A distributed cooperative positioning method considering communication interruption and related apparatuses

Abstract

This invention belongs to the field of distributed cooperative localization technology, and discloses a distributed cooperative localization method and related apparatus that considers communication interruptions; wherein, the distributed cooperative localization method includes: acquiring the relative measurements of an agent to all neighboring agents, utilizing the intermediate localization results of the agents, k Intermediate localization results of neighboring agents whose communication with the agent is interrupted at any time. k The intermediate location results sent by neighboring agents that can still communicate with the agent at any time are used to calculate... k The final localization result of the real-time agent. k The final location result of neighboring intelligent agents whose communication with the intelligent agent is interrupted at any time. The technical solution disclosed in this invention can obtain accurate distributed cooperative localization results even when communication interruption is taken into account.

Description

Technical Field

[0001] This invention belongs to the field of distributed cooperative positioning technology, and specifically relates to a distributed cooperative positioning method and related apparatus that takes into account communication interruptions. Background Technology

[0002] Currently, it is increasingly common for multiple intelligent agents to network and perform tasks. Sensors, robots, drones, and underwater vehicles can all form networks, playing a greater role than individual devices. During task execution, accurate positioning of the intelligent agents is crucial for task completion. Existing mature positioning technologies, such as GPS and inertial navigation, all have limitations. For example, GPS availability drops drastically or becomes completely unusable in many harsh environments, such as indoors, dense forests, deep seas, areas under polar ice caps, and battlefields. High-precision inertial navigation components are expensive, while ordinary inertial navigation components suffer from significant cumulative errors.

[0003] Given the above, cooperative localization technology is currently receiving widespread attention. It utilizes useful information hidden in the relative measurements between agents in a network to improve localization accuracy. Specifically, for example, the existing distributed cooperative localization algorithm based on the Extended Kalman Filter (EKF) and Belief Propagation (BP) algorithms is a representative example. It calculates the marginal posterior probability density of the agent's state based on the BP algorithm and uses EKF to calculate the marginal posterior probability density under the Gaussian distribution assumption, which can achieve good localization results.

[0004] Existing cooperative localization algorithms typically assume that relative measurements with neighbors must be received simultaneously with the intermediate localization results from neighboring agents to improve positioning accuracy. This assumption fails to account for situations where relative measurements persist despite communication interruptions. In reality, communication interference and even interruptions are not uncommon, such as electronic interference in battlefield environments. When communication is interrupted, agents executing traditional cooperative localization algorithms cannot utilize relative measurement information with their neighbors to improve positioning because they cannot receive intermediate localization results from neighboring agents. If communication between an agent and all neighboring agents is interrupted, traditional cooperative localization algorithms will degenerate into trajectory estimation algorithms, leading to a rapid increase in the agent's positioning error, resulting in reduced mission success rates and even impacting equipment survivability. Specific examples include autonomous underwater vehicles misaligning seabed topographic data, distorting the location of valuable glacier sample collection, or even causing vehicles to accidentally enter trenches and disappear permanently; and drone swarms deviating from their planned flight paths or even colliding. Therefore, there is an urgent need to develop a distributed cooperative localization scheme that considers communication interruptions. Summary of the Invention

[0005] The purpose of this invention is to provide a distributed cooperative positioning method and related apparatus that takes into account communication interruptions, in order to solve one or more of the aforementioned technical problems. The technical solution disclosed in this invention can obtain accurate distributed cooperative positioning results even when communication interruptions are considered.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a distributed cooperative localization method considering communication interruption, comprising the following steps: Based on a network composed of multiple agents, obtain k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Location results at time 1; based on k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents whose communication was interrupted; intelligent agents The intermediate positioning results are sent to k Time and Intelligent Agent The neighboring intelligent agents that can still communicate and obtain k Time and Intelligent Agent Intermediate location results sent by neighboring intelligent agents that are still able to communicate; Acquiring intelligent agents Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning resultsk Time and Intelligent Agent The final location result of the neighboring intelligent agent whose communication was interrupted.

