A method and device for selecting a cooperative node based on AOA three-dimensional positioning contribution degree

Abstract

The application relates to a method and device for selecting a cooperative node based on AOA three-dimensional positioning contribution degree, wherein the method comprises the following steps: selecting a three-dimensional positioning target space, and determining the agent nodes and anchor nodes in the target space; preliminarily estimating the positions of the agent nodes based on the known anchor nodes; calculating the Fisher information matrix of each agent node about the position according to the known anchor node information, and calculating the corresponding AOA three-dimensional positioning error limit; selecting a target agent node, traversing other agent nodes in the communication range of the target agent node, selecting the agent node with the highest contribution degree in each round of iteration as a cooperative node, and outputting the cooperative node set of the target agent node, wherein the contribution degree is defined as the decline value of the AOA three-dimensional positioning error limit of the target agent node after adding a certain agent node. Compared with the prior art, the application can greatly reduce the system energy consumption and communication load of cooperative positioning on the basis of maintaining good positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of radio communication technology, and in particular to a method and apparatus for selecting cooperative nodes based on AOA three-dimensional positioning contribution. Background Technology

[0002] With the increasing prevalence of intelligent terminals and the rapid growth of business demands, users' need for location information is becoming increasingly strong, leading to the significant development of location-based services (LBS). Wireless sensor-based location-aware networks (WSNs) are a new research hotspot catering to this demand. A WSN typically consists of anchor nodes with known location information and proxy nodes with unknown location information. The proxy nodes estimate their own location by collecting measurement signals from different anchor nodes. These measurement signals mainly include Received Signal Strength (RSS), Time of Arrival (TOA), Time Difference of Arrival (TDOA), and Angle of Arrival (AOA). Among wireless signal measurements, positioning systems based on AOA measurements can achieve sub-meter level positioning accuracy without requiring a training dataset. However, AOA measurements are often affected by multipath effects, non-line-of-sight propagation, and channel interference, leading to a sharp decline in positioning accuracy.

[0003] Given the constraints of existing system costs and practical environments, collaborative positioning methods involving other agent nodes within the agent node's communication range can significantly improve positioning accuracy even with a limited number of available anchor nodes. However, while collaborative positioning methods can utilize as many cooperating nodes as possible to enhance positioning accuracy, they also increase system energy consumption and communication load. Therefore, to achieve high positioning accuracy while reducing system complexity and improving positioning efficiency, selecting an appropriate number of nodes as cooperating nodes within the agent node's communication range is crucial.

[0004] CN108810840A discloses a node selection method based on EFIM and distance collaboration in cooperative localization, including the following steps: 1) Place proxy and anchor nodes in the scene and calculate their distances to the target node to be localized. 2) Obtain the Fisher Information Matrix (FIM) and then decompose and quantize the high-dimensional FIM to obtain a two-dimensional equivalent Fisher Information Matrix (EFIM). 3) When the target node and the anchor node collaborate, calculate the squared SPEB of the node's position error limit based on the anchor node's EFIM; when the target node and a neighboring proxy node collaborate, calculate the SPEB based on the proxy node's EFIM. Select nodes from neighboring nodes according to the new node selection criteria to construct a set of auxiliary nodes for target node localization. However, this method only uses a threshold value as the selection criterion for auxiliary nodes, which is not comprehensive enough, and the accuracy and computational cost need further improvement. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for selecting cooperative nodes based on AOA 3D positioning contribution. In AOA cooperative positioning, suitable cooperative nodes can be selected to reduce system energy consumption and improve positioning efficiency while ensuring high positioning accuracy.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A collaborative node selection method based on AOA 3D localization contribution includes the following steps:

[0008] Step 1) Select a three-dimensional positioning target space and determine the proxy nodes and anchor nodes in the target space;

[0009] Step 2) Make a preliminary estimate of the location of the proxy node based on the known anchor nodes;

[0010] Step 3) Based on the known anchor node information, calculate the Fisher information matrix of each agent node with respect to its position, and calculate its corresponding AOA three-dimensional positioning error limit;

[0011] Step 4) Select the target proxy node, traverse other proxy nodes within its communication range, select the proxy node with the highest contribution in each iteration as the collaborating node, and output the set of collaborating nodes of the target proxy node. The contribution is defined as the decrease in the AOA three-dimensional positioning error limit of the target proxy node after a certain proxy node is added.

