Multi-anchor-node cooperative UWB indoor positioning method, device, equipment and medium

Abstract

The invention discloses a multi-anchor-node cooperative UWB indoor positioning method, device and equipment and a medium, and the method comprises the steps: introducing a plurality of anchor nodes to jointly receive an ultra-bandwidth positioning pulse signal transmitted by a processing card, and constructing a signal receiving time difference matrix in combination with information such as a signal arrival timestamp, signal receiving strength and environment perception parameters; the non-line-of-sight propagation probability among the anchor nodes is calculated, signal link reliability is quantified, signal strength and anchor node type information are combined, confidence weights are given to time difference measurement of different anchor nodes, a weight matrix is formed, interference of the anchor nodes which are greatly influenced by shielding, reflection or attenuation in final positioning calculation can be effectively reduced, and positioning accuracy is improved. And the positioning robustness and accuracy are improved. Multi-source sensing data, non-line-of-sight probability modeling and a dynamic confidence weight adjustment mechanism are introduced, effective compensation and adaptive optimization of channel uncertainty and anchor node difference in the UWB indoor positioning system are realized, and the high-precision positioning capability of the system is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless positioning, and particularly relates to a multi-anchor node cooperative UWB indoor positioning method, device, equipment and medium. BACKGROUND

[0002] With the increasing demand for high-precision positioning in intelligent manufacturing, smart logistics and asset management scenarios, an indoor positioning technology based on Ultra Wide Band (UWB) has emerged. The UWB technology has high time resolution, low power consumption and strong anti-interference capability.

[0003] Traditional UWB positioning methods mostly use single anchor node or a small number of anchor nodes for configuration, and use ranging models such as Time of Arrival (TOA), Time Difference of Arrival (TDOA) or Angle of Arrival (AOA) for target positioning. The TDOA algorithm is commonly used in multi-receiving anchor node scenarios due to its advantage of not requiring clock synchronization. The target node transmits a UWB signal, which is received and recorded by multiple anchor nodes, the time difference is calculated, and a hyperboloid positioning model is constructed to estimate the target position.

[0004] However, in actual deployment, signal propagation is easily disturbed by non-line-of-sight (NLOS) effects such as shielding and reflection, resulting in a significant increase in positioning error. On the other hand, trajectory analysis of positioning results is also an important function of indoor positioning systems. Currently, simple trajectory smoothing, interpolation and other processing methods are commonly used to improve trajectory continuity and readability, but it is difficult to accurately identify changes in device motion state such as staying, moving or acceleration mutation in a noisy measurement environment.

[0005] Therefore, how to design a UWB indoor positioning method that can balance high-precision positioning and environmental interference is a technical problem to be solved. SUMMARY

[0006] Therefore, it is necessary to provide a multi-anchor node cooperative UWB indoor positioning method, device, equipment and medium that can balance high-precision positioning and environmental interference in view of the problems of the prior art.

[0007] In a first aspect, the embodiments of the present application provide a multi-anchor node cooperative UWB indoor positioning method, including the following steps: The system acquires reported data from multiple anchor nodes. The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node. The reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card. The anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters. The ultra-wideband positioning pulse signal includes the processing card identifier and transmission timestamp. Calculate the signal reception time difference between each pair of anchor nodes based on the arrival timestamps of each signal to obtain a reception time difference matrix; Calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix. The confidence weight of each anchor node in the reception time difference matrix is ​​determined based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, thus obtaining the weight matrix. The location information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix are processed based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card in a continuous time period.

[0008] Preferably, the step of calculating the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data to obtain the non-line-of-sight propagation probability matrix includes: Arrival time offset detection is performed based on the signal arrival timestamp to obtain time distribution characteristics; Based on the received signal strength, signal strength abrupt change detection is performed to obtain the strength attenuation characteristics; Environmental occlusion detection is performed based on the environmental perception parameters to obtain environmental occlusion features; Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, the non-line-of-sight propagation probability of each anchor node is calculated to obtain the non-line-of-sight propagation probability matrix.

[0009] Preferably, the confidence weight of each anchor node is represented by the following formula: (1); (2); (3); in, For anchor nodes Confidence weights For anchor node index; The confidence level of the received signal strength is the value of the mapped signal. The variance of ranging stability is negatively correlated with the weights; Weight for anchor node type; For anchor nodes The signal reception strength; For anchor nodes Anchor node type; For anchor nodes The non-line-of-sight propagation probability; For anchor nodes Historical ranging stability variance; This represents the total number of anchor nodes; This represents the expected value of the received signal strength. and It is a constant.

[0010] Preferably, the method further includes: Based on the multiple positioning values ​​of the processing card within a continuous time period, an initial trajectory of the processing card is constructed; the initial trajectory includes multiple positions of the processing card and the time points corresponding to the multiple positions; Based on spatial variation patterns and velocity smoothness constraints, anomaly detection is performed on the initial trajectory to obtain trajectory anomaly types; the trajectory anomaly types include normal, abrupt, continuous offset, and occlusion. Based on the filtering method corresponding to the trajectory anomaly type, the initial trajectory is fitted and denoised to obtain the smooth trajectory of the processing card; the filtering method includes Kalman filtering, extended Kalman filtering and particle filtering.

[0011] Preferably, the step of performing anomaly detection on the initial trajectory to obtain the trajectory anomaly type includes: If the initial trajectory exhibits a sudden change in velocity, then the trajectory anomaly type is determined to be the sudden change. If the fusion mean square error of the initial trajectory exceeds a preset threshold, then the trajectory anomaly type is determined to be occlusion. The speed is expressed by the following formula: (4); The fusion mean square error is expressed by the following formula: (5); in, The processing is stopped at a certain time point. speed, The processing is stopped at a certain time point. Location, The processing is stopped at a certain time point. Location; The processing is stopped at a certain time point. fusion mean square error For anchor nodes The relative time difference of signal reception; to be an anchor node a predicted time difference of signal reception.

