13 results about "Unscented particle filter" patented technology

The invention provides a pipeline defect prediction and evaluation method and system based on AKF and SR-UPF. The method comprises the following steps: obtaining observation data of a pipeline; based on the observation data, a growth rule of pipeline defects is obtained through a preset mechanism model; introducing random disturbance in the defect growth process through a preset random process model based on the growth rule of the pipeline defect, and generating a pipeline defect prediction result; wherein the mechanism model carries out dynamic estimation and correction on parameter uncertainty and noise distribution through adaptive Kalman filtering, and the random process model carries out particle weight and distribution optimization through square root unscented particle filtering. The problems that in the prior art, a pipeline corrosion and crack growth reliability evaluation method is insufficient in prediction precision and low in calculation efficiency are solved.

The invention discloses an IGBT life prediction method based on adaptive unscented particle filtering of time sequence form perception optimization, and belongs to the technical field of health prediction and reliability engineering. The method aims at the problems that a traditional filtering algorithm is prone to falling into local optimum in a high-dimensional parameter space and prediction trajectory forms are discontinuous, and comprises the steps that firstly, an IGBT nonlinear degradation model is established through a state-space equation; performing Sigma point sampling and nonlinear propagation at a particle level by adopting an unscented transformation and particle filtering cooperation mechanism, and fusing process noise to realize dynamic state prediction; constructing a Gaussian likelihood function by using the deviation between a predicted value and an actual observed value to generate a system residual sequence; an FCD fusion index is innovatively introduced, the index fuses a Pearson correlation coefficient and a Frechet distance to synchronously quantify trend consistency and form similarity, and self-adaptive genetic optimization is driven accordingly; and repeating the behaviors before a preset termination condition is met, and continuously updating the parameters of the state-space equation.

According to the electromechanical transient estimation method and system based on robust H infinite unscented particle filtering, on the basis of a dynamic estimation model of a generator, unscented particle filtering iteration solution is adopted; determining an estimation error covariance matrix by using a positive scalar parameter of a system uncertainty error defined based on a robust H infinity theory, a state prediction error covariance matrix and a cross covariance matrix of a predicted value and a measured value, and updating a Kalman gain; based on the state variable, estimating an error covariance matrix to establish suggested distribution, and resampling from the suggested distribution to obtain a new particle set; updating a dynamic estimation model of the generator by using the corrected system noise covariance matrix and the observation noise covariance matrix, and solving an obtained state variable estimation value by adopting unscented particle filter iteration; taking the state variable estimation value and the weighted mean value of the weight thereof as an electromechanical transient estimation result of the generator; the problems of accuracy and robustness caused by communication noise and uncertainty of model parameters in the dynamic estimation process of the generator are solved.

The application relates to a river flood discharge capacity dynamic evaluation and hydrological early warning method. The method comprises the following steps: based on a parameter sensitive observation subset, sequentially updating an initial hidden variable in a low-dimensional hidden space through an unscented particle filter algorithm to obtain an optimized hidden variable, and inputting the optimized hidden variable into a river dynamic resistance field generator based on an initial dynamic roughness field to perform decoding processing to obtain a dynamic roughness field of a current period; based on the dynamic roughness field and a state sensitive observation subset, performing analysis and update processing on a state variable field of a hydrodynamic model through a deterministic ensemble Kalman filter algorithm to obtain an assimilated water level field and a flow velocity field; according to the water level field, the flow velocity field and the dynamic roughness field, the maximum flood discharge capacity of a section is calculated in combination with dike top elevation data, and a flood discharge capacity dynamic index is calculated based on the maximum flood discharge capacity of the section and a current actual flow. The method can realize real-time dynamic quantification of river flood discharge capacity and predictive evaluation of flood risk.

The present invention provides a method for predicting the early life expectancy (RUL) of lithium batteries based on a fusion model of transfer learning and an unscented particle filter (UPF), belonging to the field of power batteries and energy storage technology. The present invention uses a moving average method and residual analysis method to smooth and denoise the original data set, and divides the processed data into source domain data and target domain data. The source domain data is input into a Mogrifier LSTM neural network for training to obtain a pre-trained model, and the pre-trained model is fine-tuned using a small amount of target domain data to achieve RUL prediction for other batteries of the same type but under different experimental environments. To further improve prediction accuracy, a double-exponential capacity decay empirical model is sampled to establish a state equation. The RUL prediction value output by the fine-tuned model is used as the observation value of the UPF, and the posterior capacity prediction value is obtained through each iterative update. It is then determined whether it reaches the capacity threshold, thereby achieving the remaining service life prediction of the lithium battery.

The invention provides a CNN-DBN pseudo measurement modeling-based power distribution network prediction auxiliary state estimation method and a related device, and the method comprises the steps: collecting the historical data, meteorological data, date data and economic data of the node injection power of a power distribution network, carrying out the data preprocessing, and constructing an input vector; performing feature extraction on the input vector by using a pre-constructed convolutional neural network to obtain data after feature extraction; inputting the data after feature extraction into a pre-trained deep belief network to obtain prediction data of the node injection power of the power distribution network; and using the prediction data as pseudo measurement data, combining the real-time measurement data, and adopting an unscented particle filter algorithm to estimate the prediction auxiliary state of the power distribution network. According to the method, pseudo measurement data is generated through CNN-DBN modeling, state estimation is carried out through unscented particle filtering in combination with real-time measurement data, and the accuracy and reliability of real-time state estimation of the power distribution network are improved.

