Self-Organizing Network Formation Navigation Method, System and Storage Medium Based on Relative Positioning

Abstract

The present invention provides a self-organizing network formation navigation method, system and storage medium based on relative positioning, including: obtaining the basic information of the self-organizing network, selecting the optimal target formation based on a preset self-organizing network formation network protocol, splitting the optimal target formation and sending it to each self-organizing network node; obtaining the real-time relative measurement results between adjacent self-organizing network nodes through a preset multi-sensor fusion position estimator of Kalman filter, generating the current relative position estimation information and estimation error; calculating and generating a motion control strategy by comparing the current task objectives of the nodes based on the relative position estimation information and estimation error through a preset distributed adaptive decision-making and scheduling algorithm, and executing the motion control strategy to perform self-organizing network formation. The present invention solves the problems of unstable positioning and navigation and limited performance of unmanned self-organizing network clusters caused by satellite denial or weak satellite signals in the existing situation.

Description

Technical Field

[0001] The present invention relates to the field of networking positioning technology, and in particular to a self-organizing network formation navigation method, system and storage medium based on relative positioning. Background Art

[0002] The existing mainstream multi-machine network relative position estimation technology framework is mainly composed of three parts: observation signal screening module, Kalman filter estimator and fused inertial navigation position result solver. Among them, the observation signal screening module mainly screens out high-quality navigation sources and their pseudo-range estimation results from the wireless signals received by the node, including beacon data that provides pseudo-range estimation, navigation source data, etc.; the Kalman filter estimator mainly filters the screened navigation sources, pseudo-range estimation results, and related quality information to obtain the optimal estimation results under given constraints; the fused inertial navigation position result solver is mainly based on the Kalman estimation results, integrates the inertial navigation sensor information, and periodically calculates the current position result to avoid the problem of Kalman filter solution stagnation caused by unstable wireless signals.

[0003] The existing mainstream distributed adaptive decision-making and scheduling algorithms in multi-machine networks mainly include: centralized algorithms such as pilot-follower and distributed algorithms based on consistency. Among them, the pilot-follower algorithm is that the pilot node moves according to the task plan, and the other nodes are adaptively coordinated and scheduled according to the preset formation goals and geometric architecture based on the motion state of the pilot node; the distributed algorithm based on consistency needs to achieve consistent consensus on parameters such as position and speed among the nodes in the network through information interaction, that is, each network node reaches a consensus on the network topology geometric state estimation and motion state estimation, which is equivalent to each node can obtain global information, and each node calculates the position difference required by the target task based on this, and finally realizes control and scheduling. In addition, in simple specific scenarios, the algorithm can be used to guide the movement of nodes to reach the target position to form a target formation by artificially designing cost functions such as potential functions or stream functions.

[0004] The existing network relative position estimation methods have low utilization rates of wireless signals and inertial navigation sensors, do not fully exploit the utility of poor quality measurement information, and do not effectively fuse multi-source data, resulting in poor positioning accuracy and slow convergence. In terms of distributed adaptive decision-making and scheduling algorithms, the pilot-follower algorithm is highly dependent on the pilot node, the system robustness is low, and the advantages of multi-machine collaboration are not fully utilized; although the consistency-based distributed algorithm is easy to implement in a distributed manner, and the convergence conditions in the absence of noise have been relatively well studied, the convergence and accuracy in the presence of noise are worrying, because it does not fully consider the positioning stage error, which can easily lead to inaccurate network positioning and limited formation capabilities, affecting the normal use of equipment. Summary of the invention

[0005] The present invention provides a self-organizing network formation navigation method, system and storage medium based on relative positioning, which are used to solve the problems of unstable positioning and navigation and limited performance of unmanned self-organizing network cluster formation caused by existing satellite denial or weak satellite signals.

[0006] The present invention provides a self-organizing network formation navigation method based on relative positioning, comprising:

[0007] Obtaining basic information of the ad hoc network, selecting the optimal target formation based on a preset ad hoc network formation network protocol, splitting the optimal target formation and sending it to each ad hoc network node;

[0008] Through the preset Kalman filter multi-sensor fusion position estimator, the real-time relative measurement results between adjacent ad hoc network nodes are obtained to generate the current relative position estimation information and estimation error;

[0009] Through a preset distributed adaptive decision-making scheduling algorithm, based on the relative position estimation information and the estimation error comparison node's current task target, a motion control strategy is calculated and generated, and the motion control strategy is executed to perform self-organizing network formation.