[0012] Preferably, the method further comprises: in response to the acquired frequency modulation scheduling request of the anchor node, querying a frequency modulation map, and allocating optimal frequency bands and time slots for each of the anchor nodes based on low interference and low delay principles; The frequency modulation map is obtained by the following method: acquiring channel monitoring indicators of idle time slots of each of the anchor nodes; the channel monitoring indicators include received signal strength indicators, interference strength, and signal strength change rate of each frequency band; using a sliding time window to estimate the interference density of the channel monitoring indicators to obtain a frequency band-time slot historical interference heat map; obtaining a task density index according to the number of ranging times and the positioning request response frequency of each of the anchor nodes within a preset period; obtaining an activity density heat map of the processing card according to the historical smooth trajectory of the processing card within a preset period; obtaining the frequency modulation map according to the frequency band-time slot historical interference heat map, the task density index, and the processing card activity density heat map.

[0013] Preferably, the method further comprises: based on a preset processing card movement path, acquiring initial signal arrival time stamps of a plurality of anchor nodes; calculating mutual measurement time differences between each of the anchor nodes according to the initial signal arrival time stamps; calculating relative distances between each of the anchor nodes according to each of the mutual measurement time differences to obtain a local anchor node topology map; using a graph optimization algorithm to calculate the actual positions of the anchor nodes according to the local anchor node topology map, and correcting the position information of the anchor nodes according to the actual positions of each of the anchor nodes.

[0014] In a second aspect, the embodiments of the present application provide a multi-anchor node cooperative UWB indoor positioning device, comprising: a data acquisition module configured to acquire reported data of a plurality of anchor nodes; the reported data includes signal arrival time stamps, signal reception strengths, and environmental perception parameters of each of the anchor nodes; the reported data is recorded by the anchor nodes in response to receiving a same ultra-wideband positioning pulse signal sent by a processing card; the anchor nodes include heterogeneous anchor nodes configured to acquire local environmental parameters; the ultra-wideband positioning pulse signal includes a processing card identifier and a transmission time stamp; a time difference calculation module, configured to calculate a signal receiving time difference between each pair of the anchor nodes according to the signal arrival time stamps of each of the anchor nodes, and obtain a receiving time difference matrix; a non-line-of-sight propagation calculation module, configured to calculate a non-line-of-sight propagation probability of a signal link of each of the anchor nodes according to the reported data, and obtain a non-line-of-sight propagation probability matrix; a dynamic weight calculation module, configured to determine a confidence weight of each of the anchor nodes in the receiving time difference matrix according to the non-line-of-sight propagation probability matrix, the signal receiving intensity and an anchor node type of each of the anchor nodes, and obtain a weight matrix; a positioning module, configured to process the position information of the plurality of anchor nodes, the weight matrix and the receiving time difference matrix based on a dynamic weighted least squares positioning algorithm, and obtain positioning information of the processing card; the positioning information includes a plurality of positioning values of the processing card in continuous time.

[0015] In a third aspect, an electronic device is provided, and the electronic device includes: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method steps described above.

[0016] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is configured to execute the method steps described above.

[0017] Compared with the prior art, the application has the following beneficial effects: by introducing a plurality of anchor nodes to jointly receive the ultra-wideband positioning pulse signals transmitted by the processing card, and combining the signal arrival time stamp, signal reception strength and environmental perception parameters and other multi-dimensional information, a signal reception time difference matrix with better recognition is constructed. At the same time, part of the anchor nodes are heterogeneous anchor nodes with environmental perception capability, which can perceive local shielding, channel changes and other non-line-of-sight propagation factors, further improving the adaptability to environmental disturbances. By calculating the non-line-of-sight propagation probability between each anchor node, the reliability of the signal link is quantified, and combined with the signal strength and anchor node type information, the time difference measurement of different anchor nodes is given a differentiated confidence weight to form a weight matrix. The matrix can effectively reduce the interference degree of anchor nodes that are greatly affected by shielding, reflection or attenuation in the final positioning calculation, thereby improving the robustness and accuracy of the overall positioning result. Based on the weighted confidence information, a dynamic weighted least squares positioning algorithm is adopted, which dynamically adjusts the participation degree of each anchor node based on the fusion of multiple anchor node position information and reception time difference, to complete high-precision estimation of the position of the processing card. By introducing multi-source perception data, non-line-of-sight probability modeling and dynamic confidence weight adjustment mechanism, the effective compensation and adaptive optimization of channel uncertainty and anchor node difference in the UWB indoor positioning system are realized, which not only guarantees the high-precision positioning capability of the system, but also greatly improves the practicability and deployment robustness of the system in complex dynamic scenes such as industry, warehouse and medical treatment. BRIEF DESCRIPTION OF DRAWINGS

[0018] The exemplary embodiments of this application can be more fully understood with reference to the following drawings in which:

[0019] Figure 1 A flow chart of a multi-anchor node cooperative UWB indoor positioning method provided by an exemplary embodiment of the application is shown; Figure 2 A structural schematic diagram of a multi-anchor node cooperative UWB indoor positioning device provided by an exemplary embodiment of the application is shown; Figure 3 A schematic diagram of an electronic device provided by an exemplary embodiment of the application is shown; Figure 4 A schematic diagram of a computer readable medium provided by an exemplary embodiment of the application is shown. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms without being limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be thoroughly and completely understood, and so that the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0021] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for the purpose of description and cannot be understood as indicating or implying relative importance.

[0022] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as there is no conflict.

