28results about How to "Effective estimate" patented technology

The invention discloses an accumulation detection and parameter estimation method for a high-speed target signal with unknown time information, which is applied to the field of radar signal processing and aims to solve the problems of poor accumulation detection performance, inaccurate parameter estimation and the like under the condition that the time information is unknown in the prior art. The method comprises the following steps: firstly, establishing a high-speed target echo signal model with unknown time information; thirdly, performing pulse compression on the echo signal and realizing coherent accumulation through RFT to obtain an initial radial distance coarse estimation value and an accurate estimation value of the radial speed; then, the pulse pressure echo signals are extracted based on the estimation parameters; then, SFT processing is carried out on the extracted signals, and estimated values of the starting time and the ending time are obtained; and finally, obtaining an accurate estimation value of the initial radial distance by using the obtained initial radial speed and the starting and ending time. And effective accumulation detection and high-precision parameter estimation of the target are realized through scaling Fourier transform.

The invention provides a near-field information source positioning method and system. The method comprises the following steps: on the basis of a direction of arrival (DOA) and a receiving signal model in a near-field environment, creating a fourth-order accumulation matrix only containing DOA information; performing eigenvalue decomposition on the fourth-order accumulation matrix to obtain a strong signal subspace; bringing the strong signal subspace into the noise subspace to form a first extended noise subspace; a DOA representing a weak signal source is determined by using a MUSIC algorithm and the first extended noise subspace; creating a covariance matrix of the received signal; carrying out eigenvalue decomposition on the covariance matrix and expanding a noise subspace to construct a two-dimensional spatial spectrum; the DOA of the weak signal source is input into the two-dimensional spatial spectrum for one-dimensional search to obtain the positioning information of the weak signal source, and the positioning information comprises the distance of the weak signal source, so that the accuracy of positioning the near-field signal source in a scene where strong and weak signals coexist is improved.

The application relates to a real-valued super-resolution direction estimation method of a high-order acoustic field sensor array, which uses a received signal covariance matrix to approximately calculate an estimated value of noise power, avoids the iterative operation process of noise power in the prior art, improves the calculation efficiency, and reduces the floating-point operation amount. By constructing an augmented matrix, the array receiving data matrix and the array manifold matrix with a multi-dimensional structure of an element are made into Hermitian matrices, so that the unitary transformation processing of the related parameters of the high-order acoustic field sensor array is realized. On the basis of obtaining the array receiving data matrix and the array manifold matrix in the real number domain, the array receiving data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method with a variable exponential factor, and the completely real-valued sparse approximate minimum variance direction estimation with a variable exponential factor is realized.

The application relates to a deep-sea shadow zone target distance and azimuth fast high-precision estimation method. First, a large-aperture horizontal array is divided into two sub-arrays with a certain horizontal interval, and target azimuth estimation is performed on array receiving signals of the two sub-arrays; second, based on target azimuth estimation results of the two sub-arrays and geometric relation with the target, the sound line propagation distance of the target is estimated, and the sound line propagation distance is converted into the horizontal distance between the target and the receiving array through a virtual source model; finally, the horizontal distance obtained is utilized to correct the horizontal array system direction-finding error in combination with geometric knowledge. Compared with a traditional two-station direction-finding cross positioning method, the positioning precision of the method is higher. The basic principle and implementation scheme of the method are verified through computer numerical simulation, and the result shows that in a typical deep-sea environment, the method can effectively estimate the distance and azimuth of a near-sea surface target.

The invention relates to the technical field of self-adaptive wave trap groups, in particular to an improved cascade structure self-adaptive wave trap group and an instantaneous frequency detection method thereof, which can overcome the respective defects of CANFB, TCANFB and PPANFB and realize more efficient multi-frequency detection. According to the invention, the method is a series-parallel adaptive wave trap group (Cascade Pre-filled and Parallel Update ANFB, CPANFB), the calculation efficiency is high, and the calculation amount is obviously reduced while the detection precision is ensured; compared with the PPANFB, the CPANFB does not need to accurately set an initial value, so that the robustness and adaptability of the system are enhanced; by optimizing the design of pre-filtering and main filtering, the CPANFB can maintain high frequency estimation precision in various signal-to-noise ratio environments.

