26 results about "Motion parameter estimation" patented technology

The invention provides a target third-order motion parameter estimation method and device based on high-order phase fusion, and the method comprises the steps: building a third-order polynomial phase model based on a radar echo signal, and carrying out the high-order phase fusion estimation, and obtaining an estimation value of a third-order motion parameter; performing third-order phase compensation based on the estimated value of the third-order motion parameter to obtain an estimated value of a second-order motion parameter; performing phase compensation on the slow time complex sequence based on the estimated value of the third-order motion parameter and the estimated value of the second-order motion parameter to obtain a compensation sequence; and converting the compensation sequence into a single-frequency slow-time complex sequence, and estimating motion parameters of the to-be-measured target based on the single-frequency slow-time complex sequence. Through step-by-step compensation of the third-order phase term and the second-order phase term, coherent focusing of target energy can be realized under the condition of not depending on dense grid search, so that the robustness and precision of maneuvering target detection are improved, and meanwhile, the requirements of a vehicle-mounted system on real-time performance and computing power cost are considered.

The invention discloses a star point motion parameter estimation method for a high-dynamic star sensor. The method specifically comprises the following steps: (1) acquiring a star map under a dynamic condition through a fixed star sensor; (2) carrying out filtering preprocessing on the fixed star sensor image obtained in the step (1); (3) carrying out threshold segmentation on the image processed in the step (2) and carrying out connected domain extraction to obtain rough star points; (4) selecting the brightest star point in the star points obtained in the step (3); (5) obtaining a sub-pixel star point track by using the brightest star point adjacent area obtained in the step (4) and adopting a Zernike moment sub-pixel edge extraction method; and (6) utilizing the sub-pixel star point trajectory obtained in the step (5), and adopting least square fitting to obtain star point motion parameters. The method has the beneficial effects that the precision of star point trailing length and angle estimation is improved, so that the extraction precision of star points in a dynamic environment is improved, and the precision of a fixed star sensor is improved.

The application discloses a kind of high-efficiency motorized weak target long-time coherent accumulation detection methods, belong to radar target detection field;Including the three-scale decomposition of motorized target radial motion parameter variable;In target blur scale motion parameter variable search space eliminates Doppler frequency blur effect;Through Keystone conversion correction first-order range migration caused by motorized target baseband speed;In target coarse scale baseband motion parameter variable search space, estimate motorized target coarse scale baseband motion parameter by GIFT conversion;In target fine scale baseband motion parameter variable search space, estimate motorized target fine scale baseband motion parameter, obtain temporary range-doppler spectrum;According to motorized target motion parameter variable three-scale decomposition model, estimate the complete motion parameter of motorized target;Based on motorized target motion parameter estimation, obtain motorized target range-doppler spectrum.The method has the characteristics of low computational complexity, Doppler blur effective compensation, high detection performance.

The application provides a multi-target motion parameter estimation method and device based on MIMO radar, which comprises: performing down-conversion processing on echo signals observed by each receiving array of a MIMO radar system to obtain identification results of baseband signals corresponding to each receiving array; constructing a covariance matrix corresponding to each receiving array based on the identification results; performing eigenvalue decomposition on each covariance matrix, and determining a noise subspace corresponding to each receiving array based on the results of the eigenvalue decomposition of each receiving array; and determining an estimation result of a motion parameter of each target to be measured based on the noise subspace corresponding to each receiving array and an array response matrix; wherein the array response matrix comprises array response characteristics of the receiving array and signal characteristics of the echo signals observed by the receiving array. The application can effectively reduce the calculation load in a multi-target scene, thereby overcoming the limitation of direct estimation methods in the application of multi-target scenes.

This invention provides a radar motion parameter estimation method and apparatus based on information criterion values. It acquires raw radar echo data, performs range processing and target extraction to obtain a target slow-time complex sequence. Based on the target slow-time complex sequence, it constructs multi-order motion candidate models, calculates the residual sum of squares, and calculates the information criterion value based on the residual sum of squares. The optimal model order is determined based on the information criterion value, and the target radial motion parameters are inverted and output based on the phase coefficients corresponding to the optimal model order. Instead of pre-fixing the use of a first-order, second-order, or third-order model, it determines the optimal model order after constructing multi-order candidate models and evaluating them using the calculated information criterion value. This approach adaptively determines the appropriate motion model order for the target based on the phase evolution characteristics of the signal itself, and completes motion parameter estimation accordingly. This ensures estimation accuracy while suppressing unnecessary model complexity, thus improving its engineering application value.

