165 results about "3d localization" patented technology

The invention provides a three-dimensional positioning method and system for a satellite-borne multi-base radar system based on weighted linear iteration, and the method comprises the steps: obtaining radar echo data of a plurality of receivers, carrying out the effective detection of a target, and carrying out the searching of a pitch angle and a space taper angle of a target signal through a two-dimensional adaptive filtering technology, obtaining two-dimensional angle estimation information of the target at different bases and different visual angles; constructing a nonlinear equation set according to two-dimensional angle information and slope distance information of the target under different visual angles, and performing first-order Taylor expansion on the nonlinear equation set to obtain a corresponding linear overdetermined equation set; and forming a precision matrix according to the known measurement precision of the radar system, and carrying out normalization operation and iterative solution on the linear overdetermined equation to obtain the three-dimensional coordinate of the target in the earth-centered earth-fixed coordinate system, thereby obtaining the latitude, longitude and height information of the target, and realizing the high-precision three-dimensional positioning of the multi-static radar system.

The invention discloses a dual-machine target positioning method based on semantic prior and spatial intersection constraint. The method comprises the following steps: 1, acquiring images of a camera 1 and a camera 2, and determining the spatial orientation of a camera coordinate system relative to a reference world coordinate system; step 2, obtaining a target normalized coordinate under a coordinate system of the camera 1; step 3, obtaining a target normalized coordinate under a coordinate system of the camera 2; 4, converting the normalized coordinates of the target into geometric rays in a three-dimensional space; 5, constructing an optimization function with the minimum distance sum of squares; step 6, performing semantic-level foreground and background division on the image content through a YOLOv5 network, and constructing a three-dimensional space feature point library of a semantic background; and 7, performing single-view semantic neighborhood retrieval and depth compensation by using the three-dimensional space feature point library of the semantic background constructed in the step 6, and realizing target three-dimensional positioning in a high-altitude long-distance complex scene. According to the invention, the detection robustness and the positioning precision of the high-altitude long-distance target in the environment are improved.

The invention discloses a digital twinning-based three-dimensional visual management system and a digital twinning-based three-dimensional visual management method for an anode assembly workshop. The system comprises a triple binding index module, a unified time axis alignment module, a view cone inverse solution positioning module, a safety linkage arrangement module and a resume playback module, and is provided with a process template library and a network isolation / main / standby module. Three-dimensional positioning and PTZ linkage of an AI / threshold event are realized by establishing mapping of a three-dimensional object, a camera and a PLC point location; the batch / bracket / station events, the PLC time sequence and the video slices are indexed in a unified mode; shadow verification, interlocking check, instruction issuing, read-back and video consistency confirmation and trace leaving are executed according to the process, and a safe closed loop from warning to disposal is formed. The method comprises the steps of access and calibration, triple binding, time axis alignment, event positioning and stream taking, linkage processing and resume filing. The positioning and handling efficiency can be improved, the misoperation risk is reduced, and cross-production-line reuse and tracing evidence obtaining are supported.

The invention discloses a physical object three-dimensional positioning method and system based on video data, and relates to the technical field of three-dimensional positioning. The method is used for solving the problems of low object space positioning precision, unstable pose estimation and poor time sequence continuity in a video scene. Firstly, an input video stream is analyzed, an object segmentation mask is generated, feature points are extracted, camera motion parameters are calculated through inter-frame matching, and a scene sparse three-dimensional point cloud is reconstructed; a candidate three-dimensional bounding box is generated according to the segmentation mask and the point cloud, an optimal bounding box is selected by combining geometric matching degree and feature similarity evaluation, and a preliminary three-dimensional positioning result of the object is obtained; a positioning result and historical frame motion data are fused, a space-time constraint optimization model is constructed, and the six-degree-of-freedom pose of the object is solved; and finally, neural radiation field representation is established based on the pose, the pose and neural parameters are optimized through combination of micro rendering and back propagation, continuous and accurate updating of the three-dimensional position of the object is realized, and the positioning stability and robustness in a dynamic scene are improved.

