Distributed sensor network cooperative positioning method, device, equipment and storage medium

Abstract

This application provides a method, apparatus, device, and storage medium for cooperative localization in distributed sensor networks. It relates to the field of information fusion. The method includes: a distance extension formula based on a combination of RSSI ranging and a sixth-order Cayley-Menger determinant; constructing a rigid graph model with distance constraints between nodes; dynamically verifying the network topology using graph theory; and employing a distributed optimization algorithm to solve for node distances and positioning coordinates. This application aims to solve for the problems of high energy consumption and strong node density dependence in traditional DV-Hop algorithms, significantly enhancing the robustness of WSNs in scenarios with poor signal conditions or sparse nodes. It can effectively suppress errors caused by broken paths and topological irregularities, and is suitable for various applications such as underwater sensor network localization, unmanned surface vessel swarming, the Internet of Things, disaster early warning, and smart cities.

Description

Technical Field

[0001] This application relates to the field of information fusion technology, and in particular to a method, apparatus, device and storage medium for collaborative positioning of distributed sensor networks. Background Technology

[0002] Wireless sensor networks (WSNs), as a key technology at the intersection of multiple disciplines, have become a core tool for monitoring complex environments due to their distributed architecture and intelligent characteristics. Through the collaborative work of miniature sensor nodes, WSNs achieve autonomous perception, data fusion, and real-time monitoring of target areas. Their self-organization and low latency significantly reduce deployment costs and improve system reliability. From military monitoring to smart cities, WSNs have been widely applied in critical areas such as national defense security, industrial control, and disaster early warning, becoming a technological cornerstone of digital transformation. Among them, distributed collaborative positioning technology based on rigid distance geometry determination, as a core direction for breaking through the engineering bottlenecks of WSNs, is leading a paradigm shift in network positioning algorithms.

[0003] In the implementation of WSN functionality, node spatial positioning technology is the core bottleneck determining system performance. Positioning methods based on the Global Positioning System (GPS) suffer from high cost, high energy consumption, and signal blockage, making it difficult to meet the low-cost, low-power design requirements of WSNs. While cooperative positioning mechanisms based on reference nodes can calculate global position from accurate local coordinates, positioning errors in complex electromagnetic environments can easily lead to data mapping disorder and situational awareness distortion. Furthermore, in specific scenarios such as inverse synthetic aperture radar (ISAR) and high-frequency ground wave radar (HFGWR), the system is often limited by hardware conditions (such as antenna aperture limitations and low-frequency interference), often only acquiring distance measurement information and not azimuth measurement information. This positioning problem relying solely on distance measurement has significant nonlinear characteristics, requiring solutions to the nonlinear filtering problem under Euclidean distance mapping to improve tracking accuracy. However, traditional methods suffer from high computational complexity and poor stability, making it difficult to meet the real-time requirements of dynamic network environments. Therefore, a high-precision, low-complexity distributed cooperative positioning method is urgently needed to adapt to the engineering deployment of WSNs in complex scenarios. Summary of the Invention

[0004] This application provides a distributed sensor network cooperative localization method, apparatus, device, and storage medium. For node localization with a known finite number of nodes with localization capabilities and a finite number of distance measurement information, it develops a DV-Hop localization method based on Euler's four-point formula and an RSSI localization method based on a sixth-order Cayley-Menger determinant. By abstracting multi-hop paths into "S-shaped" and "U-shaped" basic topological units, and combining Euler's four-point formula and the sixth-order Cayley-Menger determinant, accurate calculation of end-to-end straight-line distance is achieved. Furthermore, RSSI ranging is combined with the sixth-order Cayley-Menger determinant for improved localization, avoiding the problems of high energy consumption and strong node density dependence of traditional methods. This application can improve the accuracy, environmental adaptability, and computational efficiency of node localization with a known finite number of nodes with localization capabilities and a finite number of distance measurement information, solving the problems of high energy consumption and strong node density dependence of traditional methods.

[0005] Firstly, this application provides a distributed sensor network cooperative localization method, including:

[0006] S1: Establish a sensor network with randomly distributed nodes; wherein the nodes include anchor nodes and non-anchor nodes, and there are at least three anchor nodes in the sensor network;

[0007] S2: Calculate the observation distance between each node and its 1-hop neighbor node based on the RSSI value of each node and the 1-hop neighbor node, and store the observation distance in the distance matrix. Search in the distance matrix. If there is a direct distance between a non-anchor node and three anchor nodes, perform trilateration and update the non-anchor node as an anchor node.

