136 results about "Nonlinear motion" patented technology

The invention discloses a visual prediction method for space-time manifold and implicit state deduction of a robot, and relates to the technical field of robot space visual perception. The motion state and the visual image of the robot are collected; calculating carrier pose offset and generating a reverse compensation matrix; modeling an image target into a visual cone probability manifold, and extracting an explicit manifold observation vector; monitoring the confidence coefficient in real time through an observation quality evaluation network, and activating an implicit deduction mode during observation degradation; reading historical time sequence characteristics by using an improved Transform model, and performing prediction and deduction on a future nonlinear motion state of the target; performing coordinate correction and probability field decoding on a prediction result in combination with the reverse compensation matrix, and outputting a three-dimensional prediction trajectory and a spatial covariance ellipsoid; according to the method, the problem that traditional visual tracking fails under the conditions of violent shaking of a carrier and target shielding is solved, and continuous and foresight physical scale positioning and safety decision making of a robot on a dynamic target are achieved.

The invention relates to the technical field of solar cell processing, in particular to a laser light-transmitting scribing control method and system for a solar thin-film cell. The control method comprises the steps that a large-size substrate is divided into a plurality of galvanometer view fields through a dynamic flight continuous machining mode, a continuous snake-shaped machining path is generated, the laser machining platform is driven to continuously move at the constant speed, and the galvanometer scanning system is synchronously triggered to start lineation in a splicing area in advance. According to the method, a time domain coupling equation of galvanometer scanning frequency-platform movement speed-laser pulse sequence is established, and dynamic compensation of an optical path and self-adaptive adjustment of energy density are achieved in the continuous machining process. Based on a view field expansion algorithm of a nonlinear kinematics model, the phase deviation of a scanning vector and a motion vector is effectively inhibited, and the geometric continuity of a splicing interface in ultra-long stroke machining is guaranteed.

The present invention discloses a laser metal curved surface automatic focus processing system and method thereof, including: a multimodal data fusion device, a curved surface detection module with a secondary tracking structure, integrated 3D structured light vision and vibration sensing equipment; a dynamic focus correction module, configured with an adaptive energy control unit and a beam shaping component; a nonlinear motion control console, including a six-degree-of-freedom parallel mechanism motion platform, and its servo control system integrates a time-space phase compensation algorithm; a core control unit, the core control unit has an embedded fusion algorithm model, and establishes a nonlinear mapping between material removal efficiency η and multivariable dynamic parameters. The processing quality level of the present invention is improved through the multi-source data fusion of 3D structured light and vibration sensing, the curvature recognition accuracy is improved, and the focus offset is reduced in combination with the axial vibration compensation mechanism, thereby effectively improving the processing quality.

The invention discloses a multi-modal data collaborative intelligent detection and three-dimensional positioning method for marine ships. The method comprises the following steps: performing refined preprocessing on collected multi-source heterogeneous data, and performing preliminary target detection; performing dynamic weighted fusion on the preprocessed multi-source heterogeneous data, and realizing accurate coordinate recovery of the ship target in a three-dimensional space by combining depth data of the laser radar; and predicting and updating the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. Through multi-modal data perception and preliminary processing, heterogeneous data deep fusion and three-dimensional positioning, and advanced multi-target continuous tracking and state estimation, abundant texture information of a visible light image, anti-interference capability of thermal imaging and accurate space depth data of a laser radar are fully utilized, and a multi-tracking mechanism cooperation strategy is integrated. The motion state of the recognized target is predicted and updated, and the problem of tracking interruption caused by nonlinear motion or transient shielding of the marine target is effectively solved.

