A non-equilibrium hybrid AOA / RSSI cooperative positioning method based on a mesh network

Abstract

The application belongs to wireless positioning technology and relates to a non-balanced mixed AOA / RSSI cooperative positioning method based on a Mesh network. The method comprises the following steps: acquiring AOA / RSSI measurement information of a main anchor node to an unknown node and position coordinate information of the main anchor node, and calculating a first position estimation coordinate of the unknown node; acquiring RSSI measurement information of a secondary anchor node to the unknown node and position coordinate information of the secondary anchor node, and calculating a second position estimation coordinate of the unknown node; fusing the first position estimation coordinate and the second position estimation coordinate to obtain a preliminary position estimation coordinate of the unknown node; acquiring RSSI measurement information of other unknown nodes to the current unknown node and preliminary position estimation coordinates of the other unknown nodes, and calculating a final position estimation coordinate of the unknown node. The application can further improve positioning precision by using a Mesh network without additional hardware devices.

Description

Technical Field

[0001] This invention pertains to wireless positioning technology, specifically relating to an unbalanced hybrid AOA / RSSI cooperative positioning method based on a mesh network. Background Technology

[0002] Wireless positioning technology utilizes devices such as radio frequency identification (RFID) and sensors to measure parameters like the time, amplitude, and phase of received radio waves. Based on relevant algorithms, it determines the position of the object being measured, enabling the location, monitoring, and tracking of specific targets. Currently, it is widely used in navigation, robot tracking, virtual reality, and military target positioning. Several common wireless positioning systems include the following:

[0003] Common positioning systems in wireless positioning technology mainly include those based on Angle-of-Arrival (AOA) and those based on Received Signal Strength Indicator (RSSI). AOA-based systems require all anchor nodes to be equipped with array antennas or directional antennas to estimate the AOA angle, placing high demands on anchor node hardware and resulting in high costs. The location estimation range of the unknown node is determined by the intersection area of ​​the AOA angles formed by multiple anchor nodes and the unknown node; this method requires at least two anchor nodes for positioning. While this method offers high positioning accuracy, AOA estimation data processing is complex and susceptible to non-line-of-sight effects. In contrast, RSSI-based positioning systems require only a single antenna transceiver for all anchor nodes to obtain the RSSI value, resulting in lower hardware requirements and costs. RSSI-based systems are further divided into geometric methods and fingerprint positioning methods. The geometric method converts the RSSI received by the receiver into a measured distance using a path loss model. Each distance range is a circle, and the intersection range of the circles formed by multiple anchor nodes and the unknown node represents the estimated location range of the unknown node. This method requires at least three anchor nodes for localization, but its accuracy is relatively low. The fingerprint localization method pre-measures the signals from multiple anchor nodes to various locations, acquiring various signal features and constructing an offline fingerprint database for each location. During actual localization, the signal from the terminal to be located is matched online with the fingerprint database to achieve localization. This method requires extensive preliminary data surveys and regular data updates.

[0004] Both of the aforementioned positioning methods have certain shortcomings. Therefore, researchers combined AOA (Optical Angle Alignment) and RSSI (Remote RSSI) technologies to propose a hybrid positioning system based on AOA / RSSI. This hybrid system requires all anchor nodes to be equipped with array antennas or directional antennas to achieve AOA angle estimation. It can be used for positioning via a single anchor node or multiple anchor nodes. When using a single anchor node, because the anchor node can perform angle and distance estimation, only one anchor node is needed to locate the unknown node. However, due to measurement errors, the positioning error range of a single anchor node is relatively large, resulting in low positioning accuracy. When using multiple anchor nodes, the overlapping area of ​​the intersection region of the AOA angles formed by multiple anchor nodes and the unknown node, and the intersection region formed by the RSSI ranging circle, is used as the position estimation range of the unknown node. This method can effectively improve positioning accuracy, but it also requires multiple anchor nodes to be equipped with AOA array antennas, resulting in higher costs.