The application provides a spatial precession cone target micro-motion parameter estimation method based on deep learning under echo defect conditions; the method comprises the following steps: generating a time-frequency image dataset containing different defect degrees; pre-processing the time-frequency graph, including defect simulation, curve repair and peak feature enhancement; constructing a double-branch anti-defect parameter estimation network (DAPE-Net), which has a shared feature extraction backbone, a space-time attention mechanism, a double-branch feature decoupling structure and an independent regression head, realizes end-to-end estimation of precession period and precession angle from the defective time-frequency graph, and verifies the robustness and high precision of the model under various defect proportions through training and testing; experimental results show that under certain defect conditions, the model can accurately estimate the micro-motion period and precession angle of the target, and is significantly better than mainstream deep learning models, and is suitable for real-time estimation of radar target micro-motion parameters in complex electromagnetic environments.

The present disclosure provides a kind of based on coherent array detector moving target laser synthetic aperture sparse imaging method, comprising: using narrowband laser emission signal to irradiate the target to be detected, and the target to be detected exists rotation and translation;The multiple frames of laser complex images of the target to be detected are collected by laser local oscillator coherent array detector;On the basis of the motion parameter estimation of the target to be detected, the multiple frames of laser complex images are carried out Fourier inverse transform in digital domain, and the multiple frames of spatial sampling signals are obtained, and the multiple frames of spatial sampling signals are carried out phase compensation and motion compensation;The spatial sampling center is calculated, and the multiple frames of spatial sampling signals are spliced, and large range space sparse sampling signal is formed;By lengthening observation time, the sparsity of the large range space sparse sampling signal is reduced, and by compressed sensing processing, the high-resolution image reconstruction and image sidelobe suppression of large range space sparse sampling signal are realized.

This invention discloses a plate motion analysis method based on GNSS and SLR stations of the Eurasian Plate, belonging to the field of geodesy and surveying engineering technology. The method includes: acquiring raw coordinate time-series data from GNSS and SLR stations and preprocessing them; decomposing the preprocessed sequence using singular spectrum analysis (SSA) to reconstruct and separate trend, periodic, and noise terms; performing Fast Fourier Transform (FFT) on the reconstructed signal to extract frequency domain features; establishing a comprehensive model of linear velocity plus periodic correction by combining time and frequency domain features; and analyzing the motion characteristics of the Eurasian Plate based on this model to achieve motion trend prediction and anomaly detection. This invention, through the combination of SSA and FFT, significantly improves the accuracy of motion parameter estimation and the ability to identify weak signals, providing a reliable method for plate motion analysis and earthquake prediction.

The application discloses a SAR motion parameter estimation method based on accurate measurement of time of arrival and direction of arrival, which comprises the following steps: reading N LFM direction of arrival alpha n and corresponding measurement value of time of arrival t n , performing cubic polynomial fitting on the line of sight angle alpha n by using a least square method to smooth measurement error; performing quadratic polynomial fitting on the time of arrival t n by using a least square method to obtain a linear term coefficient p1 and a quadratic term coefficient p2; obtaining a radar pulse repetition interval T by using a statistical pulse time of arrival difference; updating a detection pulse serial number n; solving a radar initial distance r0; and solving a radar flight speed v and a flight direction beta by using the initial distance r0, the direction of arrival alpha n and the time of arrival t n . The application has the characteristic that a signal waveform of a SAR emission is synthesized with an LFM pulse, accurate estimation of SAR motion parameters is realized, the variation rate parameter of the line of sight angle is avoided, and the robustness of the algorithm is improved and the requirement for the direction finding accuracy is reduced.