The invention discloses a vehicle positioning method and system, and relates to unmanned driving. According to IMU data and laser radar data, a fast-lio algorithm is used to construct a global prior map; constructing a local point cloud map according to laser radar data collected in real time; recognizing and rejecting dynamic point clouds, and carrying out NDT registration on residual static point clouds and the global prior map to obtain a laser odometer pose; extracting feature points of the camera data by adopting a shii-tomasi algorithm; a Lucas-Kanade optical flow method is used to acquire motion vectors of the feature points; calculating the speed of the feature points according to the motion vector, removing the dynamic feature points with the speed exceeding a preset threshold value, and obtaining the pose of the visual odometer; reckoning the position and course of the vehicle through a CTRV model to obtain a track reckoning result; and carrying out multi-source heterogeneous data fusion by adopting a non-destructive Kalman filtering algorithm. Aiming at the defects of dynamic environment interference and non-linear motion trail modeling existing in traditional open air and mines, the positioning stability of the unmanned vehicle is improved.

The invention discloses a fish multi-target tracking method based on nonlinear motion modeling and category constraint, and the method comprises the following steps: constructing a basic fish target detection model which is used for generating a fish target detection frame; constructing a target tracking data set in an MOT format for target tracking and a target identification data set for appearance feature extraction, and constructing a basic fish appearance feature re-identification model for extracting appearance features of a fish target; and constructing a target tracking model associated with a nonlinear filter based on detection confidence weighting and category gating. A confidence-weighted extended Kalman filtering algorithm is provided, and the algorithm dynamically adjusts weight distribution of a detection result in a track updating process by analyzing a confidence score of a target detection frame in real time. According to the category gating mechanism, a category similarity penalty term is introduced into a cost matrix of data association, and by establishing an association barrier of category perception, it is ensured that detection frames and estimation frames of different categories are not wrongly associated to the same identity.

The invention discloses a multi-information fusion hydraulic cylinder internal leakage fault diagnosis method, and relates to the technical field of hydraulic cylinder fault diagnos.The hydraulic cylinder internal leakage fault diagnosis method comprises the steps that firstly, a hydraulic cylinder heavy load, time-varying and nonlinear motion curve and a test system based on a multi-source sensor are constructed, and internal leakage fault evolution under the actual working condition is simulated; and secondly, developing a hybrid deep learning semi-supervised feature fusion framework based on a convolutional auto-encoder, a multi-head attention mechanism, residual connection and a bidirectional sequential network, and enhancing the characterization learning ability of a multi-source signal in a complex and multi-noise environment through omnibearing deep feature decoupling. And finally, feature extraction and fusion are realized by using the displacement error and the pressure signal, and fault diagnosis is completed. Experiments show that compared with a comparison model, the diagnosis accuracy of the provided model and feature fusion strategy is improved by 3.95%; under-3 decibel noise, the accuracy is improved by 2.33%, and the scheme has high accuracy, reliability and generalization performance.

The invention discloses a wind turbine blade semi-coupling aeroelastic modeling method considering nonlinear deformation, and belongs to the technical field of fan simulation calculation. In combination with calculation parameters used by three-party software, establishing a blade structured load model and a blade correction aerodynamic model, calculating an aerodynamic load and a structural load, and superposing: superposing the aerodynamic load, the gravity load and the centrifugal force load of the blade as an external load of a nonlinear structure control equation of the blade, and taking the external load as a calculation parameter; according to the method, the blade nonlinear structure model and the semi-coupling simulation process of the three-party wind turbine simulation software are constructed, and under the same simulation environment and load conditions, the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation, so that the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation calculation. The higher-precision non-linear motion response characteristics of the super-long flexible wind turbine blade are calculated, and a good effect can be achieved in the aspects of initial structure design and response evaluation of the large wind turbine blade.

The invention discloses an ultra-precision global bridge rotation angle measurement method, and relates to the technical field of angle measurement. The method comprises the following steps: continuously shooting a bridge motion image sequence, and recording timestamps; key points on the bridge deck slab are selected through a feature detection algorithm, a spatio-temporal data matrix is constructed, and data standardization processing is carried out. Linear principal component analysis is adopted to extract global features of overall movement of the bridge deck; and extracting local nonlinear motion characteristics in the residual error through nonlinear principal component analysis. And calculating the displacement of the feature points based on the reconstruction matrix in combination with an lk optical flow method. A least square method is adopted to fit displacement data, and an angle is calculated through an arc tangent function on the basis. The method has the characteristics of high precision and global coverage, is suitable for the fields of bridge health monitoring, safety evaluation and the like, and can effectively capture the overall and local motion characteristics of the bridge.

