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

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 following steps: determining a path loss factor and reference signal strength of a free space propagation model by utilizing known distances and signal strength among N reference nodes; m reference nodes with the strongest target node receiving signals are selected, coordinates of the reference nodes are obtained, and the distance between the reference nodes and the target node is calculated based on the determined free space propagation model; calculating an initial coordinate value of the target node by using a genetic algorithm based on the coordinates of the M reference nodes and the measurement distance between the M reference nodes and the target node; and calculating a final coordinate value of the target node by using an improved Gaussian-Newton algorithm based on the initial coordinate value of the target node. According to the method, the influence of multipath interference and shadow fading on RSSI positioning can be effectively reduced, and the precision and stability of three-dimensional positioning are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication and positioning technology, and in particular to a three-dimensional space positioning method and a server based on a wireless mesh network, which are used to improve the positioning accuracy and stability of nodes in a wireless sensor network. Background Art

[0002] With the rapid development of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have been widely used in smart homes, industrial monitoring, logistics management, etc. Accurate node positioning is the basis for efficient data transmission and network management.

[0003] At present, RSSI-based positioning methods have been widely used in wireless sensor networks. RSSI positioning methods measure the signal strength between reference nodes and target nodes, and estimate the distance between nodes in combination with the free space propagation model to achieve node positioning. This type of method has the advantages of simple implementation and low cost, and is suitable for scenarios with large-scale node deployment.

[0004] The Gauss-Newton iteration method plays an important role in node positioning technology in wireless sensor networks due to its high convergence speed and positioning accuracy. However, the traditional Gauss-Newton iteration method has shortcomings in practical applications, such as sensitivity to initial values, inflexible step size adjustment, and insufficient robustness in complex environments such as multipath interference and shadow fading. These problems make it difficult for the algorithm to meet actual needs in terms of accuracy and stability in three-dimensional spatial positioning.

[0005] To solve the above problems, a positioning method that can improve RSSI positioning accuracy and stability in complex environments is urgently needed. This method should be insensitive to initial values, flexible in step adjustment, and have good noise robustness, and can effectively cope with adverse conditions such as multipath interference and shadow fading, so as to meet the high-precision and high-stability requirements of actual three-dimensional spatial positioning. Summary of the invention

[0006] In response to the above problems, the present invention proposes a three-dimensional spatial positioning method and server based on a wireless mesh network, which uses an improved Gauss-Newton iteration method combined with a genetic algorithm to optimize the initial coordinate position, and introduces a dynamic step adjustment mechanism to enhance the adaptability and robustness of the algorithm. The genetic algorithm provides global optimization capabilities, effectively avoiding the defect that traditional methods are prone to falling into local optimality; the dynamic step adjustment mechanism flexibly adjusts the step size according to the residual ratio and gradient direction consistency of the current iteration, ensuring that the iterative algorithm can converge quickly and maintain stability at different iteration stages. In addition, the present invention also processes the noise in the RSSI data through a weighting factor, significantly reducing the impact of multipath interference and shadow fading on the positioning results, thereby greatly improving the accuracy and reliability of three-dimensional spatial positioning, especially in complex and high-interference environments. Excellent positioning performance can still be maintained.

[0007] In a first aspect of the present invention, the present invention proposes a three-dimensional spatial positioning method based on a wireless mesh network, wherein the mesh network includes N reference nodes and at least one target node; the method includes:

[0008] Using the known distances and signal strengths between N reference nodes, determine the path loss factor and reference signal strength of the free space propagation model, N ≥ 4;

[0009] Select M reference nodes with the strongest receiving signals from the target node, obtain their coordinates and calculate the distance to the target node based on the determined free space propagation model, M ≥ 4;

[0010] Based on the coordinates of M reference nodes and the measured distances between them and the target node, the initial coordinates of the target node are calculated using a genetic algorithm.

