33 results about "Optical flow estimation" patented technology

The invention discloses a panoramic imaging method and device, equipment and a medium, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the projection transformation of an original image collected by a vehicle camera, and generating an aerial view; determining the size of an adaptive target window according to the distance from each pixel to a central pixel in the aerial view, and performing optical flow estimation on each pixel in two adjacent frames of aerial views in the size of the target window based on vehicle motion information measured by an inertial measurement unit to obtain a dense optical flow field; extracting inner points from the dense optical flow field by using a preset improved RANSAC algorithm, and estimating global motion parameters based on an extracted inner point set; fusing the global motion parameters and motion parameters measured by the inertial measurement unit and the positioning system by using a Kalman filtering method to obtain fused motion parameters; and projecting the historical panoramic image to an aerial view coordinate system by using a homography matrix calculated based on fusion motion parameters, and splicing the historical panoramic image with the current aerial view to generate a new panoramic image.

The application provides a soft tissue real-time tracking and navigation method based on inter-frame deformation field guidance and a related device. The method comprises the following steps: S1. acquiring an intraoperative endoscope image sequence, and extracting a current frame and a historical frame; S2. performing dense optical flow estimation on the current frame and the historical frame to obtain an inter-frame deformation field; S3. performing spatial transformation on a historical mask based on the inter-frame deformation field to obtain a deformation compensation segmentation prior; S4. performing prior guided fusion on the segmentation prior and a current frame feature to obtain a refined anatomical segmentation mask; S5. performing key point matching based on the refined mask and a pre-stored atlas to obtain a spatial registration parameter; and S6. rendering a safety boundary based on the registration parameter and the deformation field, and outputting a safety navigation view. The application also provides a related device corresponding to the method, and the related device comprises a device, an electronic device, a computer readable storage medium and a computer program product.

This invention discloses a motion blur compensation image enhancement method for high-speed cameras, relating to the field of computer vision technology. The method includes acquiring raw sequence data recorded by a high-speed imaging device, analyzing overlapping regions of adjacent frames in the sequence using a temporal convolutional network to determine the temporal continuity features corresponding to highly overlapping frames, obtaining a separated frame sequence, and using an optical flow estimation algorithm to track the object's movement trajectory based on the separated frame sequence. If the optical flow vector exceeds a preset threshold, it is judged as an interference factor in a dynamic scene, resulting in a motion-compensated frame sequence. This motion blur compensation image enhancement method for high-speed cameras not only improves the accuracy of image enhancement but also enables real-time and accurate extraction of key information frames in complex dynamic environments, optimizing the defect detection process. It has broad application prospects, particularly in high-speed motion analysis and industrial inspection, significantly improving detection efficiency and accuracy.

This invention discloses an event-visual-inertial semantic simultaneous localization and map building method, which is applicable to navigation and localization of mobile robots, unmanned vehicles and drones. This method acquires event data streams from event cameras and inertial data from IMUs, and optionally standard camera images. Based on the event data, it constructs an event activity surface, performs coarse-to-fine event corner detection on it, constructs an event representation for event optical flow estimation, and estimates the event optical flow. It then uses the event optical flow to perform temporal tracking of event corners to obtain multi-time-stack related observations. Based on the event corners and event representations, it extracts descriptive information, performs loop closure detection and relocalization, and introduces loop closure constraints as additional residual terms into a sliding window graph optimization. Within the sliding window, it combines residuals from events, images, IMUs, edge detection, and loop closure relocalization, and sets adaptive weights for multi-source fusion to obtain a six-degree-of-freedom pose sequence. It performs target detection on the event data stream, outputting detection boxes, categories, and confidence scores. Based on the event optical flow, it implements missed detection compensation and motion consistency checks to identify dynamic target regions, eliminates dynamic corners and their observation constraints, and generates an object-level semantic map based on pose estimation. This improves the robustness of localization mapping and environmental understanding in complex lighting, high-speed motion, and dynamic interference scenarios.

