9 results about "Robust filtering" patented technology

The application discloses an automatic centering device and method for laser engraving of a cylindrical part, and belongs to the technical field of laser beam processing auxiliary equipment. The device comprises a base turntable assembly, a flexible clamping platform superimposed on the base turntable assembly and driven to translate by a double-shaft servo cylinder, a part clamping mechanism, a grating ruler, a non-contact laser range finder, and a control system electrically connected with the above components. The method comprises the steps of coarse positioning, laser path calibration, equal-angle multi-position acquisition and robust filtering and noise reduction, eccentric vector and main shaft direction angle closed solution, adaptive damping servo reverse compensation, predictive sampling point iterative convergence, etc. The application has the comprehensive advantages of strong anti-interference, fast solution, stable convergence, and short beat, and can complete automatic centering of the cylindrical part in a full-digital way under one-time clamping by means of robust filtering to suppress outlier readings, closed solution to avoid numerical iteration, adaptive damping to suppress overshoot, predictive sampling point to shorten the beat, and length criterion to ensure reasonable criterion.

The application provides a non-Gaussian noise suppression method and device with adaptive kernel width, and belongs to the field of inertial base combined navigation algorithm and state estimation. The method solves the problems that the robust filtering method based on fixed kernel width is difficult to adapt to time-varying noise characteristics, and the scheme depending on an optimization algorithm has the problem of insufficient real-time performance. The method comprises the following steps: initializing filter parameters according to a SINS / DVL combined navigation system; performing time updating to obtain a predicted state vector and a predicted state covariance matrix at the current moment; updating a measurement noise covariance matrix through a variational Bayesian method, wherein the measurement noise covariance matrix is modeled as an inverse Wishart distribution; adaptively updating a kernel width parameter according to a filter innovation at the current moment and the measurement noise covariance matrix; and updating a state quantity estimation value and a state covariance matrix at the current moment through a fixed-point iteration algorithm by using the updated kernel width. The method is used in the field of underwater resource exploration.

The application discloses a kind of unmanned aerial vehicle flight control method and system, for solving the protection level failure caused by navigation error non-gaussian distribution and the problem of frequent false trigger of flight control of unmanned aerial vehicle in complex electromagnetic environment of city low altitude. Method includes obtaining bottom sensor original observation value, using robust filtering algorithm to suppress multipath interference and output residual error;Non-gaussian error boundary is constructed based on set theory geometric envelope algorithm, and the rigidity quantitative index of navigation reliability is deduced by protection level;Dynamic mapping operation risk alarm limit, and introduce time anti-shake tolerance window to filter transient signal burr;When reliability is continuously broken down, trigger state machine to execute stepwise autonomous degradation. The application breaks the limitation of traditional gaussian hypothesis, and considers the absolute safety and task continuity of flight.

The application belongs to the technical field of navigation, and proposes an adaptive robust filtering navigation method based on multivariate t distribution and Bayesian shrinkage. First, multi-source heterogeneous sensor observation data is obtained. Second, the multi-source heterogeneous sensor data is modeled based on multivariate t distribution, and the innovation of the multi-source heterogeneous sensor observation data is extracted. Then, a continuous confidence factor is constructed, the fault detection is converted into a Bayesian inference problem, and the confidence diagnosis of the observation innovation is performed based on the confidence factor. Then, the innovation discount factor is constructed based on the confidence factor, and the weighted fusion target function is constructed based on the observation fitting term and the virtual robust term, so as to obtain the equivalent innovation observation, the equivalent observation noise covariance matrix, the equivalent innovation covariance and the robust Kalman gain. Finally, the observation information weight is dynamically adjusted, the equivalent parameters are used to complete the robust filtering iteration, and the optimal navigation solution is obtained. The application can improve the navigation positioning precision and robustness in the multi-source fusion navigation process.