[0010] According to a relative positioning-based ad hoc network formation navigation method provided by the present invention, the acquisition of ad hoc network basic information, the selection of the optimal target formation based on a preset ad hoc network formation network protocol, the splitting of the optimal target formation and the distribution to each ad hoc network node specifically include:

[0011] The preset self-organizing network formation network protocol includes collecting basic information of the self-organizing network, establishing the optimal task target, and disassembling the task target and sending it to each node;

[0012] The basic information of the ad hoc network includes the identity representation of the node terminal, the estimation result of the initial position of the node terminal and the estimation error covariance;

[0013] The estimation result of the initial position of the node terminal and the estimation error covariance are derived from prior information or from the solution result of the absolute positioning or relative positioning method of the node terminal.

[0014] According to a relative positioning-based self-organizing network formation navigation method provided by the present invention, the establishing of the optimal task goal specifically includes:

[0015] Given an equivalence class of a target formation in a self-organizing network, the self-organizing network estimates the network position information based on the relative measurements between nodes;

[0016] Based on the position estimation results and estimation errors, the optimal target formation is selected in the target formation equivalence class and broadcast to each node;

[0017] Each node performs relative measurement on its neighboring nodes, and each node uses Kalman filtering or other methods to obtain the estimation result of the sub-formation in which it is located and the covariance of the estimation error;

[0018] Each node compares the shape difference between the sub-formation and the target sub-formation, establishes a motion control strategy, and each node executes the motion control strategy.

[0019] According to a relative positioning-based self-organizing network formation navigation method provided by the present invention, the multi-sensor fusion position estimator using a preset Kalman filter obtains real-time relative measurement results between adjacent self-organizing network nodes, generates current relative position estimation information and estimation error, specifically including:

[0020] The multi-sensor fusion position estimator uses each node to periodically perform real-time relative measurement on adjacent nodes, and fuses the relevant information obtained by the current sensor of the node, and obtains the current relative position estimation information and estimation error through filtering algorithms such as Kalman filtering, where the estimation error is represented by the estimation error covariance matrix.

[0021] According to a self-organizing network formation navigation method based on relative positioning provided by the present invention, the adjacent nodes perform real-time relative measurement in the following ways: distance measurement, angle measurement, laser radar, and visual positioning;

[0022] The position estimation methods include: extended Kalman filter, Bayesian filter, particle filter, and least squares.

[0023] According to a self-organizing network formation navigation method based on relative positioning provided by the present invention, the preset distributed adaptive decision scheduling algorithm is used to calculate and generate a motion control strategy based on the relative position estimation information and the estimation error comparison node current task target, and the motion control strategy is executed to perform self-organizing network formation, which specifically includes:

[0024] The distributed adaptive decision-making and scheduling algorithm uses the real-time position and motion state information of each node to periodically compare the shape difference between the sub-formation in which it is located and the target sub-formation, calculates and solves its own motion control strategy and executes it.

[0025] The present invention also provides a self-organizing network formation navigation system based on relative positioning, the system comprising:

[0026] A communication and navigation ad hoc network module is used to obtain basic information of the ad hoc network, select the optimal target formation based on a preset ad hoc network formation network protocol, split the optimal target formation and send it to each ad hoc network node;

[0027] The relative positioning module is used to obtain the real-time relative measurement results between adjacent ad hoc network nodes through a preset Kalman filter multi-sensor fusion position estimator, and generate current relative position estimation information and estimation error;

[0028] The formation control module is used to calculate and generate a motion control strategy based on the relative position estimation information and the estimated error comparison node's current task target through a preset distributed adaptive decision-making scheduling algorithm, and execute the motion control strategy to perform self-organizing network formation.

[0029] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the self-organizing network formation navigation method based on relative positioning as described in any one of the above is implemented.

[0030] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the relative positioning-based self-organizing network formation navigation method as described in any one of the above is implemented.