[0012] The Fischer information matrix of the proxy node regarding its location is as follows:

[0013]

[0014] in, This is a set of proxy nodes whose locations are unknown. This represents the Fisher information matrix of the k-th proxy node calculated based on the known anchor node information, and its expression is: in,

[0015]

[0016] d kj Let d represent the distance between the k-th proxy node and the j-th anchor node. kj =||(x k ,y k ,z k )-(x j ,y j ,z j )||, This represents the projected distance between the k-th proxy node and the j-th anchor node in the xy-plane, i.e. and These represent the azimuth and elevation angles of the k-th proxy node and the j-th anchor node, respectively.

[0017]

[0018] λ kj =Δ 2 PQSNR kj , Δ=2πd a / λ, P and Q represent the number of array elements on the x-axis and y-axis, respectively; d a λ and λ represent the element spacing and wavelength of the antenna, respectively; A kj S(f) represents the amplitude of the received waveform at the kth proxy node when the j-th anchor node sends a signal; S(f) represents the Fourier transform of the known signal s(t); N0 / 2 represents the two-sided power spectral density of the signal noise.

[0019]

[0020]

[0021]

[0022] The AOA 3D positioning error limit of the proxy node is expressed as: Where σ k This represents the AOA 3D positioning error limit of the k-th agent node.

[0023] Before selecting the agent node with the highest contribution in step 4), a set of cooperative nodes within the communication distance threshold of the kth agent node is selected by setting the communication distance threshold of the kth agent node, thus initially excluding cooperative nodes with a small contribution to the kth agent node.

[0024] The process of selecting a target proxy node, traversing other proxy nodes within its communication range, and selecting the proxy node with the highest contribution in each iteration as the collaborating node specifically involves:

[0025] In the s-th iteration, calculate the contribution w of the i-th node to the k-th proxy node. k s i Its expression is:

[0026]

[0027] and Let the Fischer information matrices of the k-th agent node after the (s-1)-th iteration and the s-th iteration be represented by the following relationship: When s = 1, This represents the Fischer information matrix at the k-th proxy node based on the anchor node information; This represents the Fischer information added by the k-th agent node after the i-th node joins the cooperative positioning network. The expression for this information is: I3 represents a 3×3 identity matrix; σ i This represents the positioning error limit of the i-th agent node.

[0028] A collaborative node selection device based on AOA 3D positioning contribution includes:

[0029] The target space selection module is used to select a three-dimensional positioning target space and determine the proxy nodes and anchor nodes in the target space;

[0030] The coarse positioning module is used to initially estimate the location of the proxy node based on the known anchor nodes;

[0031] The AOA 3D positioning error limit calculation module is used to calculate the Fisher information matrix of each agent node with respect to its position based on the known anchor node information, and to calculate its corresponding AOA 3D positioning error limit.

[0032] The collaborative node selection module is used to select the target agent node, traverse other agent nodes within its communication range, select the agent node with the highest contribution in each iteration as the collaborative node, and output the set of collaborative nodes of the target agent node. The contribution is defined as the decrease in the AOA three-dimensional positioning error limit of the target agent node after a certain agent node is added.

[0033] The Fischer information matrix of the proxy node regarding its location is as follows:

[0034]

[0035] in, This is a set of proxy nodes whose locations are unknown. This represents the Fisher information matrix of the k-th proxy node calculated based on the known anchor node information, and its expression is: in,

[0036]

[0037] d kj Let d represent the distance between the k-th proxy node and the j-th anchor node. kj =||(x k ,y k ,z k )-(x j ,y j ,z j )||, This represents the projected distance between the k-th proxy node and the j-th anchor node in the xy-plane, i.e. and These represent the azimuth and elevation angles of the k-th proxy node and the j-th anchor node, respectively.

[0038]

[0039] λ kj =Δ 2 PQSNR kj , Δ=2πd a / λ, P and Q represent the number of array elements on the x-axis and y-axis, respectively; d a λ and λ represent the element spacing and wavelength of the antenna, respectively; A kj S(f) represents the amplitude of the received waveform at the kth proxy node when the j-th anchor node sends a signal; S(f) represents the Fourier transform of the known signal s(t); N0 / 2 represents the two-sided power spectral density of the signal noise.