The method comprises the following steps: 1) collecting continuous frame images; 2) building an optical flow estimation network, the input of two continuous frame images is respectively extracted by a depth perception feature encoder to obtain a fusion feature map, then zero optical flow is taken as an initial optical flow estimation, and multiple iteration optimizations are carried out: in each iteration, a self-adaptive feature alignment module is entered for feature alignment, a global-local collaborative refinement module is entered for global-local feature collaborative refinement, and an optical flow prediction module is entered for optical flow updating, the optical flow of one-half resolution of the input image is up-sampled to the original image resolution, and a final optical flow map is output; 3) training the optical flow estimation network; 4) inputting the continuous frame image pairs in a test set into the optical flow estimation network for optical flow estimation. The application alleviates the feature matching difficulty in strong occlusion and large range weak texture area, and improves the precision and robustness of optical flow estimation.

This invention relates to the field of video image processing technology and discloses a method for denoising high-speed motion continuous frames. The invention receives a raw video sequence in Bayer format and establishes a multi-scale image pyramid with a variance-stabilized domain. At each level of the pyramid, based on local brightness statistics, local texture contrast information, and estimated motion amplitude, a motion texture discriminability index representing texture change relative to noise intensity is calculated. This index is used to dynamically adjust the data fidelity term and smoothing constraint term in the variational optical flow estimation process to obtain a motion-compensated image. Subsequently, this index is mapped to an alignment deviation estimate and a similarity measurement range, multi-frame spatiotemporal fusion weights are calculated, and a primary fusion image is generated. Finally, an adaptive filtering threshold is calculated based on this index, high-frequency texture details are extracted from the differential data and superimposed back onto the primary fusion image, and a denoised video sequence is output after inverse transformation processing.

The application belongs to the technical field of river surface flow velocity measurement, and discloses a method for predicting water flow surface flow velocity based on improved RAFT optical flow. Firstly, in the feature extraction stage, the feature descriptor information output by the SuperPoint network is fused, the representation ability of key features is strengthened, the reliability of feature matching is improved, and problems such as ambiguous optical flow estimation results are solved. Secondly, after correlation calculation, an evaluation module is introduced, the evaluation mode is used to calculate a matching quality evaluation index according to a 4D correlation body and a context feature map, and whether to enable a moving window function is judged according to the size of the evaluation index and a preset threshold value, so that the 4D correlation body is kept or updated. By introducing the moving window function, the problem of false features caused by optical flow discontinuity and noise in the water flow scene is solved, and the evaluation mechanism is constructed to dynamically decide the enabling time of the moving window function, so that the balance between the estimation accuracy and efficiency of the optical flow is realized.

A variable frame rate video generation method based on optical flow estimation, which introduces optical flow supervision information into an OpFode-Net model, the OpFode-Net model comprising an encoder-decoder structure; the encoder uses an ODE-ConvGRU to embed input video sequence X T into a hidden state h T ; wherein the ODE-ConvGRU uses a ConvGRU as a node of a neural ODE and embeds it into the neural ODE to realize dynamic modeling of the video sequence; the decoder starts from h T , and uses an ODE solver to generate a new video frame at any time step S, which can realize more accurate prediction results and achieve optimal performance in video interpolation and video prediction tasks.

This application discloses a deep learning-based method and system for human perception and distance measurement. The method acquires image pairs of non-rigid human body regions using a binocular camera, establishes initial pixel correspondences using dense optical flow estimation, and refines the corresponding point sets by combining semantic segmentation and feature enhancement processing. Then, it eliminates disparity anomalies using neighborhood consistency constraints, transforms the data into continuous depth information, and reconstructs a spatial point cloud through triangulation. Based on this, it constructs a fine-grained boundary depth field covering the entire human body region by dynamically tracking non-rigid deformations and fusing binocular geometric constraints. This application addresses the shortcomings of existing methods in sparse matching of non-rigid human body surfaces, depth discontinuities, and handling of occluded regions, achieving high-precision perception of the spatial distribution of various parts of the human body. This effectively supports safe collaboration and intelligent interaction of robots in complex dynamic scenarios.

The application discloses a kind of based on machine learning's tunnel unmanned plane autonomous flight system, comprising: multi-sensor data acquisition and pre-processing module, for collecting multi-source data and generating pre-processing data set;Improved Hector SLAM positioning module, for executing the introduction information entropy weight and the scanning matching of multi-source constraint, output two-dimensional map and horizontal pose estimation;Weak light vision incremental estimation module, for output light flow estimation visual incremental displacement;Unscented Kalman filter fusion module, for fusing multi-source information, output three-dimensional pose fusion result;Risk tensor construction module, for updating map and generating three-channel risk tensor;Improved A3C strategy control module, for fusing strategy output flight control instruction;Flight control and data acquisition module, for driving flight and generating inspection report.The application realizes the data fusion control effect of unmanned plane autonomous flight accurate navigation and efficient inspection in tunnel scene.