[0011] Based on the initial coordinate value of the target node, the final coordinate value of the target node is calculated using the improved Gauss-Newton algorithm. 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, wherein the mesh network includes N reference nodes and at least one target node; the server includes: a receiver, at least one processor;

[0012] The receiver is used to receive signal strength values ​​between M reference nodes and the target node and signal strength values ​​between N reference nodes reported by at least one gateway, where M≥4 and N≥4;

[0013] The at least one processor is configured to:

[0014] Using the known distances and signal strengths between N reference nodes, determine the path loss factor and reference signal strength of the free space propagation model, N ≥ 4;

[0015] Select M reference nodes with the strongest receiving signals from the target node, obtain their coordinates and calculate the distance to the target node based on the determined free space propagation model, M ≥ 4;

[0016] Based on the coordinates of M reference nodes and the measured distances between them and the target node, the initial coordinates of the target node are calculated using a genetic algorithm.

[0017] Based on the initial coordinate value of the target node, the final coordinate value of the target node is calculated using the improved Gauss-Newton algorithm.

[0018] Through the embodiments of the present invention, the present invention can effectively reduce the impact of multipath interference and shadow fading on RSSI positioning, and significantly improve the accuracy and stability of three-dimensional positioning. The method combines the genetic algorithm to optimize the initial estimate with the improved Gauss-Newton iterative algorithm, which enhances the adaptability of the algorithm in complex environments. Experimental verification shows that the algorithm exhibits excellent positioning performance in various complex three-dimensional environments, especially under non-line-of-sight (NLOS) conditions, and can still maintain a highly reliable positioning effect. In addition, the algorithm has the characteristics of fast convergence and high robustness, and is suitable for a variety of application scenarios such as smart cities, industrial automation, and the Internet of Things, and has broad application prospects and significant practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of the architecture of a wireless mesh system according to an embodiment of the present invention;

[0020] Figure 2 is a flow chart of a three-dimensional spatial positioning method based on a wireless mesh network according to an embodiment of the present invention;

[0021] Figure 3 is a flow chart of a genetic algorithm according to an embodiment of the present invention;

[0022] Figure 4 is a flow chart of an improved Gauss-Newton iteration method according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of the structure of a server according to an embodiment of the present invention;

[0024] Figure 6 It is a schematic diagram of the structure of a server according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0026] Figure 1 is an application scenario diagram of a three-dimensional spatial positioning method based on a wireless mesh network provided according to an embodiment of the present invention, such as Figure 1 As shown, the scenario includes at least a server, a gateway, a target node and multiple reference nodes. The target node and the multiple reference nodes form a wireless mesh network. These nodes are connected to form a mesh structure by wireless means. Each terminal node in the network has an automatic routing function, and each terminal node communicates with its adjacent terminal nodes. It is a dynamic and continuously expandable network structure, and any two nodes can maintain wireless interconnection. In this wireless mesh network, the target node refers to a specific node in the network. It may be the initiator, receiver, or simply a relay station participating in the forwarding of data. Multiple reference nodes are nodes that play a key role in the network. They can provide stable reference points for positioning services, time synchronization, or as a basis for selecting data transmission paths.

[0027] The present application provides a three-dimensional spatial positioning method based on a wireless mesh network, such as Figure 2 As shown, the mesh network includes N reference nodes and at least one target node; the method includes:

[0028] 101. Determine the path loss factor and reference signal strength of the free space propagation model using the known distances and signal strengths between N reference nodes, N ≥ 4;

[0029] In an embodiment of the present invention, since the reference nodes are fixedly distributed in the wireless mesh network, the actual distance between the reference nodes is known, so it is only necessary to receive the signal strength values ​​between the reference nodes in real time. The signal strength values ​​between the reference nodes reflect the actual strength of the signal after propagation between the reference nodes in the current wireless mesh network environment. By continuously monitoring and acquiring these values, the current free space propagation model can be constructed, that is, the parameter information in the model is determined. The free space propagation model describes the propagation law of the wireless signal in an ideal free space environment. Since there may be various interferences and changes in the wireless environment, real-time acquisition of the signal strength value can more accurately reflect the actual situation of the current network, thereby obtaining more practical model parameters. The free space propagation model parameters obtained in the above manner can be applied to the positioning and ranging of the target node. By measuring the signal strength between the target node and multiple reference nodes, and combining the known propagation model parameters, the location information of the target node can be estimated.