[0040]

[0041]

[0042]

[0043] The AOA 3D positioning error limit of the proxy node is expressed as: Where σ kThis represents the AOA 3D positioning error limit of the k-th agent node.

[0044] Before selecting the agent node with the highest contribution, the collaborative node selection module selects a set of collaborative nodes within the communication distance threshold of the kth agent node by setting a communication distance threshold for the kth agent node, thus initially excluding collaborative nodes with a small contribution to the kth agent node.

[0045] The process of selecting a target proxy node, traversing other proxy nodes within its communication range, and selecting the proxy node with the highest contribution in each iteration as the collaborating node specifically involves:

[0046] In the s-th iteration, calculate the contribution of the i-th node to the k-th proxy node. Its expression is:

[0047]

[0048] and Let the Fischer information matrices of the k-th agent node after the (s-1)-th iteration and the s-th iteration be represented by the following relationship: When s = 1, This represents the Fischer information matrix at the k-th proxy node based on the anchor node information; This represents the Fischer information added by the k-th agent node after the i-th node joins the cooperative positioning network. The expression for this information is: I3 represents a 3×3 identity matrix; σ i This represents the positioning error limit of the i-th agent node.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) This invention solves the problem of selecting cooperative nodes in AOA three-dimensional cooperative positioning. It uses the Fisher information matrix to solve the positioning error limit of cooperative positioning, and combines the uncertainty of the cooperative nodes themselves to conduct a quantitative analysis on the contribution of the cooperative nodes to the positioning error limit.

[0051] (2) Based on the contribution of cooperative nodes to the AOA positioning error limit, the present invention selects cooperative nodes, which can achieve higher positioning accuracy with fewer cooperative nodes, thereby greatly reducing the system energy consumption and communication load of cooperative positioning.

[0052] (3) This invention utilizes an iterative cooperative node selection method. Although this method cannot guarantee global optimality, it can ensure that the selection result of cooperative nodes in each iteration is optimal, thereby achieving high positioning accuracy. Therefore, this method can not only achieve good positioning performance, but also greatly reduce time overhead.

[0053] (4) This invention is applicable to the selection of cooperative nodes in AOA three-dimensional cooperative positioning, and can also be extended to the selection of cooperative nodes in AOA two-dimensional cooperative positioning. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2 This is a schematic diagram illustrating how the average positioning error limit varies with the number of selected cooperative nodes in an embodiment of the present invention.

[0056] Figure 3 Comparison of the average positioning error limits of the collaborative node selection method based on AOA 3D positioning contribution, the collaborative node selection method based on distance, the collaborative node selection method based on simulated annealing, and the random collaborative node selection method under different signal-to-noise ratio scenarios. Among them, (3a) is the comparison diagram under the 3dB signal-to-noise ratio scenario, and (3b) is the comparison diagram under the 5dB signal-to-noise ratio scenario.

[0057] Figure 4 The chart compares the time overhead of the following methods when the signal-to-noise ratio is 3dB: the collaborative node selection method based on AOA 3D positioning contribution, the collaborative node selection method based on distance, the collaborative node selection method based on simulated annealing, and the random collaborative node selection method. Detailed Implementation

[0058] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0059] This embodiment provides a collaborative node selection method based on AOA 3D positioning contribution, such as... Figure 1 As shown, it includes the following steps:

[0060] Step 1) Select a three-dimensional positioning target space And determine the proxy nodes (unknown nodes) and anchor nodes (known nodes) in the target space.

[0061] In this embodiment, it is assumed that the selected target space contains N a One agent node and N bThere are N anchor nodes, where the sets of proxy nodes and anchor nodes are respectively represented as N. a =[1,2,…,N a ] and N b =[N a +1,N a +2,…,N a +N b Let the set of proxy nodes with unknown locations be the parameter. Where p k p represents the position of the k-th proxy node. k =[x k ,y k ,z k ] T , k∈N a .

[0062] Step 2) Make a preliminary estimate of the location of the proxy node based on the known anchor node.