The application discloses a water surface flow velocity detection method based on deep learning optical flow estimation, and belongs to the motion estimation task in the computer vision technical field. The method comprises the following steps: constructing an optical flow estimation network of water surface flow velocity, constructing an optical flow dataset for water surface flow velocity estimation, training an optical flow estimation network model of water surface flow velocity, and testing and using the optical flow estimation network model of water surface flow velocity. The application fully utilizes the advantages of the deep learning optical flow estimation model in the motion estimation field, uses a multi-scale fusion feature extraction network, calculates the multi-scale correlation between adjacent frames by using patch convolution, introduces a KPA attention mechanism, designs a multi-scale attention fusion optimization network, and the average velocity measurement error is only 0.056 m / s. The water surface flow velocity detection without tracer is realized, and the recognition precision of the model in the interference of uneven illumination and complex background environment is improved.

This invention discloses a dynamic gesture recognition method based on spatiotemporal interaction and rate awareness. The method includes spatiotemporal decoupling feature extraction, a rate feature injection module, and a spatiotemporal feature injection interaction module. The spatiotemporal decoupling feature extraction consists of a 3D convolutional layer, an Inception module, and a Transformer. The rate feature injection module extracts visual rhythm features based on similarity calculation and optical flow estimation algorithms, and uses these visual rhythm features to enhance the initial features. The spatiotemporal feature injection interaction module consists of a spatial feature injection module and a temporal feature interaction module, which are implemented based on an attention mechanism. The spatiotemporal decoupling feature extraction serves as the backbone network, while other modules perform feature injection and enhancement. This invention relates to human-computer interaction, gesture recognition algorithms, computer vision, and other fields, and has the advantages of comprehensive features and accurate recognition.

This invention relates to the field of optical flow estimation in computer vision, specifically a method for optical flow estimation that combines pure compressed domain information with multiple attention mechanisms. The method includes: at the video receiver, extracting and receiving motion vectors and residual information from the compressed domain, and converting them into an image; at the optical flow estimation end, using a sliding attention algorithm to generate local context information for motion vectors to fill discontinuous motion vector regions, and using a window dynamic cross-attention algorithm to match untrusted motion vector regions with high residual value regions, correcting untrusted motion vector regions using cross-attention; inputting the corrected motion vectors, residual information, and the optical flow estimated from the previous frame into a U-Net backbone network incorporating a channel attention algorithm and a warm-start strategy to obtain the final estimated optical flow. The algorithm proposed in this invention effectively solves the problem of high computational time complexity in practical applications of optical flow estimation using video frames.

The application discloses a kind of motion estimation methods for fusing deep learning feature optical flow and binocular vision, comprising: based on controllable adaptive histogram equalization preprocessing to driving image dataset;Construct the optical flow feature extraction model based on deep learning, and the moving target is identified training;Distance measurement is carried out by binocular camera, and the target position is obtained;Vehicle motion speed is acquired.Compared with traditional optical flow speed measurement, this method is based on deep learning optical flow and binocular imaging principle, and the motion parameter estimation of carrier displacement and speed can be realized according to video data, which solves the problem that traditional optical flow estimation method is too sensitive and cannot be stably estimated when driving at night or in weak light environment, and further improves reliability.Meanwhile, this method avoids the cumulative error of traditional inertia sensor and the shortcomings of poor anti-interference ability and low update frequency of GPS positioning speed measurement.

The application relates to a kind of instantaneous event intelligent analysis methods based on multi-modal spatiotemporal feature fusion.The application relates to a kind of instantaneous event intelligent analysis methods based on multi-modal spatiotemporal feature fusion.The application first uses a light-weight model to quickly identify possible event candidate regions, and simultaneously enhances the sensitivity to weak events by combining the brightness jump signal; subsequently, high-precision optical flow estimation and local texture change analysis are used to separate event-related local anomalies from dynamic backgrounds; in the time sequence dimension, the occurrence time of the event is accurately determined by fusing multiple clues such as firelight appearance, brightness peak and optical flow enhancement; further, the stable positioning of the event center is realized by combining the event region centroid trajectory, time sequence brightness distribution and geometric constraints. The overall process does not require large-scale training data, and still maintains good robustness in complex environments, and is suitable for multi-type sudden event analysis in monitoring video and unmanned aerial vehicle inspection.