[0008] S3: Each node propagates the distance matrix information to its 2-hop neighbor nodes and receives feedback information, then updates the distance matrix;

[0009] S4: Each node searches based on the updated distance matrix. If a node satisfies the application conditions of the distance extension formula, the straight-line distance between nodes 2 hops away from the node is calculated using the distance extension formula, and the distance matrix is ​​updated. Non-anchor nodes that satisfy the trilateration condition are updated to anchor nodes. The application conditions of the distance extension formula are that two nodes are connected by three common intermediate nodes, and the distances between the two nodes and the three intermediate nodes are known.

[0010] S5: Repeat S3-S4, incrementing the corresponding jump count by 1 in each round until all nodes are located or the maximum number of iterations is reached.

[0011] In one possible design, the observation distance between each node and its 1-hop neighbor is calculated based on the RSSI values ​​of each node and its 1-hop neighbor, including:

[0012] Initialize the hop count matrix Hops, setting the diagonal elements to 0, direct neighbors to 1, and non-direct neighbors to infinity; wherein, the direct neighbors are neighboring nodes whose communication distance with the current node is less than or equal to the node's communication radius;

[0013] Calculate the true Euclidean distance L between node i and node j. ij ;

[0014] Based on the true Euclidean distance L between node i and node j ij Calculate the RSSI value using the following formula:

[0015]

[0016] In the formula, Let A represent the RSSI value received by node i from node j in the t-th probe, where A represents the baseline RSSI value at the reference distance, n represents the path loss exponent, and Q represents the noise variance. This represents the Gaussian white noise term; if Q = 0, there is no noise.

[0017] Calculate the average RSSI value;

[0018] The observation distance is calculated based on the average RSSI value using the following formula:

[0019]

[0020] In the formula, Represents the observation distance between nodes estimated based on the average RSSI value; A and n are the same as above.

[0021] The observation distance is determined using the following formula:

[0022]

[0023] In the formula, R is the node communication radius; Z d This is the final observation distance used.

[0024] In one possible design, the true Euclidean distance L between node i and node j is calculated using the following formula. ij :

[0025]

[0026] In the formula, L ij Let x represent the true Euclidean distance between node i and node j. i and y i Represents the x and y coordinates of node i, x j and y j Represents the x and y coordinates of node j;

[0027] The average RSSI value is calculated using the following formula:

[0028]

[0029] In the formula, T represents the number of detections; This represents the average RSSI between node i and node j.

[0030] In one possible design, each node searches based on the updated distance matrix. If a node satisfies the application conditions of the distance extension formula, the straight-line distance between that node and nodes within two hops of it is calculated using the distance extension formula, and the distance matrix is ​​updated. Non-anchor nodes that satisfy the trilateration condition are updated to anchor nodes, including:

[0031] For each pair of nodes i and j with a hop count of 2 and not yet directly connected, if the number of common neighbors is ≥3, select the three optimal nodes from the common neighbors to form a node combination; wherein, the optimal three nodes are selected by choosing the three nodes with the most stable geometric distribution or the smallest sum of side lengths.

[0032] Based on the node combination, a search is performed in the distance matrix. According to the distance between each node in the node combination and nodes i and j, a sixth-order Cayley-Menger determinant is constructed. The distance between nodes i and j, which have a hop count of 2 and are not directly connected, is calculated using the distance extension formula.

[0033] The distance matrix is ​​updated based on the distance between node i and node j. A search is performed in the updated distance matrix. If there is a direct distance between a non-anchor node and the three anchor nodes, trilateration is performed, and the non-anchor node is updated to an anchor node.

[0034] In one possible design, based on the distances between each node in the node combination and nodes i and j, a sixth-order Cayley-Menger determinant is constructed. The distance between nodes i and j, which have a hop count of 2 and are not yet directly connected, is then calculated using the distance extension formula, including:

[0035] The sixth-order Cayley-Menger determinant is represented as:

[0036]

[0037] In the formula, CM is a sixth-order Cayley-Menger determinant, and d in1 d in2 and d in3 Let d be the distance between node i and nodes n1, n2, and n3, where nodes n1, n2, and n3 are three nodes in a node combination. jn1 djn2 and d jn3 Let d be the distance between node j and nodes n1, n2, and n3. ij Let d be the distance between node i and node j. n1n2 and d n1n3 Let d be the distance between node n1 and nodes n2 and n3. n2n3 M is the distance between node n2 and node n3; 12 (5) M is the cofactor of the fifth order after removing the first row and second column. 13 (5) M is the cofactor of the fifth order after removing the first row and third column. 14 (5) M is the cofactor of the fifth order after removing the first row and fourth column. 15 (5) M is the cofactor of order 5 after removing the first row and fifth column. 16 (5) is the cofactor of the fifth order after removing the first row and sixth column;