The invention discloses an unmanned aerial vehicle detection and tracking method based on a multi-modal image, and belongs to the field of target detection and tracking. The unmanned aerial vehicle in the air is detected through the combined action of multiple modes, so that the reliability of unmanned aerial vehicle detection in various environments is realized, and identification and classification of the unmanned aerial vehicle are realized; in the tracking stage, a prediction structure in which LSTM and a Kalman filter are fused is introduced, so that the state modeling capability under the conditions of nonlinear motion and shielding of a target is enhanced, and the stability and continuity of tracking are improved; a fusion multi-stage matching strategy is adopted to effectively improve the matching accuracy and robustness in a complex environment; meanwhile, a confidence shunt strategy and a trajectory management mechanism are combined, so that the response speed to a new target and the fault-tolerant capability to a lost target are improved; through combination of global motion estimation and height information judgment, accurate correction and target distinguishing of the track of the unmanned aerial vehicle are realized, aliasing and misjudgment are avoided, and the detection and tracking capability of the unmanned aerial vehicle is remarkably improved.

The invention relates to a three-degree-of-freedom redundant parallel mechanism suitable for a wave compensation ship and a compensation method thereof.The three-degree-of-freedom redundant parallel mechanism comprises a lower deck and an upper deck, the lower deck and the upper deck are arranged in parallel, a plurality of wave compensation branch chains are connected between the lower deck and the upper deck, and the wave compensation branch chains comprise redundant branch chains and non-redundant branch chains; the redundant branch chains and the non-redundant branch chains located in the same row or the same column are arranged in a crossed mode, the non-redundant branch chains comprise the first non-redundant branch chain and the second non-redundant branch chain, the non-redundant branch chains are arranged in an isosceles triangle mode with the second non-redundant branch chain as the vertex, and the rest of the non-redundant branch chains are the redundant branch chains. The position precision of the mechanism is guaranteed through the position control branch chains, time-varying nonlinear motion in the heaving, rolling and pitching directions is dynamically compensated in real time, meanwhile, the redundant structural force control branch chains balance driving force of all the branch chains by adjusting the driving force, the bearing capacity of the mechanism can be improved, and good overall rigidity is achieved.

The invention discloses a radar target detection method and system based on deep learning and a storage medium, and relates to the technical field of target detection, and the method comprises the steps: carrying out the standardization processing of a millimeter wave radar, obtaining the standardized data, inputting the standardized data into an LSTM-KF model, and carrying out the filtering, combining data obtained by filtering with echo data to realize target detection; in order to solve the problems that a small target echo signal is weak, is susceptible to clutter interference, is complex in motion mode and the like, a weak signal change mode and a nonlinear motion characteristic of continuous multi-frame echoes are captured through LSTM, and dynamic trajectory constraint of KF is combined, so that time sequence enhancement and false alarm suppression of the weak signal are realized; the problems that traditional linear filtering is high in small target omission ratio, false alarm is difficult to control, nonlinear motion tracking lags and the like are effectively solved.

The invention discloses a video tracking control method and device for a non-linear moving target based on adaptive sliding mode control, which are suitable for accurate tracking of a dynamic target. The system is composed of a visual sensing module, a main control module and an execution mechanism, the visual sensing module collects position information of a target object in real time and transmits the position information to a main control chip through serial port communication, the main control chip calculates the error between the target position and the current position of the system and the error change rate, and a sliding mode surface is designed based on sliding mode control. Meanwhile, sliding mode control parameters are dynamically adjusted in combination with a self-adaptive control algorithm, so that the robustness of the system to uncertainty and external interference is enhanced, and meanwhile the chattering phenomenon is restrained. A stepping motor control signal is optimized by introducing a dynamic acceleration and deceleration algorithm, so that control smoothness and response precision are ensured, and real-time accurate tracking of a target object is realized. The method has strong robustness, high dynamic performance and control precision on the premise of not depending on a target kinematic model, can be widely applied to the fields of unmanned aerial vehicle tracking, intelligent monitoring, robot vision and the like, and effectively solves the problems of easy buffeting, response hysteresis, insufficient control precision and the like during nonlinear target tracking in the prior art.

