A three-dimensional space positioning method based on a wireless mesh network and a server

Abstract

The present application relates to the field of wireless communication and positioning technology, in particular to a three-dimensional space positioning method based on wireless mesh network and a server; the method comprises determining path loss factor and reference signal strength of free space propagation model by using known distance and signal strength between N reference nodes; selecting M reference nodes with strongest signal received by target node, obtaining coordinates of the M reference nodes and calculating distance from the target node based on the determined free space propagation model; calculating initial coordinate value of the target node by using genetic algorithm based on coordinates of the M reference nodes and measured distance from the target node; calculating final coordinate value of the target node by using improved Gauss-Newton algorithm based on the initial coordinate value of the target node. The present application can effectively reduce the influence of multipath interference and shadow fading on RSSI positioning, and significantly improve the accuracy and stability of three-dimensional positioning.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and positioning technology, and in particular to a three-dimensional spatial positioning method and server based on a wireless mesh network, for improving the positioning accuracy and stability of nodes in a wireless sensor network. Background Technology

[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, and other fields. Accurate node positioning is fundamental to achieving efficient data transmission and network management.

[0003] Currently, RSSI-based localization methods are widely used in wireless sensor networks. RSSI localization methods estimate the distance between nodes by measuring the signal strength between a reference node and a target node, combined with a free-space propagation model, thereby achieving node localization. These methods have advantages such as simplicity and low cost, making them suitable for scenarios with large-scale node deployments.

[0004] The Gauss-Newton iterative method plays a crucial role in node localization technology in wireless sensor networks due to its high convergence speed and positioning accuracy. However, the traditional Gauss-Newton iterative method suffers from drawbacks in practical applications, including sensitivity to initial values, inflexible step size adjustment, and insufficient robustness in complex environments such as multipath interference and shadow fading. These issues make it difficult for the algorithm to meet practical requirements in terms of accuracy and stability in 3D spatial localization.

[0005] To address the aforementioned issues, a positioning method that can improve the accuracy and stability of RSSI positioning in complex environments is urgently needed. This method should be insensitive to initial values, flexible in step size adjustment, and possess good noise robustness, effectively coping with adverse conditions such as multipath interference and shadow fading, thereby meeting the high accuracy and high stability requirements of practical three-dimensional spatial positioning. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a 3D spatial positioning method and server based on a wireless mesh network. It utilizes an improved Gauss-Newton iterative method combined with a genetic algorithm to optimize the initial coordinate position, and introduces a dynamic step-size adjustment mechanism to enhance the algorithm's adaptability and robustness. The genetic algorithm provides global optimization capabilities, effectively avoiding the pitfalls of traditional methods that easily get trapped in local optima. The dynamic step-size adjustment mechanism flexibly adjusts the step size based on 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. Furthermore, this invention uses a weighting factor to process noise in RSSI data, significantly reducing the impact of multipath interference and shadow fading on the positioning results, thereby greatly improving the accuracy and reliability of 3D spatial positioning, especially maintaining excellent positioning performance even in complex and highly interference environments.

[0007] In a first aspect, 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, where N≥4;

[0009] Select the M reference nodes that receive the strongest signals from 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;

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

[0011] Based on the initial coordinates of the target node, the final coordinates of the target node are calculated using an improved Gauss-Newton algorithm. In a second aspect, the invention also proposes a server for three-dimensional spatial positioning in a wireless mesh network, the mesh network comprising N reference nodes and at least one target node; the server comprises: a receiver and at least one processor;

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

[0013] The at least one processor is used for:

[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, where N≥4;

[0015] Select the M reference nodes that receive the strongest signals from 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;

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

[0017] Based on the initial coordinates of the target node, the final coordinates of the target node are calculated using an improved Gauss-Newton algorithm. The beneficial effects of this invention are:

[0018] Through the embodiments of this invention, the impact of multipath interference and shadow fading on RSSI positioning can be effectively reduced, significantly improving the accuracy and stability of 3D positioning. This method combines a genetic algorithm to optimize the initial estimate with an improved Gauss-Newton iterative algorithm, enhancing the algorithm's adaptability to complex environments. Experimental verification shows that the algorithm exhibits excellent positioning performance in various complex 3D environments, especially maintaining highly reliable positioning results under non-line-of-sight (NLOS) conditions. Furthermore, the algorithm features fast convergence and high robustness, making it suitable for various application scenarios such as smart cities, industrial automation, and the Internet of Things, demonstrating broad application prospects and significant practical value. Attached Figure Description

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

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

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

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

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

[0024] Figure 6 This is a schematic diagram of the server structure according to a preferred embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Figure 1 This is an application scenario diagram of a three-dimensional spatial positioning method based on a wireless mesh network provided by an embodiment of the present invention, such as... Figure 1 As shown, this scenario includes at least a server, a gateway, a target node, and multiple reference nodes. The target node and the multiple reference nodes constitute a wireless mesh network. These nodes are wirelessly connected in a mesh structure. Each terminal node in the network has automatic routing capabilities, and each terminal node communicates with its neighboring terminal nodes. It is a dynamic and continuously expandable network structure, where 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 of data transmission, the receiver, or simply a relay station participating in data forwarding. The multiple reference nodes are nodes that play a crucial role in the network. They can provide stable reference points for location services, time synchronization, or as a basis for selecting data transmission paths, etc.

[0027] This 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. 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, where N ≥ 4;

[0029] In this embodiment of the invention, since the reference nodes are fixedly distributed within the wireless mesh network, the actual distances between them are known. Therefore, it is only necessary to receive the signal strength values ​​between each reference node in real time. These signal strength values ​​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, a free-space propagation model can be constructed, i.e., the parameter information in the model can be determined. The free-space propagation model describes the propagation law of wireless signals in an ideal free-space environment. Since the wireless environment may have various interferences and changes, real-time acquisition of signal strength values ​​can more accurately reflect the actual situation of the current network, thereby obtaining model parameters that are more consistent with reality. The free-space propagation model parameters obtained in the above manner can be applied to the positioning and ranging of target nodes. By measuring the signal strength between the target node and multiple reference nodes and combining it with the known propagation model parameters, the location information of the target node can be estimated.

[0030] In some embodiments of the present invention, the methods for solving the path loss factor and reference signal strength of the free space propagation model include:

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

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

[0033] 113. Solve the objective equations using the least squares method to obtain the path loss factor and signal strength reference value in the free space propagation model.

[0034] For example, 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] Where RSSI(d) is the received signal strength at a transmission distance of d, and this value varies with the distance d. RSSI(d0) is the received signal strength at a reference distance d0, where d0 is a pre-set reference distance, and the received signal strength measured at this distance serves as a baseline value. n is the path loss exponent, which reflects how quickly the signal strength attenuates with distance during propagation. The value of the path loss exponent may differ under different propagation environments. For example, in relatively open free space, the value of n is relatively small, and the signal attenuates slowly; while in environments with many obstacles, the value of n may be large, and the signal attenuates quickly. σA random variable that follows a normal distribution can represent some random and unpredictable factors.

[0037] For example, filtering can eliminate the influence of normally distributed random variables. The signal received strength RSSI(d0) at a reference distance d0 is used as the reference value for the obtained signal strength. The reference distance d0 can be set to 1 meter, 2 meters, etc., and this invention does not impose a specific limitation on it. Based on 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 d represents the received signal strength between reference node i and reference node j. ij Let represent the true distance between reference node i and reference node j. Linearize the above formula to construct a system of linear equations, and solve it using the least squares method to obtain the parameters RSSI(d0) and n.

[0040] This invention obtains the ranging model parameters of the propagation environment by acquiring the signal strength values ​​between reference nodes in real time, thereby enabling accurate measurement of the distance between the reference node and the target node in subsequent processes.