[0007] A further improvement to the technical solution of the present invention lies in that the basis of k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent In the steps of obtaining intermediate localization results from neighboring agents during communication interruptions... First, construct a stacked motion model and a stacked internal measurement model. Then, simultaneously compute the agent based on the stacked motion model and the stacked internal measurement model. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions; among which... k Time and Intelligent Agent The identification model for neighbor agents in the event of communication interruption is as follows: ; In the formula: Represents intelligent agents Maintenance and intelligent agents Neighboring agents with communication interruption state, , Represents intelligent agents In the horizontal direction, Represents intelligent agents In the vertical direction; express exist Time and The state increments between time steps are assumed to follow a mean of . variance is The Gaussian distribution at different times They are unrelated and related to Irrelevant; Based on intelligent agents The motion evolution model,k Time and Intelligent Agent The stacking motion model constructed by the neighbor agent identification model in the case of communication interruption is as follows: ; In the formula, Represents intelligent agents status The state of its neighboring intelligent agents whose communication with it is interrupted The stacked state formed, , Represents intelligent agents The velocity in the horizontal direction, Represents intelligent agents In the vertical direction of velocity, express Time and Intelligent Agent A collection of neighboring intelligent agents whose communication is interrupted; , This represents the block diagonal matrix generating function. Represents intelligent agents The state transition matrix, Represents an identity matrix of appropriate size; , Represents intelligent agents The noise gain matrix, Represents a vector or matrix of suitable size consisting of zeros; Represents intelligent agents The process noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; ; The constructed internal measurement model of the stack is as follows: ; In the formula, , ; , ; Represents intelligent agents Acquired internal measurements The measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Simultaneous computation of the agent based on stacked motion model and stack internal measurement model intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruption ; In the formula, Indicates to Intermediate estimate, The covariance, representing the estimation error, is calculated using the following formula: ; In the formula, , and From Time-based intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Positioning results at time 1 , , , For intelligent agents Increment of neighboring agent state variance The estimate; ; ; .

[0008] A further improvement of the technical solution of the present invention lies in that the intelligent agent is calculated simultaneously based on the stacked motion model and the stacked internal measurement model. intermediate positioning results k Time and Intelligent Agent In the process of estimating the intermediate localization results of neighboring agents during communication interruptions. The specific steps for calculating the mean and variance include: First, establish information-exponentially smoothed state-space models of the horizontal and vertical positions of neighboring agents, respectively, and then establish... The state-space model is used to predict based on the established model. and get and ;in, The horizontal position of the established neighboring agents The exponentially smoothed state-space model for new information is: ; In the formula: , and These are the horizontal and trend components of the horizontal position of the neighboring agent, respectively. Let be the error term, with a mean of 0 and a variance of . Gaussian white noise; and These are the coefficients in the model; Use the maximum likelihood method to estimate the initial values ​​of the model state. and model parameters , and ; , and It is obtained by solving the following optimization problem: ; In the formula: , From the neighbor Intermediate location results sent before the communication was interrupted. Horizontal position estimation in yes Time Neighbor Intelligent Agent Intermediate estimates of its own state It is the covariance of the estimation error; Always for intelligent agents with neighbors The moment when communication was interrupted, ; Similarly, we can obtain , , and Thus establishing The state-space model is as follows: ; In the formula, ; ; Let be the error term, with a mean of 0 and a variance of . Gaussian white noise, , for Neighbors at all times Intermediate estimation results sent The covariance of the position estimation error in the middle; ; ; based on State-space model, prediction and get and The calculation formula is as follows: ; ; In the formula, .

[0009] A further improvement to the technical solution of the present invention lies in the acquisition of the intelligent agent. Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent In the steps of determining the final location result of a neighboring agent with communication interruption, First, a stacked linearized measurement model is established, and then calculations are performed simultaneously based on the stacked linearized measurement model. k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring agent whose communication was interrupted; among which, The stacked linearized measurement model is as follows: ; In the formula, , express Time-based intelligent agent Acquired neighboring intelligent agents Relative measurement, express Always able to communicate with intelligent agents A collection of neighboring intelligent agents that communicate; , express Time-based intelligent agent Neighboring intelligent agents The measurement function, ; , , , ; , , ; ; , Represents intelligent agents Neighboring intelligent agents The relative measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Next, based on the stacked linearized measurement model, Kalman filtering is used to simultaneously calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent when communication is interrupted. In the formula, Indicates to The estimate, The covariance, representing the estimation error, is calculated using the following formula: ; In the formula, , , , .

[0010] A second aspect of the present invention provides a distributed cooperative positioning system that takes into account communication interruptions, comprising: The initial localization result acquisition module is used to acquire, based on a network composed of multiple agents, the initial localization result acquisition module. k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Location results at time 1; The first intermediate positioning result acquisition module is used to obtain the results based on... k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents whose communication was interrupted; The second intermediate positioning result acquisition module is used to obtain the intelligent agent's location result. The intermediate positioning results are sent to k Time and Intelligent Agent The neighboring intelligent agents that can still communicate and obtain k Time and Intelligent Agent Intermediate location results sent by neighboring intelligent agents that are still able to communicate; The final localization result acquisition module is used to acquire the intelligent agent. Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent whose communication was interrupted.

