35 results about "Unscented particle filter" patented technology

The invention discloses a lithium battery SOC estimation method and system based on second-order differential particle filtering, and belongs to the technical field of battery management, and the method comprises the steps: building a lithium battery second-order RC battery model; carrying out model parameterization by adopting a least square algorithm with a forgetting factor; generating an importance density function by a second-order central difference Kalman filtering method SCDKF, improving a particle filtering algorithm to obtain a second-order difference particle filtering algorithm SCDPF, and adopting the SCDPF to estimate the SOC of the lithium battery. The estimation method provided by the invention is more accurate, has higher estimation precision than an unscented particle filter algorithm UPF, an unscented Kalman filter algorithm UKF and an extended Kalman filter algorithm EKF, and can accurately estimate the SOC value of the lithium battery.

There are a sort of predictive filtering and the self-demarcation of the UPF spaceflight, it relates to the spaceflight airmanship field. It applies to the self-demarcation of the spaceflight peg-top, especially it relates to a sort of spaceflight self-demarcation method of the inertia / starlight which bases the predictive filtering and the UPF (Unscented Particle Filter) information coalescence, thereby it applies to the navigation determining gesture of the spaceflight. First it establishes the self-demarcation state equations of the spaceflight, and then it makes the gesture information which is observed by the star sensing device to become the measuring equations, finally it adopts the self-demarcation arithmetic which bases the predictive filtering and the UPF spaceflight to estimate and amend the drift error of the peg-top, thereby it obtain the high exact gesture of the spaceflight.

The invention relates to a Gaussian mixture unscented particle filter algorithm employing adaptive resampling. The common single-Gaussian particle filter algorithm is optimized in two aspects, namely importance density function and importance resampling. Gaussian mixture unscented transformation is used as the importance density function for particle filtering, and system state can be estimated accurately. On the basis of traditional residual resampling, the adaptive residual resampling simple and effective is provided in order that particle degeneracy and particle impoverishment are relieved. An uncertainty model, random surfer model, is used as a state model is used in order to estimate the performance of the algorithm. Simulation results show that the algorithm is superior to the common single-Gaussian particle filter algorithm in terms of tracking precision, state estimation and particle set diversity.

The invention discloses an integrated attitude determination method based on an ant colony unscented particle filter algorithm, which relates to an inertial / astronomical integrated attitude determination method. The method comprises the following steps of: compensating gyro output data by using inertial measurement information and acquiring carrier attitude information through attitude calculation; acquiring required astronomical attitude information by using astronomical measurement information through a deterministic algorithm; fusing the astronomical attitude information with the carrier attitude information by using an ant colony unscented particle filter algorithm, solving nonlinear and noise non-Gaussian problems of a system, solving high-accuracy carrier attitude information, estimating gyro drift, and feeding back and correcting carrier attitude and compensating the gyro drift; and finally realizing on-line correction of eliminating random errors of a gyro of an inertial / astronomical integrated navigation system in real time based on the astronomical measurement information, and finishing long-term and high-accuracy combined attitude determination of a spacecraft.

The invention discloses a full-drive AUV recovery control system and an autonomous recovery method, provides a hardware structure for a full-drive AUV recovery docking control system, an autonomous recovery method that uses dynamic positioning to maintain linear tracking and hovering calibration, and A dynamic positioning state estimation method based on adaptive unscented particle filter that can improve the stability of AUV recovery and docking. The adopted adaptive unscented particle filter algorithm uses Saga-Husa to estimate the statistical characteristics of unknown noise, and uses the particle distribution to jointly approximate the state posterior distribution, which complements the advantages of particle filter and UKF algorithm. In the case of external interference, physical characteristics of acceleration and human manipulation, the tracking filtering effect is better, which effectively improves the stability of dynamic positioning and the success rate of autonomous recovery.

The invention provides an oilfield mechanical oil extraction parameter modeling method based on an unscented particle filtering neural network. The method comprises the steps of determining efficiency affecting factors and performance variables in the oil extraction process of oilfield machinery; performing dimension reduction processing on load variables in a sample so as to reconstruct a new sample, and normalizing the new sample; building a neural network model based on the normalized new sample; estimating an optimal state variable of the neural network model by using an UPFNN algorithm, and building an oilfield mechanical oil extraction process model by using the optimal state variable; inputting X^ in the normalized new sample into the oilfield mechanical oil extraction process model to acquire a prediction result, comparing the prediction result with Y^ in the normalized new sample, wherein the oilfield mechanical oil extraction process model is effective if the comparison result is less than a preset error value, otherwise repeating all of the above steps until the comparison result is less than the preset error value. According to the invention, working conditions of the oilfield machinery are predicted through mining production laws of the oilfield machinery, and a basic model is provided for mining optimal production operations of the oilfield machinery.