[0005] Building upon the hybrid positioning system based on AOA / RSSI, researchers have proposed a hybrid positioning system based on unbalanced AOA / RSSI (for situations where AOA anchor node resources are limited); such as Figure 1 As shown in the figure, only the main anchor node s1 has an array antenna or a directional antenna, which can perform AOA angle measurement and RSSI ranging; the other anchor nodes perform RSSI ranging. Among them, s i Represents the anchor node, d mi Represents the distance from the i-th anchor node to the m-th unknown node x. m RSSI measurement distance, α m Represents the path from the main anchor node s1 to the m-th unknown node x. m The AOA measurement angle, where i = 1, ..., 4. Therefore, Figure 1 There are 4 anchor nodes s i There is one measurement angle α m and 4 measured distances d mi Meanwhile, the dashed line and star region indicate the m-th unknown node x. m The location estimation range is reduced. Since all anchor nodes can perform RSSI ranging, and the AOA angle measurement information of the central anchor node is used for fusion positioning, the number of AOA anchor nodes is reduced. However, the positioning accuracy and positioning estimation range are lower than those of a typical AOA / RSSI hybrid positioning system. Summary of the Invention

[0006] To address the issue of low accuracy under limited anchor node resources, this invention proposes an unbalanced hybrid AOA / RSSI cooperative localization method based on Mesh networks.

[0007] In a first aspect, the present invention provides an unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network, the method being executed by an unknown node located in the mesh network, the method comprising:

[0008] Obtain AOA / RSSI measurement information from the main anchor node to the unknown node and the position coordinate information of the main anchor node, and calculate the first estimated position coordinates of the unknown node;

[0009] Obtain RSSI measurement information from the auxiliary anchor node to the unknown node and the position coordinate information of the auxiliary anchor node, and calculate the second position estimated coordinates of the unknown node;

[0010] The first estimated position coordinates and the second estimated position coordinates are fused to obtain the preliminary estimated position coordinates of the unknown node;

[0011] Obtain RSSI measurement information from other unknown nodes to the current unknown node, as well as the preliminary position estimate coordinates of other unknown nodes, and calculate the final position estimate coordinates of the unknown node.

[0012] In a second aspect, the present invention also provides an unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network, the method being performed by a master anchor node communicating with unknown nodes, the master anchor node being located in the mesh network, the master anchor node being deployed with an array antenna or a directional antenna to provide AOA / RSSI measurement information, the method comprising:

[0013] The AOA / RSSI measurement information of the main anchor node and the position coordinate information of the main anchor node are sent to the unknown node; the AOA / RSSI measurement information and the position coordinate information of the main anchor node are used to calculate the first estimated position coordinates of the unknown node; the first estimated position coordinates are used to fuse with the second estimated position coordinates of the unknown node calculated based on the RSSI measurement information from the auxiliary anchor node to the unknown node and the position coordinate information of the auxiliary anchor node, to obtain the preliminary estimated position coordinates of the unknown node; the preliminary estimated position coordinates of the unknown node are used to process with the RSSI measurement information from the unknown node to other unknown nodes to obtain the final estimated position coordinates of the unknown node.

[0014] In a third aspect, the present invention also provides an unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network, the method being performed by an auxiliary anchor node communicating with an unknown node, the auxiliary anchor node being located in the mesh network, the auxiliary anchor node being deployed with a single-antenna transceiver and providing RSSI measurement information, the method comprising:

[0015] RSSI measurement information and position coordinate information of auxiliary anchor nodes are sent to the unknown node to calculate the second estimated position coordinates of the unknown node. The second estimated position coordinates are fused with the first estimated position coordinates of the unknown node calculated based on the AOA / RSSI measurement information from the main anchor node to the unknown node and the position coordinate information of the main anchor node to obtain the preliminary estimated position coordinates of the unknown node. The preliminary estimated position coordinates of the unknown node are processed with the RSSI measurement information from the unknown node to other unknown nodes to obtain the final estimated position coordinates of the unknown node.

[0016] The beneficial effects of this invention are:

[0017] 1. The method of the present invention does not require additional hardware equipment. It only requires building the entire positioning system into a Mesh network, so that multiple unknown nodes can achieve cooperative communication. The effective RSSI ranging information between unknown nodes can be added to the overall positioning process to effectively improve the positioning accuracy of the system.

[0018] 2. This invention effectively reduces the number of AOA array antennas or directional antennas in a hybrid AOA / RSSI positioning system, thereby reducing positioning costs and construction complexity. Furthermore, the more unknown nodes involved in the collaboration, the higher the positioning accuracy.