[0063] In this embodiment, other existing positioning methods can also be used to make a preliminary estimate of the location of the proxy node.

[0064] Step 3) Based on the known anchor node information, calculate the Fisher information matrix of each agent node with respect to θ, the expression of which is: This represents the Fischer information matrix of the k-th proxy node calculated based on the known anchor node information, and calculates its corresponding AOA 3D positioning error limit.

[0065] Assume that all nodes in the cooperative positioning network are equipped with uniform linear array antennas, with P and Q array elements on the x and y axes, respectively. When the j-th anchor node transmits signal s(t), the received waveform at the k-th surrogate node is as follows:

[0066]

[0067] Among them, A kj Indicates the amplitude value of the received waveform; τ kj Indicates the propagation delay; α represents the azimuth angle between the k-th proxy node and the j-th anchor node; kj This represents the elevation angle between the k-th proxy node and the j-th anchor node; n kj (t) represents complex Gaussian white noise with zero mean and two-sided power spectral density of N0 / 2. and These represent the direction vectors along the x-axis and y-axis, respectively, and their expressions are: ⊙ represents the Khatri-Rao product. Δ=2πd a / λ,da λ and λ represent the element spacing and wavelength of the antenna, respectively.

[0068] Therefore, the received waveform with respect to θ can be expressed as:

[0069]

[0070] Where, r k T (t) represents the signal waveforms received from all anchor nodes at the k-th proxy node.

[0071] Then, to measure the performance of cooperative positioning, the square root of the Cramer-Rao lower bound calculated using the Fischer information matrix is ​​defined as the positioning error limit, and its expression is:

[0072]

[0073] Where tr(CRLB(θ)) represents the Cramer-Rao lower bound, i.e., the squared position error bound; J θ The Fischer information matrix representing the agent node θ whose location is unknown is expressed as follows:

[0074]

[0075] Here, f(r,θ) represents the joint probability density function of the random vector r with respect to θ. Due to the dynamic characteristics of the propagation channel and the inherent spatiotemporal independence of the signal, it is assumed here that the signal originates from N. b The received waveforms of each anchor node are independent of each other, therefore its expression is:

[0076]

[0077] in,

[0078] Based on this, the Fisher information matrix for θ can be obtained, which is expressed as:

[0079]

[0080] in,

[0081]

[0082] d kj Let d represent the distance between the k-th proxy node and the j-th anchor node. kj =||(x k ,y k ,z k )-(x j ,y j ,z j)||, This represents the projected distance between the k-th proxy node and the j-th anchor node in the xy-plane, i.e. and α kj These represent the azimuth and elevation angles of the k-th proxy node and the j-th anchor node, respectively.

[0083]

[0084] λ kj =Δ 2 PQSNR kj , Δ=2πd a / λ, P and Q represent the number of array elements on the x-axis and y-axis, respectively; d a λ and λ represent the element spacing and wavelength of the antenna, respectively; A kj S(f) represents the amplitude of the received waveform at the kth proxy node when the j-th anchor node sends a signal; S(f) represents the Fourier transform of the known signal s(t); N0 / 2 represents the two-sided power spectral density of the signal noise.

[0085]

[0086]

[0087]

[0088] The AOA 3D positioning error limit of the proxy node is expressed as: Where σ k This represents the AOA 3D positioning error limit of the k-th agent node.

[0089]

[0090] Step 4) Select the target proxy node, traverse other proxy nodes within its communication range, select the proxy node with the highest contribution in each iteration as the collaborating node, and output the set of collaborating nodes of the target proxy node. The contribution is defined as the decrease in the AOA three-dimensional positioning error limit of the target proxy node after a certain proxy node is added.

[0091] Before selecting the proxy node with the highest contribution in this step, a communication distance threshold is set for the k-th proxy node. Select the set of cooperative nodes that are within the communication distance threshold of the k-th proxy node, i.e., SR k This initially eliminates collaborating nodes with relatively small contributions to the k-th agent node, significantly reducing the time overhead of the collaborating node selection method.

[0092] Specifically, the iterative process of this step is as follows:

[0093] Step 4-1) Let k = 1.

[0094] Step 4-2) Based on step 2), obtain the position p of the kth proxy node. k =[x k ,y k ,z k ] T .

