8results about How to "Estimated speed" patented technology

The invention discloses an electric energy meter based on a self-adaptive observer and a using and collecting terminal frequency estimation method. The method comprises the following steps: setting an initial value for a first intermediate variable estimation value; continuously sampling the voltage or the current to obtain sampling data and adding the sampling data into a cache; when a group of sampling data is newly added into the cache, calculating a calculated value of a second intermediate variable corresponding to the new sampling data based on the new sampling data, then obtaining a gradient value based on the calculated value of the second intermediate variable, and calculating a new first intermediate variable estimated value based on the gradient value and the current first intermediate variable estimated value; if the continuously obtained first intermediate variable estimates have converged, an estimate of the frequency is calculated based on the current first intermediate variable estimate. According to the method, frequency estimation can be carried out once after each sampling point is obtained, the frequency estimation speed is greatly improved, and high estimation precision can still be achieved on the occasion with large harmonic interference.

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.

The invention discloses a 3D indoor radio map estimation method (R2Net) based on 2D deep learning, a 3D indoor radio map is estimated according to a building layout map, a furniture layout map and a transmitter position, and the influence of the height of an indoor transmitter, the height of a receiver and the height of an obstacle on the radio map is considered. Since the input and output of the 2D deep learning algorithm are 2D pictures, the invention proposes that the heights of buildings, furniture and transmitters are represented by pixel values, and 3D radio maps with different heights are generated by using different output channels. And 3D radio map estimation is converted into a 2D deep learning task, so that the calculation overhead and the memory demand are reduced. The 2D deep learning method R2Net provided by the invention has strong generalization ability, enhances feature extraction according to indoor path loss features, and improves estimation precision and efficiency.

The invention provides an SOC and SOH estimation method and system of a power battery and a medium. The method comprises the following steps of iteratively executing the following steps in the charging and discharging process of the power battery: determining model parameters of a circuit equivalent model of a battery pack of the power battery; measuring state parameters of the battery pack at the current moment, wherein the state parameters comprise battery end voltage and charging and discharging current; according to the model parameters and the state parameters, estimating the battery state of the battery pack by adopting a dual-extended Kalman filter; and obtaining the SOC estimation value, the SOH estimation value and the estimation error of the battery pack when the estimation result converges. According to the method, high-precision estimation of the SOC and SOH of the battery pack can be realized, and the estimation speed can be improved while the estimation precision is kept, so that the reliability and the safety of the power battery are ensured; furthermore, the method has relatively high calculation efficiency and relatively high applicability.

The invention discloses a rapid implementation method for pulse sorting in the technical field of pulse signal processing. According to the rapid implementation method for pulse sorting, rapid sorting of known targets is achieved in a library matching mode, and the storage capacity of pulses is improved in a mode that an SDRAM is externally hung on a DSP; preprocessing the PDW by adopting a three-dimensional clustering method; an addressing mode is adopted to improve the PRI estimation speed; and a multi-DSP parallel processing method is adopted to improve the processing speed. According to the pulse sorting rapid implementation method, the processing speed of each link of pulse sorting is increased in multiple modes, the storage capacity is large, task arrangement is reasonable, and the requirements of pulse sorting in different environments can be met.

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.

A method and apparatus for wheel speed estimation including an electric powertrain having an electric motor providing a motor speed, a wheel, and a mechanical linkage between the motor and the wheel, and an electronic control unit that calculates an estimated wheel speed based on the motor speed and a mechanical dynamic model of the electric powertrain.