[0041] 102. Select the M reference nodes that receive the strongest signals from 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 this embodiment of the invention, under the free-space propagation model, a stronger signal strength means that the distance between the transmitting node (target node) and the receiving node (reference node) is relatively short, or that the loss along 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 measurements to a certain extent. Since the coordinates of all reference nodes are fixed, after determining the M reference nodes corresponding to the M strongest signal strength values ​​received by the target node, the position coordinates of these M reference nodes can be directly obtained. This position coordinate information provides the necessary positional reference for subsequent calculation of the distance between the target node and the reference nodes. Substituting the signal strength values ​​of these M reference nodes received by the target node into the relevant formulas of the free-space propagation model, and based on the determined signal strength reference values, the signal strength at the reference distance, the path loss index, and other parameters, the measured distance between the target node and the reference nodes can be deduced.

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

[0044] 103. Based on the coordinates of M reference nodes and the measured distances between them and the target node, the initial coordinate values ​​of the target node are calculated using a genetic algorithm.

[0045] In embodiments of the present invention, such as Figure 3 As shown, to avoid the search for the target node's coordinates getting trapped in local optima, a genetic algorithm is used for global search. The initial coordinates of the target node are determined using the genetic algorithm, including:

[0046] 121. Based on the genetic algorithm, select multiple initial populations from the intersection regions of M spheres formed by M reference nodes as sphere centers and the measured distances between the M reference nodes and the target node as radii, and use them as the initial population parameters;

[0047] In this embodiment of the invention, the M reference nodes are determined based on signal strength. The measured distance between the M reference nodes and the target node is also determined in step 102. M spheres can be constructed with the M reference nodes as centers and the measured distance to the target node as the sphere radius. These M spheres will form an intersection region in space. This intersection region has special significance. It is a spatial range constrained by multiple reference nodes. The target node is likely located within this intersection region. This method makes full use of spatial location information to guide the selection of the initial population. This method can reduce the global search time. Multiple individuals are randomly selected from this intersection region 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 as the starting point for the genetic algorithm iteration.

[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 individuals in the population, calculate the fitness function value for each individual in the population.

[0049] In this embodiment of the invention, the fitness function is an indicator used in genetic algorithms to evaluate the quality of each individual in the population. Here, the fitness function value of each individual in the population 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 individual in the population.

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

[0051]

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

[0053] Understandably, the fitness function guides the search direction of the genetic algorithm, causing it to tend to retain and optimize individuals with high fitness during iterations—that is, individuals more likely to be close to the true location of the target node. By continuously calculating and comparing fitness function values, the algorithm can gradually select better individuals from the population.

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

[0055] Genetic algorithms are random search algorithms that simulate the natural evolutionary process. They optimize the population through operations such as selection, crossover, and mutation, constantly updating and evolving the population to gradually approach the optimal solution. Selection refers to the continuous updating and evolution of the population, gradually approaching the optimal solution. Each generation of the population, guided by the fitness function, develops in a better direction, continuously improving the quality of individuals and bringing them closer to the true position of the target node. Crossover refers to performing a crossover operation on selected parent individuals to generate new offspring individuals. Crossover simulates the gene recombination process in biological evolution, exchanging some information from parent individuals (e.g., exchanging the encoding representing position) to produce new individuals, increasing population diversity and potentially producing better individuals. Mutation refers to performing mutation operations on individuals in the population with a certain mutation probability, that is, randomly changing certain characteristics of the individual (such as certain bits of the position encoding). Mutation can prevent the algorithm from getting trapped in local optima too early, increasing the possibility of finding the global optimum.

[0056] Through the above methods, each generation of the population, guided by the fitness function, develops in a better direction, thereby continuously improving the quality of individuals in the population and bringing them closer to the true location of the target node.