The invention relates to the technical field of GIS partial discharge high-precision three-dimensional positioning, and discloses a multi-stage iteration GIS partial discharge high-precision three-dimensional positioning method, and the technical scheme is characterized in that S1, an electromagnetic simulation model is constructed based on a GIS equipment structure, and the installation position of an ultrahigh frequency sensor is optimized through electromagnetic simulation; s2, performing wide-band adaptive sub-band reconstruction and multi-resolution feature enhancement processing on the acquired signals; s3, calculating three-dimensional focusing energy distribution by adopting a multi-band time inversion method, and extracting candidate discharge positions; s4, screening candidate positions; s5, performing multi-stage hybrid optimization on the screening result to obtain refined three-dimensional positioning coordinates; and S6, outputting a three-dimensional positioning result, and performing performance evaluation based on a confidence index. According to the method, a time delay consistency screening mechanism is introduced, false candidate points which do not conform to an actual signal propagation rule are effectively eliminated, and the accuracy and reliability of a positioning result are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle detection and computer vision, in particular to an application method of a multi-scene throwing object recognition FOD algorithm carried by an unmanned aerial vehicle, and the method comprises the steps: dynamically fusing data through a multispectral camera and a laser radar, carrying out the scene self-adaptive defogging and illumination equalization preprocessing, inputting an improved twin network-FPN model, and extracting features; then, a target frame is screened by adopting a DIOU-NMS containing a scene complexity coefficient; and three-dimensional positioning is realized by combining RTK-GPS and a laser radar, the data is transmitted to a ground terminal through 5G and is subjected to secondary verification, and the models can be dynamically switched according to the flight height. The method solves the problems of missing and false detection, poor environmental adaptability and the like of complex scene FOD recognition, data integrity is improved through multi-sensor fusion, robustness is enhanced through scene self-adaption, the model and dynamic strategy balance precision and real-time performance are improved, multi-scene adaptability is high, and the recognition effective rate exceeds 98%.

An automatic end effector tip position initialization mechanism for a micromanipulation system is provided. The method includes: S1, positioning a macro end effector tip, calculating a camera pose by using ArUco Tag, and realizing a three-dimensional positioning of the end effector tip by using a triangulation method, and moving the end effector to a area near a center of a focal plane based on a visual servo; S2, positioning a micro end effector tip; S3, acquiring calibration data based on a visual servo, calibrating the end effector tip and the petri dish, and obtaining a left calibration matrix and a right calibration matrix reflecting the transformation relationship between left and right micromanipulator coordinates and image coordinates; generating an intuitive human-computer interaction interface based on a mouse and a keyboard according to acquired position information. The method has obvious advantages in terms of operation accuracy, operation efficiency and repeatability.

The invention discloses a single-sensor type robust high-precision three-dimensional positioning method and system, and aims to improve the positioning precision and stability of a moving target in a three-dimensional scene. According to the method, a geometric constraint structure formed by a multi-label sensor is constructed, rich observation information is fused, and simultaneous acquisition of three-dimensional position and attitude information is realized. The optimal layout coordinates of a base station sensor and a label sensor are calculated by adopting a particle swarm optimization algorithm, and high-precision three-dimensional position and attitude calculation is realized by combining a weighted least square algorithm of single-point positioning, a weighted least square algorithm of rigid body positioning, robust M estimation, gross error detection and a Kalman filtering backward smoothing algorithm. According to the method, in a complex environment of non-line-of-sight signal interference, the positioning robustness can be remarkably enhanced, the elevation error is reduced, and the application feasibility and the precision reliability of a single type of sensor in an actual scene are improved.

The invention relates to a power distribution network tower AI adaptive visual inspection method and system based on edge calculation. The method comprises the following steps: acquiring a tower image and pose data through an unmanned aerial vehicle carrying an edge calculation node, and realizing flight path optimization in combination with visual SLAM and optical flow tracking; a target detection model which fuses a knowledge graph and spatial relationship modeling is adopted to identify an electric power component, and the small target detection precision is improved through image partitioning and a feature enhancement strategy; carrying out defect discrimination by utilizing a special heterogeneous classification model and multi-scale feature fusion; three-dimensional accurate positioning of the defective part is realized based on reverse ray tracing and cross-modal feature matching; and the edge nodes adopt multi-target reinforcement learning resource scheduling and space-time diagram neural network fault detection to realize system resource optimization and self-healing protection. According to the invention, intelligentization, precision and full-process automation of electric power tower inspection are realized.

