Positioning methods, devices, electronic equipment and storage media

Abstract

This application proposes a positioning method, apparatus, electronic device, and storage medium. The method includes: receiving echo feature information measured by any second sensing node in a distributed sensing node for at least one sensing object; selecting multiple target nodes among the second sensing nodes measuring the target object for any target object; and determining the target location of the target object based on the echo feature information of the multiple target nodes for the target object.

Description

Technical Field

[0001] This application relates to the field of communication sensing technology, and in particular to a positioning method, device, electronic device and storage medium. Background Technology

[0002] Communication-sensing fusion is one of the key technological directions in 5G evolution (5G-Advanced, 5G-A). Based on shared hardware and software resources, it utilizes a portion of time and frequency resources to transmit sensing signals and, through signal processing of the echo signals, senses the motion state of the target. However, the sensing accuracy of related technologies still needs improvement. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first objective of this application is to propose a positioning method.

[0005] The second objective of this application is to provide a positioning device.

[0006] The third objective of this application is to propose an electronic device.

[0007] The fourth objective of this application is to provide a computer-readable storage medium.

[0008] The fifth objective of this application is to provide a computer program product.

[0009] To achieve the above objectives, a first aspect of this application proposes a positioning method applied to a first sensing node, comprising:

[0010] Receive echo characteristic information obtained by any second sensing node in the distributed sensing nodes for measuring at least one sensing object;

[0011] For any target object among the sensing objects, multiple target nodes are selected in the second sensing node that measures the target object;

[0012] The target location of the target object is determined based on the echo characteristic information of the multiple target nodes for the target object.

[0013] To achieve the above objectives, a second aspect of this application provides a positioning device applied to a first sensing node, comprising:

[0014] The receiving module is used to receive echo characteristic information measured by any second sensing node in the distributed sensing nodes for at least one sensing object;

[0015] The selection module is used to select multiple target nodes from the second sensing nodes that measure the target object for any target object among the sensing objects;

[0016] The determination module is used to determine the target location of the target object based on the echo characteristic information of the multiple target nodes for the target object.

[0017] To achieve the above objectives, a third aspect of this application provides an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement a positioning method as described in the first aspect of this application.

[0018] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement a positioning method as described in the first aspect of this application.

[0019] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements a positioning method as described in the first aspect of this application.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart illustrating a positioning method provided in an embodiment of this application;

[0023] Figure 2 A schematic flowchart illustrating a positioning method provided in another embodiment of this application;

[0024] Figure 3 A schematic diagram of a sphere and a circumference provided for another embodiment of this application;

[0025] Figure 4 A schematic diagram illustrating the initial solution location provided in another embodiment of this application;

[0026] Figure 5 This is a schematic diagram of the structure of a positioning device provided in an embodiment of this application;

[0027] Figure 6 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] Unlike radar, sensor fusion achieves seamless coverage through networking. In overlapping coverage areas of multi-station sensing, diversity gain can be obtained and sensing accuracy improved through multi-station collaborative fusion and deduplication. Related technologies include trajectory-level fusion, point cloud-level fusion, and symbol-level fusion schemes.

[0030] However, trajectory-level and point cloud-level fusion schemes rely on single-station ranging and angle measurement to obtain trajectories or point clouds, followed by multi-station fusion of the trajectory / point clouds. This approach depends on the accuracy of single-station angle measurement, while position errors caused by angle measurement errors at cell edges are significant, resulting in poor position perception accuracy. Symbol-level fusion schemes have high algorithmic complexity and require high computing power, making them difficult to apply in practical products.

[0031] To address the aforementioned problems, this application proposes a positioning method, apparatus, electronic device, and storage medium. The positioning method, apparatus, electronic device, and storage medium of this application are described below with reference to the accompanying drawings.

[0032] Figure 1 This is a flowchart illustrating a positioning method provided in an embodiment of this application. The positioning method can be applied to a first sensing node, which can be a centralized sensing node (Master Baseband Unit, MasterBBU).

[0033] like Figure 1 As shown, the positioning method includes the following steps:

[0034] Step 101: Receive echo feature information obtained by any second sensing node in the distributed sensing nodes for at least one sensing object.

[0035] The second sensing node can refer to any node in the distributed sensing node (SlaveBaseband Unit, Slave BBU).

[0036] In order to avoid wasting communication resources, the second sensing node can also refer to a node in the overlapping coverage area of ​​the distributed sensing nodes. That is, the second sensing node that reports echo feature information can simultaneously sense the sensing objects in the overlapping coverage area.

[0037] The echo characteristic information may include distance measurement, angle measurement, velocity measurement, and signal-to-clutter ratio (SCR) and other features.

[0038] Step 102: For any target object in the sensing objects, select multiple target nodes in the second sensing node of the target object.

[0039] The target object can refer to any one of at least one sensing object; the target node can be multiple nodes among the second sensing nodes that have been measured against the target object.

[0040] As an example, multiple target nodes can be selected in the second perception node of the target object, based on the set evaluation index such as SCR.

[0041] Step 103: Determine the target location of the target object based on the echo characteristic information of multiple target nodes for the target object.