An anti-robust adaptive UFastSLAM autonomous navigation method based on KLD resampling, comprising the steps of: 1. In the carrier pose estimation stage, an anti-robust adaptive factor is incorporated, and the carrier pose is estimated using the robust adaptive unscented particle filter algorithm (RAUPF); 2. In the feature state estimation stage, the robust adaptive unscented filter algorithm (RAUKF) is used to estimate and update the position information of environmental features; 3. In the particle resampling stage, an adaptive particle resampling method based on KL divergence is adopted to online and real-time adjust the required number of particles, improving the real-time performance of the algorithm while ensuring accuracy. This method can be applied to environments where the measurement noise is time-varying and there are abnormal interferences in the measurement information, improving the problems of decreased carrier positioning accuracy and low real-time performance caused by factors such as abnormal disturbances and inaccurate noise statistical characteristics.

The present invention discloses a vehicle parameter estimation method based on a BP neural network and an improved unscented Kalman filter, comprising: determining the state equation and observation equation of the unscented Kalman filter based on an established three-degree-of-freedom vehicle dynamics model; determining the input, state, and observation of the unscented Kalman filter; improving the unscented Kalman filter to obtain a state estimation result of the improved unscented Kalman filter; using the vehicle's front wheel angle and longitudinal acceleration as training inputs, and fitting the BP neural network to obtain a residual value between the state estimation result and the true sensor value; and using the state estimation result and the residual value as the actual output of the vehicle parameter prediction model. The present invention uses an improved unscented particle filter to predict and estimate vehicle dynamics parameters, and uses vehicle state parameters to construct a BP neural network fitting residual, thus addressing the current drawback that high-precision vehicle parameter sensors cannot be installed on vehicles.

The invention provides a lithium battery charge state prediction method based on a fractional order model, and relates to the technical field of lithium batteries, and the method comprises the steps: obtaining a plurality of experiment voltage values of a lithium battery at different temperatures, different working conditions and different discharge rates; constructing a fractional order equivalent circuit model; the fractional order equivalent circuit model comprises an open-circuit voltage source, a first resistor, a second resistor, a third resistor, an external voltage measuring end, a first constant phase element CPE and a second CPE; based on each experimental voltage value, carrying out iterative optimization on parameters of the fractional order equivalent circuit model by adopting an adaptive genetic algorithm; and on the basis of the trained fractional order equivalent circuit model, in combination with a real-time external input variable, a lithium battery charge state prediction value is obtained by adopting a fractional order multi-information unscented particle filtering algorithm. According to the method, the constant phase element is introduced to construct the fractional order circuit model, and the complex physical and chemical processes in the battery can be better described in combination with the fractional order calculus theory.

The application provides a satellite autonomous continuous maneuvering orbit determination method with adaptive process noise, and aims at the problem of orbit determination accuracy reduction of a satellite in a maneuvering process.The method is based on innovation, and adaptively adjusts process noise in a non-maneuvering stage and a maneuvering stage, so that the estimation accuracy reduction problem caused by fixed noise parameters is solved, and the filter can more accurately represent the dynamic state of the system.In a Gaussian noise, compared with a traditional unscented particle filter, the APNUPF exhibits better orbit determination accuracy during the maneuvering, and even if the state mutates, the APNUPF can still maintain high-precision orbit determination.In addition, in order to meet the actual engineering requirements, the robustness and reliability of the method for satellite autonomous continuous maneuvering orbit determination are comprehensively verified under non-Gaussian noise conditions.

The invention relates to the technical field of indoor navigation, in particular to an indoor and outdoor navigation positioning method and system based on an improved unscented particle filter algorithm, and the positioning method comprises the following steps: estimating the initial position of a positioning target through an extended fingerprint positioning algorithm based on LightGBM; processing the initial position of the positioning target through an improved unscented particle filtering algorithm to obtain a filtered initial position; calculating a final weight value according to the measurement weight and the map weight; and weighting the particles in each step by using the final weight value to obtain a fusion positioning result, and obtaining the real-time position of the measurement target. According to the invention, an outdoor navigation positioning part is fused with a BDS / INS / magnetometer / barometer through a particle filter optimization algorithm so as to realize accurate positioning under an outdoor condition; and a high-precision positioning result of an indoor environment can be provided based on an opportunity signal / geomagnetic module under the condition that an indoor GNSS signal is weak.

The invention relates to an unscented particle filter high-reliability OTFS channel estimation method based on a basis expansion model, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing an orthogonal time-frequency space single-input single-output system transmission model; converting a delay-Doppler domain channel estimation problem into a time domain channel estimation problem; a basis expansion model is adopted to convert a time domain channel estimation problem into a basis coefficient estimation problem; and estimating a basis coefficient and a time domain correlation coefficient by adopting unscented particle filtering so as to track a fast time-varying time domain channel impulse response. According to the method, the calculation efficiency is maintained, the estimation precision and the bit error rate performance are remarkably improved, and the channel estimation precision is improved at the same time.

This invention discloses a robot integrated navigation method based on dual-weighted optimized unscented particle filtering. The method first defines the state vector of the mobile robot and establishes a nonlinear state-space model; then, it performs unscented particle filtering (UPF) initialization to obtain an initial particle set and initial weights, and constructs the importance distribution of the particles; next, it uses unscented Kalman filtering to construct the importance distribution for each particle and samples new particles; then, it combines satellite navigation observation information and updates the particle weights through a maximum entropy-Tukey dual-weighting mechanism to obtain normalized particle weights; finally, it performs particle degradation discrimination and determines whether resampling is needed, and finally outputs the robot navigation state estimation result through weighted fusion. This invention can effectively cope with non-Gaussian noise and outlier interference caused by satellite signal obstruction and multipath effects, significantly improving the state estimation accuracy and stability of the integrated navigation system in complex scenarios.