The invention belongs to the field of space target imaging, and discloses a space target three-dimensional reconstruction method for inhibiting dark color interference through combination of brightness and transparency, which is used for solving the problems of poor imaging quality and inaccurate pose estimation of a space target. According to the method, multiple requirements of improving the quality of an input image, adjusting a pose and training a three-dimensional model are jointly completed, and low-quality imaging of an incomplete imaging system is preprocessed based on lucky imaging; and then, dense feature points are extracted by using a pre-training network based on no detector, estimation of a three-dimensional initial point cloud and an initial pose of a camera is carried out, and pose joint adjustment is carried out by combining RANSAC and Bundle AdJustlement. During training, an MLP-based optimizer is used for calculating the deviation of pose adjustment, meanwhile, training of parameters of three-dimensional Gaussian primitives is carried out, and after various physical constraints are added, a one-stop assembly line can be implemented. According to the method, the good basic shape and the sharp edge of the model are kept, the good anti-noise capability is achieved, and meanwhile the real-time performance and the robustness are achieved.

The invention provides a multi-source traffic operation data analysis method and system based on AI sensor fusion. Accurate reconstruction and dynamic monitoring of an interchange space scene of a highway section can be realized. A traffic operation data flow is generated through an AI sensor fusion acquisition technology, a detailed traffic dynamic change map is further analyzed, meanwhile, driving behavior data blocks and non-driving behavior data blocks are effectively separated through block mapping and fine extraction of road domain sensing data, a data basis is provided for deep analysis of traffic state influence factors, and the traffic state influence factors are effectively analyzed. The further optimized traffic dynamic change map is combined with an interchange space scene to accurately estimate a traffic safety risk trend and generate forward-looking traffic safety risk trend data, so that targeted safety prompt suggestions can be generated in time, the traffic safety management level and the emergency response capability of the expressway section are remarkably improved, and the traffic safety risk of the expressway section is improved. The occurrence of traffic accidents is effectively prevented and reduced, and the public travel safety is guaranteed.

The application discloses a product environment stress estimation method, system, device and storage medium, which is applied to the product evaluation technical field and comprises the following steps: receiving environment stress time series data of a measured product at each set detection position collected by a sensor; determining each stress extreme position and an environment stress value of the measured product under a determined working condition; constructing a Kriging model; inputting physical size information of the measured product, coordinate information of each set detection position, environment stress time series data, coordinate information of each stress extreme position and the environment stress value into the Kriging model and performing interpolation through grid division to obtain each environment stress field matrix of the measured product output by the Kriging model in time sequence, and determining an environment stress value of a target position at a target time. According to the scheme, the environment stress at each position of the measured product at each time can be conveniently and effectively estimated, and the accuracy is relatively high.

The invention discloses an ultrasonic imaging phase distortion correction calculation method and system based on time delay estimation, and the method comprises the steps: calculating a phase error evaluation index based on relative delay and XOR operation for each array element and each scanning angle of ultrasonic imaging, and obtaining the relative delay when the evaluation index is lower than a set threshold value; calculating a corresponding correction delay according to the obtained relative delay, and calculating an accumulated phase distortion delay according to the correction delay; calculating weight coefficients of each array element and each scanning angle in phase distortion correction, and calculating weighted average phase distortion delay of each array element in combination with the accumulated phase distortion delay; and performing ultrasonic imaging phase distortion correction by taking the weighted average phase distortion delay as an array element delay time estimation value. According to the invention, on the basis of effectively improving the precision of time delay estimation and reducing phase distortion among channel signals, the requirement of real-time ultrasonic imaging in industry is met.

