3 results about "Unscented particle filter" patented technology

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 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.

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.