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

[0058] In this embodiment of the invention, after multiple generations of genetic operations and population updates, the individuals in the population gradually converge to one or a few better solutions. The initial coordinates of the target node can be determined by selecting the individual with the highest fitness, or by using a certain rule (such as the average of multiple better individuals). This initial coordinate value is an estimate based on the current iteration results of the genetic algorithm; it reflects the best prediction of the target node's location given the reference node information and the corresponding genetic algorithm flow. This best prediction will serve as the initial coordinates of the target node in subsequent, more refined localization algorithms, i.e., the improved Gauss-Newton algorithm, to obtain a more accurate final coordinate value for the target node.

[0059] It is understood that the genetic algorithm employed in this embodiment of the invention possesses excellent global search capabilities, enabling it to explore multiple regions within a complex search space and avoid getting trapped in local optima. By initializing the population within the sphere intersection region and performing multiple generations of genetic operations, the algorithm has the opportunity to find the globally optimal or near-globally optimal target node coordinates, especially when multiple local optima exist, where its advantages are even more evident. This invention fully utilizes the distance information between the reference node and the target node, as well as the spatial geometric relationship (sphere intersection region), providing valuable prior knowledge and constraints for the genetic algorithm. This makes the genetic algorithm's search more targeted, enabling it to converge to a reasonable solution space more quickly, thus improving the efficiency and accuracy of the genetic algorithm.

[0060] 104. Based on the initial coordinates of the target node, the final coordinates of the target node are calculated using the improved Gauss-Newton algorithm.

[0061] The traditional Gauss-Newton iterative method, a classic nonlinear least squares optimization algorithm, is widely used in parameter estimation and localization. However, this method has many limitations in practical applications, especially in complex RSSI 3D localization scenarios. First, the traditional Gauss-Newton method is highly sensitive to the initial estimate. When the initial value deviates significantly from the true location, the algorithm may require a large number of iterations to converge, resulting in low computational efficiency. Furthermore, since the Gauss-Newton method relies on the calculation of the Jacobian and Hessian matrices, when the condition numbers of these matrices are high, the numerical stability of the algorithm is severely affected, easily leading to the accumulation of numerical errors during the iteration process, and even causing the algorithm to diverge. Second, traditional methods typically use a fixed step size, which cannot flexibly adapt to optimization needs at different iteration stages. When the step size is too large, it may cause oscillations during the iteration process or skip the optimal solution; while when the step size is too small, it leads to slow convergence and increased computational burden. In addition, RSSI signals themselves have significant noise and fluctuations. When the traditional Gauss-Newton method processes this noisy data, the positioning accuracy is easily affected, especially in complex environments such as multipath interference and shadow fading, where the positioning error is significant, limiting the practical application effect of the algorithm.

[0062] Based on this, this invention addresses the shortcomings of the traditional Gauss-Newton iterative method in RSSI 3D localization. It employs an improved Gauss-Newton iterative method, effectively solving the aforementioned problems through various optimization measures. First, a dynamic step-size adjustment mechanism is introduced. This mechanism comprehensively evaluates the ratio of the current residual to the previous residual and the consistency of the gradient direction, thereby dynamically adjusting the step size. This not only accelerates the convergence speed of the algorithm when approaching the optimal solution but also improves the stability and numerical accuracy of the iterative process by avoiding excessively large or small step sizes. Second, noise robustness processing is adopted. By introducing a weighting factor, each residual term is weighted according to the standard deviation of the RSSI value, reducing the impact of signal noise on the localization results and significantly improving the accuracy and reliability of localization. Furthermore, initial value optimization is performed using a genetic algorithm. A global search is used to obtain an initial estimate closer to the true location, reducing the risk of the algorithm getting trapped in local optima and further improving the global optimization capability. Figure 4 As shown, the specific implementation steps are as follows:

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

[0064] Traditional Gauss-Newton algorithms select an initial evaluation value as the starting point for the parameter vector to be evaluated. However, as mentioned above, the traditional Gauss-Newton method is highly sensitive to the initial estimate. When the initial value deviates significantly from the true position, the algorithm may require a large number of iterations to converge, resulting in low computational efficiency. Therefore, this invention uses the initial coordinates of the target node determined by the genetic algorithm as the initial evaluation value for the improved Gauss-Newton algorithm. This provides a starting point for subsequent iterative calculations, accelerating the convergence speed of the improved Gauss-Newton algorithm and preventing it from getting trapped in local optima or failing to converge.