This invention discloses a wireless positioning method based on intelligent reflective surfaces. The method comprises the following steps: S1, acquiring the location information of a base station and at least two intelligent reflective surfaces in the positioning scenario; S2, the receiving end performs timing estimation using the received signal to obtain the arrival time difference between the reflection path and path AB passing through each intelligent reflective surface, and uses the time difference to obtain the optimal estimate of the target location using a timing estimation algorithm; S3, based on the obtained optimal estimate, the optimal solution is obtained through an optimization algorithm, which is the optimal estimate of the user's location. This method provides a method for calculating virtual base stations using multiple RIS (Reflection Sources) and a method for target positioning based on virtual base stations. The wireless positioning method based on RIS presented in this invention can effectively estimate the accurate location of the user in a positioning system under RIS scenarios.

The invention belongs to the technical field of image processing, and discloses a multi-type high-voltage tower three-granularity attitude estimation system and method.The system comprises an attitude estimation model, a PAF-based key point connection module and a high-voltage power transmission tower attitude generation module; the attitude estimation model is used for carrying out key point identification under three specified granularities on the to-be-detected high-voltage power transmission tower image, and a key point identification result comprises key point position information and key point position direction information; the key point connection module based on the PAF is used for constructing key point connection according to the key points and the position direction information under each granularity; and the high-voltage power transmission tower attitude generation module is used for forming the attitude of the high-voltage power transmission tower according to the connecting lines obtained under different granularities. The method not only can effectively estimate the attitude of the high-voltage power transmission tower, but also has good generalization ability.

The present invention relates to the field of vehicle technology, and specifically provides a drivable area detection method, computer equipment, storage medium and vehicle, aiming to solve the problem of accurately determining the drivable area. To this end, the method of the present invention includes obtaining a three-dimensional point cloud in the vehicle driving environment; using a ground height estimation model based on a convolutional neural network and estimating the ground height of the current environment based on the three-dimensional point cloud in the current environment; determining non-ground point clouds that do not belong to the ground in the three-dimensional point cloud based on the ground height; performing obstacle detection on the non-ground point cloud to obtain one or more obstacles; and determining the drivable area in the vehicle driving environment based on the position of the obstacle. Through the above method, non-ground point clouds can be quickly and accurately screened out from a large number of three-dimensional point clouds, thereby improving the efficiency and accuracy of determining drivable areas using non-ground point clouds.

The invention relates to a weak supervision visual space positioning method and system. The method comprises the following steps of: generating various and real pseudo queries for each candidate box by using three complementary methods, and forming pseudo query region pairs; by calculating the single-mode similarity of the pseudo query and the real query, taking the candidate box corresponding to the pseudo query with the highest similarity as a pseudo label of the real query; secondly, estimating a confidence coefficient score of a query region pair by utilizing an image-text matching pre-training model; and finally, a visual space positioning model based on Transform is adopted to cooperate with the designed selective positioning loss based on confidence score, and supervised training is carried out on the region query pair. According to the method, a matching method based on single-mode similarity is used, a model can be helped to establish more reliable region query pairs, distribution drift between real query and pseudo query is avoided, and meanwhile, the designed selective positioning loss based on confidence can help effectively avoid overfitting of query region pairs which are wrongly associated.

The invention relates to the technical field of computer vision and pattern recognition, and discloses a head-body coordination-based sight line estimation method, which comprises the following steps of image input, feature extraction, speed information mapping, feature fusion and context association, model training and testing, performance optimization, sight line direction estimation, data enhancement and standardization and method verification. Using a cascaded Bayesian framework, images of the head, body are mapped to a three-dimensional direction and confidence compliant with the von Mises-Fisher distribution. By analyzing the body and head postures, the sight line direction can be estimated, in addition, the model adopts two feature extractors of a multi-branch structure to extract head and body features, and a Bi-LSTM recurrent neural network is combined to solve the problem that the sight line changes in a short time. Training of the method is carried out on two public data sets GAFA and 3DPW, testing is carried out in daily monitoring images, and high accuracy and robustness are achieved.