The invention relates to the technical field of computer vision, in particular to a multi-target tracking method combining a time convolution network and a self-attention mechanism, which comprises a motion model based on a TCN, dynamic ReID feature updating based on track confidence, and a rematching strategy used for correcting wrong association caused by target interaction shielding. A TCN network is combined with an attention mechanism to be used for motion modeling, and nonlinear motion prediction of a track is achieved. In addition, a track confidence coefficient is provided to measure the robustness of the track, and the ReID features are dynamically updated based on the track confidence coefficient. Meanwhile, in order to make up for wrong association caused by target interaction shielding, a rematching module is designed, and the effectiveness of the rematching module is proved through experiments. In addition, each module of the system has a modular characteristic and a relatively strong generalization ability, so that the system is suitable for a wider real scene, and future research work is possibly stimulated.

The invention provides an attitude trajectory optimization method and system based on a self-adaptive sliding window. The method comprises the following steps: acquiring visual media data and decoding the visual media data into a time sequence image frame sequence; performing attitude estimation on each frame of image to obtain a coordinate set of each attitude feature point; caching a coordinate set of the historical attitude feature points to form a variable-length dynamic trajectory cache queue; constructing a sliding window according to the coordinate sets of the attitude feature points of the current frame and the previous frame; performing abnormal data elimination on each coordinate track; performing B spline fitting on each coordinate track; sequentially executing multi-stage optimization on the coordinate track; and updating the sliding window frame by frame to obtain a sequential sequence of the optimized attitude feature points. The method has the beneficial effects that the window radius is dynamically adjusted based on the motion intensity, the window is expanded at low speed to enhance noise suppression, and the window is reduced at high speed to reduce delay; and nonparametric regression fitting is carried out by adopting a cubic B spline, so that the modeling capability of a nonlinear motion track is enhanced, and the track smoothness is improved.

The invention discloses a method for calculating the posture of a berth airplane in rainy and snowy weather. The method comprises: acquiring an aircraft cleaning point cloud; performing coarse registration on the aircraft cleaning point cloud and the target point cloud, and outputting a registered aircraft pose; and carrying out Kalman pose prediction on the aircraft pose at the next moment by utilizing the aircraft pose and adding aircraft kinematics model constraint. According to the method, the posture of the aircraft at the next moment can be accurately predicted by establishing the nonlinear kinematics model and performing state estimation by adopting the extended Kalman filtering. By weighting key areas such as a machine head and an engine, accurate alignment of the important areas is guaranteed preferentially in the registration optimization process, and the precision and robustness of a registration algorithm under severe conditions are effectively improved. Kalman prediction provides a high-quality initial value for the ICP, and the accurate result of the ICP updates the state of Kalman filtering in turn. When the ICP fails, Kalman prediction takes over seamlessly, and a set of complete fault-tolerant mechanism is formed.

The invention provides a learning enhanced Kalman filtering method for forest emergency positioning and a related device. The method comprises the following steps: acquiring time sequence measurement data of an unmanned aerial vehicle and a ground target node; inputting time sequence measurement data into an error compensation network, and outputting a ranging error compensation amount and an observation noise covariance matrix; performing distance measurement compensation on the time sequence distance measurement data, feeding back the distance measurement data subjected to error compensation and a state error of the ground target node to a state prediction network, and outputting a state transition matrix and a process noise covariance matrix; state prediction is carried out based on a state transition model and a process noise covariance matrix, Kalman filtering fusion is carried out on a prediction state, compensated distance measurement data and an observation noise covariance matrix, three-dimensional position estimation of a ground target node is determined, combined improvement of measurement correction and prior prediction precision is realized, and the accuracy of measurement correction and prior prediction is improved. On the basis of keeping the optimality of traditional filtering, modeling is carried out on time-varying noise and non-linear motion, and the positioning precision in a forest complex environment is improved.