[0038] The simplified equation obtained by simplifying the sixth-order Cayley-Menger determinant is as follows:

[0039]

[0040] In the formula, α represents the coefficient of the quadratic term, and β represents the coefficient of the linear term;

[0041] Solving the simplified equation yields the following distance extension formula:

[0042]

[0043] In the formula, d ij Let γ1, γ2, γ3, γ4, and γ5 be the extended distance to be solved, and let γ1, γ2, γ3, γ4, and γ5 be the coefficients obtained by the sixth-order Cayley-Menger determinant.

[0044] In one possible design, γ1, γ2, γ3, γ4, and γ5 are represented as:

[0045]

[0046]

[0047] In one possible design, each node propagates the distance matrix information to its two-hop neighbor nodes and receives feedback information to update the distance matrix. This includes each node propagating the information collected by S2 to its two-hop indirect nodes and correspondingly receiving the information propagated by the two-hop indirect nodes and storing it in the distance matrix.

[0048] Secondly, this application provides a distributed sensor network cooperative positioning device, the device comprising:

[0049] The sensor network construction module is configured to establish a sensor network with randomly distributed nodes; wherein the nodes include anchor nodes and non-anchor nodes, and there are at least three anchor nodes in the sensor network;

[0050] The distance matrix construction module is configured to calculate the observed distance between each node and its 1-hop neighbor nodes based on the RSSI values ​​of each node and its 1-hop neighbor nodes, and store the observed distance in the distance matrix. The module then searches the distance matrix and performs trilateration if there is a direct distance between a non-anchor node and three anchor nodes, and updates the non-anchor node to an anchor node.

[0051] The information propagation module is configured so that each node propagates the distance matrix information to its two-hop neighbor nodes and receives feedback information to update the distance matrix.

[0052] The distance calculation and matrix update module is configured so that each node searches based on the updated distance matrix. If a node meets the application conditions of the distance extension formula, the straight-line distance between the node and the node within 2 hops is calculated using the distance extension formula, and the distance matrix is ​​updated. Non-anchor nodes that meet the trilateration conditions are updated to anchor nodes. The application conditions of the distance extension formula are that two nodes are connected by three common intermediate nodes, and the distances between the two nodes and the three intermediate nodes are known.

[0053] The iteration module is configured to repeatedly execute the corresponding operations of the information propagation module and the distance calculation and matrix update module, with the corresponding hop count incremented by 1 in each round until all nodes have completed localization or the maximum number of iterations has been reached.

[0054] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the distributed sensor network cooperative localization method as described in the first aspect and various possible designs of the first aspect.

[0055] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the distributed sensor network cooperative localization method described in the first aspect and various possible designs of the first aspect.

[0056] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the distributed sensor network cooperative localization method described in the first aspect and various possible designs of the first aspect.

[0057] The distributed sensor network cooperative localization method, apparatus, device, and storage medium provided in this application have at least the following beneficial effects:

[0058] Traditional algorithms exhibit two main problems in practical applications: firstly, excessive energy consumption, as the need to double the communication radius for information exchange leads to a sharp increase in node energy consumption; secondly, over-reliance on node density results in significant performance degradation in sparse network environments. These problems severely limit the effectiveness of the algorithms in resource-constrained wireless sensor networks. To address the inherent shortcomings of traditional DV-Hop positioning algorithms, this application proposes an improved positioning scheme that integrates RSSI ranging technology with sixth-order Cayley-Menger determinant theory. In terms of ranging methods, this application uses RSSI technology instead of the traditional hop count measurement method. This improvement offers multiple advantages: it not only avoids the energy consumption requirement of doubling the communication radius but also establishes a more accurate ranging model through the correlation between signal strength and distance. For distance calculation, this application applies Cayley-Menger determinant theory. The derived distance extension formula allows for direct calculation of Euclidean distances between multi-hop nodes. This technological breakthrough brings three significant improvements: first, it completely eliminates the complex path shape determination step; second, it effectively suppresses the cumulative error caused by polyline paths, resulting in a significant improvement in positioning accuracy in irregularly topological networks; and third, it enhances adaptability to sparse networks. Furthermore, this application exhibits good scalability, allowing for easy integration of other positioning technologies through modular design, and reserving interfaces for future technology upgrades. The proposed technology fusion scheme provides new research ideas and practical methods for the field of wireless sensor network positioning. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0060] Figure 1 The flowchart of a distributed sensor network cooperative localization method provided in the embodiments of this application Figure 1 ;

[0061] Figure 2 The five-point relational graph of the sixth-order Cayley-Menger determinant provided in the embodiments of this application calculates the number of hops between each pair of nodes as 2;

[0062] Figure 3 This application provides a cyclic solution diagram utilizing solution information in an embodiment.