[0023] With reference to Figure 1 The present embodiment discloses a multi-anchor node cooperative UWB indoor positioning method, comprising the following steps: S101: Obtain the reported data of a plurality of anchor nodes; the reported data includes the signal arrival timestamp, signal reception strength and environmental perception parameters of each anchor node; the reported data is recorded by the anchor nodes in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card; the anchor nodes include heterogeneous anchor nodes for obtaining local environmental parameters; the ultra-wideband positioning pulse signal includes the processing card identifier and the transmission timestamp; Illustratively, the processing card can periodically or on-demand actively emit UWB positioning pulse signals. For example, the processing card can actively emit a positioning pulse signal once at a preset time interval. The UWB positioning pulse signal has a very short duration and a very wide spectrum bandwidth, and can contain processing card identification information and accurate transmission timestamp, as well as task priority coding, etc. The pulse signal is broadcast to multiple anchor nodes in the coverage area through the UWB channel, and is received by multiple anchor nodes deployed at various locations. Among them, the UWB positioning pulse signal transmission behavior of the processing card is controlled by TDMA (Time division multiple access) scheduling to prevent channel conflicts caused by multiple processing cards transmitting signals at the same time. Anchor nodes refer to UWB receiving devices fixed at known locations, which are used to receive signals emitted by processing cards and record relevant receiving information. Some anchor nodes are equipped with environmental perception modules such as temperature and humidity sensors, barometers, acoustic sensors, and low-cost visual perception modules, forming heterogeneous anchor nodes, which aims to obtain local environmental parameters to assist in positioning error modeling. After receiving the positioning pulse signal, the anchor node will record the accurate timestamp of the signal arrival, the received signal strength (RSS) value of the signal, and the local environmental perception parameters such as temperature, humidity, air pressure, etc. Optionally, the reported data is uploaded to the edge server or central positioning processing unit through the local network UWB backhaul link.

[0024] S102: Calculate the signal reception time difference between each pair of anchor nodes according to the arrival time stamp of each signal to obtain a reception time difference matrix; Illustratively, the processing card transmits a pulse signal once, and the receiving time of different anchor nodes has a slight difference, which can be obtained by comparing the reception time difference matrix through pairwise pairing. The matrix reflects the time delay difference on the signal propagation path, and is the key data for realizing TDOA positioning, which can effectively avoid the clock synchronization error between the processing card and the anchor node, thereby improving the practicality and accuracy of positioning.

[0025] S103: Calculate the non-line-of-sight propagation probability of the signal link of each anchor node according to the reported data to obtain a non-line-of-sight propagation probability matrix; Illustratively, it is judged whether there is a non-line-of-sight propagation phenomenon in each signal link. The signal link refers to the propagation path between the signal sent from the processing card to the signal received by a certain anchor node. Under the condition of non-line-of-sight propagation, the signal often reflects or is blocked by the wall, resulting in a longer propagation path, and thus introducing a receiving time delay and misjudgment. Specifically, based on the RSS value, the environmental perception parameter provided in the reported data, and the geometric relationship between the anchor nodes, the non-line-of-sight propagation probability of each signal link, that is, the NLOS probability, is calculated through a multi-mode feature modeling and fusion judgment model, and then a non-line-of-sight propagation probability matrix is constructed to measure the reliability of the data collected by each anchor node.

[0026] Illustratively, the signal receiving strength, the environmental perception parameter, and the geometric relationship of each anchor node are extracted from the reported data of each anchor node as link features; wherein the environmental perception parameter includes the type of the barrier, the temperature and humidity, and the electromagnetic interference index collected by the heterogeneous anchor nodes; the geometric relationship refers to the relative relationship of the known anchor nodes in the spatial distribution, including the relative distance and azimuth angle between any two anchor nodes, the angle distribution between the processing card and multiple anchor nodes, and the geometric dilution of precision (GDOP) calculated based on the anchor node distribution. Further, the above link features are input into a pre-trained statistical discrimination model or a machine learning model to obtain a probability value of the link being in a non-line-of-sight state, the probability value being a continuous value between 0 and 1, used to represent the possibility of the link being blocked or affected by multipath effect. Further, the latest collected link features can be updated and retrained based on a sliding time window to adapt to environmental changes and improve the accuracy of non-line-of-sight probability estimation. Thus, the non-line-of-sight propagation probability matrix can more truly reflect the signal propagation state in a complex indoor environment, providing a basis for the calculation of the subsequent weight matrix. The statistical discrimination model or the machine learning model establishes a mapping relationship between the features and the propagation state, and the training samples of the model come from the labeled LOS / NLOS measurement data in a typical scene. As the operating environment changes, the latest collected link features can be adjusted in parameters based on a sliding window to maintain the accuracy of the discrimination.

[0027] S104: determining the confidence weight of each anchor node in the receiving time difference matrix according to the non-line-of-sight propagation probability matrix, the signal receiving strength, and the anchor node type of each anchor node, and obtaining a weight matrix; Illustratively, the signal strength of the anchor node, whether it is a heterogeneous anchor node, the NLOS probability, and the historical ranging stability are combined for weight distribution. Specifically, the higher the signal receiving strength, the lower the non-line-of-sight probability, and the smaller the historical ranging variance, the greater the weight of the anchor node; and the data from the heterogeneous anchor node is given a structural correction weight because it has more environmental redundant information. Optionally, a weighting mechanism based on Bayesian or empirical distribution can be introduced in the weight calculation process to further adjust the influence of each anchor node in the positioning estimation.

[0028] S105: Process the position information of the plurality of anchor nodes, the weight matrix and the time difference matrix based on a dynamic weighted least square positioning algorithm to obtain the positioning information of the processing card; the positioning information includes a plurality of positioning values of the processing card in continuous time.

[0029] Under the premise that the three-dimensional space coordinates of the anchor nodes are known, the spatial position of the processing card is estimated based on the time difference matrix and the corresponding weight matrix by using a dynamic weighted least square positioning algorithm. The least square algorithm solves a set of nonlinear equations about the unknown coordinates of the processing card and the time difference of each anchor node by constructing a set of nonlinear equations about the unknown coordinates of the processing card and the time difference of each anchor node, and minimizing the sum of squares of residuals as the target. Further, a dynamic weighting mechanism is introduced, that is, the values in the weight matrix are used as the adjustment factor of each residual term, and different importance is given to the error term, so as to suppress unreliable data and enhance high-confidence data. The weight will be updated in real time with each positioning calculation, adapting to the change of data quality in different environments, while ensuring small calculation overhead, significantly improving the final positioning accuracy and environmental adaptability.

