59 results about "Motion parameter estimation" patented technology

The invention relates to the technical field of radar signal processing, and discloses a method for calibrating radar echo signals on a mobile platform, which comprises the following steps of: acquiring original echo signals of a radar on the mobile platform under different working conditions and preprocessing the original echo signals, acquiring multi-source IMU sensor and differential GPS information and performing time sequence fusion; carrying out platform motion parameter estimation on the preprocessed echo signal; and based on a preset reference signal model, performing feature extraction on the echo signal, and obtaining feature parameters for calibration through time-frequency processing and spatial domain adaptive filtering. Multi-source IMU (inertial measurement unit) data and differential GPS (global positioning system) data are fused to obtain six-degree-of-freedom continuous motion parameters of a mobile platform, and time synchronization and preprocessing of echo signals are combined to realize dynamic calibration of original radar echo signals. And the problem of amplitude, phase and Doppler frequency deviation of the echo signal caused by the attitude and the motion state of the mobile platform is solved.

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 present invention discloses an independent positioning and navigation system based on synthetic aperture radar, which relates to the field of radar signal processing technology, including using synthetic aperture radar to collect echo signals, and sparsely reconstructing the echo signals through a double-layer sparse reconstruction unit to obtain a reconstructed and restored signal; inputting the reconstructed and restored signal into a Kalman filter to estimate platform motion parameters and construct a motion compensation parameter matrix; dynamically compensating the reconstructed and restored signal according to the motion compensation parameter matrix, and outputting target positioning parameters. The present invention accurately compensates for the distance migration, Doppler frequency modulation effect and signal envelope broadening caused by target motion, and ultimately achieves the technical effect of improving the positioning accuracy, anti-interference ability and navigation performance of the system in a complex electromagnetic environment.

The invention provides a method and a device for estimating rotor motion parameters of an unmanned aerial vehicle target. The method comprises the following steps: acquiring a time sequence electromagnetic scattering characteristic parameter of an unmanned aerial vehicle target in a hovering state; calculating the time sequence electromagnetic scattering characteristic parameters by using a smooth pseudo-Wigner-Ville distribution algorithm to obtain a time-frequency distribution diagram of the unmanned aerial vehicle target; performing Fourier transform on the time-frequency distribution diagram of the unmanned aerial vehicle target to obtain a micro-Doppler frequency representation diagram of the unmanned aerial vehicle target; and based on the micro-Doppler frequency representation diagram of the unmanned aerial vehicle target, obtaining the rotor motion parameters of the unmanned aerial vehicle target in the air according to the micro-Doppler frequency spectrum estimation. According to the scheme, accurate estimation of the rotor motion parameters of the unmanned aerial vehicle target in a low signal-to-noise ratio environment can be realized.

The invention provides an image data processing method, device and equipment and a storage medium, and the method comprises the steps: obtaining a plurality of cone beam computed tomography (CBCT) image data for the same target, precisely recognizing a region with a motion artifact in an image, and carrying out the detailed motion parameter estimation of the artifact region, including the motion direction and amplitude of the target. Based on the motion parameters, refined motion compensation processing is performed on the image data of the artifact area, thereby effectively eliminating artifacts generated by motion. The beneficial effects of the scheme are that through step-by-step artifact detection, accurate motion parameter estimation and targeted motion compensation, it is ensured that the finally generated image is clear and free of artifacts, and the quality of the CBCT image is significantly improved.

The invention discloses a bistatic inverse synthetic aperture radar imaging and calibration method based on motion parameter estimation, and belongs to the technical field of radar signal processing and radar imaging. The method solves the problems that an existing method is poor in anti-noise performance, related scattering points are difficult to find, and the azimuth defocusing effect is not eliminated. According to the method, an equation set is constructed by using a bistatic ISAR echo phase coefficient, the ratio of a first-order coefficient to a constant coefficient of a bistatic coefficient and an estimated value of an effective rotation angular velocity are obtained by solving the equation set, and then the constant coefficient of the bistatic coefficient is estimated by using a receiver beam center direction and target distance information; and constructing a compensation phase based on the estimated value to effectively compensate image distortion and range defocus, thereby realizing calibration of the inverse synthetic aperture radar image. The method can be applied to bistatic inverse synthetic aperture radar imaging and calibration.