The invention relates to a multi-target tracking method under monitoring of an unmanned aerial vehicle, and belongs to the technical field of intelligent monitoring. According to the method, a traditional DeepSORT algorithm is optimized from three aspects of feature extraction, a matching strategy and motion modeling aiming at the problems of small target, frequent shielding, nonlinear motion and the like in an unmanned aerial vehicle scene. The method comprises the following steps: firstly, replacing an original feature extraction module with a lightweight OfficientNet network, and improving the small target feature discrimination through a composite scaling strategy and an MBConv module; secondly, providing a dynamic matching strategy based on EIoU, fusing position, shape and motion information, and reducing the mismatching rate of dense scenes; and finally, introducing extended Kalman filtering to process nonlinear motion trail prediction. According to the method, the MOTA index on a VisDrone data set reaches 85.6% and is improved by 12.3% compared with an original algorithm, and the tracking robustness in a complex scene is remarkably improved while the real-time performance is kept.

The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an incremental Kd-Tree laser-inertial fusion positioning method based on improvement, the method provided by the invention adopts a practical filtering scheme, the operand is small, the real-time performance is high, and the requirement of a laser radar on an illumination condition is lower than that of a camera, so that the method is very practical. The noise is much lower than that of visual measurement, so that the precision of the method is higher than that of a visual algorithm; according to the method provided by the invention, two kinds of data are fused in a deep level, so that the method is not liable to fail during strenuous exercise, and a GPU is not needed during operation. The filtering algorithm adopts an iterative error state Kalman filtering algorithm, so that the precision of the system is not very low under nonlinear motion, and the map management efficiency is also improved by the improved incremental Kd-Tree algorithm in the aspect of map management. In conclusion, aiming at a special scene without a GNSS signal, the robot can efficiently position the robot and construct a surrounding environment map only by depending on the sensor of the robot.

The invention provides a vehicle-mounted three-dimensional scene rendering method and device, a storage medium and a program product, and the method comprises the steps: obtaining the perception data of a target object, and constructing a nonlinear motion state space representing the motion process of the target object; performing prediction updating on the target object based on the state transition model of the nonlinear motion state space to obtain prediction state information and a prediction covariance matrix at the current rendering moment; extracting variance information related to the position from the covariance matrix to calculate an uncertainty factor, introducing the uncertainty factor into a graphic rendering pipeline, and dynamically adjusting at least one type of visual attribute of the three-dimensional model to realize uncertainty prompt; and when new perception data of the target object is obtained, performing fusion updating on the prediction state information, and performing smooth transition on a rendering result based on the correction amount to suppress rendering jump. According to the method, high-frame-rate continuous rendering display is realized under the condition of low-frequency updating of the sensor, and the interpretability and the safety of a rendering result are improved.

The invention discloses a video multi-target tracking method and system based on intra-layer and inter-layer fusion, and relates to the technical field of video processing and computer vision. The method comprises the following steps: acquiring a video data set containing a multi-target bounding box label, and performing preprocessing to construct a track sequence data set; constructing a Kalman-transformer hybrid frame model, and training by using the trajectory sequence data set to obtain a trained Kalman-transformer hybrid frame model; collecting an input video sequence, and performing frame-by-frame target detection on the input video sequence to obtain a candidate detection set; and inputting the candidate detection set into the trained Kalman-transformer hybrid framework model, and carrying out data association and state estimation to obtain a multi-target trajectory set. According to the method, reasoning is carried out only based on the geometric trajectory on the premise of not extracting image appearance features, real-time performance and precision are both considered, and the problem that an existing Kalman filtering method is difficult to deal with nonlinear motion and long-time shielding is solved.