[0030] In the multi-anchor node cooperative UWB indoor positioning method disclosed in the above embodiment, the same ultra-wideband positioning pulse signal is received by multiple anchor nodes in cooperation, and a time difference matrix is constructed based on the time difference received by each anchor node. Further, the non-line-of-sight propagation probability and the corresponding confidence weight are calculated by combining the signal strength, anchor node type and environmental perception parameters, and the weight matrix is introduced in the dynamic weighted least square algorithm for position solution. Through multi-source information fusion, the recognition ability of the non-line-of-sight channel is improved, so that the influence of abnormal links on the positioning result is weakened; by introducing dynamic confidence weight, the dynamic adjustment of the contribution of multiple anchor nodes is realized, and the adaptability of the system to complex environmental changes is improved; through the weight-assisted least square solution model, higher precision and stronger robustness of the processing card position estimation are realized, which can effectively resist the influence of multi-path interference, shielding and signal fluctuation and other factors on the positioning accuracy in actual complex environment. Through fine-grained modeling of the anchor node signal receiving process and weight adjustment, the fault tolerance of abnormal links is enhanced, so that the positioning system has higher environmental adaptability and real-time performance. The ability of heterogeneous anchor nodes to perceive the local environment is utilized to further enrich the channel state judgment dimension, providing a reliable basis for weight calculation and algorithm optimization. While ensuring high-precision positioning, the deployment complexity is reduced and the system robustness is improved.

[0031] In the preferred embodiment, the non-line-of-sight propagation probability of the signal link of each anchor node is calculated according to the reported data to obtain a non-line-of-sight propagation probability matrix, including: S201: Perform time of arrival offset detection according to the time of arrival timestamp to obtain time distribution characteristics; In the NLOS state, the signal usually detours, which will cause the TDOA to be larger. Comparing the signal arrival time stamp reported by each anchor node with the propagation delay under the theoretical direct view path, if there is a significant deviation or extended distribution, it is considered as the time characteristic performance of non-line-of-sight propagation, which is extracted as the time distribution feature , wherein, is the propagation time curve under the theoretical direct view path, is a historical data residual model, representing the deviation degree of the expected distribution of TDOA; S202: Signal strength mutation detection is performed according to the signal receiving strength to obtain the strength attenuation feature; Under the LOS state of UWB, the RSS and the distance show stable attenuation. The RSS is compared with the estimated strength threshold under the line of sight to identify the mutation or nonlinear attenuation behavior to form the strength attenuation feature of the anchor node link. If the RSS is much lower than the normal attenuation curve of the TDOA ranging value, such as more than 10 dB lower than the expected value, it is considered that there may be an obstruction, and the strength attenuation feature is obtained , wherein, is the expected curve of normal attenuation.

[0032] S203: Environmental obstruction detection is performed according to the environmental perception parameters to obtain the environmental obstruction feature; NLOS is common in obstructive environments such as walls, water vapor, and metal. Illustratively, the heterogeneous anchor nodes perceive temperature and humidity, pressure mutation, or light abnormalities, etc. as obstruction signals according to the environmental perception parameters to obtain the environmental obstruction feature , wherein, and are normal baselines, is a temperature and humidity mutation variable, is a light abnormality variable.

[0033] S204: The non-line-of-sight propagation probability of each anchor node is calculated according to the time distribution feature, the strength attenuation feature, and the environmental obstruction feature to obtain a non-line-of-sight propagation probability matrix.

[0034] The time distribution feature, the strength attenuation feature, and the environmental obstruction feature are fused , and then Bayesian inference, fuzzy logic discrimination, or a lightweight neural network model is used to calculate the non-line-of-sight propagation probability of each anchor node signal link to obtain the corresponding non-line-of-sight propagation probability matrix. Illustratively, a multi-feature joint evaluation model based on a multilayer perceptron (MLP) is constructed to calculate the NLOS probability of each anchor node based on the observation results . The matrix will serve as the basis for subsequent positioning weight adjustment and trajectory confidence update.

[0035] In a preferred embodiment, the confidence weight of each anchor node in the received time difference matrix is determined according to the non-line-of-sight propagation probability matrix, the signal reception strength and the anchor node type, to obtain a weight matrix, comprising: The confidence weight of each anchor node is represented by the following formula: (1); (2); (3); wherein, is the confidence weight of the anchor node , is the anchor node index; is the confidence degree of the mapped signal reception strength; is the negative correlation of the ranging stability variance and the weight; is the anchor node type weight; is the signal reception strength of the anchor node ; is the anchor node type of the anchor node ; is the non-line-of-sight propagation probability of the anchor node ; is the historical ranging stability variance of the anchor node ; is the total number of anchor nodes; is the expected value of the signal reception strength; and are constants.

[0036] In a preferred embodiment, the method further comprises: S301: constructing an initial trajectory of the processing card according to the plurality of positioning values of the processing card in the continuous time; the initial trajectory comprises a plurality of positions of the processing card and time points corresponding to the plurality of positions; Illustratively, the spatial positioning values of the processing card at each time point in the continuous time period are extracted and constructed into an initial trajectory. Each trajectory data contains a clear timestamp and three-dimensional spatial coordinates, reflecting the moving path and rhythm of the processing card in the time period. The positioning values recorded by the UWB indoor positioning system are constructed into the initial trajectory of the processing card in time sequence. The trajectory is in the form of , wherein represents the two-dimensional positioning coordinates of the th time point, represents the corresponding timestamp thereof.

