12 results about "Motion parameter estimation" patented technology

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.

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.

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 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 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 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.