[0031] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned relative positioning-based self-organizing network formation navigation methods.

[0032] The present invention provides a self-organizing network formation navigation method, system and storage medium based on relative positioning, a formation network protocol based on a communication and navigation fusion network, which realizes the splitting of formation tasks and the issuance of sub-target tasks, and a multi-sensor fusion position estimator based on Kalman filtering, which estimates the relative position and accuracy of the current multi-machine network in real time, provides information support for scheduling decision processing, and can achieve satellite denial or unstable satellite signals according to the real-time observation information of each node, ensuring the stability of the self-organizing network formation performance and formation results. In addition, the present invention designs a distributed scheduling decision scheme that integrates the current motion state, and a distributed adaptive decision scheduling algorithm. Each node performs adaptive decision scheduling based on the current real-time observation information and estimation accuracy of itself and surrounding nodes, combined with the target task. While converging to the task target quickly, it has efficient autonomous maintenance capabilities, which can effectively solve the problem of poor performance of multi-machine collaborative systems caused by slow convergence of scheduling decisions or poor maintainability when processing complex tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 This is one of the flow charts of a self-organizing network formation navigation method based on relative positioning provided by the present invention;

[0035] Figure 2 This is a second flow chart of a self-organizing network formation navigation method based on relative positioning provided by the present invention;

[0036] Figure 3 This is a third flow chart of a self-organizing network formation navigation method based on relative positioning provided by the present invention;

[0037] Figure 4 It is a module connection diagram of a self-organizing network formation navigation system based on relative positioning provided by the present invention;

[0038] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention.

[0039] Reference numerals:

[0040] 110: communication and navigation ad hoc network module; 120: relative positioning module; 130: formation control module;

[0041] 510: processor; 520: communication interface; 530: memory; 540: communication bus. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Combine the following Figure 1-Figure 3 The present invention describes a self-organizing network formation navigation method based on relative positioning, comprising:

[0044] S100, obtaining basic information of the ad hoc network, selecting an optimal target formation based on a preset ad hoc network formation network protocol, splitting the optimal target formation and sending it to each ad hoc network node;

[0045] S200, obtaining real-time relative measurement results between adjacent ad hoc network nodes through a preset Kalman filter multi-sensor fusion position estimator, and generating current relative position estimation information and estimation error;

[0046] S300, using a preset distributed adaptive decision-making scheduling algorithm, based on the relative position estimation information and the estimation error comparison node's current task target, calculate and generate a motion control strategy, and execute the motion control strategy to perform self-organizing network formation.

[0047] The present invention solves the problem of low precision of self-organizing network formation when accurate absolute position or relative position cannot be obtained through a self-organizing network formation system based on relative position. In the initialization stage, the optimal formation target with the smallest estimated formation error is selected from the target formation set equivalent in relative position, combining the size and spatial distribution of the positioning error of each node. In the iteration stage, the shape difference between the sub-formation and the target sub-formation is compared in a distributed manner, and the motion control strategy of the self-formation is calculated and executed.

[0048] Obtaining basic information of the ad hoc network, selecting the optimal target formation based on a preset ad hoc network formation network protocol, splitting the optimal target formation and sending it to each ad hoc network node, specifically including:

[0049] S101, the preset self-organizing network formation network protocol includes collecting basic information of the self-organizing network, establishing the optimal task target, and disassembling the task target and delivering it to each node;

[0050] S102, the basic information of the ad hoc network includes the identity representation of the node terminal, the estimation result of the initial position of the node terminal and the estimation error covariance;

[0051] S103: The estimation result of the initial position of the node terminal and the estimation error covariance are derived from prior information or from the solution result of the absolute positioning or relative positioning method of the node terminal.

[0052] In the present invention, the estimation result and the estimation error covariance of the initial position of the node terminal in the basic information can be replaced by the positioning measurement information, and the estimation result and the estimation error covariance of the initial position of the node terminal can be solved by directly collecting the measurement information. The basic information of the ad hoc network can be concentrated in a certain node in the ad hoc network, or it can be concentrated on a device with storage, computing and communication functions such as a server.