[0037] S302: performing abnormality detection on the initial trajectory based on the spatial variation rule and the speed smoothness constraint to obtain a trajectory abnormality type; the trajectory abnormality type comprises normal, mutation, continuous deviation and occlusion.​ The initial trajectory is applied to a plurality of statistical and pattern recognition means for trajectory anomaly detection to identify non-physical changes or unreasonable jumps in the trajectory. The spatial change rule means that the spatial position determined by physical limitations during actual movement of the processing card should show a coherent and slowly changing trend and should not have large jumps or non-continuous turns. The speed smoothness constraint means that the speed change of the processing card in a short time should be relatively smooth. If the speed difference derived from the positioning points of two adjacent time points is too large, or the instantaneous speed is far beyond the upper limit of the allowed movement of the system, it can be preliminarily judged that there is data anomaly or positioning drift at this place. Specifically, parameters such as speed change rate, path continuity and spatial position drift amplitude are analyzed to classify trajectory anomaly types, wherein ordinary indicates that the trajectory basically conforms to the continuous motion rule and does not need to be corrected; the mutation type indicates that the position difference before and after a certain time point is significant and does not conform to the physical motion ability of the processing card; the continuous offset indicates that the trajectory continuously deviates from the real channel of the processing card and may be caused by shielding or NLOS error; the shielding indicates that there are no effective positioning points or signal attenuation in multiple consecutive time periods, and it is difficult to restore the path. Optionally, the systematic deviation accumulation of the trajectory and the auxiliary inertial trajectory system such as IMU (Inertial Measurement Unit) is used to judge the drift trend by the angle or offset degree of the trajectory fitting and the IMU reference trajectory.

[0038] S303: fitting and denoising the initial trajectory according to the filtering mode corresponding to the trajectory anomaly type to obtain a smooth trajectory of the processing card; the filtering mode includes Kalman filtering, extended Kalman filtering and particle filtering.

[0039] Illustratively, for the ordinary and mutation types, Kalman filtering is adopted to smooth high-frequency jumps through a linear state space model, and for the continuous offset and shielding types, extended Kalman filtering (EKF) or particle filtering (PF) is adopted to realize trajectory reconstruction under multi-source constraints on the basis of introducing nonlinear modeling and Bayesian estimation. The reconstructed smooth trajectory effectively suppresses system noise and non-line-of-sight interference while retaining the true movement trend, providing reliable input for subsequent production behavior modeling, process state recognition and process anomaly diagnosis.

[0040] Specifically, Kalman filtering is a recursive estimation algorithm for linear systems, which realizes optimal estimation of the state by introducing a system state transition model and a measurement model, combining the current observation value and the historical state prediction. In the positioning trajectory, if the error is randomly Gaussian distributed and changes smoothly, Kalman filtering can effectively smooth the positioning points, reduce random fluctuations and is suitable for processing of trajectory anomaly of the mutation type. Specifically, the position value at the current time is predicted according to the state transition equation and covariance matrix . Further, the predicted value is corrected in combination with the current actual measurement value , , wherein, represents a current position estimation of the processing card, is an observation value, and A, B, H, Q, R are model parameters respectively.

[0041] Extended Kalman filter is a nonlinear extension of Kalman filter, which is used to deal with the situation that the system state transition and observation model do not meet the linear assumption. Due to the introduction of nonlinear disturbance caused by occlusion or reflection, the trend of trajectory change often presents a nonlinear mode, at which time the ordinary Kalman filter cannot be modeled and the EKF needs to be used to realize stronger state correction capability. Specifically, the EKF first linearizes the nonlinear state transition function and the observation function , and uses the Jacobian matrix of the first-order Taylor expansion to replace the original model, so as to convert it into a linear filtering problem. Specifically, in the prediction stage , ; in the update stage , , wherein and are the first-order Jacobian matrices of the state function and the observation function respectively, which are dynamically constructed at each step.

[0042] Particle filter is a Bayesian filter based on Monte Carlo method, which is suitable for any nonlinear, non-Gaussian system. By generating a large number of possible state samples (i.e. particles) to simulate the probability distribution of the system state, and updating the particle weight through importance sampling and resampling strategy, the optimal state is estimated. When there are high-noise non-Gaussian disturbances such as severe occlusion and continuous deviation in the trajectory, the accuracy of Kalman filter decreases, while particle filter can realize robust trajectory reconstruction in complex scenes through non-parametric modeling. Specifically, initialize the particle set: generate possible states , and set the initial weight . According to the system state transition model, each particle is predicted, and combined with the measurement value , the weight of each particle is calculated through the observation model , in order to prevent particle degradation, the particle group is resampled according to the weight, and the weighted average of the current all particle states is obtained to get the estimated position.

[0043] In the preferred embodiment, the initial trajectory is subjected to anomaly detection to obtain the trajectory anomaly type, including: S401: if the initial trajectory has a speed jump, it is determined that the trajectory anomaly type is the mutation; S402: If the fusion mean square error of the initial trajectory exceeds the preset threshold, the trajectory anomaly type is determined to be occlusion. The speed is expressed by the following formula: (4); The fusion mean square error is expressed by the following formula: (5); in, To process the time point speed, To process the time point Location, To process the time point Location; To process the time point fusion mean square error For anchor nodes The relative time difference of signal reception; For anchor nodes The expected time difference for signal reception.

[0044] Indicative, if If the speed exceeds the set speed threshold, it indicates that the positioning is unreliable and the signal may be blocked. Alternatively, if the weights in the weight matrix are concentrated in a very small number of anchor nodes or the weight matrix is ​​extremely sparse, it is determined to be channel blockage.

[0045] In a preferred embodiment, the method further includes: S501: In response to the frequency modulation scheduling request obtained from the anchor node, query the frequency modulation map and allocate the optimal frequency band and time slot to each anchor node based on the principles of low interference and low latency. The frequency modulation spectrum was obtained using the following method: S5011: Obtain channel monitoring indicators for each anchor node during idle time slots; channel monitoring indicators include received signal strength indication, interference intensity, and signal strength change rate for each frequency band; S5012: Using a sliding time window, interference density is estimated for channel monitoring indicators to obtain a frequency band-time slot historical interference heatmap. S5013: Based on the number of ranging operations and the frequency of positioning request responses of each anchor node within a preset period, a task density index is obtained; S5014: Obtain the activity density heat map of the processing card based on the historical smooth trajectory of the processing card within a preset period; S5015: Based on the historical interference heatmap of the frequency band-time slot, the task density index, and the activity density heatmap of the processing card, the frequency modulation spectrum is obtained.