[0011] A further improvement to the technical solution of the present invention lies in that the basis of k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent In the steps of obtaining intermediate localization results from neighboring agents during communication interruptions... First, construct a stacked motion model and a stacked internal measurement model. Then, simultaneously compute the agent based on the stacked motion model and the stacked internal measurement model. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions; among which... k Time and Intelligent Agent The identification model for neighbor agents in the event of communication interruption is as follows: ; In the formula: Represents intelligent agents Maintenance and intelligent agents Neighboring agents with communication interruption state, , Represents intelligent agents In the horizontal direction, Represents intelligent agents In the vertical direction; express exist Time and The state increments between time steps are assumed to follow a mean of . variance is The Gaussian distribution at different times They are unrelated and related to Irrelevant; Based on intelligent agents The motion evolution model, k Time and Intelligent Agent The stacking motion model constructed by the neighbor agent identification model in the case of communication interruption is as follows: ; In the formula, Represents intelligent agents status The state of its neighboring intelligent agents whose communication with it is interrupted The stacked state formed, , Represents intelligent agents The velocity in the horizontal direction, Represents intelligent agents In the vertical direction of velocity, express Time and Intelligent Agent A collection of neighboring intelligent agents whose communication is interrupted; , This represents the block diagonal matrix generating function. Represents intelligent agents The state transition matrix, Represents an identity matrix of appropriate size; , Represents intelligent agents The noise gain matrix, Represents a vector or matrix of suitable size consisting of zeros; Represents intelligent agents The process noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; ; The constructed internal measurement model of the stack is as follows: ; In the formula, , ; , ; Represents intelligent agents Acquired internal measurements The measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Simultaneous computation of the agent based on stacked motion model and stack internal measurement model intermediate positioning resultsk Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruption ; In the formula, Indicates to Intermediate estimate, The covariance, representing the estimation error, is calculated using the following formula: ; In the formula, , and From Time-based intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Positioning results at time 1 , , , For intelligent agents Increment of neighboring agent state variance The estimate; ; ; .

[0012] A further improvement of the technical solution of the present invention lies in that the intelligent agent is calculated simultaneously based on the stacked motion model and the stacked internal measurement model. intermediate positioning results k Time and Intelligent Agent In the process of estimating the intermediate localization results of neighboring agents during communication interruptions. The specific steps for calculating the mean and variance include: First, establish information-exponentially smoothed state-space models of the horizontal and vertical positions of neighboring agents, respectively, and then establish... The state-space model is used to predict based on the established model. and get and ;in, The horizontal position of the established neighboring agents The exponentially smoothed state-space model for new information is: ; In the formula: , and These are the horizontal and trend components of the horizontal position of the neighboring agent, respectively. Let be the error term, with a mean of 0 and a variance of . Gaussian white noise; and These are the coefficients in the model; Use the maximum likelihood method to estimate the initial values ​​of the model state. and model parameters , and ; , and It is obtained by solving the following optimization problem: ; In the formula: , From the neighbor Intermediate location results sent before the communication was interrupted. Horizontal position estimation in yes Time Neighbor Intelligent Agent Intermediate estimates of its own state It is the covariance of the estimation error; Always for intelligent agents with neighbors The moment when communication was interrupted, ; Similarly, we can obtain , , and Thus establishing The state-space model is as follows: ; In the formula, ; ; Let be the error term, with a mean of 0 and a variance of . Gaussian white noise, , for Neighbors at all times Intermediate estimation results sent The covariance of the position estimation error in the middle; ; ; based on State-space model, prediction and get and The calculation formula is as follows: ; ; In the formula, .

[0013] A further improvement to the technical solution of the present invention lies in the acquisition of the intelligent agent. Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent In the steps of determining the final location result of a neighboring agent with communication interruption, First, a stacked linearized measurement model is established, and then calculations are performed simultaneously based on the stacked linearized measurement model. k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring agent whose communication was interrupted; among which, The stacked linearized measurement model is as follows: ; In the formula, , express Time-based intelligent agent Acquired neighboring intelligent agents Relative measurement, express Always able to communicate with intelligent agents A collection of neighboring intelligent agents that communicate; , express Time-based intelligent agent Neighboring intelligent agents The measurement function, ; , , , ; , , ; ; , Represents intelligent agents Neighboring intelligent agents The relative measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Next, based on the stacked linearized measurement model, Kalman filtering is used to simultaneously calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent when communication is interrupted. In the formula, Indicates to The estimate, The covariance, representing the estimation error, is calculated using the following formula: ; In the formula, , , , .

[0014] In a third aspect, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a distributed cooperative positioning method considering communication interruption as described in any one of the first aspects of the present invention.