[0042] Among them, the target location of the target object can be obtained by jointly solving the position based on the echo feature information of multiple target nodes for the target object.

[0043] In this embodiment, echo feature information measured by any second sensing node in the distributed sensing nodes for at least one sensing object is received; for any target object among the sensing objects, multiple target nodes are selected among the second sensing nodes measuring the target object; the target position of the target object is determined based on the echo feature information of the multiple target nodes for the target object. By performing feature-level sensing fusion using the echo feature information of multiple target nodes, the problem of significant decrease in position accuracy caused by single-station angle measurement with increasing coverage distance in related technologies can be avoided, improving the edge position sensing accuracy while balancing algorithm complexity and the amount of data transmitted through the sensing node interface.

[0044] This embodiment provides another positioning method. Figure 2 This is a flowchart illustrating a positioning method provided in an embodiment of this application, which can be applied to a first sensing node.

[0045] like Figure 2 As shown, the positioning method may include the following steps:

[0046] Step 201: Receive echo feature information obtained by any second sensing node in the distributed sensing nodes for at least one sensing object.

[0047] In one possible embodiment of this application, the echo characteristic information includes at least one of the following: echo signal-to-noise ratio; line-of-sight path (LoS path) presence information; and measured values ​​of echo characteristic parameters.

[0048] Among them, the line-of-sight path existence information is used to indicate whether there is a LoS path between the second sensing node and the sensing object; the echo characteristic parameters may include distance and angle (including azimuth and elevation angle).

[0049] Step 202: For any target object among the sensing objects, determine the second sensing node for measuring the target object from the distributed sensing nodes.

[0050] In one possible embodiment of this application, the echo feature information also includes the location information of the centroid of the point cloud cluster of the sensed object.

[0051] The second sensing node can estimate distance, angle, and velocity based on the range-velocity-angle (RVA) spectrum, and perform point cloud clustering to obtain the location information of the cluster centroid and the distance, angle, and velocity features of the cluster centroid.

[0052] In one possible embodiment of this application, for any two echo feature information from different second sensing nodes, the distance between the centroids of the point cloud cluster is determined based on the location information; in response to the distance being less than a distance threshold, the corresponding echo feature information is associated and bound, wherein the echo feature information that is associated and bound belongs to the same sensing object; based on the associated and bound echo feature information corresponding to the target object in the sensing object, the second sensing node for measuring the target object is determined from the distributed sensing nodes.

[0053] Among them, based on the distance (e.g., Euclidean distance) between the centroids of multiple stations (multiple second sensing nodes), sensing objects can be matched. Those with a distance less than the distance threshold are matched as the same object, thereby filtering out echo feature information belonging to the same target.

[0054] The echo feature information includes sensing node source information. For the target object in the sensing object, the second sensing node that has measured the target object can be determined in the distributed sensing nodes based on the sensing node source information included in the echo feature information of the target object.

[0055] Step 203: Based on the set sensing node selection conditions, select the target node based on the echo characteristic information of the target object by the second sensing node of the target object.

[0056] When performing feature-level fusion, not all sensory feature data of the second sensing nodes with coverage are included in the fusion for the target object. The location calculation accuracy may be reduced when one of the following situations occurs: (1) The SCR of the second sensing node measuring the target object is reduced due to interference, occlusion, etc., resulting in unreliable ranging and angle measurement results; (2) In dense urban scenes, some second sensing nodes participating in the fusion may only have a non-line-of-sight path (NLoS path) between them and the target object. The ranging and angle measurement results based on the NLoS path may have large errors. If the measurement results of these second sensing nodes are selected for joint location calculation, the error propagation may cause the calculation accuracy to decrease; (3) When performing feature-level fusion, if the multiple second sensing nodes participating in the fusion do not meet a certain geometric distribution, the calculation accuracy will also be reduced.

[0057] To improve location awareness accuracy, in one possible embodiment of this application, the selection criteria for sensing nodes include at least one of the following: the echo signal-to-noise ratio (SNR) is greater than or equal to a SNR threshold; there is a line-of-sight path between the distributed sensing nodes and the target object; the location difference between the predicted location and the calculated location of the target object is within a set difference range, wherein the predicted location is predicted based on the historical location of the target object, and the calculated location is calculated based on the measured values ​​of the echo characteristic parameters; and the mean square error of the echo characteristic parameter measurements taken multiple times for the target object is greater than or equal to a mean square error threshold.

[0058] One approach is to use the Kalman filter algorithm to predict the location of the target object based on its historical location.

[0059] In one possible embodiment of this application, a node that meets the selection criteria for a sensing node can be selected from the second sensing nodes of the target object and used as the target node.

[0060] In one possible embodiment of this application, based on the sensing node selection criteria, multiple candidate nodes that meet the sensing node selection criteria are selected from the second sensing nodes of the target object; in response to the number of candidate nodes being greater than or equal to a number threshold, the multiple candidate nodes are arranged and combined to obtain at least one node combination; the index value of any node combination under a set measurement index is obtained; based on the index value, a target node combination is selected from at least one node combination to obtain the target node.