[0046] Illustratively, each anchor node actively carries out channel listening operation in its idle time slot to obtain channel listening indicators within the coverage frequency range. Among them, the received signal strength indication (RSSI) is used to evaluate whether there is a high-power interference source in a certain frequency band; the interference power reflects the electromagnetic interference degree caused by non-system transmission sources in the frequency band; and the signal strength change rate is used to judge the interference volatility and stability of a certain frequency band.

[0047] Further, in order to dynamically evaluate the interference situation of the frequency band and the time slot in the historical dimension, the sliding time window mechanism is used to aggregate and statistically analyze the listening indicators. Specifically, in the sliding window, the interference density estimation value of each frequency band in different time slots is continuously accumulated to form an interference heat map of frequency band-time slot two-dimensional mapping. This map can intuitively reflect the interference intensity change trend of a certain frequency band in a certain time period, and further provide historical basis for interference avoidance.

[0048] Further, the task density indicators of each anchor node in a preset period are counted. The task density is the sum of the ranging times and the frequency of participating in positioning requests of a certain anchor node in the period, which reflects the load of the anchor node in the network and is one of the factors that need to be considered in frequency resource allocation.

[0049] In order to further improve the foresight and accuracy of spectrum scheduling, the processing card activity density analysis is carried out combined with the historical trajectory data of the processing card. With the trajectory sequence generated by the trajectory smoothing module, the processing card distribution frequency of different regions in different periods can be counted in the spatial dimension, and a processing card activity density heat map can be constructed to predict the possible positioning demand peak in some regions in the near future.

[0050] Optionally, based on the interference heat map, the task density indicator and the processing card activity density heat map, a global frequency adjustment map is constructed by fusion , wherein, is the frequency band in the time interference density, is the task density of the anchor node in the time , and is the processing card heat of the frequency band in the time corresponding region. The map provides each anchor node with information about when and in which frequency band the communication interference is the smallest and the response is the most timely.

[0051] When an anchor node needs to participate in the positioning task and initiate the frequency modulation scheduling request, the frequency modulation map is queried, and the selection of the frequency band-time slot pair is based on two key scheduling principles: one is low interference priority, that is, the node is preferentially selected for the combination of the frequency band and the time slot with lower interference; the second is low delay response priority, that is, the spectrum resource with smaller task density and shorter response delay is preferentially selected. On the basis of meeting the two priorities, the current optimal frequency band and time slot pair are allocated to the requesting node to complete the spectrum scheduling task.

[0052] The mechanism realizes dynamic spectrum management based on data perception without changing the hardware structure of the anchor node, effectively improves the cooperative communication efficiency between the anchor nodes, avoids the redundant occupation of the spectrum resource and the overlapping of the interference, and thus enhances the stability and robustness of the entire UWB indoor positioning system.

[0053] In the preferred embodiment, the method further comprises: S601: Based on the preset processing card movement path, the preset signal arrival time stamps of the plurality of anchor nodes are obtained; Illustratively, when deployed, the position information of part of the anchor nodes is usually statically calibrated, but due to the installation error of the anchor nodes, environmental disturbance or part of the anchor nodes being mobile nodes, the actual positions thereof can be offset from the preset values, resulting in a decrease in the overall positioning accuracy. Specifically, based on the preset processing card movement path, the processing card is controlled to move on a specified area or track. Since the processing card periodically broadcasts the UWB signal, the plurality of anchor nodes distributed in the area can simultaneously receive the signal and record the arrival time stamps of the signal.

[0054] S602: The mutual measurement time difference between each two anchor nodes is calculated according to the signal arrival time stamps; The signal arrival time stamps recorded between the plurality of anchor nodes are compared in pairs, and the TDOA of any two anchor nodes to the same signal is calculated.

[0055] S603: The relative distance between each two anchor nodes is calculated according to the mutual measurement time difference, and a local anchor node topology is obtained: According to the fact that the signal propagation speed in space is constant, that is, the speed of light, the time difference can be multiplied by the propagation speed to convert into the spatial distance difference. By collecting the mutual measurement time difference data between the plurality of anchor nodes, a local topology representing the relative geometric relationship between the anchor nodes can be constructed. The edges in the topology represent the relative distances between the anchor nodes, and the nodes represent the anchor nodes.

[0056] S604: The actual positions of the anchor nodes are calculated according to the local anchor node topology by using a graph optimization algorithm, and the known anchor node position information is corrected according to the actual positions of the anchor nodes.

[0057] Specifically, a non-linear least squares optimization algorithm such as the Levenberg-Marquardt algorithm or a pose graph optimization method can be used, and the relative distance constraints between the anchor nodes are used as the cost function of the graph edge, and the actual spatial coordinates of each anchor node are calculated through iterative optimization. The actual position of the anchor node in the optimization result is compared with the preset calibration position, and the position information of the anchor node deviating more than a set threshold is corrected, so as to construct a more accurate anchor node global coordinate graph. The self-correcting mechanism of the anchor node position information is realized, and the positioning stability and maintenance convenience are significantly improved without external manual recalibration.

[0058] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or stages or steps or stages in other steps.

[0059] Based on the same inventive concept, the embodiments of the present application also provide a multi-anchor node cooperative UWB indoor positioning device for implementing the above-mentioned multi-anchor node cooperative UWB indoor positioning method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more multi-anchor node cooperative UWB indoor positioning device embodiments provided below can refer to the limitations of the multi-anchor node cooperative UWB indoor positioning method described above, which will not be repeated here.