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

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

[0067]

[0068] Among them, w j Let x be the weighting factor, and b be the coordinates of the target node. j Let z be the coordinates of the j-th reference node. j This is the measured distance between the j-th reference node and the target node.

[0069] Let X k Given the estimated value for the k-th iteration, performing a first-order Taylor expansion of the error function at the current estimation point yields the linear approximation equation:

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

[0071] Among them, J k Let ΔX be a Jacobian matrix. k Let r be the position update amount, and the residual be 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 the 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 this embodiment of the invention, the Jacobian matrix is ​​a matrix containing the partial derivatives of the objective function with respect to each parameter, describing 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, and this angle plays an important role in determining the update direction of the parameter vector. Calculating these two quantities is to more accurately adjust the parameter vector to be evaluated during the iteration process, moving it in the direction that reduces the objective function.

[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. Calculating these two quantities provides the necessary basis for determining the estimated values ​​of the parameter vector to be evaluated in the next step, thus guiding the algorithm to iterate towards a better direction.

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

[0078]

[0079] Where, α k Where W is the dynamic step size, and r is the weight matrix. k Let be the residual vector of the k-th iteration.

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

[0081] In this embodiment of the invention, the estimated value of the parameter vector to be evaluated is determined based on the calculated Jacobian matrix and the angle between the gradient directions. This can be achieved, for example, by solving a system of linear equations or using some iterative formulas to obtain a new estimated value. Then, the parameter vector to be evaluated is updated based on this estimated value, that is, the current parameter vector to be evaluated is replaced with the new estimated value, thereby achieving iterative updating of the parameters.

[0082] The update process of the dynamic iteration step size can be represented as follows:

[0083]

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

[0085]

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

[0087]

[0088] Where, α max and α min These represent the maximum and minimum values ​​of the step size, β is the step size adjustment gain factor, γ is the step size adjustment attenuation factor, and θ is the step size adjustment decay factor. k δ 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 the maximum number of iterations, the final coordinate value of the target node is determined.

[0090] In this embodiment of the invention, the iteration process stops when the error function corresponding to the target node converges or the maximum number of iterations is reached, and the final coordinate value of the target node is determined. The error function is a function used to measure the difference between the currently estimated target node coordinate value and the true value. If the error function converges, it means the algorithm has found a point sufficiently close to the optimal solution; if the maximum number of iterations is reached, the iteration stops even if the error function has not fully converged, to avoid the algorithm looping infinitely. For example, when ΔX... k When the value is less than a preset threshold ε, or when the maximum number of iterations is reached, the iteration stops, and the optimized coordinate value X is output. This ensures that the algorithm terminates within a reasonable time and obtains a reliable target node coordinate value. By judging the convergence of the error function and the number of iterations, the algorithm's operation can be effectively controlled, ensuring its stability and effectiveness.

[0091] Based on the same technical concept, see [link / reference] Figure 5 This application also provides a server for three-dimensional spatial positioning of a wireless mesh network, the mesh network including 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 some or all of the operations performed by any device in the above method embodiments.