The application discloses an image repairing method for continuous slice electron microscope data and belongs to the technical field of image repairing. The application uses the information of adjacent frames of a missing area, provides effective information for the information missing area of a damaged frame through optical flow estimation and weighted interpolation operation; an encoding network capable of effectively learning the multi-scale fusion features of a damaged image and a reference image is proposed; the skip-connection operation is used in the decoding part to recover the effective information lost in the encoding process, and the final prediction result is output; a structure consistency loss function is introduced when training the model to optimize the repairing result, the difference between the optical flow between the repaired frame and the front and rear frames and the true value is calculated, so that the network can repair the visual and semantic reasonable and clear slice image along the section and slice direction. The application solves the problems that the traditional and existing deep learning methods cannot process the inconsistent semantic information, the insufficient visual clarity and the incoherent structure of the front and rear frames.

The application discloses an unsupervised optical flow estimation method and system for different exposure low dynamic range images, and relates to the technical field of optical flow estimation. First, based on an intensity mapping function (IMF), the brightness of low dynamic range images with different exposures is normalized. Then, based on a RAFT algorithm, the low dynamic range images after brightness normalization are subjected to preliminary optical flow estimation. Based on the result of the preliminary optical flow estimation, the RAFT algorithm is trained by using an unsupervised learning method. Finally, the low dynamic range images after brightness normalization are subjected to final optical flow estimation by using the trained RAFT algorithm. The application can be applied to images with different exposures and images under more complex lighting conditions, and the best optical flow estimation result can be achieved, which is more efficient and more robust than existing methods.

This invention discloses a method for estimating motion optical flow in low-light scenes, belonging to the field of computer vision technology. The method includes constructing a spatial domain frequency decomposition encoder to decompose the initial features of the low-light image into high- and low-frequency components, encode local and global motion features respectively, and fuse them. Feature refinement is completed by downsampling and multilayer perceptron (MLP) residual fusion operation. A frequency domain sensing motion enhancement module is constructed to map the spatial domain motion features to the frequency domain for modulation and fusion. After frequency sensing channel attention optimization, enhanced motion features are obtained through gated residual connections. A motion prior sensing attention module is constructed to perform high-dimensional semantic space mapping on the enhanced features of adjacent frames. After calculating semantic similarity weights, prior motion vectors are obtained by coordinate weighted aggregation. A 4D cost volume is constructed based on the enhanced features. Combined with the prior motion vectors, the prior motion vectors are iteratively updated using a gated recurrent unit (GRU) update operator to obtain the motion optical flow estimation result.

The application discloses an infrared video deblurring method based on cross-modal style transfer and sparse attention guided by optical flow, which comprises the following steps: constructing an infrared video deblurring dataset; generating infrared optical flow data by using a cross-modal style transfer technology; constructing a global motion aggregation optical flow estimation network, and training an optical flow estimation model by using the infrared optical flow data; constructing an infrared video deblurring network, guiding spatio-temporal feature fusion by using the optical flow estimation model, and constructing a joint loss function to calculate errors; training to obtain a deblurring model; processing input blurred infrared video, and outputting clear infrared video. The application effectively captures long-distance spatio-temporal dependence between adjacent frames, balances numerical accuracy and structural details of the image, eliminates frame jitter, significantly improves the structural similarity of infrared video deblurring, generates clear infrared video with high precision and meeting the time sequence consistency constraint, and has wide application and promotion value in complex industrial scenes.

This invention belongs to the field of river surface velocity measurement technology and discloses a method for predicting water surface velocity based on improved RAFT optical flow. Firstly, this invention integrates feature descriptor information output by the SuperPoint network during the feature extraction stage. By strengthening the representational ability of key features, it improves the reliability of feature matching and solves problems such as ambiguity in optical flow estimation results. Secondly, this invention introduces an evaluation module after correlation calculation. This evaluation module calculates the matching quality evaluation index based on the 4D correlators and context feature maps, and determines whether to enable the moving window function based on its magnitude with a preset threshold, thereby maintaining or updating the 4D correlators. By introducing the moving window function, it solves the problems of optical flow discontinuity and false features caused by noise in water flow scenarios. Simultaneously, it constructs an evaluation mechanism to dynamically decide when to enable the moving window function, achieving a balance between the accuracy and efficiency of optical flow estimation.