[0019] 3. This invention takes into account the possibility of non-fully connected unknown nodes. That is, when some unknown nodes do not meet the fully connected Mesh network structure (there is a two-hop or more information routing distance from the node to the current node with the highest connectivity), it is impossible to perform point-to-point RSSI ranging on all unknown nodes. Therefore, this invention uses the measured unknown nodes as pseudo-anchor nodes to perform secondary positioning on the unknown nodes to be measured. By reducing the position estimation range corresponding to the measurement error of the anchor nodes, the accuracy is improved, and finally the final position estimation coordinates of the unknown nodes to be measured are obtained. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a hybrid positioning system based on unbalanced AOA / RSSI in the prior art;

[0021] Figure 2 This is a schematic diagram of an unbalanced hybrid AOA / RSSI cooperative localization system based on a Mesh network according to an embodiment of the present invention;

[0022] Figure 3 This is a basic flowchart of the unbalanced hybrid AOA / RSSI cooperative localization method based on Mesh networks according to an embodiment of the present invention;

[0023] Figure 4 This is a flowchart illustrating the implementation of another embodiment of the unbalanced hybrid AOA / RSSI cooperative localization method based on a Mesh network according to the present invention.

[0024] Figure 5 This is a basic flowchart of an unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network according to another embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of an unbalanced hybrid AOA / RSSI preliminary localization node based on a Mesh network according to an embodiment of the present invention;

[0026] Figure 7 This is a schematic diagram of an unbalanced hybrid AOA / RSSI cooperative localization node based on a Mesh network, according to an embodiment of the present invention. Detailed Implementation

[0027] 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.

[0028] This invention proposes an unbalanced hybrid AOA / RSSI cooperative localization method based on mesh networks. An unbalanced hybrid AOA / RSSI cooperative localization system is deployed in a mesh network, such as... Figure 2 As shown, the cooperative positioning system mainly includes a central processor, a gateway, anchor nodes, and unknown nodes. On the positioning side, there is only one main anchor node s1 equipped with an array antenna to provide AOA / RSSI estimation. The remaining auxiliary anchor nodes are equipped with simple single-antenna transceivers to provide RSSI estimation, for a total of N anchor nodes. Simultaneously, M unknown nodes are considered on the side to be positioned. Point-to-point RSSI ranging is performed between the unknown nodes, and these nodes collectively construct a mesh network.

[0029] Figure 3 This is a flowchart of an unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network according to an embodiment of the present invention, as shown below. Figure 3 As shown, the method is executed by an unknown node located in the Mesh network, and the method includes:

[0030] 101. Obtain the AOA / RSSI measurement information from the main anchor node to the unknown node and the position coordinate information of the main anchor node, and calculate the first estimated position coordinates of the unknown node;

[0031] In this embodiment, the main anchor node can perform AOA and RSSI measurements with the unknown node. Since the main anchor node simultaneously has AOA angle estimation and RSSI distance estimation functions, single-anchor node AOA-RSSI positioning based on combined angle and distance measurements can be achieved. The unknown node can obtain corresponding AOA and RSSI measurement information from the main anchor node. The AOA measurement information provides angle information, and the RSSI measurement information provides distance information. Using only the measurement information provided by the main anchor node, the unknown node can calculate its first estimated position coordinates.

[0032] 102. Obtain RSSI measurement information from the auxiliary anchor node to the unknown node and the position coordinate information of the auxiliary anchor node, and calculate the second position estimated coordinates of the unknown node;

[0033] In this embodiment, the auxiliary anchor nodes can communicate with the unknown node via RSSI, meaning they can perform RSSI measurements on the unknown node. When the number of auxiliary anchor nodes is greater than or equal to two, since the main anchor node already has RSSI measurement capabilities, at least three anchor nodes have RSSI distance estimation capabilities. In this case, multi-anchor node positioning can be performed based on the RSSI distance information. The unknown node can obtain the corresponding RSSI measurement information from the auxiliary anchor nodes. When there are enough auxiliary anchor nodes (i.e., the number of auxiliary anchor nodes is ≥3), only the RSSI distance between the unknown node and three or more auxiliary anchor nodes needs to be used. Through the measurement information provided by the auxiliary anchor nodes, the unknown node can calculate its second estimated position coordinates. Alternatively, it can combine the RSSI measurement information of the main anchor node and three or more auxiliary anchor nodes to calculate its second estimated position coordinates. When there are insufficient auxiliary anchor nodes (i.e., only two auxiliary anchor nodes), it is necessary to combine the RSSI measurement information of the main anchor node; all three anchor nodes must provide RSSI measurement information together for the unknown node to calculate its second estimated position coordinates.