[0060] Referring to Figure 2 The embodiment discloses a multi-anchor node cooperative UWB indoor positioning device, comprising: A data acquisition module 201 is configured to acquire reported data of a plurality of anchor nodes; the reported data comprises signal arrival time stamps, signal reception strengths and environment perception parameters of the anchor nodes; the reported data is recorded by the anchor nodes in response to receiving a same ultra-wideband positioning pulse signal sent by a processing card; the anchor nodes comprise heterogeneous anchor nodes configured to acquire local environment parameters; the ultra-wideband positioning pulse signal comprises a processing card identifier and a transmission time stamp; A time difference calculation module 202 is configured to calculate signal reception time differences between each two anchor nodes according to the signal arrival time stamps, and obtain a reception time difference matrix; The non-line-of-sight propagation calculation module 203 is configured to calculate a non-line-of-sight propagation probability of a signal link of each anchor node according to the reported data, and obtain a non-line-of-sight propagation probability matrix; The dynamic weight calculation module 204 is configured to determine a confidence weight of each anchor node in the received time difference matrix according to the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, and obtain a weight matrix; The positioning module 205 is configured to process the position information of the plurality of anchor nodes, the weight matrix, and the received time difference matrix based on a dynamic weighted least squares positioning algorithm, and obtain positioning information of the processing card; the positioning information includes a plurality of positioning values of the processing card in continuous time.

[0061] In the preferred embodiment, the non-line-of-sight propagation calculation module 203 is further configured to: perform time of arrival offset detection according to the signal time of arrival stamps, and obtain time distribution characteristics; perform signal strength mutation detection according to the signal reception strength, and obtain strength attenuation characteristics; perform environment occlusion detection according to the environment perception parameters, and obtain environment occlusion characteristics; calculate the non-line-of-sight propagation probability of each anchor node according to the time distribution characteristics, the strength attenuation characteristics, and the environment occlusion characteristics, and obtain the non-line-of-sight propagation probability matrix.

[0062] In the preferred embodiment, the trajectory smoothing module is further configured to: construct an initial trajectory of the processing card according to a plurality of positioning values corresponding to continuous time of the processing card; the initial trajectory includes a plurality of positions of the processing card and time points corresponding to the plurality of positions; perform abnormality detection on the initial trajectory based on spatial variation rules and speed smoothness constraints, and obtain a trajectory abnormality type; the trajectory abnormality type includes normal, mutation, continuous offset, and occlusion; perform fitting and denoising processing on the initial trajectory according to a filtering method corresponding to the trajectory abnormality type, and obtain a smoothed trajectory of the processing card; the filtering method includes Kalman filtering, extended Kalman filtering, and particle filtering.

[0063] In the preferred embodiment, the frequency modulation scheduling module is further configured to: in response to the obtained frequency modulation scheduling request of the anchor node, query a frequency modulation spectrum, and allocate optimal frequency bands and time slots for each anchor node based on low interference and low delay principles; The frequency modulation spectrum is obtained by the following method: obtain channel monitoring indicators performed by idle time slots of each anchor node; the channel monitoring indicators include received signal strength indication, interference strength, and signal strength change rate of each frequency band; perform interference density estimation on the channel monitoring indicators by using a sliding time window, and obtain a frequency band-time slot historical interference heat map; According to the number of ranging times and the positioning request response frequency of each anchor node in a preset period, a task density index is obtained; According to the historical smooth trajectory of each processing card in a preset period, a processing card activity density heat map is obtained; According to the frequency band-time slot historical interference heat map, the task density index and the processing card activity density heat map, a frequency modulation spectrum is obtained.

[0064] In the preferred embodiment, it also includes a calibration module for: Based on the preset processing card moving path, the initial signal arrival time stamp of the plurality of anchor nodes is obtained; According to the initial signal arrival time stamp, the mutual measurement time difference between each two anchor nodes is calculated; According to each mutual measurement time difference, the relative distance between each two anchor nodes is calculated, and a local anchor node topology map is obtained; The actual position of the anchor node is calculated by using a graph optimization algorithm according to the local anchor node topology map, and the position information of the anchor node is corrected according to the actual position of each anchor node.

[0065] The embodiments of the application also provide an electronic device corresponding to the method provided by the foregoing embodiments. The electronic device can be an electronic device for a server, such as a server, including a stand-alone server and a distributed server cluster, etc., to execute the above method. The electronic device can also be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to execute the above method.

[0066] Please refer to Figure 3 , which shows a schematic diagram of an electronic device provided by some embodiments of the application. As Figure 3 shown, the electronic device 30 includes a processor 300, a memory 301, a bus 302 and a communication interface 303, the processor 300, the communication interface 303 and the memory 301 are connected through the bus 302; the memory 301 stores a computer program executable on the processor 300, and the processor 300 executes the computer program to perform the foregoing method of the application.

[0067] Among them, the memory 301 can contain a high-speed random access memory (RAM: Random Access Memory), and can also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which can be wired or wireless), and the Internet, a wide area network, a local network, a metropolitan area network, etc. can be used.

[0068] The bus 302 can be an ISA bus, a PCI bus, an EISA bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. The memory 301 is configured to store programs, and the processor 300 executes the programs after receiving execution instructions. The method disclosed in any of the embodiments of the present application can be applied to the processor 300 or implemented by the processor 300.

[0069] The processor 300 can be an integrated circuit chip having a processing capability of signals. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 300 or an instruction in the form of software. The processor 300 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), and the like; can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-program gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Each method, step and logic block diagram disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, or other mature storage media in the art. The storage medium is located in the memory 301, and the processor 300 reads the information in the memory 301 and combines the hardware to complete the steps of the above method.

[0070] The electronic device provided by the embodiments of the present application and the method provided by the embodiments of the present application have the same beneficial effects as the method they adopt, run or implement.

[0071] The embodiments of the present application also provide a computer readable medium corresponding to the method provided by the preceding embodiments. Please refer to Figure 4 The computer readable storage medium shown in the figure is an optical disc 40, and a computer program (i.e. program product) is stored on the optical disc 40. When the computer program is run by a processor, the preceding method is executed.