The invention discloses an interventional operation continuum catheter tail end stress estimation method and a force feedback system, and aims to solve the problem that catheter tail end force feedback lags behind or is low in accuracy in the interventional operation process. The continuum catheter tail end stress estimation method comprises the following steps: applying a beam theory to elastic mechanical modeling of a catheter, and performing force / moment balance analysis on a model under the condition of considering tail end external force so as to establish an elastic mechanical model of the catheter; a plurality of data sets of external force, internal force and joint configuration are obtained through an elastic mechanical model to form a training set and a test set of a BP neural network model, the BP neural network model is trained to obtain constant parameters of a nonlinear mapping function with the internal force and instantaneous joint configuration as variables and the external force as a dependent variable, and estimation of the external force borne by the catheter is achieved. Simulation test results show that the continuum catheter tail end stress estimation method has good reliability and feasibility.

The application discloses a compressed sensing shallow sea acoustic channel estimation method based on an atom threshold and backtracking, and relates to the technical field of underwater communication. The application improves the gOMP algorithm by setting a reasonable atom screening threshold, introducing a backtracking thought and an iteration stopping condition based on noise, and applies the improved gOMP algorithm to a compressed sensing channel estimation framework of an underwater acoustic orthogonal frequency division multiplexing system, and proposes a gOMP channel estimation method based on an atom threshold and backtracking. The method performs secondary atom screening by setting a reasonable atom threshold, introduces a backtracking step and sets an iteration condition under noise, and is applied to a sparse shallow channel OFDM system. Experiments show that the method improves channel estimation accuracy, shortens estimation time by reducing algorithm calculation amount, and improves spectrum utilization rate by using fewer pilots.

The present invention provides a multi-source fusion positioning method and device in a large-scale indoor scene, including controlling a pre-built inertial odometer to collect three-dimensional positioning information; using a dynamic time warping algorithm to detect different types of indoor landmark points; extracting the motion characteristics of pedestrian multi-source indoor trajectories as input vectors, and extracting the reference error value of each step of the pedestrian multi-source indoor trajectory as an output vector to construct a multi-layer perception network; using an anti-error Kalman filter to fuse the three-dimensional positioning information, landmark point position information and multi-source fingerprints, eliminating abnormal observations, and obtaining updated three-dimensional positioning information. The present invention comprehensively considers the important factors that affect the accuracy of multi-source fusion positioning in a large-scale indoor space, models and evaluates the multi-source trajectory, and eliminates interference from the external environment; a unified anti-error Kalman filter is designed to combine different positioning sources, thereby achieving effective estimation of the three-dimensional position of pedestrians in a large-scale indoor space.

The present invention proposes a method for dynamic altitude control of an underwater robot based on DVL information, which belongs to the field of underwater robot control technology. The method includes: estimating the horizontal distance and vertical water depth of the beam detection area according to the real-time information of two forward beams of DVL and the real-time inclination information of the underwater robot; fitting the historical horizontal distance and vertical water depth of the forward area by a quadratic polynomial, and generating an altitude trajectory in combination with the expected altitude; discretizing the fitted trajectory, and determining the preview distance in combination with the expected speed and the comprehensive curvature, and smoothly adjusting the preview distance by a moving average method; calculating the expected pitch angle of the underwater robot according to the longitudinal error between the preview point position and the current position, adjusting the inclination of the underwater robot in real time, and realizing accurate altitude path tracking. By implementing the method of the embodiment of the present invention, the underwater robot can be controlled to track the undulating navigation of the seabed with high precision and high safety, so as to improve its near-seabed altitude observation capability.

The present invention provides an intelligent collaborative control method for autonomous fly-by and collision threat avoidance of spacecraft, comprising the following steps: using a hybrid state machine to establish a hybrid model of the autonomous fly-by and collision threat avoidance control system, giving the control target corresponding to each state, and monitoring and managing the behavior state of the fly-by spacecraft based on the state machine model; designing a motion planner to give a reference motion trajectory of the fly-by spacecraft to ensure the flexibility of the conversion of each behavior state of the spacecraft; designing a reduced-order observer to estimate the unknown maneuvering parameters for the target spacecraft in view of the uncertainty of the maneuvering parameters for the controller design; designing a multi-modal motion planning and control strategy according to the control targets of different states to realize multi-modal adaptive tracking control. The present invention solves the problem that the spacecraft can effectively avoid collision threat events of space targets while realizing autonomous fly-by, thereby improving the mission execution capability and space operation safety of the spacecraft.