The application relates to a medium earth orbit (MEO) multi-channel SAR ground moving target motion parameter estimation method and belongs to the field of radar signal processing. A signal model of a distance equation of a first channel and a second channel of MEO SAR based on third-order Taylor expansion is established, a first-order phase coefficient, a second-order phase coefficient, a third-order phase coefficient and an intermediate parameter are estimated based on a CPF method, along-course velocity and radial acceleration in the second-order phase coefficient are adjusted by combining multi-channel information, and along-course velocity, radial velocity, along-course acceleration and radial acceleration of the target are estimated based on a coupling relationship. The method solves the problem of difficult motion parameter estimation of a moving target caused by the coupling relationship.

This application discloses a radar low-altitude target detection method and system based on least squares inversion, involving radar and data processing technology. The method includes: establishing the target's motion trajectory based on multipath echo signals received by the radar, and establishing a multipath observation model based on the motion trajectory; establishing residuals and a target function based on the multipath observation model; performing inversion iteration using least squares based on the established residuals and target function, solving the problem along the iteration direction based on the iteration step size; and outputting the inverted parameters for target tracking. This application achieves adaptive compensation for multipath effects by modeling multipath bias and jointly inverting parameters such as target range and elevation angle, thereby improving the accuracy of radar estimation of low-altitude target motion parameters.

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 provides a target motion parameter estimation method and device based on a distributed MIMO radar, which comprises the following steps: based on a preset echo signal expression model, signal expression is performed on echo signals of a target to be measured observed by each receiving array of a MIMO radar system; wherein the echo signal expression model comprises an angle of arrival expression term, a time of arrival expression term and a Doppler frequency offset expression term, and the angle of arrival expression term, the time of arrival expression term and the Doppler frequency offset expression term are constructed based on a motion parameter term; the results of signal expression corresponding to each receiving array are subjected to down-conversion processing respectively, so as to obtain baseband signal expression results corresponding to each receiving array; and based on the baseband signal expression results corresponding to each receiving array, a maximum likelihood estimation method is used to determine an estimation result of the motion parameter of the target to be measured. The application can realize high-precision estimation of the motion parameter of the target to be measured.

The application discloses a kind of robust frequency-modulated stepped frequency ISAR imaging radar velocity estimation methods, belong to radar technical field, first construct multiple frames frequency-modulated stepped frequency data model;Again, the velocity estimation and compensation of rough accuracy are realized using the same frequency point offset estimation of multi-frequency point synthesis;Then, the high-resolution imaging of the coarse resolution distance unit where the special point is located is obtained by the IFFT of intra-frame sub-pulse dimension, and the high-precision velocity estimation and compensation are realized using the high-resolution image of multiple special point synthesis;Finally, ISAR imaging is realized through the FFT between frames.The application realizes the estimation and compensation of velocity gradually using two-step method, first realizes the estimation and compensation of large motion amount by rough estimation, then realizes the residual motion estimation and compensation of high precision by synthesis high resolution;Offset estimation is realized using envelope cross correlation, motion parameter estimation is realized using offset fitting, and the amount of calculation is small;The robustness of algorithm is improved using the characteristics that multiple frequency points are not easily disturbed simultaneously, and the robustness of high-resolution velocity estimation is improved using the motion identity of multiple special points.

The invention discloses a space target rotation motion parameter estimation method and device based on multi-modal feature fusion. The method comprises the steps of obtaining a to-be-processed image sequence and an internal high-frequency sight vector sequence corresponding to each frame of image in the to-be-processed image sequence; processing by adopting the trained residual network and a first layer in the trained double-layer long short-term memory network to obtain a visual feature vector after each frame of image is fused and an intra-frame motion feature vector of each frame of image, projecting the visual feature vector and the intra-frame motion feature vector to the same dimension space, and then splicing to obtain a visual feature vector and an intra-frame motion feature vector; obtaining a multi-modal feature vector fusing visual information and motion information of each frame of image; and performing macroscopic motion time sequence modeling on the multi-modal feature vector corresponding to the image sequence to be processed by adopting a second layer in the trained double-layer long-short-term memory network to obtain an aggregation total feature, and performing regression processing on the aggregation total feature to obtain an estimated rotation motion vector. According to the invention, the accuracy of motion parameter estimation can be improved.