The invention relates to an eccentric head five-axis numerical control cutter shaft proper amount control method and device, and the method comprises the steps: determining an initial cutter location point position and a target cutter shaft vector in a workpiece coordinate system according to the geometrical characteristics and machining requirements of a to-be-machined workpiece; based on the physical structure parameters of the eccentric rotating workbench, determining the space offset of the rotating center and the workpiece reference through a pre-constructed space offset relation model; calculating the motion compensation amount of the spindle head and the rotating shaft according to the current angle of the rotating shaft of the workbench and the space offset; the motion compensation amount is converted into a motion control instruction of a numerical control system, a rotating shaft of the eccentric rotating workbench is driven to be linked with a spindle head, the direction of a cutter shaft is continuously controlled, and therefore the purposes that high-precision cutter shaft pointing control of the eccentric rotating workbench in five-axis linkage machining is achieved, and non-linear motion errors caused by rotation center deviation are eliminated are achieved.

The present invention relates to a zoom lens, specifically to a high-performance long focal length continuous zoom lens, which solves the technical problems of complex mechanical structure and high processing difficulty of existing zoom lenses. The continuous zoom lens provided by the present invention includes a front fixed group, a zoom group, a compensation group, and a rear fixed group arranged in sequence from the object side to the image side. The rear fixed group includes a rear fixed group I, a rear fixed group II, and a rear fixed group III arranged in sequence from the object side to the image side. Among them, the zoom group makes a linear motion between the front fixed group and the compensation group to achieve zooming, and the compensation group makes a non-linear motion between the zoom group and the rear fixed group to compensate for the change in the axial position of the image plane during the zooming process. The present invention adopts a 6-group 20-lens structure, and the lenses used in each lens group are all spherical mirrors, which has good processability. The zoom group makes a linear motion to achieve zooming, the compensation group makes a non-linear motion to achieve image plane stability, with a large magnification, a large field of view, low distortion, and good imaging quality.

The application discloses a rotor coupling dynamics calculation method of complex three-dimensional shape layout. A coordinate conversion relationship caused by a front protrusion / after-sweeping / downward-reversing configuration is established; nonlinear elastic deformation of elastic blades waving, oscillating and twisting and nonlinear coupling relationship among them are described; nonlinear motion caused by the complex three-dimensional shape layout is described based on the coordinate conversion relationship and the nonlinear coupling relationship; and the relationship among strain energy, kinetic energy and virtual work of external force of the rotor is established based on the Hamilton principle according to the description of the nonlinear motion, and a finite element method of a 15-degree-of-freedom beam element is used to establish a rotor dynamics equation. The application can effectively reflect the structural coupling characteristics and coupling degree of the waving-torsion direction caused by the front / rear sweep of the blade tip, effectively reflect the structural coupling characteristics and coupling degree of the oscillation-torsion direction caused by the upward / downward reversal of the blade tip, and effectively reflect the rotor dynamics characteristic behaviors caused by the three-dimensional shape of the blade.

The invention relates to a multi-target tracking method and device based on a diffusion model, and the method comprises the steps: obtaining the ROI features of an input image of a front frame and an input image of a rear frame through a feature extraction module; a diffusion detection head structure based on the DDIM process is used for detecting targets in a front frame and a rear frame, and meanwhile, a trajectory prediction module based on a decoupling diffusion model is used for predicting the trajectory of an image of the front frame; and effectively associating the predicted trajectory with the obtained detection result by using a distance calculation algorithm mixed with IoU and ReID so as to obtain a final target tracking result. Therefore, the problems that an existing multi-target tracking technology is difficult to cope with linear and non-linear moving targets at the same time, ID switching is prone to occurring in a dense scene, and tracking stability and robustness are poor are solved.