[0053] Establish optimal mission objectives, including:

[0054] S201, given an equivalence class of a self-organizing network target formation, the self-organizing network estimates network position information based on relative measurements between nodes;

[0055] S202, based on the position estimation result and the estimation error, selecting the optimal target formation in the target formation equivalence class and broadcasting it to each node;

[0056] S203, each node performs relative measurement on neighboring nodes, and each node uses Kalman filtering or other methods to obtain an estimation result of the sub-formation in which it is located and a covariance of the estimation error;

[0057] S204 , each node compares the shape difference between the sub-formation and the target sub-formation, establishes a motion control strategy, and each node executes the motion control strategy.

[0058] In the present invention, the number of nodes is assumed to be N, and the estimated result of the three-dimensional initial position is recorded as where x i , i = 1, 2, ..., N is a three-dimensional vector, representing the coordinates of the i-th node in the x-direction, y-direction and z-direction in the plane rectangular coordinate system. Given the formation mission goal, it is recorded as where ξ i , i = 1, 2, ..., N is a three-dimensional vector, representing the x-, y-, and z-coordinates of the i-th node in the target formation in the plane rectangular coordinate system. The task goal of the self-organizing network formation is to make the shape of the formation after movement as close as possible to the set formation shape target through motion control. The shape refers to the relative geometric position structure between the nodes in the self-organizing network, which remains unchanged under the overall translation and / or rotation transformation. The optimization problem corresponding to the node formation in the self-organizing network can be expressed as:

[0059]

[0060] in is the motion control vector, where c i , i = 1, 2, ..., N is a three-dimensional vector, representing the motion control components of the i-th node in the x-direction, y-direction and z-direction in the plane rectangular coordinate system; I N is the N×N identity matrix, 1 N is an N-dimensional column vector whose elements are all 1, represents the Kronecker product, is a three-dimensional rotation matrix, and its value range is the three-dimensional rotation group

[0061] Order Collection is the equivalence class of the target formation Ξ. The optimization problem corresponding to the self-organizing network formation can be simplified as:

[0062]

[0063] Among them, the first optimization problem in the formula In this method, it is called the optimal target formation selection problem. In this method, the optimal target formation is approximately calculated by the following formula:

[0064]

[0065] where λ max (H) represents the maximum eigenvalue of the matrix H, ν max (H) represents the eigenvector corresponding to the maximum eigenvalue of the matrix H, the matrix H = ∏TL∏, where {e 1 , e 2 , ..., e m} is composed of Zhang Cheng's linear subspace (denoted as ), m is a set of standard orthogonal bases The dimension of ; where the matrix L is the solution to the following convex optimization problem:

[0066] minimize-log det L

[0067]

[0068]

[0069] In the formula Representation Matrix The maximum eigenvalue of The expression is as follows:

[0070]

[0071] In addition, the present invention is also compatible with two-dimensional formation navigation problems, that is, it is applicable to ground node formation navigation scenarios. In view of the fact that the horizontal height of each node remains unchanged during the movement, this method can transform the three-dimensional problem into a two-dimensional problem and use the following simplified method to reduce the computational complexity:

[0072] The estimated result of the initial horizontal position of N nodes in the ad hoc network is recorded as where x i , i = 1, 2, ..., N is a two-dimensional vector, representing the x- and y-coordinates of the ith node in the plane rectangular coordinate system. The given target formation is denoted as where ξ i , i = 1, 2, ..., N is a two-dimensional vector, representing the x- and y-coordinates of the i-th node in the formation mission target in the plane rectangular coordinate system. The following formula can be constructed to approximately calculate the optimal target formation in a two-dimensional scenario.

[0073]

[0074] in, represents the maximum eigenvalue of the matrix Ψ, which is the The estimated error covariance matrix of . Optionally, the above formula can also be approximated as:

[0075] Ξ * =(Ξcosτ+γsinτ)

[0076] Through the preset Kalman filter multi-sensor fusion position estimator, the real-time relative measurement results between adjacent ad hoc network nodes are obtained, and the current relative position estimation information and estimation error are generated, including:

[0077] The multi-sensor fusion position estimator uses each node to periodically perform real-time relative measurement on adjacent nodes, and fuses the relevant information obtained by the current sensor of the node, and obtains the current relative position estimation information and estimation error through filtering algorithms such as Kalman filtering, where the estimation error is represented by the estimation error covariance matrix.