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 present invention relates to a real-time posture estimation system for a small ground carrier, comprising: a current carrier posture result processing module, a current moment environment grid map publishing module, a carrier posture history matrix updating module, a carrier motion parameter estimation module, a next moment carrier posture prediction module and a laser SLAM scanning callback function. The present invention greatly improves the working performance of the prior art under the high dynamic maneuvering state of the carrier without introducing a large amount of calculation.

The invention discloses a rigid registration CT (Computed Tomography) motion parameter estimation and motion compensation method and system. The 3D-2D rigid registration CT motion parameter estimation and motion compensation method based on exhaustion search strategy optimization is carried out through six steps. According to the method, aiming at the problems of huge computing power demand and storage occupation caused by an exhaustive search mechanism, the motion parameter interference decoupling method is used for optimizing the exhaustive search strategy, so that the demand of the exhaustive projection search strategy on computing resources is effectively reduced while the motion parameter estimation precision is remarkably improved. Besides, in consideration of motion artifact characterization differences of different stages in the iteration process, the invention provides a time-varying data consistency measurement method which is used for more accurately measuring data consistency between re-projection and measurement projection, so that the accuracy of parameter estimation is further improved.

The present invention relates to a method and system for extracting radar target motion characteristics. The method includes preprocessing radar one-dimensional range profile data to obtain the phase signal of the target; using empirical mode decomposition to separate the signal of the phase signal to obtain a plurality of intrinsic mode functions; and performing motion parameter estimation on the intrinsic mode functions respectively based on the spectrum analysis method of Fourier analysis to obtain the radar target motion characteristics. The present invention can adapt to the actual situations of more diverse, high-speed motion, and motion coupling of radar targets, and the extracted target motion characteristics have higher accuracy.

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.

The invention discloses long-time monitoring based on kinematics and mechanics priori under complex working conditions of AI + vision + IMU fusion, and belongs to the technical field of large-scale civil structure health monitoring, and the method comprises the following steps: firstly, respectively collecting image data and motion data of a civil structure by using a vision camera and an IMU sensor; decoupling the rotation motion of the camera through a mathematical model based on kinematics and mechanics priori knowledge; only one control point is used, decoupling information and collected data are combined, and six-degree-of-freedom motion parameters are estimated through a measurement equation optimized through AI; and finally calculating displacement information and performing error compensation. The system comprises a data acquisition module, a rotary motion decoupling module, a six-degree-of-freedom motion parameter estimation module and a displacement measurement and compensation module. According to the invention, the hardware cost is reduced, the measurement flexibility and adaptability are improved, and the method is suitable for long-time monitoring under complex working conditions.

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 present disclosure provides a kind of long-distance high-speed weak target detection method and device based on DP-RFT, belong to radar detection technical field.In the target detection process, first, dynamic programming (DP) algorithm is used to estimate target motion parameters, and the DP parameter estimation result is used as the prior information of Radon Fourier transform (RFT) algorithm to solve the blind speed sidelobe problem of RFT long-time accumulation;Then, using the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value for compensation.Then, the target echo after motion parameter compensation is synthesized to obtain the target high-resolution one-dimensional range image.The present application can estimate the target motion parameters and synthesize the target high-resolution one-dimensional range image in the case of range ambiguity, avoid blind speed sidelobe, reduce the amount of calculation, so as to realize the stable detection of long-distance high-speed weak target.

