Positioning method and device, electronic equipment and storage medium

Abstract

The invention provides a positioning method and device, electronic equipment and a storage medium, and the method comprises the steps: receiving echo feature information obtained through measurement of any second sensing node in distributed sensing nodes for at least one sensing object; for any target object in the sensing objects, selecting a plurality of target nodes from second sensing nodes for measuring the target object; and determining the target position of the target object according to the echo feature information of the plurality of target nodes for the target object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication sensing, and in particular to a positioning method and device, electronic equipment and a storage medium. BACKGROUND

[0002] Communication sensing fusion is one of the key technical directions of 5G evolution (5G-Advanced, 5G-A). On the basis of sharing of software and hardware resources, a part of time-frequency resources is used to transmit sensing signals, and the motion state of the target is sensed through signal processing of the echo signals. However, in the related art, the sensing accuracy of the sensing fusion scheme still needs to be improved. SUMMARY

[0003] The present application aims to at least partly solve one of the technical problems in the related art.

[0004] To this end, a first object of the present application is to provide a positioning method.

[0005] A second object of the present application is to provide a positioning device.

[0006] A third object of the present application is to provide electronic equipment.

[0007] A fourth object of the present application is to provide a computer-readable storage medium.

[0008] A fifth object of the present application is to provide a computer program product.

[0009] To achieve the above objects, a first aspect of the present application provides a positioning method applied to a first sensing node, comprising:

[0010] receiving echo feature information measured by any second sensing node in a distributed sensing node for at least one sensing object;

[0011] for any target object in the sensing object, selecting a plurality of target nodes in the second sensing node measuring the target object;

[0012] determining a target position of the target object according to echo feature information of the target object measured by the plurality of target nodes.

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

[0014] a receiving module configured to receive echo feature information measured by any second sensing node in a distributed sensing node for at least one sensing object;

[0015] A selecting module is configured to select a plurality of target nodes in a second sensing node of any target object in the sensing objects;

[0016] A determining module is configured to determine a target position of the target object according to echo feature information of the target object from the plurality of target nodes.

[0017] To achieve the above object, the third aspect of the present application provides an electronic device, comprising: a processor and a memory connected with the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the positioning method according to the first aspect of the present application.

[0018] To achieve the above object, the fourth aspect of the present application provides a computer readable storage medium, which stores computer execution instructions; when the computer execution instructions are executed by a processor, the computer execution instructions are used to implement the positioning method according to the first aspect of the present application.

[0019] To achieve the above object, the fifth aspect of the present application provides a computer program product, which comprises a computer program; when the computer program is executed by a processor, the computer program implements the positioning method according to the first aspect of the present application.

[0020] It should be understood that the contents described in this part are not intended to identify the key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above aspects and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A flowchart of a positioning method according to an embodiment of the present application;

[0023] Figure 2 A flowchart of a positioning method according to another embodiment of the present application;

[0024] Figure 3 A schematic diagram of a sphere and a circumference according to another embodiment of the present application;

[0025] Figure 4 A schematic diagram of an initial solution position according to another embodiment of the present application;

[0026] Figure 5 A structural schematic diagram of a positioning device according to an embodiment of the present application;

[0027] Figure 6 FIG. 1 is a block diagram of an electronic device according to an embodiment of the disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the disclosure are described below in detail with reference to examples shown in the drawings, in which the same or similar numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the disclosure, and cannot be understood as limiting the disclosure.

[0029] Unlike radar, sensor fusion can achieve seamless coverage through networking. In the overlapping coverage area of multi-station sensing, diversity gain can be obtained through multi-station cooperative fusion and deduplication, and sensing accuracy and other performances can be improved. In the related art, multi-station sensing fusion schemes include trajectory-level fusion schemes, point cloud-level fusion schemes, and symbol-level fusion schemes.

[0030] However, the trajectory-level and point cloud-level fusion schemes rely on single-station ranging and angle measurement to obtain trajectories or point clouds, and then perform multi-station fusion of trajectories / point clouds. This way relies on single-station angle measurement accuracy, and the position error caused by angle measurement error at the cell edge is large, resulting in poor position sensing accuracy. The symbol-level fusion scheme has high algorithm complexity and high requirements for computing power, and is difficult to apply in actual products.

[0031] To solve the above problems, the disclosure provides a positioning method, device, electronic device and storage medium. The positioning method, device, electronic device and storage medium of the embodiments of the disclosure are described below with reference to the drawings.

