60 results about "Velocity estimation" patented technology

The invention relates to a fast distributed humanoid robot state estimation method, system and device and a medium. The method comprises the following steps: acquiring IMU sensor data and vision measurement data; non-linear attitude estimation is carried out based on IMU sensor data and visual measurement data, and floating base attitude estimation information at the next moment is obtained; contact information and leg joint information are obtained, after physical constraint is carried out based on the contact information, linear movement window estimation is carried out according to the leg joint information and the attitude estimation information of the floating base at the next moment, and speed and position estimation information of the floating base at the next moment is obtained; and when linear movement window estimation is carried out, speed estimation information and position estimation information are optimized by adopting a marginalization method. According to the method, the calculation complexity can be reduced, and meanwhile, the accuracy of state estimation and the resistance to noise and model uncertainty are improved.

The invention provides an RFID trajectory tracking system based on least square speed adaptation, and the method comprises the steps: firstly building a half-wavelength observation distance expansion model, and effectively solving the problem of error divergence caused by a conventional phase unwrapping scheme; then, generating an initial position candidate set by using a triangular centroid weighting algorithm, performing instantaneous speed estimation by using a least square algorithm, and introducing a dynamic threshold-weight constraint mechanism to obtain a high-quality initial state set; thirdly, speed estimation based on least square is fused in the EKF, speed data are updated through the least square method before each round of antenna scheduling is started, target tracking trajectories estimated based on different initial positions are obtained, and the problem that in the target tracking process, the speed suddenly changes or the tracking error is large during steering is solved; and finally, dynamically deleting the tracking trajectory with the zero weight, and selecting the trajectory with the maximum weight value as the final tracking trajectory of the target, thereby effectively reducing the calculation complexity of the algorithm. The invention provides an RFID trajectory tracking system which is convenient and simple to deploy, can realize trajectory tracking of a target tag, and is suitable for most indoor positioning scenes.

A finite time dynamic mechanical arm control method based on a speed reconstruction strategy comprises the steps that firstly, a mechanical arm system physical model containing joint torque is constructed, joint tracking errors are defined, and a mechanical arm system state space model is obtained; 2, constructing a finite-time high-bandwidth gain speed estimator, defining a state estimation error, and obtaining an estimation error dynamic equation; 3, defining an auxiliary state to obtain an auxiliary state dynamic equation; 4, constructing a finite time non-recursive adaptive controller; and 5, obtaining a final input torque of the controlled system, and outputting the final input torque as a joint torque. According to the method, through the processes of error modeling, state estimation, auxiliary conversion, control law design and closed-loop feedback, the dynamic tracking problem of the mechanical arm is converted into the finite time convergence problem of the error state, meanwhile, dynamic gain and robust compensation are introduced, the adaptability of a system to parameter uncertainty and external disturbance is improved, and the system reliability is improved. And a high-precision and high-robustness control target is realized.

The invention provides an IMU (Inertial Measurement Unit) speed estimation method and system based on multi-scale time sequence feature coding and Mama. The method belongs to the field of IMU speed estimation, and comprises the following steps: S1, acquiring original time sequence data which is generated by an IMU and contains three-axis acceleration and three-axis angular velocity, and intercepting a section of historical time sequence data with a fixed length by taking a current moment as an end point to construct a historical window; s2, inputting the original time sequence data in the historical window into a multi-scale one-dimensional convolution encoder, processing the input data by the encoder by using at least two convolution kernels with different sizes in parallel so as to extract time sequence characteristics under different receptive fields, and fusing output characteristics of each branch to obtain a potential characteristic sequence; and S3, inputting the potential feature sequence into a Mama model to carry out time sequence dependency relationship modeling, and extracting an output state of the Mama model at the end of the sequence as a solved three-dimensional speed output. According to the method, the IMU speed estimation precision can be improved.