[0015] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the distributed cooperative positioning method considering communication interruption as described in any one of the first aspects of the present invention.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention specifically discloses a distributed cooperative localization method that considers communication interruptions, enabling accurate distributed cooperative localization results even when communication interruptions occur. Specifically, the technical solution of this invention reconstructs the factor graph for scenarios where communication interruptions occur during cooperative localization, allowing the agent to continuously improve localization by utilizing the identification model of neighboring agents and relative measurements of neighboring agents even during communication interruptions. Compared to traditional cooperative localization algorithms that cannot cooperate during communication interruptions, or even degenerate into trajectory estimation algorithms, the technical solution of this invention can continuously improve localization using relative measurements during communication interruptions, improving the accuracy of coordinated localization and effectively preventing the rapid increase of localization errors.

[0017] In a preferred embodiment of the present invention, when the motion evolution model of the neighboring agent is unknown, an identification model is constructed based on the historical intermediate positioning results sent by the neighboring agent using a time series model, which further ensures that the agent can still obtain accurate positioning results in the event of communication interruption. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a cooperative positioning factor map under a communication interruption scenario in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a distributed cooperative positioning method considering communication interruption in a specific embodiment of the present invention. Figure 3 This is a schematic diagram of a mesh-like topology simulation scenario in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of a chain-like topology simulation scenario in a specific embodiment of the present invention; Figure 5 This is a schematic diagram of the absolute position RMSE of agent 5 in a grid-like topology scenario in a specific embodiment of the present invention; Figure 6 This is a schematic diagram of the absolute position RMSE of agent 5 in a chain topology scenario in a specific embodiment of the present invention. Figure 7 This is a schematic diagram of a distributed cooperative positioning system that takes into account communication interruption in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Based on the technical solutions disclosed in the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0022] Please see Figure 1 and Figure 2 The present invention provides a distributed cooperative positioning method that considers communication interruption. The entire calculation process is as follows: Figure 1The backpropagation (BP) algorithm is executed on the factor graph constructed for the communication interruption scenario, as shown below, specifically including the following steps: In a network composed of multiple agents Time-based intelligent agent The maintenance state is defined as follows In the formula, express Time-based intelligent agent The state is defined as , and Representing intelligent agents respectively Position and velocity in the horizontal and vertical directions; express Time-based intelligent agent Maintaining neighboring intelligent agents The state is defined as ; express Time and Intelligent Agent The set of neighboring agents whose communication was interrupted; in addition, using express Time and Intelligent Agent A set of neighboring intelligent agents that communicate normally.

[0023] Time-based intelligent agent In acquiring internal measurements Afterwards, it can calculate intelligent agents. Intermediate localization results and the agent Intermediate localization results of neighboring agents during communication interruption In the formula, Represents intelligent agents status The state of its neighboring intelligent agents The stacked state formed, Yes Intermediate estimate, This is the covariance of the corresponding estimation error. In the agent... Obtain relative measurements of neighboring intelligent agents Intermediate location results sent by neighboring intelligent agents with normal communication. After that, it can calculate Time-based intelligent agent The final localization result and its relationship with the intelligent agent The final location result of the neighboring intelligent agent when communication is interrupted. ,in It is an intermediate estimate of the neighboring agent's own state. It is the covariance of the corresponding estimation error. Indicates to The estimate, The covariance represents the estimation error.

[0024] Step 1, Time-based intelligent agent In acquiring internal measurements Then, computational agents intermediate positioning results and k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruption ;in, intelligent agent Need to be based on neighboring intelligent agents Establish the neighbor agent state based on the intermediate estimation results sent in the past. Recognition Model In the formula, express exist Time and The state increments between time steps are assumed to follow a mean of . variance is The Gaussian distribution at different times They are unrelated and related to Irrelevant, thus constructing The stacked motion model. Next, the intelligent agent... Construct a measurement model of the stack's internal structure. Then utilize... Location results at any time , Internal measurement of time and the corresponding stack internal measurement model, The stacked motion model uses linear minimum mean square error estimation to calculate the intermediate localization results of itself and its neighboring agents whose communication with it has been interrupted.

[0025] First, it is necessary to establish information exponentially smoothed state-space models for the horizontal and vertical positions of neighboring agents, and then establish... The state-space model is used to predict the identification model required based on the established state-space model. and get and In the formula, For intelligent agents Increment of neighboring agent state variance The estimate.

[0026] Choose an innovation-exponentially smoothed state-space model with additive error, linear trend, and no seasonal component for the horizontal or vertical positions of neighboring agents, with the horizontal position as the starting point. For example, the established model is as follows: ; In the formula, , and These are the horizontal and trend components of the horizontal position of the neighboring agent, respectively. Let be the error term, assuming it has a mean of 0 and a variance of . Gaussian white noise; and These are the coefficients in the model.

