3results about How to "Estimated speed" patented technology

The application discloses a kind of robust direction of arrival estimation method based on ADMM-Net, by expanding ADMM algorithm into model-driven deep network ADMM-Net to improve DOA estimation accuracy, speed up DOA estimation speed and have robustness to array disturbance.Firstly, the sparse transformation of source and array received multi-shot data is carried out by space over-complete dictionary, and the DOA estimation is converted into compressed sensing sparse recovery problem;Then, ADMM algorithm is expanded, and model-driven deep network ADMM-Net with explainability is formed;ADMM-Net is used to reconstruct source power spectrum and carry out DOA estimation.The present application can learn the hyperparameters in iterative algorithm and array disturbance, solve the problem that existing compressed sensing DOA estimation method based on calculation speed is slow, and fails under the condition of array disturbance, realize fast, accurate, explainable and robust DOA estimation.

This invention presents a novel health status estimation method for lithium-ion batteries based on novel health features. First, it obtains the charging voltage curve under constant current and constant voltage charging conditions, using the voltage difference at equal starting voltages and time intervals as the novel health feature. Then, since the starting voltage and time interval are two parameters affecting the novel health feature, they are optimized, resulting in an optimized starting voltage and multiple optimized time intervals from the same charging voltage curve. Combining the optimized starting voltage with each optimized time interval yields multiple novel health features extracted from the same charging voltage curve. Finally, a lithium battery health status estimation model is constructed based on a multiple linear regression model. The model is trained using the extracted novel health features and corresponding health status values, and the trained model is then used for lithium battery health status estimation. This method has low computational complexity and significantly improves estimation speed while maintaining accuracy, making it applicable even when current battery management systems have insufficient computing power.

Embodiments of the present application provide a target motion state detection method, device, mobile device and storage medium. The method comprises: obtaining three-dimensional laser point clouds of a current frame and a previous frame observed by a laser radar; performing rasterized point cloud coding on the three-dimensional laser point clouds of the current frame and the previous frame to obtain two-dimensional raster images of the two frames; estimating an optical flow of each raster in the two-dimensional raster image of the current frame by using a change in image intensity of each raster in the two-dimensional raster images of the two frames; obtaining an envelope box of the target on the two-dimensional raster image of the current frame, determining an optical flow intensity of the envelope box based on the optical flow of the rasters occupied by the envelope box; and detecting a motion state of the target based on the optical flow intensity of the envelope box. Embodiments of the present application do not perform target tracking on multiple frames. The embodiments avoid the dependence on target detection consistency and target tracking, determine the motion state of the target by fusing the optical flow intensity of each raster, and reduce the probability of dynamic and static estimation errors.