[0063] Figure 4 This application provides an extended virtual neighborhood solution for multi-hop distance graphs.

[0064] Figure 5 , Figure 6 , Figure 7 This is a randomized network topology diagram provided in an embodiment of this application;

[0065] Figure 8 The localization error diagrams of the RSSI-CY-DV-Hop method, the original DV-Hop algorithm, and the RSSI-DV-Hop algorithm under different total numbers of nodes in a random network topology provided for embodiments of this application;

[0066] Figure 9 The positioning error diagrams of the method (RSSI-CY-DV-Hop) of this application, the original DV-Hop algorithm, and the RSSI-DV-Hop algorithm under different communication radii in random network topologies provided for embodiments of this application;

[0067] Figure 10 The positioning error diagrams of the method (RSSI-CY-DV-Hop) of this application, the original DV-Hop algorithm, and the RSSI-DV-Hop algorithm under different numbers of anchor nodes in a random network topology provided for embodiments of this application;

[0068] Figure 11 This is a structural diagram of the distributed sensor network cooperative positioning device provided in an embodiment of this application.

[0069] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0070] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0071] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0072] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0073] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0074] Example 1:

[0075] This application provides a distributed sensor network cooperative localization method, such as... Figure 1 The diagram shown is a detailed flowchart of the distributed sensor network cooperative localization method provided in this application embodiment. The distributed sensor network cooperative localization method includes the following steps 1 to 5.

[0076] Step 1: Establishing the network topology, including the following steps 101-102.

[0077] Step 101: Determine the network area size L, the total number of nodes N, the number of anchor nodes B, and the number of unknown nodes (NB), and set the communication radius to R.

[0078] Step 102: Randomly generate N node coordinates within a preset two-dimensional area, and record the anchor node coordinate matrix bc and the unknown node coordinate matrix uc to verify the positioning.

[0079] Step 2: RSSI ranging measures the distance between each pair of nodes with a hop count of 1 and constructs the matrix, including the following steps 201-207.

[0080] Step 201: Initialize the hop count matrix Hops, set the diagonal elements to 0, set the direct neighbors (distance ≤ R) to 1, and set the non-direct neighbors to infinity.

[0081] Step 202: Calculate the true Euclidean distance between nodes, using the following formula:

[0082]

[0083] In the formula L ij This represents node i(x) i ,y i ) and node j(x j ,y j The Euclidean distance between them.

[0084] Step 203: Generate RSSI values ​​based on distance and perform multiple probes and calculations, using the following formula:

[0085]

[0086] In the formula, Let A represent the RSSI value received by node i from node j in the t-th probe, where A represents the baseline RSSI value at the reference distance, n represents the path loss exponent, and Q represents the noise variance. Gaussian white noise term, if Q=0, then there is no noise.

[0087] Step 204: Calculate the average RSSI value:

[0088]

[0089] In the formula: T represents the number of detections; This represents the average RSSI between node i and node j.

[0090] Step 205: Calculate the observation distance from the average RSSI, using the following formula.

[0091]

[0092] In the formula, Represents the observation distance between nodes estimated based on the average RSSI value; A and n are the same as above.

[0093] Step 206: Next, determine the hop count between each pair of nodes. If a node is outside the communication range or is itself, the observation distance between the nodes is 0, indicating that communication is impossible. If the hop count is 1, the observation distance between the two nodes is the estimated observation distance based on RSSI, and the final observation distance Z will be used. d It is stored in the distance matrix. The formula is as follows:

[0094]

[0095] In the formula, R is the node communication radius; L ij Z represents the true Euclidean distance; d This is the final observation distance used.

[0096] Step 207: Search in the distance matrix. If there is a direct distance between it and the three anchor nodes, trilateration can be performed, and the node attribute is changed to anchor node.