[0027] The initial values ​​of the model state need to be estimated using the maximum likelihood method. and model parameters , and Specifically, the joint probability density of the horizontal position at historical moments is the product of the probability densities of the information at each moment, and then the neighboring agents are used. The horizontal position estimate from the intermediate positioning results is used to replace the true horizontal position. , , , The log-likelihood function is: ; In the formula, , From neighboring intelligent agents Intermediate location results sent before the communication was interrupted. Horizontal position estimation in; Always for intelligent agents with neighbors The moment when communication was interrupted.

[0028] right Taking the partial derivative and setting it to 0, we get The maximum likelihood estimate is: ; Substituting the above equation into the expression for the log-likelihood function, and taking the log-likelihood function... If we multiply the constant term by a factor of 1, then the objective function to be optimized is: ; Therefore, the maximum likelihood method is used to estimate. , , The optimization problem is defined as: ; Similarly, we can obtain , , and Thus establishing The state-space model is as follows: ; In the formula, ; ; Let be the error term, assuming it has a mean of 0 and a variance of . Gaussian white noise is used because the position estimates from neighboring agents are used when estimating the variance of the error term in the exponentially smoothed state-space model of the innovation. To account for the uncertainty of the position estimates, the noise is reduced in the estimation process. Time to take and The diagonal matrix composed of the maximum likelihood estimates and The sum of the position estimation error covariance matrices in the intermediate localization results of the neighboring agents at each time step, i.e. In the formula This indicates that the elements within the parentheses will be arranged into a focus matrix. for Neighbors at all times Intermediate location results sent The covariance of the position estimation error in the middle; ; .

[0029] Based on this model, prediction and get and The calculation formula is as follows: ; ; In the formula, .

[0030] The constructed The stacking motion model is as follows: ; In the formula, , This represents the block diagonal matrix generating function. Represents intelligent agents The state transition matrix, Represents an identity matrix of appropriate size; , Represents intelligent agents The noise gain matrix, Represents a vector or matrix of suitable size consisting of zeros; Represents intelligent agents The process noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; .

[0031] The constructed internal measurement model of the stack is as follows: ; In the formula, , ; , ; This represents measurement noise, where the covariance is... Zero-mean Gaussian white noise.

[0032] The agent is calculated using linear minimum mean square error estimation. Intermediate localization results and the agent Intermediate localization results of neighboring agents during communication interruption The calculation formula is: ; In the formula, , , ; ; ; .

[0033] Step 2, Intelligent Agent Obtain its relative measurements to neighboring intelligent agents Intermediate location results sent by neighboring intelligent agents with normal communication. Simultaneously calculate k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent when communication is interrupted. .

[0034] The model definition for a single relative measurement is as follows:

[0035] In the formula: For measurement functions; To measure noise, the covariance is Zero-mean Gaussian white noise.

[0036] The calculation of the final localization result is equivalent to the update step of performing Kalman filtering based on the stacked linearized measurement model. Here we take... and Perform a first-order Taylor expansion of the relative measurement model at the operating point, and then apply the agent... The linearized equations of all available relative measurements are stacked together to obtain the stacked linearized measurement model as follows: ; In the formula: , express Time-based intelligent agent Acquired neighboring intelligent agents Relative measurement; ; ; In the formula, , ; ; , , ; ; ; Based on this stacked linearized measurement model, Kalman filtering is used to simultaneously calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent when communication is interrupted. The calculation formula is: ; In the formula: .

[0037] The distributed cooperative localization process considering communication interruption proposed in this invention is as follows: Figure 2 As shown.

[0038] This invention experiments with the proposed distributed cooperative localization algorithm considering communication interruptions in two dynamic scenarios, and compares its performance with that of algorithms that do not experience communication interruptions and those that discard relative measurements related to interrupted neighbors after communication interruptions. The agents move in approximately uniform linear motion, and the motion evolution model is as follows: ; In the formula: ; Process noise The covariance is Zero-mean Gaussian white noise.

[0039] Measurement noise of internal measurements The covariance is Zero-mean Gaussian white noise. Relative measurements can provide the position and orientation measurements of neighboring agents relative to themselves, i.e. ; And measurement noise The covariance is The noise is zero-mean Gaussian white noise. The difference between the two scenarios lies in the different communication and measurement topologies between the agents. Scenario one is as follows: Figure 3 As shown, the nine agents form a grid-like topology; Scenario 2 is as follows. Figure 4 As shown, nine agents form a grid-like topology. Black dots represent agents, and the numbers above and to the right of each black dot indicate the agent's ID. Dashed lines indicate that when communication is stable, two connected agents are neighbors, capable of relative measurements and communication.