The present invention discloses a pipeline leakage detection and leakage position positioning device and method, including: an inlet pressure sensor and an inlet flowmeter are installed at the inlet of the pipeline to be tested; an outlet pressure sensor and an outlet flowmeter are installed at the outlet of the pipeline to be tested; a node pressure sensor is installed at a preset node of the pipeline to be tested; the output ends of the inlet pressure sensor, the inlet flowmeter, the node pressure sensor, the outlet pressure sensor and the outlet flowmeter are all connected to the input end of the acquisition card, and the output end of the acquisition card is connected to the input end of the particle filter; wherein the number of node pressure sensors is one or more; the particle filter completes detection and positioning based on a particle filter algorithm. The present invention requires a small number of sensors, takes a short time to detect leakage, and has high positioning accuracy; it can detect and locate slow leakage and small leakage in long-distance oil pipelines; and has good real-time performance and large-scale application prospects.

This invention discloses an outdoor Wi-Fi fingerprint positioning method and system based on high-gain antenna anchor point assistance, relating to the field of wireless communication technology. It addresses the problems of high workload in offline data acquisition and poor positioning accuracy and stability under limited signal information in outdoor Wi-Fi fingerprint positioning. The method involves acquiring offline outdoor Wi-Fi signal acquisition data, extracting Wi-Fi fingerprint information to form an offline information database; using a high-gain antenna to select several acquisition points within the target area as graph anchor points, extracting WAP names and multi-directional signal strength antenna acquisition information from the acquisition data to form an anchor point information database; constructing a multi-source relationship graph structure based on the offline and anchor point information databases; building and training a multi-source graph neural network positioning model based on the multi-source graph to form a usable positioning system; acquiring online outdoor Wi-Fi signal acquisition data and updating it based on the constructed multi-source graph; using the updated multi-source graph and the trained model for positioning, and extracting the positioning results of online nodes from the positioning output.

The invention relates to the technical field of adaptive control, and discloses an adaptive bed temperature coordination method and related equipment. The method comprises the following steps: acquiring disturbance information influencing the water content of discharged materials, and a set temperature and an actual temperature of a boiling bed; wherein the disturbance information comprises at least one of slurry amount change, slurry concentration change or steam fluctuation; based on the actual temperature of the boiling bed, the set temperature and the disturbance information, determining the total disturbance output by an observer; and the opening degree of a feeding valve of the centrifugal machine is adjusted based on the total disturbance amount. According to the method, the slurry drying effect can be improved.

The invention discloses a protocol reverse analysis method, a computer readable storage medium and a program product, and relates to the field of information countermeasure, and the method comprises the steps: employing variable sliding window coarse search, and determining the minimum width of a sliding window containing the length of a whole frame; determining the width of a fixed sliding window according to the minimum width of the sliding window, and determining a frame header and a frame tail by adopting the fixed sliding window for precise search; segmenting the data stream according to the frame header and the frame tail; determining whether the frame type is fixed-length or variable-length according to a result of accurate search of the fixed sliding window; carrying out statistics on each frame of segmented data, and determining the length of a synchronization word; forming a frame structure-keyword template according to priori knowledge; the frame structure analysis result comprises a frame header, a frame tail, a synchronous word length and a frame type, and matching the frame structure analysis result with a frame structure-keyword template to obtain an analytic value and a speculative value of an actual keyword or obtain a new sample. On the basis of demodulation of a physical layer and a link layer, parameter identification is carried out on a communication protocol under the condition of no prior information or less prior information for a data stream.

