10results about How to "Effective estimate" patented technology

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 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 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.

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 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.

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 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.

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 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.