[0061] The quantity threshold can be 2 or 3; the measurement index can include the geometric dilution of precision (GDOP); the nodes included in the target node combination are the target nodes, and the combination with the smallest GDOP can be selected as the target node combination from at least one node combination.

[0062] The candidate nodes can be arranged and combined according to a set number, that is, each node combination includes a set number of nodes. For example, if the number threshold is 3, the set number can be 3.

[0063] The formula for calculating the GDOP of any combination of nodes is as follows:

[0064]

[0065] P=(H T R -1 H) -1

[0066]

[0067] Where trace(P) represents the trace operation on the position error covariance matrix P; H represents the stack of observation matrices of all nodes in the node combination; R represents the measurement noise covariance matrix; h i Let (x) represent the observation matrix of the i-th node in the node combination. i y i , z i (x, y, z) represents the coordinates of the i-th node in the node combination, and (x, y, z) represents the coordinates of the target object.

[0068] It should be noted that if there are no candidate nodes that meet the selection criteria for the second sensing node of the target object, that is, the number of candidate nodes is 0, the position of the target object can be estimated based on the historical trajectory, velocity and other information of the target object, and algorithms such as Kalman filtering. If the number of candidate nodes is greater than 0 and less than the number threshold, for example, the number of candidate nodes is 1, the position of the target object can be estimated based on the echo feature information of this candidate node, without multi-station fusion calculation.

[0069] Step 204: Based on the echo characteristic parameter measurement values ​​of at least one target node, the position of the target object is calculated to obtain the reference position of the target object.

[0070] In one possible embodiment of this application, based on the echo signal-to-noise ratio of the target node for the target object, two reference nodes are selected from multiple target nodes; based on the measured values ​​of the echo characteristic parameters of the reference nodes for the target object, the position of the target object is calculated to obtain the reference position of the target object.

[0071] In this process, any two nodes can be selected from the target nodes as reference nodes; to improve the accuracy of the position calculation, the two nodes with the highest SCR in the target nodes can also be selected as reference nodes.

[0072] The process of calculating the position of the target object based on two reference nodes is as follows:

[0073] Draw a sphere based on the distance measurements from two reference nodes. Assume the coordinates of the two reference nodes are (x1, y1, z1) and (x2, y2, z2), and the distance measurements from the two reference nodes to the target object are d1 and d2, respectively. Then the equations of the spheres are (x-x1). 2 +(y-y1) 2 +(z-z1) 2 =d1 2 (x-x2) 2 +(y-y2) 2 +(z-z2) 2 =d2 2 Two spheres intersecting form a circle, as shown below. Figure 3 As shown, Figure 3 This is a schematic diagram of a sphere and a circle. The elliptical part in the diagram represents the circle. The equation of the circle can be obtained by solving the equations of the two spheres simultaneously.

[0074] Draw two rays based on the azimuth / elevation angles measured from the two reference nodes. If the two rays are coplanar, calculate the intersection point S. ′ (x ′ ,y ′ ,z ′ If the two rays are skew, then connect the two points closest to each other on the two rays and calculate the midpoint S of the line. ′ (x ′ ,y ′ ,z ′ ).

[0075] like Figure 4 As shown, Figure 4 This is a schematic diagram of the initial solution position; select the intersection circle at point S. ′ The nearest point is used as the initial solution position P0(x0,y0,z0), which means S ′ The projection onto the circumferential plane is connected to the center of the circle. The intersection of the line connecting the two lines with the circumference is taken to obtain the initial solution position. This initial solution position is the reference position.

[0076] Based on the distance and angle measurement data of two reference nodes, the initial solution position is obtained through geometric calculation. Then, based on multi-station data, the final position is calculated iteratively using the least squares method. The advantages of this approach are improved convergence speed, reduced iteration complexity, and avoidance of multiple solutions in local convergence.

[0077] Step 205: For any target node, obtain the reference value of the echo characteristic parameter based on the reference position.

[0078] The echo characteristic parameters include range, azimuth, and elevation. Based on the coordinates of the reference position (x0, y0, z0) and the coordinates of the nth target node (x... n ,y n ,z n The distance reference value can be obtained as follows: The azimuth reference value is: The reference value for pitch angle is:

[0079] Step 206: Update the reference position based on the difference between the measured value of the echo characteristic parameter and the reference value of the echo characteristic parameter corresponding to any target node.

[0080] In one possible embodiment of this application, an error function is determined based on the echo feature parameter weights corresponding to any target node and the difference between the measured echo feature parameter values ​​and the reference echo feature parameter values; the Jacobian matrix is ​​determined based on the error function; an increment equation is determined based on the position increment to be solved, the Jacobian matrix, the echo feature parameter weights corresponding to any target node, and the difference between the measured echo feature parameter values ​​and the reference echo feature parameter values; the increment equation is solved to obtain the increment value of the position increment, and the reference position is updated based on the increment value.