An unmanned aerial vehicle time-frequency image reconstruction method based on deep learning belongs to the technical field of radar signal processing and image reconstruction, solves the technical problem that unmanned aerial vehicle time-frequency images are difficult to identify and analyze under the condition of low signal-to-noise ratio, and comprises the following steps: S1, performing short-time Fourier transform on echo signals, and establishing an unmanned aerial vehicle radar echo model STFT; s2, designing a SelfNet model based on a convolutional neural network, and reconstructing an unmanned aerial vehicle time-frequency curve graph; s3, according to an unmanned aerial vehicle radar echo model STFT and a time-frequency curve output image, establishing an unmanned aerial vehicle time-frequency image data set, and performing training by using a SelfNet model; and S4, reconstructing the time-frequency image data set of the unmanned aerial vehicle by using the trained SelfNet model weight. The method can be used for actually measuring data, the precision of the time-frequency image of the unmanned aerial vehicle under the low signal-to-noise ratio can be improved, and follow-up multi-class classification and micro-motion parameter estimation of the unmanned aerial vehicle are facilitated.

A system for rigid motion correction for magnetic resonance imaging (MRI) of a subject includes an input for receiving motion corrupted k-space data for the subject acquired using an MRI system, a motion parameter estimation module coupled to the input and configured to estimate motion parameters based on the motion corrupted k-space data, a motion correction neural network coupled to the input and the motion parameter estimation module and configured to generate motion corrected k-space data based on the motion corrupted k-space data and the estimated motion parameters, and a reconstruction module coupled to the motion correction neural network and configured to generate a motion corrected image from the motion corrected k-space data.

The invention relates to the field of golf sensing, and discloses a golf swing data calculation method and system based on machine vision, and the method comprises the following steps: S1, obtaining an image sequence containing a golf club swing process, and processing the image sequence to obtain an initial motion track of a club head; s2, outputting a prediction rod type and an initial motion parameter estimation value through a first pre-training model; s3, processing the image sequence to construct a six-degree-of-freedom pose of the club head, and calculating based on the six-degree-of-freedom pose to obtain a group of swing data; the system comprises an initial analysis module, an initial analysis module and a data calculation module. According to the method, the two-dimensional positioning model and the three-dimensional geometric model which are accurately matched with the type of the golf club are adaptively and dynamically selected from the model library through the preliminarily predicted type of the golf club, so that pertinent geometric priori knowledge is obtained through subsequent refined calculation, and the effect of accurately analyzing different golf clubs is achieved.

The invention provides a DP-RFT-based long-distance high-speed weak target detection method and device, and belongs to the technical field of radar detection. In a target detection process, a dynamic programming (DP) algorithm is firstly adopted to estimate target motion parameters, and a DP parameter estimation result is used as prior information of a Radon Fourier transform (RFT) algorithm to solve the problem of blind speed sidelobe accumulated for a long time by the RFT; and then taking the target motion parameter obtained by the DP algorithm as a center, performing coherent accumulation by using an RFT algorithm, and obtaining a target motion parameter estimation value for compensation. And then target high-resolution one-dimensional range profile synthesis is carried out on the target echo after motion parameter compensation. According to the method, the target motion parameters can be estimated and the target high-resolution one-dimensional range profile can be synthesized under the condition of range ambiguity, so that the blind speed sidelobe is avoided, the operand is reduced, and the stable detection of the long-distance high-speed weak target is realized.

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 invention discloses a uniformly accelerated motion target echo processing and motion parameter estimation method and device. The method comprises the following steps: acquiring a frequency coding signal target echo of a uniformly accelerated motion target; performing baseband echo processing to obtain echo after pulse compression; distance compensation and frequency domain transformation processing are carried out, and echoes after fast time Fourier transformation are obtained; second-order non-standard Keystone transformation processing is carried out, and a second-order non-standard transformation signal is obtained; performing acceleration estimation and compensation processing to obtain compensation echoes; performing first-order non-standard Keystone transformation processing to obtain a first-order non-standard transformation signal; echo phase-coherent accumulation processing is carried out, and echoes after phase-coherent accumulation are obtained; motion parameter estimation is carried out to obtain a motion parameter estimation result, and the motion parameter estimation result comprises the target initial distance and the target speed. According to the invention, echo processing and motion parameter estimation are realized, the estimation accuracy is improved, and the processing calculation amount is reduced. The method can be widely applied to the technical field of radar signal processing.

