6 results about "Sparse methods" patented technology

This invention discloses a sparse method and related equipment for quadrant amplitude mapping arrays. The method, through recursive partitioning and a deterministic amplitude mapping mechanism, eliminates the randomness of traditional probabilistic methods, ensuring the uniqueness and stability of the output layout under the same input conditions, thereby significantly improving the repeatability and engineering reliability of the design results. The method in this embodiment achieves high-precision matching between amplitude distribution and cell position through sparse cell allocation based on recursive quadrant partitioning, significantly reducing sidelobe levels while lowering the standard deviation of performance fluctuations. Furthermore, the method in this embodiment is independent of specific array geometry and naturally supports rectangular, circular, and irregular arrays with internal obstacles through its recursive processing mechanism, demonstrating excellent versatility and flexibility, and can be widely applied in the field of data processing technology.

Embodiments herein describe a sparse-dense-sparse (SDS) process that achieves a better pruning scheme that benefits from pruning-friendliness relative to one-shot pruning schemes. The SDS process performs a first pruning to generate a sparse ML model followed by reconstruction to generate a re-dense ML model, followed by a second pruning to generate another sparse ML model. By pruning a ML model and then re-constructing the ML model, the ML model can be made more pruning-friendly by performing data and / or weight regularization. As a result, performing the second pruning in the SDS process can result in a smoother weight distribution and lower perplexity relative to one-shot pruning.

The application discloses a kind of support dynamic sparse and static sparse hardware accelerator, comprising: data segmentation and reordering module is used to the input data is segmented to adapt to the size of spatial accelerator hardware;Spatial accelerator module is used to support the operation of sparse attention mechanism;Weight summation module is used to support the segmentation of hybrid attention mode, the result obtained by the calculation of segmented input data is merged to obtain the final output;Pattern matching module includes matrix multiplication operation module, double tuning sequencer and sliding window comparator, for hybrid attention mode matching.The application simultaneously supports static sparse method and dynamic sparse method, and can carry out efficient static and dynamic sparse attention calculation.Saves at least 6.1 times of calculation amount, while maintaining the accuracy of output result.

This invention belongs to the field of radar signal processing and intelligent sensing technology, specifically relating to a target protection mask prior-guided low-rank sparse reconstruction method for FMCW radar interference suppression. The method includes the following steps: 1. Acquiring FMCW radar observation signals and preprocessing them; 2. Performing time-frequency transformation on the preprocessed FMCW radar observation signals to extract basic features; 3. Accurately locating the interference region in the time-frequency domain, forming a binary mask prior, and constructing a mandatory target protection region; 4. Constructing a low-rank sparse matrix and a target protection low-rank sparse decomposition model based on mask prior guidance; 5. Solving the target protection low-rank sparse decomposition model to obtain the decomposition results; 6. Reconstructing and post-processing the decomposition results, outputting the interference suppression results, and evaluating the effect. This method combines the intuitiveness of time-frequency detection methods with the reconstruction capability of low-rank sparse methods, reducing the probability of target echoes being misclassified into sparse terms.

The application discloses a multi-baseline INSAR phase unwrapping method, system, device and medium, wherein the method comprises the following steps: constructing a to-be-clustered data set: acquiring intercept information corresponding to each pixel according to interferograms corresponding to different vertical baselines, then combining position information of each pixel as multi-dimensional clustering features of the pixel, and taking the pixel as a to-be-clustered target to obtain the to-be-clustered data set; wavelet clustering processing: performing clustering processing on the to-be-clustered data set based on wavelet clustering; cluster result correction: correcting cluster labels of pixels in each noise cluster; cluster-by-cluster phase unwrapping: based on the corrected cluster distribution, calculating a blur vector of each cluster by using a closed solution formula or a sparse-TSPA method, and calculating absolute phases of each pixel in a corresponding cluster based on the blur vector of each cluster. When large-size interferograms are processed, the application has more advantages than existing multi-baseline phase unwrapping methods in terms of efficiency, accuracy and adaptability of a baseline ratio.

This invention provides a UPA near-field channel estimation method based on two-dimensional block sparsity, comprising: representing the UPA near-field channel matrix as the sum of the outer products of the horizontal and vertical ULA near-field steering vectors; constructing improved DFT dictionaries in the horizontal and vertical directions respectively; representing the channel matrix as a total coefficient matrix under these dictionaries, which presents a two-dimensional block sparse structure determined by the outer product of the horizontal and vertical block sparse representation vectors, thereby transforming channel estimation into a two-dimensional block sparse signal recovery problem; and solving the problem using the 2D-PCSBL algorithm, in which the accuracy parameter of each sparse coefficient in the prior distribution is determined by the weighted sum of its own hyperparameter and the hyperparameters of its two-dimensional neighbors, to capture the sparse coupling characteristics of the UPA near-field channel in the horizontal and vertical dimensions. This invention achieves high-precision channel estimation with fewer pilots and a lower signal-to-noise ratio, and its computational complexity is comparable to that of the one-dimensional block sparse method.