The invention belongs to the technical field of rigid body posture estimation, and discloses a rigid body posture estimation method, system and device based on generalized correlation entropy geometric filtering, and the method comprises the steps: obtaining target motion information, and building a discrete nonlinear kinematics model; the discrete nonlinear kinematics model is initialized; sigma points of a posterior state covariance matrix and a process noise covariance matrix are generated respectively, and the sigma points are propagated and updated through manifold fast unscented transformation; obtaining a prediction state mean value, a priori state covariance matrix and a square root of the priori state covariance matrix; updating the central parameters based on the information of the historical moments; calculating a pseudo measurement matrix and updating the gain; correcting the state estimation by using the gain; judging whether convergence occurs or not, and if not, continuing iteration; otherwise, outputting a posterior state estimation value as a final rigid body posture estimation result at the moment. According to the method, the problem that the robustness and precision of rigid body posture estimation in a non-zero mean value and non-Gaussian noise environment are reduced is effectively solved.

This invention relates to an active disturbance rejection control method and system suitable for biomimetic chest cavity motion simulation. The method includes: acquiring and preprocessing a nonlinear motion trajectory signal to generate an original target position command; processing the original target position command using a tracking differentiator to generate a smoothly transitioning desired position signal and desired velocity signal; receiving the actual position feedback signal of the controlled object and the control input of the previous control cycle through an extended state observer, and estimating the actual position state observation value, actual velocity state observation value, and total disturbance state observation value of the controlled object in real time; calculating the position tracking error and velocity tracking error based on the desired position signal, desired velocity signal, and the actual position state observation value and actual velocity state observation value of the controlled object, and calculating the initial state feedback control quantity; generating an actual control command and sending it to the motor driver to drive the biomimetic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory.

The invention relates to a curtain driving method and device. The method comprises the steps that the curtain driving device obtains a starting instruction; the curtain driving device responds to the starting instruction and drives the curtain to execute sectional type nonlinear motion control according to a segmentation strategy, the segmentation strategy is determined according to target parameters obtained in advance, and the target parameters are changeable; wherein the sectional type nonlinear motion control at least comprises the following steps: in a first preset proportion interval of a target stroke determined according to the target parameter, gradually propelling a curtain to act by a curtain driving device according to an action interval determined by a sectional strategy; and in a second preset proportion interval of the target stroke, the curtain driving device is divided into at least one step of action, and the stroke of each step of action is larger than that of each step of action in the first preset proportion interval, so that different action rhythms are distributed in different stroke proportion intervals, and the target stroke can be adjusted according to the different action rhythms. And a sectional type nonlinear motion control mode in the whole target stroke is formed.

The application discloses a kind of unmanned aerial vehicle detection tracking methods based on multi-modal image, belong to target detection and tracking field.The present application detects aerial unmanned aerial vehicle by the joint action of multiple modes to realize the reliability of unmanned aerial vehicle detection under various environments, realize the identification and classification of unmanned aerial vehicle;In the tracking stage, the prediction structure of the fusion of LSTM and Kalman filter is introduced, the state modeling ability under the nonlinear motion of target and the shielding condition is enhanced, and the stability and continuity of tracking are improved;Adopting the multi-stage matching strategy effectively improves the matching accuracy and robustness in complex environment;At the same time, the confidence shunt strategy and trajectory management mechanism are combined, the response speed to new target and the fault tolerance to lost target are improved;Combined with global motion estimation and height information determination, the precise correction of unmanned aerial vehicle trajectory and target differentiation are realized, aliasing and misjudgment are avoided, and the detection and tracking ability of unmanned aerial vehicle is significantly improved.

The present invention belongs to the field of UWB indoor positioning and target tracking. Aiming at the adaptability problem of target tracking methods to nonlinear motion in indoor UWB positioning scenarios, a nonlinear motion target tracking method based on robust extended Kalman filtering is proposed. Based on the use of Taylor approximation of nonlinear state transfer function and observation function, robust threshold control is performed on the error between estimated observation value and actual observation value to achieve adaptive correction of target state estimation. This can not only improve the robustness of the model to noise but also improve the accuracy of state correction, thereby obtaining a more accurate UWB positioning target trajectory.