[0032] Figure 1 FIG. 1 is a flowchart of a positioning method according to an embodiment of the disclosure. The positioning method can be applied to a first sensing node, which can be a centralized sensing node (Master Baseband Unit, Master BBU).

[0033] As shown in FIG. 1, the positioning method includes the following steps: Figure 1

[0034] Step 101, receiving echo feature information measured by any second sensing node in the distributed sensing node for at least one sensing object.

[0035] The second sensing node can be any node in the distributed sensing node (Slave Baseband Unit, Slave BBU).

[0036] ​The second sensing node can also be a node in the distributed sensing nodes in the overlapping coverage area, that is, the second sensing node reporting the echo characteristic information can simultaneously sense the sensing object in the overlapping coverage area.

[0037] The echo characteristic information can include a distance measurement value, an angle measurement value, a speed measurement value, and an echo signal-to-clutter ratio (SCR), and the like.

[0038] In step 102, for any target object in the sensing object, a plurality of target nodes are selected from the second sensing nodes measuring the target object.

[0039] The target object can be any one of the at least one sensing object, and the target node can be a plurality of nodes in the second sensing nodes measuring the target object.

[0040] As an example, the plurality of target nodes can be selected from the second sensing nodes measuring the target object according to a set evaluation index such as SCR.

[0041] In step 103, a target position of the target object is determined according to the echo characteristic information of the target object from the plurality of target nodes.

[0042] The target position of the target object can be obtained by position joint calculation according to the echo characteristic information of the target object from the plurality of target nodes.

[0043] In this embodiment, the echo characteristic information measured by any second sensing node in the distributed sensing nodes for the at least one sensing object is received, for any target object in the sensing object, a plurality of target nodes are selected from the second sensing nodes measuring the target object, and a target position of the target object is determined according to the echo characteristic information of the target object from the plurality of target nodes. The feature-level sensing fusion of the echo characteristic information of the plurality of target nodes can avoid the problem of a large decrease in position accuracy caused by the single station angle measurement with an increase in coverage distance in the related art, improve the edge position sensing accuracy, and take into account the algorithm complexity and the transmission data amount of the sensing node interface.

[0044] This embodiment provides another positioning method, Figure 2 A flowchart of a positioning method provided by the embodiment of the present application is shown in FIG. 1. The positioning method can be applied to a first sensing node.

[0045] As shown in FIG. 1, the positioning method can include the following steps: Figure 2

[0046] ​Step 201, receiving echo feature information measured by any second sensing node of the distributed sensing nodes for at least one sensing object.

[0047] In a possible embodiment of the present application, the echo feature information includes at least one of the following: echo signal-to-clutter ratio; line-of-sight (LoS) path existence information; echo feature parameter measurement value.

[0048] The LoS path existence information is used to indicate whether there is a LoS path between the second sensing node and the sensing object; the echo feature parameter can include distance, angle (including azimuth and elevation).

[0049] Step 202, determining, from the distributed sensing nodes, a second sensing node that measures a target object in the sensing object.

[0050] In a possible embodiment of the present application, the echo feature information further includes position information of a point cloud cluster center of the sensing object.

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

[0052] In a possible embodiment of the present application, for any two echo feature information from different second sensing nodes, the distance between point cloud cluster centers is determined according to the position 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 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, a second sensing node that measures the target object is determined from the distributed sensing nodes.

[0053] The sensing objects can be matched according to the distances (such as Euclidean distances) of the multi-station (multiple second sensing nodes) cluster centers, and the matching with a distance less than a distance threshold is the same object, so that echo feature information belonging to the same target is screened out.

[0054] The sensing node source information is included in the echo feature information; for a target object in the sensing object, the second sensing node that measures the target object can be determined in the distributed sensing nodes based on the sensing node source information included in the associated and bound echo feature information corresponding to the target object, that is, the second sensing node that measures the target object is determined.

[0055] In step 203, according to the set sensing node selection condition, the echo feature information of the target object is selected based on the second sensing node for measuring the target object.

[0056] In the feature-level fusion, not all sensing feature data of the second sensing node covering the target object participates in the fusion. When one of the following situations occurs, the position calculation accuracy may be reduced: (1) the second sensing node for measuring the target object has a reduced SCR due to interference, obstruction, etc., resulting in unreliable ranging and angle measurement results; (2) in a dense urban area, there may be only a non-line-of-sight path (NLoS path) between some second sensing nodes participating in the fusion and the target object, and the ranging and angle measurement results based on the NLoS path may have large errors, and the error propagation caused by selecting these measurement results for joint position calculation may reduce the calculation accuracy; (3) when performing feature-level fusion, if the multiple second sensing nodes participating in the fusion do not satisfy a certain geometric distribution, the calculation accuracy may also be reduced.