[0078] Among them, the real-time relative measurement methods of adjacent nodes include: distance measurement, angle measurement, laser radar, and visual positioning;

[0079] The position estimation methods include: extended Kalman filter, Bayesian filter, particle filter, and least squares.

[0080] By using a preset distributed adaptive decision-making scheduling algorithm, based on the relative position estimation information and the estimation error comparison node's current task target, a motion control strategy is calculated and generated, and the motion control strategy is executed to perform self-organizing network formation, specifically including:

[0081] The distributed adaptive decision-making and scheduling algorithm uses the real-time position and motion state information of each node to periodically compare the shape difference between the sub-formation in which it is located and the target sub-formation, calculates and solves its own motion control strategy and executes it.

[0082] In the present invention, the method for calculating the control strategy of each node is as follows. The optimal target formation determined based on the self-organizing network formation network protocol is denoted as * ,Ξ * The target position corresponding to the middle node i is recorded as Let the set of neighbor nodes of node i be in The number of elements in is denoted by N i , let node i use the multi-sensor fusion position estimator to estimate its neighboring nodes The estimated position of Then the control strategy of node i can be calculated according to the following formula:

[0083]

[0084] The self-organizing network formation method based on relative position proposed in the embodiment of the present invention solves the problem of low precision of the self-organizing network formation when the accurate absolute position or relative position cannot be obtained. In the initialization stage, combined with the size and spatial distribution of the positioning error of each node, the optimal formation target with the smallest estimated formation error is selected from the set of target formations that are equivalent in the relative position. In the iteration stage, the shape difference between the sub-formation and the target sub-formation is compared in a distributed manner, and the motion control strategy itself is calculated and executed. The self-organizing network formation navigation type mentioned in the embodiment of the present invention includes but is not limited to application scenarios such as formation navigation of unmanned facilities such as drones, unmanned vehicles, and unmanned boats. It can also be used for application scenarios such as paratrooper assembly, emergency search and rescue, and other satellite denial or weak signals that require positioning and navigation formations.

[0085] refer to Figure 4 The present invention also discloses a self-organizing network formation navigation system based on relative positioning, the system comprising:

[0086] The communication and navigation ad hoc network module 110 is used to obtain basic information of the ad hoc network, select the optimal target formation based on a preset ad hoc network formation network protocol, split the optimal target formation and send it to each ad hoc network node;

[0087] The relative positioning module 120 is used to obtain the real-time relative measurement results between adjacent ad hoc network nodes through a preset Kalman filter multi-sensor fusion position estimator, and generate current relative position estimation information and estimation error;

[0088] The formation control module 130 is used to calculate and generate a motion control strategy based on the relative position estimation information and the estimation error comparison node's current task target through a preset distributed adaptive decision-making scheduling algorithm, and execute the motion control strategy to perform self-organizing network formation.

[0089] Among them, the communication and navigation self-organizing network module 110, the preset self-organizing network formation network protocol includes the collection of basic information of the self-organizing network, the establishment of the optimal task target, and the disassembly of the task target to each node;

[0090] The basic information of the ad hoc network includes the identity representation of the node terminal, the estimation result of the initial position of the node terminal and the estimation error covariance;

[0091] The estimation result of the initial position of the node terminal and the estimation error covariance are derived from prior information or from the solution result of the absolute positioning or relative positioning method of the node terminal.

[0092] Establish optimal mission objectives, including:

[0093] Given an equivalence class of a target formation in a self-organizing network, the self-organizing network estimates the network position information based on the relative measurements between nodes;

[0094] Based on the position estimation results and estimation errors, the optimal target formation is selected in the target formation equivalence class and broadcast to each node;

[0095] Each node performs relative measurement on its neighboring nodes, and each node uses Kalman filtering or other methods to obtain the estimation result of the sub-formation in which it is located and the covariance of the estimation error;

[0096] Each node compares the shape difference between the sub-formation and the target sub-formation, establishes a motion control strategy, and each node executes the motion control strategy.