A method for adapting a causal FIR filter is provided. The causal FIR filter is configured to estimate velocities by filtering position measurement data in accordance with a frequency response of an electro-mechanical system involved in a task. The method uses a processor coupled with stored instructions to implement the method. The stored instructions, when executed by the processor, carry out steps of the method. The method includes collecting a sequence of position measurement data and a corresponding sequence of ground truth velocities of a component of the electro-mechanical system performing the task. The method further includes adapting the causal FIR filter for estimating velocities of the component performing the task by updating one or more coefficients of the causal FIR filter to reduce a difference between the sequence of ground truth velocities and estimated velocities produced by filtering the sequence of position measurement data by the causal FIR filter.

The invention discloses a multiphase flow bubble tracking and speed measuring and calculating method based on improved YOLO11, and belongs to the technical field of multiphase flow bubble detection and tracking, and the method comprises the steps: obtaining a multiphase flow bubble image data set; dividing the multiphase flow bubble image data set into a training set and a verification set; yOLO11 is improved to obtain an improved YOLO11 network model, and a multiphase flow bubble segmentation model based on the improved YOLO11 is obtained; inputting the training set into the model for training to obtain an optimal segmentation model; and carrying out performance evaluation on the model by utilizing a verification set, and inputting the model into a multiphase flow bubble identification and speed measurement and calculation system based on a graphical user interface after the precision requirement is met, so as to carry out bubble segmentation, tracking and speed measurement and calculation on an actual image. According to the method, under the condition that the model parameter quantity and the calculation quantity are greatly reduced, the bubble segmentation effect with high operation precision and high speed can be achieved, and equipment deployment and application are more convenient.

The invention discloses a multi-target optimization tracking control method for a heterogeneous unmanned cluster system based on MADDPG, and the method comprises the following steps: building a dynamic model of the heterogeneous unmanned cluster system, and carrying out the isomorphism of the heterogeneous unmanned cluster system through speed estimation; establishing a communication topology model of the heterogeneous unmanned cluster system, designing an adjacent matrix weight, and quantifying the information interaction strength between the heterogeneous unmanned cluster systems; describing a plurality of optimization objectives of the heterogeneous unmanned cluster system and executor saturation constraint conditions of the optimization objectives; modeling a multi-target optimization tracking control problem of the heterogeneous unmanned cluster system into a reinforcement learning problem, and designing MADDPG-based tracking control algorithm elements; and a learnable weight parameter is established, a target weight coefficient is updated online by using gradient back propagation, and dynamic optimization of multi-target weight is realized. Through an adaptive target weight adjustment mechanism and a saturation constraint and heterogeneous unified modeling framework, the tracking control problem under the influence of multi-target conflict, actuator saturation constraint and heterogeneous characteristics is solved, and the cooperative tracking control effect and robustness of a heterogeneous unmanned cluster system in a dynamic environment are improved.

A method for adapting a causal FIR filter is provided. The causal FIR filter is configured to estimate velocities by filtering position measurement data in accordance with a frequency response of an electro-mechanical system involved in a task. The method uses a processor coupled with stored instructions to implement the method. The stored instructions, when executed by the processor, carry out steps of the method. The method includes collecting a sequence of position measurement data and a corresponding sequence of ground truth velocities of a component of the electro-mechanical system performing the task. The method further includes adapting the causal FIR filter for estimating velocities of the component performing the task by updating one or more coefficients of the causal FIR filter to reduce a difference between the sequence of ground truth velocities and estimated velocities produced by filtering the sequence of position measurement data by the causal FIR filter.

The invention relates to a traffic flow speed estimation method based on a Gaussian mixture model and hierarchical Bayesian, and belongs to the technical field of intelligent traffic systems. The method comprises four steps of data preparation and preprocessing, traffic state clustering division, hierarchical Bayesian random parameter model construction, and model verification and performance evaluation. Through deep fusion of the Gaussian mixture model and the hierarchical Bayesian model, data-driven traffic state identification and random parameter estimation are realized, traffic flow heterogeneity can be effectively described, model parameter explosion is avoided, and model prediction precision and generalization ability are improved.