[0081] For example, the formula for calculating the difference between the measured value and the reference value of the echo characteristic parameter corresponding to the target node is as follows:

[0082]

[0083] in, This represents the difference between the measured distance value and the reference distance value of the nth target node, also known as the distance residual. This represents the difference between the measured azimuth value and the reference azimuth value of the nth target node, also known as the azimuth residual. This represents the difference between the measured pitch angle value and the reference pitch angle value of the nth target node, i.e., the pitch angle residual; d n θ represents the distance measurement value of the nth target node relative to the target object. n This represents the azimuth angle measurement value of the nth target node relative to the target object. This represents the pitch angle measurement value of the nth target node relative to the target object.

[0084] For example, the formula for calculating the error function is as follows:

[0085]

[0086] Where E represents the error function; Indicates the azimuth weight. Indicates the pitch angle weight. and A value greater than 0 and less than 0.5, where N represents the total number of target nodes.

[0087] Considering the relationship between the positional error caused by angle E and the distance between the sensing object and the sensing node, That is, it is proportional to the distance. Therefore, in one possible embodiment of this application, for any target node, the azimuth weight and pitch weight are determined based on the distance measurement value of the target node to the target object; wherein, the azimuth weight and pitch weight are less than the distance weight.

[0088] Among them, the overlapping coverage area is mostly located at the edge of perception. Compared with the position calculation based on angle, the position calculation based on distance is more accurate. Therefore, this application considers the distance measurement result as the main factor to determine the target position, and the angle measurement data is only used to assist in adjusting and correcting the position calculated based on distance. That is, when iteratively solving, the distance weight is greater than the azimuth weight and the pitch weight.

[0089] For example, the formulas for calculating the azimuth weight and pitch weight can be: That is, the greater the distance, the greater the error, the lower the reliability of the angle measurement data, and the smaller the weight setting.

[0090] By setting weights and using multi-station ranging as the primary method for position calculation at the sensing edge, the problem of amplified angle measurement errors at the sensing edge can be avoided, thereby improving sensing accuracy.

[0091] To obtain the optimal solution position, the error function E should be minimized, at which point the target position is optimal. Since directly calculating the partial derivative of the error function E and setting it to zero is computationally expensive and difficult to obtain a closed-form solution, this application proposes an iterative optimization algorithm, as follows:

[0092] Take the partial derivative of each error term in the error function E with respect to (x,y,z) to construct the Jacobian matrix J:

[0093]

[0094] Based on the weights of the echo feature parameters corresponding to the target node and the differences between the measured values ​​and reference values ​​of the echo feature parameters, the error vector e is determined:

[0095]

[0096] Based on the position increment, Jacobian matrix, and error vector to be solved, the incremental equation is constructed as follows:

[0097] (J T WJ)[Δx,Δy,Δz] T =J T We

[0098] Where W represents the weight diagonal matrix, Δx, Δy, and Δz represent the position increments to be solved.

[0099] Since the error vector e, Jacobian matrix J, and weight diagonal matrix W are all known in the incremental equation, the position increment [Δx, Δy, Δz] can be solved through the incremental equation; the reference position after updating the reference position based on the increment value is (x0+Δx, y0+Δy, z0+Δz).

[0100] Step 207: Determine the target location based on the updated reference location.

[0101] In one possible embodiment of this application, in response to the reference position update count being greater than or equal to a count threshold, and / or the increment value being less than an increment value threshold, the updated reference position is used as the target position.

[0102] In one possible embodiment of this application, in response to the reference position update count being less than a count threshold, and / or in response to the increment value being greater than or equal to an increment value threshold, the step of obtaining the reference value of the echo characteristic parameter is returned based on the updated reference position until the reference position update count is greater than or equal to the count threshold, and / or the increment value is less than the increment value threshold.

[0103] Specifically, in response to the reference position update count being less than a threshold, and / or in response to the increment value being greater than or equal to an increment value threshold, the process returns to step 205. Based on the updated reference position, the echo characteristic parameter reference value is obtained, and step 206 is executed based on the recalculated echo characteristic parameter reference value. It should be noted that during the iterative execution of steps 205 and 206, the coordinates of the reference position are dynamically updated.

[0104] Specifically, in response to the reference position update count being greater than or equal to the count threshold, and / or the increment value being less than the increment value threshold, i.e., in response to stopping iteration, the reference position after the last update is taken as the target position.

[0105] In this embodiment, the target node is selected according to the set sensing node selection conditions, which can avoid the problem of decreased sensing accuracy due to unreliable measurement information or large errors of the node. The reference position of the target object is obtained first, and then the reference position is updated based on the difference between the measured value of the echo feature parameter and the reference value of the echo feature parameter. The target position is then determined based on the updated reference position, which can improve the convergence speed, reduce the algorithm complexity, and avoid the problem of multiple solutions in local convergence.

[0106] To clearly illustrate the positioning method of this application, this embodiment is described in conjunction with the Master BBU and Slave BBU.

[0107] (I) Feature Acquisition

[0108] Each slave BBU estimates distance, angle, and velocity based on the RVA spectrum and performs point cloud clustering to obtain the distance, angle, and velocity features of the cluster centroids. Simultaneously, it determines the location of static clutter such as buildings based on environmental reconstruction, thereby determining whether there is a Loss of Place (LoS) path between the sensing object and the slave BBU / base station.