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

[0095] Select the M reference nodes that receive the strongest signals from 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;

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

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

[0098] In a preferred embodiment of the present invention, such as 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 content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0100] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include ROM, RAM, disk, or optical disk, etc.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which 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 One reference node and at least one target node; characterized in that, The method includes the following steps: use N Given the known distances and signal strengths between several reference nodes, determine the path loss factor and reference signal strength for the free-space propagation model. N ≥4; Select the target node with the strongest received signal. M Given a reference node, obtain its coordinates and calculate the distance to the target node based on a defined free-space propagation model. M≥ 4; based on M The initial coordinates of the target node are calculated using a genetic algorithm based on the coordinates of each reference node and the measured distance between the reference node and the target node. Based on the initial coordinates of the target node, the final coordinates of the target node are calculated using an improved Gauss-Newton algorithm, specifically including: The initial parameter vector to be evaluated is initialized based on the improved Gauss-Newton algorithm to obtain the 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. The estimated value of the parameter vector to be evaluated is determined based on the Jacobian matrix and the gradient direction angle, and the parameter vector to be evaluated is updated according to the estimated value of the parameter vector to be evaluated. Based on the updated parameter vector to be evaluated, the coordinate values ​​of the target node are updated according to a dynamic iteration step size; the dynamic iteration step size is determined by: If the residual vector of the current iteration is less than the residual vector of the previous iteration and the angle between the current gradient and the previous gradient is less than the gradient consistency direction threshold, then the step size of the current iteration is determined according to the step size adjustment gain factor, the maximum step size value and the minimum step size value of the previous iteration. If the residual vector of the current iteration is not less than the residual vector of the previous iteration 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 is determined according to the step size adjustment decay factor, the minimum step size value and the maximum step size of the previous iteration. When the error function corresponding to the target node converges or reaches the maximum number of iterations, the final coordinate value of the target node is determined.

2. The three-dimensional spatial positioning method based on a wireless mesh network according to claim 1, characterized in that, The methods for solving the path loss factor and reference signal strength in the free space propagation model include: Get N The signal strength values ​​between each reference node and the known distances between the reference nodes; use N Based on the signal strength values ​​between reference nodes and the known distances between reference nodes, construct a set of objective equations; The objective equations are solved using the least squares method to obtain the path loss factor and signal strength reference value in the free space propagation model.

3. The three-dimensional spatial positioning method based on a wireless mesh network according to claim 1, characterized in that, The basis M The initial coordinates of the target node are calculated using a genetic algorithm, based on the coordinates of each reference node and the measured distance between them. Based on genetic algorithm from M The reference node is the center of the sphere. M The measured distance between each reference node and the target node is formed by the radius. M Multiple initial populations are selected in the intersection region of each sphere and used as initial population parameters; based on M The measured distance between each reference node and the target node M The distance between each reference node and an individual in the population is used to calculate the fitness function value for each individual in the population. Based on the fitness function value of each individual in the population, the population is updated using selection, crossover, and mutation operations; Based on the updated population, determine the initial coordinates of the target node.

4. The three-dimensional spatial positioning method based on a wireless mesh network according to claim 3, characterized in that, The fitness function value of each individual in the population is determined by... M The measured distance between each reference node and the target node M The reciprocal of the difference between the distances between each reference node and an individual in the population is used to determine this.

5. A server for three-dimensional spatial positioning of a wireless mesh network, the mesh network comprising... N One reference node and at least one target node; characterized in that, The server, used to implement the three-dimensional spatial positioning method based on a wireless mesh network as described in claim 1, comprises: a receiver and at least one processor; The receiver is used to receive reports from at least one gateway. M The signal strength values ​​between the reference node and the target node and N Signal strength values ​​between reference nodes, M≥ 4, N ≥4; The at least one processor is used for: use N Given the known distances and signal strengths between several reference nodes, determine the path loss factor and reference signal strength for the free-space propagation model. N ≥4; Select the target node with the strongest received signal. M Given a reference node, obtain its coordinates and calculate the distance to the target node based on a defined free-space propagation model. M≥ 4; based on M The initial coordinates of the target node are calculated using a genetic algorithm based on the coordinates of each reference node and the measured distance between the reference node and the target node. Based on the initial coordinates of the target node, the final coordinates of the target node are calculated using an improved Gauss-Newton algorithm.

6. A server according to claim 5, characterized in that, The server also includes: a transmitter; The transmitter is used to send the final coordinates of the target node to the target node.

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