The invention belongs to the technical field of automatic driving and environmental perception, and particularly relates to a 4D millimeter-wave radar-camera omnidirectional speed estimation method based on a CAN bus, and the method specifically comprises the steps: S1, data collection and coordinate system unification; s2, vehicle motion information provided by a vehicle CAN bus is utilized to carry out pre-compensation on motion of the vehicle, and the absolute radial speed is obtained; s3, constructing a speed constraint condition by using optical flow and depth information, simplifying an inter-frame transformation matrix into a unit matrix in combination with motion compensation, and greatly simplifying an inter-frame motion transformation relationship; and S4, establishing three constraint conditions by combining the optical flow, the depth and the radial speed of the radar, constructing a linear equation set about the omnidirectional speed of the target, and directly solving the linear equation set to obtain a closed form solution of the three-dimensional omnidirectional speed of the target. According to the method, the accurate target depth and radial speed provided by the 4D millimeter wave radar and the optical flow information provided by the camera are fused, the CAN bus is innovatively introduced to carry out pre-compensation on the motion of the vehicle, a linear constraint model of multi-source information fusion is constructed, and complete estimation of the three-dimensional omnidirectional speed of the target is achieved. The method effectively overcomes the limitation of a single sensor in speed estimation, and improves the accuracy and robustness of speed estimation.

The present invention provides a terahertz near-field MIMO-OFDM multi-target position and velocity joint estimation method, including a terahertz near-field MIMO-OFDM model establishment step: establishing a near-field MIMO-OFDM signal model, representing the received signal in the near-field MIMO-OFDM signal model as a third-order tensor; a tensor decomposition step: decomposing the received signal tensor, and based on the uniqueness of the tensor decomposition, using the factor matrix after the tensor decomposition to estimate the estimated value of the intermediate parameter; a parameter preliminary estimation step: using the estimated value of the intermediate parameter and the least squares LS algorithm to obtain a preliminary solution to the iterative initial point to establish an optimization equation; a precise positioning and velocity estimation step: performing a Taylor expansion on the nonlinear term of the optimization equation at the initial point to perform a second-order Taylor approximation, converting the optimization equation into a convex optimization equation, and using the cvx toolbox to iteratively solve the convex optimization equation to obtain the estimated value of the target position and velocity. The present invention improves the accuracy of target parameter estimation by jointly processing the inter-antenna phase difference and the inter-subcarrier phase difference through tensor decomposition.

The invention provides a flow velocity estimation method and device, equipment and a storage medium, and the method comprises the steps: carrying out the normalization processing of a flow velocity change rate at a current moment, and obtaining a normalized flow velocity change rate; determining a target flow velocity change rate in the normalized flow velocity change rates at the multiple moments; determining a target process excitation noise covariance matrix and a target observation noise covariance matrix corresponding to the target flow velocity change rate in a pre-established first relationship between the change rate and the process excitation noise covariance and a pre-established second relationship between the change rate and the observation noise covariance; and based on the target process excitation noise covariance matrix, the target observation noise covariance matrix and the first flow velocity, calculating a flow velocity estimation value at the current moment. According to the embodiment of the invention, the excitation noise covariance matrix and the observation noise covariance matrix of the target process in the Kalman filter are determined according to the flow velocity change rate and the size of the fluid, so that the sudden change fluid can be quickly followed, and the jump fluid data can be effectively filtered.

The present invention relates to a method, system, device and medium for fast decentralized humanoid robot state estimation. The method includes: acquiring IMU sensor data and visual measurement data; performing non-linear attitude estimation based on the IMU sensor data and visual measurement data to obtain the attitude estimation information of the floating base at the next moment; acquiring contact information and leg joint information, performing physical constraints based on the contact information, and then performing linear moving window estimation according to the leg joint information and the attitude estimation information of the floating base at the next moment to obtain the speed and position estimation information of the floating base at the next moment; when performing linear moving window estimation, the marginalization method is used to optimize the speed estimation information and position estimation information. The present invention can improve the accuracy of state estimation and the resistance to noise and model uncertainty while reducing the computational complexity.

