CN-119854936-B - Three-dimensional space positioning method based on wireless mesh network and server

CN119854936BCN 119854936 BCN119854936 BCN 119854936BCN-119854936-B

Abstract

The invention relates to the technical field of wireless communication and positioning, in particular to a three-dimensional space positioning method based on a wireless mesh network and a server; the method comprises the steps of utilizing known distances and signal strengths among N reference nodes to determine path loss factors and reference signal strengths of a free space propagation model, selecting M reference nodes with strongest received signals of a target node, obtaining coordinates of the M reference nodes, calculating the distance between the M reference nodes and the target node based on the determined free space propagation model, calculating initial coordinate values of the target node by utilizing a genetic algorithm based on the coordinates of the M reference nodes and the measured distances between the M reference nodes and the target node, and calculating final coordinate values of the target node by utilizing an improved Gaussian-Newton algorithm based on the initial coordinate values of the target node. The invention can effectively reduce the influence of multipath interference and shadow fading on RSSI positioning, and remarkably improve the precision and stability of three-dimensional positioning.

Inventors

ZHANG YI
Tu Weiye

Assignees

重庆邮电大学

Dates

Publication Date: 20260512
Application Date: 20250115

Claims (6)

1. The three-dimensional space positioning method based on the wireless mesh network is characterized by comprising the following steps of: Determining a path loss factor and reference signal strength of a free space propagation model by using known distances and signal strengths among N reference nodes, wherein N is more than or equal to 4; selecting M reference nodes with strongest received signals of a target node, acquiring coordinates of the M reference nodes, and calculating the distance between the M reference nodes and the target node based on a determined free space propagation model, wherein M is more than or equal to 4; Calculating initial coordinate values of the target nodes by using a genetic algorithm based on the coordinates of the M reference nodes and the measured distances between the M reference nodes and the target nodes; Based on the initial coordinate value of the target node, calculating the final coordinate value of the target node by utilizing an improved Gaussian-Newton algorithm, wherein the method specifically comprises the following steps: Initializing a parameter vector to be evaluated based on an improved Gaussian-Newton algorithm to obtain an initial parameter vector to be evaluated, wherein the initial parameter vector to be evaluated is an initial coordinate value of the target node; calculating a Jacobian matrix and a gradient direction included angle based on a residual vector corresponding to the target node and the initial parameter vector to be evaluated; Determining an estimated value of the parameter vector to be evaluated based on the jacobian matrix and the gradient direction included angle, and updating the parameter vector to be evaluated according to the estimated value of the parameter vector to be evaluated; Updating the coordinate value of the target node according to a dynamic iteration step length based on the updated parameter vector to be evaluated, wherein the determination mode of the dynamic iteration step length comprises the following steps: if the residual vector of the current iteration process is smaller than the residual vector of the previous iteration process and the included angle between the current gradient and the previous gradient is smaller than the gradient consistency direction threshold, determining the step length of the current iteration process according to the step length adjustment gain factor, the maximum step length value and the step length minimum value of the previous iteration process; If the residual vector of the current iteration process is not smaller than the residual vector of the previous iteration process and the included angle between the current gradient and the previous gradient is smaller than the gradient consistency direction threshold, determining the step length of the current iteration process according to the step length adjustment attenuation factor, the minimum step length value and the step length maximum value of the previous iteration process; and when the error function corresponding to the target node converges or reaches the maximum iteration number, determining the final coordinate value of the target node.
2. The three-dimensional space positioning method based on the wireless mesh network according to claim 1, wherein the solving method of the path loss factor and the reference signal strength of the free space propagation model comprises: Acquiring signal intensity values among N reference nodes and known distances among the reference nodes; constructing a target equation set by using the signal intensity values among the N reference nodes and the known distances among the reference nodes; And solving the target equation set by using a least square method to obtain a path loss factor and a signal strength reference value in the free space propagation model.
3. The three-dimensional space positioning method based on a wireless mesh network according to claim 1, wherein the calculating the initial coordinate values of the target node by using a genetic algorithm based on the coordinates of the M reference nodes and the measured distances between the M reference nodes and the target node comprises: selecting a plurality of initial populations from M sphere intersection areas formed by taking M reference nodes as sphere centers and the measurement distances between the M reference nodes and the target node as radiuses based on a genetic algorithm, and taking the initial populations as initial population parameters; Calculating an fitness function value of each population individual based on the measured distances between the M reference nodes and the target nodes and the distances between the M reference nodes and the population individuals; Based on the fitness function value of each population individual, the population is updated by adopting selection, crossing and mutation operations; And determining an initial coordinate value of the target node based on the updated population.
4. A three-dimensional space positioning method based on a wireless mesh network according to claim 3, wherein the fitness function value of each population individual is determined by the inverse of the difference between the measured distances between M reference nodes and the target nodes and the distances between M reference nodes and the population individuals.
5. The three-dimensional space positioning method based on the wireless mesh network is characterized by comprising a receiver and at least one processor, wherein the receiver is used for receiving the three-dimensional space positioning information of the wireless mesh network; The receiver is used for receiving signal intensity values between M reference nodes and target nodes and signal intensity values between N reference nodes, wherein the signal intensity values are reported by at least one gateway, M is more than or equal to 4, and N is more than or equal to 4; the at least one processor is configured to: Determining a path loss factor and reference signal strength of a free space propagation model by using known distances and signal strengths among N reference nodes, wherein N is more than or equal to 4; selecting M reference nodes with strongest received signals of a target node, acquiring coordinates of the M reference nodes, and calculating the distance between the M reference nodes and the target node based on a determined free space propagation model, wherein M is more than or equal to 4; Calculating initial coordinate values of the target nodes by using a genetic algorithm based on the coordinates of the M reference nodes and the measured distances between the M reference nodes and the target nodes; And calculating the final coordinate value of the target node by utilizing an improved Gaussian-Newton algorithm based on the initial coordinate value of the target node.
6. The server of claim 5, wherein the server further comprises: a transmitter; the transmitter is configured to transmit the final coordinate value of the target node to the target node.