The embodiment of the invention discloses a target position global optimization method and system based on gradient search and TDOA-AOA cooperation, and aims to solve the problem of low positioning precision caused by poor high-dimensional observation of a pure time difference of arrival positioning system under the condition of ground station arrangement. A fitness function containing a distance difference and an angle residual error is constructed, a nonlinear positioning equation is solved and converted into an objective function minimization problem, and a gradient optimizer is introduced for global optimization. According to the method, exploration and development capabilities are balanced through a gradient search rule, and local optimum is effectively avoided by using a local escape operator. Simulation shows that the height estimation error is remarkably reduced while the horizontal positioning precision is kept, and the overall performance of three-dimensional positioning is improved.

The present application belongs to the technical field of image processing, and particularly relates to a real-time positioning method and system for mechanical arm operation, which comprises the following steps: calculating an initial matching cost volume of a stereo image pair; generating an adaptive aggregation path field according to a local structure tensor of the image, and determining an adaptive penalty term based on a structure strength; performing cost aggregation along the adaptive path field by using the adaptive penalty term to obtain an aggregated cost volume; determining a disparity map according to the aggregated cost volume and solving a three-dimensional pose of a target point to be drilled. The present application generates an aggregation path that conforms to the scene geometry and dynamically adjusts the smoothing constraint, thereby significantly improving the accuracy and robustness of three-dimensional positioning in a complex environment and providing reliable real-time pose guidance for high-precision operation of the mechanical arm.

This invention discloses an assisted firefighting method and system based on UAV information and image processing technology. The method includes: step S1, using a UAV equipped with preset equipment to collect images of a fire scene; step S2, performing smoke removal preprocessing on the fire scene images to obtain a preprocessed image; step S3, performing target detection on the preprocessed image to obtain a target detection result; step S4, performing visual target localization at the fire scene based on the UAV's gimbal pose information and the target detection result; and step S5, using the target detection result and the visual target localization to assist in the scheduling of firefighting resources, thus completing the assisted firefighting based on UAV information and image processing technology. The improved YOLOv8 model is used to perform target detection on the preprocessed image. Based on the camera imaging principle, and combining the target detection result with the UAV's monocular gimbal sensor data, the fire source target is three-dimensionally located to assist in the scheduling of firefighting resources.

The application discloses a real-time leakage positioning monitoring system and method based on electrical resistance tomography, relates to the technical field of process tomography and industrial safety monitoring, and solves the technical problem that it is difficult to intelligently identify, three-dimensionally position and quantitatively evaluate leakage events; the system comprises a sensor array, a multi-channel data acquisition and excitation module, a real-time data processing and image reconstruction module, a leakage intelligent identification and positioning module, a man-machine interaction and data management module; the core lies in that a multi-layer nested annular array and a sparse measurement strategy are adopted to improve data acquisition efficiency, a physical information neural network is introduced to reconstruct an image, a measurement topological coding matrix and a physical residual correction module are used to reconstruct the image, and finally, a time-space dual-domain leakage probability field model is constructed, a virtual sensor network and an improved D-S evidence reasoning framework are combined to intelligently identify and position a leakage diffusion mode.

The application relates to a Beidou RTK measurement point cloud self-adaptive filtering processing method, belonging to the field of high-precision three-dimensional positioning technology based on the Beidou system. The method firstly determines whether it is suitable to directly take the median filtering result as the filtering output according to the data amplitude span state of any measurement point in the Beidou RTK measurement point cloud and the effective satellite quantity when the point cloud is acquired, and quantifies the reliability of the median filtering result through the data fluctuation and direction change of the local neighborhood of the measurement point when it is not suitable, so that the fusion filtering result determination of the median filtering result and the original measurement value is completed in the case that the direct median filtering result is not suitable for filtering output. The application can avoid the reverse filtering optimization effect of the median filtering in the Beidou RTK measurement point cloud, and improve the RTK measurement point cloud data filtering accuracy of the median filtering.

This invention relates to the field of orchard navigation technology, specifically a 3D method for orchard operations based on a grid map. The method includes: constructing an orchard point cloud map using a mapping algorithm; filtering the acquired 3D point cloud map and constructing a triangular grid map of the orchard, embedding the orchard's GPS information into the triangular grid map; based on the triangular grid map with GPS information, performing ICP registration between the point cloud data scanned by the LiDAR and the points on the triangular grid map using the RMCL 3D positioning algorithm, outputting the 3D pose of the operation robot in the orchard; updating the 3D pose of the operation robot in the orchard using GNSS information; planning the navigation path of the operation robot using grid navigation based on the GPS information of the target point; and controlling the operation robot to dynamically avoid obstacles and navigate to the target point. This invention enables precise positioning and autonomous navigation of the operation robot in hilly orchards, allowing it to complete tasks such as orchard inspection, plant protection, harvesting, and transportation.

