the reciprocal of the TDOA measurement residual is introduced based on the Jacobian matrix; The target location is determined by iteratively updating the unknown target location based on the adaptive weight update strategy.

2. The method according to claim 1, characterized in that, The process of selecting one sensor from multiple sensors located at known underwater positions as a reference point and calculating the time difference matrix of the target signal arriving at different sensors includes: Based on the arrival time of the target signal received by each sensor, an arrival time matrix is ​​constructed. ; Select the sensor corresponding to the minimum time in the matrix as the reference point, and calculate the time difference matrix of the target signal arriving at different sensors. .

3. The method according to claim 2, characterized in that, The step of performing k iterations based on the initial position estimate to obtain the unknown position of the target and the distance from the target to each sensor includes: After k iterations, the unknown position of the target is obtained from the initial position estimate. ; Obtain the known positions of each sensor ; The distances from the target to each sensor are calculated based on the unknown location of the target and the known locations of each sensor. : ； in, Indicates the number of sensors.

4. The method according to claim 3, characterized in that, The construction of the Jacobian matrix based on the distance and the time difference matrix includes: Based on the time difference matrix, a nonlinear positioning equation is established: ； in, The speed of sound underwater. For the first The second iteration Predicted time difference values ​​for each sensor; By using Taylor expansion and linearization, the Jacobian matrix is ​​constructed. : 。 5. The method according to claim 4, characterized in that, The adaptive weight update strategy based on the reciprocal of the TDOA measurement residual, introduced based on the Jacobian matrix, includes: The predicted time difference value is determined based on the nonlinear positioning equation. Calculate the weight matrix based on the predicted time difference and the Jacobian matrix; The weighted least squares update is calculated based on the predicted time difference, the Jacobian matrix, and the weight matrix.

6. The method according to claim 5, characterized in that, The calculation of the weight matrix based on the predicted time difference and the Jacobian matrix includes: Predicted values ​​based on time difference and Jacobi matrix Calculate the weight matrix : ； in, For the first The second iteration Time difference prediction values ​​of each sensor To prevent extremely small values ​​from being divided by zero, they are set to a fixed constant.

7. The method according to claim 6, characterized in that, The calculation of the weighted least squares update based on the predicted time difference, the Jacobian matrix, and the weight matrix includes: Predicted values ​​based on time difference Jacobian matrix and weight matrix The weighted least squares update amount was calculated. : 。 8. The method according to claim 6, characterized in that, The step of iteratively updating the unknown target location based on the adaptive weight update strategy to determine the target location includes: The unknown position of the target is iteratively updated using the following formula: ； when When the value is less than the set threshold, stop the iteration and... The value is used as the target location.

9. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-8.

10. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer instructions; The processor is configured to invoke computer instructions stored in the memory, causing the electronic device to perform the method as described in any one of claims 1-8.

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