To improve the perception performance of an integrated communication and perception system using offset constant envelope OFDM (OFDM), this paper proposes a distance and velocity estimation method based on offset constant envelope OFDM. This method uses an offset constant envelope OFDM signal as the transmission signal for the integrated communication and perception system and uses a 2D-FFT algorithm based on conjugate multiplication to estimate target range and velocity. This method effectively reduces the impact of a non-flat signal spectrum and achieves superior distance and velocity estimation performance.

The present invention discloses a slope-compensated acceleration estimation method based on trajectory data, comprising the following steps: 1. obtaining trajectory data of a vehicle traveling on a slope and a horizontal section; 2. calibrating the vehicle's free flow speed using the trajectory data of the horizontal section; 3. analyzing the slope trajectory data to construct a slope acceleration model; 4. assuming that the expected acceleration of the horizontal section and the compensated acceleration of the slope conform to a normal distribution, according to the acceleration model, obtaining the probability density of the observed acceleration, the posterior probability of the compensated acceleration, and the joint probability distribution of the two; 5. Based on the joint probability distribution, constructing a compensated acceleration estimation model using an expectation-maximization algorithm and performing iterations to obtain the mean and variance of the expected acceleration of the horizontal section and the mean and variance of the compensated acceleration of the slope. The present invention can identify the additional compensated acceleration given to the vehicle by the driver on a slope, thereby better explaining the impact of driving behavior on road capacity.

The invention provides a UUV rapid iteration positioning and speed measuring method based on one-way communication, and relates to the technical field of acoustic positioning. The method comprises the specific implementation steps that unknown UUV nodes acquire known information through one-way communication with anchor nodes, a ranging equation is constructed, a linear-nonlinear iterative optimization algorithm is executed according to the known information, and coordinate data and speed data of the unknown UUV nodes are updated according to the result of the algorithm. According to the technical scheme, rapid positioning and speed measurement of the unknown UUV node can be completed only through one-way communication, the exposure risk of the unknown UUV node is reduced, and the accuracy of positioning and speed measurement is improved through the linear-nonlinear iterative optimization algorithm.

Methods and apparatus, including computer program products, implementing, and using techniques for estimating wind velocity. Measurements are obtained, which indicate two or more trajectories traversed by flying balls. Individual wind velocity estimates are determined for the two or more trajectories, wherein the determination comprises comparing a model acceleration of the ball with an observed acceleration of the ball, derived from the measurements. The aggregated wind velocity estimate is calculated as a weighted average of the determined two or more individual wind velocity estimates. The aggregated wind velocity estimate is used for generating ball trajectory information to be presented on an output device.

To provide a speed estimation device, an evaluation system, and a speed estimation method that can improve the accuracy of estimating elastic wave velocity.SOLUTION: A speed estimation device of an embodiment has a spectrum information extraction unit, a peak frequency extraction unit, a range determination unit, a specification unit, and an elastic wave velocity estimation unit. The spectrum information extraction unit extracts, for each of different measurement points, spectrum information of one or more elastic waves generated from impact on the measurement points in a target member. The peak frequency extraction unit extracts one or more peak frequencies in every spectrum information extracted for each of the measurement points. The range determination unit determines a frequency range of the elastic wave on the basis of the one or more peak frequencies obtained for each of the measurement points. The specification unit specifies the peak frequency at each measurement point on the basis of the spectrum information and the frequency range. The elastic wave velocity estimation unit estimates the elastic wave velocity at each measurement point on the basis of the peak frequency and thickness information of the target member.SELECTED DRAWING: Figure 8

Velocity estimation using intelligent reflecting surfaces (e.g., using a computerized tool), is enabled. For example, a system can comprise at least one processor, and at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations. The operations can comprise based on a first signal between a receiver and a target object, determining a second signal between the receiver and the target object, wherein the first signal comprises a direct signal, and wherein the second signal comprises an indirect signal conveyed via an intelligent reflecting surface, determining a Doppler frequency of the second signal, and based on the Doppler frequency, determining a velocity estimation of the target object.