This invention discloses a method for autonomous operation of humanoid robots based on ROS. It employs a decision-making and control framework that fuses hierarchical finite state machines with YOLOv8 vision to achieve continuous autonomous execution of multiple tasks in complex unstructured environments. The method includes: system initialization and standardized processing of multimodal perception data; target 3D localization fused with YOLOv8 and coordinate transformation; task scheduling and state transitions using hierarchical finite state machines; segmented navigation and motion planning of joint trajectories; and ROS command issuance and full-process closed-loop control. This invention addresses the rigidity of traditional script-based control by fusing hierarchical state machines with visual servoing. Combined with a full-process anomaly handling mechanism and multi-target scene processing rules, it can dynamically adapt to changes in complex unstructured environments, effectively avoiding problems such as state freezes and erroneous grasping.

A method may include receiving, by a processor, a three-dimensional (3D) location of each of a plurality of electrodes of a catheter from one or more sensors during an ablation session; generating, by the processor, a 3D graphical representation that identifies a location of each electrode of the plurality of electrodes with a first indication; and connecting, by the processor, pairs of adjacent first indications in the constellation-like 3D graphical representation with second indications. These second indications may indicate a location between the electrodes where energy is delivered. The method may include displaying, by the processor, a 3D graphical representation including the first indication and the second indication on a display device. The method may include identifying a pair of adjacent first and third indications not connected by a second indication, the third indication may indicate a location between the electrodes where no energy is delivered.

The present application provides a kind of complex stratum structure and topographic effect considering slope microseismic event 3D positioning method, the technical scheme includes S1: the theoretical travel time model suitable for complex slope environment is built;S2: the actual travel time difference is extracted from slope monitoring data;S3: based on the 3D positioning of theoretical and observed travel time difference matching, the ambient noise surface wave tomography technology is applied to slope velocity modeling, without artificial seismic source, high resolution velocity structure can be obtained;Realize 3D ray tracing under the constraint of digital elevation model, ensure that wave propagation path meets physical law, positioning accuracy reaches 3 m, can reach 1.5 m in dense array coverage area, compared with traditional method, it is improved by one order of magnitude;Successfully applied to complex environment with more than 700m topographic relief and extreme velocity variation, can be applied to microseismic monitoring in various complex geological environments.

The utility model discloses a three-dimensional spatial calibration module for realizing spatial three-dimensional positioning and camera calibration, which comprises a cubic framework, calibration target surfaces are respectively arranged on six surfaces of the cubic framework, and laser etching checkerboards, dot matrixes or ArUco coding patterns are respectively arranged on the six calibration target surfaces. And the six surfaces of the cubic skeleton are respectively provided with an identification surface coding module. According to the utility model, multi-surface pattern arrangement is adopted, and multi-angle three-dimensional calibration can be realized by a single module; the structural cubic design meets spatial analytic geometry constraints, and the reconstruction stability is improved; a built-in attitude sensing system records calibration attitudes and improves reproduction and error analysis capability; the modular quick-change base design supports quick plugging and mobile calibration; a wireless tag and visual coding combined recognition mechanism enhances the recognition and matching efficiency of an automatic system.

The invention relates to the technical field of computer vision and industrial automation, in particular to a hoisting equipment real-time positioning and trajectory tracking method based on a vision algorithm, which comprises the following steps of: S1, acquiring a video stream in real time through a camera deployed in a hoisting operation area, and preprocessing an image; according to the method, accurate mapping from two-dimensional image features to a global three-dimensional space in a complex hoisting scene is realized through a multi-source constraint fusion self-correction three-dimensional pose solving mechanism, and a trajectory prediction and'trajectory-load 'associated safety system is constructed based on a three-dimensional positioning result; the closed-loop control from passive trajectory tracking to active prediction planning, overload early warning and equipment linkage protection is realized, and the positioning precision, robustness and safety protection level of hoisting operation are greatly improved.

This invention relates to the field of engineering cost technology, and discloses a method and system for generating cost schemes driven by a component system. The method includes: extracting annotation information from 2D CAD drawings and identifying the spatial positioning attributes of components; generating 3D bounding boxes for component positioning based on multi-view geometric projection rules; inferring spatial adjacency relationships of components and calculating adjacency geometric parameters through bounding box intersection detection; expanding the component system to add spatial adjacency trigger dependency definitions; parsing bill of quantities items to match adjacency records and calculating linked quota quantities; and generating a cost combination scheme including linked quotas. This invention solves the technical problem of not being able to automatically identify and measure linked quotas dependent on spatial adjacency relationships when a BIM model is lacking.