The invention relates to a method and device for estimating motion parameters of a moving target, electronic equipment and a medium, and belongs to the technical field of wireless communication signal processing.The method comprises the steps that an MISO system receiving signal model between the moving target and an antenna array is built; the method comprises the following steps: decomposing an MISO system receiving signal model into a plurality of factor nodes, constructing a factor graph based on the plurality of factor nodes, and dividing the factor graph into a plurality of function modules; and performing parameter estimation on a functional module on the factor graph by applying a hybrid message passing algorithm combined with a BP-EP-MF rule to obtain the estimated distance, angle, radial speed and tangential speed from the moving target to the antenna array. According to the method, the MISO system receiving signal model is decomposed into a plurality of factors for mixed message transmission, so that the calculation complexity of the MISO system receiving signal model is reduced.

The application discloses a target motion parameter estimation method and system based on a bistatic Doppler time-frequency curve; a plane rectangular coordinate system is established with a receiver position as an origin, a transmitter position is a known fixed coordinate, and a target is assumed to be a uniform linear motion; according to analytical expressions of distances between the transmitter and the target and between the receiver and the target, a bistatic equivalent path length is constructed, and an analytical expression of Doppler variation with time is derived based on a Doppler physical model; a target Doppler time-frequency curve is sampled at multiple time points to obtain a Doppler measurement vector, and outputs of the analytical model at corresponding time points are combined to form a model vector, so that an observation equation with target motion parameters as unknown quantities is formed; and a nonlinear least square algorithm is used to estimate the target motion parameters. The application has the advantages of not needing distance measurement, complete theoretical basis, high estimation precision and easy engineering implementation, and can be used for multi-station radar target trajectory matching and information fusion.

The application discloses a moving target CPA through feature detection method based on Doppler-Warping transformation and belongs to the technical field of underwater sound. The application combines the high gain characteristic of Doppler-Warping transformation and the real-time processing capability of a time sliding window, and proposes a moving target CPA through feature detection method. In each time window, firstly, a target motion model is established according to three motion parameters of a CPA time, a CPA distance and a target motion speed, and a Doppler-Warping operator corresponding to different parameter combinations is constructed; then, the time domain signal in the window is subjected to Doppler-Warping transformation by using the operator; finally, the optimal parameters are determined by searching for the minimum value of a spectrum entropy, and whether the line spectrum belongs to the moving target passing through the CPA is judged according to whether the signal-to-noise ratio gain before and after the transformation exceeds a set threshold value. The application can effectively extract the CPA through feature of the moving target and meet the requirements of target detection and motion parameter estimation in engineering.

The application discloses an ISAR phase correlation translational motion compensation and imaging method based on a motion parameter estimation network, relates to the field of imaging technology, and comprises the following steps: receiving echo of a spatial target inverse synthetic aperture radar; pre-processing the echo to obtain a compensated echo sequence; processing the compensated echo sequence by using a trained motion parameter estimation network model to obtain a distance grid offset coarse estimation value of a target center position of each echo; multiplying the distance grid offset coarse estimation value by a distance resolution unit to obtain a distance coarse estimation value, fitting the distance coarse estimation value to obtain a translational motion parameter coarse estimation value, taking the translational motion parameter coarse estimation value as an optimized imaging parameter of a preset imaging model, iteratively optimizing the translational motion parameter coarse estimation value to obtain an optimized imaging parameter; obtaining translational motion error terms of each echo according to the optimized imaging parameter; compensating the translational motion error terms of each echo to obtain an imaging result by coherently accumulating the compensated echoes.

Computer-implemented method for determining at least one vehicle skidding condition, comprising: Receiving at least one motion parameter of a vehicle based on signals captured by at least one vehicle sensor; estimating a self-aligning torque and at least one lateral axis force based on at least one motion parameter; estimating the tire caster for at least one tire of the vehicle using the self-aligning torque and the at least one lateral axle force; Determining at least one vehicle skidding condition based on the estimated tire trail; the computer-implemented method further includes the following: a condition of decreasing tire trail is determined and an initial vehicle skidding condition is determined in response to the condition of decreasing tire trail; and / or The estimated tire caster is compared with a predetermined tire caster threshold, and a second vehicle skidding condition is determined based on the comparison.