[0097] Step 3: Each node propagates the information collected in the previous step to its two-hop neighbor nodes, and correspondingly receives the information propagated by these two-hop neighbor nodes, storing it in the distance matrix. In short, each node propagates the distance matrix information to its two-hop neighbor nodes, receives feedback information, and updates the distance matrix.

[0098] In some embodiments, step 3 includes: each node propagating the information collected in step 2 to indirect nodes two hops away, and correspondingly receiving the information propagated by these two-hop-distance nodes and storing it in a distance matrix. Specifically, each node packages and sends the following information from its stored distance matrix: its measured distances to all one-hop neighbors (RSSI back-calculated values) / its own attributes (whether it is an anchor node) and known anchor node coordinates (if it is an anchor node). Each node simultaneously receives data packets from multiple two-hop nodes, including the sending node's neighbor distance table, the sending node's attribute status, and the sending node's known anchor node information. Step 3 prepares necessary parameters for multi-hop distance calculation, such as the five-point coplanar relationship (current node + two-hop nodes + three common neighbors).

[0099] Step 4: Each node searches based on the updated distance matrix. If a node meets the application conditions of the distance extension formula, the straight-line distance between the node and the node within 2 hops is calculated using the distance extension formula, and the distance matrix is ​​updated. Non-anchor nodes that meet the trilateration conditions are updated to anchor nodes. The application conditions of the distance extension formula are that two nodes are connected by three common intermediate nodes, and the distances between the two nodes and the three intermediate nodes are known.

[0100] The distance calculation and matrix update between multi-hop nodes include the following steps 401-405.

[0101] Step 401: Use the Floyd-Warshall algorithm to calculate the shortest hop count between any two nodes and store it in the node hop count matrix. Initially, set the distance matrix to the initial distance matrix, and use the updated distance matrix for subsequent iterations.

[0102] Step 402: Extract the list of common neighbors. For each pair of nodes i and j, record their common neighbors (nodes with a hop count of 1) and store them in neighbors{i,j}, which represents the list of direct neighbors of nodes i and j.

[0103] Step 403: For each pair of nodes i and j with 2 hops and not yet directly connected, if the number of common neighbors is ≥3, select the optimal combination of three nodes from the common neighbors. The selection criteria for the optimal combination of three nodes are: the most stable geometric distribution or the minimum sum of side lengths.

[0104] Step 404: For the selected optimal combination of three nodes (n1, n2, n3), search in the distance matrix and construct a sixth-order Cayley-Menger determinant with the distances of nodes i and j. Then, use the distance extension formula to calculate the distance between nodes i and j, which have a hop count of 2 and are not yet directly connected. The formula is as follows:

[0105]

[0106] Specifically, a sixth-order Cayley-Menger determinant can be expressed as about The quadratic equation:

[0107]

[0108] Since the five points are coplanar, the constant term γ can be ignored, and the equation simplifies to:

[0109]

[0110] α is the coefficient of the quadratic term, representing the geometric constraint on the distance relationship between nodes. β is the coefficient of the linear term, reflecting the influence of known distances on unknown distances.

[0111] Solving the simplified equation using the quadratic formula yields the following distance extension formula:

[0112]

[0113] In the formula, d ij Let γ1, γ2, γ3, γ4, and γ5 be the extended distance to be solved, and let γ1, γ2, γ3, γ4, and γ5 be the coefficients obtained by the sixth-order Cayley-Menger determinant.

[0114]

[0115] Step 405: Update the distance matrix with the calculated distances, search the distance matrix for non-anchor nodes, and if the conditions are met, perform random trilateration and change the node attribute to anchor node.

[0116] Step 5: Repeat steps 3 and 4, incrementing the corresponding jump count by 1 for each round. Set an iteration counter and a maximum number of iterations, and continue until all nodes are located or the maximum number of iterations is reached.

[0117] Example 2:

[0118] The data in step 1 of Example 1 is modified as follows:

[0119] Step 1: Establishing the network topology, including the following steps 101-102.

[0120] Step 101: Determine the network area size L = 100, the total number of nodes N = 50-300, the number of anchor nodes B = 20, and the number of unknown nodes (NB) = 30-270. Set the communication radius to R = 20m.

[0121] Step 102: Randomly generate 50-300 node coordinates within a preset 100m×100m two-dimensional area, and record the anchor node coordinate matrix bc and the unknown node coordinate matrix uc to verify the positioning.

[0122] Steps 2 to 5 are the same as in Example 1.