[0040] The simulation process is divided into three stages. In the first stage, the agents start from... Figure 3 and Figure 4 Starting from the initial position shown, communication is stable, and each agent executes a distributed cooperative localization algorithm, utilizing motion evolution models, internal measurements, and relative measurements to improve its localization; At this point, the simulation enters the second phase, and communication between agent 5 and its neighboring agents is interrupted. In scenario one, agent 5 loses communication with agents 2, 4, 6, and 8; in scenario two, agent 5 loses communication with agents 4 and 6. At this point, the simulation needle enters the third stage, and communication between agent 5 and its neighboring agents returns to normal. The distributed cooperative localization algorithm considering communication interruptions proposed in this paper mainly works in the second stage.

[0041] Furthermore, the initial state of the agent ,in It consists of initial position and velocity, the initial position is as follows: Figure 3 and Figure 4 As shown, the initial velocity is , In the simulation, 1000 Monte Carlo simulations were performed for each scenario, and the simulation results are as follows: Figure 5 and Figure 6As shown in the figure, we use the root mean square error (RMSE) of the agent's absolute position to measure the localization performance of the algorithm. Absolute position refers to the agent's position in a fixed external coordinate system. In the figure, DR describes the effect of using a trajectory extrapolation algorithm, where agents do not cooperate and each agent only uses a motion evolution model and internal measurements for localization; GiveUp describes the effect of abandoning the use of relative measurements related to the interrupted neighbor during the second stage of the simulation when communication is interrupted. For agent 5, this is equivalent to using the trajectory extrapolation algorithm in the second stage; Normal describes the localization effect when communication is not interrupted throughout the simulation; ETS describes the localization effect using the distributed cooperative localization algorithm considering communication interruption proposed in this invention. It can be seen that the cooperative localization algorithm considering communication interruption described in this paper performs consistent with the original algorithm when communication is stable, and its localization effect during communication interruption is better than abandoning the use of relative measurements related to the interrupted neighbor agent, indicating that the algorithm successfully utilizes these relative measurements to improve localization.

[0042] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0043] Please see Figure 7 In this embodiment of the invention, a distributed cooperative positioning system considering communication interruption is provided, comprising: The initial localization result acquisition module is used to acquire, based on a network composed of multiple agents, the initial localization result acquisition module. k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Location results at time 1; The first intermediate positioning result acquisition module is used to obtain the results based on... k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents whose communication was interrupted; The second intermediate positioning result acquisition module is used to obtain the intelligent agent's location result. The intermediate positioning results are sent to k Time and Intelligent Agent The neighboring intelligent agents that can still communicate and obtain k Time and Intelligent Agent Intermediate location results sent by neighboring intelligent agents that are still able to communicate; The final localization result acquisition module is used to acquire the intelligent agent. Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent whose communication was interrupted.

[0044] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to execute operations of a distributed cooperative positioning method considering communication interruptions.

[0045] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the distributed cooperative positioning method considering communication interruptions in the above embodiments.