[0109] (II) Feature Reporting

[0110] Slave BBUs within the overlapping coverage area report echo characteristic information to the master BBU via an interface (other slave BBUs, i.e., slave BBUs not within the overlapping coverage area, do not report echo characteristic information). The reported echo characteristic information includes:

[0111] Perception ID

[0112] Cluster centroid location (latitude, longitude, altitude)

[0113] Features of the cluster centroid include distance, angle, velocity, SCR, and the presence of a Loss-of-Stake (LoS) path.

[0114] (III) Object Matching

[0115] Based on multi-station spatiotemporal alignment (feature-level fusion has lower requirements for multi-station time synchronization; the master BBU can achieve spatial alignment by performing azimuth / elevation and latitude / longitude calibration through high-precision parameter calibration), the sensing objects are matched according to the Euclidean distance of the centroids of the multi-station (multiple slave BBU) clusters. Matches with an Euclidean distance less than the distance threshold are considered to be the same sensing object. This allows the sensing IDs belonging to the same sensing object, as well as the corresponding distance, angle, velocity, SCR, and whether a Loss path exists, to be selected.

[0116] (iv) Selection of participating fusion base stations based on specific sensing objects

[0117] For a given sensing object, not all sensing data from covered nodes participate in the fusion process during feature-level fusion. The accuracy of location calculation may deteriorate if any of the following situations occur:

[0118] ① Some Slave BBUs involved in the fusion have reduced SCR due to interference, occlusion, etc., resulting in unreliable ranging and angle measurement results;

[0119] ② In dense urban scenarios, some Slave BBUs participating in the fusion may only have an NLoS path between them and the sensed object. However, the ranging and angle measurement results based on the NLoS path may have large errors. If the measurement results of these Slave BBUs are selected for joint position calculation, the existing error propagation may cause the calculation accuracy to deteriorate.

[0120] ③ When performing feature-level fusion, if the multiple Slave BBUs involved in the fusion do not meet a certain geometric distribution (for example, the angle between the sensing object and the two stations is very small), it will also lead to a deterioration in the solution accuracy.

[0121] Furthermore, different sensing objects are located in different environments, and the optimal combination of fusion sensing nodes required is also different. Therefore, this application proposes a fusion sensing node selection mechanism centered on sensing objects. That is, for any target object among the sensing objects, the fusion base station is selected by comprehensively considering multiple factors such as the target object's SCR, whether there is a Loss path between the target object and the Slave BBU, the difference between the calculated position and the predicted position, the mean square error of ranging / angle measurement, and the geometric accuracy factor.

[0122] For example, the selection criteria for sensing nodes include at least one of the following:

[0123] Based on the SCR reported by the slave BBU, the ranging and angle measurement values ​​of slave BBUs with echo SCR below a certain threshold are filtered out.

[0124] Based on whether a Loss path exists as reported by the slave BBU, slave BBUs that do not have a Loss path are filtered out.

[0125] Based on algorithms such as Kalman filtering, the position of the target object is predicted, and the measurement data of the SlaveBBU that is not within the set difference range between the calculated position and the predicted position (e.g., the Euclidean distance between the calculated position and the predicted position is higher than the set threshold) is filtered out.

[0126] If the root mean square error of the angle and distance measurements reported by the slave BBU is higher than the root mean square error threshold, it may be due to interference, and the measurement data of that slave BBU should be filtered out.

[0127] If there are three or more Slave BBUs (candidate nodes) after filtering based on the sensing node selection criteria, then the candidate nodes are arranged and combined to obtain at least one node combination; the GDOP of each node combination is calculated. GDOP is the core indicator for measuring the quality of the geometric distribution of the positioning system. The smaller the value, the better the geometric distribution; the node combination with the smallest GDOP is selected to obtain the target node.

[0128] (V) Joint Location Solution

[0129] The solution is jointly calculated based on the ranging (e.g., Time of Flight (TOF)) and angle (e.g., Angle of Arrival (AOA)) data of multiple target nodes. Specifically, the initial solution position is first obtained through geometric calculation based on the ranging and angle data of two reference nodes. Then, the final position is calculated through iterative optimization using the least squares method based on the measurement data of multiple target nodes, thereby improving the convergence speed, reducing the iteration complexity, and avoiding the problem of multiple solutions in local convergence.

[0130] Among them, the overlapping coverage area is mostly located at the edge of perception. Compared with the position calculation based on angle, the position calculation based on distance is more accurate. Therefore, this application considers the distance measurement result as the main factor to determine the target position, and the angle measurement data is only used to assist in adjusting and correcting the position calculated based on distance. That is, when iteratively solving, the distance weight is greater than the azimuth weight and the pitch weight.

[0131] For example, the steps for joint location calculation include:

[0132] ① Determining the initial solution location

[0133] Select the two nodes with the highest SCR among the target nodes as reference nodes;

[0134] The initial calculated position, also known as the reference position, is obtained by calculating the distance and angle measurements of two reference nodes.