[0123] The established network topology is as follows Figure 6 As shown, the total number of nodes is 200, the number of anchor nodes is 20, and the number of unknown nodes is 180. After the experiment, the method proposed in this application (RSSI-CY-DV-Hop) is compared with the original DV-Hop algorithm and the RSSI-DV-Hop algorithm. The results are as follows: Figure 8 As shown in the figure, experimental results demonstrate that RSSI-CY-DV-Hop exhibits superior positioning accuracy across the entire node density variation range, with its average positioning error consistently remaining at a low level. Particularly under high node deployment density, the method shows a significant reduction in error compared to the original DV-Hop algorithm. This performance improvement is primarily attributed to the introduction of the Cayley-Menger determinant in multi-hop distance estimation. The proposed method can directly estimate the straight-line distance between cross-hop nodes, effectively alleviating the distance overestimation problem caused by path polymorphism in traditional hop count accumulation models. This application demonstrates stronger robustness and higher positioning accuracy.

[0124] Example 3:

[0125] The data in step 1 of Example 1 is modified as follows:

[0126] Step 1: Establishing the network topology.

[0127] The network area size is determined to be L=100, the total number of nodes is N=100, the number of anchor nodes is B=20 and the number of unknown nodes (NB) is 80. The communication radius is set to R=15-40m.

[0128] 100 node coordinates are randomly generated within a preset 100m×100m two-dimensional area. The anchor node coordinate matrix bc and the unknown node coordinate matrix uc are recorded to verify the positioning.

[0129] Steps 2 to 5 are the same as in Example 1.

[0130] The established network topology is as follows Figure 7 As shown, the total number of nodes is 100, the number of anchor nodes is 20, and the number of unknown nodes is 80. After the experiment, the method proposed in this application (RSSI-CY-DV-Hop) is compared with the original DV-Hop algorithm and the RSSI-DV-Hop algorithm. The results are as follows: Figure 9As shown in the figure, experimental results show that the average positioning error of all three methods gradually decreases with the gradual increase of the communication radius. Expanding the communication radius increases the number of neighbors between nodes, enhancing the reliability of ranging information. Specifically, when the communication radius reaches 30 meters, the average positioning error under the conventional network topology tends to stabilize and converge. This phenomenon is mainly attributed to the saturation effect of node density—when the communication range is large enough, the connectivity between nodes is close to its maximum value, and further increasing the communication radius has limited effect on improving ranging accuracy. Therefore, with the expansion of the communication radius, the overall connectivity of the network is improved, especially in the originally sparse local areas, where the hop count estimation error and RSSI ranging deviation between nodes are effectively corrected, thus significantly improving the overall positioning accuracy.

[0131] Example 4:

[0132] The data in step 1 of Example 1 is modified as follows:

[0133] Step 1: Establishing the network topology:

[0134] The network area size is determined to be L = 100, the total number of nodes is N = 100, the number of anchor nodes is B = 5-30 and the number of unknown nodes (NB) is 95-70. The communication radius is set to R = 20m.

[0135] 100 node coordinates are randomly generated within a preset 100m×100m two-dimensional area. The anchor node coordinate matrix bc and the unknown node coordinate matrix uc are recorded to verify the positioning.

[0136] Steps 2 to 5 are the same as in Example 1.

[0137] The established network topology is as follows Figure 8 As shown, the total number of nodes is 100, the number of anchor nodes is 10, and the number of unknown nodes is 80. After the experiment, the method proposed in this application (RSSI-CY-DV-Hop) is compared with the original DV-Hop algorithm and the RSSI-DV-Hop algorithm. The results are as follows: Figure 10 As shown in the figure, experimental results indicate that as the proportion of beacon nodes gradually increases, unknown nodes in the network can obtain more distance measurement information from anchor nodes, thereby effectively improving the overall positioning accuracy and gradually reducing the average positioning error. When the number of beacon nodes exceeds 15, the error reduction trend slows down significantly and enters a relatively stable state, indicating that the network has reached a certain information coverage saturation at this point, and further increasing the number of beacon nodes has limited effect on improving the overall positioning performance. In summary, this application consistently demonstrates the best positioning accuracy, outperforming the compared algorithms.

[0138] Example 5:

[0139] This application also provides a distributed sensor network cooperative positioning device, such as... Figure 11 As shown, the distributed sensor network cooperative positioning device includes:

[0140] The sensor network construction module 1101 is configured to establish a sensor network with randomly distributed nodes; wherein the nodes include anchor nodes and non-anchor nodes, and there are at least three anchor nodes in the sensor network;

[0141] The distance matrix construction module 1102 is configured to calculate the observation distance between each node and its 1-hop neighbor node based on the RSSI value of each node and the 1-hop neighbor node, and store the observation distance in the distance matrix. If a non-anchor node has a direct distance to three anchor nodes, trilateration is performed and the non-anchor node is updated to an anchor node.