[0030] In some embodiments of the present invention, the path loss factor and the reference signal strength of the free space propagation model are solved in the following manner:

[0031] 111. Obtain signal strength values ​​between N reference nodes and known distances between the reference nodes;

[0032] 112. Using the signal strength values ​​between the N reference nodes and the known distances between the reference nodes, construct a target set of equations;

[0033] 113. Solve the target equation group using the least square method to obtain the path loss factor and the signal strength reference value in the free space propagation model.

[0034] Exemplarily, according to the free space propagation model, the relationship between the actual distance between reference nodes and the signal strength value can be expressed as:

[0035]

[0036] Among them, RSSI (d) is the signal receiving strength at the transmission distance d, and this value changes with the change of distance d. RSSI (d0) is the signal receiving strength at the reference distance d0. Here, distance d0 is a pre-set reference distance. The signal receiving strength measured at this distance is used as a benchmark value. n is the path loss index, which reflects the speed at which the signal strength decays with distance during the propagation process. The value of the path loss index may be different in different propagation environments. For example, in a relatively open free space, the value of n is relatively small, and the signal decays slowly; while in an environment with more obstacles, the value of n may be large, and the signal decays quickly. X σIt is a random variable that obeys the normal distribution and can represent some random and unpredictable factors.

[0037] For example, the influence of random variables that obey the normal distribution can be eliminated by filtering, and the signal receiving strength RSSI (d0) at the reference distance d0 is used as the signal strength reference value obtained by solving. The reference distance d0 can be set to 1 meter, 2 meters, etc., and the present invention does not make specific limitations on this. According to this method, the following equation can be obtained:

[0038] RSSI ij =RSSI(d0)-10×n·log 10 (d ij ) (2)

[0039] Among them, RSSI ij represents the signal receiving strength between reference node i and reference node j, d ij Represents the true distance between reference node i and reference node j. The above formula is linearized to construct a linear equation system, and the parameters RSSI (d0) and n are obtained by solving them using the least squares method.

[0040] The present invention obtains the distance measurement model parameters of the propagation environment by obtaining the signal strength values ​​between the reference nodes in real time, so that the measured distance between the reference node and the target node can be accurately obtained in the subsequent process.

[0041] 102. Select M reference nodes with the strongest receiving signals of the target node, obtain their coordinates, and calculate the distances to the target node based on the determined free space propagation model, where M≥4;

[0042] In an embodiment of the present invention, under the free space propagation model, a strong signal strength means that the distance between the transmitting node (target node) and the receiving node (reference node) is relatively close, or the loss on the propagation path is relatively small. Therefore, selecting these reference nodes with the strongest signal strength can improve the accuracy and reliability of subsequent distance measurement to a certain extent. Since the coordinates of all reference nodes are fixed, after determining the M reference nodes corresponding to the strongest M signal strength values ​​received by the target node, the position coordinates of these M reference nodes can be directly obtained. These position coordinate information provides the necessary position reference for the subsequent calculation of the distance between the target node and the reference node. The signal strength values ​​of the M reference nodes received by the target node are substituted into the relevant formulas of the free space propagation model, and the measured distance between the target node and the reference node is inverted according to the parameters such as the signal strength reference value, the signal strength at the reference distance, and the path loss index that have been determined.

[0043] In a preferred embodiment of the present invention, the M reference nodes are non-coplanar reference nodes.