This invention discloses an attention-integrated multimodal federated learning method and system for risk monitoring. On the client side, an attention-integrated hybrid early fusion module dynamically injects multimodal features into globally shared latent variables through attention mechanisms and entropy-based gating mechanisms. On the server side, a plug-and-play dual-track federated aggregation strategy decouples state aggregation from model aggregation. The state track employs robust filtering based on the geometric median, while the model track supports algorithm-independent parameter updates. Between communication rounds, a neighborhood-weighted personalized reference mechanism constructs a gradient-based semantic topology to balance global consistency and local task preferences. This invention simultaneously addresses the technical challenges of modality incompleteness, data statistical heterogeneity, and Byzantine adversarial robustness in multimodal federated learning, thus providing an efficient and robust collaborative learning solution for disaster risk perception under privacy-preserving conditions.

The application discloses a noise-induced score function-based Kalman filtering method for target tracking, and belongs to the technical field of intelligent perception and state estimation. In view of the problem that sensors such as radars and millimeter wave radars are easily interfered by multipath reflection and other pulse interference in a complex environment, leading to abnormal measurement, the application constructs a class of noise-induced M score function, and rewrites the observation update step of Kalman filtering into a generalized M estimation form; meanwhile, an online adaptive update algorithm of a noise scale parameter is designed according to a measurement residual, and collaborative adaptive estimation of system state and noise parameters is realized. The application does not need prior noise distribution assumption, can automatically soft-restrain abnormal measurement under pulse interference, and significantly improves the robustness and precision of state estimation. Compared with traditional Kalman filtering and existing robust filtering methods, the application has higher estimation precision and stronger anti-interference ability in applications such as radar target tracking, automatic driving front vehicle tracking and unmanned aerial vehicle tracking.

This invention relates to the field of traffic control systems, specifically to a checkpoint travel time estimation method based on mixed-integer optimization and spatial KNN, comprising the following steps: cleaning mapped checkpoint data, calculating initial spatiotemporal parameters, and removing anomalies based on absolute median; decomposing travel time and removing anomaly dwellers; dividing the road network according to traffic flow thresholds, estimating high-traffic road segments using mixed-integer optimization verification and Bayesian dynamic fusion, and performing parameter extrapolation based on spatial KNN topological constraints and congestion characteristics inverse solution for low-traffic road segments; fusing the results from dual-source road segments to construct a global parameter vector, and iteratively optimizing through an iterative convergence mechanism to output a high-precision global travel time field. This method utilizes absolute median to construct a robust filtering mechanism to remove extreme anomalies; it performs parameter extrapolation for low-traffic road segments, achieving high-precision estimation while ensuring physical interpretability, accurately reconstructing the dynamic operating state of the road network.

The present application relates to the field of satellite positioning technology, and particularly relates to a robust filtering method based on satellite quality evaluation, comprising the following steps: step 1: a receiver collects navigation messages in real time; step 2: single point positioning is used to obtain the receiver position and signal quality evaluation parameters; step 3: satellite signal quality evaluation is performed to determine whether the observation matrix is full rank; step 4: a robustness factor is calculated, and a Kalman gain is calculated; step 5: multi-epoch joint observation is performed to establish full-rank complete geometric constraints to solve new predicted values; and step 6: the current epoch parameters are retained, and the next epoch is solved. The scheme of the present application has the advantages that it can be used to reduce the influence of weak satellite signal strength, multipath effect and the like on positioning accuracy, and improve the continuous positioning calculation capability.

This invention discloses a bridge multi-source fusion positioning system and method based on digital twins, belonging to the field of navigation and positioning technology. Specifically, it involves constructing a bridge digital twin model containing environmental semantic annotation information; collecting BeiDou positioning data, IMU inertial data, and UWB ranging data and performing spatiotemporal synchronization; calculating real-time positioning quality indicators based on BeiDou positioning data; identifying the current environmental state in real time based on multi-source positioning data and predicting future motion trajectories using the digital twin model; adaptively switching positioning states based on the predicted trajectory and real-time positioning quality indicators; fusing multi-source positioning data using a robust improved capacitive Kalman filter algorithm; dynamically adjusting the observation noise covariance based on the Mahalanobis distance of the innovation vector; and outputting the fused positioning result. This invention provides prior environmental information through the digital twin model, and combined with adaptive state switching and robust filtering fusion, enables continuous and reliable centimeter-level positioning in complex environments.