[0142] The information propagation module 1103 is configured to allow each node to propagate the distance matrix information to its two-hop neighbor nodes and receive feedback information to update the distance matrix.

[0143] The distance calculation and matrix update module 1104 is configured so that each node searches based on the updated distance matrix. If a node meets the application conditions of the distance extension formula, the straight-line distance between the node and the node within 2 hops is calculated using the distance extension formula, and the distance matrix is ​​updated. Non-anchor nodes that meet the trilateration conditions are updated to anchor nodes. The application conditions of the distance extension formula are that two nodes are connected by three common intermediate nodes, and the distances between the two nodes and the three intermediate nodes are known.

[0144] The iteration module 1105 is configured to repeatedly execute the corresponding operations of the information propagation module and the distance calculation and matrix update module, with the corresponding hop count incremented by 1 in each round until all nodes have completed localization or the maximum number of iterations has been reached.

[0145] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0146] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0147] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0148] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0149] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the distributed sensor network cooperative positioning method described in the above embodiments.

[0150] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the distributed sensor network cooperative positioning method in the above embodiments.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0152] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0153] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0154] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0155] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0156] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0157] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0158] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0159] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0160] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A distributed sensor network cooperative positioning method, characterized in that, The method comprises: S1: establishing a node random distribution sensing network; wherein the nodes comprise anchor nodes and non-anchor nodes, and at least three anchor nodes exist in the sensing network; S2: calculating the observation distance of each node and the 1-hop neighbor node according to the RSSI value of each node and the 1-hop neighbor node, and storing the observation distance in a distance matrix, searching in the distance matrix, if there is a direct distance between the non-anchor node and the three anchor nodes, performing trilateration, and updating the non-anchor node to an anchor node; S3: each node propagates the distance matrix information to the 2-hop neighbor node and receives feedback information, and updates the distance matrix; S4: each node searches based on the updated distance matrix, if the node meets the application condition of the distance extension formula, the straight-line distance between the 2-hop nodes from the node is calculated by using the distance extension formula, and the distance matrix is updated, and the non-anchor node meeting the trilateration condition is updated to an anchor node; wherein the application condition of the distance extension formula is that two nodes are connected by three common intermediate nodes, and the distances of the two nodes and the three intermediate nodes are known; S5: repeating S3-S4, corresponding to the number of hops, and increasing 1 round by round, until all nodes complete positioning or reach the maximum iteration number.

2. The distributed sensor network cooperative positioning method of claim 1, wherein, According to the RSSI value of each node and the 1-hop neighbor node, the observation distance of each node and the 1-hop neighbor node is calculated, comprising: Initializing the hop matrix Hops, setting the diagonal elements as 0, the direct neighbors as 1, and the non-direct neighbors as infinity; wherein the direct neighbor is the neighbor node whose communication distance with the current node is less than or equal to the node communication radius; Real Euclidean distance L of the computing node i and the node j ij ; According to the real Euclidean distance L between the node i and the node j ij , the RSSI value is calculated, and the calculation formula is: wherein denotes the RSSI value received by node i from node j in the tth probe, A denotes the reference RSSI value at the reference distance, n denotes the path loss exponent, and Q denotes the noise variance, denotes a Gaussian white noise term, which is absent if Q = 0. Calculating the average RSSI value; According to the average RSSI value, the observation distance is calculated by the following formula: wherein represents the estimated observed distance between nodes based on the average RSSI value; A, n are as above. The observation distance is determined by the following formula: where R is the node communication radius; L ij represents the true Euclidean distance; Z d is the observed distance for the end use.

3. The distributed sensor network cooperative positioning method of claim 2, wherein, The real Euclidean distance L between node i and node j is calculated by the following formula ij : wherein L ij represents the true Euclidean distance between node i and node j, x i and y i represent the x-coordinate and y-coordinate of node i, x j and y j represent the x-coordinate and y-coordinate of node j; The average RSSI value is calculated by the following formula: In the formula, T represents the number of detections; represents the average value of RSSI between node i and node j.