The invention discloses a three-dimensional positioning method and system for a bimodal target, and relates to the technical field of indoor positioning, and the method comprises the steps: collecting the phase ranging data of a connected state base station and the received signal strength indication data of a broadcast state base station, and forming a bimodal perception data set; selecting a static positioning model or a dynamic positioning model according to the motion state of a to-be-positioned point, and converting a positioning problem into a nonlinear optimization problem based on the bimodal perception data set; and solving the nonlinear optimization problem by adopting a CBS algorithm to obtain the position estimation of the point to be positioned in the three-dimensional space. According to the method, three-dimensional positioning of a static target and smooth track positioning of a dynamic target are realized at the same time, the optimization range is effectively constrained, the system deployment complexity and the calculation load are reduced, the precision and robustness of indoor three-dimensional positioning are effectively improved, and the method is suitable for application scenes with strict requirements for quick response and low-cost deployment.

The application relates to the technical field of image processing, and discloses a vehicle surface defect identification method and system based on artificial intelligence. The method comprises the following steps: acquiring a vehicle surface image through polarization filtering multi-angle imaging; applying HSV color conversion and texture equalization processing to standardize the image; combining parallax analysis to construct a three-dimensional deformation feature and extract a defect feature vector; and utilizing a position perception convolutional neural network to identify the defect and output a severity heat map. According to the application, in a complex light environment, the tiny defects on the surface of vehicles with various colors and materials can be accurately identified and evaluated, and the accurate three-dimensional positioning and severity quantification of the defects can be realized.

A kind of open pit model dynamic updating method based on real-time positioning of excavator and SDF belongs to the technical field of digital mine. The initial pit triangular mesh model is voxelized to generate a three-dimensional symbolic distance field based on the definition of vertical coordinate symbol. The three-dimensional positioning data of the center of rotation of the excavator is collected in real time through the GNSS-RTK positioning module, and the working state is judged based on the preset time window and displacement threshold. When the excavator switches from displacement state to digging state, a spherical mining influence domain is constructed with the current positioning data as the center of the sphere and the maximum digging radius as the radius, and the voxel symbol distance field value in the domain is set to 0. A control point set is constructed by boundary constraint points and background constraint points, and global symbol distance field smoothing reconstruction is realized by using radial basis function interpolation. The zero value isosurface is extracted by moving cube algorithm to generate the updated pit model. The present application realizes high-frequency model updating synchronized with operation, has low deployment cost and strong robustness, and solves the problems of model updating lag and visualization penetration.

The application provides a deep foundation pit anti-floating risk assessment method based on monitoring data, and relates to the technical field of deep foundation pit anti-floating. The method comprises the following steps: carrying out layered multi-source heterogeneous monitoring on the deep foundation pit, obtaining a water pressure deformation data set and extracting spatial node and time sequence parameters; calculating the spatial distribution gradient of buoyancy and the dynamic resistance threshold; then establishing a local mechanical coordinate system in the effective stress coupling area, extracting a structure deformation mutation sequence from the data set; subsequently constructing a space-time coupling graph, generating a nonlinear attenuation path covering a weak area through a feature fusion algorithm; finally extracting a local anti-floating risk set along the path, generating a global anti-floating evolution graph through time domain smoothing and three-dimensional space mapping, and outputting an evaluation result containing three-dimensional positioning and automatic hardware control instructions. The application realizes space-time linkage analysis of foundation water pressure and structure deformation, and significantly improves the fine early warning and entity control ability of anti-floating risk under complex working conditions of deep foundation pit.

The invention provides an intelligent nuclear power embedded part detection and acceptance method based on a binocular camera, belongs to the technical field of detection and acceptance, is an intelligent detection algorithm based on binocular vision and a deformable attention mechanism, and aims to break through the technical bottleneck of three-dimensional positioning and multi-scale feature fusion of embedded parts in a complex scene. A three-dimensional pyramid network with reserved features is designed, multi-scale features are fused in a layered mode, binocular geometric consistency is kept, and the problem of information loss in cross-resolution feature fusion is effectively solved; parallax perception position coding is innovatively introduced, binocular parallax information and two-dimensional position coding are deeply fused, and the three-dimensional space perception capability is enhanced; a non-parametric anchor-based target query mechanism is provided, query embedding covered by dense space is generated by multiplexing low-resolution three-dimensional features, the model convergence efficiency and the detection precision are improved, the detection cost is reduced, the detection efficiency is improved, and the detection safety is guaranteed.