[0097] The relative positioning module 120 uses a multi-sensor fusion position estimator to periodically perform real-time relative measurements on adjacent nodes using each node, and fuses the relevant information obtained by the current sensor of the node. Through filtering algorithms such as Kalman filtering, the current relative position estimation information and estimation error are obtained, where the estimation error is represented by an estimation error covariance matrix.

[0098] The formation control module 130, through a distributed adaptive decision-making scheduling algorithm, uses the real-time position and motion state information of each node to periodically compare the shape difference between the sub-formation it is in and the target sub-formation, calculates and solves its own motion control strategy and executes it.

[0099] The self-organizing network formation navigation system based on relative positioning provided by the present invention can realize the stability of self-organizing network formation performance and formation results under satellite denial or unstable satellite signals according to the real-time observation information of each node. In addition, the present invention designs a distributed scheduling decision-making scheme that integrates the current motion state, which has efficient autonomous maintenance capabilities while converging to the task goal quickly, and can effectively solve the problem of poor performance of multi-machine collaborative systems caused by slow convergence speed of scheduling decisions or poor maintainability when processing complex tasks.

[0100] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute a self-organizing network formation navigation method based on relative positioning, the method comprising:

[0101] Obtaining basic information of the ad hoc network, selecting the optimal target formation based on a preset ad hoc network formation network protocol, splitting the optimal target formation and sending it to each ad hoc network node;

[0102] Through the preset Kalman filter multi-sensor fusion position estimator, the real-time relative measurement results between adjacent ad hoc network nodes are obtained to generate the current relative position estimation information and estimation error;

[0103] Through a preset distributed adaptive decision-making scheduling algorithm, based on the relative position estimation information and the estimation error comparison node's current task target, a motion control strategy is calculated and generated, and the motion control strategy is executed to perform self-organizing network formation.

[0104] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0105] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute a relative positioning-based self-organizing network formation navigation method provided by the above methods, the method comprising:

[0106] Obtaining basic information of the ad hoc network, selecting the optimal target formation based on a preset ad hoc network formation network protocol, splitting the optimal target formation and sending it to each ad hoc network node;

[0107] Through the preset Kalman filter multi-sensor fusion position estimator, the real-time relative measurement results between adjacent ad hoc network nodes are obtained to generate the current relative position estimation information and estimation error;

[0108] Through a preset distributed adaptive decision-making scheduling algorithm, based on the relative position estimation information and the estimation error comparison node's current task target, a motion control strategy is calculated and generated, and the motion control strategy is executed to perform self-organizing network formation.

[0109] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for self-organizing network formation navigation based on relative positioning provided by the above methods is implemented, and the method includes:

[0110] Obtaining basic information of the ad hoc network, selecting the optimal target formation based on a preset ad hoc network formation network protocol, splitting the optimal target formation and sending it to each ad hoc network node;

[0111] Through the preset Kalman filter multi-sensor fusion position estimator, the real-time relative measurement results between adjacent ad hoc network nodes are obtained to generate the current relative position estimation information and estimation error;

[0112] Through a preset distributed adaptive decision-making scheduling algorithm, based on the relative position estimation information and the estimation error comparison node's current task target, a motion control strategy is calculated and generated, and the motion control strategy is executed to perform self-organizing network formation.

[0113] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0114] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A self-organizing network formation navigation method based on relative positioning, characterized in that, it includes: Obtain the basic information of the self-organizing network, select the optimal target formation based on the preset self-organizing network formation network protocol, split the optimal target formation and send it to each self-organizing network node; Through a preset multi-sensor fusion position estimator based on Kalman filter, obtain the real-time relative measurement results between adjacent self-organizing network nodes, and generate the current relative position estimation information and estimation error; Through a preset distributed adaptive decision-making scheduling algorithm, compare the current task objectives of the nodes based on the relative position estimation information and estimation error, calculate and generate a motion control strategy, and execute the motion control strategy to form a self-organizing network; Among them, obtaining the basic information of the self-organizing network, selecting the optimal target formation based on the preset self-organizing network formation network protocol, splitting the optimal target formation and sending it to each self-organizing network node specifically includes: The preset self-organizing network formation network protocol includes the collection of basic information of the self-organizing network, the establishment of the optimal task objective, and the decomposition and distribution of the task objective to each node; The basic information of the self-organizing network includes the identity representation of the node terminal, the estimation result of the initial position of the node terminal and the estimation error covariance; The estimation result of the initial position of the node terminal and the estimation error covariance are derived from prior information or from the solution results of the absolute positioning or relative positioning method of the node terminal.

