6results about How to "Reduce mean square error" patented technology

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

The application discloses a short-term and nowcasting precipitation prediction method based on a multimodal RNN, relates to the technical field of short-term and nowcasting precipitation prediction, and aims to provide a short-term and nowcasting precipitation prediction method with high accuracy, which comprises the following steps: acquiring a radar graph and a meteorological element graph; training a multimodal model by using the radar graph and the meteorological element graph; and performing precipitation prediction by using the trained multimodal prediction model; wherein the multimodal prediction model comprises a radar module and a meteorological element module, and the multimodal prediction model obtains a short-term and nowcasting precipitation prediction result according to a radar prediction graph; the method takes radar information as the main part and meteorological element information as the auxiliary part to perform precipitation prediction, fuses the two modes, and improves the accuracy of short-term and nowcasting precipitation prediction.

This invention proposes a reconfigurable metasurface beamforming design method based on CGAN and Gumbel-Sinkhorn. This method constructs a conditional generative adversarial network (GAN) as the inverse design network. The generator takes the target beam pattern as a conditional input to generate the corresponding encoding matrix. The Gumbel-Sinkhorn operator is introduced to discretize this continuous matrix, transforming it into an approximately doubly random permutation matrix. The encoding is sorted and selected by multiplying this permutation matrix with a predefined structure vector (SV), outputting a discrete 0 / 1 metasurface encoding array. A noisy forward prediction network is trained to predict the beam pattern corresponding to a given encoding array. In the training of the inverse design network, the discretized encoding matrix output by the generator is input into the noisy-trained forward prediction network. The mean square error between the predicted beam pattern and the target beam pattern is calculated as the target loss term to optimize the network parameters and accurately learn the complex mapping relationship from the beam pattern to the encoding array.

This invention discloses a distributed synthetic aperture radar (SAR) cooperative anti-jamming method and system. The method includes: acquiring distributed SAR echo signals with interference; estimating the relative time delay of interference signals in the data received by different stations of the distributed SAR system, and constructing a low-rank enhancement matrix in the frequency domain; arranging the echo data matrix and the low-rank enhancement matrix in the frequency domain of each receiving station into a new matrix to be processed according to pulses, and constructing a distributed cooperative interference suppression convex optimization problem model for the newly constructed data matrix; solving the distributed cooperative interference suppression convex optimization problem to obtain the distributed SAR echo signal data after interference suppression, and rearranging it according to the radar to which it belongs into an initial structure for subsequent imaging. Compared with traditional interference suppression methods, the SAR image results recovered by the method of this invention have lower mean square error and higher structural similarity, and can handle noise-type interference that monostatic SAR cannot handle, that is, this invention has more effective interference suppression capability.

The invention relates to the technical field of geophysical exploration signal processing, and discloses a GPR echo signal denoising method based on improved WTD and VMD. Comprising the following steps: adaptively optimizing a decomposition modal number and a penalty factor of the VMD by utilizing an HHO algorithm; performing 2D-VMD decomposition on the original GPR data by using the optimal parameter to obtain a plurality of IMFs; dividing the IMF into a signal IMF and a noise IMF through a correlation coefficient; a wavelet threshold parameter is optimized and improved by using a PSO algorithm, and the noise IMF is processed; and after reconstruction, direct waves are removed by adopting an averaging method. Through a dual parameter optimization mechanism, an improved threshold function with continuity and no deviation and a differential threshold processing strategy, adaptive high-precision denoising of a GPR signal is realized, the signal-to-noise ratio is effectively improved, a weak target signal is reserved, direct wave interference is remarkably suppressed, and the method has the advantages of high robustness and high robustness. The method has the advantages of high parameter adaptive capacity, high denoising precision, high engineering practicability and the like.

The application discloses a kind of multi-mode wind energy resource month scale prediction revision method and system based on U-Net, belong to wind energy resource evaluation and climate numerical prediction cross technical field, for the month scale wind speed of climate mode output is revised.The method constructs climatic wind speed field using ERA5 reanalysis, calculates month scale 10m wind speed anomaly as revision benchmark;Obtain the month scale historical return data of multiple dynamic climate models, extract 10m wind speed and multiple layers meteorological elements, uniform interpolation and standardization, form sample set.Based on sample set, the U-Net revision model with encoding and decoding structure is constructed, and the feature combination and hyperparameter are optimized through cross-validation, to learn the nonlinear mapping from multi-mode prediction field to ERA5 anomaly field.The optimal model is used to revise the future month scale prediction, to generate wind speed products with more similar spatial structure and amplitude distribution to observations, to provide high credible wind energy climate information for wind power planning, power generation planning and power grid dispatching.