[0034] In some embodiments, assuming there are a total of N main anchor nodes and N-1 auxiliary anchor nodes, the unknown node can determine N nonlinear equations based on the RSSI measurement distances from the N anchor nodes to the unknown node; taking the main anchor node as the reference node, the N nonlinear equations are converted into N-1 linear equations through iteration; the second position estimated coordinates of the unknown node can be obtained by using the least squares method.

[0035] 103. The first estimated position coordinates and the second estimated position coordinates are fused to obtain the preliminary estimated position coordinates of the unknown node;

[0036] In this embodiment, a weighted fusion method can be used to fuse the first estimated position coordinates and the second estimated position coordinates, as follows:

[0037] in, This represents the estimated initial position coordinates of the unknown node m. This represents the estimated coordinates of the first position of the unknown node m. The second position estimated coordinates of the unknown node m are represented; η is a positive parameter, 0≤η≤1.

[0038] In some embodiments, the positive parameter η can be determined based on the accuracy of AOA and RSSI measurements or empirical values, and those skilled in the art can specify it according to the actual situation.

[0039] In some embodiments, the positive parameter η can be determined by a network model. The first and second estimated position coordinates of the positioning node, given their known true positioning coordinates, are input into the neural network model. Preliminary estimated position coordinates of the positioning node are predicted. The residuals between the true positioning coordinates and the predicted preliminary estimated position coordinates are compared, and the neural network model is optimized until the neural network model converges or the residual comparison result meets a preset requirement. The parameter value of the neural network model is then determined; this parameter value corresponds to the positive parameter. The neural network model can be any existing network model, such as a convolutional neural network model, a recurrent neural network model, etc.

[0040] In some embodiments, the positive parameter η can be determined by an intelligent optimization algorithm. The first and second estimated position coordinates of the positioning node, given their known true positioning coordinates, are input into the intelligent optimization algorithm. The algorithm iteratively optimizes the algorithm until the optimal positive parameter is determined. The intelligent optimization algorithm can be any existing intelligent optimization algorithm, such as a genetic algorithm, ant colony optimization algorithm, or differential evolution algorithm.

[0041] 104. Obtain RSSI measurement information from other unknown nodes to the current unknown node and preliminary position estimation coordinates of other unknown nodes, and calculate the final position estimation coordinates of the unknown node.

[0042] In this embodiment of the invention, considering that the unknown nodes belong to the Mesh network, that is, the unknown nodes cooperate and communicate with each other, the point-to-point ranging information can be added to the overall position estimation process to achieve cooperative positioning; therefore, RSSI ranging between unknown nodes is considered.

[0043] In some embodiments of the present invention, if all unknown nodes are fully connected, that is, all unknown nodes are in the Mesh network, this embodiment determines the residual distance between unknown nodes and other unknown nodes based on the RSSI ranging information from unknown nodes to other unknown nodes and the calculated distance between the preliminary estimated coordinates of the unknown nodes and other unknown nodes. A first residual distance objective function is constructed according to the path distance weight between unknown nodes and other unknown nodes. The first residual distance objective function is solved to find the preliminary unknown estimated coordinates of other unknown nodes that minimize the residual distance, which are the final estimated coordinates of the unknown nodes.

[0044] The path distance weight between unknown nodes and other unknown nodes is determined based on the RSSI distance information between them. Unknown nodes with larger RSSI distances have smaller weights, and those with smaller RSSI distances have larger weights. For example, the path distance weight can be the reciprocal of the RSSI distance between unknown node m and other unknown node k, denoted as w. mk =1 / d mk .

[0045] In some embodiments of the present invention, considering that the reciprocal of the distance between an unknown node m and other unknown nodes k may cause the weight value to exceed 1, this embodiment also normalizes the reciprocal of the distance so that the weight value is in the interval [0,1].

[0046] It is understood that, in this embodiment of the invention, since the unknown node is fully connected, meaning that the unknown node is in the same mesh network as other unknown nodes, the unknown node can perform RSSI measurements with all other unknown nodes. Therefore, the unknown node can obtain the corresponding RSSI measurement information from other unknown nodes, which is used to provide distance information. Through other unknown nodes connected to the current unknown node, the final estimated coordinates of the current unknown node can be obtained jointly. This calculation process is similar to calculating the second estimated coordinates of the unknown node through auxiliary anchor nodes. Both can determine multiple nonlinear equations based on the RSSI measurement distances from other unknown nodes to the current unknown node. Taking any node as a reference node, the multiple nonlinear equations are converted into multiple linear equations through iteration. The final estimated coordinates of the unknown node can then be obtained using the least squares method.