Description

Three-dimensional space positioning method based on wireless mesh network and server Technical Field The invention relates to the technical field of wireless communication and positioning, in particular to a three-dimensional space positioning method based on a wireless mesh network and a server, which are used for improving the positioning accuracy and stability of nodes in a wireless sensor network. Background With the rapid development of internet of things (IoT), wireless sensor networks (Wireless Sensor Networks, WSNs) are widely used in the fields of smart home, industrial monitoring, logistics management, and the like. Accurate node location is the basis for achieving efficient data transmission and network management. At present, a positioning method based on RSSI is widely applied to a wireless sensor network. The RSSI positioning method is used for estimating the distance between the nodes by measuring the signal intensity between the reference node and the target node and combining a free space propagation model, so that the positioning of the nodes is realized. The method has the advantages of simple realization, low cost and the like, and is suitable for a scene of large-scale node deployment. The Gaussian-Newton iteration method plays an important role in the node positioning technology in the wireless sensor network because of higher convergence speed and positioning accuracy. However, the conventional gaussian-newton iterative method has the disadvantages of sensitivity to initial values, inflexibility in step adjustment, insufficient robustness in complex environments such as multipath interference and shadow fading in practical application, and the like. These problems lead to difficulties in the accuracy and stability of the algorithm in three-dimensional spatial positioning to meet practical requirements. In order to solve the above problems, a positioning method capable of improving the positioning accuracy and stability of RSSI in a complex environment is needed. The method is insensitive to initial values, flexible in step length adjustment, good in noise robustness and capable of effectively coping with adverse conditions such as multipath interference and shadow fading, and therefore the requirements of high accuracy and high stability of actual three-dimensional space positioning are met. Disclosure of Invention Aiming at the problems, the invention provides a three-dimensional space positioning method based on a wireless mesh network and a server, wherein an initial coordinate position is optimized by utilizing an improved Gaussian-Newton iteration method and combining a genetic algorithm, and a dynamic step length adjustment mechanism is introduced to enhance the adaptability and the robustness of the algorithm. The dynamic step length adjustment mechanism flexibly adjusts the step length according to the residual ratio and gradient direction consistency of the current iteration, and ensures that the iterative algorithm can not only quickly converge but also maintain stability in different iteration stages. In addition, the invention also processes the noise in the RSSI data through the weighting factors, and obviously reduces the influence of multipath interference and shadow fading on the positioning result, thereby greatly improving the precision and reliability of three-dimensional space positioning, and particularly, the invention can still maintain excellent positioning performance under complex and high-interference environments. In a first aspect of the present invention, the present invention provides a three-dimensional spatial positioning method based on a wireless mesh network, where the mesh network includes N reference nodes and at least one target node, and the method includes: Determining a path loss factor and reference signal strength of a free space propagation model by using known distances and signal strengths among N reference nodes, wherein N is more than or equal to 4; selecting M reference nodes with strongest received signals of a target node, acquiring coordinates of the M reference nodes, and calculating the distance between the M reference nodes and the target node based on a determined free space propagation model, wherein M is more than or equal to 4; Calculating initial coordinate values of the target nodes by using a genetic algorithm based on the coordinates of the M reference nodes and the measured distances between the M reference nodes and the target nodes; And calculating the final coordinate value of the target node by utilizing an improved Gaussian-Newton algorithm based on the initial coordinate value of the target node. In a second aspect of the present invention, the present invention also proposes a server for three-dimensional spatial positioning of a wireless mesh network, the mesh network comprising N reference nodes and at least one target node; The receiver is used for receiving signal intensity values between M reference nodes and