[0095] Step 4-3) Define a Boolean vector to represent the cooperative node selected by the k-th agent node, i.e., S k =[β k1 ,β k2 ,…,β kNa ], when β ki When β = 1, it means that the i-th agent node is selected as the collaborating node of the k-th agent node; otherwise, β = 1. ki =0. Initialized to S k =[0,0,…,0].

[0096] Step 4-4) Set the communication distance threshold for the k-th proxy node.

[0097] Steps 4-5) Initialize the collection SR k This represents the set of nodes to be collaborated with, determined based on the communication distance threshold of the k-th proxy node.

[0098] Steps 4-6) Calculate the distance d between the k-th proxy node and the i-th proxy node. ki =||(x k ,y k ,z k )-(x i ,y i ,z i )||,i∈N a / {k}. If Then add the i-th proxy node to the set SR k Otherwise, the i-th proxy node is not added to the set SR. k .

[0099] Steps 4-7) Initialization This represents the set of collaborating nodes currently selected by the k-th agent node.

[0100] Step 4-8) Let s = 1, where s represents the current iteration number.

[0101] Steps 4-9) In the s-th iteration, calculate the i-th node to be cooperated with (i∈SR) k \S selContribution to the k-th agent node

[0102] Suppose that in the s-th iteration, after the i-th node to be collaborated with joins the collaborative localization network, the joint probability density function of the k-th agent node is updated as follows:

[0103]

[0104] Therefore, when the i-th node to be collaborated with joins the collaborative positioning network, the Fischer information matrix of the k-th agent node is updated as follows:

[0105]

[0106] in, Let f(x) represent the Fisher information matrix of the kth agent node after the (s-1)th iteration; This represents the Fisher information matrix of the k-th agent node after the s-th iteration; when s=1, Its expression is referenced in formula (7).

[0107] This represents the expression for the Fischer information added by the k-th agent node after the i-th node joins the cooperative positioning network. χ ki and Λ ki The expressions are referenced in formulas (8) and (9).

[0108] Therefore, based on formula (12), the positioning error limit of the k-th proxy node after the i-th node to be cooperated with joins the cooperative positioning network can be obtained as follows:

[0109]

[0110] This represents the square of the positioning error limit of the k-th agent node after the (s-1)-th iteration; This represents the reduction in positioning error limit of the k-th proxy node after adding the i-th node to be collaborated with in the s-th iteration.

[0111] Therefore, this invention combines the location uncertainty of the cooperative node, i.e., the positioning error limit of the proxy node obtained in step 3), with the reduction in the positioning error limit after the cooperative node joins the network, to jointly define the contribution of the cooperative node, which is expressed as follows:

[0112]

[0113] Where I represents a 3×3 identity matrix. σ iThis represents the positioning error limit of the i-th agent node, and its expression is given by formula (10).

[0114] Step 4-10) Sort the contributions of all nodes to be collaborated on, and select the node with the largest contribution in the s-th iteration. And add it to set S sel ,Right now

[0115] Step 4-11) Update the Fischer information matrix of the k-th agent node based on the information of the newly added collaborating node. in, Let f(x) represent the Fischer information matrix of the kth agent node after the s-th iteration. Let f(x) represent the Fischer information matrix of the kth agent node after the (s-1)th iteration. Represents a collaborative node The Fischer information added after joining the cooperative positioning network.

[0116] Step 4-12) Update S according to the selected collaborating node. k .

[0117] Step 4-13) Determine s = n pre If yes, proceed to step 4-14); if no, then s = s + 1, proceed to step 4-9), where n pre This indicates the number of predefined collaborative nodes selected.

[0118] Step 4-14) Determine if k = N a If yes, proceed to step 4-15; if no, then k = k + 1, otherwise proceed to step 4-2).

[0119] Step 4-15) After execution, output the cooperative nodes selected by all agent nodes in the entire target space.

[0120]

[0121] This embodiment utilizes an iterative collaborative node selection method, which ensures that the selection result of the collaborative node is optimal in each iteration, avoiding the use of a globally optimal method and greatly reducing the computational complexity of the collaborative node selection method.