[0047] In this embodiment of the invention, the maximum connectivity structure of the current unknown node can be found to determine whether the current unknown node belongs to the maximum connectivity structure (i.e., the maximum structure in which all unknown nodes in the network are fully connected). If it does, other unknown nodes are used for cooperative positioning to output the final position estimate coordinates of the other unknown nodes. These other unknown nodes are then upgraded to pseudo-anchor nodes. Through multi-pseudo-anchor node cooperative positioning, the final position estimate coordinates are output.

[0048] In a preferred embodiment of the present invention, this embodiment can further filter other unknown nodes required by the current unknown node, eliminating some nodes with low importance to positioning, so as to improve positioning accuracy. That is, the current unknown node with the maximum connectivity is taken as the central node, the path distance RSSI ranging information of all measured unknown nodes within the one-hop communication range of the central node is calculated, and the mean of the RSSI ranging information is taken as the cluster radius of the central node; the nearest clusters of the central node are determined according to the distance relationship between the central node and the cluster radius; the distance from the central node to the cluster center of the nearest cluster is calculated, other unknown nodes in the nearest clusters that meet the preset threshold are found, and these other unknown nodes are taken as pseudo anchor nodes and included in the final position estimation coordinate judgment of the current unknown node.

[0049] Since the above cooperative positioning method is based on the fully connected state of the cooperative unknown nodes, that is, each unknown node can communicate with the others and obtain the RSSI measurement distance between them. However, in the actual communication environment, there may be some unknown nodes that are not connected. Therefore, when some unknown nodes do not meet the fully connected Mesh network structure (the node has a two-hop or more information routing distance to the current node with the highest connectivity), this embodiment will update the location for the semi-connected situation.

[0050] In other embodiments of the present invention, if an unknown node is partially connected, that is, some unknown nodes are not connected through the Mesh network, the point-to-point RSSI ranging of the unknown node is considered to be invalid. In this embodiment, other unknown nodes with known final position estimated coordinates within one hop of the communication distance of the unknown node are identified. Using the final position estimated coordinates of the other unknown nodes and the RSSI ranging information from the other unknown nodes to the unknown node, the residual distance between the unknown node and other unknown nodes is determined. A second residual distance objective function is constructed according to the path distance weight between the unknown node and other unknown nodes. The second residual distance objective function is solved, and the final position estimated coordinates of the other unknown nodes that minimize the residual distance are the final position estimated coordinates of the unknown node.

[0051] The path distance weight between unknown nodes and other unknown nodes is determined based on the RSSI ranging information between them. Unknown nodes with larger RSSI distances have smaller weights, and those with smaller RSSI distances have larger weights. For example, the path distance weight can be the reciprocal of the RSSI distance between unknown node m and other unknown nodes q, denoted as w. mq =1 / d mq .

[0052] In some embodiments of the present invention, considering that the reciprocal of the distance between an unknown node m and other unknown nodes q may cause the weight value to exceed 1, this embodiment also normalizes the reciprocal of the distance so that the weight value is in the interval [0,1].

[0053] In this embodiment of the invention, the maximum connectivity structure of the current unknown node can be found to determine whether the current unknown node belongs to the maximum connectivity structure (i.e., the maximum structure in which all unknown nodes are fully connected in the network). If it does not belong to the maximum connectivity structure, it can be determined whether there are pseudo-anchor nodes within one hop range of the current unknown node. If they exist, the cooperative positioning of multiple pseudo-anchor nodes is still used to output the final position coordinate estimate. Otherwise, the preliminary position estimate coordinates of the current unknown node are directly used as the final position estimate coordinates.