[0135] ② Position Iteration Optimization

[0136] Based on the initial calculated positions, the measurement data of multiple target nodes are iteratively optimized using the least squares method (with greater weight given to ranging errors, and the weight of angle errors related to distance). Specifically, this includes:

[0137] Residual calculation: For any target node, the distance residual, azimuth residual, and elevation residual are calculated based on the difference between the measured value and the reference value of the echo characteristic parameter corresponding to the target node.

[0138] Error function Hou Jian: Based on the distance residual, azimuth residual and elevation residual corresponding to each target node, as well as the distance weight, azimuth weight and elevation weight corresponding to each target node, a least squares error function is constructed.

[0139] Jacobian matrix calculation: Take the partial derivative of each error term in the error function with respect to (x,y,z) to construct the Jacobian matrix.

[0140] Incremental equation solution and position update: Based on the distance residual, azimuth residual and elevation residual corresponding to each target node, the error vector is determined; the incremental equation is constructed according to the position increment to be solved, the Jacobian matrix and the error vector; the incremental equation is solved to obtain the incremental values ​​[Δx, Δy, Δz]; the reference position is updated based on the incremental values.

[0141] Iteration Termination: Repeat the above steps to iterate and optimize until the incremental value [Δx, Δy, Δz] is less than the incremental value threshold, or the number of times the reference position is updated reaches the set number of iterations, and the target position is obtained.

[0142] This application adds features such as distance, angle, velocity, SCR, and presence of Loss-of-Stake (LoS) paths to the information exchanged between the slave BBU and master BBU. Centered on the sensing object, it comprehensively considers multiple factors including SCR, presence of LoS paths, difference between calculated and predicted positions, mean square error of ranging / angle measurement, and geometric precision factor to determine the target nodes for each sensing object involved in fusion. This avoids error propagation problems, optimizes the distribution of sensing nodes, and improves feature-level fusion accuracy. For any sensing object, the precise position is jointly determined based on the ranging and angle measurement data of multiple corresponding target nodes. This effectively avoids the problem of significant deterioration in position accuracy due to increased coverage distance in single-station angle measurement, effectively improving the position accuracy at the sensing edge. Furthermore, it employs a two-step position calculation process: geometry-based initial position calculation followed by least-squares iterative optimization. This improves convergence speed, reduces algorithm complexity, and avoids multiple solutions in local convergence. Additionally, by assigning different weights to distance and angle, it enables ranging-based calculation at the sensing edge, further enhancing position calculation accuracy.

[0143] To achieve the above embodiments, this application also proposes a positioning device.

[0144] Figure 5 This is a schematic diagram of a positioning device provided in an embodiment of this application. The device is applied to a first sensing node.

[0145] like Figure 5 As shown, the positioning device 500 includes:

[0146] The receiving module 510 is used to receive echo feature information measured by any second sensing node in the distributed sensing nodes for at least one sensing object.

[0147] The selection module 520 is used to select multiple target nodes in the second sensing node of the target object for any target object in the sensing object;

[0148] The determination module 530 is used to determine the target location of the target object based on the echo characteristic information of multiple target nodes for the target object.

[0149] Optionally, module 520 is specifically used for:

[0150] For any target object among the sensing objects, determine the second sensing node for measuring the target object from the distributed sensing nodes;

[0151] Based on the set sensing node selection conditions, the target node is selected based on the echo characteristic information of the target object by the second sensing node measuring the target object.

[0152] Optionally, the echo characteristic information includes at least one of the following: echo signal-to-noise ratio; line-of-sight path presence information; and measured values ​​of echo characteristic parameters.

[0153] The selection criteria for sensing nodes include at least one of the following:

[0154] The echo signal-to-noise ratio is greater than or equal to the signal-to-noise ratio threshold.

[0155] There is a line-of-sight path between the distributed sensing nodes and the target object;

[0156] The positional difference between the predicted position and the calculated position of the target object is within a set range. The predicted position is obtained based on the historical position of the target object, while the calculated position is obtained based on the position calculation of the echo characteristic parameter measurement value.

[0157] The mean square error of the echo characteristic parameter measurements taken multiple times for the target object is greater than or equal to the mean square error threshold.

[0158] Optionally, module 520 is specifically used for:

[0159] Based on the selection criteria for sensing nodes, multiple candidate nodes that meet the selection criteria are selected from the second sensing nodes of the target object.

[0160] In response to the number of candidate nodes being greater than or equal to a threshold, multiple candidate nodes are arranged and combined to obtain at least one node combination;

[0161] Get the indicator value of any combination of nodes under the set measurement indicators;

[0162] Based on the index value, select the target node combination from at least one node combination to obtain the target node.

[0163] Optionally, the echo feature information also includes the location information of the centroid of the point cloud cluster of the perceived object, and the selection module 520 is specifically used for:

[0164] For any two echo feature information from different second sensing nodes, the distance between the centroids of the point cloud cluster is determined based on the location information.