The application relates to a traffic flow speed estimation method based on a Gaussian mixture model and a hierarchical Bayesian method, and belongs to the technical field of intelligent transportation systems. The method comprises four steps of data preparation and preprocessing, traffic state clustering division, hierarchical Bayesian random parameter model construction, model verification and performance evaluation. Through the deep fusion of the Gaussian mixture model and the hierarchical Bayesian model, the application realizes data-driven traffic state recognition and random parameter estimation, can effectively depict traffic flow heterogeneity, avoids model parameter explosion, and improves the model prediction precision and generalization ability.

Systems and methods to implement Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can exceed four times the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Derivation of the estimator yields explicit probabilities of unwrapping errors and the posterior probability distribution for the velocity estimate; this in turn allows for optimized design of the phase-encoded acquisition.

The present invention provides a multi-target position and velocity joint estimation method based on terahertz near-field MIMO-OFDM, comprising: a terahertz near-field MIMO-OFDM model establishment step: establishing a near-field MIMO-OFDM signal model, and representing a received signal in the near-field MIMO-OFDM signal model as a third-order tensor; a tensor decomposition step: decomposing the received signal sensor, and on the basis of the uniqueness of the tensor decomposition, performing estimation by using a factor matrix obtained after the tensor decomposition to obtain estimated values of intermediate parameters; a parameter preliminary estimation step: using the estimated values of the intermediate parameters and a least squares (LS) algorithm for preliminary calculation so as to obtain iteration starting points, and establishing an optimization equation; and a precise positioning and velocity estimation step: performing Taylor expansion and second-order Taylor approximation on nonlinear terms of the optimization equation at the starting points, converting the optimization equation into a convex optimization equation, and using a cvx toolbox to iteratively solve the convex optimization equation to obtain estimated values of the position and the velocity of a target. The present invention improves the accuracy of target parameter estimation by jointly processing an inter-antenna phase difference and an inter-subcarrier phase difference by means of tensor decomposition.

The invention provides a speed estimation method and a training method and device of a speed estimation model, and the method comprises the steps: obtaining input data in real time through a speed estimation model obtained through training under the indication of a fuselage speed estimation instruction, the body speed information of the foot-type robot is estimated based on the body sensing data of the foot-type robot included in the input data, and the speed estimation model does not need to acquire the sole contact state of the foot-type robot, so that the accuracy of body speed estimation is improved.

The invention discloses a maneuvering target tracking method based on multi-model filtering. The method comprises the following steps: establishing a state equation and a measurement equation of a tracking system; performing multi-model interaction according to an interactive multi-model filtering method; parallel filtering is carried out, a feedback learning item at the current moment is used as a reference item, and state estimation and error covariance matrixes of different models at the next moment are obtained; carrying out model probability updating; fusing the state estimation of each model based on the updated probability to obtain a target state and a covariance matrix, and obtaining a feedback learning item at the next moment based on the target state and the covariance matrix; and multiple iterations are carried out to realize tracking of the target at subsequent moments. According to the method disclosed by the invention, the position estimation precision and the speed estimation precision of the maneuvering target are remarkably improved.