[0054] It is understood that in this embodiment of the invention, since the unknown node is partially connected, that is, the unknown node is in the same mesh network as other unknown nodes, but a disconnection may occur. If an unknown node moves and leaves the fully connected structure of the mesh network, the unknown node only communicates with some unknown nodes. These unknown nodes are within the one-hop communication range of the unknown node, and the unknown node can communicate with these unknown nodes. Therefore, the unknown node can obtain the corresponding RSSI measurement information from these unknown nodes. The RSSI measurement information is used to provide distance information. Therefore, these unknown nodes can be upgraded to pseudo-anchor nodes to update the position of the moved unknown node. Similarly, by using the unknown nodes connected to the current unknown node, the final estimated position coordinates of the current unknown node can be obtained jointly. This calculation process is similar to the calculation of the second estimated position coordinates of the unknown node through auxiliary anchor nodes. Both can determine multiple nonlinear equations based on the RSSI measurement distances from other unknown nodes to the current unknown node. Taking any node as a reference node, the multiple nonlinear equations are converted into multiple linear equations through iteration. The final estimated position coordinates of the unknown node can then be obtained using the least squares method.

[0055] Figure 4 This is a flowchart of another embodiment of the unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network, as shown below. Figure 4As shown, the method is performed by a master anchor node that communicates with unknown nodes. The master anchor node is located in a mesh network and is equipped with an array antenna or a directional antenna to provide AOA / RSSI measurement information. The method includes:

[0056] 201. Send the AOA / RSSI measurement information of the main anchor node and the position coordinate information of the main anchor node to the unknown node;

[0057] 202. The AOA / RSSI measurement information and the position coordinate information of the main anchor node are used to calculate the first position estimated coordinates of the unknown node;

[0058] 203. The first estimated position coordinates are used to fuse with the second estimated position coordinates of the unknown node calculated based on the RSSI measurement information from the auxiliary anchor node to the unknown node and the position coordinate information of the auxiliary anchor node, to obtain the preliminary estimated position coordinates of the unknown node.

[0059] 204. The preliminary position estimate coordinates of the unknown node are used to process with RSSI measurement information based on the unknown node to other unknown nodes to obtain the final position estimate coordinates of the unknown node.

[0060] Figure 5 This is a flowchart of an unbalanced hybrid AOA / RSSI cooperative localization method based on a mesh network according to another embodiment of the present invention, as shown below. Figure 5 As shown, the method is performed by an auxiliary anchor node that communicates with an unknown node. The auxiliary anchor node is located in a mesh network and is equipped with a single-antenna transceiver, providing RSSI measurement information. The method includes:

[0061] 301. Send RSSI measurement information and the position coordinate information of the auxiliary anchor node to the unknown node;

[0062] 302. The unknown node sends RSSI measurement information and the position coordinate information of the auxiliary anchor node to calculate the second position estimated coordinates of the unknown node;

[0063] 303. The second estimated position coordinates are used to fuse with the first estimated position coordinates of the unknown node calculated based on the AOA / RSSI measurement information from the main anchor node to the unknown node and the position coordinate information of the main anchor node, to obtain the preliminary estimated position coordinates of the unknown node.

[0064] 304. The preliminary position estimate coordinates of the unknown node are used to process with RSSI measurement information based on the unknown node to other unknown nodes to obtain the final position estimate coordinates of the unknown node.

[0065] Figure 6This is a schematic diagram of an unbalanced hybrid AOA / RSSI preliminary localization node based on a Mesh network according to an embodiment of the present invention, as shown below. Figure 6 As shown, Figure 6 Only the main anchor node s1 has an array antenna or a directional antenna, capable of performing AOA angle measurement and RSSI ranging; the remaining anchor nodes perform RSSI ranging. Among them, s i Represents the anchor node, d mi Represents the distance from the i-th anchor node to the m-th unknown node x. m RSSI measurement distance, α m Represents the path from the main anchor node s1 to the m-th unknown node x. m The AOA measurement angle, where i = 1, ..., 4. Therefore, Figure 1 There are 3 anchor nodes s i There is one measurement angle α m and 3 measured distances d mi Meanwhile, the dashed line and star region indicate the m-th unknown node x. m The location estimation range. This embodiment of the invention can utilize primary and secondary anchor nodes to perform AOA / RSSI estimation, achieving preliminary location estimation of unknown nodes and providing a positioning basis for collaborative positioning among unknown nodes.

[0066] Figure 7 This is a schematic diagram of an unbalanced hybrid AOA / RSSI cooperative localization node based on a Mesh network according to an embodiment of the present invention, as shown below. Figure 7 As shown, Figure 7 Taking a fully connected structure consisting of five unknown nodes as an example, since all five unknown nodes are connected, each of the five unknown nodes can obtain the corresponding RSSI measurement information from the other four unknown nodes. Taking unknown node m and unknown node k as an example, the calculated distance between their coordinates is estimated by using the preliminary positions of unknown node m and unknown node k. By combining the path distance weights between unknown node m and unknown node k, the final estimated coordinates of unknown node m are determined. This embodiment of the invention utilizes unknown nodes for cooperative localization, achieving final position estimation for unknown nodes and effectively improving system positioning accuracy.