[0044] 103. Based on the coordinates of the M reference nodes and the measured distances between the M reference nodes and the target node, the initial coordinate value of the target node is calculated using a genetic algorithm;

[0045] In the embodiment of the present invention, Figure 3 As shown, in order to avoid the search for the coordinate value of the target node falling into the local optimum, a genetic algorithm is used to perform a global search. The use of the genetic algorithm to determine the initial coordinate value of the target node includes:

[0046] 121. Based on the genetic algorithm, multiple initial populations are selected from the intersection area of ​​M spheres formed by taking the M reference nodes as the sphere center and the measured distance between the M reference nodes and the target node as the radius, and the initial population parameters are used;

[0047] In an embodiment of the present invention, the M reference nodes are determined based on signal strength, and the measured distances between the M reference nodes and the target node position are also determined through step 102. M spheres can be constructed with the M reference nodes as the sphere center and the measured distances to the target node as the sphere radius, and these M spheres will form an intersection area in space. This intersection area has a special meaning. It is a spatial range under the common constraints of multiple reference nodes. The target node is likely to be located in this intersection area. This method makes full use of spatial position information to guide the selection of the initial population. This method can reduce the global search time, and randomly select multiple individuals from this intersection area as the initial population. Each individual represents a possible three-dimensional coordinate position, which is used to indicate the coordinate value of the target node. In this way, multiple individuals with representativeness and diversity can be selected, which can serve as the starting point for the iteration of the genetic algorithm.

[0048] 122. Based on the measured distances between the M reference nodes and the target node, and the distances between the M reference nodes and the population individuals, the fitness function value of each population individual is calculated;

[0049] In an embodiment of the present invention, the fitness function is an indicator used to evaluate the quality of each population individual in the genetic algorithm. Here, the fitness function value of each population individual is determined by the reciprocal of the difference between the measured distance between the M reference nodes and the target node and the distance between the M reference nodes and the population individual.

[0050] Exemplarily, the fitness function can be expressed as follows:

[0051]

[0052] Where N is the number of individuals in the population, M is the number of reference nodes, fit(i') is the fitness of individual i', d i'jis the distance between reference node j and individual i', r j is the distance between reference node j and the target node.

[0053] It is understandable that the fitness function guides the search direction of the genetic algorithm, making the algorithm more inclined to retain and optimize individuals with high fitness during the iteration process, that is, individuals that are more likely to be close to the true location of the target node. By continuously calculating and comparing the fitness function value, the algorithm can gradually select better individuals in the population.

[0054] 123. Based on the fitness function value of each individual in the population, the population is updated by using selection, crossover, and mutation operations;

[0055] Among them, the genetic algorithm is a random search algorithm that simulates the natural evolution process. It gradually optimizes the population through operations such as selection, crossover, and mutation. The population is constantly updated and evolved, and gradually approaches the optimal solution. The selection operation refers to the continuous updating and evolution of the population, gradually approaching the optimal solution. Under the guidance of the fitness function, each generation of the population develops in a better direction, so that the quality of individuals in the population is continuously improved and closer to the true position of the target node. The crossover operation refers to the crossover operation of the selected parent individuals to generate new offspring individuals. The crossover operation simulates the gene recombination process in biological evolution. By exchanging part of the information of the parent individuals (for example, exchanging the code representing the position), new individuals are generated, the diversity of the population is increased, and it is possible to produce better individuals. The mutation operation refers to the mutation operation of individuals in the population with a certain mutation probability, that is, random changes are made to certain characteristics of the individuals (such as certain bits of the position code). The mutation operation can prevent the algorithm from falling into the local optimal solution too early and increase the possibility of searching for the global optimal solution.

[0056] Through the above method, each generation of the population develops in a better direction under the guidance of the fitness function, so that the quality of individuals in the population continues to improve and gets closer to the actual position of the target node.

[0057] 124. Based on the updated population, determine the initial coordinate value of the target node.