[0057] To improve the position sensing accuracy, in one possible embodiment of the present application, the sensing node selection condition includes at least one of the following: the echo signal-to-jamming ratio is greater than or equal to a signal-to-jamming ratio threshold; there is a line-of-sight path between the distributed sensing node and the target object; the position difference between the predicted position of the target object and the calculated position of the target object is within a set difference range, wherein the predicted position is predicted based on the historical position of the target object, and the calculated position is calculated based on the echo feature parameter measurement value; the mean square error of the echo feature parameter measurement value for multiple measurements of the target object is greater than or equal to a mean square error threshold.

[0058] The Kalman filtering algorithm can be used to perform position prediction based on the historical position of the target object.

[0059] In one possible embodiment of the present application, among the second sensing nodes for measuring the target object, the nodes that satisfy the sensing node selection condition can be selected as the target nodes.

[0060] In one possible embodiment of the present application, according to the sensing node selection condition, among the second sensing nodes for measuring the target object, multiple candidate nodes that satisfy the sensing node selection condition are selected; 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 index is obtained; and based on the index value, the target node combination is selected from the at least one node combination to obtain the target node.

[0061] The quantity threshold value can be 2 or 3; the setting measurement index can include a geometric dilution of precision (GDOP); and the nodes included in the target node combination are target nodes, and the combination with the minimum 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 the setting quantity, that is, the setting quantity of nodes are included in each node combination. For example, when the quantity threshold value is 3, the setting quantity can be 3.

[0063] The GDOP of any node combination can be calculated according to the following formula:

[0064]

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

[0066]

[0067] The trace(P) represents a trace operation on the position error covariance matrix P; H represents a stacked observation matrix of all nodes in the node combination, R represents a measurement noise covariance matrix; h i represents an observation matrix of the i th node in the node combination, and (x i , y i , z i ) 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 is no candidate node satisfying the sensing node selection condition in the second sensing node for measuring 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, speed and other information of the target object, based on the Kalman filtering algorithm; if the number of candidate nodes is greater than 0 and less than the quantity threshold value, for example, the number of candidate nodes is 1, the position of the target object can be estimated according to the echo characteristic information of the candidate node, and the multi-station fusion calculation is not performed.

[0069] In step 204, the position of the target object is calculated based on the echo characteristic parameter measurement value of at least one target node, and the reference position of the target object is obtained.

[0070] In one possible embodiment of the present application, two reference nodes are selected from a plurality of target nodes according to the echo signal-to-clutter ratio of the target nodes for the target object; and the position of the target object is calculated based on the echo characteristic parameter measurement value of the reference nodes for the target object, and the reference position of the target object is obtained.

[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 angle measurements from the two reference nodes, including azimuth and elevation angles. 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] The initial solution position is obtained through geometric calculation based on the ranging and angle measurement data of two reference nodes, and the final position is obtained through least square iterative optimization based on multi-station data, which has the advantages of improving convergence speed, reducing iteration complexity and avoiding local convergence multi-solution problem.

[0077] In step 205, for any target node, the echo characteristic parameter reference value is obtained according to the reference position.

[0078] Wherein, the echo characteristic parameters include distance, azimuth angle and elevation angle, and the distance reference value, the azimuth angle reference value and the elevation angle reference value can be obtained according to the coordinates (x0, y0, z0) of the reference position and the coordinates (x n ,y n ,z n ) of the nth target node. The distance reference value is: The azimuth angle reference value is: The elevation angle reference value is:

[0079] In step 206, the reference position is updated according to the difference between the echo characteristic parameter measurement value and the echo characteristic parameter reference value corresponding to any target node.

[0080] In one possible embodiment of the present application, an error function is determined according to the echo characteristic parameter weight corresponding to any target node and the difference between the echo characteristic parameter measurement value and the echo characteristic parameter reference value, a Jacobian matrix is determined according to the error function, an increment equation is determined based on the position increment to be solved, the Jacobian matrix, the echo characteristic parameter weight corresponding to any target node and the difference between the echo characteristic parameter measurement value and the echo characteristic parameter reference value, the increment equation is solved to obtain an increment value of the position increment, and the reference position is updated based on the increment value.