The invention relates to the technical field of image processing, solves the problems of insufficient local motion estimation and low processing efficiency in the prior art, and provides an image stabilization method, device and equipment based on image block matching and a medium. Obtaining a first image block set and a plurality of second image block sets, and performing feature matching on the first image block set and the plurality of second image block sets to obtain a matching point set; performing local motion parameter estimation according to the matching point set to obtain a target local motion parameter; obtaining an image stabilization transformation matrix according to the target local motion parameters; and carrying out image stabilization processing on the current frame image according to the image stabilization transformation matrix to obtain a target image. According to the invention, the stability of the video can be improved, and higher user satisfaction can be realized in practical application.

This invention proposes a system and method for estimating the motion parameters of an elastic seabed acoustic source based on acoustic field interference phenomena. First, the normal mode characteristics and interference structure characteristics of the elastic seabed waveguide are analyzed. Then, based on the analyzed interference structure characteristics, a two-dimensional Fourier transform of the time-frequency interference structure is performed to extract the spectral density peaks. Finally, based on the analyzed normal mode characteristics of the elastic seabed waveguide and the calculated coordinates of the spectral density peak points, the motion parameters of the acoustic source are estimated. This invention analyzes the surface wave characteristics of this ubiquitous marine environment based on the normal mode characteristics of the elastic seabed waveguide, identifies the normal mode that plays a major role in the acoustic field interference structure of the elastic seabed waveguide, optimizes the original two-dimensional Fourier transform algorithm for estimating the motion parameters of the acoustic source, and accurately achieves the estimation of the motion parameters of the acoustic source in this marine environment waveguide.

The application discloses a radar weak target multi-mode motion parameter estimation and coherent accumulation method, which comprises the following steps: S1, performing pulse compression on the radar echo of a maneuvering target along the distance direction to obtain an echo signal; S2, dividing the echo signal in a CPI based on a short-time window to obtain a sub-CPI signal; S3, correcting the range migration of the signal in the sub-CPI by using a short-time Keystone transformation, and extracting a range signal in the azimuth direction; S4, performing a short-time fractional Fourier transformation on the range signal in the azimuth direction, estimating the instantaneous motion parameters in the sub-CPI, estimating the motion mode conversion time based on the instantaneous motion parameters in the sub-CPI, and obtaining the number of motion modes and the motion parameters in each mode; and S5, constructing a phase compensation function according to the motion parameter estimation, compensating the range migration and Doppler migration in the original echo, and realizing long-time coherent accumulation. The application avoids high-dimensional search of parameters, has low calculation complexity, high calculation efficiency, and good engineering application prospect.

The application relates to the field of visual control, in particular to a robot motion parameter estimation method and system based on a static spraying image, and aims to solve the problems of improving the efficiency and accuracy of robot spraying motion parameter planning. The method comprises the following steps: acquiring an initial single-frame static test spraying deposition image, converting the image from RGB to HSV color space, obtaining a background plate mask image, performing perspective projection transformation on the background plate mask image to obtain a background plate area correction image, and obtaining the mapping ratio of the image space to the Cartesian space; obtaining the width and height of the minimum circumscribed rectangle of the effective spraying area outer contour according to the background plate area correction image, and mapping the width and height to the Cartesian space based on the mapping ratio to obtain the width and height of the robot spraying area, and obtaining the lateral movement speed and longitudinal movement interval of the robot. The scheme provided by the application effectively solves the problems of low robot spraying efficiency, poor quality consistency and high artificial dependence.

The invention discloses an industrial product crack defect detection method and system based on image recognition, and relates to the technical field of computer vision and industrial detection. According to the invention, multi-modal synchronous acquisition and dynamic adaptive enhancement are carried out through the image acquisition module, and multi-spectral modal complementation is utilized to suppress composite interference and reinforce crack characteristics; double-source motion parameter estimation and crack form constraint fuzzy kernel optimization are adopted through a fuzzy compensation module, motion blur is repaired in combination with simplified iteration reconstruction, and the integrity of the crack form is guaranteed; geometric-gray double features are extracted through a crack identification module, and texture-independent accurate identification is realized through form-area constraint and dynamic weight fusion; a differentiation strategy is made by means of a collaborative optimization module, and parameter self-adaptive optimization is achieved through smooth updating. The core pain point of micro crack detection under complex working conditions is effectively solved, the requirements of industrial production for detection precision and anti-interference capability are met, and the method is suitable for industrial product quality control under a high-speed production scene.