[0165] In response to a distance less than a distance threshold, the corresponding echo feature information is associated and bound, wherein the echo feature information that is associated and bound belongs to the same sensing object;

[0166] Based on the echo feature information associated with the target object in the sensing object, the second sensing node for measuring the target object is determined from the distributed sensing nodes.

[0167] Optionally, the echo characteristic information includes measured values ​​of echo characteristic parameters, and the determination module 530 is specifically used for:

[0168] Based on the echo characteristic parameter measurements of at least one target node, the position of the target object is calculated to obtain the reference position of the target object;

[0169] For any target node, obtain the reference value of the echo characteristic parameters based on the reference position;

[0170] The reference position is updated based on the difference between the measured value of the echo characteristic parameter and the reference value of the echo characteristic parameter corresponding to any target node;

[0171] Determine the target location based on the updated reference location.

[0172] Optionally, module 530 is specifically used for:

[0173] The error function is determined based on the weight of the echo characteristic parameter corresponding to any target node, and the difference between the measured value and the reference value of the echo characteristic parameter.

[0174] Determine the Jacobian matrix based on the error function;

[0175] The incremental equation is determined based on the position increment to be solved, the Jacobian matrix, the weights of the echo characteristic parameters corresponding to any target node, and the difference between the measured values ​​and reference values ​​of the echo characteristic parameters.

[0176] Solve the incremental equation to obtain the incremental value of the position increment, and update the reference position based on the incremental value.

[0177] Optionally, module 530 is specifically used for:

[0178] In response to the reference position update count being less than the count threshold, and / or in response to the increment value being greater than or equal to the increment value threshold, the step of obtaining the reference value of the echo feature parameter is returned based on the updated reference position until the reference position update count is greater than or equal to the count threshold, and / or the increment value is less than the increment value threshold.

[0179] Optionally, the echo characteristic parameters include at least one of range, azimuth, and elevation angles, and the determining module 530 is further configured to:

[0180] For any target node, the azimuth weight and pitch weight are determined based on the distance measurement value of the target node to the target object; wherein, the azimuth weight and pitch weight are less than the distance weight.

[0181] Optionally, module 530 is specifically used for:

[0182] Based on the echo signal-to-noise ratio of the target node for the target object, two reference nodes are selected from multiple target nodes;

[0183] Based on the echo characteristic parameter measurements of the target object at the reference node, the position of the target object is calculated to obtain the reference position of the target object.

[0184] In this embodiment, echo feature information measured by any second sensing node in the distributed sensing nodes for at least one sensing object is received; for any target object among the sensing objects, multiple target nodes are selected among the second sensing nodes measuring the target object; the target position of the target object is determined based on the echo feature information of the multiple target nodes for the target object. By performing feature-level sensing fusion using the echo feature information of multiple target nodes, the problem of significant decrease in position accuracy caused by single-station angle measurement with increasing coverage distance in related technologies can be avoided, improving the edge position sensing accuracy while balancing algorithm complexity and the amount of data transmitted through the sensing node interface.

[0185] It should be noted that the foregoing explanation of the positioning method embodiment also applies to the positioning device of this embodiment, and will not be repeated here.

[0186] Figure 6This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 600 in this embodiment is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0187] like Figure 6 As shown, the above-mentioned electronic device 600 includes:

[0188] The memory 601 and the processor 602 are connected by a bus 603. The memory 601 stores a computer program, and when the processor 602 executes the program, it implements the positioning method of the present application embodiment.

[0189] Bus 603 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0190] Electronic device 600 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 600, including volatile and non-volatile media, removable and non-removable media.

[0191] Memory 601 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 604 and / or cache memory 605. Electronic device 600 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 606 can be used to read and write non-removable, non-volatile magnetic media (… Figure 6 Not shown; usually referred to as a "hard drive"). Although Figure 6Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 603 via one or more data media interfaces. Memory 601 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0192] A program / utility 608 having a set (at least one) of program modules 607 may be stored, for example, in memory 601. Such program modules 607 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 607 typically perform the functions and / or methods described in the embodiments of this application.

[0193] Electronic device 600 can also communicate with one or more external devices 609 (e.g., keyboard, pointing device, display 611, etc.), and with one or more devices that enable a user to interact with the electronic device 600, and / or with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 612. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 613. Figure 6 As shown, network adapter 613 communicates with other modules of electronic device 600 via bus 603. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0194] The processor 602 executes various functional applications and data processing by running programs stored in the memory 601.

[0195] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the positioning method in the embodiments of this application, and will not be repeated here.

[0196] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0197] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0198] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0199] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0200] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0201] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0202] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0203] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0204] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0205] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0206] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0207] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0208] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A positioning method, characterized in that, When applied to the first sensing node, the following steps are included: Receive echo feature information measured by any second sensing node in the distributed sensing nodes for at least one sensing object; the echo feature information includes measured values ​​of echo feature parameters; For any target object among the sensing objects, multiple target nodes are selected in the second sensing node that measures the target object; Based on the measured echo characteristic parameters of at least one target node, the position of the target object is calculated to obtain the reference position of the target object; For any of the target nodes, obtain reference values ​​for echo characteristic parameters based on the reference position; The reference position is updated based on the difference between the measured value of the echo characteristic parameter and the reference value of the echo characteristic parameter corresponding to any of the target nodes; The target location of the target object is determined based on the updated reference location.