The present invention provides a velocity estimation method based on phase unwrapping, which belongs to the field of radar signal processing for high-speed rendezvous target miss distance measurement systems. The method first extracts valid data frames containing target motion information based on the signal-to-noise ratio of the baseband echo signal. Then, phase unwrapping is used to obtain the measured two-way distance corresponding to each sampling point of the target echo. The search two-way distance corresponding to each sampling moment before the target impact is then calculated. Considering that target velocity changes relatively slowly in long-range segments, where the non-stationary characteristics of the signal are less pronounced, the error between the search two-way distance and the measured two-way distance within this range is used to reflect the deviation between the search velocity and the target motion velocity. This method can effectively improve the estimation accuracy of non-stationary signals.

This disclosure provides systems, methods, and devices for wireless communications that support speed estimation, such as speed estimation with radio frequency (RF) sensing. In a first aspect, a velocity of an object is determined based on Doppler measurements from a plurality of transmit / receive points (TRPs). In a second aspect, a velocity of the object is determined based on angle of arrival (AoA) information received from the plurality of TRPs. The AoA information may include distance, AoA, time, or a combination thereof. In a third aspect, a velocity of the object is determined based on an orientation of the object during a sensing operation performed by the TRP. Other aspects and features are also claimed and described.

This invention discloses a method for estimating the remaining average velocity and controlling the flight time of a variable-speed missile, comprising the following steps: constructing a missile motion model and a differential geometry guidance model; predicting the remaining flight path of the missile under proportional guidance, and constructing a flight path error dynamic equation in the form of biased proportional guidance; designing a flight path control guidance law based on the optimal error dynamics method; predicting the missile velocity profile and estimating the remaining average velocity; converting the desired remaining flight time into the desired remaining flight path, and achieving flight time control through the flight path control guidance law. This invention relates to the field of aircraft dynamics, guidance, and control, and can effectively improve the accuracy of existing missile remaining average velocity estimation algorithms and reduce flight time control errors. It can provide an effective solution to the problem of time-varying multi-missile cooperative guidance.

The invention provides an inertial navigation error suppression method based on an LSTM-MMAE hybrid architecture, and the method comprises the steps: carrying out the data preprocessing of original IMU data, and obtaining a standardized time sequence; capturing a long-term time sequence dependency relationship of the standardized time sequence through a gating mechanism to obtain a speed increment predicted value; the noise covariance matrix of the speed increment predicted value and the observation noise covariance matrix of the theoretical trajectory speed are adjusted and fused through a plurality of parallel filtering models with different parameters, and optimal fusion estimation coping with different operation tasks is obtained; and carrying out speed prediction on the optimal fusion estimation to obtain optimal fusion speed estimation, and carrying out iteration on the optimal fusion speed estimation to obtain a target motion track. According to the method, through fusion of the LSTM neural network and multi-model adaptive estimation, IMU error accumulation is dynamically inhibited in a GPS failure scene, and the positioning precision and the system reliability are improved.

This invention discloses an AUV velocity estimation method based on a centrosymmetric multicell Kalman filter. First, a systematic analysis of the state-space model of the autonomous underwater vehicle (AUV) is performed. Then, combining the properties of centrosymmetric multicells and the concept of Kalman filtering, the reachable set of the AUV system state is calculated and optimized. The velocity range of the AUV is calculated using the optimal reachable set, enabling effective velocity monitoring. This method is applicable to AUVs with various configurations and different motion states. Compared with existing technologies, the proposed method can not only accurately estimate the velocity of the AUV but also provide velocity range estimates, thereby achieving effective velocity state monitoring and significantly improving the reliability and stability of the AUV.

A method is provided for estimating a velocity of an object located in the environment of a vehicle. Detections of a range, an azimuth angle and a range rate of the object are acquired for at least two different points in time via a sensor. A cost function is generated which depends on a first source and a second source. The first source is based on a range rate velocity profile which depends on the range rate and the azimuth angle, and the first source depends on an estimated accuracy for the first source. The second source is based on a position difference which depends on the range and the azimuth angle for the at least two different points in time, and the second source depends on an estimated accuracy for the second source. By minimizing the cost function, a velocity estimate is determined for the object.