4. The distributed sensor network cooperative positioning method of claim 1, wherein, Each node searches based on the updated distance matrix, if the node meets the application condition of the distance extension formula, the straight-line distance between the 2-hop nodes from the node is calculated by using the distance extension formula, and the distance matrix is updated, and the non-anchor node meeting the trilateration condition is updated to an anchor node, comprising: For each pair of nodes i and j with a hop number of 2 and not directly connected, if the number of common neighbors is greater than or equal to 3, the optimal three nodes are selected from the common neighbors to form a node combination; wherein the optimal three nodes are selected in the following manner: the three nodes with the most stable geometric distribution or the smallest sum of edge lengths are selected as the optimal three nodes; Based on the node combination, searching in the distance matrix, according to the distance of each node in the node combination and nodes i and j, constructing a six-order Cayley-Menger determinant, and using the distance extension formula to calculate the distance between nodes i and j with a hop number of 2 and not directly connected; According to the distance between the node i and the node j, the distance matrix is updated, and searching in the updated distance matrix, if there is a direct distance between the non-anchor node and the three anchor nodes, performing trilateration, and updating the non-anchor node to an anchor node.

5. The distributed sensor network cooperative positioning method of claim 4, wherein, According to distances of each node in the node combination to nodes i and j, a sixth-order Cayley-Menger determinant is constructed, and a distance extension formula is used to calculate a distance between nodes i and j which are not directly connected and have a hop number of 2, including: The sixth-order Cayley-Menger determinant is determined as: wherein CM is a sixth order Cayley-Menger determinant, d in1 , d in2 , and d in3 are distances between node i and nodes n1, n2, and n3, which are three nodes in a node combination, d jn1 , d jn2 , and d jn3 are distances between node j and nodes n1, n2, and n3, d ij is a distance between node i and node j, d n1n2 and d n1n3 are distances between node n1 and nodes n2 and n3, d n2n3 is a distance between node n2 and n3; M 12 (5) is a fifth order minor removing the first row and second column, M 13 (5) is a fifth order minor removing the first row and third column, M 14 (5) is a fifth order minor removing the first row and fourth column, M 15 (5) is a fifth order minor removing the first row and fifth column, M 16 (5) is a fifth order minor removing the first row and sixth column; The sixth-order Cayley-Menger determinant is simplified to obtain a simplified equation, which is expressed as: In the formula, α represents a quadratic term coefficient, and β represents a linear term coefficient; The simplified equation is solved to obtain the distance extension formula as follows: where d ij are the coefficients obtained by the six-order Cayley-Menger determinant.

6. The distributed sensor network cooperative positioning method of claim 5, wherein, γ1, γ2, γ3, γ4, and γ5 are expressed as: 。 7. The distributed sensor network cooperative positioning method of claim 1, wherein, Each node propagates distance matrix information to 2-hop neighbor nodes and receives feedback information to update the distance matrix, including each node propagating information collected by S2 to indirect nodes which are 2 hops away from the node, and corresponding information propagated by nodes which are 2 hops away from the node is received and stored in the distance matrix.

8. A distributed sensor network cooperative positioning apparatus, characterized by, The device comprises: A sensor network construction module configured to establish a sensor network in which nodes are randomly distributed, wherein the nodes include anchor nodes and non-anchor nodes, and at least three anchor nodes exist in the sensor network; A distance matrix construction module configured to calculate observed distances between each node and 1-hop neighbor nodes according to RSSI values of the nodes and the neighbor nodes, and store the observed distances in a distance matrix, search in the distance matrix, if there is a direct distance between a non-anchor node and three anchor nodes, perform trilateration, and update the non-anchor node to an anchor node; An information propagation module configured to propagate distance matrix information to 2-hop neighbor nodes by each node and receive feedback information to update the distance matrix; A distance calculation and matrix updating module configured to search based on the updated distance matrix by each node, if a node meets an application condition of a distance extension formula, calculate a straight-line distance between a node which is 2 hops away from the node by using the distance extension formula, and update the distance matrix, and update a non-anchor node which meets a trilateration condition to an anchor node; wherein the application condition of the distance extension formula is that two nodes are connected by three common intermediate nodes, and distances of the two nodes and the three intermediate nodes are known; An iteration module configured to repeatedly execute corresponding operations of the information propagation module and the distance calculation and matrix updating module, and increase a corresponding hop number by 1 round by round until all nodes complete positioning or a maximum iteration number is reached.

9. An electronic device, comprising: Comprise: A processor, and a memory connected in communication with the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the distributed sensor network cooperative positioning method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the distributed sensor network cooperative positioning method in any one of claims 1-7.

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