[0122] The simulation environment in this embodiment is 50×50×50m. 3In a three-dimensional space, 30 proxy nodes and 10 anchor nodes are distributed throughout the environment. The anchor node positions are (0,0,0), (0,50,50), (12.5,0,50), (12.5,50,0), (25,0,0), (25,50,50), (37.5,0,50), (37.5,50,0), (50,0,0), (50,50,50), and the proxy nodes are randomly distributed in the target area. Furthermore, this invention sets P = 3, Q = 4, and d... a =λ2 and ∫|s(t)| 2 dt = 1. The positioning error limit in this invention refers to the average value of 100 Monte Carlo experiments.

[0123] Figure 2 The paper presents the variation of the average positioning error limit with the number of selected cooperative nodes when using the cooperative node selection method based on AOA 3D positioning contribution proposed in this invention. The average positioning error limit refers to the average of the positioning error limits of 30 agent nodes. The results show that the cooperative node selection method proposed in this invention not only maintains good positioning performance but also significantly reduces the system energy consumption and communication load of cooperative positioning.

[0124] Figure 3 Comparisons of the average positioning error limits of the following methods are presented, respectively, for signal-to-noise ratios of 3dB and 5dB: the collaborative node selection method based on AOA 3D positioning contribution, the distance-based collaborative node selection method, the simulated annealing-based collaborative node selection method, and the random collaborative node selection method. The distance-based collaborative node selection method selects collaborative nodes based on the distance between the node to be collaborated with and the proxy node. The simulated annealing-based collaborative node selection method constructs a solution space based on the positions of all nodes to be collaborated with, and then uses the simulated annealing algorithm to find the optimal solution to minimize the positioning error limit. The random collaborative node selection method randomly selects nodes from the nodes to be collaborated with for collaborative positioning. The results show that the proposed collaborative node method has higher positioning accuracy.

[0125] Figure 4 A comparison of the time costs of four collaborative node selection methods—based on AOA 3D localization contribution, distance-based, simulated annealing-based, and random—is presented, all with a signal-to-noise ratio (SNR) of 3dB. The results show that while the time cost of the proposed collaborative node selection method is slightly longer than that of the distance-based and random methods (both on the order of seconds), it is significantly shorter than that of the simulated annealing-based method. Therefore, the proposed collaborative node selection method achieves good localization performance within an acceptable time cost.

[0126] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A collaborative node selection method based on AOA 3D localization contribution, characterized in that, Includes the following steps: Step 1) Select a 3D positioning target space and determine the proxy nodes and anchor nodes in the target space; Step 2) Make a preliminary estimate of the location of the proxy node based on the known anchor nodes; Step 3) Based on the known anchor node information, calculate the Fisher information matrix of each agent node with respect to its location, and calculate its corresponding AOA three-dimensional positioning error limit; Step 4) Select the target proxy node, traverse other proxy nodes within its communication range, select the proxy node with the highest contribution in each iteration as the collaborating node, and output the set of collaborating nodes of the target proxy node. Specifically, selecting the target proxy node, traversing other proxy nodes within its communication range, and selecting the proxy node with the highest contribution in each iteration as the collaborating node involves: In the s In the nth iteration, calculate the th i The node to be collaborated with the first k Contribution of each proxy node Its expression is: and Indicates the first k The agent node is at the ( s -1) after the iteration and the th s The Fischer information matrix after the next iteration has the following relationship: ;when hour, , Indicates the first k A Fischer information matrix based on anchor node information at each proxy node; Indicates when the first i After the node to be cooperated joins the cooperative positioning network, for the node to be cooperated... k The Fischer information added by each proxy node is expressed as follows: ; ; Represent a The identity matrix; Indicates the first i The positioning error limit of each agent node.