[0046] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0047] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0048] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0049] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed cooperative localization method considering communication interruption, characterized in that, Includes the following steps: Based on a network composed of multiple agents, obtain k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Location results at time 1; based on k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents whose communication was interrupted; intelligent agents The intermediate positioning results are sent to k Time and Intelligent Agent The neighboring intelligent agents that can still communicate and obtain k Time and Intelligent Agent Intermediate location results sent by neighboring intelligent agents that are still able to communicate; Acquiring intelligent agents Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent whose communication was interrupted; Wherein, the basis k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent In the steps of obtaining intermediate localization results from neighboring agents during communication interruptions... First, construct a stacked motion model and a stacked internal measurement model. Then, simultaneously compute the agent based on the stacked motion model and the stacked internal measurement model. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions; among which... k Time and Intelligent Agent The identification model for neighbor agents in the event of communication interruption is as follows: ； In the formula: Represents intelligent agents Maintenance and intelligent agents Neighboring agents with communication interruption state, , Represents intelligent agents In the horizontal direction, Represents intelligent agents In the vertical direction; express exist Time and The state increments between time steps are assumed to follow a mean of . variance is The Gaussian distribution at different times They are unrelated and related to Irrelevant; Based on intelligent agents The motion evolution model, k Time and Intelligent Agent The stacking motion model constructed by the neighbor agent identification model in the case of communication interruption is as follows: ； In the formula, Represents intelligent agents status The state of its neighboring intelligent agents whose communication with it is interrupted The stacked state formed, , Represents intelligent agents The velocity in the horizontal direction, Represents intelligent agents In the vertical direction of velocity, express Time and Intelligent Agent A collection of neighboring intelligent agents whose communication is interrupted; , This represents the block diagonal matrix generating function. Represents intelligent agents The state transition matrix, Represents an identity matrix of appropriate size; , Represents intelligent agents The noise gain matrix, Represents a vector or matrix of suitable size consisting of zeros; Represents intelligent agents The process noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; ; The constructed internal measurement model of the stack is as follows: ； In the formula, , ; , ; Represents intelligent agents Acquired internal measurements The measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Simultaneous computation of the agent based on stacked motion model and stack internal measurement model intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruption ; In the formula, Indicates to Intermediate estimate, The covariance, representing the estimation error, is calculated using the following formula: ； In the formula, , and From Time-based intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Positioning results at time 1 , , , For intelligent agents Increment of neighboring agent state variance The estimate; ； ； 。 2. The distributed cooperative positioning method considering communication interruption according to claim 1, characterized in that, The intelligent agent is calculated simultaneously based on the stacked motion model and the stack internal measurement model. intermediate positioning results k Time and Intelligent Agent In the process of estimating the intermediate localization results of neighboring agents during communication interruptions. The specific steps for calculating the mean and variance include: First, establish information-exponentially smoothed state-space models of the horizontal and vertical positions of neighboring agents, respectively, and then establish... The state-space model is used to predict based on the established model. and get and ;in, The horizontal position of the established neighboring agents The exponentially smoothed state-space model for new information is: ； In the formula: , and These are the horizontal and trend components of the horizontal position of the neighboring agent, respectively. Let be the error term, with a mean of 0 and a variance of . Gaussian white noise; and These are the coefficients in the model; Use the maximum likelihood method to estimate the initial values ​​of the model state. and model parameters , and ; , and It is obtained by solving the following optimization problem: ； In the formula: , From the neighbor Intermediate location results sent before the communication was interrupted. Horizontal position estimation in yes Time Neighbor Intelligent Agent Intermediate estimates of its own state It is the covariance of the estimation error; Always for intelligent agents with neighbors The moment when communication was interrupted, ; Similarly, we can obtain , , and Thus establishing The state-space model is as follows: ； In the formula, ; ; Let be the error term, with a mean of 0 and a variance of . Gaussian white noise, , for Neighbors at all times Intermediate estimation results sent The covariance of the position estimation error in the middle; ; ; based on State-space model, prediction and get and The calculation formula is as follows: ； ； In the formula, .

3. The distributed cooperative positioning method considering communication interruption according to claim 2, characterized in that, The acquisition agent Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent In the steps of determining the final location result of a neighboring agent with communication interruption, First, a stacked linearized measurement model is established, and then calculations are performed simultaneously based on the stacked linearized measurement model. k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring agent whose communication was interrupted; among which, The stacked linearized measurement model is as follows: ； In the formula, , express Time-based intelligent agent Acquired neighboring intelligent agents Relative measurement, express Always able to communicate with intelligent agents A collection of neighboring intelligent agents that communicate; , express Time-based intelligent agent Neighboring intelligent agents The measurement function, ; , , , ; , , ; ; , Represents intelligent agents Neighboring intelligent agents The relative measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Next, based on the stacked linearized measurement model, Kalman filtering is used to simultaneously calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent when communication is interrupted. In the formula, Indicates to The estimate, The covariance, representing the estimation error, is calculated using the following formula: ； In the formula, , , , .