This invention discloses a visual perception method and system for autonomous driving in mines based on YOLO real-time inference, relating to the field of environmental perception technology for autonomous driving in mines. The method employs an adaptive deconvolution deblurring algorithm based on inter-frame optical flow estimation to calculate the image plane displacement caused by low-frequency, high-amplitude vibrations from continuous image frames acquired by an onboard camera. Deconvolution operations are then performed on directional motion blur and edge texture degradation. The processed image frames are input into a YOLO real-time inference network with reconfigured anchor frame parameters. These anchor frame parameters are obtained by clustering the labeled dimensions of fallen rocks and eroded rock blocks from the slope in the mine. By performing optical flow deconvolution restoration operations on the blurred frames from the mine's bumpy terrain and reconfiguring the anchor frame parameters based on the clustering results of mine obstacle sizes, this invention enables the YOLO real-time inference network to maintain stable bounding box outputs and continuous trajectory markings for fallen rocks and eroded rock blocks under vibration conditions on unpaved roads.

The invention discloses a long-term semantic VSLAM method and system for dynamic accumulated noise suppression and ghosting elimination, and relates to the technical field of robot visual navigation and map construction, and the method comprises the following steps: collecting a current frame image in the continuous inspection process of a robot, and carrying out the processing of the current frame in the processing of the current frame; semantic mask extraction based on the instance segmentation model and dense optical flow field calculation based on the current frame and the previous key frame are executed in parallel; according to the method, potential dynamic object categories and accurate contours in the images can be recognized by adopting the YOLACT instance segmentation model, the large displacement situation possibly brought by the motion of the robot is coped with by combining a parallel execution optical flow estimation algorithm, semantic understanding of the environment and accurate synchronous capture of motion information are achieved, and the method has the advantages of being high in practicability and high in practicability. And the optical flow generated by the motion of the object and the background optical flow caused by the motion of the camera are effectively distinguished by measuring and analyzing the direction consistency of the optical flow vector, so that the accuracy and robustness of dynamic judgment are improved.

Embodiments of the present application disclose a multi-person behavior recognition method and device, computer equipment and a storage medium, wherein the method comprises: acquiring image data in a detection area; performing frame cutting processing on the image data to obtain a plurality of static pictures; inputting the plurality of static pictures in the form of a picture sequence into a human behavior prediction model for processing to obtain a human behavior recognition result. The present application combines multi-target tracking, key point detection and optical flow estimation method, can effectively perform real-time behavior recognition on a moving human body of a multi-target, has good compatibility for behavior detection of a human body moving greatly in a short time period, improves the robustness and accuracy of the model in different scene recognition, and makes the model have good understanding ability for complex content images.

This application provides a feature domain optical flow determining method and a related device, and relates to the field of video or picture compression technologies based on artificial intelligence (AI). The method specifically includes: obtaining a picture domain optical flow between a current frame and a reference frame; performing multi-scale feature extraction on the reference frame, to obtain M feature maps of the reference frame, where M is an integer greater than or equal to 1; and performing M times of feature domain optical flow estimation based on the M feature maps of the reference frame and the picture domain optical flow between the current frame and the reference frame, to obtain M feature domain optical flows. A feature domain optical flow obtained by using the solutions of this application is more accurate and more stable, thereby improving inter-prediction accuracy.

The application relates to the technical field of artificial intelligence, and discloses a gas leakage detection method and device, electronic equipment, a system and a storage medium. The method comprises the following steps: acquiring a detection image sequence of a to-be-detected object, wherein the detection image sequence is collected under the condition that the to-be-detected object is soaked in liquid; performing optical flow estimation according to the detection image sequence to obtain optical flow vector data corresponding to the detection image sequence; and determining a gas leakage detection result of the to-be-detected object according to optical flow vectors corresponding to bubble pixel points representing bubbles in the optical flow vector data. The application can automatically identify the gas leakage condition of the to-be-detected object by using the detection image sequence, and improves the gas leakage detection efficiency.