2. The collaborative node selection method based on AOA three-dimensional positioning contribution as described in claim 1, characterized in that, The Fischer information matrix of the proxy node regarding its location is as follows: in, This is a set of proxy nodes whose locations are unknown. This represents the first anchor node calculated based on known anchor node information. k The Fischer information matrix of each proxy node is expressed as follows: ,in, Indicates the first k The agent node and the first j The distance between each anchor node, i.e. , Indicates the first k The agent node and the first j Anchor nodes at xy The projected distance on the plane, i.e. ; and They represent the first k The agent node and the first j The azimuth and elevation angles of each anchor node; , , , P and Q They represent x shaft and y The number of on-axis array elements; and These represent the element spacing and wavelength of the antenna, respectively. Indicates when the first j The anchor node sends a signal at the [number]th anchor node. k The amplitude values ​​of the received waveform at each proxy node; Represents a known signal Fourier transform; The two-sided power spectral density represents the signal-to-noise ratio; 。 3. The method for selecting collaborative nodes based on AOA three-dimensional positioning contribution as described in claim 2, characterized in that, The AOA 3D positioning error limit of the proxy node is expressed as: ,in Indicates the first k AOA 3D positioning error limit for each agent node .

4. The method for selecting collaborative nodes based on AOA three-dimensional positioning contribution as described in claim 1, characterized in that, Before selecting the agent node with the highest contribution in step 4), the first step is to set the... k The communication distance threshold of the proxy node is selected at the _th_ node. k The set of cooperative nodes within the communication distance threshold of the first agent node is initially excluded from consideration. k Collaborating nodes with relatively small contributions from agent nodes.

5. A collaborative node selection device based on AOA three-dimensional positioning contribution, characterized in that, include: The target space selection module is used to select a three-dimensional positioning target space and determine the proxy nodes and anchor nodes in the target space; The coarse positioning module is used to initially estimate the location of the proxy node based on the known anchor nodes; The AOA 3D positioning error limit calculation module is used to calculate the Fisher information matrix of each agent node with respect to its position based on the known anchor node information, and to calculate its corresponding AOA 3D positioning error limit. The collaborative node selection module is used to select a target proxy node, traverse other proxy nodes within its communication range, select the proxy node with the highest contribution in each iteration as a collaborative node, and output the set of collaborative nodes of the target proxy node. Specifically, selecting the target proxy node, traversing other proxy nodes within its communication range, and selecting the proxy node with the highest contribution in each iteration as a collaborative node involves: In the s In the nth iteration, calculate the th i The node to be collaborated with the first k Contribution of each proxy node Its expression is: and Indicates the first k The agent node is at the ( s -1) after the iteration and the th s The Fischer information matrix after the next iteration has the following relationship: ;when hour, , Indicates the first k A Fischer information matrix based on anchor node information at each proxy node; Indicates when the first i After the node to be cooperated joins the cooperative positioning network, for the node to be cooperated... k The Fischer information added by each proxy node is expressed as follows: ; ; Represent a The identity matrix; Indicates the first i The positioning error limit of each agent node.

6. The collaborative node selection device based on AOA three-dimensional positioning contribution as described in claim 5, characterized in that, The Fischer information matrix of the proxy node regarding its location is as follows: in, This is a set of proxy nodes whose locations are unknown. This represents the first anchor node calculated based on known anchor node information. k The Fischer information matrix of each proxy node is expressed as follows: ,in, Indicates the first k The agent node and the first j The distance between each anchor node, i.e. , Indicates the first k The agent node and the first j Anchor nodes at xy The projected distance on the plane, i.e. ; and They represent the first k The agent node and the first j The azimuth and elevation angles of each anchor node; , , , P and Q They represent x shaft and y The number of on-axis array elements; and These represent the element spacing and wavelength of the antenna, respectively. Indicates when the first j The anchor node sends a signal at the [number]th anchor node. k The amplitude values ​​of the received waveform at each proxy node; Represents a known signal Fourier transform; The two-sided power spectral density represents the signal-to-noise ratio; 。 7. The collaborative node selection device based on AOA three-dimensional positioning contribution as described in claim 6, characterized in that, The AOA 3D positioning error limit of the proxy node is expressed as: ,in Indicates the first k AOA 3D positioning error limit for each agent node .

8. The collaborative node selection device based on AOA three-dimensional positioning contribution as described in claim 5, characterized in that, Before selecting the agent node with the highest contribution, the collaboration node selection module sets the first... k The communication distance threshold of the proxy node is selected at the _th_ node. k The set of cooperative nodes within the communication distance threshold of the first agent node is initially excluded from consideration. k Collaborating nodes with relatively small contributions from agent nodes.

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