[0081] Exemplarily, the calculation formula of the difference between the echo characteristic parameter measurement value and the echo characteristic parameter reference value corresponding to any target node is as follows:

[0082]

[0083] Wherein, d represents the difference between the distance measurement value and the distance reference value of the nth target node, that is, the distance residual, represents the difference between the azimuth angle measurement value and the azimuth angle reference value of the nth target node, that is, the azimuth angle residual, represents the difference between the elevation angle measurement value and the elevation angle reference value of the nth target node, that is, the elevation angle residual; d n represents the distance measurement value of the nth target node for the target object, n represents the azimuth angle measurement value of the nth target node for the target object, represents the pitch angle measurement value of the nth target node for the target object.

[0084] Exemplarily, the calculation formula of the error function is as follows:

[0085]

[0086] wherein E represents the error function; represents the azimuth angle weight, represents the pitch angle weight, and is greater than 0 and less than 0.5, and N represents the total number of target nodes.

[0087] The relationship between the position error caused by the angle E and the distance between the perceived object and the perceived node is that is, it is proportional to the distance, and therefore, in a possible embodiment of the present application, for any target node, the azimuth angle weight and the pitch angle weight are determined based on the distance measurement value of the target node for the target object; wherein the azimuth angle weight and the pitch angle weight are less than the distance weight.

[0088] wherein the overlapping coverage area is mostly located at the perception edge, and compared with the angle-based position solution, the distance-based position solution is more accurate, and therefore, the present application considers that the ranging result is mainly used to determine the target position, and the angle measurement data is only used to assist in adjusting and correcting the distance-based solution position, that is, when performing iterative solution, the distance weight is greater than the azimuth angle weight and the pitch angle weight.

[0089] Exemplarily, the calculation formula of the azimuth angle weight and the pitch angle weight can be as follows: that is, the farther the distance, the greater the error, the lower the referenceability of the angle measurement data, and the smaller the weight setting.

[0090] By setting the weight and mainly using multi-station ranging to perform position solution at the perception edge, the problem of magnification of angle measurement error at the perception edge can be avoided, and the perception accuracy can be improved.

[0091] wherein in order to obtain the optimal solution position, the error function E should be minimized, and at this time, the corresponding target position is optimal. Since it is complex to directly take the partial derivative of the error function E and set it to zero for solution, and it is difficult to obtain a closed-form solution, the present application proposes a suboptimal algorithm of iterative optimization, which is as follows:

[0092] The partial derivative of each error term in the error function E with respect to (x, y, z) is taken to form a Jacobian matrix J:

[0093]

[0094] Based on the echo characteristic parameter weight corresponding to the target node, and the difference between the echo characteristic parameter measurement value and the echo characteristic parameter reference value, an error vector e is determined:

[0095]

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

[0097] (J T WJ)[Δx,Δy,Δz] T =J T We

[0098] Wherein, W represents a weight diagonal matrix, Δx,Δy,Δz represents the position increment to be solved.

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

[0100] Step 207, determining the target position according to the updated reference position.

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

[0102] In a possible embodiment of the present application, in response to the reference position update times being less than the times threshold value, and / or, in response to the increment value being greater than or equal to the increment value threshold value, according to the updated reference position, the step of obtaining the echo characteristic parameter reference value is returned to be executed until the reference position update times are greater than or equal to the times threshold value, and / or, the increment value is less than the increment value threshold value.

[0103] Wherein, in response to the reference position update times being less than the times threshold value, and / or, in response to the increment value being greater than or equal to the increment value threshold value, step 205 is returned to obtain the echo characteristic parameter reference value according to the updated reference position, and step 206 is executed based on the recalculated echo characteristic parameter reference value. It should be noted that in the process of iteratively executing step 205 and step 206, the coordinates of the reference position are dynamically updated and changed.

[0104] Wherein, in response to the reference position update times being greater than or equal to the times threshold value, and / or, the increment value being less than the increment value threshold value, namely, in response to stopping iteration, the last updated reference position 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 characteristic information obtained by any second sensing node in the distributed sensing nodes for measuring at least one sensing object; For any target object among the sensing objects, multiple target nodes are selected in the second sensing node that measures the target object; The target location of the target object is determined based on the echo characteristic information of the multiple target nodes for the target object.

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 echo feature information includes measured values ​​of echo feature parameters. Determining the target location of the target object based on the echo feature information of the multiple target nodes for the target object includes: 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 is determined based on the updated reference location.

7. The method according to claim 6, 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.

8. The method according to claim 7, characterized in that, Determining the target location 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.

9. The method according to claim 7, 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.

10. The method according to claim 6, 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.

11. A positioning device, characterized in that, Applied to the first sensing node, including: 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; 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; 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.

12. 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 1-10.

13. 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 1-10.

14. 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 1-10.

Images