[0067] 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.

[0068] 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 non-balanced hybrid AOA / RSSI cooperative localization method based on mesh networks, characterized in that, The method is executed by an unknown node located in a Mesh network, and the method includes: Obtain AOA / RSSI measurement information from the main anchor node to the unknown node and the position coordinate information of the main anchor node, and calculate the first estimated position coordinates of the unknown node; Obtain RSSI measurement information from the auxiliary anchor node to the unknown node and the position coordinate information of the auxiliary anchor node, and calculate the second position estimated coordinates of the unknown node; The first estimated position coordinates and the second estimated position coordinates are fused to obtain the preliminary estimated position coordinates of the unknown node; The process involves acquiring RSSI measurement information from other unknown nodes to the current unknown node, as well as preliminary position estimates of other unknown nodes, and calculating the final position estimates of the unknown node. The final position estimates are obtained by: determining the residual distance between the unknown node and other unknown nodes based on the RSSI ranging information from the unknown node to other unknown nodes and the calculated distance between the unknown node and the preliminary position estimates of other unknown nodes; constructing a first residual distance objective function according to the path distance weights between the unknown node and other unknown nodes; and solving the first residual distance objective function to find the preliminary unknown estimates of other unknown nodes that minimize the residual distance. These preliminary unknown estimates are the final position estimates of the unknown node. The final position estimated coordinates; the method to obtain the final position estimated coordinates of an unknown node also includes, when the point-to-point RSSI ranging of the unknown node fails, identifying other unknown nodes within one hop of the unknown node whose final position estimated coordinates are known; using the final position estimated coordinates of other unknown nodes, and the RSSI ranging information from other unknown nodes to the unknown node, determining the residual distance between the unknown node and other unknown nodes, and constructing a second residual distance objective function according to the path distance weight between the unknown node and other unknown nodes, solving the second residual distance objective function, and finding the final position estimated coordinates of other unknown nodes that minimize the residual distance is the final position estimated coordinates of the unknown node.

2. The non-balanced hybrid AOA / RSSI cooperative localization method based on mesh networks according to claim 1, characterized in that, The method of obtaining preliminary position estimate coordinates includes using a weighted fusion method to fuse the first position estimate coordinates and the second position estimate coordinates.

3. The non-balanced hybrid AOA / RSSI cooperative localization method based on mesh networks according to claim 1, characterized in that, The method is performed by a master anchor node that communicates with unknown nodes. The master anchor node is located in a mesh network and is equipped with an array antenna or a directional antenna to provide AOA / RSSI measurement information. The method includes: The AOA / RSSI measurement information of the main anchor node and the position coordinate information of the main anchor node are sent to the unknown node; the AOA / RSSI measurement information and the position coordinate information of the main anchor node are used to calculate the first estimated position coordinates of the unknown node; the first estimated position coordinates are used to fuse with the second estimated position coordinates of the unknown node calculated based on the RSSI measurement information from the auxiliary anchor node to the unknown node and the position coordinate information of the auxiliary anchor node, to obtain the preliminary estimated position coordinates of the unknown node; the preliminary estimated position coordinates of the unknown node are used to process with the RSSI measurement information from the unknown node to other unknown nodes to obtain the final estimated position coordinates of the unknown node.

4. The non-balanced hybrid AOA / RSSI cooperative localization method based on mesh networks according to claim 1, characterized in that, The method is performed by an auxiliary anchor node communicating with an unknown node. The auxiliary anchor node is located in a mesh network and is equipped with a single-antenna transceiver, providing RSSI measurement information. The method includes: RSSI measurement information and position coordinate information of auxiliary anchor nodes are sent to the unknown node to calculate the second estimated position coordinates of the unknown node. The second estimated position coordinates are fused with the first estimated position coordinates of the unknown node calculated based on the AOA / RSSI measurement information from the main anchor node to the unknown node and the position coordinate information of the main anchor node to obtain the preliminary estimated position coordinates of the unknown node. The preliminary estimated position coordinates of the unknown node are processed with the RSSI measurement information from the unknown node to other unknown nodes to obtain the final estimated position coordinates of the unknown node.

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