The invention discloses an unmanned aerial vehicle anti-interference and noise-reduction control method based on a composite extended state observer, and belongs to the technical field of unmanned aerial vehicle control. A filter is embedded in the extended state observer, so that the influence of zero-mean measurement noise on the attitude angular velocity is reduced, and time-varying interference is extended as a new state quantity, so that the attitude angular velocity of the unmanned aerial vehicle is improved. Effective estimation of time-varying interference is realized, estimation of the time-varying interference is improved, and the influence of measurement noise on the attitude angular velocity, especially control input, is greatly weakened. Time-varying interference is estimated through an extended state observer, and unknown dynamics caused by an external wind field and dynamics influenced by measurement noise are approximated by adopting a radial basis function neural network, so that the estimation performance of lumped interference is effectively improved; in addition, a decoupling term is embedded in the extended state observer, so that the conservative property of the control method is reduced, and the application range of the control method is expanded.

The invention relates to the technical field of signal processing, in particular to a weighted fractional Fourier transform parameter estimation method based on a maximum entropy criterion. The method comprises the following steps: solving a WFRFT of an input signal twiddle factor in a [0, 2] interval, and generating a local signal; solving an entropy value of a local signal of an input signal twiddle factor in a [0, 2] interval, and calculating a maximum value of the entropy value; constructing a sparse optimization function according to the input sparse coefficient, the entropy function of the local signal and the maximum value of the entropy function, solving the maximum peak value of the normalized entropy value in a twiddle factor [0, 2] range, and calculating the position of the maximum peak value of the twiddle factor; and estimating a twiddle factor according to the maximum position of the peak value. According to the method, effective estimation under unknown signal parameters is realized, a transformation order estimation problem is converted into a sparse optimization problem, a sparse reconstruction algorithm is used for solving, and relatively high parameter estimation accuracy can also be realized in a low signal-to-noise ratio environment.

This invention discloses a beam-domain channel estimation method for spatially non-stationary large-scale MIMO, comprising the following steps: First, a beam-domain channel model for spatially non-stationary large-scale MIMO is constructed using the visible region; then, based on the sparsity of the beam-domain channel and the influence of power leakage, the beam-domain channel estimation problem is transformed into a sparse channel reconstruction problem; next, based on the sparse structure of the cross-blocks of the beam-domain channel and the power ratio threshold, a sparsity-adaptive matched pursuit scheme based on the beam-domain structure is proposed; finally, simulation results verify that the proposed scheme has lower pilot overhead and higher accuracy and effectiveness compared with traditional schemes. The beam-domain channel estimation method for spatially non-stationary large-scale MIMO provided by this invention can be effectively applied to channel estimation with non-stationary characteristics, and has significant advantages in estimation accuracy and complexity.

The invention discloses a signal detection method and system based on iterative estimation in composite Gaussian clutter, belonging to the field of radar target detection. First, it is assumed that clutter from different distance units has the same covariance matrix structure, but has unknown power mismatch, and the mismatch amount is a non-random non-negative number. Then, the clutter covariance matrix estimation value is constructed using the power mismatch amount obtained by the iterative estimation and the data to be detected, the training sample, and the iterative estimation. Then, the test statistic is calculated, and the detection threshold is determined according to the false alarm probability. Finally, the size between the test statistic and the detection threshold is compared. If the test statistic is greater than the detection threshold, it is determined that the target exists, otherwise it is determined that the target does not exist. The detection method and system of the invention take into account the problem of power mismatch in the training sample in the composite Gaussian clutter, obtain all the mismatch amounts through iterative estimation, and realize clutter suppression, signal accumulation and target judgment in an integrated manner, simplify the process, and improve the detection performance.

The application provides an infrared dim small target detection method and device based on annular structure local contrast, comprising the following steps: S1. An original image to be detected is acquired to perform edge suppression processing, and an edge suppression weighting coefficient is output; S2. An annular structure is constructed according to the original image to be detected; S3. The image background is estimated according to the discrete degree of the gray value of the peripheral annular background region, and the target is pre-enhanced according to the pixel value in the central region, the annular structure local contrast is obtained according to the ratio between the image background estimation result and the pre-enhancement processing result, and the target enhancement image is calculated using the annular structure local contrast; and S4. The final target detection image is obtained according to the edge suppression weighting coefficient and the target enhancement image. The application can effectively estimate the image background, enhance the difference between the dim small target and the background, and thus quickly and accurately realize the detection of the dim small target.