[0058] In an embodiment of the present invention, after multiple generations of genetic operations and population updates, the individuals in the population gradually converge to one or several better solutions. The individual with the highest fitness can be selected, or the initial coordinate value of the target node can be determined according to a certain rule (such as the average value of multiple better individuals, etc.). This initial coordinate value is an estimated value obtained based on the current genetic algorithm iteration result, which reflects the best guess of the target node position under given reference node information and corresponding genetic algorithm process. This best guess will be used as the initial coordinate value of the target node in the subsequent more refined positioning algorithm, that is, the improved Gauss-Newton algorithm, to obtain a more accurate final coordinate value of the target node.

[0059] It is understandable that the genetic algorithm adopted in the embodiment of the present invention has good global search capability, can explore multiple areas in a complex search space, and avoid falling into a local optimal solution. By initializing the population in the sphere intersection area and undergoing multiple generations of genetic operations, the algorithm has the opportunity to find the target node coordinate value that is globally optimal or close to the global optimal, especially in the case of multiple local optimal solutions, which can better reflect its advantages. The present invention makes full use of the distance information between the reference node and the target node and the spatial geometric relationship (sphere intersection area), and provides valuable prior knowledge and constraints for the genetic algorithm. This makes the search of the genetic algorithm more targeted, can converge to a reasonable solution space more quickly, and improves the efficiency and accuracy of the genetic algorithm.

[0060] 104. Based on the initial coordinate value of the target node, the final coordinate value of the target node is calculated using an improved Gauss-Newton algorithm.

[0061] Among them, the traditional Gauss-Newton iteration method, as a classic nonlinear least squares optimization algorithm, is widely used in fields such as parameter estimation and positioning. However, this method has many limitations in practical applications, especially in the RSSI three-dimensional positioning scenario in complex environments. First, the traditional Gauss-Newton method is highly sensitive to the initial estimate. When the initial value deviates far from the true position, the algorithm may require a large number of iterations to converge, resulting in low computational efficiency. In addition, since the Gauss-Newton method relies on the calculation of the Jacobian matrix and the Hessian matrix, when the condition number of these matrices is high, the numerical stability of the algorithm is seriously affected, which easily leads to the accumulation of numerical errors during the iteration process, and even causes the algorithm to diverge. Secondly, the traditional method usually adopts a fixed step size, which cannot flexibly adapt to the optimization requirements at different iteration stages. When the step size is too large, it may cause oscillation or skip the optimal solution during the iteration process; when the step size is too small, it will lead to slow convergence and increase the computational burden. In addition, the RSSI signal itself has large noise and fluctuations. When the traditional Gauss-Newton method processes these noisy data, the positioning accuracy is easily affected. Especially in complex environments such as multipath interference and shadow fading, the positioning error is significant, which limits the practical application effect of the algorithm.

[0062] Based on this, the embodiment of the present invention aims at the shortcomings of the traditional Gauss-Newton iteration method in RSSI three-dimensional positioning. The present invention adopts an algorithm based on the improved Gauss-Newton iteration method, and effectively solves the above-mentioned problems through various optimization measures. First, a dynamic step size adjustment mechanism is introduced, which comprehensively evaluates the ratio of the current residual to the previous residual and the consistency of the gradient direction, so as to dynamically adjust the step size. This not only speeds up the convergence speed of the algorithm when it is close to the optimal solution, but also improves the stability and numerical accuracy of the iterative process by avoiding excessive or small step sizes. Secondly, noise robust processing is adopted. By introducing weighting factors, each residual term is weighted according to the standard deviation of the RSSI value, which reduces the impact of signal noise on the positioning results and significantly improves the accuracy and reliability of positioning. In addition, the initial value optimization is performed in combination with the genetic algorithm, and the initial estimate that is closer to the actual position is obtained through global search, which reduces the risk of the algorithm falling into the local optimum and further improves the global optimization capability. Figure 4 As shown, the following are the specific implementation steps:

[0063] 141. Initialize the parameter vector to be evaluated based on the improved Gauss-Newton algorithm to obtain an initial parameter vector to be evaluated; the initial parameter vector to be evaluated is the initial coordinate value of the target node;

[0064] Among them, the traditional Gauss-Newton algorithm will select an initial evaluation value as the starting point of the parameter vector to be evaluated. As mentioned above, the traditional Gauss-Newton method is highly sensitive to the initial estimated value. When the initial value deviates far from the true position, the algorithm may require a large number of iterations to converge, resulting in low computational efficiency. Based on this, the embodiment of the present invention uses the initial coordinate value of the target node determined by the genetic algorithm as the initial evaluation value of the improved Gauss-Newton algorithm, which can provide a starting point for subsequent iterative calculations. Accelerate the convergence speed of the improved Gauss-Newton algorithm and avoid the improved Gauss-Newton algorithm from falling into a local optimal solution or non-convergence.

[0065] 142. Calculate the Jacobian matrix and the gradient direction angle based on the residual vector corresponding to the target node and the initial parameter vector to be evaluated;

[0066] In this embodiment of the present invention, the residual vector corresponding to the target node can be represented by the following residual function:

[0067]

[0068] Among them, w j is the weighting factor, X is the coordinate of the target node, b j is the coordinate of the jth reference node, z j is the measured distance between the jth reference node and the target node.

[0069] Let X k is the estimated value of the kth iteration, and the error function is expanded by the first order Taylor at the current estimated point to obtain the linear approximate equation:

[0070] F(X k+1 )≈F(X k )+J k ΔX k =0 (5)

[0071] Among them, J k is the Jacobian matrix, ΔX k is the position update amount, the residual r k Defined as the difference between the actual distance and the measured distance:

[0072] r i =||Xb i ||-z i (6)

[0073]

[0074] 143. Determine an estimated value of the parameter vector to be evaluated based on the Jacobian matrix and the gradient direction angle, and update the parameter vector to be evaluated according to the estimated value of the parameter vector to be evaluated;

[0075] In an embodiment of the present invention, the Jacobian matrix is ​​a matrix containing the partial derivatives of the objective function with respect to each parameter, which describes the local linear approximation of the objective function at the current parameter point. The gradient direction angle reflects the angle between the gradient direction of the objective function at the current point and a reference direction, which plays an important role in determining the update direction of the parameter vector later. The two quantities are calculated in order to more accurately adjust the parameter vector to be evaluated during the iteration process so that it moves in the direction in which the objective function decreases.

[0076] The Jacobian matrix provides information about the rate of change of the objective function at the current point, while the gradient direction angle helps determine the update direction of the parameter vector. The calculation of these two quantities can provide the necessary basis for determining the estimated value of the parameter vector to be evaluated in the next step, thereby guiding the algorithm to iterate in a more optimal direction.

[0077] According to the iterative update formula of the improved Gauss-Newton method, it can be expressed as:

[0078]

[0079] Among them, α k is the dynamic step size, W is the weight matrix, r k is the residual vector of the kth iteration.

[0080] 144. Based on the updated parameter vector to be evaluated, update the coordinate value of the target node according to a dynamic iteration step size;

[0081] In an embodiment of the present invention, an estimated value of the parameter vector to be evaluated is determined based on the calculated Jacobian matrix and the gradient direction angle. This is achieved, for example, by solving a linear equation system or using some iterative formulas to obtain a new estimated value. Then, the parameter vector to be evaluated is updated based on the estimated value, that is, the current parameter vector to be evaluated is replaced with the new estimated value to achieve iterative update of the parameters.

[0082] Among them, the update process of the dynamic iteration step can be expressed as follows:

[0083]

[0084] r k =||X k -b j ||-z j (11)

[0085]

[0086] is the variance of the RSSI value, reflecting the degree of measurement noise. The larger the variance, the smaller the weight. is the gradient vector, then:

[0087]

[0088] Among them, α max and α min are the maximum and minimum values ​​of the step length, β is the step length adjustment gain factor, γ is the step length adjustment attenuation factor, θ k is the angle between the current gradient and the previous gradient, and δ is the threshold for gradient direction consistency.