The invention discloses a cross-modal pedestrian re-identification method and system for deep ground emergency rescue, and the method comprises the steps: extracting the multi-layer features of two modals through a double-flow neural network, carrying out the multi-granularity semantic enhancement of the features through a self-adaptive multi-granularity enhancement module, introducing a cross-modal fusion attention module, fusing a cross-modal adjacency relation based on a graph structure, and carrying out the multi-modal semantic enhancement of the features through a self-adaptive multi-granularity enhancement module. A multi-head image attention mechanism is adopted to improve robustness and generalization ability; according to the method, a cross-modal aggregation loss and joint loss optimization strategy is combined, discrimination of cross-modal features is effectively improved, a near-real-time digital twinborn model of an underground space is constructed, a two-dimensional cross-modal Re-ID result is coupled with incremental three-dimensional reconstruction based on Gaussian splashing, and three-dimensional positioning of an identified person in a digital twinborn scene is realized. According to the method, the pedestrian feature expression capability can be improved, the modal difference can be reduced, rescue workers can be expanded from two-dimensional video information to three-dimensional position information of the workers, and the rescue searching and positioning efficiency and the directness of command decision are remarkably improved.

This application discloses a multimodal data collaborative intelligent detection and 3D localization method for maritime vessels, comprising: fine preprocessing of collected multi-source heterogeneous data and preliminary target detection; dynamic weighted fusion of the preprocessed multi-source heterogeneous data, combined with depth data from lidar, to achieve accurate coordinate recovery of the vessel target in 3D space; and prediction and updating of the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. By fully utilizing the rich texture information of visible light images, the anti-interference capability of thermal imaging, and the precise spatial depth data of lidar through multimodal data perception and preliminary processing, heterogeneous data deep fusion and 3D localization, and advanced multi-target continuous tracking and state estimation, this method integrates a multi-tracking mechanism collaborative strategy to predict and update the motion state of the identified target, effectively addressing the problem of tracking interruption caused by nonlinear motion or temporary occlusion of maritime targets.

This invention provides a method, system, and medium for three-dimensional positioning and displacement measurement that integrates microwave and vision technologies. The method includes: rigidly connecting at least one microwave sensor and at least one vision sensor as a joint sensing unit; establishing a spatial transformation relationship between the coordinate systems of the two sensors through calibration; fusing the target's radial distance and angle information provided by the microwave sensor with the target's two-dimensional imaging information provided by the vision sensor; and directly solving for the target's three-dimensional coordinates in the selected coordinate system using an optimization algorithm; further combining the high-precision one-dimensional displacement information from the microwave sensor with the two-dimensional displacement information from the vision sensor to reconstruct the target's complete displacement vector in three-dimensional space. This invention, through a compact heterogeneous sensor fusion architecture, effectively solves the problems of limited dimensionality of single-sensor modes, high cost of multi-sensor networking, system complexity, and poor engineering applicability, achieving high-precision, low-cost, and full-dimensional non-contact three-dimensional positioning and displacement measurement.

This invention discloses a multi-algorithm collaborative 3D positioning method based on UWB and IMU. The method includes: constructing a 3D Cartesian coordinate system based on multiple UWB base stations; measuring the distance between the tag and the UWB base stations through bilateral bidirectional ranging to obtain the corresponding distance variables; establishing a nonlinear equation system using a trilateration algorithm, substituting the distance variables to transform it into a linear error equation system, and obtaining a set of 3D coordinates of the tag using Newton's iteration method; obtaining a nonlinear equation system using the time difference of arrival, obtaining a linear error equation system based on Taylor expansion approximation, and obtaining another set of 3D coordinates of the tag using the least squares method; weighted averaging the two sets of 3D coordinates to obtain the initial 3D coordinates; constructing a state transition model based on the initial 3D coordinates, velocity components, and acceleration components; predicting the state at the next moment using Kalman filtering and updating the tag's 3D coordinates; and outputting the final 3D spatial positioning result. This invention can achieve high-precision 3D positioning.