The invention belongs to the technical field of signal processing, and particularly relates to a beam-pumping unit motion parameter estimation method based on a multi-scale convolution noise reduction auto-encoder. Comprising the following steps that acceleration signal data in the operation process of the beam-pumping unit are collected through an acceleration sensor; performing sliding sampling on the processed acceleration data through a sliding rectangular frame with a fixed length to obtain acceleration sampling data with a fixed length; constructing a multi-scale convolution noise reduction auto-encoder model; performing random corrosion processing on the acceleration sampling data to obtain corrosion data; using the sampling data and the corresponding corrosion data to train a multi-scale convolution noise reduction auto-encoder model; acceleration data is collected and processed in real time, and noise reduction processing is carried out on the acceleration data through the trained multi-scale convolution noise reduction auto-encoder model. And calculating motion parameters of the oil pumping unit by using the noise reduction acceleration data, wherein the motion parameters comprise the stroke and the stroke frequency. The method reduces stroke and stroke frequency calculation errors caused by data acquisition loss and noise errors.

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 invention discloses a method and apparatus for processing echoes and estimating motion parameters of uniformly accelerated moving targets. The method includes: acquiring the frequency-coded target echo signal of a uniformly accelerated moving target; performing baseband echo processing to obtain a pulse-compressed echo; performing range compensation and frequency domain transformation processing to obtain a fast-time Fourier transform echo; performing second-order non-standard Keystone transform processing to obtain a second-order non-standard transform signal; performing acceleration estimation and compensation processing to obtain a compensated echo; performing first-order non-standard Keystone transform processing to obtain a first-order non-standard transform signal; performing echo coherent accumulation processing to obtain a coherently accumulated echo; and performing motion parameter estimation to obtain motion parameter estimation results, including the target's initial range and target velocity. This invention achieves echo processing and motion parameter estimation, improves estimation accuracy, and reduces the computational load. This invention can be widely applied in the field of radar signal processing technology.

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 invention discloses an ISAR coherent translation compensation and imaging method based on a motion parameter estimation network, and relates to the technical field of imaging, and the method comprises the steps: receiving an echo of a space target inverse synthetic aperture radar; preprocessing the echo to obtain a compensated echo sequence; processing the compensated echo sequence by adopting a trained motion parameter estimation network model to obtain a distance grid offset rough estimation value of the central position of the target in each echo; multiplying the distance grid offset coarse estimation value by the distance resolution unit to obtain a distance coarse estimation value, fitting to obtain a translation parameter coarse estimation value, taking the translation parameter coarse estimation value as a preset imaging parameter to be optimized of an imaging model, and performing iterative optimization to obtain an optimized imaging parameter; obtaining a translation error term of each echo according to the optimized imaging parameters; and compensating the translation error term of each echo, and performing coherent accumulation on each compensated echo to obtain an imaging result. According to the invention, the imaging result is clearer and more accurate.

A bistatic inverse synthetic aperture radar imaging and calibration method based on motion parameter estimation belongs to the field of radar signal processing and radar imaging technology. The present invention solves the problems of poor anti-noise performance, difficulty in finding associated scattering points, and failure to eliminate azimuth defocusing effects in existing methods. The present invention uses bistatic ISAR echo phase coefficients to construct an equation group, obtains the ratio of the first-order coefficient to the constant coefficient of the bistatic coefficient and the estimated value of the effective rotational angular velocity by solving the equation group, and then estimates the constant coefficient of the bistatic coefficient using the receiver beam center direction and target distance information; constructs a compensation phase based on the estimated value to effectively compensate for image distortion and range defocusing, thereby achieving calibration of the inverse synthetic aperture radar image. The method of the present invention can be applied to bistatic inverse synthetic aperture radar imaging and calibration.