[0089] 145. When the error function corresponding to the target node converges or reaches a maximum number of iterations, the final coordinate value of the target node is determined.

[0090] In an embodiment of the present invention, when the error function corresponding to the target node converges or reaches the maximum number of iterations, the iteration process is stopped and the final coordinate value of the target node is determined. The error function is a function used to measure the difference between the current estimated target node coordinate value and the true value. If the error function converges, it means that the algorithm has found a point close enough to the optimal solution; if the maximum number of iterations is reached, even if the error function has not completely converged, the iteration is stopped to avoid an infinite loop of the algorithm. For example, when ΔX k When the error is less than the preset threshold ε or reaches the maximum number of iterations, the iteration is stopped and the optimized coordinate value X is output. This ensures that the algorithm can be completed within a reasonable time and a reliable target node coordinate value is obtained. By judging the convergence of the error function and the number of iterations, the operation process of the algorithm can be effectively controlled to ensure the stability and effectiveness of the algorithm.

[0091] Based on the same technical concept, see Figure 5 The embodiment of the present application also provides a server for three-dimensional spatial positioning of a wireless mesh network, wherein the mesh network includes N reference nodes and at least one target node; the server includes a receiver 201 and at least one processor 202; the receiver 201 and the processor 202 cooperate to perform part or all of the operations performed by any device in the above method embodiment.

[0092] The receiver 201 is used to receive signal strength values ​​between M reference nodes and the target node and signal strength values ​​between N reference nodes reported by at least one gateway, where M≥4 and N≥4;

[0093] The at least one processor 202 is configured to:

[0094] Using the known distances and signal strengths between N reference nodes, determine the path loss factor and reference signal strength of the free space propagation model, N ≥ 4;

[0095] Select M reference nodes with the strongest receiving signals from the target node, obtain their coordinates and calculate the distance to the target node based on the determined free space propagation model, M ≥ 4;

[0096] Based on the coordinates of M reference nodes and the measured distances between them and the target node, the initial coordinates of the target node are calculated using a genetic algorithm.

[0097] Based on the initial coordinate value of the target node, the final coordinate value of the target node is calculated using an improved Gauss-Newton algorithm.

[0098] In a preferred embodiment of the present invention, Figure 6 As shown, the server further includes: a transmitter 203; the transmitter 203 is used to send the final coordinate value of the target node to the target node.

[0099] All relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

[0100] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which can include: ROM, RAM, disk or CD, etc.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional spatial positioning method based on a wireless mesh network, wherein the mesh network includes N reference nodes and at least one target node; characterized in that: The method comprises the following steps: Using the known distances and signal strengths between N reference nodes, determine the path loss factor and reference signal strength of the free space propagation model, N ≥ 4; Select M reference nodes with the strongest receiving signals from the target node, obtain their coordinates and calculate the distance to the target node based on the determined free space propagation model, M ≥ 4; Based on the coordinates of M reference nodes and the measured distances between them and the target node, the initial coordinates of the target node are calculated using a genetic algorithm. Based on the initial coordinate value of the target node, the final coordinate value of the target node is calculated using an improved Gauss-Newton algorithm.

2. A three-dimensional spatial positioning method based on a wireless mesh network according to claim 1, characterized in that: The path loss factor and reference signal strength of the free space propagation model are solved in the following ways: Obtain signal strength values ​​between N reference nodes and known distances between the reference nodes; Using the signal strength values ​​between N reference nodes and the known distances between the reference nodes, a target set of equations is constructed; The target equation group is solved by using the least square method to obtain the path loss factor and the signal strength reference value in the free space propagation model.