The application provides an optical flow estimation method and device, which can improve the accuracy of optical flow estimation from two adjacent image frames to any time between the two adjacent image frames. The method can include: obtaining a first image frame and a second image frame, the first image frame and the second image frame being any two adjacent image frames in an image sequence, the image sequence being obtained by shooting a target scene; obtaining a first event frame, the first event frame being used to describe the brightness change of the target scene in a time period between the first image frame and the second image frame; determining a target optical flow based on the first image frame, the second image frame and the first event frame, the target optical flow being an optical flow from the first image frame to a target time, the target time being any time between the first image frame and the second image frame.

The application is suitable for the technical field of intelligent terminals, and provides a vehicle-mounted image processing method and device, a terminal device and a storage medium. The method comprises the following steps: acquiring a to-be-repaired video frame and adjacent video frames thereof collected by a vehicle-mounted camera; calculating an optical flow field based on the to-be-repaired video frame and the adjacent video frames and an optical flow estimation network; performing multi-scale feature extraction on the to-be-repaired video frame and the adjacent video frames to obtain original multi-scale features of the to-be-repaired video frame and multi-scale features of the adjacent video frames; aligning the multi-scale features of the adjacent video frames to a coordinate system of the to-be-repaired video frame based on the optical flow field; and inputting the original multi-scale features of the to-be-repaired video frame and the aligned multi-scale features of the adjacent video frames into a repair model based on a space-time attention mechanism to perform information fusion and reconstruction, so as to obtain a repaired video frame. The application can improve the accuracy of image repair in a vehicle-mounted dynamic scene and meet the real-time requirement of a vehicle-mounted system.

The application discloses a SPAD depth optical flow estimation and reconstruction method based on a counter overflow interval, belongs to the technical field of neuromorphic visual perception and image processing, and effectively improves the timing stability under a low photon counting condition through short-time statistical modeling and overlapping time window coding, so that the structural representation of a dynamic area is more reliable; a shared weight pulse neural network coding structure is adopted to reduce the parameter scale and enhance the consistency of cross-time window modeling, and the characteristics of passive SPAD sparse pulse data are adapted; an alternating update mechanism of image reconstruction and optical flow is introduced in the decoding stage, so that the optical flow can perform timing alignment and constraint on the reconstruction process, and the reconstruction feature can inversely refine the optical flow, thereby obtaining higher reconstruction robustness in a high-speed dynamic scene; through joint optimization of image reconstruction loss and optical flow loss, the structural recovery capability of an image at a key moment is improved, and the finally reconstructed image has higher clarity and stability.

The application discloses an unsupervised optical flow estimation method based on instance mask guided smoothing loss, combines a M2Flow neural network architecture core part with a brand-new mask guided smoothing loss function, and carries out neural network unsupervised training under a PWC-Lite 4-Frame pyramid framework. The training process is completed by relying on the image photometric consistency constraint and the dynamic warm-up strategy of the loss function, and does not need artificial optical flow data labeling. The trained neural network can realize significant optimization effect: on the boundary sharpening and overflow suppression level, a sharp and accurate flow field fault is formed at the complex instance boundary of traffic signs, fast moving vehicles and the like, and the overflow problem of the flow field to the static background is effectively avoided; on the textureless area robustness level, for large-area sky, vehicle body, reflective glass curtain wall and the like low-texture scene, the texture-independent instance constraint is relied on to suppress the noise and the false image caused by reflection and the error motion diffusion, and the flow field inside the instance is kept uniform and pure.

This invention discloses a real-time video localization method based on three-dimensional imaging simulation of magnetic field signals of ferromagnetic materials, relating to the detection of ferromagnetic materials in a medical MRI room. The method includes: piecewise fitting of the magnetic field gradient using B-spline basis functions; omnidirectional calibration of the array using a rotating platform to establish a nonlinear mapping matrix between the sensor output and the actual magnetic field strength; extraction of target shape features; optical flow estimation of video frames to compensate for the magnetic field measurement delay caused by target motion; establishment of an association matrix between the target state vector and the measured value; reconstruction of the three-dimensional spatial distribution of the ferromagnetic material using an inversion method based on a perspective transformation model; and fusion of the three-dimensional imaging results with the real-time video. Optical flow estimation of video frames compensates for the magnetic field measurement delay caused by target motion, and estimation of multiple modes in parallel improves the convergence speed of state estimation.