2. The method according to claim 1, characterized in that, For any target object among the sensing objects, in the second sensing node measuring the target object, multiple target nodes are selected, including: For any target object among the sensing objects, a second sensing node for measuring the target object is determined from the distributed sensing nodes; Based on the set sensing node selection conditions, the target node is selected based on the echo characteristic information of the target object by the second sensing node that measures the target object.

3. The method according to claim 2, characterized in that, The echo characteristic information includes at least one of the following: echo signal-to-noise ratio; line-of-sight path presence information; Echo characteristic parameter measurements; The selection criteria for the sensing nodes include at least one of the following: The echo signal-to-noise ratio is greater than or equal to the signal-to-noise ratio threshold. There is a line-of-sight path between the distributed sensing nodes and the target object; The positional difference between the predicted position and the calculated position of the target object is within a set difference range, wherein the predicted position is obtained based on the historical position of the target object, and the calculated position is obtained based on the position calculation of the echo characteristic parameter measurement value; The mean square error of the echo characteristic parameter measurements taken multiple times for the target object is greater than or equal to the mean square error threshold.

4. The method according to claim 2, characterized in that, The step of selecting the target node based on the set sensing node selection conditions and the echo characteristic information of the second sensing node measuring the target object for the target object includes: Based on the sensing node selection criteria, multiple candidate nodes that satisfy the sensing node selection criteria are selected from the second sensing nodes that measure the target object. In response to the number of candidate nodes being greater than or equal to a number threshold, the multiple candidate nodes are arranged and combined to obtain at least one node combination; Obtain the index value of any of the node combinations under the set measurement index; Based on the index value, a target node combination is selected from the at least one node combination to obtain the target node.

5. The method according to claim 2, characterized in that, The echo feature information also includes the location information of the centroid of the point cloud cluster of the sensing object. The step of determining the second sensing node for measuring the target object from the distributed sensing nodes for any target object among the sensing objects includes: For any two echo feature information from different second sensing nodes, the distance between the centroids of the point cloud cluster is determined based on the location information. In response to the distance being less than a distance threshold, the corresponding echo feature information is associated and bound, wherein the echo feature information that is associated and bound belongs to the same sensing object; Based on the echo feature information associated with the target object in the sensing objects, a second sensing node for measuring the target object is determined from the distributed sensing nodes.

6. The method according to claim 1, characterized in that, The step of updating the reference position based on the difference between the measured value of the echo characteristic parameter and the reference value of the echo characteristic parameter corresponding to any of the target nodes includes: An error function is determined based on the weight of the echo feature parameter corresponding to any of the target nodes, and the difference between the measured value of the echo feature parameter and the reference value of the echo feature parameter. Determine the Jacobian matrix based on the error function; The incremental equation is determined based on the position increment to be solved, the Jacobian matrix, the weight of the echo feature parameter corresponding to any of the target nodes, and the difference between the measured value of the echo feature parameter and the reference value of the echo feature parameter. Solve the incremental equation to obtain the incremental value of the position increment, and update the reference position based on the incremental value.

7. The method according to claim 6, characterized in that, Determining the target location of the target object based on the updated reference location includes: In response to the reference position update count being less than a count threshold, and / or in response to the increment value being greater than or equal to an increment value threshold, the step of obtaining the echo feature parameter reference value is returned to be executed based on the updated reference position until the reference position update count is greater than or equal to the count threshold, and / or the increment value is less than the increment value threshold.

8. The method according to claim 6, characterized in that, The echo characteristic parameters include at least one of range, azimuth, and elevation angles, and the method further includes: For any of the target nodes, azimuth weights and pitch weights are determined based on the distance measurement values ​​of the target nodes relative to the target object; wherein the azimuth weights and pitch weights are less than the distance weights.

9. The method according to claim 1, characterized in that, The method of calculating the position of the target object based on the echo characteristic parameter measurements of at least one target node to obtain the reference position of the target object includes: Based on the echo signal-to-noise ratio of the target node for the target object, two reference nodes are selected from the plurality of target nodes; Based on the echo characteristic parameter measurements of the target object by the reference node, the position of the target object is calculated to obtain the reference position of the target object.

10. A positioning device, characterized in that, Applied to the first sensing node, including: The receiving module is used to receive echo feature information measured by any second sensing node in the distributed sensing nodes for at least one sensing object; the echo feature information includes measured values ​​of echo feature parameters; The selection module is used to select multiple target nodes from the second sensing nodes that measure the target object for any target object among the sensing objects; A determination module is configured to: calculate the position of the target object based on the measured echo characteristic parameters of at least one target node to obtain a reference position of the target object; for any target node, obtain a reference value of the echo characteristic parameters based on the reference position; update the reference position based on the difference between the measured echo characteristic parameters and the reference value of the echo characteristic parameters corresponding to any target node; and determine the target position of the target object based on the updated reference position.

11. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-9.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-9.

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