3. The three-dimensional space positioning method based on wireless mesh network according to claim 1, characterized in that: The method of calculating the initial coordinate value of the target node by using a genetic algorithm based on the coordinates of the M reference nodes and the measured distances between the reference nodes and the target node includes: Based on the genetic algorithm, multiple initial populations are selected from the intersection area of ​​M spheres formed by taking M reference nodes as the sphere center and the measured distance between the M reference nodes and the target node as the radius, and used as the initial population parameters; Based on the measured distances between the M reference nodes and the target node, and the distances between the M reference nodes and the individuals in the population, the fitness function value of each individual in the population is calculated; Based on the fitness function value of each individual in the population, the population is updated by using selection, crossover and mutation operations; Based on the updated population, the initial coordinate value of the target node is determined.

4. The three-dimensional space positioning method based on wireless mesh network according to claim 3 is characterized in that: The fitness function value of each population individual is determined by the reciprocal of the difference between the measured distances between the M reference nodes and the target node and the distances between the M reference nodes and the population individual.

5. A three-dimensional space positioning method based on a wireless mesh network according to claim 1 or 3, characterized in that: The step of calculating the final coordinate value of the target node based on the initial coordinate value of the target node by using the improved Gauss-Newton algorithm comprises: Initialize the parameter vector to be evaluated based on the improved Gauss-Newton algorithm to obtain an initial parameter vector to be evaluated; the initial parameter vector to be evaluated is the initial coordinate value of the target node; Based on the residual vector corresponding to the target node and the initial parameter vector to be evaluated, the Jacobian matrix and the gradient direction angle are calculated; Determine an estimated value of the parameter vector to be evaluated based on the Jacobian matrix and the gradient direction angle, and update the parameter vector to be evaluated according to the estimated value of the parameter vector to be evaluated; Based on the updated parameter vector to be evaluated, updating the coordinate value of the target node according to a dynamic iteration step; When the error function corresponding to the target node converges or reaches a maximum number of iterations, the final coordinate value of the target node is determined.

6. A three-dimensional space positioning method based on a wireless mesh network according to claim 5, characterized in that: The dynamic iteration step size is determined by the ratio of the residual vector of the current iteration process to the residual vector of the previous iteration process and the consistency of the gradient direction.

7. A three-dimensional space positioning method based on a wireless mesh network according to claim 6, characterized in that: The method for determining the dynamic iteration step size includes: If the residual vector of the current iteration process is smaller than the residual vector of the previous iteration process and the angle between the current gradient and the previous gradient is smaller than the gradient consistency direction threshold, the step size of the current iteration process is determined according to the step size adjustment gain factor, the maximum step size value and the minimum step size of the previous iteration process; If the residual vector of the current iteration process is not less than the residual vector of the previous iteration process and the angle between the current gradient and the previous gradient is less than the gradient consistency direction threshold, the step size of the current iteration process is determined according to the step size adjustment attenuation factor, the minimum step size value and the maximum step size of the previous iteration process.

8. A server for three-dimensional spatial positioning of a wireless mesh network, wherein the mesh network comprises N reference nodes and at least one target node; characterized in that: The server comprises: a receiver, at least one processor; The receiver is used to receive signal strength values ​​between M reference nodes and the target node and signal strength values ​​between N reference nodes reported by at least one gateway, where M≥4 and N≥4; The at least one processor is configured to: Using the known distances and signal strengths between N reference nodes, determine the path loss factor and reference signal strength of the free space propagation model, N ≥ 4; Select M reference nodes with the strongest receiving signals from the target node, obtain their coordinates and calculate the distance to the target node based on the determined free space propagation model, M ≥ 4; Based on the coordinates of M reference nodes and the measured distances between them and the target node, the initial coordinates of the target node are calculated using a genetic algorithm. Based on the initial coordinate value of the target node, the final coordinate value of the target node is calculated using an improved Gauss-Newton algorithm.

9. A server according to claim 8, characterized in that: The server also includes: a transmitter; The transmitter is used to send the final coordinate value of the target node to the target node.

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