[0072] It should be noted that the examples of the computer-readable storage medium can also include, but are not limited to, a phase change memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), other types of random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, or other optical, magnetic storage medium, and the like, which are not listed one by one here.

[0073] The computer-readable storage medium provided in the above embodiments of the present application has the same inventive concept as the method provided in the embodiments of the present application, and has the same beneficial effects as the method adopted, run or implemented by the application stored therein.

[0074] It should be noted that the flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the system, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code containing one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0076] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, and for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interface, device or unit, and can be electrical, mechanical or other forms.

[0077] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0078] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0079] The functions, if realized in the form of software functional units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application can be embodied in the form of software products, and the computer software products are stored in a storage medium, including a number of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various program code storage media.

[0080] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A UWB indoor positioning method with multi-anchor node collaboration, characterized in that, Includes the following steps: The system acquires reported data from multiple anchor nodes. The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node. The reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card. The anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters. The ultra-wideband positioning pulse signal includes the processing card identifier and transmission timestamp. Calculate the signal reception time difference between each pair of anchor nodes based on the arrival timestamps of each signal to obtain a reception time difference matrix; Calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix. The confidence weight of each anchor node in the reception time difference matrix is ​​determined based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, thus obtaining the weight matrix. The location information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix are processed based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card in a continuous time period.

2. The method according to claim 1, characterized in that, The step of calculating the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtaining the non-line-of-sight propagation probability matrix, includes: Arrival time offset detection is performed based on the signal arrival timestamp to obtain time distribution characteristics; Based on the received signal strength, signal strength abrupt change detection is performed to obtain the strength attenuation characteristics; Environmental occlusion detection is performed based on the environmental perception parameters to obtain environmental occlusion features; Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, the non-line-of-sight propagation probability of each anchor node is calculated to obtain the non-line-of-sight propagation probability matrix.

3. The method according to claim 1, characterized in that, The confidence weight of each anchor node is represented by the following formula: （1）； （2）； （3）； in, For anchor nodes Confidence weights For anchor node index; The confidence level of the received signal strength is the value of the mapped signal. The variance of ranging stability is negatively correlated with the weights; Weight for anchor node type; For anchor nodes The signal reception strength; For anchor nodes Anchor node type; For anchor nodes The non-line-of-sight propagation probability; For anchor nodes Historical ranging stability variance; This represents the total number of anchor nodes; This represents the expected value of the received signal strength. and It is a constant.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Based on the multiple positioning values ​​of the processing card within a continuous time period, an initial trajectory of the processing card is constructed; the initial trajectory includes multiple positions of the processing card and the time points corresponding to the multiple positions; Based on spatial variation patterns and velocity smoothness constraints, anomaly detection is performed on the initial trajectory to obtain trajectory anomaly types; the trajectory anomaly types include normal, abrupt, continuous offset, and occlusion. Based on the filtering method corresponding to the trajectory anomaly type, the initial trajectory is fitted and denoised to obtain the smooth trajectory of the processing card; the filtering method includes Kalman filtering, extended Kalman filtering and particle filtering.

5. The method according to claim 4, characterized in that, The anomaly detection of the initial trajectory to obtain trajectory anomaly types includes: If the initial trajectory exhibits a sudden change in velocity, then the trajectory anomaly type is determined to be the sudden change. If the fusion mean square error of the initial trajectory exceeds a preset threshold, then the trajectory anomaly type is determined to be occlusion. The speed is expressed by the following formula: （4）； The fusion mean square error is expressed by the following formula: （5）； in, The processing is stuck at the time point speed, The processing is stuck at the time point Location, The processing is stuck at the time point Location; The processing is stuck at the time point fusion mean square error For anchor nodes The relative time difference of signal reception; For anchor nodes The expected time difference for signal reception.

6. The method according to claim 4, characterized in that, The method further includes: In response to the frequency modulation scheduling request obtained from the anchor node, the frequency modulation spectrum is queried, and the optimal frequency band and time slot are allocated to each anchor node based on the principles of low interference and low latency. The frequency modulation spectrum was obtained through the following method: Obtain channel monitoring metrics for each anchor node during its idle time slots; the channel monitoring metrics include received signal strength indication, interference intensity, and signal strength change rate for each frequency band; By using a sliding time window to estimate the interference density of the channel monitoring indicators, a historical interference heatmap of the frequency band and time slot is obtained. The task density index is obtained based on the number of ranging operations and the frequency of positioning request responses of each anchor node within a preset period. Based on the smoothed trajectory of the processing card within a preset period, an activity density heatmap of the processing card is obtained; The frequency modulation spectrum is obtained based on the historical interference heatmap of the frequency band-time slot, the task density index, and the activity density heatmap of the processing card.

7. The method according to claim 4, characterized in that, The method further includes: Based on the preset processing card movement path, the initial signal arrival timestamps of multiple anchor nodes are obtained; Calculate the mutual measurement time difference between each pair of anchor nodes based on the initial signal arrival timestamp; Calculate the relative distance between each pair of anchor nodes based on the mutual measurement time difference to obtain a local anchor node topology diagram; The actual position of the anchor node is calculated based on the local anchor node topology using a graph optimization algorithm, and the position information of the anchor node is corrected based on the actual position of each anchor node.

8. A UWB indoor positioning device with multi-anchor node collaboration, characterized in that, include: The data acquisition module is used to acquire the reported data from multiple anchor nodes; The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node; the reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card; the anchor node includes heterogeneous anchor nodes for acquiring local environmental parameters; the ultra-wideband positioning pulse signal includes the processing card identifier and the transmission timestamp; The time difference calculation module is used to calculate the signal reception time difference between each anchor node based on the arrival timestamp of each signal, and to obtain the reception time difference matrix. The non-line-of-sight propagation calculation module is used to calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix. The dynamic weight calculation module is used to determine the confidence weight of each anchor node in the reception time difference matrix based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, and obtain the weight matrix. The positioning module is used to process the position information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card within a continuous time period.

9. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for performing the method according to any one of claims 1 to 7.

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