4. A distributed cooperative positioning system considering communication interruption, characterized in that, include: The initial localization result acquisition module is used to acquire, based on a network composed of multiple agents, the initial localization result acquisition module. k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Location results at time 1; The first intermediate positioning result acquisition module is used to obtain the results based on... k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents whose communication was interrupted; The second intermediate positioning result acquisition module is used to obtain the intelligent agent's location result. The intermediate positioning results are sent to k Time and Intelligent Agent The neighboring intelligent agents that can still communicate and obtain k Time and Intelligent Agent Intermediate location results sent by neighboring intelligent agents that are still able to communicate; The final localization result acquisition module is used to acquire the intelligent agent. Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent whose communication was interrupted; Wherein, the basis k- 1-moment intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- The positioning results at time 1, using k Time-based intelligent agent Internal measurement, intelligent agent The motion evolution model, k Time and Intelligent Agent A neighbor agent identification model for communication interruptions uses linear minimum mean square error estimation to compute the agent's identity. intermediate positioning results k Time and Intelligent Agent In the steps of obtaining intermediate localization results from neighboring agents during communication interruptions... First, construct a stacked motion model and a stacked internal measurement model. Then, simultaneously compute the agent based on the stacked motion model and the stacked internal measurement model. intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions; among which... k Time and Intelligent Agent The identification model for neighbor agents in the event of communication interruption is as follows: ； In the formula: Represents intelligent agents Maintenance and intelligent agents Neighboring agents with communication interruption state, , Represents intelligent agents In the horizontal direction, Represents intelligent agents In the vertical direction; express exist Time and The state increments between time steps are assumed to follow a mean of . variance is The Gaussian distribution at different times They are unrelated and related to Irrelevant; Based on intelligent agents The motion evolution model, k Time and Intelligent Agent The stacking motion model constructed by the neighbor agent identification model in the case of communication interruption is as follows: ； In the formula, Represents intelligent agents status The state of its neighboring intelligent agents whose communication with it is interrupted The stacked state formed, , Represents intelligent agents The velocity in the horizontal direction, Represents intelligent agents In the vertical direction of velocity, express Time and Intelligent Agent A collection of neighboring intelligent agents whose communication is interrupted; , This represents the block diagonal matrix generating function. Represents intelligent agents The state transition matrix, Represents an identity matrix of appropriate size; , Represents intelligent agents The noise gain matrix, Represents a vector or matrix of suitable size consisting of zeros; Represents intelligent agents The process noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; ; The constructed internal measurement model of the stack is as follows: ； In the formula, , ; , ; Represents intelligent agents Acquired internal measurements The measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Simultaneous computation of the agent based on stacked motion model and stack internal measurement model intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruption ; In the formula, Indicates to Intermediate estimate, The covariance, representing the estimation error, is calculated using the following formula: ； In the formula, , and From Time-based intelligent agent Location results k Time and Intelligent Agent Neighboring agents whose communication is interrupted k- Positioning results at time 1 , , , For intelligent agents Increment of neighboring agent state variance The estimate; ； ； 。 5. A distributed cooperative positioning system considering communication interruption according to claim 4, characterized in that, The intelligent agent is calculated simultaneously based on the stacked motion model and the stack internal measurement model. intermediate positioning results k Time and Intelligent Agent In the process of estimating the intermediate localization results of neighboring agents during communication interruptions. The specific steps for calculating the mean and variance include: First, establish information-exponentially smoothed state-space models of the horizontal and vertical positions of neighboring agents, respectively, and then establish... The state-space model is used to predict based on the established model. and get and ;in, The horizontal position of the established neighboring agents The exponentially smoothed state-space model for new information is: ； In the formula: , and These are the horizontal and trend components of the horizontal position of the neighboring agent, respectively. Let be the error term, with a mean of 0 and a variance of . Gaussian white noise; and These are the coefficients in the model; Use the maximum likelihood method to estimate the initial values ​​of the model state. and model parameters , and ; , and It is obtained by solving the following optimization problem: ； In the formula: , From the neighbor Intermediate location results sent before the communication was interrupted. Horizontal position estimation in yes Time Neighbor Intelligent Agent Intermediate estimates of its own state It is the covariance of the estimation error; Always for intelligent agents with neighbors The moment when communication was interrupted, ; Similarly, we can obtain , , and Thus establishing The state-space model is as follows: ； In the formula, ; ; Let be the error term, with a mean of 0 and a variance of . Gaussian white noise, , for Neighbors at all times Intermediate estimation results sent The covariance of the position estimation error in the middle; ; ; based on State-space model, prediction and get and The calculation formula is as follows: ； ； In the formula, .

6. A distributed cooperative positioning system considering communication interruption according to claim 5, characterized in that, The acquisition agent Relative measurements of all neighboring agents, utilizing agents intermediate positioning results k Time and Intelligent Agent Intermediate localization results of neighboring agents during communication interruptions k Time and Intelligent Agent The intermediate location results sent by neighboring intelligent agents that can still communicate are used to calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent In the steps of determining the final location result of a neighboring agent with communication interruption, First, a stacked linearized measurement model is established, and then calculations are performed simultaneously based on the stacked linearized measurement model. k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring agent whose communication was interrupted; among which, The stacked linearized measurement model is as follows: ； In the formula, , express Time-based intelligent agent Acquired neighboring intelligent agents Relative measurement, express Always able to communicate with intelligent agents A collection of neighboring intelligent agents that communicate; , express Time-based intelligent agent Neighboring intelligent agents The measurement function, ; , , , ; , , ; ; , Represents intelligent agents Neighboring intelligent agents The relative measurement noise is assumed to have a mean of 0 and a variance of . Gaussian white noise; Next, based on the stacked linearized measurement model, Kalman filtering is used to simultaneously calculate... k Time-based intelligent agent Final positioning results k Time and Intelligent Agent The final location result of the neighboring intelligent agent when communication is interrupted. In the formula, Indicates to The estimate, The covariance, representing the estimation error, is calculated using the following formula: ； In the formula, , , , .

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the distributed cooperative positioning method considering communication interruptions as described in any one of claims 1 to 3.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the distributed cooperative positioning method considering communication interruption as described in any one of claims 1 to 3.

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