2. The self-organizing network formation navigation method based on relative positioning according to claim 1, characterized in that, The establishment of the optimal task objective specifically includes: Given the equivalent class of the self-organizing network target formation, the self-organizing network estimates the network position information based on the relative measurement between nodes; Based on the position estimation result and estimation error, select the optimal target formation in the target formation equivalent class and broadcast it to each node; Each node performs relative measurement on its neighbor nodes, and each node uses Kalman filter or other methods to obtain the estimation result of the sub-formation it belongs to and the covariance of the estimation error; Each node compares the shape difference between the sub-formation and the target sub-formation, establishes a motion control strategy, and each node executes the motion control strategy.

3. The self-organizing network formation navigation method based on relative positioning according to claim 1, characterized in that, Through a preset multi-sensor fusion position estimator based on Kalman filter, obtain the real-time relative measurement results between adjacent self-organizing network nodes, and generate the current relative position estimation information and estimation error, specifically including: The multi-sensor fusion position estimator uses each node to perform real-time relative measurement on adjacent nodes periodically, and fuses the relevant information obtained by the current sensors of the nodes, and obtains the current relative position estimation information and estimation error through the Kalman filter algorithm, where the estimation error is represented by the estimation error covariance matrix.

4. The self-organizing network formation navigation method based on relative positioning according to claim 3, characterized in that, The real-time relative measurement methods between adjacent nodes include: distance measurement, angle measurement, lidar, visual positioning; The methods of position estimation include: extended Kalman filter, Bayesian filter, particle filter, least squares.

5. The self-organizing network formation navigation method based on relative positioning according to claim 1, characterized in that, through the preset distributed adaptive decision-making and scheduling algorithm, based on the relative position estimation information and the estimation error, comparing the current task objectives of the nodes, calculating and generating a motion control strategy, and executing the motion control strategy to perform self-organizing network formation, specifically including: The distributed adaptive decision-making and scheduling algorithm uses each node to periodically compare the shape differences between the sub-formation it belongs to and the target sub-formation based on the real-time position and motion state information, calculates and solves its own motion control strategy and executes it.

6. A self-organizing network formation navigation system based on relative positioning, characterized in that, the system includes: A communication and navigation self-organizing network module, configured to obtain basic self-organizing network information, select an optimal target formation based on a preset self-organizing network formation network protocol, split the optimal target formation and send it to each self-organizing network node; A relative positioning module, configured to obtain real-time relative measurement results between adjacent self-organizing network nodes through a preset multi-sensor fusion position estimator of Kalman filter, and generate current relative position estimation information and estimation error; A formation control module, configured to compare the current task objectives of the nodes based on the relative position estimation information and the estimation error through a preset distributed adaptive decision-making and scheduling algorithm, calculate and generate a motion control strategy, and execute the motion control strategy to perform self-organizing network formation; wherein, obtaining the basic self-organizing network information, selecting an optimal target formation based on a preset self-organizing network formation network protocol, splitting the optimal target formation and sending it to each self-organizing network node, specifically including: The preset self-organizing network formation network protocol includes collection of basic self-organizing network information, determination of optimal task objectives, and decomposition and distribution of task objectives to each node; The basic self-organizing network information includes the identity representation of the node terminal, the estimation result of the initial position of the node terminal, and the estimation error covariance; The estimation result of the initial position of the node terminal and the estimation error covariance are derived from prior information or from the solution results of the absolute positioning or relative positioning method of the node terminal.

7. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the self-organizing network formation navigation method based on relative positioning according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the self-organizing network formation navigation method based on relative positioning according to any one of claims 1 to 5.

9. A computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the self-organizing network formation navigation method based on relative positioning according to any one of claims 1 to 5.

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